Precipitation scale effect of the TRMM satellite in Tianshan,China

High-resolution precipitation data is conducive to objectively describe the spatial-temporal variability of regional precipitation,and the study of downscaling techniques and spatial scale effects can provide technical and theoretical support to improve the spatial resolution and accuracy of satellite precipitation data.In this study,we used a machine learning algorithm combined with a regression algorithm RF-PLS(Random Forest-Partial Least Squares)to construct a downscaling model to obtain three types of high-resolution TRMM(Tropical Rainfall Measuring Mission)downscaled precipitation data for the years 2000-2017 at 250 m,500 m,and 1km.The scale effects with topographic and geomorphological features in the study area were analysed.Finally,we described the spatial and temporal variation of precipitation based on the optimal TRMM downscaled precipitation data.The results showed that:1)The linear relationships between the TRMM downscaled precipitation data obtained by each of the three downscaled models(PLS,RF,and RF-PLS)and the precipitation at the observation stations were improved compared to the linear relationships between the original TRMM data and the precipitation at the observation stations.The accuracy of the RF-PLS model was better than the other two models.2)Based on the RF-PLS model,the resolution of the TRMM data was increased to three different scales(250 m,500 m,and 1 km),considering the scale effects with topographic and geomorphological features.The precipitation simulation effect with a spatial resolution of 500 m was better than the other two scales.3)The annual precipitation was the highest in the areas with extremely high mountains,followed by the mediumhigh mountain,high mountain,medium mountain,medium-low mountain,plain,low mountain,and basin.

GIS-Based and Data-Driven Bivariate Landslide-Susceptibility Mapping in the Three Gorges Area, China

A detailed landslide-susceptibility map was produced using a data-driven objective bivariate analysis method with datasets developed for a geographic information system （GIS）. Known as one of the most landslide-prone areas in China, the Zhongxian-Shizhu Segment in the Three Gorges Reservoir region of China was selected as a suitable case because of the frequency and distribution of landslides. The site covered an area of 260.93 km^2 with a landslide area of 5.32 km^2. Four data domains were used in this study, including remote sensing products, thematic maps, geological maps, and topographical maps, all with 25 m × 25 m pixels. Statistical relationships for landslide susceptibility were developed using landslide and landslide causative factor databases. All continuous variables were converted to categorical variables according to the percentile divisions of seed cells, and the corresponding class weight values were calculated and summed to create the susceptibility map. According to the map, 3.6% of the study area was identified as high-susceptibility. Extremely low-, very low-, low-, and medium-susceptibility zones covered 19.66%, 31.69%, 27.95%, and 17.1% of the area, respectively. The high- and medium-hazardons zones are along both sides of the Yangtze River, being in agreement with the actual distribution of landslides.

Regional Scale Rainfall- and Earthquake-triggered Landslide Susceptibility Assessment in Wudu County, China

Wudu County in northwestern China frequently experiences large-scale landslide events. High-magnitude earthquakes and heavy rainfall events are the major triggering factors in the region. The aim of this research is to compare and combine landslide suseeptibility assessments of rainfall- triggered and earthquake-triggered landslide events in the study area using Geographical Information System （GIS） and a logistic regression model. Two separate susceptibility maps were produeed using inventories reflecting single landslide-triggering events, i.e., earthquakes and heavy rain storms. Two groups of landslides were utilized： one group eontaining all landslides triggered by extreme rainfall events between 1995 and 2003 and the other group containing slope failures caused by the 2008 Wenchuan earthquake. Subsequently, the individual maps were combined to illustrate the loeations of maximum landslide probability. The use of the resulting three landslide susceptibility maps for landslide forecasting, spatial planning and for developing emergency response actions are discussed. The eombined susceptibility map illustrates the total landslide susceptibility in the study area.

Landslide Susceptibility Assessment of the Youfang Catchment using Logistic Regression

A detailed landslide susceptibility map was produced in the Youfang catchment using logistic regression method with datasets developed for a geographic information system(GIS).Known as one of the most landslide-prone areas in China, the Youfang catchment of Longnan mountain region,which lies in the transitional area among QinghaiTibet Plateau, loess Plateau and Sichuan Basin, was selected as a representative case to evaluate the frequency and distribution of landslides.Statistical relationships for landslide susceptibility assessment were developed using landslide and landslide causative factor databases.Logistic regression(LR)was used to create the landslide susceptibility maps based on a series of available data sources: landslide inventory; distance to drainage systems, faults and roads; slope angle and aspect; topographic elevation and topographical wetness index, and land use.The quality of the landslide susceptibility map produced in this paper was validated and the result can be used fordesigning protective and mitigation measures against landslide hazards.The landslide susceptibility map is expected to provide a fundamental tool for landslide hazards assessment and risk management in the Youfang catchment.