The present study assessed the impact of climate change in the Anandapur catchment of Baitarani River basin, India, using the Soil and Water Assessment Tool (SWAT) hydrological model. The future climatic alterations under two Representative Concentration Pathways (RCPs), i.e. 4.5 and 8.5 scenarios, are quantified by an ensemble of two different CMIP5 models, i.e. CNRM-CM5.0, GFDL-CM3.0. The outcomes of this study reveal that the future rainfall and temperature may experience an increasing trend with gradual shifting of monsoon from mid-June to mid-May. The average annual streamflow experienced the highest increase during the period 2071–2095, whereas the highest average annual ET is observed for the period 2046–2070 under both the RCPs and resulting in comparatively slower GWR over the basin. In order to implement suitable adaptation strategies for a possible flood scenario on the concerned study basin, three critical sub-basins, namely, sub-basin 1, 4, and 5, were identified. Furthermore, the altered streamflow and ET dynamics may result in a significant shifting in the conventional agricultural practice in the coming future time scales. Conclusively, the outcomes of this study have potential implications for policy makers in formulating the policies related to sustainable water resources management in future scenarios.
The agricultural sector is the largest consumer of the water resources potential of India. The ever‐increasing demand for this finite resource has led to water shortage in the Anandapur sub‐basin of the Baitarani River basin, which comes under the hot and subhumid region of India. The yield of maize, green gram and winter wheat in the basin was simulated for the Rabi season (October–February) using SWAT, considering both rainfed and irrigated scenarios. As observed, water stress progressively increased with a shift in the sowing period of crops from 15 October to 5 November at an interval of 10 days. In the present work, irrigation was scheduled considering the early sowing period, i.e. 15 October. Model performance was evaluated in terms of Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2) and percentage bias (PBIAS). The performance of the model was found to be satisfactory for crop yield simulation during the calibration and validation periods. An optimal irrigation strategy was analysed considering three irrigation settings, i.e. S2 (sufficient irrigation), S3 (50% depletion of available water content, i.e. AWC) and S4 (70% depletion of AWC). The reduction in crop yield in S4 (deficit irrigation) was found to be relatively insignificant compared to full irrigation under scenario S2. © 2020 John Wiley & Sons, Ltd.
Climate change is one of the primary drivers that alters the natural balance of hydrologic cycle and leads to onset of hydrologic extreme situations. Among those extremes, drought is the most devasting and complex catchment hazard caused because of climate change. In this context, it is quite essential to study the implications of climatic and catchment alterations on different types of drought processes. The present study analyzed the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), hydrological drought by Streamflow Drought Index (SRI), and Agricultural Standardized Precipitation Evapotranspiration Index (aSPEI) across multiple time scales of the future climate change scenarios. Further, this study attempted a correlation-based approach to identify the suitable drought index to characterize the agricultural drought and critical drought index estimates over the study region. The aSPEI is proved to be an improvement over the conventional SPEI for analyzing the agricultural drought characteristics. The 6-month time scale is found to be the most suitable reference period for drought monitoring with highest correlation estimate of 0.688 across all the three study regions. Individually, catchment and climate variables failed to represent the drought dynamics over the catchment, whereas the combined model adequately represented the drought dynamics over the study region. The relative impact of different process components revealed that the precipitation in the climate model and baseflow index in catchment model have significant impact on short-term drought prediction, while in the combined model, the baseflow index alone is sufficient. The methodology suggested herein could be adopted in any global catchment to represent the drought process, and subsequently, identifies the drivers of drought with utmost accuracy.
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