Diagnosis toward predicting mean annual runoff in ungauged basins

Gao, Yuan; Yao, Lili; Chang, Ni-Bin; Wang, Dingbao

doi:https://doi.org/10.5194/hess-25-945-2021

Articles | Volume 25, issue 2

https://doi.org/10.5194/hess-25-945-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-25-945-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 2

Research article

|

24 Feb 2021

Research article |

| 24 Feb 2021

Diagnosis toward predicting mean annual runoff in ungauged basins

Yuan Gao, Lili Yao, Ni-Bin Chang, and Dingbao Wang

Data sets

The US MOPEX data set J. Schaake, S. Z. Cong, and Q. Y. Duan https://hydrology.nws.noaa.gov/pub/gcip/mopex/US_Data/

Land cover Multi-Resolution Land Characteristics Consortium. National Land Cover Database (NLCD) https://www.mrlc.gov/data?f\%5B0\%5D=category\%3Aland\%20cover&f\%5B1\%5D=region\%3Aconus

Web Soil Survey Soil Survey Staff http://websoilsurvey.sc.egov.usda.gov/

Short summary

Mean annual runoff prediction is of great interest but still poses a challenge in ungauged basins. The purpose of this study is to diagnose the data requirement for predicting mean annual runoff in ungauged basins based on a water balance model, in which the effects of climate variability are explicitly represented. The performance of predicting mean annual runoff can be improved by employing better estimation of soil water storage capacity including the effects of soil, topography, and bedrock.