the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The benefits and trade-offs of multi-variable calibration of WGHM in the Ganges and Brahmaputra basins
Abstract. While global hydrological models (GHMs) are affected by large uncertainties regarding model structure, forcing and calibration data, and parameters, observations of model output variables are rarely used to calibrate the model. Pareto dominance-based multi-objective calibration, often referred to as Pareto-Optimal Calibration (POC), may serve to estimate model parameter sets and analyse trade-offs among different objectives during calibration. Within a POC framework, we determined optimal parameter sets for the WaterGAP Global Hydrology Model (WGHM) in the two largest basins of the Indian subcontinent—the Ganges and the Brahmaputra, collectively supporting nearly 580 million inhabitants. The selected model parameters, determined through a multi-variable multi-signature sensitivity analysis, were estimated using up to four types of observations: in-situ streamflow (Q), GRACE and GRACE Follow-On total water storage anomalies (TWSA), LandFlux evapotranspiration (ET), and surface water storage anomalies (SWSA) derived from multi-satellite observations. While our sensitivity analysis assured that the model parameters that are most influential for the four variables were identified in a transparent and comprehensive way, the rather large number of calibration parameters, 10 for the Ganges and 16 for the Brahmaputra, had a negative impact on parameter identifiability during the calibration process. Calibration against observed Q resulted to be crucial for reasonable streamflow simulations, while additional calibration against TWSA was crucial for the Ganges basin and helpful for the Brahmaputra basin to obtain a reasonable simulation of both Q and T. Calibrating also against the other two observation types enhanced the overall model performance and enabled a more accurate representation of the water balance. We identified several trade-offs among the calibration objectives, with the nature of these trade-offs closely tied to the physiographic and hydrologic characteristics of the study basins. The trade-offs were particularly pronounced in the Ganges basin, in particular between Q and SWSA, as well as between Q and ET. When considering the observational uncertainty of the calibration data, model performance decreases in most cases. This indicates an overfitting to the singular observation time series by the calibration algorithm. We therefore propose a transparent algorithm to identify high-performing Pareto solutions under consideration of observational uncertainties of the calibration data. Recognizing these uncertainties, we anticipate that actual model performance may be lower in roughly 90 % of cases.
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RC1: 'Comment on egusphere-2023-2324', Anonymous Referee #1, 02 Feb 2024
I enjoyed reading the manuscript.
My main concerns are; 1) the temporal only calibration of a distributed hydrologic model and 2) use of coarse meteo inputs while era5-land offers 0.1 inputs.
Other comments:
Section 3.3: More details on the SA should be provided. Morris is an elaborated SA method as compared to the one at a time local methods so that much more runs are required in Morris. How many runs were required for a 24 parameter model (Line 263).
Can Morris identify effects of parameter interactions on the sensitivities like in Sobols’ method? Why did you choose Morris instead of looking at Jacobian matrix in simple terms?
L402: 5 times run of first year? I couldn’t understand how? 1985-89 spin up run and one time should be enough to reach equilibrium, shouldn’t be?
Eq2: Why only NSE is used as performance metric? Why only temporal calibration is pursued for a distributed hydrologic model which can produce flux maps? How did you deal with unit differences from satellite AET (watt/m2) and model outputs at mm/day? The same may apply to Grace anomaly values and recharge output of the model.
NSE is a bias sensitive metric and it might be necessary to use bias insensitive spatial pattern metrics in the calibration.
Introduction misses recent works on satellite based evaluation and calibration of the distributed hydrologic models using actual ET. Also, trade offs in multi objective Pareto calibration of hydrologic models have been studied in the literature. Please update your literature review with studies from 2018 to Jan 2024 from top journals (HESS and WRR). Compare your results with them in the discussions.
Conclusion: Very different than conventional conclusion sections. Detailed results (numbers) should not be given here but just the conclusions drawn from the results should be provided in bullets. It is lengthy and not easy to follow. Research Questions are repeated and probably not necessary.
The reader needs the main messages from the study and not the repetition of the results.
Citation: https://doi.org/10.5194/egusphere-2023-2324-RC1 -
RC2: 'Comment on egusphere-2023-2324', Anonymous Referee #2, 09 Feb 2024
This study presents a very thorough analysis of multi-variable calibrations considering different variables for a global hydrological model. The model was applied on two exemplary basins using in-situ and multi-satellite data. The authors did an excellent job in considering a variety of aspects that are important for modelling (e.g., required number of model runs, Pareto frontier, parameter sensitivity/importance). I also liked the selection of the authors of the data that was considered for the multi-variable calibration scenarios.
I am recommending minor revision because even though the modelling analyses seem thorough, the presentation for the reader could be improved. As a reader, it was rather difficult to extract the main aspects of this research as the text was not very concise. Additionally, a slightly unconventional structure regarding the results and discussion, and conclusion was used. I recommend shortening the manuscript or summarizing several points into one point to make it easier for future readers to follow it and to get the main points of the study. This is a very general recommendation, so I have picked out examples for you to explain what I mean.
- Make your sentences more concise:
Lines 554-566 could for example be shortened into something like this: “Several parameters influence most or all response variables across various signatures. However, certain parameters affect only one or two signatures of the response variables. For instance, the Runoff Coefficient (SL-RC) significantly influences monthly means (MM) of ET in the Ganges basin and MTS of streamflow. Similarly, the snow melt temperature (SN-MT) is important for some cases in snow-dominated catchments in the Brahmaputra basin. These parameters may also affect other response variables and signatures to some extent but do not meet the defined threshold for calibration selection (Figure 4).” - You created ten (lovely) figures and twelve tables. This is nice in the sense of replicability. However, in my opinion, this is too much to present in the main text. Please consider moving some of the tables that are not essential for the main outcomes of this study to the supplementary. Table 4 could be deleted entirely as it does not contain additional information to Figure 4.
- Regarding the structure of the paper: For me the content of the conclusion chapter would be (the main) part of the discussion. Overall, the discussion part had become a bit short by being combined with the results. I recommend renaming the current conclusion chapter and writing a more common conclusion chapter. This will help the reader a lot to understand what you did. Also shorten the current conclusion chapter and do not present the results again.
Minor comments or examples:
1 Introduction:
- You switch between the terms multi-variable, multi-signature, and multi-objective throughout the manuscript. Please clearly define them in the introduction and use the terms consistently throughout the manuscript. E.g., line 595: “multi-objective” and in the title of the manuscript: “multi-variable”. I assume the same is meant in both cases.
- Line 34: “T” not explained anywhere.
- Lines 53-53: local or regional hydrological models
- Lines 62-64: Abbreviations are placed inconsistently. Maybe do: For example, the Water - Global Assessment and Prognosis (WaterGAP) Global Hyrdological Model (WGHM, Müller...)..
- Lines 70-75: Add a reference
- Lines 88-91: Sentence is a bit difficult to follow. Please rephrase.
- Lines: 138-144: Maybe mention earlier!?
- Line 175: First time using the term “signature”. Mention and explain it before.
- Lines 191-193: Delete.
2 Study area:
- Table 1: Over which period are the means calculated (e.g., mean summer temperatures)?
- How did you decide on the two basins? What are the differences between the two basins? What was the reasoning for not choosing two very different basins (regarding climate, geology, water abstractions etc.) to see the influence of these characteristics on the modelling scenarios?
Highlight these differences or similarities between the basins also in the interpretation and comparison of the modeling results of the two basins. Why were different parameters selected between the different basins (Figure 4)? E.g., lines 693-696: Why do you think that is the case? Is there any explanation for that?
3 Data and methods
- Why not present available data in the chapter study area?
- Line 343: Title of chapter 3.2.5 Water balance closure is a bit confusing as it’s a subchapter of 3.3 observations. Water balance closure is not an observation. Maybe call it storage change (which is also not exactly an observation, but sill might fit better)?
- Lines 436-448: Move them after line 465.
- Line 473: One comma too many
4 Results and discussion
- Lines 554-566: Explanation of parameters could also be in the method section when parameters are being presented or is this meant as a discussion?
- Line 583: Maybe add a short sentence why P-PM was added later or refer to the method section (lines 263-268). Why is EP-NM not added?
- Line 648: Livneh and Lettenmaier (2012)
- Table 6 and 7: Are those the NSE values of the calibration? I am not sure if I got that correctly, but due to data scarcity you could not calculate the NSE for all variables for the validation period (only for Q and TWSA). Is that correct?
- Figure 6 and Figure 7 are a bit small.
- The authors chose to have a combined results and discussion chapter. Sometimes an explanation as to why the results turned out the way they did was missing.
- Lines 864-865: From the following text of that paragraph, I still did not understand why the Ganges and Brahmaputra basins had different identifiability regarding their parameter comparison. Could you please explain that more clearly?
- Could you add an outlook at the end of this section considering the following points: What do you expect for other basins? Could this method be applied to other basins? What would be the challenges?
5 Conclusion
- Lines 1031-1032: repetitive
Citation: https://doi.org/10.5194/egusphere-2023-2324-RC2 - Make your sentences more concise:
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