Terrestrial and groundwater storage characteristics and their quantification in the Chitral (Pakistan) and Kabul (Afghanistan) river basins using GRACE/GRACE-FO satellite data

Tariq, Aqil; Ali, Shoaib; Basit, Iqra; Jamil, Ahsan; Farmonov, Nizom; Khorrami, Behnam; Khan, Muhammad Ahsan; Sadri, Samira; Baloch, Muhammad Yousuf Jat; Islam, Fakhrul; Junaid, Muhammad; Hatamleh, Wesam Atef

doi:10.1016/j.gsd.2023.100990

Cited by 18 publications

(5 citation statements)

References 49 publications

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“…The calculated power was also compared with the observed cutter bar power to validate the developed model. The calculated values of idle cutting power and the total cutting power required for the cumin crop were compared with the observed values using the predicted and actual graphs [49][50][51][52] .…”

Section: Cutter Bar Powermentioning

confidence: 99%

Force and power requirement for development of cumin harvester: a dynamic approach

Kumar,

Sahoo,

Kushwaha

et al. 2024

Sci Rep

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An experimental setup was developed for simulating the field conditions to determine the force and power required for cutting cumin crops in dynamic conditions. The effect of cutter bar speeds, forward speeds, and blade type on cutting force and power requirement for cutting cumin were also studied. Experiments were carried out at three levels: cutter bar speeds, forward speeds, and blade type. The results showed that all the factors significantly affected cutting force. The cutting force followed a decreasing trend with the increase in cutter bar speed. Whereas it followed an increasing trend with the increase in forward speed. The maximum cutting force for all three blades was observed at a cutter bar speed of 2.00 strokes.s-1 and forward speed of 0.46 m.s−1. The idle power and actual power required for cutting the cumin crop were also determined based on the cutting force. The results obtained were validated by the power drawn from the power source while operating the cutter bar blades. The R2 values for Blade-B1, Blade-B2, and Blade-B3 were 0.90, 0.82, and 0.88, respectively. The cutting force was primarily affected by the cutter bar speed, resulting in PCR values of 74.20%, 82.32%, and 81.75% for Blade-B1, Blade-B2, and Blade-B3, respectively, followed by the forward speed, which also had an impact on PCR values of 16.60%, 15.27%, and 18.25% for Blade-B1, Blade-B2, and Blade-B3, respectively. The cutting force for Blade-B1, Blade-B2, and Blade-B3 varied from 15.96 to 58.97 N, 21.08 to 76.64 N, and 30.22 to 85.31, respectively, for the selected range of cutter bar speed and forward speed. Blade-B1 had 18 and 30% less power consumption than Blade-B2 and Blade-B3, respectively.

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Section: Cutter Bar Powermentioning

confidence: 99%

Force and power requirement for development of cumin harvester: a dynamic approach

Kumar,

Sahoo,

Kushwaha

et al. 2024

Sci Rep

Self Cite

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“…Thus, the coarse resolution may introduce uncertainties in estimations for smaller basins (Scanlon et al., 2016). To address this limitation, CSR products have undergone downscaling from 0.25° to finer resolutions across various regions (Ali, Ran, Khorrami, et al., 2024; Ali, Ran, Luan, et al., 2024; Khorrami, Ali, & Gündüz, 2023). Building on these previous efforts, the first objective of this study was to enhance the resolution of CSR mascon GRACE/GARCE‐FO data to 1 km 2 resolution to illuminate regional heterogeneity and hotspots of TWS and GWS deletion to support local‐scale management of vulnerabilities in the Indus Basin.…”

Section: Introductionmentioning

confidence: 99%

Downscaled‐GRACE Data Reveal Anthropogenic and Climate‐Induced Water Storage Decline Across the Indus Basin

Arshad,

Mirchi,

Taghvaeian

et al. 2024

Water Resources Research

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GRACE (Gravity Recovery and Climate Experiment) has been widely used to evaluate terrestrial water storage (TWS) and groundwater storage (GWS). However, the coarse‐resolution of GRACE data has limited the ability to identify local vulnerabilities in water storage changes associated with climatic and anthropogenic stressors. This study employs high‐resolution (1 km2) GRACE data generated through machine learning (ML) based statistical downscaling to illuminate TWS and GWS dynamics across twenty sub‐regions in the Indus Basin. Monthly TWS and GWS anomalies obtained from a geographically weighted random forest (RFgw) model maintained good consistency with original GRACE data at the 25 km2 grid scale. The downscaled data at 1 km2 resolution illustrate the spatial heterogeneity of TWS and GWS depletion within each sub‐region. Comparison with in‐situ GWS from 2,200 monitoring wells shows that downscaling of GRACE data significantly improves agreement with in‐situ data, evidenced by higher Kling‐Gupta Efficiency (0.50–0.85) and correlation coefficients (0.60–0.95). Hotspots with the highest TWS and GWS decline rate between 2002 and 2023 were Dehli Doab (−442, −585 mm/year), BIST Doab (−367, −556 mm/year), Rajasthan (−242, −381 mm/year), and BARI (−188, −333 mm/year). Based on a general additive model, 47%–83% of the TWS decline was associated with anthropogenic stressors mainly due to increasing trends of crop sown area, water consumption, and human settlements. The decline rate of TWS and GWS anomalies was lower (i.e., −25 to −75 mm/year) in upstream sub‐regions (e.g., Yogo, Gilgit, Khurmong, Kabul) where climatic factors (downward shortwave radiations, air temperature, and sea surface temperature) explained 72%–91% of TWS/GWS changes. The relative influences of climatic and anthropogenic stressors varied across sub‐regions, underscoring the complex interplay of natural‐human activities in the basin. These findings inform place‐based water resource management in the Indus Basin by advancing the understanding of local vulnerabilities.

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“…Evaluating groundwater quality is essential for conserving the ecological environment, and it forms the basis for the sustainable use of local groundwater resources [1][2][3]. Rising populations, industrial development, and modern lifestyles have increased water use [4][5][6]. The most accessible source of fresh water on the planet is groundwater, and it is essential for human health and industrial and agricultural purposes [7,8].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms for Predicting the Water Quality Index

Hussein,

Jat Baloch,

Nigar

et al. 2023

Water

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Groundwater is one of the water resources used to preserve natural water sources for drinking, irrigation, and several other purposes, especially in industrial applications. Human activities related to industry and agriculture result in groundwater contamination. Therefore, investigating water quality is essential for drinking and irrigation purposes. In this work, the water quality index (WQI) was used to identify the suitability of water for drinking and irrigation. However, generating an accurate WQI requires much time, as errors may be made during the sub-index calculations. Hence, an artificial intelligence (AI) prediction model was built to reduce both time and errors. Eighty data samples were collected from Sakrand, a city in the province of Sindh, to investigate the area’s WQI. The classification learners were used with raw data samples and the normalized data to select the best classifier among the following decision trees: support vector machine (SVM), k-nearest neighbors (K-NN), ensemble tree (ET), and discrimination analysis (DA). These were included in the classification learner tool in MATLAB. The results revealed that SVM was the best raw and normalized data classifier. The prediction accuracy levels for the training data were 90.8% and 89.2% for the raw and normalized data, respectively. Meanwhile, the prediction accuracy levels for the testing data were 86.67 and 93.33% for the raw and normalized data, respectively.

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Terrestrial and groundwater storage characteristics and their quantification in the Chitral (Pakistan) and Kabul (Afghanistan) river basins using GRACE/GRACE-FO satellite data

Cited by 18 publications

References 49 publications

Force and power requirement for development of cumin harvester: a dynamic approach

Force and power requirement for development of cumin harvester: a dynamic approach

Downscaled‐GRACE Data Reveal Anthropogenic and Climate‐Induced Water Storage Decline Across the Indus Basin

Machine Learning Algorithms for Predicting the Water Quality Index

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