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

Haseeb, Muhammad; Farid, Hafiz Umar; Khan, Zahid Mahmood; Anjum, Muhammad Naveed; Ahmad, Akhlaq; Mubeen, Muhammad

doi:10.1007/s10661-023-11546-6

Cited by 3 publications

(1 citation statement)

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“…The arid and semi-arid zones appear most vulnerable, exhibiting the largest absolute gap between CWR and supply annually and seasonally. These zones are the main agricultural regions of the country, and the food demand stress in these regions is enormously increasing, which increases the water demand in these regions (Qureshi, 2011;Rafiqul Islam, 2022;Hasan and Abed, 2023;Haseeb et al, 2023). This aligns with research showing heightened evapotranspiration in arid agricultural areas due to warming (Ahmed et al, 2021).…”

Section: Vulnerability Assessment Of Climatic Zones and Strategic Wat...supporting

confidence: 68%

Assessing the impact of future climate scenarios on crop water requirements and agricultural water supply across different climatic zones of Pakistan

Shafeeque,

Bibi

2023

Front. Earth Sci.

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This study investigates the potential impacts of future climate change on crop water requirements (CWR) in different climatic zones of Pakistan and the subsequent implications for agricultural water demand and supply. Using the latest CMIP6 climate projections, we focused on Rabi and Kharif crop seasons, which are crucial for the growth of major crops in Pakistan. An empirical climate-crop and a hydrological model based on the Budyko theory were modified and forced to project future CWR changes and the potential widening of the water demand-supply gap until 2,100. Our results indicate a significant rise in mean annual CWR across all zones and emissions scenarios, with increasing rates at 2.30–2.57 mm/yr under SSP585 and 1.0–1.26 mm/yr under SSP245. Both Rabi and Kharif seasons show rising CWR, notably more under SSP585 (Kharif: 8%–14%, Rabi: 12%–15%) than SSP245 (Kharif: 4%–7%, Rabi: 6%–8%). The demand-supply gap is expected to grow notably, with arid and semi-arid zones being the most affected. Compared to 2015–2025, by 2091–2,100, the gap increased by 7%–15% (SSP245) and 15%–28% (SSP585) for Kharif and 7%–13% (SSP245) and 13%–32% (SSP585) for Rabi. To address these challenges, we recommend strategies like enhancing irrigation efficiency, adjusting crop patterns, and developing heat-resilient crops. Our insights aim to inform policy decisions on agriculture and water management in Pakistan under future climate change.

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Section: Vulnerability Assessment Of Climatic Zones and Strategic Wat...supporting

confidence: 68%

Assessing the impact of future climate scenarios on crop water requirements and agricultural water supply across different climatic zones of Pakistan

Shafeeque,

Bibi

2023

Front. Earth Sci.

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Water resource mapping, monitoring, and modeling using geospatial approaches

Gupta,

Gupta

2024

Developments in Environmental Science

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A Comparative Analysis of Sediment Concentration Using Artificial Intelligence and Empirical Equations

Khalid,

Ghumman,

Pasha

2024

Hydrology

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Morphological changes in canals are greatly influenced by sediment load dynamics, whose estimation is a challenging task because of the non-linear behavior of the sediment concentration variables. This study aims to compare different techniques including Artificial Intelligence Models (AIM) and empirical equations for estimating sediment load in Upper Chenab Canal based on 10 years of sediment data from 2012 to 2022. The methodology involves utilization of a newly developed empirical equation, the Ackers and White formula and AIM including 20 neural networks with 10 training functions for both Double and Triple Layers, two Artificial Neuro-Fuzzy Inference System (ANFIS), Particle Swarm Optimization, and Ensemble Learning Random Forest models. Sensitivity analysis of sediment concentration variables has also been performed using various scenarios of input combinations in AIM. A state-of-the-art optimization technique has been used to identify the parameters of the empirical equation, and its performance is tested against AIM and the Ackers and White equation. To compare the performance of various models, four types of errors—correlation coefficient (R), T-Test, Analysis of Variance (ANOVA), and Taylor’s Diagram—have been used. The results of the study show successful application of Artificial Intelligence (AI) and empirical equations to capture the non-linear behavior of sediment concentration variables and indicate that, among all models, the ANFIS outperformed in simulating the total sediment load with a high R-value of 0.958. The performance of various models in simulating sediment concentration was assessed, with notable accuracy achieved by models AIM11 and AIM21. Moreover, the newly developed equation performed better (R = 0.92) compared to the Ackers and White formula (R = 0.88). In conclusion, the study provides valuable insights into sediment concentration dynamics in canals, highlighting the effectiveness of AI models and optimization techniques. It is suggested to incorporate other AI techniques and use multiple canals data in modeling for the future.

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

Cited by 3 publications

References 46 publications

Assessing the impact of future climate scenarios on crop water requirements and agricultural water supply across different climatic zones of Pakistan

Assessing the impact of future climate scenarios on crop water requirements and agricultural water supply across different climatic zones of Pakistan

Water resource mapping, monitoring, and modeling using geospatial approaches

A Comparative Analysis of Sediment Concentration Using Artificial Intelligence and Empirical Equations

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