Evaluation of satellite-based and reanalysis precipitation datasets by hydrologic simulation in the Chenab river basin

Ougahi, Jamal Hassan; Mahmood, Syed Amer

doi:10.2166/wcc.2022.410

Cited by 24 publications

(20 citation statements)

References 99 publications

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“…The ERA5-Land precipitation dataset had inappropriate accuracy in estimating minimum discharges and overestimated them. Although the results of this study indicate that the PERSIANN-CDR dataset performs better than ERA5-Land in simulating runoff on monthly and daily scales, although the findings of Ougahi et al (2022) [81] contradict these results. However, the modeling results of Usman et al (2022) [82] showed that the accuracy of simulating runoff using both the APHRODITE and PERSIANN-CDR datasets was comparable and better than ERA5-Land, which is consistent with the findings of this study.…”

Section: Monthly Results Evaluationcontrasting

confidence: 75%

Evaluation of Climatological Precipitation Datasets and Their Hydrological Application in the Hablehroud Watershed, Iran

Salehi,

Gharechelou,

Golian

et al. 2024

Water

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Hydrological modeling is essential for runoff simulations in line with climate studies, especially in remote areas with data scarcity. Advancements in climatic precipitation datasets have improved the accuracy of hydrological modeling. This research aims to evaluate the APHRODITE, PERSIANN-CDR, and ERA5-Land climatic precipitation datasets for the Hablehroud watershed in Iran. The datasets were compared with interpolated ground station precipitation data using the inverse distance weighted (IDW) method. The variable infiltration capacity (VIC) model was utilized to simulate runoff from 1992 to 1996. The results revealed that the APHRODITE and PERSIANN-CDR datasets demonstrated the highest and lowest accuracy, respectively. The sensitivity of the model was analyzed using each precipitation dataset, and model calibration was performed using the Kling–Gupta efficiency (KGE). The evaluation of daily runoff simulation based on observed precipitation indicated a KGE value of 0.78 and 0.76 during the calibration and validation periods, respectively. The KGE values at the daily time scale were 0.64 and 0.77 for PERSIANN-CDR data, 0.62 and 0.75 for APHRODITE precipitation data, 0.50 and 0.66 for ERA5-Land precipitation data during the calibration and validation periods, respectively. These results indicate that despite varying sensitivity, climatic precipitation datasets present satisfactory performance, particularly in poorly gauged basins with infrequent historical datasets.

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Section: Monthly Results Evaluationcontrasting

confidence: 75%

Evaluation of Climatological Precipitation Datasets and Their Hydrological Application in the Hablehroud Watershed, Iran

Salehi,

Gharechelou,

Golian

et al. 2024

Water

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“…The dataset was preferred over other reanalyses for its high spatial resolution (0.25 o × 0.25 o ) and temporal resolution. Some recent studies have compared the performance of ERA5 against other reanalysis products and have shown that ERA5 is highly reliable for hydrological applications including the study of extremes (Bhattacharyya et al, 2022;Mahto and Mishra, 2019;Ougahi and Mahmood, 2022).…”

Section: Study Area and Datamentioning

confidence: 99%

Seasonal variations in the dynamic and thermodynamic response of precipitation extremes in the Indian subcontinent

2022

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Precipitation extremes are a major impact-relevant implication of climate change. Rising temperatures increase the moisture holding capacity at a rate of ≈ 7%K −1 , called the Clausius-Claypeyron (CC) scaling, which can lead to intense precipitation which last for short duration. At a regional level, the scaling of extremes deviates from this expected scaling rate. Large scale circulation dynamics and local variability in thermodynamic influences are suspected to cause these deviation, but these drivers differ across seasons. In the present study, we use ERA5 reanalysis to evaluate the seasonal changes in precipitation-temperature scaling rates over the Indian subcontinent. We further determine the deviations from the expected CC scaling rate, and the precipitation extremes are decomposed to their dynamic and thermodynamic contribution across different seasons. It is found that significant seasonal contrast exists in the dynamic and thermodynamic contributions, with the latter dominating during the Indian summer monsoon season, while the former being higher during the pre-monsoon and post-monsoon season. Further analysis highlights that the lower dynamic contribution is attributed to drop in dew . point temperatures and Convective Available Potential Energy during extremes. The primary drivers causing the extremes in different seasons are also pointed out, further improving the understanding of how the intensity and frequency of precipitation extremes changes spatially across different seasons, and what are the physical drivers causing these changes.

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“…This creates problems for monitoring or forecasting the risk of hydrometeorological hazards like flooding and landslides [19]. However, remote sensing and reanalysis precipitation datasets hold significance in these regions where data availability is limited [20]. Precipitation stands as the primary contributor to uncertainty which leads to varying optimal ranges for the fine-tuned parameters [21].…”

Section: Introductionmentioning

confidence: 99%

Combining Hydrological Models and Remote Sensing to Characterize Snowpack Dynamics in High Mountains

Ougahi,

Rowan

2024

Remote Sensing

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Seasonal snowpacks, characterized by their snow water equivalent (SWE), can play a major role in the hydrological cycle of montane environments with months of snow accretion followed by episodes of melt controlling flood risk and water resource availability downstream. Quantifying the temporal and spatial patterns of snowpack accumulation and its subsequent melt and runoff is an internationally significant challenge, particularly within mountainous regions featuring complex terrain with limited or absent observational data. Here we report a new approach to snowpack characterization using open-source global satellite and modelled data products (precipitation and SWE) greatly enhancing the utility of the widely used Soil and Water Assessment Tool (SWAT). The paper focusses on the c. 23,000 km2 Chenab river basin (CRB) in the headwaters of the Indus Basin, globally important because of its large and growing population and increasing water insecurity due to climate change. We used five area-weighted averaged satellite, gridded and reanalysis precipitation datasets: ERA5-Land, CMORPH, TRMM, APHRODITE and CPC UPP. As well as comparison to local weather station data, these were used in SWAT to model streamflow for evaluation against observed streamflow at the basin outlet. ERA5-Land data provided the best streamflow match-ups and was used to infer snowpack (SWE) dynamics at basin and sub-basin scales. Snow reference data were derived from remote sensing and modelled SWE re-analysis products: ULCA-SWE and KRA-SWE, respectively. Beyond conventional auto-calibration and single-variable approaches we undertook multi-variable calibration using R-SWAT to manually adjust snow parameters alongside observed streamflow data. Characterization of basin-wide patterns of snowpack build-up and melt (SWE dynamics) were greatly strengthened using KRA-SWE data accompanied by improved streamflow simulation in sub-basins dominated by seasonal snow cover. UCLA-SWE data also improved SWE estimations using R-SWAT but weakened the performance of simulated streamflow due to under capture of seasonal runoff from permanent snow/ice fields in the CRB. This research highlights the utility and value of remote sensing and modelling data to drive better understanding of snowpack dynamics and their contribution to runoff in the absence of in situ snowpack data in high-altitude environments. An improved understanding of snow-bound water is vital in natural hazard risk assessment and in better managing worldwide water resources in the populous downstream regions of mountain-fed large rivers under threat from climate change.

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Evaluation of satellite-based and reanalysis precipitation datasets by hydrologic simulation in the Chenab river basin

Cited by 24 publications

References 99 publications

Evaluation of Climatological Precipitation Datasets and Their Hydrological Application in the Hablehroud Watershed, Iran

Evaluation of Climatological Precipitation Datasets and Their Hydrological Application in the Hablehroud Watershed, Iran

Seasonal variations in the dynamic and thermodynamic response of precipitation extremes in the Indian subcontinent

Combining Hydrological Models and Remote Sensing to Characterize Snowpack Dynamics in High Mountains

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