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Fenta AA, Tsunekawa A, Haregeweyn N, Yasuda H, Tsubo M, Borrelli P, Kawai T, Belay AS, Ebabu K, Berihun ML, Sultan D, Setargie TA, Elnashar A, Arshad A, Panagos P. An integrated modeling approach for estimating monthly global rainfall erosivity. Sci Rep 2024; 14:8167. [PMID: 38589610 PMCID: PMC11001900 DOI: 10.1038/s41598-024-59019-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 04/05/2024] [Indexed: 04/10/2024] Open
Abstract
Modeling monthly rainfall erosivity is vital to the optimization of measures to control soil erosion. Rain gauge data combined with satellite observations can aid in enhancing rainfall erosivity estimations. Here, we presented a framework which utilized Geographically Weighted Regression approach to model global monthly rainfall erosivity. The framework integrates long-term (2001-2020) mean annual rainfall erosivity estimates from IMERG (Global Precipitation Measurement (GPM) mission's Integrated Multi-satellitE Retrievals for GPM) with station data from GloREDa (Global Rainfall Erosivity Database, n = 3,286 stations). The merged mean annual rainfall erosivity was disaggregated into mean monthly values based on monthly rainfall erosivity fractions derived from the original IMERG data. Global mean monthly rainfall erosivity was distinctly seasonal; erosivity peaked at ~ 200 MJ mm ha-1 h-1 month-1 in June-August over the Northern Hemisphere and ~ 700 MJ mm ha-1 h-1 month-1 in December-February over the Southern Hemisphere, contributing to over 60% of the annual rainfall erosivity over large areas in each hemisphere. Rainfall erosivity was ~ 4 times higher during the most erosive months than the least erosive months (December-February and June-August in the Northern and Southern Hemisphere, respectively). The latitudinal distributions of monthly and seasonal rainfall erosivity were highly heterogeneous, with the tropics showing the greatest erosivity. The intra-annual variability of monthly rainfall erosivity was particularly high within 10-30° latitude in both hemispheres. The monthly rainfall erosivity maps can be used for improving spatiotemporal modeling of soil erosion and planning of soil conservation measures.
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Affiliation(s)
- Ayele A Fenta
- International Platform for Dryland Research and Education, Tottori University, Tottori, 680-0001, Japan.
| | - Atsushi Tsunekawa
- Arid Land Research Center, Tottori University, 1390 Hamasaka, Tottori, 680-0001, Japan
| | - Nigussie Haregeweyn
- International Platform for Dryland Research and Education, Tottori University, Tottori, 680-0001, Japan
| | - Hiroshi Yasuda
- Organization for Educational Support and International Affairs, Tottori University, Koyama Minami 4-101, Tottori, 680-8550, Japan
| | - Mitsuru Tsubo
- Arid Land Research Center, Tottori University, 1390 Hamasaka, Tottori, 680-0001, Japan
| | - Pasquale Borrelli
- Department of Environmental Sciences, University of Basel, 4056, Basel, Switzerland
- Department of Science, Roma Tre University, Rome, Italy
| | - Takayuki Kawai
- Graduate School of International Resource Sciences, Akita University, 1-1 Tegatagakuen-Machi, Akita, 010-8502, Japan
| | - Ashebir S Belay
- Department of Earth Science, Bahir Dar University, P.O. Box 79, Bahir Dar, Ethiopia
| | - Kindiye Ebabu
- Arid Land Research Center, Tottori University, 1390 Hamasaka, Tottori, 680-0001, Japan
- College of Agriculture and Environmental Sciences, Bahir Dar University, P.O. Box 1289, Bahir Dar, Ethiopia
| | - Mulatu L Berihun
- Faculty of Civil and Water Resource Engineering, Bahir Dar Institute of Technology, Bahir Dar University, P.O. Box 26, Bahir Dar, Ethiopia
- Tropical Research and Education Center, University of Florida, Gainesville, FL, 33031, USA
| | - Dagnenet Sultan
- Faculty of Civil and Water Resource Engineering, Bahir Dar Institute of Technology, Bahir Dar University, P.O. Box 26, Bahir Dar, Ethiopia
| | - Tadesual A Setargie
- Arid Land Research Center, Tottori University, 1390 Hamasaka, Tottori, 680-0001, Japan
- Faculty of Civil and Water Resource Engineering, Bahir Dar Institute of Technology, Bahir Dar University, P.O. Box 26, Bahir Dar, Ethiopia
| | - Abdelrazek Elnashar
- Department of Natural Resources, Faculty of African Postgraduate Studies, Cairo University, Giza, 12613, Egypt
| | - Arfan Arshad
- Department of Biosystems and Agricultural Engineering, Oklahoma State University, Stillwater, OK, 74075, USA
| | - Panos Panagos
- European Commission, Joint Research Centre (JRC), 21027, Ispra, VA, Italy
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Abstract
Research on precipitation regularity in the past 120 years is an important link in analyzing the precipitation characteristics of watersheds. This paper systematically analyzes the characteristic changes of centennial precipitation data in the Haihe River basin with the help of CRU data, PCI, SPI, and the Pearson type III curve. The results show that the spatial and temporal distribution of precipitation in the Haihe River basin has a more obvious inconsistency. The temporal distribution shows the characteristics of relatively stable in the early period and increasing fluctuation in the later period, the concentration of precipitation gradually decreases, and the overall drought level decreases. The spatial distribution shows a general pattern of gradually decreasing from southwest to northeast, the overall trend of summer precipitation changes from stable to north–south extremes, and the distribution probability of extreme precipitation events in the basin decreases from southeast to northwest, while the drought-prone area transitions from the northeast to the west and southwest of the basin. Under the influence of both climate change and human activities, the seasonal distribution of precipitation tends to be average, the area affected by extreme precipitation rises, and the arid area shifts to the inland area.
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Zhou X, Yu J, Li J, Li S, Zhang D, Wu D, Pan S, Chen W. Spatial correlation among cultivated land intensive use and carbon emission efficiency: A case study in the Yellow River Basin, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:43341-43360. [PMID: 35094255 DOI: 10.1007/s11356-022-18908-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 01/23/2022] [Indexed: 06/14/2023]
Abstract
Considering the current global goal of carbon neutrality, the relationship between cultivated land intensive use (CLIU) and carbon emission efficiency (CEE) should be explored to address the global climate crisis and move toward a low-carbon future. However, previous work in this has been conducted at provincial/regional scales and few have identified the spatial correlation between CLIU and CEE at the scale of large river basins. Therefore, this study explored the spatiotemporal characteristics of CLIU, cultivated land carbon emissions (CLCE), and CEE, as well as the spatial correlation between CLIU and CEE in the Yellow River Basin (YRB), China. A comprehensive evaluation model, the Intergovernmental Panel on Climate Change (IPCC) coefficient methodology, existing data envelopment analysis model, and bivariate spatial autocorrelation models were used to analyze statistical data from 2005 to 2017. We found that the overall CLIU and CLCE values in the YRB exhibited a continuous increase; the average carbon emission total efficiency and carbon emission scale efficiency first decreased and then increased, and the average carbon emission pure technical efficiency gradually decreased. Areas of high CLCE were concentrated in eastern areas of the YRB, whereas those of high CLIU, carbon emission total efficiency, carbon emission scale efficiency, and carbon emission pure technical efficiency predominantly appeared in the eastern areas, followed by central and western areas of the YRB. Spatial analysis revealed a significant spatial dependence of CLIU on CEE. From a global perspective, the spatial correlations between CLIU and CEE changed from positive to negative with time. Moreover, the aggregation degree between CLIU and CEE gradually decreases with time, while the dispersion degree increases with time, and the spatial correlation gradually weakens. The local spatial autocorrelation further demonstrates that the number of high-low and low-high clusters between CLIU and CEE gradually increases over time, while the number of high-high and low-low clusters gradually decreased over time. Collectively, these findings can help policymakers formulate feasible low-carbon and efficient CLIU policies to promote win-win cooperation among regions.
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Affiliation(s)
- Xiao Zhou
- Department of Land Resources Management, School of Public Administration, China University of Geosciences, Wuhan, 430074, China
- Key Laboratory of Rule of Law Research, Ministry of Natural Resources, Wuhan, 430074, China
| | - Juan Yu
- Department of Land Resources Management, School of Public Administration, China University of Geosciences, Wuhan, 430074, China
- Key Laboratory of Rule of Law Research, Ministry of Natural Resources, Wuhan, 430074, China
| | - Jiangfeng Li
- Department of Land Resources Management, School of Public Administration, China University of Geosciences, Wuhan, 430074, China
- Key Laboratory of Rule of Law Research, Ministry of Natural Resources, Wuhan, 430074, China
| | - Shicheng Li
- Department of Land Resources Management, School of Public Administration, China University of Geosciences, Wuhan, 430074, China
- Key Laboratory of Rule of Law Research, Ministry of Natural Resources, Wuhan, 430074, China
| | - Dou Zhang
- Department of Environmental Science and Engineering, Fudan University, Shanghai, 200438, China
| | - Di Wu
- Department of Land Resources Management, School of Public Administration, China University of Geosciences, Wuhan, 430074, China
- Key Laboratory of Rule of Law Research, Ministry of Natural Resources, Wuhan, 430074, China
| | - Sipei Pan
- Department of Land Resources Management, School of Public Administration, China University of Geosciences, Wuhan, 430074, China
- Key Laboratory of Rule of Law Research, Ministry of Natural Resources, Wuhan, 430074, China
| | - Wanxu Chen
- Department of Geography, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China.
- Research Center for Spatial Planning and Human-Environmental System Simulation, China University of Geosciences, Wuhan, 430074, China.
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China.
- School of Geography and Information Engineering, East Lake New Technology Development Zone, China University of Geosciences, No. 68, Jincheng Street, Wuhan, Hubei Province, 430078, People's Republic of China.
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Cui B, Zhang Y, Liu L, Xu Z, Wang Z, Gu C, Wei B, Gong D. Spatiotemporal Variation in Rainfall Erosivity and Correlation with the ENSO on the Tibetan Plateau since 1971. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182111054. [PMID: 34769576 PMCID: PMC8583552 DOI: 10.3390/ijerph182111054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 10/17/2021] [Accepted: 10/19/2021] [Indexed: 11/16/2022]
Abstract
Soil erosion is a serious ecological problem in the fragile ecological environment of the Tibetan Plateau (TP). Rainfall erosivity is one of the most important factors controlling soil erosion and is associated with the El Niño southern oscillation (ENSO). However, there is a lack of studies related to the spatial distribution and temporal trends of rainfall erosivity on the TP as a whole. Additionally, the understanding of the general influence of ENSO on rainfall erosivity across the TP remains to be developed. In this study, long-term (1971-2020) daily precipitation data from 91 meteorological stations were selected to calculate rainfall erosivity. The analysis combines co-kriging interpolation, Sen's slope estimator, and the Mann-Kendall trend test to investigate the spatiotemporal patten of rainfall erosivity across the TP. The Oceanic Niño Index (ONI) and multivariate ENSO Index (MEI) were chosen as ENSO phenomenon characterization indices, and the relationship between ENSO and rainfall erosivity was explored by employing a continuous wavelet transform. The results showed that an increasing trend in annual rainfall erosivity was detected on the TP from 1971 to 2020. The seasonal and monthly rainfall erosivity was highly uneven, with the summer erosivity accounting for 60.36%. The heterogeneous spatial distribution of rainfall erosivity was observed with an increasing trend from southeast to northwest. At the regional level, rainfall erosivity in the southeastern TP was mainly featured by a slow increase, while in the northwest was more destabilizing and mostly showed no significant trend. The rainfall erosivity on the whole TP was relatively high during non-ENSO periods and relatively low during El Niño/La Niña periods. It is worth noting that rainfall erosivity in the northwest TP appears to be more serious during the La Niña event. Furthermore, there were obvious resonance cycles between the rainfall erosivity and ENSO in different regions of the plateau, but the cycles had pronounced discrepancies in the occurrence time, direction of action and intensity. These findings contribute to providing references for soil erosion control on the TP and the formulation of future soil conservation strategies.
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Affiliation(s)
- Bohao Cui
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; (B.C.); (L.L.); (Z.W.); (C.G.); (B.W.); (D.G.)
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;
| | - Yili Zhang
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; (B.C.); (L.L.); (Z.W.); (C.G.); (B.W.); (D.G.)
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;
- Correspondence:
| | - Linshan Liu
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; (B.C.); (L.L.); (Z.W.); (C.G.); (B.W.); (D.G.)
| | - Zehua Xu
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Beijing 100085, China
| | - Zhaofeng Wang
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; (B.C.); (L.L.); (Z.W.); (C.G.); (B.W.); (D.G.)
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;
| | - Changjun Gu
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; (B.C.); (L.L.); (Z.W.); (C.G.); (B.W.); (D.G.)
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;
| | - Bo Wei
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; (B.C.); (L.L.); (Z.W.); (C.G.); (B.W.); (D.G.)
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;
| | - Dianqing Gong
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; (B.C.); (L.L.); (Z.W.); (C.G.); (B.W.); (D.G.)
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;
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Abstract
The construction of healthy transportation is an important ingredient for promoting the healthy development of cities. The establishment of an urban traffic evaluation mechanism can provide an important basis for the construction of healthy transportation. This study focused on the impact of precipitation on traffic speed and developed an urban traffic vulnerability index. This index reflects the degree of traffic affected by precipitation, which is calculated based on the traffic congestion index under different rainfall intensities. The traffic vulnerability indices of 41 major cities in China under rainfall conditions were evaluated. Based on the above traffic vulnerability indexes, the impact of socioeconomic factors on urban traffic vulnerability was analyzed. The three key findings of this study are as follows: there was a positive correlation between the vulnerability index and the gross domestic product (GDP); the urban population (POP) had a significant impact on the urban traffic vulnerability; and urban car ownership had little impact on traffic vulnerability. Based on these findings, possible measures to improve urban traffic vulnerability are proposed. The construction of an index system provides a basis for enhancing the urban traffic assessment mechanism, promoting the development of urban physical examinations and building healthy transportation and healthy cities.
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Examining Soil Erosion Responses to Grassland Conversation Policy in Three-River Headwaters, China. SUSTAINABILITY 2021. [DOI: 10.3390/su13052702] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Soil erosion in the Three-River Headwaters (TRH) region has continued to intensify in recent decades due to human activities and climate change. To reverse this situation, the Chinese central government has launched the Subsidy and Incentive System for Grassland Conservation (SISGC). As a sign of the effectiveness of SISGC implementation, the dynamic changes of soil erosion can provide timely feedback for decision makers and managers. The Revised Universal Soil Loss Equation (RUSLE) model was used to simulate the spatial distribution of soil erosion before and after SISGC implementation, and Mann–Kendall (MK) test to reveal the effect of policy implementation. The results showed that: (1) the soil erosion in the TRH was mainly mild (83.83% of the total eroded area), and the average soil erosion rate and the total erosion were 13.63 t ha−1 y−1 and 323.58 × 106 t y−1 respectively before SISGC implementation; (2) SISGC implementation has curbed soil erosion. After SISGC implementation, the total soil erosion decreased by 3.80%, which showed obvious differences between grassland types; (3) The influences of SISGC were mainly because it has increased vegetation cover, further decreasing soil erosion. However, soil erosion in Alpine grassland has deteriorated, indicating direct targeted policymaking should be on the agenda. Furthermore, SISGC should be continued and grassland-type-oriented to restore the grassland ecosystem.
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