case study drought in maharashtra

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• Published: 02 January 2024

Drought Atlas of India, 1901–2020

• Dipesh Singh Chuphal   ORCID: orcid.org/0000-0002-0662-2906 1   na1 ,
• Anuj Prakash Kushwaha 2   na1 ,
• Saran Aadhar 3 &
• Vimal Mishra   ORCID: orcid.org/0000-0002-3046-6296 1 , 2  

Scientific Data volume  11 , Article number:  7 ( 2024 ) Cite this article

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• Natural hazards

India has been considerably affected by droughts in the recent past. Despite the considerable impacts of droughts on agriculture and water resources, long-term datasets to examine droughts and their consequences at appropriate spatial and temporal scales have been lacking in India. Datasets that provide drought information are mostly available for a short period and at coarser resolutions, therefore, these do not comprehend the information regarding the major droughts that occurred in the distant past at administrative scales of decision-making. To fill this critical gap, we developed the high-resolution (0.05°) and long-term monthly precipitation and temperature datasets for the 1901–2021 period. We used long-term high-resolution precipitation and temperature to estimate droughts using standardized precipitation and evapotranspiration index (SPEI). As SPEI considers the role of air temperature in drought estimation, it can be used to examine meteorological, agricultural, and hydrological droughts. Using high-resolution SPEI, we developed drought atlas for India (1901–2020) that can provide comprehensive information on drought occurrence, impacts, and risks in India.

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Background & summary.

Droughts are hydroclimatic extreme events that lead to prolonged periods of water scarcity, impacting agricultural production and food security worldwide 1 , 2 . Specifically, in monsoon-dominated regions like India, droughts have been recurrent 3 , 4 , 5 and caused major famines in the 19 th and 20 th centuries 6 . The southwest monsoon rainfall in India is the primary source of agricultural water 7 and groundwater recharge 8 , 9 , accounting for 80% of the total annual rainfall. Droughts in India due to the weakening of the southwest monsoon are closely linked to Indian Ocean warming and El Nino/Southern Oscillation (ENSO) 7 , 10 , 11 , 12 . Also, the diverse physiographic conditions and significant variability in rainfall patterns across India contribute to the varying intensities of drought events 13 .

India is highly vulnerable to drought with about two-thirds of its area prone to drought 14 , 15 , 16 . Being an agricultural-dominant country and home to 1.4 billion people, droughts in India profoundly impact agricultural productivity, water resource management, and socio-economic well-being. India has witnessed a rise in the frequency, severity, and duration of droughts over the recent decades, which is projected to be further exacerbated by climate change 4 , 10 , 17 , 18 , 19 . With the increasing food demand due to rising population and urbanization 20 , 21 , the impact of droughts is expected to become more severe in the future. Additionally, unsustainable pumping of groundwater adds further to the drought-induced challenges, increasing the risks in the future 22 , 23 .

Understanding the observed droughts and their patterns is crucial to reduce the vulnerability of India’s population to future drought events. Trends and variability of droughts in the Indian monsoon region have been greatly examined, however, mostly at a coarser spatial resolution 3 , 10 . Additionally, there have been studies on a particular region 24 , 25 and for a specific drought year 17 , 26 . While Aadhar & Mishra 27 developed high-resolution precipitation and temperature for monitoring droughts in South Asia, its temporal coverage is limited from 1981 to 2020. Therefore, the available high-resolution datasets do not provide information on the severe droughts that occurred in the distant past. Despite its importance for the climate change adaptation and decision making, the long-term (1901–2021) high-resolution (0.05°) drought product for India has been lacking. Long-term reconstruction of droughts at higher spatial resolution is crucial to understand the impacts of some of the worst droughts in the past at local and regional scales. In addition, high-resolution and long-term drought reconstruction can be valuable for climate change adaptation, providing insights for policy interventions. Most of the available drought-related data sets are at coarser spatial resolution or with limited temporal coverage. To fill these crucial research gaps that hinder the decision-making at a local scale (Taluk level), we develop a high-resolution and long-term gridded drought assessment product based on the Standardized Precipitation Evapotranspiration Index (SPEI) 28 , 29 spanning the period from 1901–2021. We developed the high-resolution and long-term monthly precipitation and air temperature datasets for the 1901–2021 period to estimate the SPEI, which overcomes the limitations of the Palmer Drought Severity Index (PDSI) 30 and Standardized Precipitation Index (SPI) 31 by taking into account the multi-scale characteristics of droughts and the influence of rising temperatures on atmospheric water demand. The high-resolution SPEI dataset is then used to develop a long-term drought atlas 32 , 33 , 34 of India, which can assist in policymaking, disaster-risk management, and climate change adaptation.

We developed a drought atlas of India using high-resolution (0.05°) precipitation and maximum and minimum temperatures. The existing observed precipitation and temperature for India are available at the coarser spatial resolution (0.25°) for the 1901–2021 period. We developed high-resolution gridded precipitation and temperature by integrating the high-resolution products available for shorter periods and using the Quantile-Quantile (QQ) mapping for bias-correction. The bias-corrected precipitation from CHIRPS 35 at 0.05° was used as the reference data for correcting the gridded precipitation from IMD 36 at 0.05°. Similarly, bias-corrected temperature from ERA5-Land reanalysis 37 was used to correct gridded temperature 38 , 39 at 0.05°. The performance of high-resolution data in terms of bias, seasonality, and spatial pattern was carefully examined against the bias-corrected CHIRPS precipitation and ERA5-Land temperature. The flow chart of the overall methodology to develop the drought atlas of India is shown in Fig.  1 .

Flow chart of the overall methodology used to develop drought atlas for India.

Development of high-resolution precipitation and air temperature dataset

We used satellite-based and reanalysis data products from CHIRPS and ERA5-Land to develop high-resolution precipitation and temperature. However, these hybrid datasets (CHIRPS and ERA5-Land) exhibit bias in space and time compared to observed datasets due to inadequate sampling, lack of ground-based observations, and error correction processes 40 , 41 . Consequently, the direct application of these datasets in studies related to climate change and hydroclimatic extremes may not be appropriate and straightforward. Several bias correction methods have been developed to address this challenge 42 , 43 , 44 , 45 , 46 , 47 . Bias correction involves a statistical transformation to modify the distribution of modelled data so that it closely resembles the observed data. We used the distribution (Quantile-Quantile) mapping bias correction method to reduce the bias in these datasets and making them consistent with the observed datasets. The distribution mapping method efficiently reduces bias for mean and interannual variations and also for extreme events 48 . Aadhar & Mishra 27 compared linear scaling 27 , 49 , 50 and distribution mapping 43 , 50 for the bias correction of precipitation and temperature over South Asia and demonstrated that distribution mapping performs better than the linear scaling. Detailed information on distribution mapping methods is available in previous studies 27 , 43 , 49 .

The high-resolution bias-corrected gridded precipitation was developed using gridded precipitation from India Meteorological Department (IMD) and CHIRPS. IMD precipitation is available for 1901–2021 at 0.25° spatial resolution, while CHIRPS precipitation is available from 1981 to 2021 at 0.05° spatial resolution. Since CHIRPS precipitation is a combined product of satellite observations, in-situ data, and observed climatology 35 , 51 , it has bias and random errors 27 , 52 . To remove the bias, first, we aggregated the CHIRPS precipitation from 0.05° to 0.25° spatial resolution to perform the bias correction. Next, we bias-corrected the aggregated CHIRPS precipitation (Raw data) using the IMD precipitation (Reference data) at 0.25° spatial resolution for the period 1981–2021. The bias correction of CHIRPS precipitation was performed using the distribution (Quantile-Quantile) mapping method as described in Aadhar & Mishra 27 . During the bias correction of CHIRPS precipitation at 0.25°, scaling factors (SF) were estimated for the distribution mapping. Further, these scaling factors estimated at 0.25° were also applied to bias-correct the CHIRPS precipitation data at 0.05° spatial resolution. Considering the bias-corrected CHIRPS precipitation at 0.05° as reference data, we bias-corrected the regridded IMD precipitation (Raw data) at 0.05° to construct the high-resolution and long-term precipitation data over India. The bias correction of IMD precipitation at 0.05° was performed using the same distribution mapping method. The stepwise description to construct the bias-corrected high-resolution precipitation data from 1901 to 2021 is shown in Fig.  2 . The overall methodology to develop high-resolution precipitation product is described in detail in Aadhar and Mishra 27 .

Steps to construct high-resolution (0.05°) precipitation data.

Next, we constructed the high-resolution and long-term maximum and minimum air temperatures over India using gridded temperatures from IMD, Princeton 38 , and ERA5-Land reanalysis. Maximum and minimum temperature from IMD is available for 1951–2021 at the spatial resolution of 0.25°. Gridded temperature from IMD is unavailable for the 1901–1950 period, therefore, we used the bias-corrected temperature from the Princeton database for the 1901–1950 period at 0.25° spatial resolution. The Princeton temperature data has been used in several hydrological applications in the Indian subcontinent 18 , 53 , 54 . The bias correction of Princeton temperature was performed using the same distribution mapping method 43 , 50 . The temperature data from Princeton was bias-corrected against IMD for the period 1951–2010 and scaling factors were estimated. The scaling factors were applied to bias-correct the Princeton temperature for the period 1901–1950 at the spatial resolution of 0.25°. Finally, the bias-corrected Princeton temperature for the 1901–1950 period and IMD temperature for the 1951–2021 period at 0.25° spatial resolution were used for further analysis.

To construct the high-resolution temperature data, we used the ERA5-Land temperature for the period 1951–2021 at 0.1° spatial resolution. ERA5-Land reanalysis is also a combined product of weather models and observations from the satellite and in-situ measurements 37 . Compared to the observed datasets, ERA5-Land reanalysis consists of bias in air temperature 48 . Therefore, the bias correction of ERA5-Land temperature (Raw data) was performed using the observed IMD temperature data (Reference data) at the spatial resolution of 0.25°. To perform the bias correction, the ERA5-Land temperature was aggregated from 0.1° to 0.25° spatial resolution. The correction in aggregated ERA5-Land temperature was performed using the distribution mapping and scaling factors were estimated at spatial resolution of 0.25°. Similar to precipitation, the scaling factors were applied to bias-correct the ERA5-Land temperature at 0.05°. We constructed the high-resolution ERA5-Land temperature at 0.05° from 0.1° spatial resolution using the elevation-based SYMAP algorithm 55 , 56 , 57 . The SYMAP algorithm 55 was also used to regrid the bias-corrected Princeton and IMD temperature data (Observed-Temperature data) at 0.05° from the spatial resolution of 0.25° for the period 1901–2021. Finally, we used the bias-corrected ERA5-Land temperature (Reference data) at 0.05° to bias-correct the regridded observed temperature data (Raw data) at the spatial resolution of 0.05° for the period 1901–2021 using the distribution mapping method. The stepwise description to construct the bias-corrected high-resolution temperature data from 1901 to 2021 is shown in Figure  S1 .

Development of high-resolution and long-term drought index

We estimated high-resolution and long-term (1901–1921) SPEI to analyze droughts in India. SPEI is a standardized index that depends on both precipitation and potential evapotranspiration (PET), incorporating the impact of temperature on atmospheric water demand 31 . SPEI primarily focuses on meteorological aspects and does not directly incorporate agricultural or hydrological factors, such as soil moisture or streamflow. However, SPEI at an appropriate duration can be well correlated with streamflow and soil-moisture based drought indicators. We used high-resolution bias-corrected maximum and minimum temperature data to estimate PET. We employed the Hargreaves method 58 for estimating PET due to the inadequacy of meteorological observations required for the Penman-Monteith method 59 . We fitted the log-logistic distribution to the data and estimated the SPEI values using the available SPEI package in R 60 . We categorized the SPEI values into distinct drought categories as abnormal drought (−0.8 to −0.5), moderate drought (−1.3 to −0.8), severe drought (−1.6 to −1.3), extreme drought (−2.0 to −1.6), and exceptional drought (less than −2.0) in our study 27 , 61 . The SPEI values greater than −0.5 indicate normal or wet conditions. PET based on the Hargreaves method can be estimated as:

where R A represents mean monthly extra-terrestrial radiation (MJm −2 /day), which is a function of latitude and day of the year 59 , T max represents monthly mean daily maximum temperature (°C), T min represents monthly mean daily minimum temperature (°C), and T represents monthly mean temperature (°C).

SPEI was estimated at 1-month, 4-month, and 12-month time scales. The 1-month SPEI is essential for assessing the short-term meteorological drought and supports immediate decision-making. The 4-month SPEI monitors seasonal drought or wet conditions, providing insights into agricultural droughts. In contrast, the 12-month SPEI is more suitable for assessing the impact of droughts on surface and groundwater resources. We used 1-month SPEI to estimate monthly drought conditions for the summer monsoon months (JJAS) individually. We used 4-month SPEI at the end of September and January to estimate drought conditions for the summer monsoon and winter monsoon (ONDJ), respectively. Moreover, 12-month SPEI at the end of December and May were used to estimate drought conditions for the calendar year (Jan-Dec) and water year (Jun-May), respectively. Further, the gridded SPEI was used to evaluate the mean SPEI for India at country, states (including union territories), districts, and taluka (sub-district) levels. We computed mean SPEI for grids corresponding to each geographical level (country, states, districts, and talukas).

Data Records

The drought atlas of India covering the period 1901–2020 at the taluka level has been made available through the Zenodo repository 62 . The repository also includes the gridded SPEI values at 1-month, 4-month, and 12-month time scales for India at 0.05° spatial resolution from 1901 to 2021. Moreover, standardized SPEI corresponding to different geographical levels has also been aggregated in the repository. Interested users can refer to the readme file available in the same repository for information regarding the data format and details.

Technical Validation

We bias-corrected the raw CHIRPS precipitation aggregated at 0.25° against the reference IMD precipitation for the period 1981–2016 (Figure  S2 ). The raw CHIRPS precipitation exhibited both dry and wet biases in the mean annual precipitation (Figure  S2A ). Raw precipitation underestimated rainfall in the Kutch region, lower Himalayas, and parts of the Western Ghats while overestimated in Northeast India and South India regions. The bias-corrected CHIRPS precipitation showed a considerably lower bias for most regions of India than the raw CHIRPS precipitation (Figure  S2A , B ). We compared the monthly mean climatology of raw (CHIRPS), reference (IMD), and bias-corrected (CHIRPS) precipitation (Figure  S2C ). The corrected precipitation showed a good agreement with reference precipitation (Figure  S2C ). We compared the mean annual IMD regridded precipitation (raw) and bias-corrected high-resolution precipitation (corrected) against the reference precipitation (bias-corrected CHIRPS) at 0.05° for the period 1981–2020 (Figure  S3 ). We note that the spatial variability of the reference precipitation was well represented in the bias-corrected high-resolution precipitation (Figure  S3B , C ), however, we find some differences in the raw precipitation (Figure  S3A ). Both datasets (reference and corrected) effectively captured the regions with high (North-East India, Western Ghats) and low (parts of Rajasthan and Western India) mean annual precipitation. Furthermore, we compared the mean monthly bias-corrected high-resolution precipitation against CHIRPS (already available high-resolution precipitation) data available at 0.05°.

We find a significant difference in all-India averaged monthly rainfall from 1981 to 2020 between the two precipitation datasets (Figure  S3d ). We quantified the performance improvement due to bias correction of the mean monthly precipitation over India by evaluating the Nash-Sutcliffe efficiency (NSE) 63 , coefficient of determination (R 2 ), and root-mean-square error (RMSE). We find an increase in NSE from 0.96 to 0.98, while the R 2 improved from 0.97 to 0.99 after the bias correction. Moreover, the RMSE for monthly precipitation was reduced from 12 to 8 mm/month after the bias correction. Evaluation of NSE, R 2 , and RMSE for the homogenous rainfall zones (Figure  S8 ) also showed significant improvements after the bias correction (Table  S1 ). For instance, in the case of Hilly regions, NSE increased from 0.36 to 0.78, R 2 increased from 0.73 to 0.79, and RMSE decreased from 57 to 33 mm/month.

Similar to precipitation, we bias-corrected the raw ERA5-Land temperatures (maximum and minimum) aggregated at 0.25° against the reference IMD temperature for 1981–2016 (Figure  S4 ). We observed a predominantly cold bias over the Indian region in ERA5-Land maximum temperature, except for the Kutch region (Figure  S4A ). In contrast, the ERA5-Land minimum temperature exhibited a warm bias in most areas (Figure  S4d ). Nevertheless, a significant reduction in bias was observed after the bias correction (Figure  S4B , E ). Additionally, we compared the monthly mean climatology of raw (ERA5-Land), reference (IMD), and bias-corrected (ERA5-Land) maximum and minimum temperatures (Figure  S4C , F ). We find that the corrected temperatures exhibited a good agreement with the reference IMD temperature. We also bias-corrected the Princeton temperature (maximum and minimum) before 1950 against IMD-Temperature (refer to Methods for detail) at 0.25° (Figure  S5 ). The mean annual Princeton-Temperature over India before 1950 showed a significant cold bias of 3 °C compared to IMD-Temperature after 1950 (Figure  S5A , C ). Nonetheless, a consistent temperature trend was observed between 1901–2010 after the bias correction (Figure  S5B , D ). The bias-corrected Princeton temperature (1901–1950) and IMD temperature (1951–2021) were regridded at 0.05° spatial resolution, which were used as raw data to construct the long-term high-resolution temperature data.

Next, we compared the mean annual regridded IMD (raw) temperatures (maximum and minimum) and bias-corrected high-resolution temperatures against the reference temperatures (bias-corrected ERA5-Land) at 0.05° for the period 1981–2016 (Figures  S6 , S7 ). The spatial variability of the reference temperature was well represented in the bias-corrected high-resolution maximum (Figure  S6B , C ) and minimum (Figure  S7B , C ) temperature. The significant difference in monthly mean ERA5-Land (already available high-resolution temperature data) and bias-corrected high-resolution temperatures over India at 0.05° is evident (Figures  S6D , S7D ). Similar to precipitation, we estimated the NSE, R 2 , and RMSE values for the bias-corrected mean monthly maximum and minimum temperatures across India (Tables  S2 , S3 ). The application of bias correction showed significant improvements in the skills. Furthermore, we evaluated the NSE, R 2 , and RMSE for the homogenous rainfall zones (Figure  S8 , Tables  S2 , S3 ) and found consistent improvements in the skills after the bias correction. The final bias-corrected high-resolution (0.05°) precipitation and temperature were used to estimate the SPEI drought index over India between 1901–2021.

To examine if the high-resolution dataset captures the spatial and temporal variability in major droughts, we used the time series of average SPEI over India to assess drought occurrences during the summer monsoon season, water year, and calendar year from 1901 to 2021 (Fig.  3 ). We calculated the standardized SPEI from the mean SPEI aggregated using the gridded data for an admirative region (state, district, and taluk). The summer monsoon of 2002 ranked as the most severe monsoon season drought followed by 1972, 1987, and 1918, based on SPEI values lower than −2.0 (Fig.  3A ). Similarly, the worst events for the water year drought were observed in 1965, 2002, and 1972 (Fig.  3B ). The droughts in 2002, 1965, 1972, 1918, and 2009 were identified as the five most exceptional calendar year droughts in India (Fig.  3C ). The occurrence of droughts exhibited fluctuations across different decades (Fig.  3 ). Between 1901 and 1920, there was one extreme/exceptional drought year (SPEI between −3.0 and −1.6). However, from 1921 to 1960, the incidence of drought decreased significantly, with no exceptional drought events recorded during this period. Most of the Indian monsoon region was wet during this period 3 . Subsequently, from 1961 to 1987, the frequency of droughts increased, which was associated with the influence of the El Nino Southern Oscillation 10 . We also estimated the annual drought area coverage (%) between 1901−2021 during the monsoon season, water year, and calendar year in India (Figure  S9 ). We considered the grids with SPEI values below −0.5 to calculate the total drought area. More than 60% of the total geographical area of India was under drought during the exceptional (SPEI less than −2.0) drought events (Figure  S9 ), which signifies the severity of these observed droughts in India.

Drought estimates in India based on interannual variability of SPEI. ( A ) Z-score of India’s average 4-month SPEI at the end of September (Summer monsoon: JJAS) for the period 1901–2021, ( B ) Z-score of India’s average 12-month SPEI at the end of May (Water year: June-May) for the period 1901–2020, ( C ) Z-score of India’s average 12-month SPEI at the end of December (Calendar year: January-December) for the period 1901–2021.

We examined the drought conditions for states, districts, and talukas during the worst monsoon season (2002), water year (1965), and calendar year (2002) droughts in India (Fig.  4 , S10 , S11 ). The peninsular and north-western parts of India were the most affected regions during the 2002 monsoon season drought, whereas the top northernmost part of India remained unaffected (Fig.  4A–C ). The drought situation affected more than 23 states, 522 districts, and 3623 talukas, with the SPEI ranging between −2.0 to −0.5 (Fig.  4D–F ). Similarly, the central and eastern parts of India were the most affected regions during the worst water year drought in 1965 (Figure  S10A – C ). More than 80% of the total states (27), districts (584), and talukas (3666) in India were under drought (Figure  S10D – F ). Moreover, the 2002 calendar year drought significantly affected the eastern, north-western, and southern parts of India (Figure  S11A – C ). During this period, over 70% of the total states (25), districts (548), and talukas (3676) experienced drought situations (Figure  S11D – F ). The 1965 water year drought was more severe in terms of areal coverage than the 2002 monsoon season and calendar year droughts (Figure  S9 ).

Worst summer monsoon season drought in India (2002) between 1901–2021 based on SPEI. ( A – C ) Spatial representation of Z-score of SPEI values across India at State, District, and Taluka (Sub-district) levels. ( D – F ) Distribution of States, Districts, and Talukas based on SPEI values for the year 2002.

As a next step of data validation, we analyzed the impacts of the summer monsoon season droughts of 2002 and 2009 on the major crop yield in India (Fig.  5 ). We obtained yearly crop data for Indian districts from the ICRISAT database ( http://data.icrisat.org/dld/ ), available from 1990 onwards and corresponding to India’s district boundaries before 2015. The years 2002 and 2009 witnessed two recent monsoon droughts of exceptional and extreme categories for which crop data is available in the ICRISAT database. The change in yield for a year is calculated by taking the difference between the yield of the current year and the yield of the previous year. We primarily focused on Rice and Maize, which are the two most essential rainy-season crops due to their higher water demands for growth. The impact of the summer monsoon season drought is evident in the production of these two crops (Fig.  5 ). The 2002 drought mainly affected the north-western, southern, and eastern regions of India, leading to substantial reductions in crop yield in those areas (Fig.  5A–C ). On the other hand, the monsoon drought of 2009 had a more pronounced impact on the east-central and north-western regions of India, resulting in a reduction in crop yield in these areas (Fig.  5D–F ). While Rice is not a significant crop in north-western India, including Rajasthan and Gujarat (Figure  S12A ), drought impact on its yield in this region was relatively insignificant. However, the decline in Maize yield in the same region was evident, as north-western states are significant producers of maize in India (Figure  S12B ). These results emphasize the effectiveness of the high-resolution data in capturing the drought events that cause significant crop loss in drought-affected regions of India.

Impact of drought on major crops in India. ( A ) Drought-affected districts in India during the 2002 summer monsoon based on SPEI (Z-score). ( B, C ) Change in the yield (Kilogram/hectare) of Rice and Maize in 2002 compared to 2001 at the district level. ( D ) Drought-affected districts in India during the 2009 summer monsoon based on SPEI (Z-score). ( E, F ) Change in the yield (Kilogram/hectare) of Rice and Maize in 2009 compared to 2008 at the district level. Crop production data was obtained from the ICRISAT database available from the year 1990. The grey colour in the Fig. ( B,C,E,F ) represents missing data. The year 2002 and 2009 were two recent monsoon season droughts (SPEI less than −2.0) in India.

To further demonstrate the effectiveness of high-resolution data, we analyzed the frequency of severe and exceptional drought events (SPEI less than −1.6) that occurred in India’s states, districts, and talukas between 1901–2021 (Fig.  6 ). At the state level, the northernmost part of India (Ladakh) has the least frequency of these events, while Himachal Pradesh (just below Ladakh) demonstrated the highest occurrence of such drought events (Fig.  6A ). Notably, a high spatial variability was observed within states when examined at the district and taluka levels (Fig.  6B,C ). Also, as we move to the higher spatial resolution, the frequency of drought events crossing the threshold (SPEI less than −1.6) increases (Fig.  6A–C ). This is because the averaging of SPEI values across larger spatial areas reduces variability, leading to higher z-scores (standardized values). The occurrences of drought events were predominantly clustered between 6 and 10 for the majority of states (Fig.  6D ). The number of drought events was concentrated between 6 and 10 for most of the states (Fig.  6D ). However, at the district level, the concentration of these events was observed between 5 and 11 occurrences and between 4 and 9 occurrences at the taluka level (Fig.  6E,F ).

Frequency of severe and exceptional droughts occurred in India. Number of drought events based on Z-score of SPEI values (SPEI less than −1.6) across India between 1901–2021 at ( A ) State, ( B ) District, ( C ) Taluka (Sub-district) levels. ( D – F ) Distribution of States, Districts, and Talukas based on the number of droughts events that occurred between 1901–2021.

As a next step of our high resolution data validation, we showed 64 significant impacts of the 2002 drought across various sectors in India (Fig.  7 ). During this drought, approximately 56% of India’s area experienced moderate to exceptional drought conditions, affecting 300 million people and 150 million cattle (Fig.  7 ). The economic impact of the drought was also substantial. The country experienced a reduction in per capita income due to the loss of over 1250 million person-days of employment. Additionally, an estimated economic loss of about 8.7 billion USD was reported due to crop damage, which reduced the country’s agricultural gross domestic product (GDP) by 3.1% (Fig.  7 ).

Impacts of the 2002 drought on different sectors of India.

Finally, using the high-resolution (0.05°) SPEI, we developed the Drought Atlas of India for each year between 1901 and 2020. The atlas includes the taluka-wise drought condition of summer monsoon, winter monsoon, calendar year, water year, and monsoon months (JJAS) for each year. As an example, we show drought condition for 1972 (Fig.  8 ), which was the second most exceptional monsoon season drought in India (Fig.  3A ). The severity of the 1972 drought was exceptionally high for all the selected seasons (except winter monsoon) and for all monsoon months (Fig.  8 ).

Drought condition in India for different seasons and time scales at taluka level. Drought condition based on SPEI (Z-score) for ( A ) summer monsoon (JJAS), ( B ) winter monsoon (ONDJ), ( C ) calendar year (January-December), ( D ) water year (June-May), ( E ) June, ( F ) July, ( G ) August, ( H ) September are represented at taluka level along with the total drought area in km 2 (for SPEI less than −0.5) and mean drought intensity.

Usage Notes

The gridded SPEI data are available at 0.05° spatial resolution from 1901 to 2021 at 1-month, 4-month, and 12-month scales. Gridded SPEI data and drought atlas plots can be accessed from the Zenodo repository 62 . Each year’s drought atlas plot shows drought-affected areas of different categories (Normal to Exceptional) across different talukas in India, highlighting the drought-prone areas, which can be directly used for future drought-related studies. High-resolution SPEI data can be used for analyzing the droughts at the basin and sub-basin levels.

We checked the accuracy of bias-corrected data against the reference data and noted significant improvements in its performance. However, despite the bias correction, potential bias may still exist 65 , 66 . The application of bias correction and interpolation techniques may also introduce random errors in the precipitation and temperature data 42 , 67 . Moreover, due to limited observations of climate variables, we estimated PET using the Hargreaves method, which may result in an overestimation of PET and drought 4 , 68 , 69 .

Code availability

Code to estimate SPEI can be downloaded from: https://github.com/sbegueria/SPEI .

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Acknowledgements

We appreciate data availability from India Meteorological Department (IMD): https://www.imdpune.gov.in/cmpg/Griddata/Rainfall_25_Bin.html ; ERA5-Land: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land ; CHRIPS: https://data.chc.ucsb.edu/products/CHIRPS-2.0/ ; Sheffield: https://hydrology.soton.ac.uk/data/pgf/v3/0.25deg/daily/ . All the datasets are freely available and can be downloaded after registration.

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These authors contributed equally: Dipesh Singh Chuphal, Anuj Prakash Kushwaha.

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Civil Engineering, Indian Institute of Technology (IIT) Gandhinagar, Gandhinagar, India

Dipesh Singh Chuphal & Vimal Mishra

Earth Sciences, Indian Institute of Technology (IIT) Gandhinagar, Gandhinagar, India

Anuj Prakash Kushwaha & Vimal Mishra

Civil & Infrastructure Engineering, Indian Institute of Technology (IIT) Jodhpur, Jodhpur, India

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V.M. designed the study. A.P.K., D.S.C. and S.A. performed analysis and wrote the first draft. All the authors contributed to the writing.

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Chuphal, D.S., Kushwaha, A.P., Aadhar, S. et al. Drought Atlas of India, 1901–2020. Sci Data 11 , 7 (2024). https://doi.org/10.1038/s41597-023-02856-y

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• 1. Introduction
• a. Selection of river basins
• b. Climate and agricultural setting
• a. Data collection and preparation
• b. Standardized precipitation index
• c. Drought frequency analysis
• a. Meteorological droughts
• b. Estimation of monsoon rainfall vis-à-vis drought return periods
• c. SPI vis-à-vis SCPI and SCAI
• d. Monsoon condition and NDVI
• 5. Conclusions

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(a) Location of the study basin and distribution of rainfall over the Maharashtra State. Rainfall classes are based on the natural breaks. The pink color in the inset map of India shows Madhya Maharashtra Subdivision. K = Karha, Y = Yerala, M = Man, A = Agrani, and S = Sina river basins. Projected rainfall and temperature data were collected for the area demarcated by the red dashed square. (b) Distribution of the selected rainfall stations over the study area. Source of TRMM data: http://www.geog.ucsb.edu/~bodo/TRMM/ .

Time series plot of SPI values for annual monsoon rainfall over the Madhya Maharashtra Subdivision. Basic data source: Indian Institute of Tropical Meteorology (IITM), Pune.

Basin-wise drought severity classes for the yearly monsoon rainfall. Asterisks (*) denote El Niño events (from weak to very strong).

(a) Time series plot of observed and projected average SPI for five basins. IODMI = Indian Ocean Dipole Mode Index (average for monsoon period). Rainfall data for the year 2014 were not available. (b) Linear relationship between SPI (average for five basins) and SOI for the monsoon period. Dots in the blue and red boxes represent above average (with positive SOI) and below average (with negative SOI) rainfall years. Asterisks (*) denote significant relationship at the 95% confidence level.

Basin-wise monsoon rainfall during the droughts of variable return periods.

Temporal variations in SPI and SCPI of major crops for all the study basins. Pink bands represent regionwide severe drought years. The dashed red line shows the severe drought of 1997. Sugarcane and sorghum productivity data were missing for some years.

Annual variations in SPI and SCAI of major crops for the study area. The dashed red lines represent severe to extreme meteorological drought years. The wettest year in the record (1998) is represented by a dashed green line.

Histograms showing the percentage area under different NDVI classes in winter (December) for the years 1998 and 2002. The NDVI values were derived from the analysis of Landsat-5 TM data.

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Droughts and Agriculture in the Semi-Arid Region of Maharashtra, Western India

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In the prevailing climate change scenario, to cope with drought, it is necessary to understand the characteristics of meteorological droughts in water-scarce regions to formulate judicial plans for the utilization of water resources. The present investigation, therefore, endeavored to assess the intensity and frequency of droughts over the five semiarid river basins in Maharashtra during the past (1980–2013) and future (2015–50). The study was carried out with the application of standardized precipitation index (SPI) methodology. The agricultural and satellite [normalized difference vegetation index (NDVI)] data were analyzed to understand the effects of meteorological droughts. Although the study area experienced three severe rainfall droughts in 1985/86, 2002/03, and 2011/12, higher frequency of low-intensity droughts is observed, particularly after 2000. The estimation suggests occurrence of moderate, severe, and extreme droughts once in 6, 28, and 50 years, respectively. Among the selected basins, the Agrani, the Karha, and the Man are expected to experience intense droughts and hence require special attention in drought management. The study also highlights that El Niño events considerably retard the monsoon rainfall. However, the occurrence of the positive phase of the Indian Ocean dipole in the El Niño years reduces the intensity of droughts. As agricultural productivity and cropped areas heavily depend on the monsoon rainfall, the meteorological droughts result in agricultural droughts. Moreover, the future warming (by 1.02°C) over the study area is very likely to exacerbate the meteorological droughts (estimated to occur in the 2030s) and increase the agricultural water demand, further adding to an already difficult water management challenge in the study basins.

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Drought is a major climatic hazard resulting from the deficiency of water falling below the expected amount for protracted period, which is a case of hydrological extreme. Among all the natural hazards, drought has maximum impact in terms of damage to society ( Mishra and Singh 2010 ). Although the frequency, duration, and intensity of droughts vary in different climatic and hydrological regimes, it has been experienced in almost all the regions of the world ( Hisdal and Tallaksen 2000 ). During the drought period, water scarcity affects all human activities in general and agricultural activities in particular, leading to reductions in agricultural production and productivity in the arid and semiarid regions ( Das et al. 2003 ; Pandey et al. 2008 ).

To quantify various characteristics of drought, operational definitions are constructed in the form of drought indices ( Smakhtin and Hughes 2004 ). As drought is a relative phenomenon ( Van Loon 2015 ), various region-wise indices and methods are being used for its quantification. The Palmer drought severity index (PDSI), developed by Palmer (1965) , is a widely used drought index. It was constructed for topographically homogeneous regions and was based on the data on soil moisture, precipitation, temperature, and evapotranspiration ( Palmer 1965 ; Maliva and Missimer 2012 ). Given the requirement of data on multiple weather parameters, the calculation of PDSI is quite complex ( Maliva and Missimer 2012 ) and has been assessed, criticized, and modified for several reasons during the last 50 years. The standardized precipitation index (SPI), introduced by McKee et al. (1993) , overcomes the limitations of the PDSI, as it is simple to calculate and requires minimum data. As the SPI has the advantage of applicability for all types of climatic regions, thereby allowing spatial comparison of drought, it has been extensively used to assess regional droughts in all parts of the world, particularly during the last two decades. Based on the same methodology, attempts have been made to develop the standardized runoff index (SRI) ( Shukla and Wood 2008 ) and standardized water-level index (SWI) ( Bhuiyan et al. 2006 ) to quantify the hydrological droughts.

In India, about 15.8% (50.8 Mha) of the geographical area is arid and nearly 37.6% (123.4 Mha) is characterized by semiarid climatic conditions ( Ajai et al. 2009 ). Therefore, drought is one of the major disasters, as it affects the agrarian economy of the country. To understand the spatial and temporal characteristics of drought over all or some meteorological subdivisions in India, several scientific studies ( Sinha Ray and Shewale 2001 ; Gore and Sinha Ray 2002 ; Shewale and Kumar 2005 ; TERI 2014 ; and many others) have been devoted to this work. Since it is a drought-prone state, identification and projection of drought in Maharashtra are the main focus of hydrological studies. Although it is an example of an industrialized state, more than 50% of the population depend on the agriculture and allied activities for their livelihood ( Kalamkar 2011 ), which increases the vulnerability of drought disaster. Because of high annual variability, the semiarid region in the state frequently suffers from the water scarcity problem. Particularly, the Madhya Maharashtra and Marathwada Subdivisions ( Chowdhury and Abhyankar 1984 ) are observed with higher drought frequency. Based on a Markov chain model, Khambete and Biswas (1984) have identified drought-prone zones in the state. The chronic drought-prone zone in Maharashtra (covering most of the present study area) registered the highest frequency of drought ( Deosthali 2002 ). The drought studies carried out by Gore and Sinha Ray (2002) and Gore et al. (2010) emphasize the same fact. The occurrence of drought affects the production and productivity of sorghum and pearl millet crops in this region of the state ( Gore and Sinha Ray 2002 ). With the application of remote sensing techniques, Messina (2013) has attempted to map the effects of 2003 drought conditions in the Krishna basin. It is reported that the drought and flood disasters in 2003 and 2005, respectively, have devoured more funds than the planned budget of the rural and agriculture sectors in Maharashtra State for 2002–07 ( World Bank 2008 ). The recent drought experienced in 2012 has adversely affected the agrarian economy of Maharashtra ( Udmale et al. 2014 ). The Intergovernmental Panel on Climate Change ( IPCC 2013 ) has estimated an increase in frequency of drought over the semiarid regions of India (including the present study area). Moreover, the projected rise in temperature (by 1.5°–3°C) over Maharashtra is very likely to amplify drought intensity in the semiarid region, which may increase agrarian stress ( TERI 2014 ).

On this background, the present study endeavors to evaluate the drought intensity and frequency over five semiarid river basins in Maharashtra. Additionally, with the use of projected data, future drought events are estimated. Understanding of the linkage between drought and El Niño events is another objective of this study. As the drought disaster adversely affects agriculture, an attempt has also been made to assess the connection of monsoon condition with agricultural productivity and cropped area. Additionally, the effect of drought on the agricultural cropped area is confirmed by remote sensing techniques.

2. Study area

About 83% of the land area of Maharashtra is characterized by semiarid climatic conditions ( World Bank 2003 ; Kalamkar 2011 ). On account of the orographic effect, the leeward side of the Western Ghats receives a very scanty amount (<700 mm) of precipitation ( Fig. 1a ) ( Gadgil 2002 ). Almost the entire Madhya Maharashtra Subdivision falls in this low rainfall zone ( Fig. 1a ). The monsoon precipitation over this subdivision is observed with higher coefficients of variation (24%–57%) ( Guhathakurta and Saji 2013 ), which results in a 20%–30% probability of drought ( Gadgil 2002 ; Gore et al. 2010 ). Several regionwide droughts of moderate to extreme intensity were experienced in Madhaya Maharashtra between 1871 and 2016 ( Fig. 2 ), which have adversely affected the agricultural crops ( Gore and Sinha Ray 2002 ).

Citation: Weather, Climate, and Society 11, 4; 10.1175/WCAS-D-18-0131.1

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For the present investigation, the Sina, Karha, Yerala, Agrani, and Man basins were selected, which cover about 24 000 km 2 ( Fig. 1a ) in area. These river basins from low-rainfall zone meet the following criteria:

The river does not have its source in the high-rainfall zone of the Western Ghats but within the rain shadow zone.

At least a part of the basin falls in the chronic to severe drought-prone zone, with annual rainfall of <600 mm and annual water deficit of >900 mm.

Adequately long (>20 years) hydrological (rainfall, discharge and groundwater level) data are available.

Among the selected river basins, the Sina (12 365 km 2 ) and Karha (1141 km 2 ) are the largest and smallest basins, respectively.

The selected river basins drain part of Madhya Maharashtra Subdivision and experience a semiarid tropical monsoon (Köppen classification Bsh) type of climate ( IMD 2005 ). Being a part of the rain shadow zone of the Western Ghats, the selected basins receive annual precipitation between 500 and 750 mm, about 90% of which falls during the five months (June–October) of the southwest monsoon season. Although these basins are the part of same climatic zone, they exhibit notable spatial variation in monsoon rainfall (Sina 584 mm, Man 453 mm, Agrani 313 mm, Yerala 502 mm, and Karha 490 mm). The mean daily temperature is generally above 22°C, except during winter (18°–22°C). The mean maximum temperature varies between 30° and 40°C from May to October, but is generally below 32°C in the remaining months of the year ( IMD 2005 ). Given the high ambient temperatures throughout the year, the potential evapotranspiration (PET) tends to be high (between 1600 and 1800 mm), which results in annual water deficiency of more than 900 mm ( Dikshit 1983 ). By considering probability of drought occurrence, the study basins are the part of chronically drought-prone area ( Khambete and Biswas 1984 ).

Agro-climatologically, the selected basins are part of the scarcity zone of Maharashtra, where agriculture is rain dependent ( Kalamkar 2011 ). Therefore, the cultivation of short-duration and drought-tolerant crops (sorghum, pearl millet, gram pigeon pea) is the chief characteristic of the agriculture ( Deosthali 2002 ; Kalamkar 2011 ). Sorghum and pearl millet are the dominant crops, which cover about 50%–60% of the cropped area. Because of their lower market value, the traditional crops in this region are being replaced by cash crops such as sugarcane, maize, and onion ( World Bank 2008 ; Kalamkar 2011 ; Todmal and Kale 2016 ).

3. Material and methods

As the present study is mainly focused on the meteorological droughts, daily monsoon rainfall data (June–October) of 40 well-distributed stations over the selected river basins were collected from the India Meteorological Department (IMD) and Hydrological Data Users Group (HDUG) for about three decades (1981–2013). The acquired rainfall data from the Agriculture Department of Maharashtra State (ADMS) were used to fill the missing rainfall records (about 5%). The remaining ~7% missing values were filled by adopting the linear regression approach. The Thiessen polygon method was used to calculate the average basin rainfall over each of the selected river basins.

One of the major objectives of this investigation was to understand the effect of meteorological droughts on the agriculture in the selected semiarid river basins. For this, the agricultural crop productivity and cropped area data of 37 talukas, which partially or completely fall in the selected river basins, were acquired from the ADMS for the period between 1980 and 2014. The missing records in the agricultural data (about 2%) were estimated by averaging the succeeding and preceding year’s values. To derive the average productivity of each crop in the study area (average for five basins), yearly average productivity values were calculated for the entire study area (average of 37 taluka-wise values). There is a good relation between summer monsoon rainfall and NDVI values during winter (October–December) or the postmonsoon season over the Indian region ( Revadekar et al. 2012 ) as well as in the Upper Krishna basin ( Dodamani et al. 2015 ). The postmonsoon cropped area, which has considerable share of greenery cover in the study area ( Gumma et al. 2011 ), is heavily dependent on the rainfall received during the rainy season. Therefore, to confirm the findings on cropped area vis-à-vis meteorological droughts in the selected river basins, the present study has attempted to identify the pattern and range of NDVI values (for vegetation cover including the cultivated cropped area) over the study area for the drought (2002) and surplus (1998) monsoon years. The vegetation cover and agriculture during the postmonsoon period represent monsoon condition in that year. For this, the Landsat-5 TM freely available images (30 m) were downloaded for the postmonsoon months (November and December). Additionally, the difference between class-wise NDVI values in wet and drought years was verified with the use of the Student’s t test. To understand the connection between observed droughts over the present study area (basins) with the Southern Oscillation index (SOI), monthly SOI data were collected from the National Oceanic and Atmospheric Administration (NOAA) website ( www.noaa.gov ). As it is based on the methodology given by Ropelewski and Jones (1987) , the lower index values (negative values) indicate weak to strong El Niño events. The linkage of monsoon rainfall with El Niño is weakening at the national level ( Kumar et al. 1999 ; Shewale and Kumar 2005 ). In the present investigation, therefore, an attempt has been made to correlate monsoon rainfall and average SOI for the monsoon period.

Apart from this, to estimate the future (up to 2050) drought events and temperature changes, the projected Coordinated Regional Downscaling Experiment (CORDEX) data were acquired from the Indian Institute of Tropical Meteorology (IITM), Pune, for the geographical area demarcated by 16°45′–19°25′N latitude and 73°45′–76°25′E longitude ( Fig. 2 ) to cover all the five semiarid basins under study. CORDEX South Asia data include simulations performed by the Swedish Meteorological and Hydrological Institute (SMHI) and from the Rossby Centre regional climate model (RCM) Rossby Center Atmospheric Model version 4 (RCA4), using CMIP5 GCM EC-Earth ( Hazeleger et al. 2011 ; Strandberg et al. 2014 ). This model is based on the representative concentration pathway 4.5, which assumes peak greenhouse gas emission around 2040. As the present investigation attempts to highlight expected climatic changes over study areas/basins in the near-term future (up to 2050), the RCA4 climate projection model was selected. The yearly projected mean temperature and monsoon rainfall values for the period between 2015 and 2050 were obtained by averaging all pixel values in the demarcated area for each year.

The SPI, developed by McKee et al. (1993) , is an index used worldwide to identify meteorological drought conditions by their intensity, as it requires only precipitation data as an input. It is the standardized deviation of rainfall from its long-term mean. Generally, the precipitation data are not normally distributed. Therefore, the application of appropriate probability distribution is the prerequisite to transform the data. Generally, gamma or Pearson type III probability distributions are applied for this transformation ( McKee et al. 1993 ; Heim 2002 ). Before deciding the appropriate probability distribution for the basin-wise rainfall data, the goodness of fit for the available distributions was tested in the MINITAB and Easy Fit software. In the present study, the gamma distribution was used, as it was observed to be most suitable for all the rainfall data series. The SPI can be calculated for 1-, 3-, 6-, 9-, 12- and 24-month time scales. In the case of the present study area, the monsoon period is observed for five months (June–October). As a result, the agricultural practices are normally active for a short duration (during the monsoon and postmonsoon periods). Therefore, only the monsoon period was considered for the calculation of the SPI. The methodology explained by Naresh Kumar et al. (2009) was adopted to calculate the SPI.

As the SPI is based on the concept of probability, return periods of droughts of variable intensity (moderate to extreme) were estimated by using less-than probabilities. Therefore, the same distribution and its parameters were employed to estimate the cumulative probabilities. Based on these probability values, the return periods were calculated. The reciprocal of probability is considered as return period ( Muthreja 1986 ; Shaw 1994 ). Statistically, the return period should not be estimated for the period of more than 2 times the empirical (observed) dataset ( Muthreja 1986 ). In the present study, the rainfall data for the last three decades were considered. Therefore, the estimation of return period up to 50 years (SPI = −2.05) was considered. In the SPI methodology, drought is implied when the index value falls below −1.0. Hence, the return period of such drought (SPI = −1.0) is termed as the return period of drought with minimum intensity. To present basin-wise drought frequencies, the estimated return periods were plotted against the corresponding rainfall values. From these interpolated series, the basin-wise rainfall amounts during the droughts of the return periods of 6, 10, 15, 25, and 50 years were derived. To identify the average return periods of moderate (SPI from −1 to −1.49), severe (SPI from −1.5 to −2), and extreme (SPI < −2) droughts, the estimated return periods in these categories were averaged.

4. Results and discussion

All the study basins exhibit region-wide consistency of SPI during the extremely wet and dry years. It suggests considerable spatial coverage of extreme events in rainfall. The drought events such as 1986, 2003, and 2012 were experienced in all the study basins. As against this, 1998 is the only extremely wet monsoon year, which is widespread. Another wet year is 2010, which was experienced in all the study basins, except in Agrani. The remaining dry, wet, and near-normal years do not display agreement with respect to space and time. Among all the droughts, the intensity (from SPI < −1.5 to −2.5) and coverage (100% study area) of the drought of 2003 was at a maximum, as the highest SPI was observed in all the study basins, except in Sina. The 2003 drought event is observed in the Sina basin with moderate to severe intensity. Interestingly, the 2002 drought event was observed in all the basins except Sina and Man; contradictorily, the drought of 1994 was observed only in the Sina and Man basins. This drought phenomenon is mainly observed in the Sina and Man basins. The years 1985 and 1986 are also identified as the deficient years over the Sina, Karha, and Yerala basins. However, among all the study basins, the Man basin experienced this drought phenomenon only for one year (1986) ( Fig. 3 ). During the drought of 2012, the Yerala and Agrani basins were severely affected (with SPI < −1.5) whereas the other basins experienced moderate water scarcity. These spatial anomalies are probably governed by the uneven spatial distribution of monsoon rainfall.

Based on the average monsoon rainfall over the study basins, Fig. 4a exhibits a broad picture of drought events over the entire study area (average SPI classes for five basins). During the gauge period (1981–2013), three major and regionwide drought events can be noticed ( Figs. 3 and 4a ) in 1985/86, 2002/03, and 2011/12. These droughts were experienced for two consecutive years, where the second drought year is observed with additional water scarcity. The 1985/86 drought was one of a regionwide drought and was experienced over the study area (selected five basins) and over all the four subdivisions of Maharashtra ( Sinha Ray and Shewale 2001 ) as well. Figure 4a shows the lowest SPI over the study area in the year 2003, as it was the worst drought recorded after 1972 (in terms of coverage) and has adversely affected the land use and land cover in the Krishna basin (including the present study area) ( Messina 2013 ). Similarly, the drought condition in 2012 (with SPI < −1.5) severely affected the agrarian economy in the rain shadow zone of Maharashtra ( Udmale et al. 2014 ), as it has larger coverage over the study area ( Fig. 3 ). The study carried out by Purandare (2013) has reported that the intensive irrigation for sugarcane cultivation has intensified the 2012 drought. Apart from this, based on the projected monsoon rainfall data, an attempt has been made to estimate the future drought events ( Fig. 4a ). The severe and consecutive meteorological drought events (consecutively for four years) are expected to occur between 2029 and 2032 and between 2035 and 2038. It is pertinent to mention here that the annual mean temperature over the semiarid region of Maharashtra has increased (by 0.16°C decade −1 ) during the last four decades and resulted in increase of potential evapotranspiration ( Todmal et al. 2018 ). The projected annual mean temperature data used in the present investigation indicate rise in temperature by 1.02°C up to 2050, which will notably be amplified after 2030. The study conducted by the World Bank (2008) corroborates the same fact. It suggests that under the climate change scenario, future drought events over the semiarid region of Maharashtra will very likely be aggravated due to the warming condition.

During the recent decades, the connection between Indian monsoon rainfall and El Niño has become weaker ( Kumar et al. 1999 ; Shewale and Kumar 2005 ). The monsoon rainfall over the study basins showed a significant relationship with the SOI (average for the monsoon months). The events with negative SOI during June to October retard the monsoon in the study area (five basins). The comparable results obtained by Todmal and Kale (2016) corroborate the same fact. However, there is weak linkage between occurrences of droughts and El Niño events. During the gauge period, although about 60% of the negative SPI years were associated with negative SOI ( Fig. 4b ), only two droughts (1986 and 1994) occurred during El Niño years. It can also be noticed that El Niño was active in 2002 and 2004; however, the most extreme drought of 2003 was not associated with El Niño. Similarly, another regionwide drought of 2012 was also not associated with El Niño. It is observed that the occurrence of the Indian Ocean dipole (IOD) is the cause for weakening of the relationship between the Indian summer monsoon and Southern Oscillation index ( Saji et al. 1999 ; Webster et al. 1999 ). The significant relationship between SPI and IOD obtained for the present study area suggest a partial influence of IOD on the monsoon rainfall over the semiarid region of Maharashtra. The positive IOD during the El Niño year reduces the effect of El Niño over India to some extent ( Ashok et al. 2004 ). Under such circumstances, it can be stated that the intensity and the coverage of 1994 and 1997 droughts over the study area ( Figs. 3 and 4a ) were controlled by the positive IOD, as the Indian Ocean dipole mode index (average for monsoon months) values were observed at 0.91 and 0.88 respectively in these two years ( Fig. 4a ).

Based on the return period obtained for the empirical rainfall data, the interpolated values of the variable return periods with corresponding rainfall are plotted in Fig. 5 . As the return periods of drought are calculated by using the SPI methodology, years with SPI values less than −1.0 are treated as drought years. Therefore, in each basin, the drought of least intensity (SPI −1.0) occurs once in six years, albeit there are basin-wise variations in the monsoon rainfall. In spite of being in the same climatic region, interbasin variations in the monsoon rainfall during the droughts of same return period can be noticed ( Fig. 5 ). Among all the study basins, the Sina basin reveals comparatively higher rainfall amounts (300–425 mm) during the drought of variable return periods between 6 and 50 years, followed by the Yerala basin. In the Agrani basin, moderate to extreme droughts are observed with lower monsoon rainfall between 150 and 225 mm. By and large, the Karha and Man basins broadly display similar amount of monsoon rainfall during the drought events ( Fig. 5 ). These basins are experiencing the once-in-six-year drought event (when the SPI = −1) with the monsoon rainfall amount of ~320 mm. Among the study basins, the Agrani basin reveals the highest deficiency of rainfall during the selected drought events of variable return periods. It is pertinent to mention here that the Karha, Agrani, and Man basins mostly fall in the chronic to severe meteorological drought-prone zone identified by Khambete and Biswas (1984) . Additionally, the study conducted by Dikshit (1983) has found that these rivers drain the area which is characterized by the highest water deficiency (900–1100 mm) in the state. There are three pockets in India (observed in Gujarat-Punjab, Maharashtra, and Karnataka States) with lowest crop potential ( Biswas and Nayar 1984 ). One of these pockets (in Maharashtra) is the area drained by the Karha, Man, and Agrani Rivers where drought disaster with consecutive 10 dry weeks has the highest frequency (once in three years) ( Khambete and Biswas 1984 ; Deosthali 2002 ). Such frequent water scarcity events adversely affect the agricultural productivity, particularly during the rabi (postmonsoon) season ( Khambete and Biswas 1984 ).

The drought events experienced over the study area are also classified in drought categories according to their intensity (SPI value). It can be observed that the drought frequency is inversely associated with the drought intensity. In other words, the extreme drought events (SPI ≤ −2.0) occur very rarely and vice versa. In the study area, extreme droughts were experienced in 1994 and 2003. However, in the Man and Sina basins (about 70% of the study area), these two droughts were observed with relatively less intensity and hence were categorized as severe droughts, which occur once in 28 years. Among these two drought events, the drought of 2003 was the worst experienced by the study area after the 1972 drought conditions ( Todmal 2016 ). Interestingly, these two severe drought events (1972 and 2003) have an interval of about 30 years, which is almost comparable with the drought frequency results ( Table 1 ) obtained in the present study. It supports the estimation of extreme drought conditions in the early 2030s ( Fig. 4a ), which are expected to occur with intervals of ~30 years from the 2003 drought. The droughts of 2011 and 2012 were observed with moderate to severe intensity over the study basins. The moderate drought event is experienced once in a decade. As these droughts are comparatively less intense, they may occur consecutively for more than two years (e.g., the droughts of 1985/86 and 2011/12). It can be noticed that majority of the droughts from this category have occurred after 2000 ( Table 1 ). Although marginally below-normal rainfall or mild dryness (SPI between 0 and −0.99) is not treated as a drought event, it can potentially exacerbate the drought condition if it occurs successively after severe drought. In spite of less intensity, these events probably cause serious socioeconomic damages to a large extent due to the occurrence once in 3–3.5 years. In the Fifth Assessment Report, the IPCC (2013) has estimated an increase in intensity and frequency of droughts over the semiarid regions of South Asia (including the present study area). From Table 1 it is apparent that the frequency of droughts has increased over the study area during the last two decades, perhaps partly due to human-induced climate change. Similar inferences regarding increased frequency of droughts at the all-India level were drawn by Gore et al. (2010) .

Average drought return periods for the study region. The listed drought events have occurred at least in one or more basins. The frequencies of moderate, severe, and extreme drought events are averaged for the study area. The droughts with severe intensity are observed with moderate and extreme intensity in some basins. The drought of 1994 was observed with extreme and severe intensity in the Sina and other basins, respectively.

Figure 6 displays the connection between the SCPI and SPI for the average monsoon rainfall over the entire study region. It can be stated that the agricultural productivity is heavily dependent on the monsoon rainfall, as all the selected crops show positive and statistically significant relationship with monsoon rainfall (SPI) ( Table 2 ). Particularly, the rainfed crops such as sorghum and gram ( r 2 = 0.51 and 0.41, respectively) reveal comparatively good agreement with SPI. As mentioned earlier, the study basins experienced severe and regionwide droughts in 1985/86, 2002/03, and 2012. Being rainfed crops, the productivity of sorghum, pearl millet, and gram crops was affected to a greater extent during these drought years ( Fig. 6 ). Exceptionally, SCPI values for all the selected crops other than sorghum are near normal in the drought of 2002 (SPI = −1.17). Among the high water-requiring crops, the productivity of wheat exhibits a notable decline during the drought years, as it requires higher amount of water (than millet). This can be observed during the regionwide droughts in 1986, 2002/03, and 2012 ( Fig. 6 ), when almost all the selected talukas registered below-average yield of wheat. Similarly, the moderate drought of 1997 resulted in the failure of wheat crop to a considerable extent. The worst droughts (with minimum SPI) observed over the study area are in 2003 followed by 2012 and 1986. Among them, the drought of 2003 affects the productivity of rainfed crops and irrigated crops as well. During the last three decades, the extreme drought condition in 2003 resulted in a maximum decline in the productivity of sorghum, sugarcane, and wheat (SCPI = −2.32, −3.26, and −2.13, respectively). As compared to them, pearl millet, gram, and pigeon pea (SCPI = −1.32, −1.83, and −0.52, respectively) are the less affected crops in 2003. Although sugarcane is irrigated through surface water resources, perhaps due to acute rainfall deficiency for two consecutive years, the maximum decline in sugarcane productivity (SCPI = −3.26) was registered in 2003 ( Fig. 6 ).

Coefficient of correlation ( r 2 ) between agricultural drought indices and SPI for the entire study region. Asterisks (*) denote a statistically significant relationship at the 95% confidence level.

Figure 7 show the temporal fluctuations based on SCAI values for the major crops in the study area. It is evident that lower intensity meteorological droughts (mild to moderate) do not seem to have any noteworthy impact on the crop hectarage. The insignificant relationships between SCAI and SPI for all the selected crops (except wheat) corroborate the same fact ( Table 2 ). The majority of the selected crops are rain-dependent; these are sown during the early part of the monsoon season, irrespective of the performance of the monsoon during the following months. Therefore, even during the below-normal monsoon year, the area under rainfed crops, as per the government records, is broadly comparable with the cropped area during the normal or wet monsoon year. Perhaps due to this reason, the impact of severe droughts in the 1980s and 1990s is not evident for sorghum, which is a rainfed crop and covers the maximum area in the basins. It is only the severe and extreme meteorological droughts that have a negative impact on the hectarage of rainfed crops ( Fig. 7 ). Almost all the crops exhibit below-average hectarage in 2003, which was an extreme meteorological drought year, preceded by a moderate to severe hydrometeorological drought year (2002). The impact is particularly observed in the case of sorghum, pearl millet, gram, pigeon pea, maize, and wheat crops. Even the area under sugarcane had declined ( Fig. 7 ), which reflects a reduction in the surface and subsurface water available for irrigation. The 2003 meteorological drought, therefore, could also be broadly classified as an agricultural drought year. It is important to mention here that the regionwide drought of 2003 and the flood disaster in 2005 together have consumed more than INR 175 billion, which is more than state’s planned budget (INR 150 billion) for irrigation, agriculture, and rural development for 2002–07 ( World Bank 2008 ; TERI 2012 ). Similarly, the area under almost all the crops recorded an above-average area in 2010, which was an excess monsoon year ( Fig. 7 ). Surprisingly, the impact of 1998, which was the wettest year on record, is not apparent from the plot. However, there is a remarkable increase in the variability in the crop hectarage after the 1998 wet year.

It is a well-established fact that the cropped area under rainfed crops in Maharashtra is getting replaced by cash crops/high water-requiring crops ( Kalamkar 2011 ; Todmal and Kale 2016 ). In the present investigation, same fact is apparent. Broadly, the below-average area under rainfed crops is observed during the recent years (particularly after 2002). Further, the cropped area under sugarcane, onion, and maize exhibits an increasing trend, particularly after 1987. During the last two decades, the area under sugarcane and onion is very frequently above the long-term mean ( Fig. 7 ). In comparison, the rainfed crops show a considerable decline during the first decade of this century (2000–10). Perhaps because of the availbility of surface water irrigation facility, in spite of the increase in drought frequency and intensity the cultivation of high water-requiring crops (sugarcane and wheat) is increasing (after 1995) at the cost of rainfed crops (sorghum and pearl millet). The obtained results compare well with the study carried out by Todmal et al. (2018) . There was an unusual increase in the area under sorghum crops in 2008. The reasons for that could not be ascertained.

On account of the limitation in the agricultural data (mentioned in previous section), the variations in the agricultural cropped area do not show good agreement with SPI ( Table 2 ). However, Dodamani et al. (2015) have found a good positive relationship between NDVI and SPI for the upper Krishna basin. Therefore, in order to ascertain the role of monsoon rainfall to determine variations in agricultural cropped area in the study area, freely available satellite images of Landsat-5 TM for the years 1998 and 2002 (SPI values 2.21 and −1.17, respectively) were analyzed. From Fig. 8 it is evident that the frequency of positive values is higher during the wet year (1998) and the frequency of negative values is higher during the dry year (2002). The frequency plot shifts from right (positive NDVI) to left (negative NDVI) with a decrease in the monsoon rainfall. The positive NDVI values indicate dense or healthy vegetation cover and vice versa ( Myneni et al. 1995 ; Pettorelli et al. 2005 ). The average NDVI value for the wet monsoon (1998) and drought monsoon years are +0.14 and −0.17, respectively. Additionally, the result obtained from a Student’s t test suggests that the basin-wise NDVI values for the year 2002 are significantly lower than those that are observed in 1998. Thus, it is clear that the area under vegetation cover (and cropped area) significantly drops during the drought years.

The present investigation has confirmed that the study area has experienced three major region-wide drought events in 1986, 2003, and 2012. During the droughts of severe to extreme intensity, the estimated monsoon precipitation is notably low (<330 mm); this is the worth considerable finding to manage the water scarcity challenge, particularly in the Agrani and Karha basins. The meteorological droughts over the study area are mainly accountable for the agricultural droughts. Therefore, in order to sustain the agrarian economy through better agricultural yield, drought disaster management authorities should focus on the severe to extreme droughts and the mild droughts too (as they occur more frequently). The role of anthropogenic activities and climate change in the rain shadow zone of Maharashtra need to verify, as the frequency of droughts augmented during the recent years. The significant relationships between SOI and SPI and between SOI and IOD (dipole mode index) that have emerged from the present investigation can be incorporated to precisely forecast the monsoon as well as droughts over the study area. The future rise in temperature (up to 2050) will not only amplify the meteorological droughts (through augmentation of evaporation rate and agricultural water demand) over the study basins but also adversely affect the agricultural productivity, which may lead to severe socioeconomic damages. Therefore, under the climate change scenario, to cope with the future drought disaster (in the early and late 2030s) in the semiarid region of Maharashtra, it is pertinent to increase the level of water literacy of society; additionally, agronomists and water resources managers must have well-defined strategies.

Acknowledgments

The author would like to thank all the government agencies for supplying the required data for this study. The author also expresses his sincere gratitude to Dr. Vishwas S. Kale for his valuable guidance to carry out the present work. The author is grateful to the anonymous reviewers for their comments and suggestions, which helped in improving this manuscript.

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Why do parts of Maharashtra experience different water stress levels? | Explained

Even when wells and reservoirs run dry in marathwada, maharashtra’s coastal areas experience severe flooding.

Published - June 26, 2024 05:00 am IST

There is a mass migration of people out of dry Marathwada to Western Maharashtra’s Sangli, Kolhapur, Pune, Satara, Solapur, and Ahmednagar, a.k.a. the sugar belt. | Photo Credit: Emmanual Yogini/The Hindu

After the deficient monsoon last year, the Maharashtra government declared many parts of the state to be drought-hit earlier this year. Much of the Marathwada region received less than 75% of its average rainfall. Its impact manifested across the region over this summer as wells ran dry, and officials brought tankers to provide drinking water and water for irrigation. Multiple reservoirs in the region, especially in Beed and Dharashiv districts, were reported to have 0% ‘live’ water storage left. This situation is in sharp contrast with the State’s coastal areas, where rainfall has often been in excess, leading to severe flooding.

Such paradoxical conditions are in fact visible across the country, where different climates, agroecological features, water sources, and human-driven land use changes have created multiple challenges in different landscapes, exacerbated by climate change. This diversity is why climate adaptation measures have been challenging to formulate and implement. They have to be tailored to the precise drivers of risk in a place and to communities’ needs. Such analysis is particularly important in regions like Marathwada, which has faced a string of droughts over the last few decades, resulting in the loss of thousands of farmers’ lives. The region’s predicament is shaped by its geographical location, topography, soil type, agricultural practices, and crop choices.

What is the rain-shadow effect?

Marathwada lies in the rain-shadow region of the Western Ghats. When moist winds from the Arabian Sea encounter these mountains, they rise and cool, causing heavy rainfall (2,000-4,000 mm) on the western side. But by the time these winds cross the Ghats and descend into Western Maharashtra and Marathwada, they lose most of their moisture, leaving Marathwada relatively much drier (600-800 mm).

A 2016 study by IIT Gandhinagar researchers indicated that climate change is worsening the situation in central Maharashtra. The region has experienced an increasing trend in drought severity and frequency of late. As a result, Marathwada and North Karnataka have emerged as the second driest regions in India after the country’s northwest region.

What’s the effect of water demand for crops?

The agricultural practices of Marathwada are not well suited to its low-rainfall regime. A major contributor to the region’s water crisis is sugarcane cultivation. Sugarcane requires about 1,500-2,500 mm of water in its growing season — outstripping what natural rainfall in the region can provide. While pulses and millets require four or five irrigations across the crop life, sugarcane needs to be irrigated almost every day.

Indeed, the traditional crops of this area — including cotton, pulses, and millets — require relatively less water. But the area under sugarcane along with the number of sugarcane mills increased steadily between the 1950s and the 2000s. The extent of sugarcane cultivation plateaued in the past decade due to the limits of water availability. Still, the crop currently occupies 4% of the total cropped area in the region but consumes 61% of the irrigation water. As a result, the average river outflow in the upper Bhima basin has almost halved .

Long-standing government support for sugarcane pricing and sales has expanded water-intensive sugarcane irrigation, which has restricted the irrigation of more nutritious crops. For every one acre of sugarcane, for example, four acres of traditional crops are deprived of water. Since December 2023, the Indian government has been promoting sugarcane-juice-based ethanol production, which may not be a wise decision for this water-starved area. The country needs its sugar but 82% of the sugar grown in Maharashtra comes from low-rainfall areas.

The State has also been incentivising sugarcane production in the region for decades. The interests are deeply entrenched now as many sugar mills are owned by leading politicians. The Maharashtra Water and Irrigation Commission in 1999 recommended that sugarcane should be banned in areas that receive less than 1,000 mm of rainfall per year, but production has only increased.

What is the local soil composition like?

Marathwada’s soil composition further complicates its water management challenges. The region has predominantly clayey black soil, locally called “regur”. This soil is fertile and retains moisture well. However, it has a low infiltration rate, meaning that when it does rain, the water is either logged or runs off rather, but doesn’t percolate down to recharge groundwater.

To capture this high runoff, Maharashtra has been building many dams — such that it is today the State with the most large dams in the country (1,845), more than double the next State on the list.

The clayey black soils have low hydraulic conductivity and hold on to the water for a long time after rains. The clay particles are so small (<2 micrometres) that they have a high affinity to water particles, even holding on to them against gravity. WELL Labs’ work in the region has found that a lot of farmers in the area face crop loss due to such water-logging.

What are the effects of topographic variations?

Within Marathwada, water scarcity is not uniform: the uplands and the valleys behave differently. The area has parallel tributaries of the Godavari and the Krishna flowing southeast. Each tributary flows in the valley and is separated by a gently sloping hill. The valleys have perennial groundwater while the uplands have seasonal groundwater. This is because groundwater slowly moves underground from upland areas to the valleys.

Typical hydrogeological cross-section of a Deccan trap basalt micro-watershed to illustrate the occurrence of groundwater bodies with related water-supply prospects and management needs. | Photo Credit: World Bank (Case Profile Collection no. 18)

The wells in upland areas often dry up a few months after the monsoons — and this is where the water scarcity is most acute. The photographs of drinking water scarcity and dry wells that emerge from Marathwada are often from upland areas. They are at a natural disadvantage and must be given special support.

To ensure source sustainability of the drinking water sources in the region, for example, the State government should consider pumping the water uphill and improving surface water storage for drinking. This may be expensive but will improve the resilience of these disadvantaged areas.

Can Marathwada become more water-resilient?

We know climate change is expected to induce more frequent and more intense droughts in Marathwada and Western Maharashtra. It is imperative then that drinking water sources and livelihoods in the region become more resilient. This in turn needs both supply-side and demand-side solutions.

Supply-side solutions are about making the most of the available resources. They include classical watershed management work (such building water-conserving structures like contour trenches, earthen bunds, gully plugs, small check dams, etc.). 

But water conservation structures are also not a panacea. Rainwater that runs off agricultural fields also carries the very soil that doesn’t allow the water to percolate. So a lot of these structures accumulate silt and stop working after a season or two. Funds under the Mahatma Gandhi National Rural Employment Guarantee Scheme could be used to address these specific challenges, such as designing silt-trapping mechanisms and organising training programmes for farmers on periodic desilting.

How can we manage water demand?

Supply-side solutions ultimately don’t create new water. They only help capture some of the rain more effectively. In a low-rainfall region, we must still manage water demand, including by practising water-efficient irrigation, cultivating drought-resistant crops, and diversifying livelihoods.

Ultimately, we must control sugarcane production, if not reduce it. Marathwada must shift to other high-value, low-water-using horticultural crops, while sugarcane production — both for food and for ethanol — must move to wetter states like Uttar Pradesh, Bihar, and West Bengal, which receive more rainfall.

Marathwada’s water crisis is a stark reminder of a delicate balance between agricultural practices and environmental sustainability. By adopting more sustainable policies and agricultural practices, drought-prone regions in peninsular India can mitigate their water crisis and build a more resilient future in the face of climate change.

Vivek Grewal is a groundwater hydrologist and managing partner (technical consulting) at WELL Labs. Veena Srinivasan is the executive director of WELL Labs.

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community water management / water / water (natural resource) / Maharashtra / The Hindu Explains

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Humanitarian Response to Maharashtra Drought Disaster: Marathwada Case Study

The ramification of climate change has led to an upsurge in humanitarian aid in the fashion of providing basic sustenance like food, shelter and medical care. The aftermath is chaotically weighted against the impoverished poverty-stricken masses with the infinitesimal collateral assets to overcome climate shocks and stresses. Humanitarian relief can help to focus on the repercussions of climate-related crunches, but a massive escalation in international efforts is needed to alleviate and acclimate to global warming, curtail the liability of disasters and restrain the suffering. Paltry levels of precipitation over the years coupled with insufficient and irregular rain gave rise to precipitated drought conditions in western parts of India. It is the most distressed and has recorded moderate to severe drought conditions in most of its districts.

When societies are affected by drought, FAO caters for support to help them quickly get back on their feet and start producing food. In the aftermath of drought, cash transfer mechanisms are provided to the neediest and underprivileged people, while refurbishing vital irrigation framework, water reservoirs and feeder roads which will boost food production in the longer term. In the most drought-prone areas, people are provided with cattle to rebuild their herds and ensure they can keep producing milk as a source of income. Farmers are encouraged and provided with quality seeds and farming inputs as well as given new ideas about investing in drought-resistant techniques which can be adapted, in time for the next rains. Millets and other drought-resistant crops are also advised to be grown in regular intervals to avoid the scarcity of food shortage and famine.

More than fifteen per cent of the citizenry accounting largely for hundred and thirty million netizens, across seventy thousand villages and two hundred and thirty urban hamlets are affected due to drought. Women and children being the most susceptible segment of the drought-affected population. In the severely distressed areas, roughly sixty million people – including nine million children, one and half million pregnant women and lactating women –  comprise the high-risk group. The obligatory duty of walking long distances to obtain water often falls on women and juvenile girls. The livelihood of the rural population has also been affected as cattle have died from starvation and agricultural production has been threatened. As a result, seasonal migration was amplified, with whole communities going to nearby cities. of families found shelter, food, water, and some work in the government’s relief camps in the most affected districts.

The paucity of water has provoked the poor and marginalised sections with thousands of litre available water. With the emergence of drought, the level of salinity and fluoride has increased in all areas. The water tables have dropped below normal and are significantly very low. Handpumps operations have broken down in several places due to poor maintenance and excessive usage. Excessive pumping of groundwater to cope with drought impacts have led to groundwater depletion, which not only poses a serious threat but is also, an important concern of Maharashtra State.

In the Marathwada region, water scarcity is not rare in summer – although its severity is exceptional at times leading the Governments at central and state levels to be prepared and to develop and come up with contingency plans. One of the large-scale governmental strategies is to authorise relief camps where families were provided with work, shelters, food, and health care. Care and protection for women and children were a priority in these camps. They are provided with health care, nutrition and education.

With the onset of the monsoon, some of the relief camps start terminating and operations get ceased for a time being, the  Government, continues to seek support from international agencies, with its efforts to help the most affected population in the mid and long-term. Indeed, with the emergency phase being called off, after the onset of the monsoon, it is of utmost importance to intensify the root causes of the crunch and bring resolutions for the long run. Drought-prone states of Maharashtra need to develop strategies and policies and mobilise adequate resources to prevent future severe droughts.

In the lexicon of great needs, it has been imperative for United Nations to carefully design its assistance. While UNICEF, UNDP, UN Women along WHO released immediate assistance through its state offices, it was decided to focus on long-term assistance to help mitigate such situations in the future.

United Nation’s acknowledgement for drought mitigation in the affected areas is a methodology based on a swift investigation conducted through field visits and via series of dialogues with Government counterparts. The predominant objective is to equip immediate relief to women and children in the water distressed localities and to curb health issues, including epidemic outbreaks like famine, diarrhoea, malnutrition and dehydration. Instant relief operations are carried out by nodal agencies like UNICEF which significantly contributes to addressing major concerns such as availability of drinking water, primary health care for women and children nutrition and health.

United Nations-supported schemes for the availability of drinking water supplement efforts through tanker supply, revitalization of handpumps, power pumps and installation of new handpumps. WHO also expediated precautionary and remedial health care system through procurement of essential drugs, vitamin supplements, iron tablets, Oral Rehydration Salt packages, disinfection of drinking water and on-site sanitation facilities.

The mid-term frame of reference to bolster the availability of drinking water in rural areas: the classical long-established response to the drought-related dearth of water has been to devise new sources, further capitalisation of existing sources or bring water to improvised areas by tankers and trains. This technique of methodology has not been altered in the last several decades, although such mediations have failed to provide lasting solutions.

Sources of Drinking water can be maintained by administering substantial environmental protection and management of the water sources at regular monitoring, with the help of community participation. This can be enacted by rain-water harvesting at catchment areas through the systematic erection of check dams and other recharge methods of architectures. This also equips an alternate source of employment to the natives, as pastoral activities have ceased due to crop failure and fiasco in the loss of cattle. UNICEF campaigns strongly about the construction, maintenance and management of these structures should be upheld at the community level via the locally elected bodies like panchayat. At the household level, rainwater rooftop harvesting will be promoted as an option to ensure household water security.

In consultation with the State Governments and nodal governing agencies, the United Nations is determined strongly to aim attention at its efforts in the mid-and long-term results to devote to drought prevention. Indeed, empiricism at the grass-root status depicts that planning at the micro-level, involvement of the localities and community-based solutions, will allow interior villages and hamlets to prevent the detrimental fallout of water scarcity. Along with the Government and civil society, Unicef works constantly to support these causes and to develop a stable and safe environment and policies. At the end of May 2000, UNICEF led a joint UN mission to identify long-term initiatives.

For a sustainable long run, UNDP, are engaged in functioning side by side with the state governments to expedite and promote the evolution of adequate and competent policies and programmes for drought-prone areas. It comprises facilitating the decentralisation and fragmentation for better management of water sources at the individual and community level. The ultimate challenge is to maintain the higher interests and greater good for all by the decision-makers in issues on water, after the onset of monsoon and termination of drought. In Maharashtra, at the request and initiative of the state government, UNDP along with various UN nodal agencies continues to support the development of a white paper on water management.

To be legitimate, impartial, principled and fair, the government has been proactive in dealing with matters related to the drought situation, but a lot more can be achieved with the advancement of science and technology. To date, the focal drought preparedness proposal and procedure consist of just donating money in the name of ministerial funds or alternative packages to the affected people. Also, the cattle shades, school programs, women empowerment schemes and initiatives are mostly undertaken by CSR or stakeholders other than the government. They do provide water by tankers or by using train water supply, but serious examination should be about the feasibility of such measures prevailing in modern times. We can utilise and call for action new water harvesting technologies to save water during the monsoons. For instance, cash crops that require lots of precipitation intake should be cultivated depending upon the availability of water. Also, the usage of green or natural pesticides and the practice of local HYV seeds should be given utmost preference over western technologies. Also providing insurance will be a great help to the farmers apart from setting up local agricultural banks which will provide loans to farmers.

For projects on large-scale water harvesting, they can rely on NREGA schemes, which will give them an interim livelihood and sustainability in the future.  The alignment of NREGA with agricultural programmes and allied sectors will lead to enhanced yields. The scope of works under NREGA is under expansion to include lands of small marginal farmers, it is now possible to significantly enhance the irrigation potential in rain-fed areas and drought-proof small-holder agriculture, leading to sustainable and higher yields.

The main aspiration of the NREGA proposal is to implement complementing recruitment chances with the auxiliary objective of eco-restoration & renewal of the natural resource base for viable rural livelihood. This will aid in transparency and accountability to permeate rural governing bodies, leading to the calcification of grassroots level democracy. The following water-based projects are listed under the domain of the NREGA scheme for drought preparedness. • Water harvesting • Desalting of tanks • Micro and minor irrigation works • Renovation of traditional water • Provision of irrigation facilities bodies • Flood control and protection works.

 Directions and guidelines given in the program are aligned to SFDRR priorities. The AIDMI team is devoted to achieving activities mentioned in the proposal in AIDMI’s ongoing projects and activities. The NDMP provides a framework and guidelines to the governmental agencies for all stages and aspects of the disaster management cycle. The NDMP is a “dynamic report” in the sense that it will be improved regularly keeping up with the ongoing global best practices and knowledge base in disaster management in lieu to the provisions of the Disaster Management Act, 2005, the guidance given in the National Policy on Disaster Management, 2009 (NPDM), and the established national practices with the country.

Poverty and risk to disasters are inextricably linked and mutually reinforcing. The poor section of the society is worst affected in case of disaster. The situation further aggravates due to the compulsion of the poor to exploit environmental resources for their survival, increasing the risk and exposure of the society to disasters, in particular those triggered by flood, drought and landslides. Poverty also compels the poor to migrate and live at physically more vulnerable locations, often on unsafe land and in unsafe shelters. These inhabitations of the poor at such locations are either because there is no other land available at a reasonable cost or it is close to the employment opportunities. The inhabitants of the poor people on marginal land are prone to all types of disasters. The type of construction of these houses further deteriorates the condition. These dwellings made up of low-cost material without giving much consideration to technical aspect are easy targets of various hazards.

 Drought is a recurrent phenomenon in Maharashtra State. Recently Maharashtra State has experienced a drought of moderate severity which commenced in 2011 and continued, expanded and further deteriorated into 2012. This drought, along with the other droughts that have occurred previously, threatened the agrarian economy of the Maharashtra State and caused considerable social and economic impacts on farming communities. Farmers were aware of the drought and also well perceived the various socio-economic and environmental impacts of drought in the Upper Bhima catchment. Failure of agriculture subsequently resulted in a lack of employment for unskilled labourers, which further exacerbated their livelihood situation and ultimately weakened the financial situation of farmers. Poor farmers affected by drought could not afford to participate in the celebration of festivals and showed a common tendency of postponement of wedding ceremonies due to drought. Less-educated farmers reported that drought-driven water scarcity has caused conflicts in society. It is also found that farmers from frequent and severe drought-affected areas considered drought as the main cause of suicidal tendencies due to lower incomes and high indebtedness. Environmental impacts of drought were perceived to be high to very high.

To mitigate the drought impacts farmers used various drought preparedness and adaptation measures. With the anticipated drought, farmers stored crop harvest (grains), stored crop residues for livestock, saved money, migrated for employment, sold livestock for income generation (and also because they were unable to provide food and water for the livestock), and sought an alternative source of income through employment under NREGA, labour for local construction work, sand mining etc. Although farmers were familiar with autonomous adaptation options in agriculture, less preference was given to their adoption. It is found that low education, small landholdings size and low incomes were major constraints in the adoption of these adaptation strategies discussed earlier.

Recurring drought is a major challenge in the Drought Prone Area of Maharashtra State in India. Agriculture (e.g., rainfed cropping and livestock) is the primordial income activity of over 64% of the state’s population. The objective of this case study is to grasp and comprehend the rural farming community’s perception of drought impacts on their socio-economic activities and environment, their adaptation at the household level and opinions on government drought mitigation measures.

Special attention should be given to while designing and formulating policies for increasing community resilience to future drought events. Also, the extent of irrigation was found to not affect the farmer’s perception of drought impacts and adoption of adaptation strategies, mainly due to a prolonged drought with moderate to severe intensity over the whole catchment. Emphasis should be given to water harvesting techniques to increase the extent of irrigation coverage. Besides household-level adaptation measures, administrative strategies played a very crucial role in adapting to drought. As a response to serious drought events in the state, the government has undertaken various relief measures. It was observed that the mitigation measures provided relief to affected households to some extent, but the level of satisfaction was still low amongst beneficiaries due to ineffective planning and management.

Responses to drought in Maharashtra. States are generally receptive under the conclusion of crisis management and poorly implemented strategies due to lack of coordination. Hence, the state calls for a change from a cognizant crisis management strategy to a more proactive game plan. This is persistent with the findings from other countries as examples through which lessons can be learnt for a greater cause and existing strategies ought to be considered for implementation in India. The case study is based on both secondary and primary data collected via a survey of 223 farming households. The results show that a decrease in the yield of cereals, horticultural crops, livestock production and loss of employment, all associated with decreased income of farmers, were the most immediate economic impacts of drought.

The NDMP assimilates substantively the technique enunciated in the Sendai Framework and help the country to meet the goals set in the framework. Equivalent water-based projects can also be used in climate change adaptation through community involvement and as means of conscious choice of livelihood. Conservation technologies should be stress-tolerant whereas providing climate-resilient varieties of seeds, drip irrigation, zero-tillage methods of agriculture, raised-bed planting, laser-levelling, Systems of Rice Intensification (SRI), can build flexible capacities to adapt with increasing water exploitation and shortage, providing “more crop per drop”. Similarly, strengthening land development practices such as land levelling, conservation bench terracing, contour and graded bunding, and pasture development prevent soil erosion and loss of organic matter. Reclamation of wastelands and degraded lands together with afforestation, horticulture plantation and agroforestry has the potential to sequester carbon both above and below ground, thereby contributing to carbon mitigation. Also, other projects such as land development, horticulture and road network development can be used for climate change adaptation in a drought situation

Conclusion :

Based on the findings for this study, the following recommendations are provided to improve farmers’ resilience and to enable farmers and governments to better

combat future droughts:

• Promotion of various micro (farm) as well as macro (National) level adaptation strategies amongst farmers with the help of government officials to cope with drought.
• Developing, introducing and implementing water harvesting practices at the community level and in situ water harvesting practices such as conservative agriculture should be introduced through community participation
• During drought, about 75% of farmers use flood irrigation practice to irrigate their crops.
• To save wastage of water, traditional flood irrigation practices should be changed to water-saving irrigation practices such as sprinkler or drip irrigation
• The introduction of crops that consume less water and drought-resistant varieties of crops should be explored as a way of increasing resilience against drought and reducing crop failure in dry spells
• Television, radio and newspapers should be used as a tool to disseminate weather information to the larger community about the current and predicted  state  of the drought and also drought adaptation practices
• Although there are government drought relief measures, community-based effective planning, implementation and management should be done to overcome the failure of the relief measures.

Written by Anamitra Banerjee

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Droughts in Maharashtra: Lack of management or vagaries of climate change?

Recent news has been flooded with reports of the severe drought situation in the Marathwada and Vidarbha regions of Maharashtra. Even more shocking are the reports of large-scale suicides by farmers due to crop losses.

Although the government has announced a relief package for drought-affected areas, these sort of quick- fix solutions are not enough to solve the real problems on the ground, argues Suneel Joshi.

Maharashtra is experiencing drought this year too. Why does this happen every year?

For this, we need to understand the geographical and climatic situation of Maharashtra. As high as 80% to 84% of the agriculture in Maharashtra is rainfed, which means that it totally depends on rainfall for its crops but there is a huge variability in rainfall in different regions of the state.

One-third of the state falls under the semi-arid climatic zone and has its agriculture dependent on the monsoons. Deficient rainfall is reported once every 5 years and drought conditions occur once every 8-9 years. Marathwada and Vidarbha have been experiencing severe drought over the last three years due to deficient rainfall and this has further worsened the situation with a drastic drop in groundwater levels, acute water shortages and severe loss of crops during the kharif and rabi seasons.     Over the last 5 years, newspapers have been full of reports related to relief being provided in the form of water tankers supplying water daily to drought-affected districts and water shortages have affected domestic needs, agriculture, livestock, and livelihoods of hundreds. The worst hit are the resource poor and marginal farmers. Despite this happening over and over again, the irrigation in the state is very low at 16% as compared to the national average of 42%. Over-dependence on private sources of groundwater use such as tube wells, bore wells, wells and piped water, limits access of farmers to water resources and has also led to over exploitation and severe drop in groundwater levels in the area.

Thus, the major problem in this area is the lack of assured water supply as no other methods of irrigation are utilised. Rather, irrigation is more developed in western Maharashtra as compared to Vidarbha and Marathwada, which needs it the most. Both regions continue to remain relatively backward in terms of socio-economic indicators as well. Is it due to geography, climate change or mismanagement of resources? Geography is a factor that we know about for a long time. Climate change has worsened the situation over the last few years, but what is more worrying is the lack of planning, short sightedness and pure disregard shown for the situation at the policy level. The vulnerable situation of the area is already known, but we still depend on dams for water, which goes to the farmers at a price.

What will the small and marginal farmers do? We still focus on water-intensive crops like sugarcane. for better and assured money. The poor farmer is forced to practice an agricultural model based on demand and supply where he is unable to get an output that is at least equal if not more than what he put in. Development of industries and cities have also put an additional load on water resources from dams, which in many cases are diverted to cities. The farmer is thus caught in a web of unending demands and a maze of circumstances from where there is no way out. What is the actual situation of farmers in the area? Who are the ones committing suicide? We have to understand the situation of a farmer in a broader context. Our policies have not looked at the overall development of farming communities. Our farmers in the area are totally dependant on land for their livelihoods. It is only a few farmers that also have other members in the family working in the cities, who can provide additional income to the family in times of crisis. And farming is a resource intensive process. You need money to buy seeds, fertilisers, pesticides, water, manpower, electricity. You put in all the money for resources and then depend on the rain and climate to do their bit. When the vagaries of climate take their toll, the farmer has no way out. He is then caught in the loan web to sow the next crop for the next season. Take the example of Vidarbha where cotton, tur and soyabeans are the important crops. Low levels of groundwater and irregular supply of electricity makes it very difficult for farmers. There is only 4% irrigation in an area where the capacity for irrigation can be as high as 65%. So the farmers have to invest in tube wells, wells and pipelines in an area which already has dangerously low levels of groundwater. So why then does farming  become unremunerative? It is this emphasis on cash crops, overdependence on monsoons, low productivity, poor irrigation facilities and dependence on wells in an area where the water tables have already gone down coupled with poor electrification, which makes it very difficult for the farmer. And do our policies make it easy for him? No way! So what happens when the farmer ends up with very less produce and cannot get back an amount even equal to what he has invested in his farm? He has to look out for another season of good harvest with which he can support his family and continue cultivation. He then falls into the trap of procuring loans in the hope of a good harvest next season.

With no guarantee of a good crop even during the next season within the limitations they have to face, the farmers continue to borrow from money lenders as they get money on demand. Getting loans from banks and cooperatives often takes long and they have to go through agents at times, who demand commissions. And mind you, my experience shows that farmers are extremely sentimental and proud with genuine attachment for the piece of land they own. It is our policies that have unfortunately been unable to understand their value and give them the respect they deserve by treating them as beggars! Farmers caught in this trap of cyclical indebtedness then have no other option, but to resort to suicide!

Why do you think this situation has arisen? What is the real problem and the ground level situation? I think this situation has arisen only because of the lack of sensitivity at the policy level to understanding agriculture as an important occupation and not only as a revenue generator but also as a food generator. None of the policies seem to be designed while keeping in mind the farmer and his convenience. We have ignored irrigation, there is no infrastructure provided to take care of the produce from the farms that can degrade fast such as onions and other vegetables. These need cold storage facilities, which still do not exist on a large scale leading to massive wastage of resources.

State and national policies are often found to favour input-oriented markets. Farmers are forced to buy genetically modified seeds at high prices. Fertilisers and pesticides also come at a price, which farmers have to buy from companies. So who benefits? It is the manufacturers who sell these at ridiculous prices and make farmers dependent on these products. Thus it is the manufacturing companies of these agricultural products that stand to gain while the farmer is left to his own destiny. The governmental system also brought in subsidies in agriculture which have put the farmer into the additional pressure of corruption by which an average farmer has lost his belief in the system. Water was also converted into a commodity, and not as a common resource to be utilised carefully by farmers. This led to diversion of water to dams, there was no effort to encourage farmers to focus on harvesting water and making it available at the local level. Focus was diverted to cash crops like sugarcane, pomegranate and other fruits while local crops like jowar, bajra, oilseeds were not equally encouraged. The groundwater levels in the area are precariously down. No efforts are being made to utilise the short span of rainfall available to harvest water and recharge the groundwater in the region. The soil in the region has also deteriorated with less capacity of absorption due to high salinity. The government has taken some steps, are those enough? Will packages serve the purpose? What has the government done? It has announced 'packages' to take care of the suicide situation in the state. Trying to compensate for what can be called as the 'failure of the system' to value and understand the needs of the farmer by giving money is extremely wrong and insensitive! Can temporary means like handing away money really solve the problems of the farmers? Is the government aware of the real problems of the farmers?

It is firstly important to understand that if at all financial help has to be given it has to be given keeping in mind a long term plan for the farmer and his family. Why does the farmer get into this situation? It is because he is totally dependant on land for his income. I come from a farming family myself. Why did my family have to migrate? We had land, cattle, but no other source of income to depend upon in times of crisis. Can we try to help people to have education or skills to be able to seek other sources of income in addition to farming? Overall educational development of the family is also important and would also help in granting status to the occupation of farming.

What should be done in your opinion to deal with this situation? I think no temporary solution will help. What ever plans we make have to be well thought out and should not be restricted over 4 to 5 years, but should focus on the next 25 years. What we do should be planned keeping in mind the farmer as an important and central unit in agriculture. The problem of suicides among farmers needs to be tackled in a holistic way.

For example:

• Policies need to be designed to improve the education and quality of life of the farmers and their households along with improvement in infrastructural facilities at the village level. Developing other additional skills or income generating activities among farmers should also be encouraged to make them better equipped to cope with uncertainties arising out of cultivation.
• Improvement in bank lending mechanisms that help and respect the farmers and provide support and training should be encouraged, rather than banks functioning as structures that treat the farmer as a poor victim that needs loan waivers.
• Non institutional lending mechanisms like moneylenders should be brought under regulation so that they stop charging the farmers high rates of interest that increase the risk of farmers of falling into debt traps.
• Efforts need to be made to improve irrigation facilities in rural areas and to stop emphasis on dams. Farmers must be encouraged to harvest and use water in their own areas sustainably and equitably. Local streams, canals in the villages should be identified, deepened and widened to enhance harvesting of water. Rivers should be considered as important units of the village and revived.
• Development should be targeted at the village and towards small groups of farmers as units to bring about real change. In our country, farmers suicides have happened due to the failure of the cooperative movement.
• For cash crops like sugarcane, grapes and other fruits, cotton, tur and soyabeans, the crop insurance has to be strengthened . Innovative methods for loan settlement should be developed to help farmers to cope in times of financial crisis.
• Dependence of farmers on seeds from manufacturers and fertilisers must be stopped by encouraging development of local seed grower families , development of organic local fertilisers and pesticides and further development in products by using Ayurveda rather than using technologies based on western models.
• The knowledge of farmers is based on their years of experience with the local climate. This should be valued and incorporated at the policy level since most of the knowledge taught even in agricultural universities is based on western models.
• We should encourage research and development that can aid our farmers such as better weather predicting systems, knowledge generation that is based on the day to day needs and queries of farmers. We should encourage better dialogue between agriculturalists and farmers who can work together to find solutions to problems.

And I think, ultimately we need to remember that farming is a tremendously satisfying activity, it is a way of life for the farmers and we have to remember that they do the job of feeding us, which is considered as a highly selfless activity in our culture.

We should stop looking at farming as an industry that focuses on exploitation of the soil and other resources such as water to produce more and more for profit. It is an incredibly spiritual endeavour, attained by being in tune with nature, and using its resources with respect, care and gratitude. Suneel Joshi, is the State co-ordinator for Jal Biradari, an NGO working for water conservation and other environmental issues.

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Drought in maharashtra.

• ACT Alliance

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1. Brief description of the emergency and impact

The current drought in Maharashtra seems to have broken all previous records, with millions of human beings and livestock suffering hunger in absence of food and fodder. Almost one-fifth of Maharashtra is reeling under drought. Out of the total 22.56 million hectares of land under cultivation, only a small 3.96 million hectares are under irrigation, while the rest is left to the mercy of the prolonged dry period. As per the official estimate, 64 out of 355 districts recorded 50% or more deficient rainfall. However, one of the main contributing factors to the effects of the drought has been shoddy management of water resources, the lack of a proper policy on water distribution and the distribution of water to industries rather than agricultural fields.

The government has declared 15 districts, comprising of 11,801 villages, drought-affected. Some of the villages are also facing drought for the second consecutive year. The dams in Maharashtra are in a sad state, in spite of the fact that the state has the maximum number of dams in the country. ACT India Forum members are assessing the situation and will be in position to share details as they progress in this direction.

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Giews country brief: india 04-september-2024, ministry of home affairs disaster management division (national emergency response centre) situation report regarding flood / heavy rainfall in the country as on 29.08.2024 at 1800 hrs, ministry of home affairs disaster management division (national emergency response centre) situation report regarding flood / heavy rainfall in the country as on 28.08.2024 at 1800 hrs, ministry of home affairs disaster management division (national emergency response centre) situation report regarding flood / heavy rainfall in the country as on 27.08.2024 at 1800 hrs.

Maharashtra's major dams at full capacity for first time since 2018, sowing complete

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Mumbai: Good news Maharashtra ! On the eve of Ganeshotsav, all the major dams of the state are 100 per cent full for the first time since 2018 and sowing operations are complete.

This means, the drought-prone areas across the state may not face water scarcity for a year.

Big dams like Ujni (Solapur), Koyna (Satara), Jayakwadi (Chhatrapati Sambhaji Nagar) Bhatsa (Thane) and Vaitrana (Palghar-Thane-Nashik) have 100 per cent water stock, according to the assessment of the Water Resources department.

“For the first time after 2018, the major dams in the state are 100 per cent full,” officials said.

The details were presented at the weekly Cabinet meeting presided over by Chief Minister Eknath Shinde.

The Agriculture department informed that the state has received 121 per cent of the average rainfall and 102 per cent sowing has been done.

“Last year around this time, the average rainfall was 81.4 per cent,” the officials said.

The Kharif area is 142.02 lakh hectares in the state out of which 144.92 lakh hectares i.e. 102 per cent has been sown. Only five talukas have received 50 to 75 percent rainfall and 305 talukas have received more than 100 percent rainfall, they added.

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Over 85 pc of Indian districts exposed to extreme climate events Study

New Delhi, Sep 6 (PTI) More than 85 per cent of districts in India are exposed to extreme climate events such as floods, droughts and cyclones, according to a new study.     The study by IPE Global and Esri India also found that 45 per cent of the districts were experiencing a "swapping" trend, where traditionally flood-prone areas were becoming drought-prone and vice versa.     Using a penta-decadal analysis, the study compiled a catalogue of extreme climate events during a 50-year period from 1973 to 2023, employing spatial and temporal modelling.     The last decade alone saw a five-fold increase in these climate extremes, with a four-fold increase in extreme flood events, it said.     Districts in eastern India are more prone to extreme flood events, followed closely by the country's northeastern and southern parts.     The study also shows there has been a two-fold increase in drought events, especially agricultural and meteorological droughts, and a four-fold increase in cyclone events.     It found that more than 60 per cent of districts in Bihar, Andhra Pradesh, Odisha, Gujarat, Rajasthan, Uttarakhand, Himachal Pradesh, Maharashtra, Uttar Pradesh and Assam were experiencing more than one extreme climate event.     Abinash Mohanty -- head of climate change and sustainability practice at IPE Global and the study's author -- said, "The current trend of catastrophic climate extremes that makes nine out of 10 Indians exposed to extreme climate events is a result of a 0.6 degree Celsius temperature rise in the last century."     "Recent Kerala landslides triggered by incessant and erratic rainfall episodes, floods in Gujarat, the disappearance of Om Parvat's snow cover, and cities getting paralysed with sudden and abrupt downpours is a testament that climate is changed. Our analysis suggests that more than 1.47 billion Indians will be highly exposed to climate extremes by 2036," he said.     The study revealed that more than 45 per cent of districts were experiencing a swapping trend, ie, some flood-prone districts were becoming more susceptible to droughts and vice versa.     The number of districts that have transitioned from experiencing floods to facing droughts surpassed those that have shifted from droughts to floods.     Districts in Tripura, Kerala, Bihar, Punjab and Jharkhand exhibit the most prominent swapping trends, it said.     The study recommended establishing a Climate Risk Observatory, a risk-informed decision-making toolkit for policymakers at the national, state, district and city levels under its National Resilience Programme, and the creation of an Infrastructure Climate Fund to support sustained investment in climate-resilient critical infrastructure and foster locally-led climate actions.     Ashwajit Singh, founder and managing director of IPE Global, said, "To meet climate goals, India must shift its budget focus from mitigation to adaptation. Current practices underfund climate resilience, risking long-term sustainability. India, in particular, experienced an 8 per cent GDP loss in 2022 and a cumulative capital wealth decrease of 7.5 per cent due to climate impacts."     Agendra Kumar -- Esri India's managing director -- said the increasing frequency and intensity of heat waves, in conjunction with intense precipitation, was causing significant impacts on lives, livelihoods and infrastructure.     A holistic, data-driven approach is essential for informed policy decisions, climate adaptation and resilience, he said.

(This story has not been edited by THE WEEK and is auto-generated from PTI)

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Architect and Interiors India

Eco conscious takes centre-stage at this 16-cottage Tadoba resort

Waghoba Eco Lodge is a resort contiguous to the forest buffer of Tadoba wildlife sanctuary in the state of Maharashtra India. The target clientele for the property is wildlife enthusiasts and conservationists.

The brief required a 16-cottage resort with responsible tourism at its core. The developers sought to restore the previously cultivated land to its original state of being a deciduous forest as part of their ecological intentions. This required interventions in the landscape to enhance biodiversity. By observing the land around and through a biodiversity survey local flora and fauna were identified.

Retreat within the wild

For biodiversity to flourish it was essential to incorporate a water body in the semi-arid climate of this region. In the summers temperature here rises to 48-degree C. By identifying the existing bund and desilting the channels a lake was made at the entrance of the resort. This lake stored the rainwater and treated wastewater. Additional afforestation with indigenous plant species provides a green corridor for the fauna to approach the lake. This lake now is home to a plethora of birds and animals. A look out hide has been introduced in the design for enthusiast and professional wildlife photographers to watch the fauna in its own habitat. 

The landscape of Todaba’s has a variety of shades of gold to dark brown for the most part of the year and green during the monsoons. Capturing of the gold to brown hue has been done in the material palette of the architectural interventions. The material palette is a combination of local sandstone and stabilized adobes made at site using local soil. 

A reflection of nature

The lake becomes the loci of the project. The main building and its welcome area, edge this lake. Here the travellers also enjoy the rising sun when they are getting ready to leave for the forest tour. Belvederes along the dining area and the lounge enjoy views of the lake, forest, and the evening skies. Below this elevated lounge is a shaded swimming pool with its deck overlooking the lake. The ceiling of the pool which has filler of pot lids create dynamic reflection while ecologically reducing material consumption.  

The guests are accommodated in the cottages placed along the east-west axis at the core of the property. Each cottage has large openings in the north and south shaded by deep verandahs. Both the study desk and bed view the buffer areas giving an experience of living in the forest. As the peak season for safaris is summer the buildings are designed to reduce the cooling loads by using passive strategies. Composite stone and adobe walls on the east and west side reduce the heat gain. Toilet and the changing area shield the room from the western sun. A small skylit roof with heat reflective glass near the vanity wash basins brings in ample amount of light inside the toilet area. Vaulted roof made of conical pottery tiles with air gaps between and ceramic mosaic on top insulates the interior spaces from the incidental heat from rooftop. 

Sticking to the roots

The interior finishes of the guest areas continue the same colour palette of light to dark brown and terracotta reds. Floor with golden brown Kota (limestone) blends well with the adobe walls. The terracotta red roof tiles create a contrast and make the vaulted ceiling standout even more. The ferrocement lighting fixtures with warm yellow light set a very cozy mood.  

Staff housing on the west is carefully located to provide quiet space and privacy to its occupants while they are resting during breaks. The senior staff housing uses similar design language of the guest rooms with adobe walls and terracotta vaulted ceiling. Junior staff dormitories are designed to offer windows near every single bed space. Load bearing fin walls act as a partition between individual beds offering some level of privacy to its users. Water from the staff housing roof is harvested and stored in the 0.1million litre of water storage tank.

All the sewage water from the property is treated using a plant-based water treatment system PHYTORID developed by National Environmental Engineering Institute NEERI and is subsequently used for growing the organic vegetables and the forest in the property. Use of terracotta either as filler in the RCC slabs or roofs for the residential spaces adds to the concept of circular economy. These are produced in the village and local areas and use the desilted soil from the lakes. Since most buildings even in rural areas are adopting RCC construction, these skills are dying out.

As locals migrate and agriculture activity reduces, the desilting of lakes stops. The non-desilting leads to drop in aquifer recharge. We used terracotta consciously to make the facility be part of a virtual cycle. We want the facility to showcase that ecological architecture is in sync with the eco-system. The vaulted roofs were done in collaboration with Centre for Science for Villages (CSV) Wardha. This is an organization which works on Gandhian principles and values. The project is a happy collaboration of ecology, social and leisure. 

Fact file: Name of the project: Waghoba Eco Lodge Location: Tadoba Total area: 3,085.91 sq m Type: Recreational and Landscape Architecture Design firm: Biome Environmental Solutions Lead Designer: Anurag Tamhanka Photography Credits: Team Pugdundee Safaris

About Biome Environmental Solutions Private Limited :

Biome Environmental Solutions Private Limited, Bengaluru, India are a multidisciplinary firm working on ecological architecture and intelligent water and sanitation designs since its inception in 1990. Through their work, they strive to showcase that an ecological design practice can be mainstream and relevant in various contexts, including geography, typology and scale, balanced by the client’s aspirations for aesthetics, functionality and budget.

Hop between the inside-out lifestyle of this contemporary masterpiece in Muzaffarnagar

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Multistage Stochastic Optimization for Semi-arid Farm Crop Rotation and Water Irrigation Scheduling Under Drought Scenarios

• Published: 05 September 2024

Cite this article

• Mahdi Mahdavimanshadi   ORCID: orcid.org/0000-0002-8853-0609 1 &
• Neng Fan 1  

Extreme weather events such as droughts have posed a significant risk to the agricultural economy of the semi-arid region in the American Southwest. To address the potential drought scenarios, which impact the precipitation and water availability, a data-driven multistage stochastic optimization model is constructed for crop rotation and water irrigation scheduling, to maximize the expected farmers’ profits over a planning horizon. The optimal decisions will be made for crop rotations, deficit level for water irrigation, crop yield response, and multi-method irrigation system scheduling. To overcome solving the multistage stochastic large-scale mixed-integer optimization model with the exponentially growing number of scenarios, we employ the stochastic dual dynamic integer programming (SDDiP) method. Numerical experiments and sensitivity analysis on drought scenarios are performed to validate the proposed approaches in a case study in Arizona.

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Data availability

The datasets analyzed during the current study are available, at https://github.com/omegayao/SBAR .

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This material is based upon funding provided by the USDA-NIFA, Grant # 2017-68005-26867. Any opinions, findings, conclusions, or recommendations expressed in this publication/work are those of the authors and do not necessarily reflect the view of the US Department of Agriculture.

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Mahdavimanshadi, M., Fan, N. Multistage Stochastic Optimization for Semi-arid Farm Crop Rotation and Water Irrigation Scheduling Under Drought Scenarios. JABES (2024). https://doi.org/10.1007/s13253-024-00651-9

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Received : 09 February 2024

Revised : 11 August 2024

Accepted : 12 August 2024

Published : 05 September 2024

DOI : https://doi.org/10.1007/s13253-024-00651-9

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• Crop rotation
• Water irrigation scheduling
• Drought scenarios
• Multistage stochastic optimization
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