Improving Rainfall Erosivity Estimates Using Merged TRMM and Gauge Data

Abstract

Soil erosion is a global issue that threatens food security and causes environmental degradation. Management of water erosion requires accurate estimates of the spatial and temporal variations in the erosive power of rainfall (erosivity). Rainfall erosivity can be estimated from rain gauge stations and satellites. However, the time series rainfall data that has a high temporal resolution are often unavailable in many areas of the world. Satellite remote sensing allows provision of the continuous gridded estimates of rainfall, yet it is generally characterized by significant bias. Here we present a methodology that merges daily rain gauge measurements and the Tropical Rainfall Measuring Mission (TRMM) 3B42 data using collocated cokriging (ColCOK) to quantify the spatial distribution of rainfall and thereby to estimate rainfall erosivity across China. This study also used block kriging (BK) and TRMM to estimate rainfall and rainfall erosivity. The methodologies are evaluated based on the individual rain gauge stations. The results from the present study generally indicate that the ColCOK technique, in combination with TRMM and gauge data, provides merged rainfall fields with good agreement with rain gauges and with the best accuracy with rainfall erosivity estimates, when compared with BK gauges and TRMM alone.

Current and future assessments of soil erosion by water on the Tibetan Plateau based on RUSLE and CMIP5 climate models

“…The R factor in this study was calculated based on the merged daily rainfall data with rigorously quality control. Teng et al, (2017) demonstrated that the improved estimates of the R factor showed higher accuracy than the simple interpolated gauge-based approach. The K factor that we modelled in this study was based on the comprehensive soil properties and environment data sets currently available and geospatial methods.…”

“…We used rainfall estimates from the TRMM 3B42 Version 7, which have a spatial resolution of 0.25°×0.25° and a temporal resolution of 3 h (Ma et al, 2017). The R factor was calculated following the approach presented in Teng et al, (2017). Collocated cokriging (ColCOK) was used to merge the daily rainfall data that from the rain gauge stations and TRMM measurements to improve the quality of the precipitation data.…”

“…Collocated cokriging (ColCOK) was used to merge the daily rainfall data that from the rain gauge stations and TRMM measurements to improve the quality of the precipitation data. The merged daily rainfall data was then used to calculate R with a power function model, which was widely used in China and implemented by the National Water Conservancy Survey (Duan et al, 2016;Teng et al, 2017).…”

The Implication of Land-Use/Land-Cover Change for the Declining Soil Erosion Risk in the Three Gorges Reservoir Region, China

“…To cover the whole and neighbouring area of TGRR, 69 stations were selected with a 60-kilometers buffer of the study area (Figure 3). The R factor was calculated following an approach widely used in China, for example by the National Water Conservancy Survey [27,28]:Ri=m∑j=1k(dji)n where R i is the R value of the half-month (MJ·mm·km −2 ·h −1 ); k is the number of days in the half-month i ; and d i j is the effective precipitation for day j (i.e., ≥12 mm) of the half-month i [29]. The parameters m and n were calculated as follows: m = 21.586 × n −7.1891 n = 0.8363 + 18.114/d 12 + 24.455/y 12 where d 12 is the average daily rainfall (≥12 mm), and y 12 is the yearly average rainfall for days with rainfall ≥12 mm.…”

Estimativa De Perda De Solo No Oeste Da Bahia (Brasil) a Partir Da Alteração Do Uso E Cobertura Da Terra

“…Segundo Hongfen et al (2017), os medidores de chuva fornecem informações valiosas sobre a quantidade e a frequência das chuvas, todavia a distribuição espacial desigual e as incoerências espaciais limitam a estimativa pluviométrica. Neste contexto, a utilização de dados de precipitação a partir de um satélite fornece uma solução alternativa para este problema, pois prop orcionam uma estimativa de precipitação espacialmente distribuída em grandes áreas.…”