Preprints
https://doi.org/10.5194/hess-2016-129
https://doi.org/10.5194/hess-2016-129
23 Jun 2016
 | 23 Jun 2016
Status: this discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion.

Modelling stream flow with a discrete rainfall–runoff model and 37 GHz PDBT microwave observations: the Xiangjiang River basin case study

Haolu Shang, Massimo Menenti, and Li Jia

Abstract. A discrete rainfall–runoff model has been developed, which uses retrievals of Water Saturated Soil (WSS) and inundation area from 37 GHz microwave observations. The model was implemented at three levels of increasing complexity using field-measured ground water table, WSS and inundated area, and precipitation data. The three levels, defined by the key-variables are: (1) precipitation and base flow; (2) overland flow, infiltrated flow and base flow; (3) overland flow, potential subsurface flow and base flow. The base flow is estimated from observed ground water table depth, while overland and infiltrated flows are estimated from precipitation and the WSS and inundated area. A linear scaling method is developed to estimate the potential subsurface flow. The three model implementations are calibrated with the gauge measurements of 10-day average river discharge in 2002 and 2005 respectively at Changsha station, downstream of Xiangjiang River basin, China. The discrete rainfall–runoff model assumes that specific runoff is determined by antecedent precipitations over a variable period of time. This duration is a model parameter varying between 10 and 150 days. The performance of the discrete rainfall–runoff model increased with the duration of antecedent precipitation for all three implementations in both years. With a duration of 150 days, the model reaches its best performance: Nash–Sutcliffe Efficiency, NSE, for the 1st implementation was ≥ 0.90 with relative RMSE ≤ 22 %; NSE ≈ 0.99 with relative RMSE ≤ 5 % for the 2nd implementation, and NSE ≥ 0.99 with relative RMSE ≤ 4 % for the 3rd one. These good performances prove that the retrievals of WSS and inundated area clearly improve model accuracy, thus justifying the choices of parameters and the method to estimate the potential subsurface flow. The set of parameters driving each implementation is an indication of dominant hydrological processes, particularly water storage, in determining the catchment response to rainfall. Significant differences in the annual water yield have been observed across the three implementations. The relative RMSE in each season demonstrates the possible recharge period of the ground water in Xiangjiang River basin.

Haolu Shang, Massimo Menenti, and Li Jia
 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Haolu Shang, Massimo Menenti, and Li Jia
Haolu Shang, Massimo Menenti, and Li Jia

Viewed

Total article views: 1,381 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
918 404 59 1,381 85 92
  • HTML: 918
  • PDF: 404
  • XML: 59
  • Total: 1,381
  • BibTeX: 85
  • EndNote: 92
Views and downloads (calculated since 23 Jun 2016)
Cumulative views and downloads (calculated since 23 Jun 2016)
Latest update: 28 Mar 2024
Download
Short summary
The key point of the discrete rainfall–runoff model is to consider the temporal differences in the redisctribution of precipitation in a catchment through the weights and the duration of antecedent precipiation. The wetness conditions at the upper and lower boundaries of soil layer are the key parameters to describe regional moisture condition. The interannual variations in model weights indicates the different catchment response between dry and wet years.