Hydrological evaluation of 14 satellite-based, gauge-based and reanalysis precipitation products in a data-scarce mountainous catchment

Saddique, Naeem; Muzammil, Muhammad; Jahangir, Istakhar; Sarwar, Abid; Ahmed, Ehtesham; Aslam, Rana Ammar; Bernhofer, Christian

doi:10.1080/02626667.2021.2022152

Cited by 12 publications

(2 citation statements)

References 78 publications

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“…Recent studies have shown that the precipitation data estimated through satellite-based observations contributed well to detecting rainfall distribution and severity in datascarce regions [22,23]. However, the satellite products may have errors due to indirect estimation, sampling uncertainty, and retrieval algorithms [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Satellite Precipitation Products for Estimation of Floods in Data-Scarce Environment

Masood

Naveed

Iqbal

et al. 2023

Advances in Meteorology

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Utilization of satellite precipitation products (SPPs) for reliable flood modeling has become a necessity due to the scarcity of conventional gauging systems. Three high-resolution SPPs, i.e., Integrated Multi-satellite Retrieval for GPM (IMERG), Global Satellite Mapping of Precipitation (GSMaP), and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), data were assessed statistically and hydrologically in the sparsely gauged Chenab River basin of Pakistan. The consistency of rain gauge data was assessed by the double mass curve (DMC). The statistical metrics applied were probability of detection (POD), critical success index (CSI), false alarm ratio (FAR), correlation coefficient (CC), root mean square error (RMSE), and bias (B). The hydrologic evaluation was conducted with calibration and validation scenarios for the monsoon flooding season using the Integrated Flood Analysis System (IFAS) and flow duration curve (FDC). Sensitivity analysis was conducted using ±20% calibrating parameters. The rain gauge data have been found to be consistent with the higher coefficient of determination (R2). The mean skill scores of GSMaP were superior to those of CHIRPS and IMERG. More bias was observed during the monsoon than during western disturbances. The most sensitive parameter was the base flow coefficient (AGD), with a high mean absolute sensitivity index value. During model calibration, good values of performance indicators, i.e., R2, Nash−Sutcliffe efficiency (NSE), and percentage bias (PBIAS), were found for the used SPPs. For validation, GSMaP performed better with comparatively higher values of R2 and NSE and a lower value of PBIAS. The FDC exhibited SPPs’ excellent performance during 20% to 40% exceedance time.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Satellite Precipitation Products for Estimation of Floods in Data-Scarce Environment

Masood

Naveed

Iqbal

et al. 2023

Advances in Meteorology

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“…The evaluation work can be generally classified into two categories: (1) direct comparison of the gridded precipitation to the corresponding rainfall gauge's precipitation data; (2) evaluation of gridded precipitation products based on their predictive ability of streamflow rate in a hydrological modeling framework. Evaluation works involved in different gridded precipitation datasets have been made over a range of scales from basin to global extent [17][18][19][20][21][22][23][24][25]. Meanwhile, recently some efforts have been put into evaluating and validating gridded precipitation over the TP and the basins within it [6,[26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Hydrological Application of Four Gridded Precipitation Datasets over a Large Southeastern Tibetan Plateau Basin

Zhang

et al. 2022

Remote Sensing

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Reliable precipitation is crucial for hydrological studies over Tibetan Plateau (TP) basins with sparsely distributed rainfall gauges. In this study, four widely used precipitation products, including the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of the water resources (APHRODITE), the High Asia Reanalysis (HAR), and the satellite-based precipitation estimates from Global Precipitation Measurement (GPM) and Tropical Rainfall Measurement Mission (TRMM), were comprehensively evaluated by combining statistical analysis and hydrological simulation over the Upper Brahmaputra (UB) River Basin of TP during 2001–2013. In respect to the statistical assessment, the overall performances of GPM and HAR are comparable to each other, and both are superior to the other two datasets. For hydrological assessment, both daily and monthly GPM-based streamflow simulations perform the best not only at the UB outlet with very good results, but they also illustrate satisfactory results at Yangcun and Lhasa hydrological stations within the UB. Runoff simulation using HAR only performs well at the UB outlet, whereas it shows poor results at both Yangcun and Lhasa stations. The simulated results based on APHRODITE and TRMM show poor performances at UB. Generally, the GPM shows an encouraging potential for hydro-meteorological investigation over UB, although with some bias in flood simulation.

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Hydrologic interpretation of machine learning models for 10-daily streamflow simulation in climate sensitive upper Indus catchments

Mushtaq,

Akhtar,

Hashmi

et al. 2024

Theor Appl Climatol

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Machine learning for hydrologic modeling has seen significant recent development and has been suggested as a valuable augmentation to physical hydrological modeling, especially in data-scarce catchments. In Pakistan, surface water flows predominantly originate from the transboundary Upper Indus sub-catchments of Chenab, Jhelum, Indus, and Kabul rivers. These catchments have large drainage areas, climate-driven streamflows, high variations in elevation, and limited streamflow gauge coverage. Hence, using machine learning models for data-driven river flow modeling may be well-suited for these catchments. However, hydrologic interpretability of machine learning models is important for the practical use of such models for these catchments. Thus, the current study besides evaluating the potential of three machine learning models (XGBOOST, Classification and Regression Trees(CART), and RandomForest) for streamflow simulation also focused on the hydrologic interpretation of machine learning models using SHapley Additive exPlananations (SHAP). All of these models performed well and the range of $$\textrm{R}^2$$ R 2 and Nasche-Efficiency for all three models lies between 0.61 to 0.90. Moreover, SHAP correctly identified minimum temperature as the most critical feature in glacier-fed Indus and Chenab catchments. It also provides logical insights into interactions between minimum temperature and precipitation for the indus and Chenab catchment. The findings of this study strongly illustrate the usefulness of SHAP analysis in interpreting the behavior of data-scarce high-elevation climate-sensitive catchments using tree-based machine learning models.

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Hydrological evaluation of 14 satellite-based, gauge-based and reanalysis precipitation products in a data-scarce mountainous catchment

Cited by 12 publications

References 78 publications

Evaluation of Satellite Precipitation Products for Estimation of Floods in Data-Scarce Environment

Evaluation of Satellite Precipitation Products for Estimation of Floods in Data-Scarce Environment

Evaluation and Hydrological Application of Four Gridded Precipitation Datasets over a Large Southeastern Tibetan Plateau Basin

Hydrologic interpretation of machine learning models for 10-daily streamflow simulation in climate sensitive upper Indus catchments

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