Sajjad Hussain

Modeling achievable yield under water-limiting environments is an imperative goal in arid, semi-arid, and drought-prone locations. This study aimed to calibrate the AquaCrop model simulation based on experience gained at al-Hashimiya District, 20 km east of Babylon, Iraq. Wheat (Triticum aestivum) in 2016–2017 and 2017–2018 was grown using deficit irrigation systems (surface and sprinkler irrigation) and different tillage methods (deep, moderate, and zero tillage). A simulation was conducted on the systems, using climate data for a period of 10 years and specific data on the study area, crops grown, and irrigation types. Then, statistical analysis was conducted with the root-mean-square error (RMSE), which refers to compatibility between the results and predictions of the measured values. For 2016–2017 and 2017–2018, RMSE values were 0.764 and 0.643 for biomass, 0.473 and 0.419 for dry matter, 0.141 and 0.154 for water productivity, and 1.59 and 0.946 for harvest index. The R2 values were 0.917 and 0.956 for dry matter, 0.982 and 0.946 for biomass, 0.869 and 0.849 for harvest index, and 0.923 and 0.943 for water productivity from 2016–2017 and 2017–2018. Mean bias error (MBE) and mean absolute error (MAE) were also derived with comparable results. The model results indicated that there is an explicit agreement with the simulated values of the wheat crop. The AquaCrop model can be used to estimate water productivity, dry matter, and biomass to improve agricultural water management and as an input into future research of alternate forms of irrigation.

Monitoring and understanding Land Use/Land Cover (LU/LC) is critical for sustainable development, as it can impact various environmental, social, and economic systems. For example, deforestation and land degradation can lead to soil erosion, loss of biodiversity, and greenhouse gas emissions, affecting the quality of soil, air, and water resources. The present research examined changes in (LU/LC) within the underdeveloped regions of Balochistan and Sindh provinces, which are situated in Pakistan. In order to monitor temporal variations of LU/LC, we employed Geographic Information System (GIS) technique, to conduct an analysis of satellite imagery obtained from the Landsat 8 Operational Land Imager (OLI) during the time period spanning from 2013 to 2023. In order to obtain an accurate LU/LC classification, we used principal component analysis (PCA) and a supervised classification approach using the maximum likelihood algorithm (MLC). According to the results of our study, there was a decrease in the extent of water bodies (− 593.24 km²) and vegetation (− 68.50 km²) by − 3.43% and − 0.40% respectively. In contrast, the area occupied by settlements in the investigated region had a 2.23% rise, reaching a total of 385.66 square kilometers. Similarly, the extent of barren land also expanded by 1.60%, encompassing a total area of 276.04 square kilometers, during the course of the last decade. The overall accuracy (94.25% and 95.75%) and K value (91.75% and 93.50%) were achieved during the year 2013 and 2023 respectively. The enhancement of agricultural output in Pakistan is of utmost importance in order to improve the income of farmers, mitigate food scarcity, stimulate economic growth, and facilitate the expansion of exports. To enhance agricultural productivity, it is recommended that the government undertake targeted initiatives that aimed at enhancing water infrastructure and optimizing land use to foster a sustainable ecological framework. Integrating the sustainable ecological framework provides a foundation for informed decision-making and effective resource management. By identifying areas of urban expansion, agricultural intensification, or alterations in natural LU/LC, stakeholders can design targeted conservation strategies, mitigating potential environmental degradation and promoting biodiversity conservation. In conclusion, the integration of GIS and Remote Sensing (RS) may effectively facilitate the monitoring of land use patterns over a period of time. This combined approach offers valuable insights and recommendations for the judicious and optimal management of land resources, as well as informing policy decisions.

The study aims to understand the hydrochemical characteristics and groundwater suitability for agricultural and drinking purposes. For this purpose, 21 groundwater samples were collected, and major physicochemical parameters such as pH, EC, TDS, temp, salinity, Ca²⁺, Mg²⁺, Na⁺, K⁺, HCO₃⁻, Cl⁻, and SO₄²⁻ were analyzed, followed by the standard analytical procedures. Different groundwater quality graphical representations were constructed by using Aqua Chem software. The results indicate groundwater samples were alkaline with fresh to moderate saline in nature, sixty-eight percent of the samples were suitable for drinking in accordance with WHO, and thirty-two percent of the samples were unsuitable due to the excess amount of different ionic concentrations derived from natural and various anthropogenic sources. Irrigation water quality parameters such as SAR, EC, PI, Na , RSBC, MR, and KR were used to understand the irrigation suitability. The US salinity diagram exemplifies that most groundwater samples fall in the C3S1 category with high salinity hazard and low alkali hazard. The Wilcox plot reveals that 80 of the samples were found under very good to permissible limits, and few samples fall with doubtful to unsuitable quality due to the excess amount of alkali and salinity. Permeability index values show that groundwater is suitable for irrigation. Three major hydrochemical facies were identified with the dominance order of mixed CaMgCl, NaCl, and CaCl. Gibbs plot suggests that evaporation and rock-water interaction are the dominant natural mechanisms controlling the groundwater chemistry in the present study area.

The current study determined the changes in Land Use/Land Change (LU/LC) and variation in land surface temperature (LST) in the Green Belt (Nasirabad district) area of Balochistan, Pakistan. To achieve this, we used GIS software (ArcMap 10.7.1) to analyze remote sensing data acquired from Landsat imagery taken in 1993, 2003, 2013, and 2023. A supervised classification technique using the maximum likelihood algorithm (MLC) was applied to generate a ground-truth LU/LC classification. Based on our findings, almost 415.28 km²(− 12.89%) of formerly undeveloped land has been transformed into urban neighborhoods and green spaces during the last three decades. Between 1993 and 2023, the study area gained 288.29 km²(8.94%) of vegetation and 136.10 km²(4.22%) of settled land. Minimum, maximum, and average LST changes were recorded as 7.50, − 5.06, and 1.22 °C for the whole thirty years. Overall, the analysis data showed that an increase in human settlements in the area investigated led to a rise in mean LST (1.22 °C). Finally, GIS and RS may be used together to track land usage over time, a crucial piece of data for eco-friendly planning. While the LU/LC and LST provide valuable insights into the rational and optimal use of land resources, the implications of policy remain constrained.

Soil erosion triggered by water and wind pose a great threat to the sustainable development of Pakistan. In this study, a combination of geographic information systems (GISs) and machine learning approaches were used to predict soil water erosion rates. The Revised Wind Erosion Equation (RWEQ) model was used to evaluate soil wind erosion, map erosion factors, and analyze the soil erosion rates for each land use type. Finally, the maps of soil water and wind erosion were spatially integrated to identify erosion risk regions and recommend land use management in Pakistan. According to our estimates, the Potohar Plateau and its surrounding regions were mostly impacted by water erosion and have a soil erosion rate of 2500–5000 t·km⁻²·a⁻¹; on the other hand, wind erosion predominated the Kharan Desert and the Thar Desert, with a soil erosion rate exceeding 15,000 t·km⁻²·a⁻¹. The Sulaiman and Kirthar Mountain Ranges were susceptible to wind–water compound erosion, which was more than 8000 t·km⁻²·a⁻¹. This study offers new perspectives on the geographic pattern of individual and integrated water–wind erosion threats in Pakistan and provides high-precision data and a scientific foundation for designing rational soil and water conservation practices.

Land use/land cover (LULC) changes are among the most significant human-caused global variations affecting the natural environment and ecosystems. Pakistan’s LULC patterns have undergone huge changes since the 1900s, with no clear mitigation plan. This paper aims to determine LULC and normalized difference vegetation index (NDVI) changes as well as their causes in Pakistan’s Southern Punjab province over four different periods (2000, 2007, 2014, and 2021). Landsat-based images of 30 m × 30 m spatial resolution were used to detect LULC changes, while NDVI dynamics were calculated using Modis Product MOD13Q1 (Tiles: h24 v5, h24 v6) at a resolution of 250 m. The iterative self-organizing (ISO) cluster method (object meta-clustering using the minimal distance center approach) was used to quantify the LULC changes in this research because of its straightforward approach that requires minimal human intervention. The accuracy assessment and the Kappa coefficient were calculated to assess the efficacy of results derived from LULC changes. Our findings revealed considerable changes in settlements, forests, and barren land in Southern Punjab. Compared to 2000, while forest cover had reduced by 31.03%, settlement had increased by 14.52% in 2021. Similarly, forest land had rapidly been converted into barren land. For example, barren land had increased by 12.87% in 2021 compared to 2000. The analysis showed that forests were reduced by 31.03%, while settlements and barren land increased by 14.52% and 12.87%, respectively, over the twenty year period in Southern Punjab. The forest area had decreased to 4.36% by 2021. It shows that 31.03% of forest land had been converted to urban land, barren ground, and farmland. Land that was formerly utilized for vegetation had been converted into urban land due to the expansion of infrastructure and the commercial sector in Southern Punjab. Consequently, proper monitoring of LULC changes is required. Furthermore, relevant agencies, governments, and policymakers must focus on land management development. Finally, the current study provides an overall scenario of how LULC trends are evolving over the study region, which aids in land use planning and management.