Quantifying irrigation water demand and supply gap using remote sensing and GIS in Multan, Pakistan.

Human interventions and rapid changes in land use adversely affect the adequate distribution of water resources. A research study was conducted to quantify the gap between demand and supply for irrigation water in Multan, Pakistan, which may lead to sustainable water management. Two remotely sensed images (Landsat 8 OLI and Landsat 5 TM) were downloaded for the years 2010 and 2020, and supervised classification method was performed for the selected land use land cover (LULC) classes and basic framework. During the evaluation, the kappa coefficient was found in the ranges of 0.83-0.85, and overall accuracy was found to be more than 80% which indicated a substantial agreement between the classified maps and the ground truth data for both years and seasons. The LULC maps showed that urbanization has increased by 49% during the last decade (2010-2020). Reduction in planting areas for wheat (9%), cotton (24%), and orchards (46%) was observed. An increase in planting areas for rice (92%) and sugarcane (63%) was observed. The changing LULC pattern may be related to variation in water demand and supply for irrigation. The irrigation water demand has decreased by 370.2 Mm3 from 2010 to 2020, due to the reduction in agricultural land and an increase in urbanization. Available irrigation water supply (canals/rainfall) was estimated as 2432 Mm3 for the year 2020 which was 26% less than that of total irrigation water demand (3281 Mm3). The findings also provide the database for sustainable water management and equitable distribution of water in the region.

Evaluating the efficiency of coarser to finer resolution multispectral satellites in mapping paddy rice fields using GEE implementation.

Timely and accurate estimation of rice-growing areas and forecasting of production can provide crucial information for governments, planners, and decision-makers in formulating policies. While there exists studies focusing on paddy rice mapping, only few have compared multi-scale datasets performance in rice classification. Furthermore, rice mapping of large geographical areas with sufficient accuracy for planning purposes has been a challenge in Pakistan, but recent advancements in Google Earth Engine make it possible to analyze spatial and temporal variations within these areas. The study was carried out over southern Punjab (Pakistan)-a region with 380,400 hectares devoted to rice production in year 2020. Previous studies support the individual capabilities of Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) for paddy rice classification. However, to our knowledge, no study has compared the efficiencies of these three datasets in rice crop classification. Thus, this study primarily focuses on comparing these satellites' data by estimating their potential in rice crop classification using accuracy assessment methods and area estimation. The overall accuracies were found to be 96% for Sentinel-2, 91.7% for Landsat-8, and 82.6% for MODIS. The F1-Scores for derived rice class were 83.8%, 75.5%, and 65.5% for Sentinel-2, Landsat-8, and MODIS, respectively. The rice estimated area corresponded relatively well with the crop statistics report provided by the Department of Agriculture, Punjab, with a mean percentage difference of less than 20% for Sentinel-2 and MODIS and 33% for Landsat-8. The outcomes of this study highlight three points; (a) Rice mapping accuracy improves with increase in spatial resolution, (b) Sentinel-2 efficiently differentiated individual farm level paddy fields while Landsat-8 was not able to do so, and lastly (c) Increase in rice cultivated area was observed using satellite images compared to the government provided statistics.

Remotely sensed image interpretation for assessment of land use land cover changes and settlement impact on allocated irrigation water in Multan, Pakistan.

Industrial revolution and rapid population growth are responsible for alteration of land into different settlements. These changes may lead to change in land use (LU) and land cover (LC). The LULC changes have impact on hydrological regimes including streams flow pattern and allocated irrigation water (water allocation through Warabandi system). The present study aimed to identify the LULC changes and settlement impact on allocated water using the unsupervised classification and normalized difference vegetation index (NDVI) of Landsat images for the years of 1990 to 2020 in Multan District. The accuracy assessment and Kappa coefficient were also investigated to evaluate quality of results derived from the classified images. The results show that the reduction in waterbody, spare, and dense vegetation was -7.6, -1.7, and -30.7%, respectively. The settlements, barren, and crop lands have increased to 25.2, 10.1, and 4.6%, respectively, from 1990 to 2020. The values of kappa coefficient (0.84-0.85) showed very good level of classification. In addition, the volume of water loss due to change of LULC from waterbody into settlements, barren land, crop land, spare, and dense vegetation was found approximately 472, 44, 133, 54, and 85 m3, respectively, in last 30 years. This volume of water is not reaching equitably to the farming community because of the LU and LC changes and urban settlements. The results indicated that remotely sensed image interpretation technique may be a useful for reallocation of water among farmers in an equitable and efficient way.

Using GIS tools to detect the land use/land cover changes during forty years in Lodhran District of Pakistan.

Land use/land cover (LULC) change has serious implications for environment as LULC is directly related to land degradation over a period of time and results in many changes in the environment. Monitoring the locations and distributions of LULC changes is important for establishing links between regulatory actions, policy decisions, and subsequent LULC activities. The normalized difference vegetation index (NDVI) has the potential ability to identify the vegetation features of various eco-regions and provides valuable information as a remote sensing tool in studying vegetation phenology cycles. Similarly, the normalized difference built-up index (NDBI) may be used for quoting built-up land. This study aims to detect the pattern of LULC, NDBI, and NDVI change in Lodhran district, Pakistan, from the Landsat images taken over 40 years, considering four major LULC types as follows: water bodies, built-up area, bare soil, and vegetation. Supervised classification was applied to detect LULC changes observed over Lodhran district as it explains the maximum likelihood algorithm in software ERDAS imagine 15. Most farmers (46.6%) perceived that there have been extreme changes of onset of temperature, planting season, and less precipitation amount in Lodhran district in the last few years. In 2017, building areas increased (4.3%) as compared to 1977. NDVI values for Lodhran district were highest in 1977 (up to + 0.86) and lowest in 1997 (up to - 0.33). Overall accuracy for classification was 86% for 1977, 85% for 1987, 86% for 1997, 88% for 2007, and 95% for 2017. LULC change with soil types, temperature, and NDVI, NDBI, and slope classes was common in the study area, and the conversions of bare soil into vegetation area and built-up area were major changes in the past 40 years in Lodhran district. Lodhran district faces rising temperatures, less irrigation water, and low rainfall. Farmers are aware of these climatic changes and are adapting strategies to cope with the effects but require support from government.

Study of land cover/land use changes using RS and GIS: a case study of Multan district, Pakistan.

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.

Development and adoption of wastewater treatment system for peri-urban agriculture in Multan, Pakistan.

The present research was conducted to assess the feasibility of biological treatment of a typical wastewater (WW) stream in Multan, Pakistan, using daily trends of WW characteristics and to design a wastewater treatment (WWT) system for that stream. The pH (5.8-6.2), temperature (24-30 °C), biological oxygen demand (BOD5: 128-265 mg/L), ultimate BOD (BODu: 227-438 mg/L), BOD/total Kjeldahl nitrogen (BOD5/TKN:5.9-11.2), BODu/BOD5 (1.6-2.0), carbonaceous BODu/nitrogenous BODu (CBODu/NBODu:1.6-2.8) of the WW was found to support the biological WWT. The inclusion of NBOD also indicated the need for nitrification-denitrification. The linear regression analysis of volatile suspended solids (VSS) with total suspended solids (TSS) indicated the high content of organic solids, which also made the WW suitable for biological treatment. The BOD/COD (chemical oxygen demand) <0.8 indicated the requirement for biomass acclimation. The major process units of the WWT system developed included a primary clarifier, cascade aeration, trickling filter, adsorption filter and chlorination contact tank. During the validation of design procedures, considerable removal of TSS (91%), TDS (46%), BOD5 (88%), COD (87%) was observed over the 15 week operational period of the secondary WWT system. The WWT system developed was appropriate as a sustainable WWT system that consumed less energy and had lower operational costs.

Assessment of Water Quality Profile Using Numerical Modeling Approach in Major Climate Classes of Asia.

A river water quality spatial profile has a diverse pattern of variation over different climatic regions. To comprehend this phenomenon, our study evaluated the spatial scale variation of the Water Quality Index (WQI). The study was carried out over four main climatic classes in Asia based on the Koppen-Geiger climate classification system: tropical, temperate, cold, and arid. The one-dimensional surface water quality model, QUAL2Kw was selected and compared for water quality simulations. Calibration and validation were separately performed for the model predictions over different climate classes. The accuracy of the water quality model was assessed using different statistical analyses. The spatial profile of WQI was calculated using model predictions based on dissolved oxygen (DO), biological oxygen demand (BOD), nitrate (NO3), and pH. The results showed that there is a smaller longitudinal variation of WQI in the cold climatic regions than other regions, which does not change the status of WQI. Streams from arid, temperate, and tropical climatic regions show a decreasing trend of DO with respect to the longitudinal profiles of main river flows. Since this study found that each climate zone has the different impact on DO dynamics such as reaeration rate, reoxygenation, and oxygen solubility. The outcomes obtained in this study are expected to provide the impetus for developing a strategy for the viable improvement of the water environment.

A linear bi-level multi-objective program for optimal allocation of water resources.

This paper presents a simple bi-level multi-objective linear program (BLMOLP) with a hierarchical structure consisting of reservoir managers and several water use sectors under a multi-objective framework for the optimal allocation of limited water resources. Being the upper level decision makers (i.e., leader) in the hierarchy, the reservoir managers control the water allocation system and tend to create a balance among the competing water users thereby maximizing the total benefits to the society. On the other hand, the competing water use sectors, being the lower level decision makers (i.e., followers) in the hierarchy, aim only to maximize individual sectoral benefits. This multi-objective bi-level optimization problem can be solved using the simultaneous compromise constraint (SICCON) technique which creates a compromise between upper and lower level decision makers (DMs), and transforms the multi-objective function into a single decision-making problem. The bi-level model developed in this study has been applied to the Swat River basin in Pakistan for the optimal allocation of water resources among competing water demand sectors and different scenarios have been developed. The application of the model in this study shows that the SICCON is a simple, applicable and feasible approach to solve the BLMOLP problem. Finally, the comparisons of the model results show that the optimization model is practical and efficient when it is applied to different conditions with priorities assigned to various water users.