Groundwater utilization for several purposes such as irrigation in agriculture, industry, and domestic use substantially impacts water storage. Groundwater Storage Anomaly (GWSA) estimates have improved owing to the Gravity Recovery and Climate Experiment (GRACE) and GRACE -Follow On (GRACE -FO) advancements. However, the characterization of GWSA fluctuation hotspots has been hindered by the coarse resolution of GRACE data. To better measure groundwater storage and depletion variations throughout an area and identify GWSA variation hotspots, a fine spatial resolution of GWSA estimations is required. Therefore, due to the coarse resolution of GRACE measurements, the eXtreme Gradient Boosting (XGBoost) model was developed to simulate fine resolution 0.1 degrees GWSA combining climatic variables (soil moisture storage, evapotranspiration, temperature, surface runoff, and rainfall) from improved spatial high resolution FLDAS (Famine Early Warning Systems Network Land Data Assimilation System) model derived data and geospatial variables (elevation, slope, and aspect) extracted from Digital Elevation Model (DEM). A correlation of 0.98 demonstrated that the XGBoost model successfully simulated groundwater storage at a finer scale over the Upper Indus Plain Aquifer (UIPA). The findings suggested that the UIPA's groundwater storage has been depleted at an annual rate of 0.44 km3/yr which was 7.94 km3 in total between 2003 and 2020. According to the results, there seems to be consistency between the downscaled and original GWSA regarding temporal and spatial variability. The results were verified to show an improved correlation of 0.77 between the downscaled and the in -situ GWSA, compared to 0.75 between the GRACE -derived and the in -situ GWSA.

In Pakistan and Afghanistan, intensive groundwater abstraction has accelerated socioeconomic development, but it also endangers the long-term sustainability of groundwater resources. Sustainable water resource management throughout river basins requires a spatiotemporal analysis of groundwater storage changes, but continuous groundwater monitoring is critical while there are few observation wells in the Chitral Kabul River Basin (CKRB). Therefore, this study uses total water storage (TWS) data from the Gravity Recovery and Climate Experiment (GRACE) and its Follow-on (GRACE-FO) and the Global Land Data Assimilation System (GLDAS) model water storage components, such as surface runoff (Qs), soil moisture content (SMS), and snow water equivalent (SWE) to determine the characteristics of groundwater storage (GWS) variations from 2003 to 2021. The seasonal decomposition LOESS method (STL) was used to assess the long-term trend, seasonal trend, and associated uncertainty of TWS and GWS time-series, while Mann-Kendall and Sen's slope estimator was used to detect and quantify the increasing and decreasing trend values of TWS and GWS time-series. The results showed that TWS indicated a general decreasing trend of -3.67 & PLUSMN; 0.98 mm/month while GWS indicated a harsh declining trend of -7.83 & PLUSMN; 0.52 mm/month from 2003 to 2021 in CKRB. The validation of GRACE-derived GWS results showed a correlation of R2 = 0.47 in comparison to in situ GWS. Spatially and temporally, according to sub-basins of CKRB, the more severe decline of TWS in Chitral-Kunar (-2.66 mm/year) and GWS in Swat (-4.34 mm/year) was observed. This research would be insightful to estimate the agroeconomic impact of the intensive ground-water in CKRB and its sub-basins.

In Pakistan, climate change is affecting water resources and also agriculture productivity. Rice-Wheat cropping zone is one of the prone regions that use water coming from upstream of the Indus Basin in Pakistan. In this study Soil and Water Assessment Tool (SWAT) model was used to evaluate the climate change adaptation practices in agriculture. The model was calibrated for the years 2005-2010 at Tarbela and Mangla reservoirs. Reasonably good performance of the calibrated model was achieved by estimated Coefficient of Determination (R-2), Nash-Sutcliffe efficiency (NSE), Percent Bias (PBIAS) at 0.87, 0.82 and 10.7 % for Tarbela and 0.70, 0.72 and 15.7 % for Mangla, respectively. Direct-Seeded Rice (DSR) practice for rice and Zero Tillage practice for wheat crops were tested in comparison to conventional methods by using SWAT. In parallel to the modeling approach, the field experiment was performed for two years i.e 2016-17 and-2017-18 at district Sahiwal. The results showed that overall water productivity of DSR was 0.58 and 0.54 kg per m(3) in the year 2017 and 2018, respectively, which was higher than Transplanting Rice Practice (TRP) having 0.43 and 0.40 kg per m(3) in 2017 and 2018, respectively. In wheat crop trails, overall water productivity of zero tillage was 1.3 and 1.2 kg per m(3) in the years 2017 and 2018, respectively. Two climate change scenarios Representative Concentration Pathways (RCP) 4.5 and 8.5 were tested in a combination of best management practices to evaluate climate change adaptation strategies for rice-wheat cropping zones. The results showed that DSR and Zero Tillage practices would be helpful in the future to adapt the expected climate change conditions without compromising the yield and water productivity of rice and wheat crops.

Irrigation water could be managed properly by mapping area of various crops. Remote sensing data can provide useful Land Use Land Cover (LULC) for assessment of different crop area and change detection. The present study was carried out with core objective to map crop area within the Indus Basin’s transboundary. Four major crops (i.e. wheat, rice, cotton and sugarcane) were identified using Normalize Difference Vegetation Index (NDVI) time series that was picked up from MODIS sensors aboard Terra (EOS AM) and Aqua (EOS PM) satellites with 250m pixel resolution. Crop phonological information was used to train each pixel intelligently for interpretation of unanalyzed NDVI data into crops. Eight days of time series data was used for identification and mapping of various crops on the basis of their phenology for the years 2008, 2010 and 2013. Error matrix was prepared to reveal mapping accurateness and ground truthing was also done in particular canal commands within the Indus basin. Furthermore, the temporal variation in cropped area was determined and for accuracy check, secondary data was matched with prepared maps. LULC maps for year 2008, 2010 and 2013 were defined for Rabi and kharif seasons.

Quantification of change in area under different crops is vital for conducting macro scale hydrological studies as spatial crop water use depends on type of crops grown. This becomes more challenging in areas with complex cropping systems like irrigated Indus basin of Pakistan. In this study, estimation of cropped area was carried out using Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation time series at six-hectare spatial resolution i.e. Soil Adjusted Vegetation Index (SAW). Two cropping years i.e. 2002-03 and 2013-14 were selected to quantify cropped area as well as assess how the cropping systems had changed over a time span of eleven years. Each pixel was trained to assign the vegetation, a particular crop cluster. Four and five major crops were discerned for rabi (winter) and kharif (summer) seasons, respectively. Wheat is dominant rabi crop while rice (a high delta crop) and cotton are dominant kharif crops. Sugarcane is an annual crop and being grown in different tracts of the basin. A confusion matrix prepared for accuracy test shows an overall accuracy of 79% and 74% for rabi and kharif seasons, respectively. The Kappa coefficient (0.64 and 0.62) expresses moderate agreement between satellite-derived map and on ground situation. Change detection indicates that wheat area has been increased significantly 4.2 mha (38.3%) from 2002 to 2013. Rice and sugarcane have also shown a significant increase of 0.69 mha (26.3%) and 0.29 mha (21.8%), respectively during the time span of eleven years. However, it is highly cautious for water management planners due to higher delta of these crops. This study provides essential information for spatial distribution of major seasonal crops grown in water stressed irrigated Indus basin for efficient agricultural monitoring and water resources management.

Spatial mapping of cropped area is always attractive for researchers working on agricultural water management and policy making (Abbas et al., 2006; Misra and Vethamony, 2015; Waqas et al., 2019).These remote sensing based datasets are used to characterize various surface and subsurface properties such as vegetation cover (for crop mapping, land changes and biodiversity), curve number (for rainfall runoff modeling) rooting depth in order to calculate soil moisture, albedo (for ET), surface roughness (for evapotranspiration), etc (Cheema and Bastiaanssen, 2010).Therefore, developing of water accounting and modeling of water balance (Molden, 1997)needs pixel information on agricultural area, to be known as water use competition within the agricultural land uses governs the water fluxes. Spatial extent of these agricultural land uses (irrigated and rainfed crops), locality and type is critical for estimating crop water requirement that varies from crop to crop (Zheng and Baetz, 1999). Compilation of crop information is not straightforward and becomes even difficult especially in areas with complex cropping systems. Irrigated Indus basin of Pakistan is one of such examples. Various studies have been carried out during the last decade to discern crops, based on spatio-temporal satellite data on vegetation (Cheema and Bastiaanssen, 2010; Saeed et al., 2017; Rehman and Kazmi, 2018). But all of these are less appropriate to use when large scale hydrological modeling studies are required due to their extent and lacking details on crops. The spatial resolution of freely available satellite datasets (moderate) and their temporal availability (in case of higher spatial resolution) made them less suitable to detect dominant crops in Pakistani farm settings that can result in mixed classes (Portmann et al., 2010). This situation is more vulnerable in mixed cropping system with varying cropping schedule. Crop sowing and harvesting windows are different in different agro-climatic zones in the basin (Gumma Pak. J. Agri. Sci., Vol. 57(2), 489-498; 2020 ISSN (Print) 0552-9034, ISSN (Online) 2076-0906 DOI: 10.21162/PAKJAS/19.8134 http://www.pakjas.com.pk

Crop yield estimation has significant importance for policy makers to make timely dicisions on import/export of particular crop. Traditionally, in Pakistan crop yield estimation is being carried out by Village Master Sampling (VMS) that is laborious and time-consuming. Satellite imagery is also being used as an alternative to estimate vegetation health and yield. Various vegetation indices are being used for the purpose however, their efficiency to estimate yield has not been tested. In this study, a comparison was performed among various satellite-based vegetation indices e.g. Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI) Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), to evaluate most appropriate index that performs better in cropping area of irrigated Indus Basin (a complex basin with spatially heterogeneous land use). A stepwise regression based model was developed for remotely sensed crop (i.e. Wheat) using multi-band MODIS and Landsat 8 products based on Land use and Land cover map developed by Semi-Supervised Classification. The results revealed that SAVI showed a fairly acceptable association with reported yield data as compared to other indices. The correlation coefficient (R2) was estimated at 0.60. Yield estimated by SAVI obtained from Landsat 8 showed good results with R2 and Pearson correlation (r), estimated at 0.74 and 0.88 as compared to SAVI obtained from MODIS with 0.63 and 0.79 respectively. The results support that SAVI vegetation indices is reliable for quick and efficient wheat area mapping under Pakistani’s farm conditions.