Your search found 2 records
1 Arasalingam, Sutharsiny; Manthrithilake, Herath; Pathmarajah, S.; Mikunthan, T.; Vithanage, M. 2020. Geo-statistical approach for prediction of groundwater quality in Chunnakam Aquifer, Jaffna Peninsula. Journal of Jaffna Science Association, 2(1):12-24.
Groundwater ; Water quality ; Aquifers ; Spatial distribution ; Forecasting ; Water properties ; Wells ; Geostatistics ; Models / Sri Lanka / Jaffna Peninsula / Valikamam / Chunnakam Aquifer
(Location: IWMI HQ Call no: e-copy only Record No: H050216)
http://journal.thejsa.org/index.php/jsaj/article/view/13/9
https://vlibrary.iwmi.org/pdf/H050216.pdf
https://vlibrary.iwmi.org/pdf/H050216.pdf
(3.03 MB) (3.03 MB)
Chunnakam aquifer is the main limestone aquifer of Jaffna Peninsula. The population of the Jaffna Peninsula depends entirely on groundwater resources to meet all of their water requirements. Thus for protecting groundwater quality in Chunnakam aquifer, data on spatial and temporal distribution are important. Geostatistics methods are one of the most advanced techniques for interpolation of groundwater quality. In this study, Ordinary Kriging and IDW methods were used for predicting spatial distribution of some groundwater characteristics such as: Electrical Conductivity (EC), pH, nitrate as nitrogen, chloride, calcium, carbonate, bicarbonate, sulfate and sodium concentration. Forty four wells were selected to represent the entire Chunnakam aquifer during January, March, April, July and October 2011 to represent wet and dry season within a year. After normalization of data, variogram was computed. Suitable model for fitness on experimental variogram was selected based on less Root Mean Square Error (RMSE) value. Then the best method for interpolation was selected, using cross validation and RMSE. Results showed that for all groundwater quality, Ordinary Kriging performed better than IDW method to simulate groundwater quality. Finally, using Ordinary Kriging method, maps of groundwater quality were prepared for studied groundwater quality in Chunnakam aquifer. The result of Ordinary Kriging interpolation showed that higher EC, chloride, sulphate and sodium concentrations are clearly shown to be more common closer to the coast, and decreasing inland due to intrusion of seawater into the Chunnakam aquifer. Also higher NO3 - - N are observed in intensified agricultural areas of Chunnakam aquifer in Jaffna Peninsula.
2 Waqas, M. M.; Waseem, M.; Ali, S.; Hopman, J. W.; Awan, Usman Khalid; Shah, S. H. H.; Shah, A. N. 2022. Capturing spatial variability of factors affecting the water allocation plans—a geo-informatics approach for large irrigation schemes. Environmental Science and Pollution Research, 29(54):81418-81429. [doi: https://doi.org/10.1007/s11356-022-20912-9]
Irrigation schemes ; Water allocation ; Plans ; Spatial variation ; Geostatistics ; Geographical information systems ; Remote sensing ; Irrigation water ; Cropping patterns ; Soil texture ; Soil salinity ; Groundwater level ; Water quality ; Irrigation systems ; Canals / Pakistan / Indus Basin Irrigation System / Lower Chenab Canal Irrigation Scheme
(Location: IWMI HQ Call no: e-copy only Record No: H051314)
(1.81 MB)
The livelihoods of poor people living in rural areas of Indus Basin Irrigation System (IBIS) of Pakistan depend largely on irrigated agriculture. Water duties in IBIS are mainly calculated based on crop-specific evapotranspiration. Recent studies show that ignoring the spatial variability of factors affecting the crop water requirements can affect the crop production. The objective of the current study is thus to identify the factors which can affect the water duties in IBIS, map these factors by GIS, and then develop the irrigation response units (IRUs), an area representing the unique combinations of factors affecting the gross irrigation requirements (GIR). The Lower Chenab Canal (LCC) irrigation scheme, the largest irrigation scheme of the IBIS, is selected as a case. Groundwater quality, groundwater levels, soil salinity, soil texture, and crop types are identified as the main factors for IRUs. GIS along with gamma design software GS + was used to delineate the IRUs in the large irrigation scheme. This resulted in a total of 84 IRUs in the large irrigation scheme based on similar biophysical factors. This study provided the empathy of suitable tactics to increase water management and productivity in LCC. It will be conceivable to investigate a whole irrigation canal command in parts (considering the field-level variations) and to give definite tactics for management.
Powered by DB/Text WebPublisher, from
