Groundwater models meticulously considering the water balance components have paramount importance in sustainable decision-making on groundwater. Plant uptakes and transpires water from the groundwater, called groundwater evapotranspiration (ETG), ‛is one of the major contributions to the water balance. ETG needs to be quantified for shallow groundwater areas as they account for major depletion in groundwater. This study evaluated the White and modified White methods to estimate ETG for the agricultural-dominated Rana watershed with a tropical savanna climate in Eastern India. This modified method uses the sine function to capture the diurnal fluctuation of groundwater and estimates daily to seasonal ETG. Additionally, a physical-based modeling strategy was adopted to estimate the ETG over the study basin as a function of soil moisture change from the surface to the saturation depth and delineate the extinction depth of ETG. Additionally, this study compared the efficacy of both the empirical methods with the physical-based model to estimate ETG in agricultural land use. Results showed that ETG was nearly 50–70

In this study trend analysis and bias correction have been done for dry (January–May) and wet (June–September) seasons under two future climate period 2021–2050 and 2051–2080 with respect to the current climate period 1980–2012 in Eastern India. The different representative concentration pathways (RCPs) of 2.6, 4.5, 6.0, and 8.5 were used to assess the future trend of the study area. Results indicate that the increasing RCP increases temperature (maximum and minimum) in all regions due to higher radiative forces (4–8.5 W/m2) with respect to the baseline temperature during the period 2051–2080. Further, the bias-corrected rainfall has a declined trend with respect to baseline, and RCP’s values for both the time slices (2021–2050 and 2051–2080) showed less scattering in the amount of rainfall for the wet season in comparison to the dry season.

Understanding the soil moisture dynamic at the watershed-scale is essential for hydrological applications (i.e., drought monitoring, flood forecasting, irrigation, etc.) and river basin management activities. Globally, in-situ measured accurate soil moisture data at the watershed-scale is quite scarce due to the high maintenance cost of large-density sensor networks and complex/challenging conditions for soil moisture field campaigns. Characterizing the soil moisture variability at the watershed-scale requires a robust in-situ monitoring strategy at the point-scale to balance representativeness and minimization of monitoring cost. Thus, this study determined an optimal sampling design to capture the spatiotemporal variability of soil moisture at the watershed-scale. The study was conducted for the typical eastern Indian conditions of extreme seasonal variability that lead from very wet (during monsoon) to dry (during hot summer). Soil moisture measurements were carried out at 83 locations in an agricultural watershed of 500 km2 for 56 days across a year. A hand-held soil moisture probe (ThetaProbe) was used for the measurements from June 2016 to July 2017. Based on the analyzes of 41,832 measurements collected during field measurements, it was found that the maximum numbers of required locations necessary to estimate watershed-mean soil moisture within ±2

Context: Sunflower is a potential summer crop for diversifying the rice-fallow system in eastern India. However, the phenolics present in the plant extract can inhibit the growth of subsequent crops. This allelopathic effect could be affected by rice establishment methods having various degrees of soil disturbance and different planting materials. In the present era of climate-conscious agriculture, technologies like residue retention/incorporation and conservation tillage are being popularized. Therefore, it is essential to assess the effects of sunflower residue incorporation on succeeding rice crop under different crop establishment methods.Objective: The present study was conducted to assess the effect of sunflower residue management options on succeeding rice crop productivity and profitability under different crop establishment methods.Methods: The experiment was laid out in split-plot design with three sunflower residue management options in the main plots, viz., no sunflower crop, removal, and incorporation of sunflower residue in main plots and three rice establishment methods in sub-plots, viz., transplanting, dry-direct and wet-direct seeding of rice. Results: The incorporation of sunflower residues reduced the germination of rice seeds by 21.5% in dry-direct seeded rice, which was avoided in wet-direct seeded and transplanted rice having pre-germinated seeds and seedlings, respectively. The weed dry weight in sunflower residue incorporated plot was 28.3% lower as compared to the plot with no preceding sunflower crop. The positive interactive effects of sunflower residue incorporation and transplanting of rice led to better weed suppression, higher crop growth and improved soil nutrient availability, resulting in the highest rice productivity and profitability. In contrast, sunflower residue incorporation and dry-direct seeding interacted negatively, resulting in the lowest rice productivity and profitability. Conclusions: Transplanting could be the best establishment for the rice crop to harness the benefits of incorporating preceding sunflower crop residue. Including sunflower as a summer crop in the rice-based cropping system and incorporating the generated residues in soil could potentially enhance the productivity and profitability of transplanted rice and, therefore, become a viable option for crop intensification in eastern India. Significance: The study identified the suitable establishment method for the succeeding rice crop in sunflower residue removed/incorporated soil. The results could be used to intensify the rice-based cropping system and develop climate-smart agriculture practices in eastern India and similar agroecological regions around the globe.

The use of energy and carbon-intensive inputs in agriculture is unsustainable as it contributes to climate change, adversely affecting crop productivity and human life. Barley, the fourth most important cereal crop, has been restricted to areas with limited resources. Maintaining a balance between productivity, profitability, and sustainability by identifying viable genotypes that adapt well to resource-restricted settings and assessing them under low energy-carbon intensive management is critical. A field experiment was conducted during 2016-17 to 2018-19 to assess the energy-carbon footprint, productivity and profitability of five barley cultivars under two contrasting tillage-residue management systems in semi-arid plains of North-West India. The zero-till + residue retention (ZT+RR) system, among the tillage-residue management options, and RD-2552 followed by BH-946, among the cultivars, provided significantly higher crop productivity and profitability. Although cultivars' responses to the tillage-residue management method were not statistically significant in terms of grain yield, they were in terms of net returns. Therefore, RD-2552 and BH-946 cultivars could provide higher profitability with the ZT+RR system as compared to those with the conventional-till + residue incorporation (CT+RI) system. Although cultivars did not affect the energy-carbon footprints of barley production, tillage-residue management methods did. The ZT+RR system enhanced the energy and carbon use efficiencies of the barley cultivation with lower energy-carbon footprints. The cultivar RD-2552 followed by BH-946 under the ZT+RR system could provide higher productivity and profitability with lower energy-carbon footprints. Adoption of conservation agriculture-based tillage-residue management practices could improve productivity and profitability of barley crop with reduced energy-carbon footprints in the semi-arid ecologies of India.

Drought monitoring and understanding of spatiotemporal patterns of drought characteristics are beneficial for sustainable agricultural water management at a regional scale. Changes in rainfall and drought patterns in future could impact decision-making in water resources allocation. The impacts of potential climate change on future meteorological droughts could be better analysed by utilizing an index that incorporates the number of rainy days information in addition to magnitude of rainfall to characterize the drought severity. In this study, we propose a bivariate copula-based multi-scalar rainfall pattern drought index (RPDI), a variant of the popular standardized precipitation index (SPI), and then the drought characteristics in India based on RPDI and SPI at different time scales (3-, 6-and 12-months) are analysed. When compared to RPDI, SPI underestimated drought severity and indicated low spatial heterogeneity of drought characteristics. The RPDI analysis identified changes in average drought severity and total number of drought events between two sample time slices (1920-1960 and 1961-2000) in the historical period. Future RPDI drought characteristics based on the coupled model inter comparison project phase 6 (CMIP6) multi-model ensemble under two emission scenarios known as shared socioeconomic pathways (SSPs): SSP126 and SSP245 are then computed. When drought characteristics in the near future (2020-2049) and far future (2070-2099) periods are compared with their baseline period (1985-2014) values, the results show that a significant declining trend is likely in the total number of drought events over the study region, while increases are projected in the maximum drought duration and maximum drought severity in future. Long-term drought characteristics extracted using 12-months scale RPDI, are likely to intensify in future under both scenarios. Considering the effects of erratic rainfall pattern on agricultural productivity, the RPDI drought monitoring framework could be utilized for efficient agricultural water management in future.

The changing climate may adversely affect crop production unless appropriate adaptation strategies are used. The appropriate level of fertilization and sowing dates are some of the simple and most economic but effective strategies which can be developed and applied for specific climatic regions. Keeping the aforesaid fact in mind, varying sowing dates were analyzed as a strategic adaptation to cope with the climate change for maize yield in Eastern India under rainfed and irrigated conditions. Evaluation of sowing dates was performed by the CERES-maize model for estimating the maize yield for the projected time periods 2021–2050 (2050s), and 2051–2080 (2080s) by using different Representative Concentration Pathways (RCP) 2.6, 4.5, 6.0, and 8.5 W m-2 respectively from the 17 Global circulation models (GCM) of CMIP5 (Coupled Model Intercomparison Project Phase 5) climate projection scenarios, and the results projected were compared with the baseline scenario of 1982–2012. The results indicated that the effect of late sowing dates (30 June and 10 July) was more in RCPs 6.0, and 8.5 for both the time periods 2050s and 2080s as during the tasseling stage the intensity of solar radiation decreased, while for the recommended (20 June), and earlier sowing (30 May and 10 June) dates reduced grain yield was recorded for the rainfed condition due to higher temperature stress. In irrigated conditions, the recommended sowing date (15 January) was suitable for time slice 2050s in all the RCP scenarios, while the earlier sowing date (25 December) was found to be suitable in the time slice period of 2080s with RCP 8.5. Thus, shifting sowing dates can be an effective management strategy to cope with climate change with less waste and efficient use of resources.

The water use, yield and profitability in three cropping sequences (rice-capsicum-baby corn, rice-rice-baby corn and rice-rice) grown under a drip irrigation (DRI) layout were compared with that in a rice-rice system under surface irrigation (SI) in a rice-dominated region of eastern India. The DRI could save 37% of irrigation water without affecting the yield in rice compared with SI. The DRI in the rice-capsicum-baby corn cropping system produced 4.63 times higher yield (rice equivalent yield, 48.2 t ha?(1)) using 59% less water (7570 m(3) ha?(1)), resulting in 11 times higher water productivity (8.72 kg rice m?(3) water), 7.63 times higher annual net income (NI) (435 000 INR ha?(1)) and 18 times higher economic water productivity (EWP) (INR 78.7 m?(3) water) with a benefit-cost ratio (BCR) of 2.87 compared with the rice-rice cropping system under SI. Use of DRI in a multi-cropping sequence is recommended for higher water productivity and net profit in rice-based cropping systems in irrigated commands.

Drought patterns are better understood by analysing the joint dependence among long‐term drought characteristics: severity, duration, and frequency. Changes in these drought characteristics are also likely due to potential climate change, for instance, increased drought severity and duration, increased frequency of drought events, and increased spatial extent of drought impacts. In this study, we performed a regional‐scale analysis of potential changes in meteorological drought characteristics in a tropical region in India under a warming future climate scenario. The meteorological drought characteristics were computed using the Standardized Precipitation Index. The Coordinated Regional Downscaling Experiment South Asia Regional Climate Model future precipitation simulation under the RCP 4.5 scenario was bias‐corrected and utilized for future drought frequency analysis. Regionalization of meteorological drought characteristics was performed using the simple k ‐means clustering technique to identify the homogeneous regions for baseline and future periods. Bivariate copula was utilized to model the dependence among the drought characteristics in order to develop the S‐D‐F curves. The changes in regionalization and regional S‐D‐F curves under future potential climate change were then investigated. The results of the study suggest an increase in drought severity by 2–3% for long‐term droughts (duration more than 5 months) in the future as indicated by upward shift and changes in the slope of the regional S‐D‐F curves. There is a projected increase in the occurrence of long‐term droughts in the future when compared to short‐term (duration of 1 to 3 months) droughts. We also observed a considerable spatial shift of drought hotspots, the severe drought‐prone regions lie in the eastern part of the study region in the future. Future projections of regional S‐D‐F curves and Spatio‐temporal drought characteristics obtained from the proposed framework may be utilized for devising drought mitigation strategies.

National Aeronautics and Space Administration's soil moisture active–passive (SMAP) mission potential to produce high-resolution soil moisture suffered adversely due to its L-band synthetic-aperture radar (SAR) failure. Other satellite-based L-/C-band SAR observations can be used within the SMAP active–passive algorithm. In this article, we evaluated the capability of ingesting ISRO's Radar Imaging Satellite-1 (RISAT-1) C-band SAR observations in the SMAP active–passive algorithm to obtain soil moisture at 1, 3, and 9 km over the agricultural region dominant by paddy that experiences seasonal flooding. We also improved the SMAP mission active–passive algorithm with a dynamic surface water bodies (ponding conditions) masking approach using the native RISAT-1 observations. The article shows that the use of surface water masks helps in mitigating the negative impact of surface water bodies in the active–passive disaggregation process. The SMAP–RISAT soil moisture retrievals at 1 and 3 km resolutions are found to have high unbiased root-mean-square error (ubRMSE) greater than 0.06 m3/m3 during very wet and high vegetative conditions. However, at low and moderate soil moisture states, the ubRMSE is below 0.06 m3/m3. Comparison of soil moisture retrievals at 9 km resolution with upscaled ground-based soil moisture measurements shows ubRMSE less than 0.04 m3/m3. This article is a precursor for estimating soil moisture for the upcoming RISAT-1A dataset over India. The findings will further help in the implementation of a microwave active–passive algorithm to retrieve soil moisture for future satellite missions involving radiometer-SAR instruments, and challenging geophysical conditions (i.e., dynamic surface water bodies).

Pedotransfer functions (PTFs) are being instrumental in saturated hydraulic conductivity ( K s ) estimation. Despite various advancements, the performance of existing generic K s predicting PTFs need augmentation. This study developed a robust K s predicting PTF using a machine learning (ML) algorithm and exhaustive data set for 324 soils with 28 properties sampled over a tropical savanna region of India. Four ML algorithms were evaluated for this purpose, and random forest (RF) outperformed all others. A substantial improvement to the prediction by RF‐based PTF was achieved through predictor selection using a hybrid wrapper‐embedded algorithm. The predictor selection algorithm selected eight pertinent predictors (HID‐S): S, Si, C, FSF, C u , GMD, D 60 , and D 10 . The mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination ( R 2 ), and Nash‐Sutcliffe efficiency (NSE) obtained as average tenfold cross‐validation scores for RF algorithm training with HID‐S were 0.87, 1.47, 0.94, and 0.94, respectively. The developed PTF (RF‐HID‐S) was evaluated alongside the recently published PTFs by Araya and Ghezzehei (2019, https://doi.org/10.1029/2018wr024357 ), within and outside the study region. In that process, it was observed that the RF‐HID‐S possessed superior prediction proficiency compared to the recently published and commonly used PTFs in both cases. These findings mark RF‐HID‐S as the most robust generalizable PTF, which may further be evaluated in different parts of the world. Moreover, looking at the performance of the eight selected predictors within and outside the study region, they can be considered for experiment design globally to make K s estimation accurate and cost‐effective.

Regional-scale precise soil moisture measurements are required for remote sensing-based soil moisture product validation besides, complimenting in several hydrological and agricultural applications. Though the gravimetric method provides the most accurate soil moisture measurements, it cannot be extended to the regional-scale due to the large number of sampling requirements. An impedance probe is a suitable substitute for the time-intensive gravimetric method; however, it needs soil/field-specific calibrations for precise measurements. The present study aims to develop a generalized calibration of an impedance probe (i.e., ThetaProbe) for precise measurements of soil moisture at the regional-scale within the root-mean-square-error (RMSE) of 0.04 m(3) m(-3) to fulfil the accuracy requirement of current satellite missions. A few methods for calibrating impedance probe were investigated using 496 gravimetric samples and coincident impedance probe measurements collected over 83 locations through field campaigns in a paddy dominated tropical Indian watershed that covers an area of 500 km(2). The manufacturer generalized calibration was found to have high RMSE (0.0523 m(3) m(-3)) and considerable bias (0.0241 m(3) m(-3)) in soil moisture measurements. Developed generalized and soil-specific calibration based on a linear regression technique that resulted in RMSE values of 0.0468 and 0.0422 m(3) m(-3), respectively. Further, a Bayesian neural network (BNN) based method, a nonlinear technique, was used for developing a generalized calibration of the impedance probe. The results illustrated that BNN-based generalized calibration (RMSE < 0.04 m(3) m(-3)) performs better than the linear regression-based calibrations (RMSE > 0.04 m(3) m(-3)). Moreover, the performance of BNN-based generalized calibration was further improved by the inclusion of soil physical properties as input and yielded an RMSE value up to 0.0352 and 0.0366 m(3) m(-3) during training and cross-validation process, respectively. (C) 2020 American Society of Civil Engineers.

This study assessed the groundwater vulnerability of the Rana groundwater basin, Odisha, India. The study attempts to optimize the DRASTIC method by modifying the weights and ratings assigned to the DRASTIC parameters using the analytical hierarchy process. Sensitivity analysis has been carried out to quantify the influence of each parameter. The groundwater vulnerability results obtained from both DRASTIC and modified DRASTIC methods were validated using the water quality index (WQI) values computed through groundwater quality data at 25 sampling locations. The results revealed that the index values generated through modified DRASTIC possessed a higher correlation with the WQI compared with the original DRASTIC method. The groundwater level and net recharge were found to be having a greater influence on groundwater vulnerability. The obtained vulnerability maps from the modified DRASTIC method revealed that about 70% of the area was under very low to low vulnerable zones, whereas 14% of the area was under high to very high vulnerable zones. It was also observed that most of the high to very high vulnerable zones were located on the agriculturally dominated areas lying in the northern part of the basin. The result obtained will play an immense role in adopting management practices to conserve the groundwater quality of the study basin. (C) 2021 American Society of Civil Engineers.

Study Region: State of Odisha, a data-scarce tropical savanna region in eastern India. Study Focus: This study evaluated the temporal variability in depth to groundwater (DTW) in the study region with heavily stressed aquifers during 1995-2015 using the modified Mann Kendall test. Subsequently, Shannon's entropy assessed spatial variability in DTW and determined the dominant Hydrological, Geological, and Climatological (HGC) factor regulating the observed spatio-temporal variability taking land use/ land cover (LULC), geomorphology, lithology, topography, and rainfall as HGC factors. New Hydrological Insights: The overall and seasonal trend analysis revealed that the study region possessed both rising and declining trends with a slightly higher percentage of wells with a rising trend. The spatial distribution of trends and the associated magnitude accentuated the unforeseen groundwater temporal variability and higher-order susceptibility of DTW to rising and declining trends. The marginal entropy revealed the higher-order spatial variability associated with deeper DTW and vice versa. Evaluation of the HGC factors revealed that LULC could explain the maximum variability in the DTW as a dominant HGC factor. It was found that the impact of LULC features on DTW variability is not straightforward, necessitating impact assessment studies in the location with significant to highly significant trends. This formulated approach can immensely contribute to the planning and management in attaining groundwater sustainability in datascarce regions.

In this study, a novel technique for grouping locations into spatially coherent homogeneous regions was presented along with its application in assessment of the impacts of potential climate change on regionalization of hydroclimatic variables. The regionalization technique based on Markov random field (MRF) model consisted of binary state variables representing low/high values, and monthly patterns of hydroclimatic variable across space and time. Changes in regionalization and regional hydroclimatic patterns over India in future under projected climate change were then investigated. The hydroclimatic variables used were monthly precipitation, monsoon precipitation and temperature, and future climate data output corresponding to RCP 4.5 and 8.5 scenarios were taken from a regional climate model (RCM) with the CNRM-CM5.0 model of Coupled Model Intercomparison Project Phase 5 (CMIP5) project as the parent general circulation model (GCM). The regions obtained for baseline (1971-2003) and future (2042-2068) periods under both scenarios were then compared. Using the MRF model, spatially coherent and homogeneous regions were obtained, and their ranking based on regional mean of the hydroclimatic variable was performed. Transitions of locations between the baseline and future regions were tracked using the Jaccard coefficient. Our findings suggest that the number of homogeneous regions and the distribution of locations among different regions are projected to change in future scenarios. Wettest regions identified during baseline period are projected to remain wetter in future, whereas regions in the moderate-to-heavy rainfall ranks have shown remarkable shift to increasingly wetter regions. There is increase in regional monthly precipitation magnitudes, and increases in regional average temperature are observed under all the scenarios in future (highest under RCP 8.5) when compared to the baseline period. Despite the uncertainty and limitations involved in the scope of the current study, the modifications in hydroclimatic patterns seen under changing climate emphasize the need for considering impacts of climate change in water resources management and regional policy and decision making.

Nitrate (NO3-) leaching is a leading process of nitrogen (N) loss in agricultural ecosystems. The present research focused on NO3- leaching and transport in soil from the packed soil columns at different depths (0-20, 0-40, 0-60 and 0-90 cm) under saturated or unsaturated conditions for a lateritic soil. The leached NO3- collected from the bottom of column was analyzed with colorimetric method and was assessed for its variance in concentration (mg/L) with variable depths (20-90 cm). The leaching of NO3- was more in saturated condition than in unsaturated conditions. The cumulative NO3- concentration detected were 23, and 17 mg/L in a saturated condition for two N applications 15 mg and 7.5 mg, respectively, and were 2.10, and 1.36 mg/L in an unsaturated condition for two N applications 19.5 mg and 9.5 mg, respectively. Depth-wise analysis of NO3- leached indicated that at a 20 cm depth leached 16, 29 and 43% higher NO3- in a saturated condition whereas 21, 38, 52% higher NO3- in the unsaturated condition in comparison to 40, 60 and 90 cm depths, respectively. The NO3- concentrations with variable depths for both conditions were analyzed with principal component analysis (PCA) and PC (Pearson correlation) scores of 99.93% and 76.41% for unsaturated and saturated conditions were obtained and clarified that N application with more water results in more NO3- being leached out. Further, the study suggests that NO3- leaching can be restricted even with limited amount of nutrient and water.