The Application of PERSIANN Family Datasets for Hydrological Modeling

This study investigates the application of precipitation estimation from remote sensing information using artificial neural networks (PERSIANN) for hydrological modeling over the Russian River catchment in California in the United States as a case study. We evaluate two new PERSIANN products includi...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-08, Vol.14 (15), p.3675
Main Authors: Salehi, Hossein, Sadeghi, Mojtaba, Golian, Saeed, Nguyen, Phu, Murphy, Conor, Sorooshian, Soroosh
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial neural networks
Benchmarks
Calibration
Classification
Climate
Climate prediction
Climatic data
Climatology
Datasets
Estimation
Evaluation
GLEAM
Hydrologic cycle
Hydrology
Modelling
Neural networks
Optimization
Performance evaluation
PERSIANN family
Precipitation
Rain
Real time
Remote sensing
River catchments
Simulation
SMAP
Stream discharge
Stream flow
Topography
VIC
VIC hydrologic model
Watersheds
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Summary:This study investigates the application of precipitation estimation from remote sensing information using artificial neural networks (PERSIANN) for hydrological modeling over the Russian River catchment in California in the United States as a case study. We evaluate two new PERSIANN products including the PERSIANN-Cloud Classification System–Climate Data Record (CCS–CDR), a climatology dataset, and PERSIANN–Dynamic Infrared Rain Rate (PDIR), a near-real-time precipitation dataset. We also include older PERSIANN products, PERSIANN-Climate Data Record (CDR) and PERSIANN-Cloud Classification System (CCS) as the benchmarks. First, we evaluate these PERSIANN datasets against observations from the Climate Prediction Center (CPC) dataset as a reference. The results showed that CCS–CDR has the least bias among all PERSIANN family datasets. Comparing the two near-real-time datasets, PDIR performs significantly more accurately than CCS. In simulating streamflow using the nontransformed calibration process, EKGE values (Kling–Gupta efficiency) for CCS–CDR (CDR) during the calibration and validation periods were 0.42 (0.34) and 0.45 (0.24), respectively. In the second calibration process, PDIR was considerably better than CCS (EKGE for calibration and validation periods ~ 0.83, 0.82 for PDIR vs. 0.12 and 0.14 for CCS). The results demonstrate the capability of the two newly developed datasets (CCS–CDR and PDIR) of accurately estimating precipitation as well as hydrological simulations.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14153675