Tamāshāgarān., Год журнала: 2024, Номер 15(2), С. 17 - 31
Опубликована: Окт. 1, 2024
Язык: Английский
Environmental Modelling & Software, Год журнала: 2025, Номер unknown, С. 106376 - 106376
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
1
Water, Год журнала: 2024, Номер 16(7), С. 1028 - 1028
Опубликована: Апрель 2, 2024
Hydrological modeling is essential for runoff simulations in line with climate studies, especially remote areas data scarcity. Advancements climatic precipitation datasets have improved the accuracy of hydrological modeling. This research aims to evaluate APHRODITE, PERSIANN-CDR, and ERA5-Land Hablehroud watershed Iran. The were compared interpolated ground station using inverse distance weighted (IDW) method. variable infiltration capacity (VIC) model was utilized simulate from 1992 1996. results revealed that APHRODITE PERSIANN-CDR demonstrated highest lowest accuracy, respectively. sensitivity analyzed each dataset, calibration performed Kling–Gupta efficiency (KGE). evaluation daily simulation based on observed indicated a KGE value 0.78 0.76 during validation periods, values at time scale 0.64 0.77 data, 0.62 0.75 0.50 0.66 These indicate despite varying sensitivity, present satisfactory performance, particularly poorly gauged basins infrequent historical datasets.
Язык: Английский
Процитировано
3
Theoretical and Applied Climatology, Год журнала: 2025, Номер 156(2)
Опубликована: Янв. 10, 2025
Язык: Английский
Процитировано
0
IOP Conference Series Earth and Environmental Science, Год журнала: 2025, Номер 1462(1), С. 012062 - 012062
Опубликована: Март 1, 2025
Abstract Studies on climatic conditions, especially rainfall small islands were very necessary for the conservation of fresh water resources. However, Pari Island, as a island, has limited observation data. Use satellite data was one ways to overcome problem. This study aims determine performance data, namely CHIRPS, TRMM, IMERG, and GSMaP islands, Island. The period used in this followed observational from 2000 2002 both daily monthly best is determined based Taylor Diagram. Furthermore, study, calculated statistical values. result showed that CHIRPS had closest match average rainfall, with coming second. climatology patterns generally consistent between except IMERG. Satellite tended overestimated than performances, contrary, IMERG least capable. Bias values required treatment such bias correction before further impact analysis.
Язык: Английский
Процитировано
0
Remote Sensing, Год журнала: 2025, Номер 17(7), С. 1154 - 1154
Опубликована: Март 25, 2025
Precipitation plays a vital role in the hydrological cycle, directly affecting water resource management and influencing flood drought risk prediction. This study proposes Bayesian Model Averaging (BMA) framework to integrate multiple precipitation datasets. The enhances estimation accuracy for simulations. BMA synthesizes four products—Climate Hazards Group Infrared with Station (CHIRPS), fifth-generation ECMWF Atmospheric Reanalysis (ERA5), Global Satellite Mapping of (GSMaP), Integrated Multi-satellitE Retrievals (IMERG)—over China’s Ganjiang River Basin from 2008 2020. We evaluated merged dataset’s performance against its constituent datasets Multi-Source Weighted-Ensemble (MSWEP) at daily, monthly, seasonal scales. Evaluation metrics included correlation coefficient (CC), root mean square error (RMSE), Kling–Gupta efficiency (KGE). Variable Infiltration Capacity (VIC) model was further applied assess how these affect runoff results indicate that BMA-merged dataset substantially improves when compared individual inputs. product achieved optimal daily (CC = 0.72, KGE 0.70) showed superior skill, notably reducing biases autumn winter. In applications, BMA-driven VIC effectively replicated observed patterns, demonstrating efficacy regional long-term predictions. highlights BMA’s potential optimizing inputs, providing critical insights sustainable reduction complex basins.
Язык: Английский
Процитировано
0
Environmental Modelling & Software, Год журнала: 2025, Номер unknown, С. 106468 - 106468
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
0
Hydrology, Год журнала: 2025, Номер 12(4), С. 89 - 89
Опубликована: Апрель 15, 2025
In this study, we analyzed the suitability of using CHIRPS, CMORPH and TRMM platforms in monitoring extreme precipitation events, precipitation–runoff relationships, seasonal/year-to-year variability Saltito semiarid sub-basin Mexican state Durango. Satellite products (SPP) 16 sites were contrasted point to with data from rainfall gauge stations a daily temporal resolution for period four years (2015–2019). Using information, constructed Rx1d, Rx2d, R25mm, RR95 indices. For runoff model based on Storm Water Management Model (SWMM) was calibrated validated data, obtained Qx1d, Qx2d, Qx3d We used bias volume (%), MSE, correlation coefficient, median evaluate ability satellite detect analyze run flow events. Although these sensors tend overestimate both levels occurrence their high spatial resolutions make them reliable tool analysis trends climate change As result, they serve as useful resource evaluating intensity region, particularly terms patterns. They also allow hydrological modeling observation relationships. This is relevant absence hydrometric which usually common vast regions developing world.
Язык: Английский
Процитировано
0
Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 133462 - 133462
Опубликована: Май 1, 2025
Язык: Английский
Процитировано
0
Theoretical and Applied Climatology, Год журнала: 2025, Номер 156(6)
Опубликована: Июнь 1, 2025
Язык: Английский
Процитировано
0
Journal of Hydroinformatics, Год журнала: 2024, Номер 26(8), С. 2026 - 2044
Опубликована: Июль 24, 2024
ABSTRACT Accurate precipitation is crucial for hydrological modelling in sparse gauge regions like the Lam River Basin (LRB) Vietnam. Gridded data from satellite and numerical models offer significant advantages such areas. However, estimates (SPEs) are subject to uncertainties, especially high variable of topography precipitation. This study focuses on enhancing accuracy Integrated Multi-satellitE Retrievals Global Precipitation Measurement (IMERG), Climate Prediction Center morphing technique (CMORPH) using Quantile Mapping (QM) technique, aligning cumulative distribution functions observed with those SPEs, assessing impact predictions. The highlights that post-correction IMERG QM performs better than other sets, model's performance LRB at different temporal scales. Nash–Sutcliffe efficiency values increased 0.60 0.77, surpassing original IMERG's 0.52 0.74, correlation coefficients improved 0.79 0.89 (compared previous 0.75–0.86) modelling. Additionally, Percent Bias (PBIAS) decreased approximately −1.66 −2.21% (contrasting initial −20.22 4.6%) corrected SPEs. These findings have implications water resource management disaster risk reduction initiatives Vietnam countries.
Язык: Английский
Процитировано
1