Temporal variability of global potential water erosion based on an improved USLE model

Assessing spatiotemporal variation in global soil erosion is essential for identifying areas that require greater attention and management under the effects of anthropogenic activities and climate change.Soil erosion can be modelled using the universal soil loss equation(USLE),which includes rainfall erosivity(R-factor),vegetation cover(C-factor),topography(LS-factor),soil erodibility(K-factor),and management practices(P-factor).However,global soil erosion modeling faces numerous challenges,including data acquisition,calculation processes,and parameter calibration under different climatic and topographic backgrounds.Thus,we presented an improved USLE-based model using highly distributed parameters.The R-,C-,and P-factors were modified by the climate zone,country,and topography.This distributed model was applied to estimate the intensity and variations in global soil erosion from 1992 to 2015.We validated the accuracy of this model by comparing simulations with measurements from 11,439 plot years of erosion data.The results showed that i)the average global erosion rate was 5.78 t ha^(-1)year^(-1),with an increase rate of 4.26×10^(-3)t ha^(-1)year^(-1);ii)areas with significantly increasing erosion accounted for 16%of the land with water erosion,whereas those with significantly decreasing erosion accounted for 7%;and iii)areas with severe erosion included the western Ghats,Abyssinian Plateau,Brazilian Plateau,south and east of the Himalayas,and western coast of South America.Intensified erosion occurred mainly on the Amazon Plain and the northern coast of the Mediterranean.This study provides an improved water erosion prediction model and accurate information for researchers and policymakers to identify the drivers underlying changes in water erosion in different regions.

A conceptual model for a process-oriented landscape pattern analysis

Linking landscape patterns to specific ecological processes has been and will continue to be the key topic in landscape ecology.However,this goal is difficult to achieve by using the traditional landscape metric based on the Patch-Mosaic Model(PMM),as they don’t integrate ecological processes with landscape patterns.In this paper,we proposed a conceptual model,i.e.,the Source-Pathway-Sink Model(SPSM),which designates the role of a landscape unit into "source", "sink",or "pathway" based on specific ecological processes during the landscape pattern analysis.While the traditional landscape metrics derived from the PMM model is visual-or geometrical-oriented and lack of linkage to ecological significance,the SPSM model is process-oriented,dynamic,and scale dependent.A comparison between the PMM and the SPSM models shows that the SPSM model is complementary to the PMM model,and can provide a simple and dynamic perspective on landscape pattern analysis.The SPSM model may represent a conceptual innovation in landscape ecology.

Effects of riparian vegetation patterns on the distribution and potential loss of soil nutrients： a case study of the Wenyu River in Beijing

A riparian ecosystem is an ecological transition zone between a river channel and terrestrial ecosystems. Riparian ecosystems play a vital role in maintaining stream health and bank stabilization. The types of riparian vegetation have changed greatly because of human activities along the Wenyu River. This study examines the impact of riparian vegetation patterns on water pollution due to soil nutrient loss. Four riparian vegetation patterns from the river channel to the upland were chosen as the focus of this study： grassland, cropland, grassland- cropland, and grassland-manrnade lawn. The different distributions of soil nutrients along vegetation patterns and the potential risk of nutrient loss were observed and compared. The results showed that riparian cropland has the lowest value of total nitrogen （TN）, total phosphorus （TP）, available nitrogen （AN）, available phosphorus （AP）, and organic matter （OM）, but it has the highest soil bulk density （BD）. The distributions of soil TN, TP, AN, AP, and OM exhibited a declining trend from the upland toward the river channel for riparian cropland, whereas a different trend was observed for the riparian grassland. The vegetation patterns of grassland-cropland and grassland- manmade lawn show that the grassland in the lower slope has more nutrients and OM but lower soil BD than the cropland or manmade lawn in the upper slope. So, the lower-slope grassland may intercept and infiltrate surface runoff from the upland. The lower-slope grassland has higher levels of soil TN, TP, AN, and AP, and thus it may become a new source of nutrient loss. Our results suggest that the management of the riparian vegetation should be improved, particularly in densely populated areas, to control soil erosion and river pollution.

Analysis and assessment of heavy metal contamination in surface water and sediments： a case study from Luan River, Northern China

Concentrations of the heavy metals Cu, Ni, Pb, Zn, Cd, and Cr were examined in surface water and sediment from the Luan River inChina,. With a decline in Cu and Ni concentration found in surface water at downstream stations. This finding suggests that water currents are a major explanatory factor in heavy metal contamination. The abundance of Cr, Pb, and Cd observed in the middle reaches of the river indicates heavy metal contamination in local areas, although there was an obvious decrease in concentrations in the water downstream of the Daheiting Reservoir. The significant rising trend in Cu, Pb, and Ni seen the sediment farther away from the river also suggests that anthropogenic activities contribute to heavy metal pollution Sediments were therefore used as environmental indicators, with sediment assessment was conducted using the geo-accumulation index （Igeo） and the potential ecological risk index （R/）. The Igeo values revealed that Cd （3.13） and Cr （2.39） had accumulated significantly in the Luan River. The R/values for most （89%） of the sampling stations were higher than 300, suggesting that sediment from the Luan River poses a severe ecological risk, with the potential ecological risks downstream higher than that in the upper and middle streams. Good correlations among Pb/Ni, Pb/Cd, Cu/Pb, and Cu/Cd in the water and Cr/Ni in the sediment were observed. Cluster analysis suggested that Cd may have various origins, being derived from anthropogenic sources.

Perceptible landscape patterns reveal invisible socioeconomic profiles of cities

Urban landscape is directly perceived by residents and is a significant symbol of urbanization development.A comprehensive assessment of urban landscapes is crucial for guiding the development of inclusive,resilient,and sustainable cities and human settlements.Previous studies have primarily analyzed two-dimensional landscape indicators derived from satellite remote sensing,potentially overlooking the valuable insights provided by the three-dimensional configuration of landscapes.This limitation arises from the high cost of acquiring large-area three-dimensional data and the lack of effective assessment indicators.Here,we propose four urban landscapes indicators in three dimensions(UL3D):greenness,grayness,openness,and crowding.We construct the UL3D using 4.03 million street view images from 303 major cities in China,employing a deep learning approach.We combine urban background and two-dimensional urban landscape indicators with UL3D to predict the socioeconomic profiles of cities.The results show that UL3D indicators differs from two-dimensional landscape indicators,with a low average correlation coefficient of 0.31 between them.Urban landscapes had a changing point in2018–2019 due to new urbanization initiatives,with grayness and crowding rates slowing,while openness increased.The incorporation of UL3D indicators significantly enhances the explanatory power of the regression model for predicting socioeconomic profiles.Specifically,GDP per capita,urban population rate,built-up area per capita,and hospital count correspond to improvements of 25.0%,19.8%,35.5%,and 19.2%,respectively.These findings indicate that UL3D indicators have the potential to reflect the socioeconomic profiles of cities.