Your search found 4 records
1 Sharma, V.; Negi, S. C.; Rudra, R. P.; Yang, S. 2003. Neural networks for predicting nitrate-nitrogen in drainage water. Agricultural Water Management, 63(3):169-183.
Drainage ; Water quality ; Subsurface drainage ; Nitrogen ; Leaching ; Fertilizers ; Models ; Networks / Canada
(Location: IWMI-HQ Call no: PER Record No: H034999)
2 Sharma, V.; Sharma, U. C. 2011. Groundwater management in Kandi Region of Jammu Province, Jammu and Kashmir, India. In Findikakis, A. N.; Sato, K. Groundwater management practices. Leiden, Netherlands: CRC Press - Balkema. pp.83-91. (IAHR Monograph)
Groundwater management ; Groundwater recharge ; Environmental effects ; Social aspects ; Economic aspects ; Rain ; Indicators / India / Kandi Region / Jammu Province / Kashmir
(Location: IWMI HQ Call no: 333.91 G000 FIN Record No: H045649)
3 Malik, Ravinder Paul Singh; Giordano, Meredith; Sharma, V.. 2014. Examining farm-level perceptions, costs, and benefits of small water harvesting structures in Dewas, Madhya Pradesh [India]. Agricultural Water Management, 131:204-211. [doi: https://doi.org/10.1016/j.agwat.2013.07.002]
Water harvesting ; Decentralization ; Cost benefit analysis ; Investment ; Smallholders ; Irrigation water ; Households ; Surveys ; Crop production ; Cropping patterns ; Livestock / India / Madhya Pradesh / Dewas
(Location: IWMI HQ Call no: PER Record No: H046099)
(0.71 MB)
A recent initiative in Madhya Pradesh, India to promote privately funded, rainwater harvesting structures on farmers’ own land has shown substantial economic and livelihood benefits. In contrast to the many poorly functioning, community managed rainwater harvesting programs, the individual or decentralized rainwater harvesting structures have led to significant improvements in availability of irrigation water, are vival of the agricultural economy of the region, and substantial increases in farmer incomes and livelihoods. Since 2006, more than 6000 farmers in the state have invested in on-farm ponds. The investments are highly cost effective and farmers are able to recover their initial investment in approximately 3 years. While longer-terms impact studies are needed, this initial assessment suggests that on-farm rainwater harvesting ponds are a promising private small irrigation option in Madhya Pradesh and similar regionsin India and elsewhere.
4 Thakur, P. K.; Garg, V.; Kalura, P.; Agrawal, B.; Sharma, V.; Mohapatra, M.; Kalia, M.; Aggarwal, S. P.; Calmant, S.; Ghosh, Surajit; Dhote, P. R.; Sharma, R.; Chauhan, P. 2021. Water level status of Indian reservoirs: a synoptic view from altimeter observations. Advances in Space Research, 68(2):619-640. [doi: https://doi.org/10.1016/j.asr.2020.06.015]
Water levels ; Estimation ; Reservoirs ; Lakes ; Inland waters ; Water resources ; Satellite observation ; Altimeters ; Time series analysis / India
(Location: IWMI HQ Call no: e-copy only Record No: H050798)
(7.37 MB)
Most of the part of India is already under water-stressed condition. In this regard, the continuous monitoring of the water levels (WL) and storage capacity of reservoirs, lakes, and rivers is very important for the estimation and utilization of water resources effectively. The long term ground observed WL of many of the water bodies is not easily available, which may be very critical for proper water resources management. Satellite radar altimetry is the remote sensing technique, which is being used to study sea surface height for the last three decades. The advancement in radar technology with time has provided the opportunity to exploit the technique to retrieve the WL of inland water bodies. In the current study, an attempt has been made to generate long term time series on WL of around 29 geometrically complicated inland water bodies in India. These water bodies are mainly large reservoirs namely Ban Sagar, Balimela, Bargi, Bhakra, Gandhi Sagar, Hasdeo, Indravati, Jalaput, Kadana, Kolab, Mahi Bajaj, Maithon, Massanjore, Pong, Ramganga, Ranapratap Sagar, Rihand, Sardar Sarovar, Shivaji Sagar, Tilaiya, Ujjani, and Ukai. The WL of these water bodies was retrieved for around two decades using the European Remote-Sensing Satellite – 2 (ERS-2), ENVISAT Radar Altimeter – 2 (ENVISAT RA-2), and Saral-AltiKa altimeters data through Ice-1 retracking algorithm. Further, an attempt has also been made to estimate the WL of gauged/ungauged lakes namely Mansarovar, Pangong, Chilika, Bhopal, and Rann of Kutch over which Saral-AltiKa pass was there. As after July 2016, the SARAL-AltiKa is operating in the drifting orbit, systematic repeated observation of WL data of all reservoirs was not possible. The data of drifted tracks of Saral-AltiKa were tested for WL estimation of Ban Sagar reservoir. As the ERS-2, ENVISAT RA-2 and Saral-AltiKa all were having almost the same passing tracks, a long term WL series of these lakes could be generated from 1997 to 2016. However, at present only Sentinel – 3 is in orbit, the continuous altimeter based WL monitoring of some of these reservoirs (Gandhi Sagar, Nathsagar, Ranapratap, Ujjani, and Ukai) was attempted through Sentinel-3A satellite data from 2016 to 2018. The accuracy of the retrieved WL was than validated against the observed WL. In most of the reservoirs, a systematic bias was found due to the different characteristics and geoid height of each reservoir. The coefficient of determination, R2 , value for a majority of reser voirs was as good as 0.9. In the case of ERS-2, the values of R2 varied for 0.44–0.97 with root mean square error (RMSE) in the range of 0.63–2.72 m. These statistics improved with the ENVISAT RA-2 data analysis, the R2 value reached more than 0.90 for around 11 reservoirs. The highest, 0.99, for Hasdeo and Shivaji Sagar Reservoirs with RMSE of 0.44 and 0.56, respectively. Further, the accuracy improved with the analysis of Saral-AltiKa data. The R2 was always more than 0.9 for each reservoir and the lowest RMSE reduced to 0.03. Therefore, it can be said that the accuracy and consistency of WL retrieval through satellite altimetry has improved with time. Furthermore, the altimeter based retrieved WL may be used in hydrological studies and can contribute to better water resources management.
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