Evaluation of Remote Sensing Rainfall Products, Bias Correction and Temporal Disaggregation Approaches, for Improved Accuracy in Hydrologic Simulations

yaswanth, pulipati; Kannan, B. A. M.; Bindhu, V. M.; Balaji, C.; Narasimhan, Balaji

doi:10.1007/s11269-023-03486-0

Cited by 9 publications

(1 citation statement)

References 38 publications

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“…Among the various SPPs available, PDIR-Now [28] is one of the most recently used satellite-based gridded datasets in hydrological models [11,29,30]. It is one of the latest PERSIANN products developed by the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine (UCI).…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Evaluation of Near-Real-Time Satellite-Based Precipitation: PDIR-Now over Saudi Arabia

Alharbi,

Dao,

Jimenez Arellano

et al. 2024

Remote Sensing

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In the past decade, Saudi Arabia has witnessed a surge in flash floods, resulting in significant losses of lives and property. This raises a need for accurate near-real-time precipitation estimates. Satellite products offer precipitation data with high spatial and temporal resolutions. Among these, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Dynamic Infrared Rain Rate near-real-time (PDIR-Now) stands out as a novel, global, and long-term resource. In this study, a rigorous comparative analysis was conducted from 2017 to 2022, contrasting PDIR-Now with rain gauge data. This analysis employs six metrics to assess the accuracy of PDIR-Now across various daily rainfall rates and four yearly extreme precipitation indices. The findings reveal that PDIR-Now slightly underestimates light precipitation but significantly underestimates heavy precipitation. Challenges arise in regions characterized by orographic rainfall patterns in the southwestern area of Saudi Arabia, emphasizing the importance of spatial resolution and topographical considerations. While PDIR-Now successfully captures annual maximum 1-day and 5-day precipitation measurements across rain gauge locations, it exhibits limitations in the length of wet and dry spells. This research highlights the potential of PDIR-Now as a valuable tool for precipitation estimation, offering valuable insights for hydrological, climatological, and water resource management studies.

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Section: Introductionmentioning

confidence: 99%

Comprehensive Evaluation of Near-Real-Time Satellite-Based Precipitation: PDIR-Now over Saudi Arabia

Alharbi,

Dao,

Jimenez Arellano

et al. 2024

Remote Sensing

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Evaluating hydrological responses of satellite precipitation products over an Indian tropical catchment through a distributed physical model

Kalura,

Pandey,

Chowdary

et al. 2024

Hydrological Processes

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Significant advancements in satellite‐based precipitation retrieval algorithms have led to the development of continuous, quasi‐global precipitation products, offering unique opportunities for hydrometeorological and climate research. In this context, six satellite‐based precipitation products (SPPs), including CHIRPS, CMORPH, GSMaP, IMERG, MSWEP, and PERSIANN, were thoroughly investigated for their application in hydrological simulations over the Wardha River basin in India. The observed gridded precipitation product developed by India Meteorological Department (IMD) has been used as reference data to evaluate the performance of SPPs. Hydrological variables such as runoff, soil moisture (SM) and evapotranspiration (ET) were simulated using the Variable Infiltration Capacity model. The performances of SPPs are critically assessed using various statistical metrics, including Pearson's correlation coefficient (R), Klinga‐Gupta Efficiency (KGE), Percent Bias (PBIAS), root mean square error (RMSE), and the RMSE to standard deviation ratio (RSR). The model‐simulated discharge was compared with streamflow observations at a single gauging site, while a spatially distributed comparison was conducted between simulated SM and ET and satellite‐based SM and ET. The IMD dataset consistently shows superior performance for discharge simulation at both daily and monthly scales, with KGE values of 0.85 and 0.91, respectively. Among SPPs, MSWEP and CHIRPS excel in simulating daily and monthly discharge, respectively. For ET simulation, CHIRPS outperforms IMD and other SPPs, achieving an overall KGE value of 0.37. In contrast, PERSIANN is the most effective for simulating SM compared to other precipitation products.

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Enhancing the reliability of hydrological simulations through global weather data assimilation in watersheds with limited data

Jayaprathiga,

Rohith,

Cibin

et al. 2024

Stoch Environ Res Risk Assess

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Evaluation of Remote Sensing Rainfall Products, Bias Correction and Temporal Disaggregation Approaches, for Improved Accuracy in Hydrologic Simulations

Cited by 9 publications

References 38 publications

Comprehensive Evaluation of Near-Real-Time Satellite-Based Precipitation: PDIR-Now over Saudi Arabia

Comprehensive Evaluation of Near-Real-Time Satellite-Based Precipitation: PDIR-Now over Saudi Arabia

Evaluating hydrological responses of satellite precipitation products over an Indian tropical catchment through a distributed physical model

Enhancing the reliability of hydrological simulations through global weather data assimilation in watersheds with limited data

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