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The quality and level of groundwater tables have rapidly declined because of intensive pumping in Punjab (Pakistan). For sustainable groundwater supplies, there is a need for better management practices. So, the identification of potential groundwater recharge zones is crucial for developing effective management systems. The current research is based on integrating seven contributing factors, including geology, soil map, land cover/land use, lineament density, drainage density, slope, and rainfall to categorize the area into various groundwater recharge potential zones using remote sensing, geographic information system (GIS), and analytical hierarchical process (AHP) for Punjab, Pakistan. The weights (for various thematic layers) and rating values (for sub-classes) in the overlay analysis were assigned for thematic layers and then modified and normalized using the AHP. The result indicates that about 17.88% of the area falls under the category of very high groundwater potential zones (GWPZs). It was found that only 12.27% of the area falls under the category of very low GWPZs. The results showed that spatial technologies like remote sensing and geographic information system (GIS), when combined with AHP technique, provide a robust platform for studying GWPZs. This will help the public and government sectors to understand the potential zone for sustainable groundwater management. [ABSTRACT FROM AUTHOR]

The rapid increase in urbanization has an important effect on cropping pattern and land use/land cover (LULC) through replacing areas of vegetation with commercial and residential coverage, thereby increasing the land surface temperature (LST). The LST information is significant to understand the environmental changes, urban climatology, anthropogenic activities, and ecological interactions, etc. Using remote sensing (RS) data, the present research provides a comprehensive study of LULC and LST changes in water scarce and climate prone Southern Punjab (Multan region), Pakistan, for 30 years (from 1990 to 2020). For this research, Landsat images were processed through supervised classification with maps of the Multan region. The LULC changes showed that sugarcane and rice (decreased by 2.9 and 1.6%, respectively) had less volatility of variation in comparison with both wheat and cotton (decreased by 5.3 and 6.6%, respectively). The analysis of normalized difference vegetation index (NDVI) showed that the vegetation decreased in the region both in minimum value (−0.05 [1990] to −0.15 [2020]) and maximum value (0.6 [1990] to 0.54 [2020]). The results showed that the built-up area was increased 3.5% during 1990–2020, and these were some of the major changes which increased the LST (from 27.6 to 28.5°C) in the study area. The significant regression in our study clearly shows that NDVI and LST are negatively correlated with each other. The results suggested that increasing temperature in growing period had a greatest effect on all types of vegetation. Crop-based classification aids water policy managers and analysts to make a better policy with enhanced information based on the extent of the natural resources. So, the study of dynamics in major crops and surface temperature through satellite RS can play an important role in the rural development and planning for food security in the study area.

To enhance the combustion efficiency of high moist coal, it is important to reduce its moisture contents. For reduction of moisture contents, the selection of efficient and low-cost drying technique based on the coal characteristics is helpful to convert the high moist coal to good quality fuel. A total of 18 Thar coal (Pakistan) samples were crushed and sieved at following size fractions (355, 500, 710 µm). These samples were dried using different techniques, namely, solar-drying, hot air-drying, oven-drying, microwave oven-drying, and flue gases drying. The morphological, physical, and chemical changes were investigated for the effectiveness of different techniques to make the Thar coal as a value-added fuel. The applied drying techniques showed a significant reduction in moisture contents from the Thar coal samples (38.11 to 7.78%). However, the flue gases drying and air-drying techniques showed highest reduction in moisture content (29–31%). Similarly, 35% increase in heating value (HV) J/kg was observed in case of flue gases drying technique. These results indicated that the flue gases and air-drying techniques may be used to reduce the moisture contents and enhance the HV of high moist Thar coal that can be used as value-added fuel in the country. [ABSTRACT FROM AUTHOR]

At field level, prediction of crop yield and determination of appropriate fertilizer application rate based on soil and landscape attributes often require for better management practices finally maximizing crop yield. The present study was designed to evaluate the potential of Artificial Neural Networks (ANNs) model to predict wheat grain yield and to determine the urea fertilizer application rate for maximizing wheat grain yield. The urea fertilizer, %sand, %silt, %clay, elevation, soil nitrogen (N), soil electrical conductivity (EC), soil phosphorus (P) and soil pH were used as input parameters and wheat grain yield was used as output. The ANNs model was trained using 124 data sets collected during growing seasons of 2008-09 to 2010-11 and evaluated using randomly selected 20 data sets of 2010-11 and 48 data sets of201 l-12. The results showed that ANNs model has the potential to predict wheat grain yield under semiarid conditions, as the mean absolute error (MAE) of 6.50% was found for training and 9.48% was observed for testing the model. To determine urea fertilizer application rate for maximizing wheat grain yield, the trained model was run for 14 urea fertilizer levels ranging from O to 400 kgurea ha·1 . It was examined that 210 kg-ureaha·1 produced maximum grain wheat yield (4200 kg ha-1) and further increase of urea fertilizer rates resulted in decrease of wheat grain yield. These results also showed that ANN s model is a useful tool to estimate the wheat grain yield response to soil and landscape attributes and to determine the optimum urea fertilizer level for maximizing the wheat grain yield in semi-arid conditions of Faisalabad. [ABSTRACT FROM AUTHOR]

Water and land both are limited resources. Current management strategies are facing multiple challenges to meet food security of an increasing population in numerous South Asian countries, including Pakistan. The study of land cover/land use changes (LCLUC) and land surface temperature (LST) is important as both provide critical information for policymaking of natural resources. We spatially examined LCLU and LST changes in district Multan, Pakistan, and its impacts on vegetation cover and water during 1988 to 2017. The LCLUC indicate that rice and sugarcane had less volatility of change in comparison with both cotton and wheat. Producer's accuracy (PA) is the map accuracy (the producer of map), but user's accuracy (UA) is the accuracy from the point of view of a map user, not the map maker. Average overall producer's and user's accuracy for the region was 85.7% and 87.7% for Rabi (winter) and Kharif (summer) seasons, respectively. The results of this study showed that 'built-up area' increased with 7.2% of all the classes during 1988 to 2017 in the Multan district. Anthropogenic activities decreased the vegetation, leading to an increase in LST in study area. Changes on LCLU and LST during the last 30 years have shown that vegetation pattern has changed and temperature has increased in the Multan district. [ABSTRACT FROM AUTHOR]

Detailed knowledge about soil characteristics and site-specific final steady infiltration rate could help to increase the irrigation water use efficiency and decrease water losses in agricultural system. The experiments were conducted on Agricultural Research Farm of Bahauddin Zakariya University, Multan, Pakistan during 2016. The cumulative infiltration depth was measured using double ring infiltrometer at selected six points of the study area. Most commonly used infiltration models such as Kostikov's, Philip's and Horton's were fitted to the field infiltration data for determination of model parameters and to find the best fit model for the study area. Kostikov's infiltration model's parameters such as empirical constant 'c' and infiltration decay constants 'k' were obtained in the ranges of 0.140-0.290 and 0.307-0.433, respectively. Philip's infiltration model's parameters such as sorptivity 'S' and conductivity constant 'A' were found in the ranges of 0.167-0.288 cm/min1/2 and -0.001 to -0.009 cm/min, respectively. Similarly, the Horton's model's 'parameter 'k' was obtained in the range of -1.619 to -1.238. The value of infiltration capacity at onset of infiltration (fo) was obtained as 1.744 to 3.491 for all the six points. The analysis showed that the infiltration models using the estimated parameters have satisfactory prediction capability at all the selected points. Horton's model provided the lowest mean values for RMSE (0.235) and highest mean values for ME (94%); and the lowest mean values for MPD (0.127). This indicated that infiltration can be well-described by the Horton's model at the selected site. [ABSTRACT FROM AUTHOR]

The performance evaluation of satellite-based precipitation products at local and regional scales is crucial for modification in satellite-based precipitation retrieval algorithms, as well as for the provision of guidance during the selection of substitute precipitation data. This study evaluated the performances of three Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) products (3B42V6, 3B42RT and 3B42V7) with a reference to rain gauge observations in the Hunza River basin, northern Pakistan. Multi-spatial (pixel and basin) and temporal (daily, monthly, seasonal and annual) resolutions were considered for performance evaluation of TMPA products. Results revealed that the spatial pattern of observed precipitation over the basin was adequately captured by 3B42V7 but misplaced by 3B42V6 and 3B42RT. All TMPA products were unable to capture the intense precipitation events. On the daily time scale, the performance of TMPA products was very poor over both spatial scales. 3B42V6 underestimated the precipitation (31.25% and 44.27% on pixel and basin scales, respectively). By contrast, 3B42RT significantly overestimated the precipitation (47.91% and 38.62% on pixel and basin scales, respectively), while 3B42V7 showed overestimation (17.30%) on pixel scale and slight underestimation (6.24%) on the basin scale. On the seasonal scale, TMPA products showed significant biases with observed precipitation data. We found that the TMPA products performed relatively better on monthly and annual time scales and overall performance of 3B42V7 product was better than that of 3B42V6 and 3B42RT. The bias in 3B42V7 was improved by 85.90% compared with 3B42V6 and by 116.16% compared with 3B42RT. Thus, it is concluded that the TMPA products were unreliable to capture the intense precipitation events and retain high errors on daily and seasonal scales. Therefore, caution should be considered while using these precipitation estimates as a substitute data in hydrology, meteorology and climatology studies in Hunza River basin. However, due to the reasonable performance of monthly and annual 3B42V7 estimates, these can be used as an acceptable substitute for applications in the region. [ABSTRACT FROM AUTHOR]

Detailed knowledge about site-specific aquifer characteristics, subsurface lithology, and groundwater potential can help to determine the depth and location of fresh groundwater quality. The present research study was carried out by conducting 80 vertical electrical sounding surveys (VESs) in Rahim Yar Khan District (RYK), Punjab, Pakistan to distinguish the fresh groundwater aquifer from saline groundwater and to evaluate the aquifer protective capacity (APC) of overburden. 1XID software (Interpex, USA) was used to accomplish the interpretation of VES data. The VES interpreted data was used to prepare spatial distribution maps of aquifer apparent resistivity (AR), layer thickness, longitudinal conductance (LC), and transverse resistance (TR) for the second, third, and fourth subsurface layers using ArcGIS 10.1. The results showed that the greater part of the study area (65%) had four subsurface geo-electric layers. The spatial distribution maps for AR showed that the fresh groundwater quality was present on the northwestern and northeastern sides of the study area for all the layers. The results also indicated that the APC of overburden increased with the increase of depth from the ground surface. Layer 4 with thickness of 57.09 m showed good APC in the northern and central parts with LC values of >0.7 mhos. Similarly, the higher values of TR showed higher yield potential in the north-eastern part as compared to the southern part. Overall analysis indicated that the spatial distribution maps of AR, layer thickness, LC, and TR should be helpful for future groundwater development in terms of quality and quantity. [ABSTRACT FROM AUTHOR]