the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 Nov 2023
The global water resources and use model WaterGAP v2.2e: description and evaluation of modifications and new features
Abstract. Water – Global Assessment and Prognosis (WaterGAP) is a modelling approach for quantifying water resources and water use for all land areas of the Earth that has served science and society since 1996. In this paper, the refinements, new algorithms and new data of the most recent model version v2.2e are described, together with a thorough evaluation of simulated water use, streamflow and total water storage anomaly against observation data. WaterGAP v2.2e improves the handling of inland sinks and now excludes not only large but also small man-made reservoirs when simulating naturalized conditions. The reservoir and non-irrigation water use data were updated. In addition, the model was calibrated against an updated and extended dataset of streamflow observations at 1509 gauging stations. The model can now be started using pre-scribed water storages and other conditions, which facilitates data assimilation as well as near real-time monitoring and forecast simulations. For specific applications, the model can consider the output of a glacier model, approximate the effect of rising CO2 concentrations on evapotranspiration or calculate the water temperature in rivers. In the paper, the publicly available standard model output is described and caveats of the model version are provided alongside the description of the model setup in the ISIMIP3 framework.
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RC1: 'Comment on gmd-2023-213', Anonymous Referee #1, 18 Jan 2024
This paper describes recent updates to the WaterGAP global water resources model (v2.2e) and provides benchmarks against observations.
As with previous versions, the model is impressive and the analysis is comprehensive. However, the improvements described in this paper, and their impacts on performance, appear fairly minor. There should be space in GMD for efforts that focus primarily on software and data updates rather than scientific ones, but revisions are needed.
1. The improvements include the treatment of reservoirs, updates to datasets for reservoirs, non-irrigation water use, and streamflow stations for calibration. Updates to model capabilities include PET representations, glaciers, and water temperature, though the latter has already been described in a previous publication. While all of these changes are justified, and no doubt an intensive effort, their impact on the modeled global water balance and distribution of NSE presented in the results does not seem to be a large change from previous versions of WaterGAP. Section 7.5.1 reports a nearly identical performance to the previous version. The scientific contribution of the new model capabilities should be more strongly justified.
2. A main focus of the updates is the reservoir model. From the previous paper (2021), the release policy is assumed to follow Hanasaki (2006) and Döll (2009), which distinguishes between irrigation and non-irrigation reservoirs. This is a simplified rule that can be applied globally, but is often inaccurate at the level of individual reservoirs. The current paper does not investigate changes to this assumption, but removes a previous limitation about the maximum storage capacity for flood prevention. The accuracy of reservoir storage shown in Supplemental Figures S1, S2 from version 2.2c leaves much room for improvement, and it is not clear that removing the storage capacity threshold will fix this. The updated results after the change are not shown.
3. More information should be included about the potential scale mismatch between reservoir outflow (a point) mapped to a larger grid cell. The same goes for the stream gage data used for calibration (Step 3 in Section 2.5.2). It is possible this information is included in previous papers, but it would help to discuss here the potential impacts of this scale mismatch.
4. The calibration process attempts to find an optimal value of gamma, the runoff coefficient, to align the modeled mean annual streamflow within either 1% or 10% of the observed. Failing this, additional correction factors are applied to the runoff. While I can appreciate the difficulty of calibrating a global model, this calibration setup would have several problems for a basin-scale study. The gamma parameter can compensate for any mass balance error without a physical relationship to the runoff curve shown in Figure 3 of the 2021 paper. The additional correction factors only worsen this problem, and many regions of the model rely on these (Fig 4 of the current paper). By calibrating to mean annual data, monthly dynamics could be lost, though the efficiency metrics reported in Figure 7 seem to be doing well at many stations.
This calibration approach may be standard for global models. But at the basin scale, we could expect to see more diagnostics applied to investigate whether the results are physically based, or to analyze how much of the calibration uncertainty comes from each component of the mass balance. The calibration is more of a bias correction that is not able to distinguish between the many degrees of freedom in the model.
Citation: https://doi.org/10.5194/gmd-2023-213-RC1 -
RC2: 'Comment on gmd-2023-213', Anonymous Referee #2, 09 Apr 2024
The manuscript presents the most recent version of the WaterGAP model (v2.2e). Refinements, new algorithms, and updated data for calibration are presented. The manuscript is well-written, and the overall evaluation section is comprehensive and competently performed. My primary concern is the link between the extensive software modifications and data increment and the overarching scientific goal of assessing global and regional water resources. Even though the modifications, data additions, and model evaluation are comprehensively described, their scientific significance is not sufficiently justified in the context of water resources assessment. In this sense, I consider revisions (mostly related to rephrasing and further discussions) to be needed before publication. Please see below for my specific comments:
Comment 1: The authors cite three papers to introduce the WaterGAP model. However, definitions necessary to understand the implemented modifications are missing. As a reader, I spent extra time reviewing the referenced papers to understand the main changes. Examples of these definitions include "naturalized mode" (L45), "standard runs" (L46-L47), and "local lakes algorithm" (L46), to name some just in section 2.1. Introducing the model's main features might be better so readers don't need to jump between papers (see related comment 2). As for GMD guidelines, for model description papers, it should be possible for independent scientists to build a model that, while not necessarily numerically identical, will produce equivalent results. In the current manuscript stage, the last is very hard to achieve.
Comment 2: The model modifications are widely discussed in Section 2. However, as a reader, it is hard to visualize the model as a whole and identify the modified components. A scheme presenting the model's overall structure with the modified stages highlighted might be helpful.
Comment 3: Following an analysis of the simulated monthly time series of reservoir water storage to observations for 16 reservoirs in the United States, the authors decided to drop off the 85% maximum storage capacity assumption implemented in version 2.2d (Müller Schmied et al. 2021) (due to model underestimation in 11 of the analyzed cases). This implies that the decision to remove the original assumption is based on a local-based analysis where ~30% of the model results did not show underestimation issues. Thus, I am failing to see the reason for extrapolating these local results to the global implementation of the model. Furthermore, Figures S1 and S2 might indicate more pressing issues related to the model's accuracy (e.g., seasonality) that might not be improved by simply removing the assumption.
Comment 4: Section 5 presents the effects of the model modifications on multiple areas and the impact of differential forcing. Overall, the effect of the implemented changes seems to be minor. However, I found it challenging to visualize the minor differences because most of the baseline results (from v2.2d) are presented in Supplementary Material. I would recommend summarizing the results as differences of v2.2e from the original implementation rather than presenting each version independently. Furthermore, this issue connects to my general comment about the lack of scientific justification for the model modifications. If model parametrizations and data changes lead to almost negligible changes, the scientific rationale for implementing them should be discussed further.
Citation: https://doi.org/10.5194/gmd-2023-213-RC2
Data sets
The global water resources and use model WaterGAP v2.2d - model output driven by gswp3-w5e5 and historical setup of direct human impacts H. Müller Schmied, T. Trautmann, S. Ackermann, D. Cáceres, M. Flörke, H. Gerdener, E. Kynast, T. A. Peiris, L. Schiebener, M. Schumacher, and P. Döll https://doi.org/10.25716/GUDE.1PQV-6477
The global water resources and use model WaterGAP v2.2d - model output driven by gswp3-w5e5 and neglecting direct human impacts H. Müller Schmied, T. Trautmann, S. Ackermann, D. Cáceres, M. Flörke, H. Gerdener, E. Kynast, T. A. Peiris, L. Schiebener, M. Schumacher, and P. Döll https://doi.org/10.25716/GUDE.0G5P-XSKK
The global water resources and use model WaterGAP v2.2e - model output driven by gswp3-w5e5 and historical setup of direct human impacts H. Müller Schmied, T. Trautmann, S. Ackermann, D. Cáceres, M. Flörke, H. Gerdener, E. Kynast, T. A. Peiris, L. Schiebener, M. Schumacher, and P. Döll https://doi.org/10.25716/GUDE.0TNY-KJPG
The global water resources and use model WaterGAP v2.2e - model output driven by gswp3-w5e5 and neglecting direct human impacts H. Müller Schmied, T. Trautmann, S. Ackermann, D. Cáceres, M. Flörke, H. Gerdener, E. Kynast, T. A. Peiris, L. Schiebener, M. Schumacher, and P. Döll https://doi.org/10.25716/GUDE.0PZW-2TVK
The global water resources and use model WaterGAP v2.2e - model output driven by 20crv3-w5e5 and historical setup of direct human impacts H. Müller Schmied, T. Trautmann, S. Ackermann, D. Cáceres, M. Flörke, H. Gerdener, E. Kynast, T. A. Peiris, L. Schiebener, M. Schumacher, and P. Döll https://doi.org/10.25716/GUDE.0K1D-ZTH5
The global water resources and use model WaterGAP v2.2e - model output driven by 20crv3-w5e5 and neglecting direct human impacts H. Müller Schmied, T. Trautmann, S. Ackermann, D. Cáceres, M. Flörke, H. Gerdener, E. Kynast, T. A. Peiris, L. Schiebener, M. Schumacher, and P. Döll https://doi.org/10.25716/GUDE.1C8E-77CV
The global water resources and use model WaterGAP v2.2e - model output driven by 20crv3-era5 and historical setup of direct human impacts H. Müller Schmied, T. Trautmann, S. Ackermann, D. Cáceres, M. Flörke, H. Gerdener, E. Kynast, T. A. Peiris, L. Schiebener, M. Schumacher, and P. Döll https://doi.org/10.25716/GUDE.1TA7-3F5W
The global water resources and use model WaterGAP v2.2e - model output driven by 20crv3-era5 and neglecting direct human impacts H. Müller Schmied, T. Trautmann, S. Ackermann, D. Cáceres, M. Flörke, H. Gerdener, E. Kynast, T. A. Peiris, L. Schiebener, M. Schumacher, and P. Döll https://doi.org/10.25716/GUDE.142E-65P$
The global water resources and use model WaterGAP v2.2e - model output driven by gswp3-era5 and historical setup of direct human impacts H. Müller Schmied, T. Trautmann, S. Ackermann, D. Cáceres, M. Flörke, H. Gerdener, E. Kynast, T. A. Peiris, L. Schiebener, M. Schumacher, and P. Döll https://doi.org/10.25716/GUDE.1Q7K-2GWV
The global water resources and use model WaterGAP v2.2e - model output driven by gswp3-era5 and neglecting direct human impacts H. Müller Schmied, T. Trautmann, S. Ackermann, D. Cáceres, M. Flörke, H. Gerdener, E. Kynast, T. A. Peiris, L. Schiebener, M. Schumacher, and P. Döll https://doi.org/10.25716/GUDE.0WKZ-74YD
Model code and software
WaterGAP v2.2e H. Müller Schmied, T. Trautmann, S. Ackermann, D. Cáceres, M. Flörke, H. Gerdener, E. Kynast, T. A. Peiris, L. Schiebener, M. Schumacher, and P. Döll https://doi.org/10.5281/ZENODO.10026943
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