| 20 Nov 2024
The general formulation for runoff components estimation and attribution at mean annual time scale
Abstract. Estimating runoff components, including surface flow, baseflow and total runoff is essential for understanding precipitation partition and runoff generation and facilitating water resource management. However, a general framework to quantify and attribute runoff components is still lacking. Here, we propose a general formulation through observational data analysis and theoretical derivation based on the two-stage Ponce-Shetty model (named as the MPS model). The MPS model characterizes mean annual runoff components as a function of available water with one parameter. The model is applied over 662 catchments across China and the contiguous United States. Results demonstrate that the model well depicts the spatial variability of runoff components with R2 exceeding 0.81, 0.44 and 0.80 for fitting surface flow, baseflow and total runoff, respectively. The model effectively simulates multi-year runoff components with R2 exceeding 0.97, and the proportion of runoff components relative to precipitation with R2 exceeding 0.94. By using this conceptual model, we elucidate the responses of surface flow and baseflow to available water and environmental factors for the first time. The surface flow is jointly controlled by precipitation and environmental factors, while baseflow is mainly influenced by environmental factors in most catchments. The universal and concise MPS model offers a new perspective on the long-term catchment water balance, facilitating broader application in large-sample investigations without complex parameterizations and providing an efficient tool to explore future runoff variations and responses under changing climate.
Greetings. The manuscript entitled “The general formulation for runoff components estimation and attribution at mean annual time scale” with the issue of estimating the various flow components for water resources management purposes. The structure and goals are clear, and the results are consistent with data. This paper can certainly be published after some major adjustments, listed below. I limited the previous revision to the Introduction and Methodology part, I think these need to be fixed before further going down the publication way. These itemized improvements would make the work more scientifically sound and robust. These considerations come from my expertise as a hydrogeologist, so they will pertain to this sphere of competency. Furthermore, I recommend incorporating ‘recommended references’ and at least having a quick glimpse at ‘further reading’ for a more precise framing of the work. Best regards.
Recommended references:
Cheng et al., 2022. Evaluation of baseflow separation methods with real and synthetic streamflow data from a watershed. Journal of Hydrology, 613, Part A, 128279. https://doi.org/10.1016/j.jhydrol.2022.128279
Schiavo, M., 2023. The role of different sources of uncertainty on the stochastic quantification of subsurface discharges in heterogeneous aquifers. J. Hydrol. 617 (4), 128930. https://doi.org/10.1016/j.jhydrol.2022.128930
Further reading:
Cartwright, I., Gilfedder, B., and Hofmann, H.: Contrasts between estimates of baseflow help discern multiple sources of water contributing to rivers, Hydrol. Earth Syst. Sci., 18, 15–30, https://doi.org/10.5194/hess-18-15-2014, 2014.
D’Oria et al., 2018. Quantifying the impacts of climate change on water resources in northern Tuscany, Italy, using high-resolution regional projections. https://doi.org/10.1002/hyp.13378
Zuecco et al., 2019. Quantification of subsurface hydrologic connectivity in four headwater catchments using graph theory. https://doi.org/10.1016/j.scitotenv.2018.07.269