Accuracy and spatio-temporal variation of high resolution satellite rainfall estimate over the Ganjiang River Basin

Hu, Qizhou; Yang, Dawen; Wang, Yintang; Yang, Hanbo

doi:10.1007/s11431-013-5176-7

Cited by 49 publications

(26 citation statements)

References 12 publications

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“…The topography of the catchment is complex and the elevation ranges from 11 to 1997 m above sea level. The catchment develops between mountains and along rivers, and low hills lie in the central part of the catchment, whereas alluvial plains govern the lower reaches [62]. The Ganjiang River catchment belongs to the subtropical moist monsoon climate zone and is one of the typical rainstorm regions in China.…”

Section: Study Area and Datamentioning

confidence: 99%

Variability of Rainfall Erosivity and Erosivity Density in the Ganjiang River Catchment, China: Characteristics and Influences of Climate Change

2018

Atmosphere

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Soil erosion is one of the most critical environmental hazards in the world. Understanding the changes in rainfall erosivity (RE) and erosivity density (ED), as well as their affecting factors, at local and catchment scales in the context of climate warming is an important prerequisite of soil erosion prevention and soil loss risk assessment. The present study identified the variability and trends of RE and ED in terms of both time and space in the Ganjiang River catchment over the period of 1960-2012, and also analyzed and discussed the impact of climate change. The results show that RE and ED in the catchment had great monthly variations and high year-to-year variability. Both presented long-term increasing trends over the entire study period. The highest RE and ED were observed in June and in the eastern and northeast parts of the catchment, which indicated that June was the most susceptible month for soil erosion in this area and the lower reaches of the Ganjiang River was the riskiest area for soil erosion. Finally, the East Asian summer monsoon and climate change were highly correlated with changes in RE and ED.

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Section: Study Area and Datamentioning

confidence: 99%

Variability of Rainfall Erosivity and Erosivity Density in the Ganjiang River Catchment, China: Characteristics and Influences of Climate Change

2018

Atmosphere

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“…Precipitation plays a significant role in hydrological and ecological processes as a key component of mass exchange and energy balance [1,2]. The spatial variations of precipitation generally influence vegetation distribution, soil moisture, and surface hydrology [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…In this study, our main goal was to map annual and monthly precipitation with a high spatial resolution over a mountainous, monsoon driven watershed. More specifically, we had four sub-objectives: (1) to test the applicability of the original TRMM 3B43 V7 precipitation data; (2) to compare three different regression techniques (MLR, ANN, and GWR) of estimating precipitation at a coarse resolution and five different interpolation methods of regression residual, choosing one method with the best performance to downscale TRMM precipitation data; (3) to of estimating precipitation at a coarse resolution and five different interpolation methods of regression residual, choosing one method with the best performance to downscale TRMM precipitation data; (3) to map the annual precipitation at a 1-km resolution based on the integrated downscaling-calibration procedure; and (4) to produce maps of monthly precipitation by disaggregating annual downscaled precipitation data using a simple fraction disaggregation method.…”

Section: Introductionmentioning

confidence: 99%

Fine-Resolution Precipitation Mapping in a Mountainous Watershed: Geostatistical Downscaling of TRMM Products Based on Environmental Variables

Zhang

et al. 2018

Remote Sensing

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Abstract:Accurate precipitation data at a high spatial resolution are essential for hydrological, meteorological, and ecological research at regional scales. This study presented a geostatistical downscaling-calibration procedure to derive the high spatial resolution maps of precipitation over a mountainous watershed affected by a monsoon climate. Based on the relationships between precipitation and other environmental variables, such as the Normalized Difference Vegetation Index (NDVI) and digital elevation model (DEM), a regression model with a residual correction method was applied to downscale the Tropical Rainfall Measuring Mission (TRMM) 3B43 product from coarse resolution (25 km) to fine resolution (1 km). Two methods, geographical difference analysis (GDA) and geographical ratio analysis (GRA), were used to calibrate the downscaled TRMM precipitation data. Monthly 1 km precipitation data were obtained by disaggregating 1 km annual downscaled and calibrated precipitation data using monthly fractions derived from original TRMM data. The downscaled precipitation datasets were validated against ground observations measured by rain gauges. According to the comparison of different regression models and residual interpolation methods, a geographically-weighted regression kriging (GWRK) method was accepted to conduct the downscaling of TRMM data. The downscaled TRMM precipitation data obtained using GWRK described the spatial patterns of precipitation reasonably well at a spatial resolution of 1 km with more detailed information when compared with the original TRMM precipitation. The results of validation indicated that the GRA method provided results with higher accuracy than that of the GDA method. The final annual and monthly downscaled precipitation not only had significant improvement in spatial resolution, but also agreed well with data from the validation rain gauge stations (i.e., R 2 = 0.72, RMSE = 161.0 mm, MAE = 127.5 mm, and Bias = 0.050 for annual downscaled precipitation during 2001 to 2015; and R 2 = 0.91, RMSE = 22.2 mm, MAE = 13.5 mm, and Bias = 0.048 for monthly downscaled precipitation during 2001 to 2015). In general, the downscaling-calibration procedure is useful for complex mountainous areas with insufficient ground gauges.

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“…Some selected examples of such studies are as follows: Nile Basin (Habib et al 2012); Iran (Katiraie-Boroujerdy et al 2013;Moazami et al 2013); Yangtze River Basin (Li et al 2013); Ganjiang River Basin, China (Hu et al 2013); and India (Prakash et al 2014). Some few other studies use a single environmental factor, mostly elevation (Dinku et al 2008;Dinku et al 2009;Hu et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Validation of satellite daily rainfall estimates in complex terrain of Bali Island, Indonesia

Rahmawati

Lubczynski

2017

Theor Appl Climatol

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Satellite rainfall products have different performances in different geographic regions under different physical and climatological conditions. In this study, the objective was to select the most reliable and accurate satellite rainfall products for specific, environmental conditions of Bali Island. The performances of four spatio-temporal satellite rainfall products, i.e., CMORPH 25 , CMORPH 8 , TRMM, and PERSIANN, were evaluated at the island, zonation (applying elevation and climatology as constraints), and pixel scales, using (i) descriptive statistics and (ii) categorical statistics, including bias decomposition. The results showed that all the satellite products had low accuracy because of spatial scale effect, daily resolution and the island complexity. That accuracy was relatively lower in (i) dry seasons and dry climatic zones than in wet seasons and wet climatic zones; (ii) pixels jointly covered by sea and mountainous land than in pixels covered by land or by sea only; and (iii) topographically diverse than uniform terrains. CMORPH 25 , CMORPH 8 , and TRMM underestimated and PERSIANN overestimated rainfall when comparing them to gauged rain. The CMORPH 25 had relatively the best performance and the PERSIANN had the worst performance in the Bali Island. The CMORPH 25 had the lowest statistical errors, the lowest miss, and the highest hit rainfall events; it also had the lowest miss rainfall bias and was relatively the most accurate in detecting, frequent in Bali, ≤ 20 mm day −1 rain events. Lastly, the CMORPH 25 coarse grid better represented rainfall events from coastal to inlands areas than other satellite products, including finer grid CMORPH 8 .

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Accuracy and spatio-temporal variation of high resolution satellite rainfall estimate over the Ganjiang River Basin

Cited by 49 publications

References 12 publications

Variability of Rainfall Erosivity and Erosivity Density in the Ganjiang River Catchment, China: Characteristics and Influences of Climate Change

Variability of Rainfall Erosivity and Erosivity Density in the Ganjiang River Catchment, China: Characteristics and Influences of Climate Change

Fine-Resolution Precipitation Mapping in a Mountainous Watershed: Geostatistical Downscaling of TRMM Products Based on Environmental Variables

Validation of satellite daily rainfall estimates in complex terrain of Bali Island, Indonesia

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