Your search found 9 records
1 Vedula, S.; Kumar, D. N.. 1996. An integrated model for optimal reservoir operation for irrigation of multiple crops. Water Resources Research; Water Resources Journal, 32(4):1101-1108; 190:57-66.
(Location: IWMI-HQ Call no: P 4173 Record No: H019273)
2 Paul, S.; Panda, S. N.; Kumar, D. N.. 2000. Optimal irrigation allocation: A multilevel approach. Journal of Irrigation and Drainage Engineering, 126(3):149-156.
Models ; Water allocation ; Irrigation water ; Irrigation scheduling ; Plant growth ; Stochastic process ; Crop production ; Water deficit ; Water distribution ; Evapotranspiration ; Soil moisture ; Rain ; Percolation ; Distributary canals / India / Punjab / Golewala Distributary Canal
(Location: IWMI-HQ Call no: PER Record No: H026238)
3 Sethi, L. N.; Kumar, D. N.; Panda, S. N.; Mal, B. C. 2002. Optimal crop planning and conjunctive use of water resources in a coastal river basin. Water Resources Management, 16(2):145-169.
Water resource management ; River basins ; Water balance ; Groundwater management ; Crop production ; Rice ; Conjunctive use ; Salinity ; Linear programming ; Models ; Optimization ; Evapotranspiration ; Leaching ; Irrigation requirements / India / Orissa
(Location: IWMI-HQ Call no: PER Record No: H030348)
4 Kumar, D. N.; Baliarsingh, F. 2003. Folded dynamic programming for optimal operation of multireservoir system. Water Resources Management, 17(5):337-353.
(Location: IWMI-HQ Call no: PER Record No: H033107)
5 Kumar, D. N.; Raju, K. S.; Sathish, T. 2004. River flow forecasting using recurrent neural networks. Water Resources Management, 18(2):143-161.
(Location: IWMI-HQ Call no: P 6940 Record No: H035130)
6 Raju, K. S.; Kumar, D. N.. 2004. Irrigation planning using genetic algorithms. Water Resources Management, 18(2):163-176.
Cropping systems ; Linear programming ; Mathematical models ; Irrigation programs ; Planning ; Irrigation requirements ; Reservoir operation / India / Andhra Pradesh / Godavari River / Sri Ram Project
(Location: IWMI-HQ Call no: P 6941 Record No: H035132)
7 Raju, K. S.; Kumar, D. N.. 2005. Fuzzy multicriterion decision making in irrigation planning. Irrigation and Drainage, 54(4):455-465.
Irrigation programs ; Irrigation systems ; Case studies ; Irrigation management ; Participatory management ; Farmer participation ; Decision making / India / Andhra Pradesh / Godavari River / Sri Ram Sugar Project
(Location: IWMI-HQ Call no: PER Record No: H037820)
8 Banerjee, C.; Kumar, D. N.. 2018. Assessment of surface water storage trends for increasing groundwater areas in India. Journal of Hydrology, 562:780-788. [doi: https://doi.org/10.1016/j.jhydrol.2018.05.052]
Surface water ; Groundwater ; Water storage ; Satellite observation ; Soil moisture ; Precipitation ; Rain ; Evapotranspiration ; River basins ; Discharges ; Uncertainty ; Models / India / Godavari River Basin / Krishna River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H048823)
(2.42 MB)
Recent studies based on Gravity Recovery and Climate Experiment (GRACE) satellite mission suggested that groundwater has increased in central and southern parts of India. However, surface water, which is an equally important source of water in these semi-arid areas has not been studied yet. In the present study, the study areas were outlined based on trends in GRACE data followed by trend identification in surface water storages and checking the hypothesis of causality. Surface Water Extent (SWE) and Surface Soil Moisture (SSM) derived from Moderate-resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) respectively, are selected as proxies of surface water storage (SWS). Besides SWE and SSM, trend test was performed for GRACE derived terrestrial water storage (TWS) for the study areas named as R1, R2, GOR1 and KOR1. Granger-causality test is used to test the hypothesis that rainfall is a causal factor of the inter-annual variability of SWE, SSM and TWS. Positive trends were observed in TWS for R1, R2 and GOR1 whereas SWE and SSM show increasing trends for all the study regions. Results suggest that rainfall is the granger-causal of all the storage variables for R1 and R2, the regions exhibiting the most significant positive trends in TWS.
9 Raju, K. S.; Kumar, D. N.. 2020. Review of approaches for selection and ensembling of GCMs [Global Climate Models]. Journal of Water and Climate Change, 11(3):577-599. [doi: https://doi.org/10.2166/wcc.2020.128]
Climate change ; Models ; Performance indexes ; Indicators ; Water resources ; Decision making ; Forecasting ; Techniques ; Precipitation ; Uncertainty / Middle East / South Asia / East Asia / South East Asia / Europe / Africa / USA / China / India / Australia / Canada / Iraq / Syrian Arab Republic / Iran Islamic Republic
(Location: IWMI HQ Call no: e-copy only Record No: H049973)
https://iwaponline.com/jwcc/article-pdf/11/3/577/716759/jwc0110577.pdf
https://vlibrary.iwmi.org/pdf/H049973.pdf
https://vlibrary.iwmi.org/pdf/H049973.pdf
(0.47 MB) (476 KB)
Global climate models (GCMs) are developed to simulate past climate and produce projections of climate in future. Their roles in ascertaining regional issues and possible solutions in water resources planning/management are appreciated across the world. However, there is substantial uncertainty in the future projections of GCM(s) for practical and regional implementation which has attracted criticism by the water resources planners. The present paper aims at reviewing the selection of GCMs and focusing on performance indicators, ranking of GCMs and ensembling of GCMs and covering different geographical regions. In addition, this paper also proposes future research directions.
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