Simulation of Runoff Hydrograph on Soil Surfaces with Different Microtopography Using a Travel Time Method at the Plot Scale

Abstract

In this study, a simple travel time-based runoff model was proposed to simulate a runoff hydrograph on soil surfaces with different microtopographies. Three main parameters, i.e., rainfall intensity (I), mean flow velocity (v m) and ponding time of depression (t p), were inputted into this model. The soil surface was divided into numerous grid cells, and the flow length of each grid cell (l i) was then calculated from a digital elevation model (DEM). The flow velocity in each grid cell (v i) was derived from the upstream flow accumulation area using v m. The total flow travel time through each grid cell to the surface outlet was the sum of the sum of flow travel times along the flow path (i.e., the sum of l i/v i) and t p. The runoff rate at the slope outlet for each respective travel time was estimated by finding the sum of the rain rate from all contributing cells for all time intervals. The results show positive agreement between the measured and predicted runoff hydrographs.

Surface microrelief changes affect the soil and water conservation benefits of rainwater harvesting tillage operation during rainfall events

“…These differences in summits and depressions lead to different microrelief responses to rainfall erosion processes in both space and time. Zhao and Wu () simulated the runoff hydrograph on the surface with mounds and depressions using a travel‐time method, and their results suggested that microrelief has a significant effect on the surface flow path and hence affects the total runoff and sediment yields.…”

Delineating wetland catchments and modeling hydrologic connectivity using lidar data and aerial imagery

“…Physically based hydrological models (e.g., Brunner and Simmons, 2012;Ameli and Creed, 2017) have not yet been integrated into our framework. However, fill-and-spill is a complex and spatially distributed hydrological process highly affected by many factors, such as surface topography, surface roughness, soil infiltration, soil properties, depression storage, precipitation, evapotranspiration, snowmelt runoff, and groundwater exchange (Tromp-van Meerveld and McDonnell, 2006a, b;Zhao and Wu, 2015;Evenson et al, 2016;Hayashi et al, 2016). Nevertheless, our study presents the first attempt to use lidar data for deriving nested wetland catchments and simulating flow paths in the broadscale Pipestem subbasin in the PPR.…”

“…In general, cells with high flow accumulation values correspond to areas of concentrated flow (e.g. stream channels), while cells with a flow accumulation value of zero correspond to the pattern of ridges (Zhu, 2016). Therefore, flow accumulation provides a basis for identifying ridgelines and delineating catchment boundaries.…”

Response to Anonymous Referee #1

“…Physically based hydrological models (e.g., Brunner and Simmons, 2012;Ameli and Creed, 2017) have not yet been integrated into our framework. However, fill-and-spill is a complex and spatially distributed hydrological process highly affected by many factors, such as surface topography, surface roughness, soil infiltration, soil properties, depression storage, precipitation, evapotranspiration, snowmelt runoff, and groundwater exchange (Tromp-van Meerveld and McDonnell, 2006a, b;Evenson et al, 2015;Zhao and Wu, 2015;Evenson et al, 2016;Hayashi et al, 2016). Nevertheless, our study presents the first attempt to use lidar data for deriving nested wetland catchments and simulating flow paths in the broad-scale Pipestem subbasin in the PPR.…”