Your search found 4 records
1 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.
2 Waqas, M. M.; Shah, S. H. H.; Awan, Usman Khalid; Waseem, M.; Ahmad, I.; Fahad, M.; Niaz, Y.; Ali, S. 2020. Evaluating the impact of climate change on water productivity of maize in the semi-arid environment of Punjab, Pakistan. Sustainability, 12(9):3905. (Special issue: Climate Resilient Sustainable Agricultural Production Systems) [doi: https://doi.org/10.3390/su12093905]
Climate change ; Impact assessment ; Water productivity ; Crop production ; Maize ; Semiarid zones ; Soil hydraulic properties ; Groundwater recharge ; Irrigation systems ; Precipitation ; Temperature ; Rain ; Models / Pakistan / Punjab / Lower Chenab Canal system
(Location: IWMI HQ Call no: e-copy only Record No: H050210)
(1.37 MB) (1.37 MB)
Impact assessments on climate change are essential for the evaluation and management of irrigation water in farming practices in semi-arid environments. This study was conducted to evaluate climate change impacts on water productivity of maize in farming practices in the Lower Chenab Canal (LCC) system. Two fields of maize were selected and monitored to calibrate and validate the model. A water productivity analysis was performed using the Soil–Water–Atmosphere–Plant (SWAP) model. Baseline climate data (1980–2010) for the study site were acquired from the weather observatory of the Pakistan Meteorological Department (PMD). Future climate change data were acquired from the Hadley Climate model version 3 (HadCM3). Statistical downscaling was performed using the Statistical Downscaling Model (SDSM) for the A2 and B2 scenarios of HadCM3. The water productivity assessment was performed for the midcentury (2040–2069) scenario. The maximum increase in the average maximum temperature (Tmax) and minimum temperature (Tmin) was found in the month of July under the A2 and B2 scenarios. The scenarios show a projected increase of 2.8 C for Tmax and 3.2 C for Tmin under A2 as well as 2.7 C for Tmax and 3.2 C for Tmin under B2 for the midcentury. Similarly, climate change scenarios showed that temperature is projected to decrease, with the average minimum and maximum temperatures of 7.4 and 6.4 C under the A2 scenario and 7.7 and 6.8 C under the B2 scenario in the middle of the century, respectively. However, the highest precipitation will decrease by 56 mm under the A2 and B2 scenarios in the middle of the century for the month of September. The input and output data of the SWAP model were processed in R programming for the easy working of the model. The negative impact of climate change was found under the A2 and B2 scenarios during the midcentury. The maximum decreases in Potential Water Productivity (WPET) and Actual Water Productivity (WPAI) from the baseline period to the midcentury scenario of 1.1 to 0.85 kgm-3 and 0.7 to 0.56 kgm-3 were found under the B2 scenario. Evaluation of irrigation practices directs the water managers in making suitable water management decisions for the improvement of water productivity in the changing climate.
3 Waseem, M.; Jaffry, A. H.; Azam, M.; Ahmad, I.; Abbas, A.; Lee, J.-E. 2022. Spatiotemporal analysis of drought and agriculture standardized residual yield series nexuses across Punjab, Pakistan. Water, 14(3):496. (Special issue: The Impacts of Climate Change on Hydrologic Extremes) [doi: https://doi.org/10.3390/w14030496]
Drought ; Agriculture ; Crop production ; Wheat ; Crop yield ; Trends ; Time series analysis ; Soil moisture ; Climate change ; Meteorological factors ; Precipitation / Pakistan / Punjab
(Location: IWMI HQ Call no: e-copy only Record No: H050918)
(3.60 MB) (3.60 MB)
Food security for the growing global population is closely associated with the variations in agricultural yield at the regional scale. Based on this perspective, the current study was designed to determine the impacts of drought on wheat production in the Punjab province, which is the agricultural hub of Pakistan. Wheat is a staple food in Pakistan, and Punjab provides a major contribution to the total wheat production of the country. Therefore, Punjab is vital to scientific concerns regarding the evaluation of climatic impacts on the annual wheat yield. The current study offers a better understanding of the drought impacts on wheat in Punjab during 2001–2019. The Standardized Precipitation Index was used to assess the impact of drought stress on the wheat yield. Its temporal evolution indicates the recurrent appearance of drought episodes during the wheat cropping season. Furthermore, meteorological drought was noticed in all study years except for 2019. The results reveal that 2002 experienced severe drought conditions. The frequency of drought was calculated as 29% for SPI-12. The relationships between soil moisture, the Standardized Yield Residual Series (SYRS), and the detrended SPI at lags of 1–12 months indicate that zones 1 and 2 are more sensitive to dry conditions. The results presented in this study provide evidence to authorities responsible for developing policies in the context of natural hazards, particularly droughts, and for preparing drought mitigation plans and implementing the adaptation strategies to minimize the effects of drought on wheat yields.
4 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.
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