Increasing trends in rainfall-runoff erosivity in the Source Region of the Three Rivers, 1961–2012

Wang, Yousheng; Cheng, Congcong; Xie, Yonghong; Liu, Baoyuan; Yin, Shuiqing; Liu, Yingna; Hao, Yanfang

doi:10.1016/j.scitotenv.2017.02.235

Cited by 60 publications

(31 citation statements)

References 48 publications

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“…Daily rainfall data is better than monthly rainfall data. Spatial distribution is characterized by a decreasing trend from the southeast to the northwest, which is in accord with previous studies [33,34,50,51].…”

Section: Discussionsupporting

confidence: 92%

Spatial and Temporal Patterns of Rainfall Erosivity in the Tibetan Plateau

Feng

Duan

et al. 2020

Water

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The Tibetan Plateau is influenced by global climate change which results in frequent melting of glaciers and snow, and in heavy rainfalls. These conditions may increase the risk of soil erosion, but prediction is not feasible due to scarcity of rainfall data in the high altitudes of the region. In this study, daily precipitation data from 1 January 1981 to 31 December 2015 were selected for 38 meteorological stations in the Tibetan Plateau, and annual and seasonal rainfall erosivity were calculated for each station. Additionally, we used the Mann–Kendall trend test, Sen’s slope, trend coefficient, and climate tendency rate indicators to detect the temporal variation trend of rainfall erosivity. The results showed that the spatial distribution of rainfall erosivity in the Tibetan Plateau exhibited a significant decreasing trend from southeast to northwest. The average annual rainfall erosivity is 714 MJ·mm·ha−1·h−1, and varies from 61 to 1776 MJ·mm·ha−1·h−1. Rainfall erosivity was mainly concentrated in summer and autumn, accounting for 67.5% and 18.5%, respectively. In addition, annual, spring, and summer rainfall erosivity were increasing, with spring rainfall erosivity highly significant. Temporal and spatial patterns of rainfall erosivity indicated that the risk of soil erosion was relatively high in the Hengduan mountains in the eastern Tibetan Plateau, as well as in the Yarlung Zangbo River Valley and its vicinity.

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Section: Discussionsupporting

confidence: 92%

Spatial and Temporal Patterns of Rainfall Erosivity in the Tibetan Plateau

Feng

Duan

et al. 2020

Water

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“…The region is experiencing gradual warming, and also becoming increasingly wetter, both in winter and summer (Figure S4). These trends in temperature and precipitation, toward progressively warmer and wetter conditions, are consistent with adjacent regions of the Tibet‐Xinjiang plateau (Ding et al, ; Wang et al, ; Xu, Knudby, Ho, Shen, & Liu, ). The broad vegetation type is alpine shrub‐steppe (also called desert‐steppe).…”

Section: Methodssupporting

confidence: 82%

“…So evidence of browning (Shen et al, 2013;Yu et al, 2010;Zhang et al, 2013) appears counterintuitive. The underlying mechanisms that could explain browning instead of greening may include altered snowmelt patterns under warmer climate (Musselman, Clark, Liu, Ikeda, & Rasmussen, 2017), declining water availability in soil (Schlaepfer et al, 2017), and accelerated erosion of topsoil with loss of soil organic matter and reduction in soil fertility (Wang et al, 2017). If these indirect mechanisms are at play (Turnbull et al, 2008), then their effects should be incorporated into land use policy, as they can be influenced by grazing mediated feedbacks at the plant-soil interface (Bagchi & Ritchie, 2010;Bagchi, Roy, et al, 2017;Klein et al, 2007).…”

Section: Discussionmentioning

confidence: 99%

“…Degradation and desertification across the larger Trans-Himalayan landscape are serious concerns that are often linked with livestock overgrazing (Shaohong et al, 2007) but remain to be adequately addressed (Bagchi, Gupta, Murthy, & Singh, 2017). Loss of ecosystem function and services, declining wildlife populations, negative impacts on human livelihoods with increase in ecological refugees, intensified soil erosion, and severe dust-storms, as well as devastating locust outbreaks, are some consequences linked to altered vegetation and climate in this ecoregion (Feng, Guo, Luo, & Qin, 2012;Neumann et al, 2015;Piao, Friedlingstein, Ciais, Viovy, & Demarty, 2007;Wang, Dong, Zhang, & Liu, 2004;Wang et al, 2017). Gradual warming and progressively wetter conditions in this cold and arid ecoregion are expected to result in greening (Fensholt et al, 2012;Zhu et al, 2016).…”

Section: Land Usementioning

confidence: 99%

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Spatial patterns of long‐term vegetation greening and browning are consistent across multiple scales: Implications for monitoring land degradation

Murthy

Bagchi

2018

Land Degrad Dev

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Understanding spatial–temporal patterns of terrestrial vegetation response to climate change (long‐term greening/browning) is important for developing strategies to mitigate degradation. Semiarid rangelands are especially susceptible to degradation, which challenges wildlife conservation and human livelihoods that depend on livestock production. In the cold‐arid Trans‐Himalayan ecosystem (northern India), temperature is increasing, and it is also becoming progressively wetter. Yet, counter‐intuitively, there are widespread concerns over degradation. We evaluated whether greening/browning patterns in long‐term satellite‐derived vegetation indices (normalized difference vegetation index [NDVI]) are consistent across different spatial and temporal scales using 6 datasets: MODIS (250 m, 500 m, 1 km, and 5.5 km), SPOT 1 km, and GIMMS 8 km. Results indicate browning in the spring and greening in late summer. Location of hotspots of degradation (browning) was broadly consistent across spatial scales (10−2–102 km2) and were found in regions with warmer temperature and at higher elevations. Broadly, the spatial/temporal pattern of browning does not coincide strongly with location and timing of human land use via livestock grazing. This geographical and seasonal context indicates that vegetation response may be more strongly related to climate than to human land use (overgrazing). Importantly, the dynamic nature of greening/browning, across space and time, is not captured by composite annual metrics (sum‐NDVI, max‐NDVI, and mean‐NDVI). This reiterates the importance of both intraannual and interannual assessments. Location of hotspots indicates that degradation occurs in a spatially contiguous manner, but these are not stationary and instead shift with seasons. Overall, the results show that evaluating the consistency of greening/browning trends across different spatial/temporal scales is critical for understanding and managing vegetation degradation.

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“…Temporal variations of the R factor, along with changes in the other soil erosion factors, reflect apparent temporal trends in the rates of soil erosion, which have significant implications for climate change impact assessment and mitigation. Studies on temporal variations of the R factor have been extensively reported based on historical records (Diodato and Bellocchi, 2009; Meusburger et al, 2012; Klik and Konecny, 2013; Ramos and Duran, 2014; Panagos et al, 2016a; Qin et al, 2016; Wang et al, 2017) and future climate projections (Nearing, 2001; Zhang et al, 2010; Shiono et al, 2013; Segura et al, 2014; Yang et al, 2015; Panagos et al, 2017a). Similar to the development of an erosivity map, high‐temporal‐resolution data usually cover limited stations and record lengths (Verstraeten and Poesen, 2006; Fiener et al, 2013), so most research in the past was based on the empirical relationship between erosivity values from high‐resolution data and those from coarse‐resolution rainfall (Angulo‐Martinez and Beguería, 2012; Ma et al, 2014; Ramos and Duran, 2014).…”

Section: Long‐term Trends In Rainfall Erosivitymentioning

confidence: 99%

Rainfall Erosivity: An Overview of Methodologies and Applications

Yin

Nearing

Borrelli

et al. 2017

Vadose Zone Journal

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The rainfall erosivity factor (R factor) is one of six erosion factors in the Universal Soil Loss Equation (USLE), which together reflect the combined effects that cause soil loss by rill and interrill erosion on hillslopes by precipitation. It is defined as the summation of event EI 30 (the product of kinetic energy and maximum 30-min intensity) over a year and calculated based on rainfall hyetograph data. The R factor was developed in the various versions of the USLE, including the definition of the individual event and the criterion for selecting events used in the calculation, the equation used to estimate the unit kinetic energy from the rainfall intensity, the estimation of erosivity from the snowmelt and thaw, and erosivity mapping. Most research on rainfall erosivity deals with any of three aspects: developing estimation methods for deriving erosivity from courser resolution rainfall data (such as daily, monthly, and annual) but with greater spatial and temporal coverages than those from hyetograph data; preparing erosivity maps including those for annual average, monthly, and 10-yr recurrence erosivity; and documenting temporal trends in erosivity. Rainfall erosivity research on these three aspects is summarized to provide a greater understanding of the R factor.

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Increasing trends in rainfall-runoff erosivity in the Source Region of the Three Rivers, 1961–2012

Cited by 60 publications

References 48 publications

Spatial and Temporal Patterns of Rainfall Erosivity in the Tibetan Plateau

Spatial and Temporal Patterns of Rainfall Erosivity in the Tibetan Plateau

Spatial patterns of long‐term vegetation greening and browning are consistent across multiple scales: Implications for monitoring land degradation

Rainfall Erosivity: An Overview of Methodologies and Applications

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