the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments
Abstract. The simultaneous incorporation of streamflow and evaporation data into sensitivity analysis and calibration approaches has a great potential to improve the representation of hydrologic processes in modelling frameworks. This work aims to investigate the capabilities of the Variable Infiltration Capacity (VIC) model in a large-sample application focused on the joint integration of streamflow and evaporation data for 189 headwater catchments located in Spain. The study has been articulated into three parts: (1) a regional sensitivity analysis for a total of 20 soil, routing and vegetation parameters to select the most important parameters conducive to an adequate representation of the streamflow and evaporation dynamics; (2) a two-fold calibration approach against daily streamflow and monthly evaporation data based on the previous parameter selection for VIC, and (3) an evaluation of model performance based on a benchmark comparison against a well-established hydrologic model for the Spanish domain and a cross-validation test using multiple meteorological datasets to assess the generalizability of the calibrated parameters. The regional sensitivity analysis revealed that only two vegetation parameters – namely, the leaf area index and the minimum stomatal resistance – were sufficient to improve the performance of VIC for evaporation. These parameters were added to the soil and routing parameter during the calibration stage. Results from the two calibration experiments suggested that, while the streamflow performance remained close in both cases, the evaporation performance was highly improved if the objectives for streamflow and evaporation were combined into a single composite function during optimization. The VIC model outperfomed the reference benchmark and the independent meteorological datasets yielded a slight to moderate loss in model performance depending on the calibration experiment considered. This investigation will help gain a better understanding of the hydrology of the Spanish catchments and will help prepare the ground for a fully gridded implementation of the VIC model in Spain.
- Preprint
(6727 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (extended)
-
RC1: 'Comment on hess-2024-57', Anonymous Referee #1, 28 Jun 2024
reply
This study investigates the capacity of the VIC model to integrate streamflow and evaporation data in a large-sample application, based on simulations for 189 headwater catchments in Spain. Utilizing multiple datasets to improve model performance is an important aspect of hydrological modeling research, making this paper potential for publication in HESS. However, some important issues are not discussed adequately, and there is room for improvement. Consequently, I recommend a major revision before publication. My major suggestions are as follows:
- Given the general imbalances in Q, E, and P, I question whether it is reasonable to use the evaporation dataset to evaluate the model directly. This brings another question: what is the most important signature provided by the evaporation data? Is it the evaporation amount or the temporal variation of evaporation? If the key information provided by the evaporation data is the amount, we can expect that the E simulation obtained by Q-only calibration will be good in the catchments with Q+E/P close to 1, and the NSEQ obtained by Q-E calibration will be lower than that from Q-only calibration (perhaps the authors can test whether the results indeed show this characteristic). Otherwise, if the key information provided by the evaporation data is the variation, conducting a bias correction on E data based on water balance before calibration makes more sense.
- An important role of adopting multiple datasets is to reduce equifinality, i.e., to reject some parameters that perform well in the simulation of only one objective. However, the authors didn’t address this issue, stopping at presenting good simulations for streamflow and evaporation. I encourage the authors to discuss the value of the evaporation dataset in reducing equifinality. A potential way to address this issue is to analyze the sensitivity of the integrated objective function to the parameters and compare it with the sensitivity of NSEQ.
Other minor and moderate issues:
-Data:
There are negative records at some stations, which can be attributed to the reservoir. So a question is whether the influence of the reservoir on streamflow is significant and whether the reservoir is simulated in the model. To my knowledge, some rivers significantly influenced by reservoirs have an extremely even interannual streamflow distribution, which is impossible to reproduce if the reservoir is not considered in the model.
-Methods:
The model performance was evaluated using NSE and its decomposition into γ, α, and β. However, to my understanding, γ, α, and β are the decomposition of KGE rather than NSE. NSE actually only quantifies the bias characteristic. I suggest the authors modify this expression. Additionally, please add the equations for these three metrics.
I am a little confused about the SST test. If I understand correctly, this seems to be the common practice in model calibration, i.e., to divide calibration and evaluation periods, with a warm-up period in each. Please correct me if I am wrong, but if this is indeed the common practice, I suggest not referring to it using a special term.
-Sensitivity Analysis:
The authors calculate the sensitivity of model performance to parameters and analyze the correlation between sensitivity and physiographic hydroclimatic characteristics. I think it would be interesting to discuss the mechanism behind this correlation, e.g., why NSEQ is more sensitive to rout1 in catchments with larger precipitation. This can also provide guidance on selecting sensitivity parameters in regions with different conditions. I encourage the authors to delve deeper into this analysis or discussion. Currently, this is only discussed by comparing with other studies, without addressing the underlying reasons.
-The Y-axis of Figure 7 is incorrect.
Citation: https://doi.org/10.5194/hess-2024-57-RC1
Data sets
A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments Patricio Yeste, Matilde García-Valdecasas Ojeda, Sonia R. Gámiz-Fortis, Yolanda Castro-Díez, Axel Bronstert, and María Jesús Esteban-Parra https://doi.org/10.5281/zenodo.10670292
Viewed
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
373 | 62 | 19 | 454 | 22 | 20 |
- HTML: 373
- PDF: 62
- XML: 19
- Total: 454
- BibTeX: 22
- EndNote: 20
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1