Contaminated landslide runout deposits in rivers - Method for estimating long-term ecological risks

Catchment-scale impacts of shallow landslides on stream water chemistry

Catchment water quality plays an important role in ecosystem and water resource management in mountainous areas. Shallow landslides triggered by earthquakes or heavy rainfall can cause a sudden and long-term deterioration in stream water quality by releasing contaminants into streams. Although many studies have been undertaken on the relationship between a single landslide and the water chemistry of a nearby river, little is known about the impact of densely distributed shallow landslides on stream water chemistry at the catchment scale. To this end, this study determined the major ion concentrations and isotopic compositions of stream water along with the shallow landslide area/catchment area ratio (LCR) in 37 headwater subcatchments in the southern part of Hokkaido, Japan, where an earthquake caused more than 6000 shallow landslides on September 6, 2018. In subcatchments with a high LCR, stream water exhibited significantly higher Ca²⁺ and HCO₃^- concentrations, while there was no correlation between the LCR and concentrations of Na⁺ and Cl^-. The δ¹⁸O and δD values of stream water plotted between the local meteoric water lines of summer and winter precipitation, indicating that they originated from meteoric water. Shallow landslides formed sliding surfaces, landslide deposits, and landslide-dammed lakes, which enhanced the interaction between the surface soil and stream water, leading to Ca-HCO₃ type water. The results showed that shallow-landslide-driven changes in stream water quality could be linearly approximated by the fraction of the landslide area at the catchment scale, which is a more versatile approach than the local framework of a single landslide and a nearby stream. In future research, these findings could be combined with a slope stability model and the background climatic, geological, topographical, and water quality conditions of a watershed to evaluate water pollution triggered by shallow landslides at the catchment scale.

Influencing Factors Analysis of Taiwan Eutrophicated Reservoirs

Treatment cost and quality of domestic water are highly correlated with raw water quality in reservoirs. This study aims to identify the key factors that influence the trophic state levels and correlations among Carlson trophic state index (CTSI) levels, water quality parameters and weather factors in four major reservoirs in Taiwan from 2000 to 2017. Weather (e.g., air temperature, relative humidity, total precipitation, sunlight percentage and cloud cover) and water quality parameters (e.g., pH, chemical oxygen demand, suspended solids (SS), ammonia, total hardness, nitrate, nitrite and water temperature) were included in the principal component analysis and absolute principal component score models to evaluate the main governing factors of the trophic state levels (e.g., CTSI). SS were washed out by precipitation, thereby influencing the reservoir transparency tremendously and contributing over 50% to the CTSI level in eutrophicated reservoirs (e.g., the Shihmen and Chengchinghu Reservoirs). CTSI levels in the mesotrophic reservoir (e.g., Liyutan Reservoir) had strong correlation with chlorophyll-a and total phosphorus. Results show that rainfall/weather factors were the key driving factors that affected the CTSI levels in Taiwan eutrophicated reservoirs, indicating the need to consider basin management and the impacts of extreme precipitation in reservoir management and future policymaking.

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

The main purpose of this study is to apply three bivariate statistical models, namely weight of evidence (WoE), evidence belief function (EBF) and index of entropy (IoE), and their ensembles with logistic regression (LR) for landslide susceptibility mapping in Muchuan County, China. First, a landslide inventory map contained 279 landslides was obtained through the field investigation and interpretation of aerial photographs. Next, the landslides were randomly divided into two parts for training and validation with the ratio of 70/30. In addition, according to the regional geological environment characteristics, twelve landslide conditioning factors were selected, including altitude, plan curvature, profile curvature, slope angle, distance to roads, distance to rivers, topographic wetness index (TWI), normalized different vegetation index (NDVI), land use, soil, and lithology. Subsequently, the landslide susceptibility mapping was carried out by the above models. Eventually, the accuracy of this research was validated by the area under the receiver operating characteristic (ROC) curve and the results indicated that the landslide susceptibility map produced by EBF-LR model has the highest accuracy (0.826), followed by IoE-LR model (0.825), WoE-LR model (0.792), EBF model (0.791), IoE model (0.778), and WoE model (0.753). The results of this study can provide references of landslide prevention and land use planning for local government.