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Abstract. Prediction of mean annual runoff is of great interest but still poses achallenge in ungauged basins. The present work diagnoses the prediction inmean annual runoff affected by the uncertainty in estimated distribution ofsoil water storage capacity. Based on a distribution function, a waterbalance model for estimating mean annual runoff is developed, in which theeffects of climate variability and the distribution of soil water storagecapacity are explicitly represented. As such, the two parameters in themodel have explicit physical meanings, and relationships between theparameters and controlling factors on mean annual runoff are established.The estimated parameters from the existing data of watershed characteristicsare applied to 35 watersheds. The results showed that the model couldcapture 88.2 % of the actual mean annual runoff on average across thestudy watersheds, indicating that the proposed new water balance model ispromising for estimating mean annual runoff in ungauged watersheds. Theunderestimation of mean annual runoff is mainly caused by theunderestimation of the area percentage of low soil water storage capacitydue to neglecting the effect of land surface and bedrock topography. Higherspatial variability of soil water storage capacity estimated through theheight above the nearest drainage (HAND) and topographic wetness index (TWI)indicated that topography plays a crucial role in determining the actualsoil water storage capacity. The performance of mean annual runoffprediction in ungauged basins can be improved by employing better estimationof soil water storage capacity including the effects of soil, topography,and bedrock. It leads to better diagnosis of the data requirement forpredicting mean annual runoff in ungauged basins based on a newly developedprocess-based model finally.
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Stochastic in Space and Time: 1. Characterizing Orographic Gradients in Mean Runoff and Daily Runoff Variability
Abstract Mountain topography alters the phase, amount, and spatial distribution of precipitation. Past efforts focused on how orographic precipitation can alter spatial patterns in mean runoff, with less emphasis on how time‐varying runoff statistics may also vary with topography. Given the importance of the magnitude and frequency of runoff events to fluvial erosion, we evaluated whether orographic patterns in mean runoff and daily runoff variability can be constrained using the global WaterGAP3 water model data. Model runoff data are validated against observational data in the contiguous United States, showing agreement with mean runoff in all settings and daily runoff variability in settings where rainfall‐runoff predominates. In snowmelt‐influenced settings, runoff variability is overestimated by the water model data. Cognizant of these limitations, we use the water model data to develop relationships between mean runoff and daily runoff variability and how these are mediated by snowmelt fraction in mountain topography globally. A global analysis of topographic controls on hydroclimatic variables using a random forest model was ambiguous. Instead, relationships between topography and runoff parameters are better assessed at the mountain range scale. Rulesets linking topography to mean runoff and snowmelt fraction are developed for three mid‐latitude mountain landscapes—British Columbia, European Alps, and Greater Caucasus. Increasing topographic elevation and relief together leads to higher mean runoff and lower runoff variability due to the increasing contribution of snowmelt. The three sets of empirical relationships developed here serve as the basis for a suite of numerical experiments in our companion manuscript (Part 2, Forte & Rossi, 2024a,https://doi.org.10.1002/2023JF007327).
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- Award ID(s):
- 1822062
- PAR ID:
- 10499670
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Earth Surface
- Volume:
- 129
- Issue:
- 3
- ISSN:
- 2169-9003
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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