Relationship between Precipitation Characteristics at Different Scales and Drought/Flood during the Past 40 Years in Longchuan River, Southwestern China

In this study, the temporal and spatial patterns of rainfall in the Longchuan River basin from 1977 to 2017 were analyzed, to assess the feature of precipitation. Based on the daily precipitation time series, the Lorenz curve, precipitation concentration index (PCI), precipitation concentration degree (PCD), and the precipitation concentration period (PCP) were used to evaluate the precipitation distribution characteristics. The PCI, PCD and PCP in five categories, defined by the fixed thresholds, were proposed to investigate the concentrations, and the average values indicated the higher concentrations in the higher intensities. The indices showed strong irregularity of daily and monthly precipitation distributions in this basin. The decrease in the PCD revealed an increase in the proportion of precipitation in the dry season. The rainy days of slight precipitation in the upper and lower basins with significant downward trends (−13.13 d/10 a, −7.78 d/10 a) led to longer dry spells and an increase in the risk of drought, even severe in the lower area. In the upper basin, the increase in rainfall erosivity was supported by the upward trend in the PCIw of heavy precipitation and the simple daily intensity index (SDII) of extreme precipitation. Moreover, the PCP of light precipitation, moderate precipitation, and heavy precipitation concentrated earlier at the end of July. The results of this study can provide beneficial reference information to water resource planning, reservoir operation, and agricultural production in the basin.

Geospatial Analysis of Soil Erosion Susceptibility and Causative Factors in Anambra State, South East, Nigeria

Land degradation is a function of soil erosion leading to soil loss and reduction in crop productivity as well as other socio-economic activities. The menace of soil erosion is challenging due to diverse factors including advertent and inadvertent anthropogenic activities. This study looks at soil erosion susceptibility and causative factors in Anambra State, both static and dynamic with the intent of identifying them, investigating spatial variability of soil loss, relate erodibility to soil properties and causative factors to soil erosion. Eight (8) prominent causative factors (CFs), were identified. These causative factors (CFs) were analyzed using ArcGIS 10.2. Sixty (60) soil samples were extracted randomly, analyzed, and tested. The study identified CFs such as Drainage Density, Erosion Density, Lineament Density, Slope Length, Land Surface Temperature, and Rainfall Erosivity, which contribute to Soil Erodibility (K - Factor). Land Surface Temperature, Soil Moisture Index, Rainfall Erosivity, and Normalized Difference Vegetation Index contributed to the loss of 8.97 ton/ha/yr, 9.1288 ton/ha/yr, 1,1134.7 ton/ha/yr, and 0.245 ton/ha/yr respectively to erosion in Anambra State. Conclusively, the dynamic causative factors influence soil susceptibility and trigger erosion in the State.

Evaluation of Various DEMs for Quantifying Soil Erosion Under Changing Land Use and Land Cover in the Himalaya

Soil erosion is one of the serious environmental threats in the Himalayas, primarily exacerbated by the steep slopes, active tectonics, deforestation, and land system changes. The Revised Universal Soil Loss Equation was employed to quantify soil erosion from the Vishav watershed in the Kashmir Himalaya, India. Topography and land use/land cover (LULC) are important driving factors for soil erosion. Most often, a Digital Elevation Model (DEM) is used in erosion models without any evaluation and testing which sometimes leads to erroneous estimates of soil erosion. For the best topographic characterization of the watershed, four publicly available DEMs with almost identical resolution (∼30 m), were evaluated. The DEMs were compared with GPS measurements to determine the most reliable among the tested DEMs for soil erosion estimation. Statistical evaluation of the DEMs with GPS data indicated that the CARTO DEM is better with root mean square error (RMSE) of 18.2 m than the other three tested DEMs viz., Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Shuttle Radar Topography Mission (SRTM), and Advanced Land Observing Satellite (ALOS). Slope length and slope steepness factors were computed from the DEMs. Crop cover and management factors were generated from the satellite-derived LULC. Moreover, rainfall data of the nearest stations were used to compute rainfall erosivity and soil erodibility factor was derived from the soil texture data generated from 375 soil samples. The simulated erosion estimates from SRTM, ALOS, and CARTO DEMs showed similar spatial patterns contrary to the ASTER estimates which showed somewhat different patterns and magnitude. The mean erosion in the study area has almost doubled from 2.3 × 106 tons in 1981 to 4.6 × 106 tons in 2019 mainly driven by the anthropogenic LULC changes. The increased soil erosion is due to the degradation of forest cover, urbanization, steep slopes, and land system changes observed during the period. In absence of the observations, the simulated soil erosion was validated with the land degradation map of the watershed which showed a good correspondence. It is hoped that the results from this work would inform policymaking on soil and water conservation measures in the data-scarce mountainous Kashmir Himalaya.

Database of rainfall erosivity factor for 141 locations in Brazil.

Soil erosion is a serious agricultural and environmental problem considered as a threat to sustainable development around the world. Rainfall is the primary cause of soil erosion, what leads the knowledge of its potential to cause soil erosion (rainfall erosivity – R-factor) to be a valuable tool for the design of land conservation best practices. As Brazil has a lack of information about rainfall erosivity, the present paper has determined the R-factor of 141 pluviographic stations distributed over Brazilian territory. Initially, erosive rainfalls were identified, and then the EI30 erosivity index was used to obtain the rainfall erosivity values. Regression models for the estimation of rainfall erosivity using daily rainfall data were established based on the correlation between the monthly average values of erosivity and the modified Fournier index. Results showed that the annual rainfall erosivity in the Brazilian stations analyzed ranged from 368.7 to 16,850.6 MJ mm ha-1 h-1 year-1. The results presented help to expand information about the spatial distribution of rainfall erosivity in Brazil, contributing to better conservation planning of land use.

Soil Erosion Assessment Using the RUSLE Model and Geospatial Techniques (Remote Sensing and GIS) in South-Central Niger (Maradi Region)

A systematic method, incorporating the revised universal soil loss equation model (RUSLE), remote sensing, and the geographic information system (GIS), was used to estimate soil erosion potential and potential area in the Maradi region of south-central Niger. The spatial trend of seasonal soil erosion was obtained by integrating remote sensing environmental variables into a grid-based GIS method. RUSLE is the most commonly used method for estimating soil erosion, and its input variables, such as rainfall erosivity, soil erodibility, slope length and steepness, cover management, and conservation practices, vary greatly over space. These factors were calculated to determine their influence on average soil erosion in the region. An estimated potential mean annual soil loss of 472.4 t/ac/year, based on RUSLE, was determined for the study area. The potential erosion rates varied from 14.8 to 944.9 t/ac/year. The most eroded areas were identified in central and west-southern areas, with erosion rates ranging from 237.1 to 944.9 t/ac/year. The spatial erosion maps can serve as a useful reference for deriving land planning and management strategies and provide the opportunity to develop a decision plan for soil erosion prevention and control in south-central Niger.

Assessing the Effect of Landuse/Land Cover (1986-2020) on Soil Loss in Alluvium Geologic Formation Using Geospatial Technology in Southern Nigeria

The occurrence of soil loss is a continuous process and occurs spatially across the earth’s surface. The study of soil loss is a necessity for proper understanding of the processes and the rate of soil loss for conservational purpose. Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) image data was acquired for 1986, 2003 and 2020 were used to derive the C factor of the RUSLE model while other factors of the model were kept fixed for the years considering their inability to change easily. The RUSLE model was used to determine the trend of the soil loss on the alluvium geologic formation considering their land use/land cover changes for 1986, 2003 and 2020. The rainfall erosivity of the study area had an average of 8201.45MJmmha-1h-1yr-1. The soil erodibility index of the soils obtained from Alluvium had an average of 0.150tons MJ-1 hmm-1. The slope length and steepness factor of the study area range from 0 to 2.574. the crop cover factor of for 1986 range from 0.52 to 0.87, 2003 range from 0.52 to 0.87 and 2020 range from 0.62 to 0.92. No active field conservation was found out within the study area as described by Wischmeier and Smith. The results obtained show that 1986, 2003 and 2020 had a soil loss of 1966.3, 2167.85 and 3361.14 tonha-1yr-1 respectively. The results show that the study area is experiencing an increased trend of soil loss. This result can serve as guide into understanding the past and current rate of soil loss for soil resource planning and management