Effect of satellite based rainfall products on river basin responses of runoff simulation on flood event

Pakoksung, Kwanchai; Takagi, Masataka

doi:10.1007/s40808-016-0200-0

Cited by 33 publications

(30 citation statements)

References 44 publications

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“…We found that daily IMERG V5 has the highest accuracy; this is consistent with the evaluation studies [17,19], which show that IMERG estimations exhibit notable improvements over TRMM to detect heavy storms and precipitation at sub-daily and daily resolution. A study in the Nan river basin of Thailand [39] revealed that mean bias of TRMM and GPM was about +4.5 mm/day and +1.54 mm/day, respectively. Our study reveals higher accuracies of TRMM and GPM products in northeastern Thailand.…”

Section: The Comparison Of Precipitation Productsmentioning

confidence: 99%

Evaluation and Hydrological Application of TRMM and GPM Precipitation Products in a Tropical Monsoon Basin of Thailand

Shi

et al. 2019

Water

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Watershed runoff is essential for water management. However, runoff materials are lacking in poorly gauged catchments and not always accessible. Microwave remote sensing offers emerging capabilities for hydrological simulation. In this study based on multi-satellite retrievals for Global Precipitation Measurement (IMERG), Tropical Rainfall Measuring Mission (TRMM) products, and World Meteorological Organization (WMO) interpolated precipitation data, we simulated runoff using a variable infiltration capacity (VIC) model and studied the differences among the results. Then, we analyzed the impacts of the runoff on a moderate-resolution imaging spectroradiometer vegetation leaf area index (LAI) during dry seasons. The results showed that (1) IMERG V5 and TRMM products are capable of monitoring the night-day rainfall diurnal cycle and have higher correlations than the WMO daily observation interpolations. However, the WMO shows less overestimation of total precipitation than remote-sensing precipitation; (2) in the downstream, the TRMM shows better runoff simulation accuracy in the tributaries, and the WMO shows better results in the mainstreams. Therefore, at basin outlets in mainstreams, the Nash-Sutcliffe efficiency coefficients of monthly runoff by the WMO are higher than the simulations by the TRMM; (3) for the whole basin during dry seasons, the LAI variation is correlated with the outlet runoff, which is similar to the correlation with threeto six-month accumulated precipitation. TRMM products can be used to depict both precipitation deficit and runoff deficit, which cause vegetation variations. Our research suggests the potential of microwave precipitation products for detailed watershed runoff simulations and water management.Water 2019, 11, 818 2 of 15 vegetation conditions, such as the normalized difference vegetation index (NDVI), a measure of the greenness or vigor of vegetation. The higher its value, the larger the vegetation density is; a low NDVI indicates stressed or small-leaf vegetation [3]. However, the NDVI can often be saturated across densely vegetated regions or during high-growth periods [4], whereas the leaf area index (LAI) has been shown to be more effective for drought monitoring [5,6]. Previous studies have shown that the multi-temporal accumulation precipitation index has a strong correlation with vegetation response [7][8][9]. The evolution of meteorological hydrological factors affects vegetation growth [10]. Both factors-precipitation and runoff-that cause variations in the vegetation LAI, should be examined separately.Dynamic watershed runoff is essential to the water supply, irrigation management, and accurate flood prediction and control. River discharge at a site is an integrated signal of water cycle processes over the catchment. However, many catchments lack runoff observation. A hydrological model is an important tool to understand water cycle processing and to fill the gaps of runoff information. The simulation accuracy mainly depends on model mechanisms and data input. The th...

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Section: The Comparison Of Precipitation Productsmentioning

confidence: 99%

Evaluation and Hydrological Application of TRMM and GPM Precipitation Products in a Tropical Monsoon Basin of Thailand

Shi

et al. 2019

Water

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“…The V4 IMERG data prior to March 2014 will also be processed retrospectively in late 2017. To date, researchers have identified both the uncertainty and error characteristics of the V3 IMERG datasets [34][35][36][37][38][39][40][41]. At present, a quantitative evaluation of the similarities and differences between the successive V3 and V4 products and of the global improvements of V4 over V3 are urgently required.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Evaluation of Two Successive V3 and V4 IMERG Final Run Precipitation Products over Mainland China

et al. 2017

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Abstract:The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Final Run (IMERGF) product has now been upgraded to Version 4 (V4), which has been available since March 2017. Therefore, it is desirable to evaluate the characteristic differences between the V4 and the previous V3 products. A comprehensive performance evaluation of the errors of the successive V3 and V4 IMERGF products is performed with a comparison of the China daily Precipitation Analysis Products (CPAP) from March 2014 to February 2015. The version 6 Global Satellite Mapping of Precipitation (GSMaP) research product (which is another Global Precipitation Measurement (GPM) based precipitation product) is also used as a comparison in this study. Overall, the IMERGF-V4 product does not exhibit the anticipated improvement for China compared to the IMERGF-V3 product. An analysis of the metrics of annual daily average precipitation over China for the IMERGF-V3 and IMERGF-V4 products indicates a decrease of the relative bias (RB) from 3.70% to −7.18%, a decrease of the correlation coefficient (CC) from 0.91 to 0.89, an increase of the fractional standard error (FSE) from 0.49 to 0.56, and an increase of the root-mean-square error (RMSE) from 0.63 mm to 0.72 mm. Compared to the IMERGF-V3 product, the IMERGF-V4 product exhibits a significant underestimation of precipitation in the Qinghai-Tibetan plateau with a much lower RB of −60.91% (−58.19%, −65.30%, and −63.74%) based on the annual (summer, autumn, and winter) daily average precipitation and an even worse performance during winter (−72.33% of RB). In comparison, the GSMaP product outperforms the IMERGF-V3 and IMERGF-V4 products and has the smallest RMSE (0.47 mm/day), highest CC (0.95), lowest FSE (0.37), and best performance of the RB (−2.39%) in terms of annual daily precipitation over China. However, the GSMaP product underestimates the precipitation more than the IMERGF-V3 product for the arid XJ region.

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“…Such satisfying results had also reported in [31,33]. It is unanticipated, in particular, the GSMaP-NRT data, as studies found that GSMaP had a significant discrepancy with observed data in rainfall estimation [68][69][70] and runoff simulation [71]. In Malaysia, rainfall estimates using SRPs have been carried out in several studies [19,20,72].…”

Section: Resultsmentioning

confidence: 60%

Application of Satellite Rainfall Products for Flood Inundation Modelling in Kelantan River Basin, Malaysia

et al. 2019

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The advent of satellite rainfall products can provide a solution to the scarcity of observed rainfall data. The present study aims to evaluate the performance of high spatial-temporal resolution satellite rainfall products (SRPs) and rain gauge data in hydrological modelling and flood inundation mapping. Four SRPs, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) - Early, - Late (IMERG-E, IMERG-L), Global Satellite Mapping of Precipitation-Near Real Time (GSMaP-NRT), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- Cloud Classification System (PERSIANN-CCS) and rain gauge data were used as the primary input to a hydrological model, Rainfall-Runoff-Inundation (RRI) and the simulated flood level and runoff were compared with the observed data using statistical metrics. GSMaP showed the best performance in simulating hourly runoff with the lowest relative bias (RB) and the highest Nash-Sutcliffe efficiency (NSE) of 4.9% and 0.79, respectively. Meanwhile, the rain gauge data was able to produce runoff with −12.2% and 0.71 for RB and NSE, respectively. The other three SRPs showed acceptable results in daily discharge simulation (NSE value between 0.42 and 0.49, and RB value between −23.3% and −31.2%). The generated flood map also agreed with the published information. In general, the SRPs, particularly the GSMaP, showed their ability to support rapid flood forecasting required for early warning of floods.

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Effect of satellite based rainfall products on river basin responses of runoff simulation on flood event

Cited by 33 publications

References 44 publications

Evaluation and Hydrological Application of TRMM and GPM Precipitation Products in a Tropical Monsoon Basin of Thailand

Evaluation and Hydrological Application of TRMM and GPM Precipitation Products in a Tropical Monsoon Basin of Thailand

Comprehensive Evaluation of Two Successive V3 and V4 IMERG Final Run Precipitation Products over Mainland China

Application of Satellite Rainfall Products for Flood Inundation Modelling in Kelantan River Basin, Malaysia

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