Satellite-Based Precipitation Datasets Evaluation Using Gauge Observation and Hydrological Modeling in a Typical Arid Land Watershed of Central Asia

Peng, Jiabin; Li, Shipeng; Huang, Yanyan; Ling, Yunan; Li, Zhengyang; Bao, Anming; Chen, Xi; Kurban, Alishir; Maeyer, Philippe De

doi:10.3390/rs13020221

Cited by 26 publications

(13 citation statements)

References 69 publications

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“…CFSR, ERA5, CHIRPS, MERRA2 and PERSIANN-CDR) is used to assess the hydrologic performance of each precipitation product. Some studies showed an improved performance of hydrological models when satellite-based precipitation datasets are employed for calibration (Bitew et al 2012;Peng et al 2021).…”

Section: Model Calibration and Validationmentioning

confidence: 99%

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

Ougahi

Mahmood

2022

Journal of Water and Climate Change

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Several satellite-based and reanalysis products with a high spatial and temporal resolution have become available in recent decades, making it worthwhile to study the performance of multiple precipitation forcing data on hydrological modeling. This study aims to examine the veracity of five precipitation products employing a semi-distributed hydrological model, i.e., the Soil and Water Assessment Tool (SWAT) to simulate streamflow over the Chenab River Basin (CRB). The performance indices such as coefficient of determination (R2), Nash–Sutcliff efficiency (NSE) and percentage bias (PBIAS) were used to compare observed and simulated streamflow at daily and monthly scales during calibration (2015–2018) and validation (2019–2020). The hydrologic performance of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA) 5-Land (ERA5) was very good at daily (calibration R2=0.83, NSE=0.81, PBIAS=−6%; validation R2=0.75, NSE=0.74, PBIAS=−9.6%) and monthly ( calibration R2=0.94, NSE=0.94, PBIAS=−3.3%; validation R2=0.91, NSE=0.89, PBIAS=−3.2%) scales. This study suggests that the ERA5 precipitation product was the most reliable of the five precipitation products, while the CHIRPS performance was the worst. These findings contribute to highlighting the performance of five precipitation products and reference in the selection of precipitation data as input data to the SWAT model in similar regions.

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Section: Model Calibration and Validationmentioning

confidence: 99%

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

Ougahi

Mahmood

2022

Journal of Water and Climate Change

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“…In contrast, CHIRPS and ERA5 perform poorly in the daily assessments. Some factors like gauge calibration play a significant role in the performance of SPPs [62]. Therefore, the fact that some products (ARC2, IMERG, PERSIANN, REF2) are gauge calibrated may explain their better performance compared to CHIRPS and ERA5.…”

Section: Discussionmentioning

confidence: 99%

Reliability of Satellite-Based Rainfall Products for Water Man-agement Studies: the Case of The Ankavia River Basin in Madagascar

Ramahaimandimby¹,

Randriamaherisoa²,

Jonard³

et al. 2022

Preprint

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Hydrological modeling for water management in large watersheds requires accurate spatially-distributed rainfall time series. In case of low coverage density of ground-based measurements, satellite precipitation products (SPP) constitute an attractive alternative, the quality of which must nevertheless be verified. The objective of this study was to evaluate, at different time scales, the reliability of six SPPs against a 2-year record from a network of 14 rainfall gauges located in the Ankavia catchment (Madagascar). The SPPs considered in this study are the African Rainfall Estimate Climatology (ARC2), the Climate Hazards group Infrared Precipitation with Station data (CHIRPS), the ECMWF Reanalysis (ERA5), the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and the African Rainfall Estimation (REF2) products. The results suggest that IMERG (R² = 0.63, slope of linear regression a = 0.96, root mean square error RMSE = 12 mm/day, mean absolute error MAE = 5.5 mm/day) outperforms other SPPs at the daily scale, followed by REF2 (R² = 0.41, a = 0.94, RMSE = 15 mm/day, MAE = 6 mm/day) and ARC2 (R² = 0.30, a = 0.88, RMSE = 16 mm/day, MAE = 6.7 mm/day). All SPPs, with the exception of the ERA5, overestimate the ‘no rain’ class (0 – 0.2 mm/day). ARC2, IMERG, PERSIANN, and REF2 all underestimate rainfall occurrence in the 0.2 – 150 mm/day rainfall range, whilst CHIRPS and ERA5 overestimate it. Only CHIRPS and PERSIANN could estimate extreme rainfall (&gt;150 mm/day) satisfactorily. According to the Critical Success Index (CSI) categorical statistical measure, IMERG performs quite well in detecting rain events in the range 2-150 mm/day, whereas PERSIANN outperforms IMERG for rain events larger than 150 mm/day. Because it performs best at daily scale, only IMERG was evaluated for time scales other than daily. At the yearly and monthly time scales, the performance is good with R² = 0.97 and 0.87, respectively. At the event time scale, the probability distribution function PDF of rain gauge values and IMERG data show good agreement. However, at hourly time scale, the correlation between ground-based measurements and IMERG data becomes poor (R2 = 0.20). Overall, the IMERG product can be regarded as the most reliable satellite precipitation source at monthly, daily and event time scales for hydrological applications in the study area, but the poor agreement at hourly time scale and the inability to detect extreme rainfall >200 mm/day may nevertheless restrict its use.

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“…Beighley et al [29] evaluated three satellite rainfall products over the Congo Basin via comparison with stream flow and water storage. Peng et al [70] used hydrologic modeling to evaluate satellite rainfall products in an arid region of Central Asia. Additional but non-quantitative checks include examining such variables as cloud cover and surface temperature.…”

Section: Discussionmentioning

confidence: 99%

Assessing the Reliability of Satellite and Reanalysis Estimates of Rainfall in Equatorial Africa

Nicholson

Klotter

2021

Remote Sensing

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This article examines the reliability of satellite and reanalysis estimates of rainfall in the Congo Basin and over Lake Victoria and its catchment. Nine satellite products and five reanalysis products are considered. They are assessed by way of inter-comparison and by comparison with observational data sets. The three locations considered include a region with little observational gauge data (the Congo), a region with extensive gauge data (Lake Victoria catchment), and an inland water body. Several important results emerge: for one, the diversity of estimates is generally very large, except for the Lake Victoria catchment. Reanalysis products show little relationship with observed rainfall or with the satellite estimates, and thus should not be used to assess rainfall in these regions. Most of the products either overestimate or underestimate rainfall over the lake. The diversity of estimates makes it difficult to assess the factors governing the interannual variability of rainfall in these regions. This is shown by way of correlation with sea-surface temperatures, particularly with the Niño 3.4 temperatures and with the Dipole Mode Index over the Indian Ocean. Some guidance is given as to the best products to utilize. Overall, any user must establish that the is product reliable in the region studied.

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Satellite-Based Precipitation Datasets Evaluation Using Gauge Observation and Hydrological Modeling in a Typical Arid Land Watershed of Central Asia

Cited by 26 publications

References 69 publications

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

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

Reliability of Satellite-Based Rainfall Products for Water Man-agement Studies: the Case of The Ankavia River Basin in Madagascar

Assessing the Reliability of Satellite and Reanalysis Estimates of Rainfall in Equatorial Africa

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