A dynamic model of litter interception that can be applied to the management of semi-arid meadow steppe with prohibition of grazing

Xie, Jingjie; He, Jie; Su, Derong; Lyu, Shihai; Diao, Zhaoyan; Bu, He; Wo, Qiang

doi:10.1016/j.ecohyd.2021.06.003

Cited by 1 publication

(1 citation statement)

References 31 publications

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“…where W 𝑖 is the storage capacity of the i-th grid located in the vegetation area (mm); f 𝑖 is the vegetation coverage of the i-th grid (0~1); LAI 𝑖 is the vegetation leaf area index of the i-th grid; h 𝑖 is the maximum water holding capacity of vegetation leaves in the i-th grid (mm); 𝑙 𝑖 is litter interception (mm), obtained by remote sensing techniques or related research [64][65][66][67]; N 𝑖 is non-capillary porosity (%), obtained by soil type; D 𝑖 is thickness of aeration zone (mm), will be described later; H 𝑖 is the depth of litter layer, generally is 50 mm. f 𝑖 , LAI 𝑖 , and h 𝑖 can be determined by remote sensing data, and their physical meaning is clear.…”

Section: Wscc and L-wsccmentioning

confidence: 99%

Land-Use and Land Cover Is Driving Factor of Runoff Yield: Evidence from A Remote Sensing-Based Runoff Generation Simulation

Yang

et al. 2022

Water

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The spatial distribution of water storage capacity has always been the critical content of the study of saturation-excess runoff. Xin′anjiang model uses the water storage capacity curve (WSCC) to characterize the distribution of water storage capacity for runoff yield calculation. However, the mathematical and physical foundations of WSCC are unclear, which is impossible to simulate runoff generation with complex basins accurately. To fill this gap, we considered the dominant role of basin physical characteristics in water storage capacity and developed a new integrated approach to solve the spatial distribution of water storage capacity (L-WSCC) to account for the spatiotemporal dynamics of their impact on runoff generation. The main contribution of L-WSCC was to confer WSCC more physical meaning and the spatial distribution of water storage capacity was explicitly represented more accurately, so as to better express the runoff generation and provide a new approach for runoff yield calculation in non-data basin. L-WSCC was applied to Misai basin in China and promising results had been achieved, which verified the rationality of the method (the mean Nash–Sutcliffe efficiency (NSE):0.86 and 0.82 in daily and hourly scale, respectively). Compared with WSCC, the performance of L-WSCC was improved (mean NSE: 0.82 > 0.78, mean absolute value of flood peak error (PE): 12.74% < 21.66%). Moreover, the results of local sensitivity analyses demonstrated that land-use and land cover was the major driving factor of runoff yield (the change of mean absolute error (ΔMAE): 131.38%). This work was significant for understanding the mechanisms of runoff generation, which can be used for hydrological environmental management and land-use planning.

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Section: Wscc and L-wsccmentioning

confidence: 99%

Land-Use and Land Cover Is Driving Factor of Runoff Yield: Evidence from A Remote Sensing-Based Runoff Generation Simulation

Yang

et al. 2022

Water

View full text Add to dashboard Cite

show abstract

A dynamic model of litter interception that can be applied to the management of semi-arid meadow steppe with prohibition of grazing

Cited by 1 publication

References 31 publications

Land-Use and Land Cover Is Driving Factor of Runoff Yield: Evidence from A Remote Sensing-Based Runoff Generation Simulation

Land-Use and Land Cover Is Driving Factor of Runoff Yield: Evidence from A Remote Sensing-Based Runoff Generation Simulation

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