Using a Kalman Filter to Assimilate TRMM-Based Real-Time Satellite Precipitation Estimates over Jinghe Basin, China

Chen, Jiaqi; Yong, Bin; Ren, Liliang; Wang, Weiguang; Bo, Chen; Lin, Junjie; Yu, Zhongbo; Li, Ning

doi:10.3390/rs8110899

Cited by 7 publications

(3 citation statements)

References 45 publications

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“…A sparsely gauged basin may not accurately represent its areal rainfall, which leads to errors in estimating discharges and forecasting flood volumes (Zhao et al 2015). On the other hand, a ground-based radar system, despite its high resolution of surface precipitation, is prone to errors in monitoring due to backscatter, attenuation, bright band effects, and signal extinction (Chen et al 2016). Advances in remote sensing technologies offer data required for hydrological modeling and flood forecasting in watersheds with limited ground observations (Khaki 2020).…”

Section: Introductionmentioning

confidence: 99%

Assessment of gridded precipitation products in the hydrological modeling of a flood-prone mesoscale basin

Khan

Guan

2021

Hydrology Research

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Precipitation plays a critical role in hydrometeorological studies. A predictive analysis of gridded rainfall datasets may provide a cost-effective alternative to conventional rain gauge observations. Here, our objective is to evaluate the performance of satellite and reanalysis precipitation products in the hydrological modeling of a mesoscale watershed. The research also examines the accuracy of hydrological simulations in a sizeable flood-prone watershed in the absence of observed data associated with the myriad water retaining structures present in the catchment. We use three precipitation products, namely Tropical Rainfall Measurement Missions (TRMM) 3B42 Version 7, Climate Forecast System Reanalysis (CFSR), and daily precipitation data recorded at multiple rain gauges in the upper Huai River Basin to simulate streamflow. The Soil & Water Assessment Tool (SWAT) is utilized for runoff modeling, while SWAT-CUP is used to perform sensitivity analysis and to calibrate and validate the simulation results. Nash–Sutcliffe efficiency, percent bias, and Kling-Gupta efficiency (KGE) are employed to evaluate modeling efficiency for three precipitation datasets on different temporal scales. The results indicate that TRMM and CFSR datasets provide satisfactory results on both daily and monthly scales. Specifically, the SWAT model performs better at monthly simulations than daily simulations for all precipitation datasets used.

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Section: Introductionmentioning

confidence: 99%

Assessment of gridded precipitation products in the hydrological modeling of a flood-prone mesoscale basin

Khan

Guan

2021

Hydrology Research

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“…Recently, remote-sensing-based precipitation (RSBP) products, such as the Global Precipitation Climatology Project (GPCP) (Schamm et al, 2014), the Tropical Rainfall Measuring Mission (TRMM) (Council, 2005), and the Climate Prediction Center Morphing Method (CMORPH) (Joyce et al, 2004), have been extensively used in ungauged or sparsely gauged areas to bridge the gap between the need for precipitation estimates and the scarcity in gauge observations (Akbari et al, 2012;Kneis et al, 2014;Li et al, 2015;Worqlul et al, 2015;Mourre et al, 2016;Wong et al, 2016). Also, data fusion across satellite and gauge observations is being conducted to further the application of RSBPs (Rozante et al, 2010;Woldemeskel et al, 2013;AriasHidalgo et al, 2013;Zhou et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Remapping annual precipitation in mountainous areas based on vegetation patterns: a case study in the Nu River basin

Zhou

Shen

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Accurate high-resolution estimates of precipitation are vital to improving the understanding of basin-scale hydrology in mountainous areas. The traditional interpolation methods or satellite-based remote sensing products are known to have limitations in capturing the spatial variability of precipitation in mountainous areas. In this study, we develop a fusion framework to improve the annual precipitation estimation in mountainous areas by jointly utilizing the satellite-based precipitation, gauge measured precipitation, and vegetation index. The development consists of vegetation data merging, vegetation response establishment, and precipitation remapping. The framework is then applied to the mountainous areas of the Nu River basin for precipitation estimation. The results demonstrate the reliability of the framework in reproducing the high-resolution precipitation regime and capturing its high spatial variability in the Nu River basin. In addition, the framework can significantly reduce the errors in precipitation estimates as compared with the inverse distance weighted (IDW) method and the TRMM (Tropical Rainfall Measuring Mission) precipitation product.

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“…Recently, remote-sensing-based precipitation (RSBP) products, such as the Global Precipitation Climatology Project (GPCP) (Schamm et al, 2014), the Tropical Rainfall Measuring Mission (TRMM) (Council, 2005), and the Climate Prediction Center Morphing Method (CMORPH) (Joyce et al, 2004), have been extensively used in ungauged or sparsely gauged areas to bridge the gap between the need for precipitation estimates and the scarcity in gauge observations (Akbari et al, 2012;Kneis et al, 2014;Li et al, 2015;Worqlul et al, 2015;Mourre et al, 2016;Wong et al, 2016). Also, data fusion across satellite and gauge observations is being conducted to further the application of RS-BPs (Rozante et al, 2010;Woldemeskel et al, 2013;Arias-Hidalgo et al, 2013;Zhou et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Remapping precipitation in mountainous area based on vegetation pattern

Zhou

Shen

et al. 2016

Preprint

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Abstract. Accurate high-resolution estimates of precipitation are vital to improve the understanding on basin-scale hydrology in mountainous areas. The traditional interpolation methods or satellite-based remote sensing products are known to have limitations in capturing spatial variability of precipitation in mountainous areas. In this study, we develop a fusion framework to improve the precipitation estimation in mountainous areas by jointly utilizing the satellite-based precipitation, gauge measured precipitation and vegetation index. The development consists of vegetation data merging, vegetation response establishment, and precipitation remapping. The framework is then applied to the mountainous area of Nu River basin for precipitation estimation. The results demonstrate the reliability of the framework in reproducing the high-resolution precipitation regime and capturing its high spatial variability in Nu River basin. In addition, the framework can significantly reduce the errors in precipitation estimates as compared with the inverse distance weighted (IDW) method and TRMM (Tropical Rainfall Measuring Mission) precipitation product.

show abstract

Using a Kalman Filter to Assimilate TRMM-Based Real-Time Satellite Precipitation Estimates over Jinghe Basin, China

Cited by 7 publications

References 45 publications

Assessment of gridded precipitation products in the hydrological modeling of a flood-prone mesoscale basin

Assessment of gridded precipitation products in the hydrological modeling of a flood-prone mesoscale basin

Remapping annual precipitation in mountainous areas based on vegetation patterns: a case study in the Nu River basin

Remapping precipitation in mountainous area based on vegetation pattern

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