Your search found 8 records
1 Karimi, Poolad; Bastiaanssen, W. G. M.; Molden, D.; Cheema, M. J. M.. 2013. Basin-wide water accounting based on remote sensing data: an application for the Indus Basin. Hydrology and Earth System Sciences, 17(7):2473-2486. [doi: https://doi.org/10.5194/hess-17-2473-2013]
River basins ; Water accounting ; Indicators ; Groundwater ; Water storage ; Water depletion ; Remote sensing ; Data ; Land use ; Land cover ; Evapotranspiration ; Precipitation ; Biomass / Pakistan / India / China / Afghanistan / Indus Basin
(Location: IWMI HQ Call no: e-copy only Record No: H045940)
http://www.hydrol-earth-syst-sci.net/17/2473/2013/hess-17-2473-2013.pdf
https://vlibrary.iwmi.org/pdf/H045940.pdf
https://vlibrary.iwmi.org/pdf/H045940.pdf
(1.82 MB) (1.84MB)
The paper demonstrates the application of a new water accounting plus (WA+) framework to produce information on depletion of water resources, storage change, and land and water productivity in the Indus basin. It shows how satellite-derived estimates of land use, rainfall, evaporation (E), transpiration (T ), interception (I ) and biomass production can be used in addition to measured basin outflow, for water accounting with WA+. It is demonstrated how the accounting results can be interpreted to identify existing issues and examine solutions for the future. The results for one selected year (2007) showed that total annual water depletion in the basin (501 km3) plus outflows (21 km3) exceeded total precipitation (482 km3). The water storage systems that were effected are groundwater storage (30 km3), surface water storage (9 km3), and glaciers and snow storage (2 km3). Evapotranspiration of rainfall or “landscape ET” was 344 km3 (69% of total depletion). “Incremental ET” due to utilized flow was 157 km3 (31% of total depletion). Agriculture depleted 297 km3, or 59% of the total depletion, of which 85% (254 km3) was through irrigated agriculture and the remaining 15% (44 km3) through rainfed systems. Due to excessive soil evaporation in agricultural areas, half of all water depletion in the basin was non-beneficial. Based on the results of this accounting exercise loss of storage, low beneficial depletion, and low land and water productivity were identified as the main water resources management issues. Future scenarios to address these issues were chosen and their impacts on the Indus Basin water accounts were tested using the new WA+ framework.
2 Kiptala, J. K.; Mohamed, Y.; Mul, Marloes L.; Cheema, M. J. M.; Van der Zaag, P. 2013. Land use and land cover classification using phenological variability from MODIS vegetation in the Upper Pangani River Basin, eastern Africa. Physics and Chemistry of the Earth, 66:112-122. [doi: https://doi.org/10.1016/j.pce.2013.08.002]
Land use ; Land cover ; Mapping ; Land classification ; Land suitability ; Phenology ; Vegetation ; River basins ; Water resources ; International waters ; Rain ; Remote sensing ; Irrigated farming ; Rainfed farming ; Calibration / Eastern Africa / Tanzania / Kenya / Upper Pangani River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H046232)
(3.30 MB)
In arid and semi-arid areas, evaporation fluxes are the largest component of the hydrological cycle, with runoff coefficient rarely exceeding 10%. These fluxes are a function of land use and land management and as such an essential component for integrated water resources management. Spatially distributed land use and land cover (LULC) maps distinguishing not only natural land cover but also management practices such as irrigation are therefore essential for comprehensive water management analysis in a river basin. Through remote sensing, LULC can be classified using its unique phenological variability observed over time. For this purpose, sixteen LULC types have been classified in the Upper Pangani River Basin (the headwaters of the Pangani River Basin in Tanzania) using MODIS vegetation satellite data. Ninety-four images based on 8 day temporal and 250 m spatial resolutions were analyzed for the hydrological years 2009 and 2010. Unsupervised and supervised clustering techniques were utilized to identify various LULC types with aid of ground information on crop calendar and the land features of the river basin. Ground truthing data were obtained during two rainfall seasons to assess the classification accuracy. The results showed an overall classification accuracy of 85%, with the producer’s accuracy of 83% and user’s accuracy of 86% for confidence level of 98% in the analysis. The overall Kappa coefficient of 0.85 also showed good agreement between the LULC and the ground data. The land suitability classification based on FAO-SYS framework for the various LULC types were also consistent with the derived classification results. The existing local database on total smallholder irrigation development and sugarcane cultivation (large scale irrigation) showed a 74% and 95% variation respectively to the LULC classification and showed fairly good geographical distribution. The LULC information provides an essential boundary condition for establishing the water use and management of green and blue water resources in the water stress Pangani River Basin.
3 Ahmad, M. D.; Kirby, J. M.; Cheema, M. J. M.. 2019. Impact of agricultural development on evapotranspiration trends in the irrigated districts of Pakistan: evidence from 1981 to 2012. Water International, 44(1):51-73. [doi: https://doi.org/10.1080/02508060.2019.1575110]
Agricultural development ; Irrigation management ; Evapotranspiration ; Irrigated farming ; Crops ; Water use ; Estimation ; Water accounting ; Groundwater ; Remote sensing ; Uncertainty / Pakistan / Indus Basin / Punjab / Balochistan / Sindh
(Location: IWMI HQ Call no: e-copy only Record No: H049118)
(3.49 MB)
Understanding time-series evapotranspiration trends is critical for water-balance assessments and sustainable water management in arid regions. In this paper, an approach is presented to understand time-series evapotranspiration trends by combining remote sensing-based evapotranspiration and agricultural statistics data and applying them to understand district-level water-use trends in the Indus basin irrigation system of Pakistan. The evapotranspiration of most districts in the Punjab increased over the period, whereas in Sindh it generally remained about the same or decreased. The trends in Punjab suggests that the already unsustainable groundwater use in some areas may become more unsustainable.
4 Ali, S.; Cheema, M. J. M.; Waqas, M. M.; Waseem, M.; Awan, Usman Khalid; Khaliq, T. 2020. Changes in snow cover dynamics over the Indus Basin: evidences from 2008 to 2018 MODIS NDSI trends analysis. Remote Sensing, 12(17):2782. (Special issue: Interactive Deep Learning for Hyperspectral Images) [doi: https://doi.org/10.3390/rs12172782]
Snow cover ; Estimation ; Mapping ; Trends ; River basins ; Catchment areas ; Temperature ; Clouds ; Landsat ; Satellite imagery ; Moderate resolution imaging spectroradiometer ; Uncertainty / Pakistan / Indus Basin / Himalayas / Chenab River Catchment / Jhelum River Catchment / Indus River Catchment / Eastern Rivers Catchment
(Location: IWMI HQ Call no: e-copy only Record No: H050209)
(4.20 MB) (4.20 MB)
The frozen water reserves on the Earth are not only very dynamic in their nature, but also have significant effects on hydrological response of complex and dynamic river basins. The Indus basin is one of the most complex river basins in the world and receives most of its share from the Asian Water Tower (Himalayas). In such a huge river basin with high-altitude mountains, the regular quantification of snow cover is a great challenge to researchers for the management of downstream ecosystems. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) daily (MOD09GA) and 8-day (MOD09A1) products were used for the spatiotemporal quantification of snow cover over the Indus basin and the western rivers’ catchments from 2008 to 2018. The high-resolution Landsat Enhanced Thematic Mapper Plus (ETM+) was used as a standard product with a minimum Normalized Difference Snow Index (NDSI) threshold (0.4) to delineate the snow cover for 120 scenes over the Indus basin on different days. All types of errors of commission/omission were masked out using water, sand, cloud, and forest masks at different spatiotemporal resolutions. The snow cover comparison of MODIS products with Landsat ETM+, in situ snow data and Google Earth imagery indicated that the minimum NDSI threshold of 0.34 fits well compared to the globally accepted threshold of 0.4 due to the coarser resolution of MODIS products. The intercomparison of the time series snow cover area of MODIS products indicated R2 values of 0.96, 0.95, 0.97, 0.96 and 0.98, for the Chenab, Jhelum, Indus and eastern rivers’ catchments and Indus basin, respectively. A linear least squares regression analysis of the snow cover area of the Indus basin indicated a declining trend of about 3358 and 2459 km2 per year for MOD09A1 and MOD09GA products, respectively. The results also revealed a decrease in snow cover area over all the parts of the Indus basin and its sub-catchments. Our results suggest that MODIS time series NDSI analysis is a useful technique to estimate snow cover over the mountainous areas of complex river basins.
5 Nazeer, A.; Waqas, M. M.; Ali, S.; Awan, U. K.; Cheema, M. J. M.; Baksh, A. 2020. Land use land cover classification and wheat yield prediction in the Lower Chenab Canal System using remote sensing and GIS. Big Data In Agriculture, 2(2):47-51. [doi: https://doi.org/10.26480/bda.02.2020.47.51]
Crop yield ; Forecasting ; Wheat ; Land use ; Land cover ; Normalized difference vegetation index ; Remote sensing ; Geographical information systems ; Landsat ; Satellite imagery ; Canals / Pakistan / Lower Chenab Canal System / Khurrian Wala Distributary / Killian Wala Distributary / Mungi Distributary
(Location: IWMI HQ Call no: e-copy only Record No: H050212)
https://bigdatainagriculture.com/paper/issue2%202020/2bda2020-47-51.pdf
https://vlibrary.iwmi.org/pdf/H050212.pdf
https://vlibrary.iwmi.org/pdf/H050212.pdf
(1.40 MB) (1.40 MB)
Reliable and timely information regarding area under wheat and its yield prediction can help in better management of the commodity. The remotely sensed data especially in combination with Geographic Information System (GIS) can provide an important and powerful tool for both, land use land cover (LULC) classification and crop yield prediction. The study objectives include LULC classification and wheat yield prediction. The study was conducted for Rabi Season from Nov. 2011 to April 2012, in the command area of three distributaries i.e. Khurrian Wala, Killian Wala and Mungi of Lower Chennai Canal (LCC) system. The Landsat-7 imagery data with spatial resolution of 30 m was used for this study. Physical features were monitored and assessed using Normalized Difference Vegetative Index (NDVI). LULC classification was done for wheat and non-wheat area which shows wheat proportion and area 87.22% and 28867.95 Ha in Khurrian wala, 71.07% and 22423.20 Ha in Killian Wala and 79.18% and 17974.34 Ha in Mungi distributary, respectively. The correlation values between maximum NDVI value and yield data were 0.45, 0.36 and 0.39 for Khurrian Wala, Killian Wala and Mungi distributary, respectively. On the basis of this correlation, average wheat yield was estimated as 3.48 T/Ha, 3.83 T/Ha and 3.80 T/Ha for Khurrian Wala, Killian Wala and Mungi distributary, respectively.
6 Ali, S.; Liu, D.; Fu, Q.; Cheema, M. J. M.; Pham, Q. B.; Rahaman, Md. M.; Dang, T. D.; Anh, D. T. 2021. Improving the resolution of GRACE data for spatio-temporal groundwater storage assessment. Remote Sensing, 13(17):3513. (Special Issue: Remote Sensing and Modelling of Water Storage Dynamics from Bedrock to Atmosphere) [doi: https://doi.org/10.3390/rs13173513]
Groundwater assessment ; Water storage ; Irrigation systems ; Aquifers ; Groundwater table ; Soil moisture ; Evapotranspiration ; Runoff ; Models ; Satellites ; Neural networks / Pakistan / Sindh / Punjab / Indus Basin Irrigation System
(Location: IWMI HQ Call no: e-copy only Record No: H050649)
(9.12 MB) (9.12 MB)
Groundwater has a significant contribution to water storage and is considered to be one of the sources for agricultural irrigation; industrial; and domestic water use. The Gravity Recovery and Climate Experiment (GRACE) satellite provides a unique opportunity to evaluate terrestrial water storage (TWS) and groundwater storage (GWS) at a large spatial scale. However; the coarse resolution of GRACE limits its ability to investigate the water storage change at a small scale. It is; therefore; needed to improve the resolution of GRACE data at a spatial scale applicable for regional-level studies. In this study; a machine-learning-based downscaling random forest model (RFM) and artificial neural network (ANN) model were developed to downscale GRACE data (TWS and GWS) from 1° to a higher resolution (0.25°). The spatial maps of downscaled TWS and GWS were generated over the Indus basin irrigation system (IBIS). Variations in TWS of GRACE in combination with geospatial variables; including digital elevation model (DEM), slope; aspect; and hydrological variables; including soil moisture; evapotranspiration; rainfall; surface runoff; canopy water; and temperature; were used. The geospatial and hydrological variables could potentially contribute to; or correlate with; GRACE TWS. The RFM outperformed the ANN model and results show Pearson correlation coefficient (R) (0.97), root mean square error (RMSE) (11.83 mm), mean absolute error (MAE) (7.71 mm), and Nash–Sutcliffe efficiency (NSE) (0.94) while comparing with the training dataset from 2003 to 2016. These results indicate the suitability of RFM to downscale GRACE data at a regional scale. The downscaled GWS data were analyzed; and we observed that the region has lost GWS of about -9.54 ± 1.27 km3 at the rate of -0.68 ± 0.09 km3/year from 2003 to 2016. The validation results showed that R between downscaled GWS and observational wells GWS are 0.67 and 0.77 at seasonal and annual scales with a confidence level of 95%, respectively. It can; therefore; be concluded that the RFM has the potential to downscale GRACE data at a spatial scale suitable to predict GWS at regional scales.
7 Shoukat, M. R.; Shafeeque, Muhammad; Sarwar, A.; Mehmood, K.; Cheema, M. J. M.. 2021. Investigating effects of deficit irrigation levels and fertilizer rates on water use efficiency and productivity based on field observations and modeling approaches. International Journal of Hydrology, 5(5):252-263. [doi: https://doi.org/10.15406/ijh.2021.05.00287]
Deficit irrigation ; Nitrogen fertilizers ; Water use efficiency ; Water productivity ; Nutrient use efficiency ; Irrigated sites ; Small farms ; Evapotranspiration ; Modelling / Pakistan / Punjab / Faisalabad
(Location: IWMI HQ Call no: e-copy only Record No: H050906)
(2.40 MB) (2.40 MB)
Investigating the effects of optimized fertilizer and irrigation levels on water use efficiency and productivity of wheat crop at small farms is of great importance for precise and sustainable agriculture in Pakistan’s irrigated areas. However, traditional farmer practices for wheat production are inefficient and unsustainable. This study aimed to investigate the effects of deficit irrigation and nitrophos fertilizer levels on bread wheat grain yield, yield parameters, nutrient use and water use efficiencies in bed planting wheat compared to traditional farmers’ practices in the flat sowing method. The two-year field experiment followed a randomized complete block design of three replications, taking three irrigation treatments according to the requirement of crop estimated by CROPWAT model (100% of ETC), deficit irrigation (80% of ETC), and deficit irrigation 60% of ETC and three nitrophos fertilizer treatments (farmer practice 120 kg N ha-1, optimized 96 kg N ha-1, and 84 kg N ha-1) at different growth stages. Crop ETC was calculated using the FAO CROPWAT 8.0 model from the last ten years (2003-2013) average climate data of the experimental station. The traditional farmer practice treatment was included as a control treatment with a flat sowing method compared with other sown-by-bed planter treatments. All treatments were provided with an equivalent amount of fertilizer at the basal dose. Before the first and second irrigation, top-dressing fertilizer was used in traditional farmers’ treatment at the third leaf and tillering stages. It was applied in optimized treatments before the first, second, and third irrigation at the third leaf, tillering and shooting stages, respectively, under the bed planting method. The deficit level of irrigation (80% of ETc) and optimized fertilizer (96 kg N ha-1) showed the optimum grain yield, nutrient use, and water use efficiencies, with 20% reduced irrigation water and fertilizer levels than traditional farming practice. The results suggest that bread wheat should be irrigated with 80% of ETC and applied 96 kg N ha-1 nitrophos fertilizer at the third leaf, tillering, and shooting stages to achieve higher grain yield and water and nutrient use efficiencies under bed planting.
8 Hussain, F.; Maeng, S.- J.; Cheema, M. J. M.; Anjum, M. N.; Afzal, A.; Azam, M.; Wu, R. - S.; Noor, R. S.; Umair, M.; Iqbal, T. 2023. Solar irrigation potential, key issues and challenges in Pakistan. Water, 15(9):1727. (Special Issue Applications of Renewable Energies in Irrigation Water Supply) [doi: https://doi.org/10.3390/w15091727]
Solar powered irrigation systems ; Solar energy ; Renewable energy ; Water scarcity ; Irrigation systems ; Drip irrigation ; Agricultural productivity ; Crop yield ; Food security ; Policies ; Groundwater extraction ; Tube wells ; Pumping ; Farmers ; Fossil fuels ; Photovoltaic systems ; Water resources ; Crops ; Water requirements ; Water scarcity ; Water management / Pakistan / Punjab
(Location: IWMI HQ Call no: e-copy only Record No: H051903)
https://www.mdpi.com/2073-4441/15/9/1727/pdf?version=1682754017
https://vlibrary.iwmi.org/pdf/H051903.pdf
https://vlibrary.iwmi.org/pdf/H051903.pdf
(4.06 MB) (4.06 MB)
Pakistan faces water scarcity and high operational costs for traditional irrigation systems, hindering agricultural productivity. Solar-powered irrigation systems (SPIS) can potentially provide a sustainable and affordable solution, but face technical, financial and policy barriers to adoption. A comprehensive study is needed to examine feasibility and identify barriers. Therefore, a comprehensive review study is conducted to identify the potential for solar irrigation, key issues and challenges related to its implementation in Pakistan. The analysis is based on published studies, technical reports and a survey of solar-powered drip irrigation systems. The use of SPIS in Pakistan is becoming a cost-effective and sustainable option for irrigation, particularly in remote and off-grid areas. However, these systems also have their challenges, such as high initial costs, maintenance and repairs, limited access to spare parts, lack of government policies and regulations, lack of technical expertise, lack of financing options and social acceptance. The most pressing issue is the risk of groundwater exploitation by using SPIS. Based on the analysis of the energy and water situation in Pakistan, it is important to sustainably use both solar energy and groundwater resources, through the implementation of effective management strategies and policies. With the right policies and investment in research and development of SPIS and groundwater, farmers can benefit by increasing crop yields, conserving water resources, reducing the cost of energy, increasing productivity and improving the standard of living and access to electricity in remote and off-grid areas. It is recommended that the adoption of solar energy be promoted to run high efficiency irrigation systems (HEIS) with urgent capacity improvement among farmers, advisors and system installers to sustainably manage water resources in SPIS. This would not only help to reduce the consumption of fossil fuels and associated environmental impacts, but also increase farmers’ income and reduce their operational costs. Moreover, the use of SPIS can improve crop yields, leading to food security and poverty reduction. Thus, the government and policymakers should consider implementing policies and incentives to encourage the large-scale adoption of solar energy in the agricultural sector.
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