Journal of Hydrology, Journal Year: 2024, Volume and Issue: unknown, P. 132460 - 132460
Published: Dec. 1, 2024
Language: Английский
The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 917, P. 170425 - 170425
Published: Jan. 29, 2024
Language: Английский
Citations
18
Journal of Hydrology, Journal Year: 2024, Volume and Issue: 630, P. 130666 - 130666
Published: Jan. 24, 2024
Language: Английский
Citations
16
Journal of Hydrology Regional Studies, Journal Year: 2024, Volume and Issue: 53, P. 101770 - 101770
Published: April 4, 2024
Target area of this study are the main agricultural production zones Austria. Most important croplands cover flat to pre-alpine areas concentrated in north, east, and southeast The novelty our is spatiotemporal assessment rainfall characteristics that drive erosivity at event level as well identification erosive distribution patterns within events. Our approach allows definition both typical extreme erosivity. Long-term high temporal resolution datasets were used apply a clustering approach, seasonal spatial analyses, distributions (isopleths) identified types (clusters). Three dominant event-types (clusters) strongly relate with Austria's seasonality complex topography. most events (cluster C1) characterized by intensity short duration. C1 have largest occurrence frequency southern Austria occur from May September. Unlike less more evenly distributed (C2 C3) highly pronounced maximum onset event.
Language: Английский
Citations
4
International Soil and Water Conservation Research, Journal Year: 2025, Volume and Issue: unknown
Published: March 1, 2025
Language: Английский
Citations
0
Earth Surface Processes and Landforms, Journal Year: 2025, Volume and Issue: 50(3)
Published: March 3, 2025
Abstract Rainfall erosivity is an essential factor affecting soil erosion, which expected to change under global climate change. Despite the existence of numerous rainfall models, there remains a scarcity research focusing on accuracy multi‐time scale models. In this study, subtropical regions central China (Hubei Province) were selected, where simulation performance six widely employed models was investigated using daily precipitation data from 70 meteorological stations spanning 2000 2020. Using optimal model, Kriging interpolation and Mann–Kendall test revealed significant temporal spatial variations in density. The results show that: (1) model more suitable for simulating Hubei Province. (2) mean annual Province 5894.25 MJ·mm·ha −1 ·h ·a , with large across regions. (3) density showed differences between different seasons, erosion most likely occur summer (June, July August). (4) distribution pattern highly consistent: long‐term high levels Xianning City, southeastern Province, risk high. findings study offer valuable insights into selection mountainous hilly areas provide reference assessing formulating control measures.
Language: Английский
Citations
0
Ecological Modelling, Journal Year: 2025, Volume and Issue: 505, P. 111120 - 111120
Published: April 8, 2025
Language: Английский
Citations
0
Remote Sensing, Journal Year: 2024, Volume and Issue: 16(7), P. 1186 - 1186
Published: March 28, 2024
Satellite precipitation products (SPPs) have emerged as an alternative to estimate rainfall erosivity. However, prior studies showed that SPPs tend underestimate erosivity but without reported bias-correction methods. This study evaluated the efficacy of two SPPs, namely, GPM_3IMERGHH (30-min and 0.1°) GPM_3IMERGDF (daily 0.1°), in estimating indices mainland China: average annual (R-factor) 10-year event (10-yr storm EI), by comparing with derived from gauge-observed hourly (Gauge-H). Results indicate yields higher accuracy than GPM_3IMERGHH, though both generally these indices. The Percent Bias (PBIAS) is −55.48% for R-factor −56.38% 10-yr EI using which reduces −10.86% −32.99% GPM_3IMERGDF. A method was developed based on systematic difference between SSPs Gauge-H. five-fold cross validation shows bias-correction, improve considerably, reduced. PBIAS decreases −0.06% 0.01%, −0.33% 0.14%, respectively, EI. estimated comparable obtained through Kriging interpolation Gauge-H better interpolated daily precipitation. Given their high temporal spatial resolution, timely updates, are viable data estimation bias correction.
Language: Английский
Citations
2
Journal of Hydrology Regional Studies, Journal Year: 2024, Volume and Issue: 56, P. 102076 - 102076
Published: Nov. 23, 2024
Language: Английский
Citations
1
The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 947, P. 174334 - 174334
Published: June 30, 2024
Language: Английский
Citations
1
Atmosphere, Journal Year: 2024, Volume and Issue: 15(9), P. 1032 - 1032
Published: Aug. 26, 2024
Rainfall erosivity is a crucial factor in the evaluation of soil erosion, significantly influencing complex relationships among water, soil, and environment. Understanding its attributes variations space time essential for effective water resource management, erosion mitigation, land-use planning. This paper utilizes daily precipitation data from 123 grid points Niyang River Basin, spanning 2008 to 2016, calculate rainfall using straightforward algorithmic model. Ordinary Kriging was used examine spatial temporal erosivity, while Spearman’s correlation analysis employed between annual various factors, including multi-year average precipitation, erosive rainfall, dry-season wet-season temperature, elevation. The results indicate year-by-year increase basin, with trend towards stabilization. over years 711 MJ·mm·hm−2·h−1, peaking at 1098 MJ·mm·hm−2·h−1 2014. A significant 93.9% concentrated wet season, maximum 191 July. left bank mainstream, especially central lower sections main river tributaries, experiences highest erosivity. factors predominantly influence showing strongest (rho = 0.93), temperature elevation have relatively minor effects. study enhances understanding forces plateau region provides scientific basis predicting loss, developing control measures, ensuring sustainable land use.
Language: Английский
Citations
1