Your search found 7 records
1 Mehrotral, R.; Kumar, N.. 1997. Pricing of water: Mechanisms and policy. In Pickford, J.; House, S.; Miles, D.; Ockelford, J.; Parr, J.; Saywell, D.; Shaw, R.; Skinner, B.; Smout, I.; Stear, R. (Eds.), Reaching the unreached - Challenges for the 21st century: Selected papers of the 22nd WEDC International Conference, New Delhi, India, 1996. London, UK; Leicestershire, UK: IT Publications; WEDC. pp.17-19.
(Location: IWMI-HQ Call no: 628.1 G000 PIC Record No: H025812)
2 Singh, H. R.; Kumar, N.. 1995. River ecology and water pollution. In Trivedy, R. K., Encyclopedia of environmental pollution and control - Vol.2. Karad, India: Enviro Media. pp.255-272.
(Location: IWMI-HQ Call no: 363.703 G635 TRI Record No: H028450)
3 Kumar, N.; Tischbein, B.; Kusche, J.; Laux, P.; Beg, M. K.; Bogardi, J. J. 2017. Impact of climate change on water resources of upper Kharun catchment in Chhattisgarh, India. Journal of Hydrology: Regional Studies, 13:189-207. [doi: https://doi.org/10.1016/j.ejrh.2017.07.008]
Climate change ; Forecasting ; Water resources ; Water balance ; Catchment areas ; Hydrology ; Models ; Groundwater ; Precipitation ; Rainfall-runoff relationships ; Temperature ; Surface runoff ; Discharges ; Percolation ; Land use ; Soils / India / Chhattisgarh / Upper Kharun Catchment
(Location: IWMI HQ Call no: e-copy only Record No: H048326)
http://www.sciencedirect.com/science/article/pii/S221458181630177X/pdfft?md5=b3f01d282a63aa0e5c3b17f5f7e21645&pid=1-s2.0-S221458181630177X-main.pdf
https://vlibrary.iwmi.org/pdf/H048326.pdf
https://vlibrary.iwmi.org/pdf/H048326.pdf
(1.78 MB) (1.78 MB)
Study region: The Upper Kharun Catchment (UKC) is one of the most important, economically sound and highly populated watersheds of Chhattisgarh state in India. The inhabitants strongly depend on monsoon and are severely prone to water stress.
Study focus: This research aims to assess the impact of climate change on water balance components.
New hydrological insights for the region: The station-level bias-corrected PRECIS (Providing REgional Climates for Impact Studies) projections generally show increasing trends for annual rainfall and temperature. Hydrological simulations, performed by SWAT (Soil and Water Assessment Tool), indicate over-proportional runoff-rainfall and under-proportional percolationrainfall relationships. Simulated annual discharge for 2020s will decrease by 2.9% on average (with a decrease of 25.9% for q1 to an increase by 23.6% for q14); for 2050s an average increase by 12.4% (17.6% decrease for q1 to 39.4% increase for q0); for 2080s an average increase of 39.5% (16.3% increase for q1 to an increase of 63.7% for q0). Respective ranges on percolation: for 2020s an average decrease by 0.8% (12.8% decrease for q1 to an increase of 8.7% for q14); for 2050s an average increase by 2.5% (10.3% decrease for q1 to 15.4% increase for q0); for 2080s an average increase by 7.5% (0.3% decrease for q1 to 13.7% increase for q0). These over-and under-proportional relationships indicate future enhancement of floods and question sufficiency of groundwater recharge.
Study focus: This research aims to assess the impact of climate change on water balance components.
New hydrological insights for the region: The station-level bias-corrected PRECIS (Providing REgional Climates for Impact Studies) projections generally show increasing trends for annual rainfall and temperature. Hydrological simulations, performed by SWAT (Soil and Water Assessment Tool), indicate over-proportional runoff-rainfall and under-proportional percolationrainfall relationships. Simulated annual discharge for 2020s will decrease by 2.9% on average (with a decrease of 25.9% for q1 to an increase by 23.6% for q14); for 2050s an average increase by 12.4% (17.6% decrease for q1 to 39.4% increase for q0); for 2080s an average increase of 39.5% (16.3% increase for q1 to an increase of 63.7% for q0). Respective ranges on percolation: for 2020s an average decrease by 0.8% (12.8% decrease for q1 to an increase of 8.7% for q14); for 2050s an average increase by 2.5% (10.3% decrease for q1 to 15.4% increase for q0); for 2080s an average increase by 7.5% (0.3% decrease for q1 to 13.7% increase for q0). These over-and under-proportional relationships indicate future enhancement of floods and question sufficiency of groundwater recharge.
4 Kumar, N.; Tischbein, B.; Beg, M. K.; Bogardi, J. J. 2018. Spatio-temporal analysis of irrigation infrastructure development and long-term changes in irrigated areas in upper Kharun Catchment, Chhattisgarh, India. Agricultural Water Management, 197:158-169. [doi: https://doi.org/10.1016/j.agwat.2017.11.022]
Irrigation systems ; Irrigation canals ; Infrastructure ; Groundwater irrigation ; Irrigation water ; Irrigated land ; Cropping patterns ; Water demand ; Spatial planning ; Mapping ; Satellite imagery ; Villages ; Catchment areas / India / Chhattisgarh / Upper Kharun Catchment
(Location: IWMI HQ Call no: e-copy only Record No: H048525)
(4.39 MB)
The Upper Kharun Catchment (UKC), which is part of the new State Chhattisgarh formed in 2000, features considerable population growth, expansion of urban areas and dynamic changes in irrigation infrastructure as well as irrigation practices (spatial extension, temporal intensification, increasing use of groundwater as source) for meeting the increasing food demand. Water intensive rice is the major crop of the area. UKC has a comprehensive canal irrigation system which provides the link to water supply from reservoirs fed from areas outside the UKC. However, water provision for irrigation via the canal system for irrigation is restricted to only post-monsoon season. As a consequence, groundwater remains the only source of irrigation water in summer and winter seasons. Improved electricity facilities and subsidy on groundwater pumping have triggered an enormous increase in groundwater withdrawals. Remote sensing satellite images along with ground observed data were used in this study to spatially identify the areas with canal and groundwater irrigation. Results reveal that in 2011, around 50% of the area of the UKC benefits from canal irrigation, whereas 29.8% area is irrigated by groundwater. Around 103 villages in the UKC have no canal infrastructures. 216 villages in UKC are considered as ‘hotspot areas’ because of high groundwater withdrawal (irrigated area exceeding 75 ha per village), There has been threefold increase in groundwater irrigated area in UKC between 1991 and 2011. The upward trend of groundwater use indicates an alarming situation towards over-exploitation and creates the need to provide and analyze data on the use of groundwater resources in the area in order to detect past and to estimate future trends referring to groundwater withdrawals. These data are a prerequisite for enabling careful and foresightful management of groundwater resources especially at spatially identified hotspot areas towards ensuring sustainable management of this resource.
5 Kumar, N.; Adeloye, A. J.; Shankar, V.; Rustum, R. 2020. Neural computing modelling of the crop water stress index. Agricultural Water Management, 239:106259. (Online first) [doi: https://doi.org/10.1016/j.agwat.2020.106259]
Crop water use ; Water stress ; Neural networks ; Models ; Evaluation ; Irrigation scheduling ; Soil moisture ; Temperature ; Wells ; Mustard ; Canopy / India / Hamirpur
(Location: IWMI HQ Call no: e-copy only Record No: H049750)
(3.26 MB)
In this study, two artificial neural network models viz. supervised Feed-Forward Back Propagation (FF-BP) and unsupervised Kohonen Self-Organizing Map (K-SOM) have been developed to predict the Crop Water Stress Index (CWSI) using air temperature, relative humidity, and canopy temperature. Field experiments were conducted on Indian mustard to observe the crop canopy temperature under different levels of irrigation treatment during the 2017 and 2018 cropping seasons. The empirical CWSI was computed using well-watered and non-transpiring baseline canopy temperatures. The K-SOM and FF-BP CWSI predictions were compared with the empirical CWSI estimates and both performed satisfactorily. Of the two, however, the K-SOM was better with R2 (coefficient of determination) of 0.97 and 0.96 for model development and validation, respectively; corresponding values for FF-BP were 0.86 and 0.75. The results of the study suggest that neural network modelling offers significant potential for reliable prediction of the CWSI, which can be utilized in irrigation scheduling and crop stress management.
6 Kumar, N.; Sinha, J.; Madramootoo, C. A.; Goyal, M. K. 2020. Quantifying groundwater sensitivity and resilience over peninsular India. Hydrological Processes, 28p. (Online first) [doi: https://doi.org/10.1002/hyp.13945]
Groundwater recharge ; Resilience ; Water balance ; Models ; Catchment areas ; Hydrology ; Evapotranspiration ; Soil moisture ; Precipitation / India
(Location: IWMI HQ Call no: e-copy only Record No: H050063)
(0.45 MB)
Groundwater in India plays an important role to support livelihoods and maintain ecosystems and the present rate of depletion of groundwater resources poses a serious threat to water security. Yet, the sensitivity of the hydrological processes governing groundwater recharge to climate variability remains unclear in the region. Here we assess the groundwater sensitivity (precipitation–recharge relationship) and its potential resilience towards climatic variability over peninsular India using a conceptual water balance model and a convex model, respectively in 54 catchments over peninsular India. Based on the model performance using a comprehensive approach (Nash Sutcliffe Efficiency [NSE], bias and variability), 24 out of 54 catchments are selected for assessment of groundwater sensitivity and its resilience. Further, a systematic approach is used to understand the changes in resilience on a temporal scale based upon the convex model and principle of critical slowing down theory. The results of the study indicate that the catchments with higher mean groundwater sensitivity (GWS) encompass high variability in GWS over the period (1988–2011), thus indicating the associated vulnerability towards hydroclimatic disturbances. Moreover, it was found that the catchments pertaining to a lower magnitude of mean resilience index incorporates a high variability in resilience index over the period (1993–2007), clearly illustrating the inherent vulnerability of these catchments. The resilience of groundwater towards climatic variability and hydroclimatic disturbances that is revealed by groundwater sensitivity is essential to understand the future impacts of changing climate on groundwater and can further facilitate effective adaptation strategies.
7 Sangeetha, B. P.; Kumar, N.; Ambalgi, A. P.; Haleem, S. L. A.; Thilagam, K.; Vijayakumar, P. 2022. IOT based smart irrigation management system for environmental sustainability in India. Sustainable Energy Technologies and Assessments, 52(Part A):101973. (Online first) [doi: https://doi.org/10.1016/j.seta.2022.101973]
Irrigation management ; Technology ; Internet ; Environmental sustainability ; Renewable energy ; Irrigation systems ; Agriculture ; Neural networks / India
(Location: IWMI HQ Call no: e-copy only Record No: H051001)
(2.90 MB)
Food and clean water make agriculture a valuable asset to humanity, as it uses water to provide us with food. Environmental destruction and rapid population growth have had a massive effect on agriculture, a detrimental impact on the world's water supplies, and crucial for sustained development. To resolve the issue, implement the intelligent irrigation method using automated and Internet of Things (IoT) technologies. This study involves an intelligent agriculture management system to produce agricultural benefits and crop production. The hybrid remote-controlled device used the Global Positioning System (GPS) with Radial Function Network (RFN) was proposed to control the irrigated system, predict the temperature, maintain the air pressure, and reduced the humidity in water content. It uses IoT sensors and the Internet of Everything (IOE) environmental data for managing and monitors intelligent solar irrigation systems. The objective is agriculture intelligent by using automation and IoT technologies. It scientifically designed to perform tasks such as weeding, irrigation, sensing humidity, attempting to scare birds and livestock, maintaining surveillance, etc., to control the geolocation of devices remotely. As a result, the design is to achieve all of its goals in terms of water use; total running costs decreased labour, energy consumption, and productivity. It is found that proposed Radial Function Network achieved 0.7734f accuracy, 0.9834 of sensitivity, 0.8955 of hit rate and 0.77 of caching rate.
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