Estimation of Peak Flow Rates for Small Drainage Areas

Liu, Baoyuan; Wang, Daan; Fu, Suhua; Cao, W. H

doi:10.1007/s11269-017-1604-y

Cited by 14 publications

(6 citation statements)

References 12 publications

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“…This power law scaling was chosen to represent aspects of fluvial landscapes that may control local habitat, such as discharge and channel width (Hicks, 2003;McGarvey & Hughes, 2008;Vannote et al, 1980). Stream discharge typically increases with drainage area with a scaling exponent of 0.5 to 1 (Di Lazzaro & Volpi, 2011;Liu et al, 2017;Ogden & Dawdy, 2003), while published values for a scaling exponent between drainage area and channel width range from 0.3 to 0.5 (Montgomery & Gran, 2001;Wolman, 1955). However, sensitivity tests we performed suggest that in our implementation of the NCM, the number of species simulated within individual drainage basins, and in the landscape as a whole, is not sensitive to the value of b.…”

Section: Neutral Community Model (Ncm) 221 Ncm Backgroundmentioning

confidence: 99%

Modeling the Evolution of Aquatic Organisms in Dynamic River Basins

Stokes

Perron

2020

JGR Earth Surface

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Rivers are conduits for aquatic organisms and host an exceptional number of species. Over geologic time, rivers and the aquatic organisms that live in them are subject to changes in topography that can alter where rivers flow. Differences in erosion rates across drainage divides cause some river basins to grow and others to shrink. Occasionally, rivers are abruptly rerouted by river captures that create both new dispersal corridors and barriers for aquatic organisms. These changes in habitat connectivity can lead to the evolution of new species, which has prompted suggestions that river captures may be a mechanism to produce high freshwater biodiversity. We test this hypothesis by building a model, Bio-SLANT (Biodiversity on Simulated LAndscapes using Neutral Theory). Bio-SLANT couples a computational landscape model that simulates river basin reorganization to a macroevolutionary model that simulates the dispersal, speciation, and extinction of organisms. We first show that modeled basin area exerts a primary control on within-basin species richness due to the species-area relationship. We then describe the effects of drainage area exchange between river basins. River capture increases species richness, but only temporarily, whereas elevated rates of speciation and extinction provide a persistent biological record of river network reorganization. When river captures are frequent, speciation rates increase more than extinction rates, resulting in a positive diversification rate under most of the biological parameters tested. We explore the implications of our results for species richness in landscapes with basins of different relative sizes and for diversification in tectonically active and inactive settings.

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Section: Neutral Community Model (Ncm) 221 Ncm Backgroundmentioning

confidence: 99%

Modeling the Evolution of Aquatic Organisms in Dynamic River Basins

Stokes

Perron

2020

JGR Earth Surface

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“…The peak flow was an important parameter to express the intensity of soil erosion or water loss on steep slopes [56]. In this study, the results in Table 1 indicated that with the increase of rainfall intensity and the concentration of precipitation, the blocking effect of different underlays on surface runoff decreased.…”

Section: Differences In the Impact Of Different Spatial Pattern Of Cypress Micro-topography And Rainfall Conditions On Peak Flowmentioning

confidence: 62%

Effects of Underlay on Hill-Slope Surface Runoff Process of Cupressus funebris Endl. Plantations in Southwestern China

Shi

2021

Forests

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Clarifying the impact of underlay (i.e., the combination of near-surface vegetation and surface micro-topography) on the surface runoff process would provide a significant theoretical basis for the adjustment of vegetation patterns and the control of soil erosion on steep slopes in mountainous areas of southwestern China. In the current study, the runoff process under different rainfall characteristics was observed based on 10 natural runoff plots, and the correlation between the spatial pattern of cypress (Cupressus funebris), micro-topography, and runoff characteristic parameters was tested using the Pearson correlation coefficient method. The coupling effects of the spatial pattern of cypress and micro-topography on surface runoff also were analyzed using the Response Surface Method (RSM). The results showed that (1) under the conditions of long-duration moderate rainfall or long-duration rainstorm, topographic relief, surface roughness, runoff path density, contagion index of cypress, and stand density of cypress were the main reasons for the difference in the peak flow of each runoff plot, while under the condition of the short-duration rainstorm, the factors previously mentioned were no longer the dominant factors; (2) under the conditions of long-duration heavy rainfall or long-duration rainstorm, the common laws reflected by the response of the peak flow to the composite index of the spatial pattern of cypress and micro-topography were that (1) when the composite index of the spatial pattern of cypress (V) was below 21 and the composite index of micro-topography (U) was below 10.5, the peak flow would not be significantly affected; (2) when U > 10.5, increasing the composite index of the spatial pattern of cypress within a certain range would promote peak flow; (3) when U < 7.5 and V > 18, the increase of V value could significantly reduce the peak flow, and on this basis, adjusting the V value to 41, the reduction rate of peak flow could reach 84%.

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“…In North America and the North Pacific, land and sea surface temperatures, precipitation, and storm tracks are determined mainly by atmospheric variability associated with the Pacific North American (PNA) pattern. The modern instrumental record indicates a recent trend towards a positive PNA phase, which has resulted in increased warming and snowpack loss in northwest North America (Liu et al 2017 ). This study used fifteen climate mode indices to increase the diversity.…”

Section: Methodsmentioning

confidence: 99%

Optimization algorithms as training approach with hybrid deep learning methods to develop an ultraviolet index forecasting model

Ahmed

Saha

et al. 2022

Stoch Environ Res Risk Assess

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The solar ultraviolet index (UVI) is a key public health indicator to mitigate the ultraviolet-exposure related diseases. This study aimed to develop and compare the performances of different hybridised deep learning approaches with a convolutional neural network and long short-term memory referred to as CLSTM to forecast the daily UVI of Perth station, Western Australia. A complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is incorporated coupled with four feature selection algorithms (i.e., genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), and differential evolution (DEV)) to understand the diverse combinations of the predictor variables acquired from three distinct datasets (i.e., satellite data, ground-based SILO data, and synoptic mode climate indices). The CEEMDAN-CLSTM model coupled with GA appeared to be an accurate forecasting system in capturing the UVI. Compared to the counterpart benchmark models, the results demonstrated the excellent forecasting capability (i.e., low error and high efficiency) of the recommended hybrid CEEMDAN-CLSTM model in apprehending the complex and non-linear relationships between predictor variables and the daily UVI. The study inference can considerably enhance real-time exposure advice for the public and help mitigate the potential for solar UV-exposure-related diseases such as melanoma.

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Estimation of Peak Flow Rates for Small Drainage Areas

Cited by 14 publications

References 12 publications

Modeling the Evolution of Aquatic Organisms in Dynamic River Basins

Modeling the Evolution of Aquatic Organisms in Dynamic River Basins

Effects of Underlay on Hill-Slope Surface Runoff Process of Cupressus funebris Endl. Plantations in Southwestern China

Optimization algorithms as training approach with hybrid deep learning methods to develop an ultraviolet index forecasting model

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