the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Use of satellite remote sensing to validate reservoir operations in global hydrological models: a case study from the CONUS
Kedar Otta
Hannes Müller Schmied
Simon N. Gosling
Abstract. Although river discharge simulations from global hydrological models have undergone extensive validation, there has been less validation of reservoir operations, primarily because of limited observational data. However, recent advancements in satellite remote sensing technology have facilitated the collection of valuable data regarding water surface area and elevation, thereby providing the ability to validate reservoir storage. In this study, we sought to establish a methodology for validation and intercomparison of reservoir storage within global hydrological model simulations using satellite-derived data. Accordingly, we chose two satellite-derived reservoir operation products, DAHITI and GRSAD, to create monthly time series storage data for seven reservoirs in the contiguous United States (CONUS) , with access to long-term ground truth data (the total catchment area accounts for about 9 % of CONUS). We assessed two global hydrological models that participated in the Inter Sectoral Model Intercomparison Project (ISIMIP) Phase 3 project, H08 and WaterGAP2, with three distinct forcing datasets: GSWP3-W5E5 (GW), CR20v3-W5E5 (CW), and CR20v3-ERA5 (CE). The results indicated that WaterGAP2 generally outperforms H08; the CW forcing dataset demonstrated superior results compared with GW and CE; the DAHITI showed better consistency with ground observations than GRSAD if temporal coverage is sufficient. Overall, our study emphasizes the potential uses of satellite remote sensing data in reservoir operations validation and underscores the importance of normalization and decomposition techniques for improved validation efficacy. The results highlight the relative performances of different hydrological models and forcing datasets, yielding insights concerning future advancements in reservoir simulation and operational studies.
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Kedar Otta et al.
Status: open (until 07 Nov 2023)
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RC1: 'Comment on hess-2023-215', Anonymous Referee #1, 12 Sep 2023
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This is an interesting study. The authors are exploring the use of satellite remote sensing to validate reservoir operations in global hydrological models (GHMs). I think if we take the perspective of the GHMs and their need to be improved as a modeling framework by moving away from assumptions on reservoir operations and parameterizations (which GHMs have a lot of), then this is a valuable study. However, if this study is just exploring if we can use satellite data to track reservoirs, I don’t think this study is innovative or adds much to the body of knowledge. That topic has been addressed and most of the relevant questions on that that the authors pose in this study have been answered much more vigorously and in more detail over the last 10 years. So the first question posed by the authors is really redundant here (we know the answer): Can satellite-based storage estimation data serve as a surrogate for ground truth data?
The authors have used too simplistic methods of using only altimeter from DAHITI (with limited sampling in space and time) and Landsat (optical) based reservoir area dataset from GRSADs. Today when we have a fleet of satellite sensors (optical, SAR, altimeters) that collectively provide more robust, high frequency and more accurate tracking of reservoirs in terms of surface area or elevation to generate storage change. So I think the authors should reorganize the paper and reduce section 3.1 and focus more on 3.2 as the key focus of the paper where there is a lot to learn for the GHM community trying to improve representation of reservoirs on global models (which also fits perfectly into the theme of the special issue of HESS).
Let me share some key references the authors miss in capturing the state of the art of satellite-based reservoir tracking:
Bonnema, M., C.H. David, R.P. d. M. Frasson, C. Oaida, & S. -H. Yun. (2022). The global surface area variations of lakes and reservoirs as seen from satellite remote sensing. Geophysical Research Letters, vol. 49, e2022GL098987. https://doi.org/10.1029/2022GL098987
Cooley, S.W., J.C. Ryan and L.C. Smith (2021), Human alteration of global water storage variability, Nature, vol. 591, pages 78–81
Das, P., F. Hossain, S. Khan, N. K. Biswas, H. Lee, T. Piman, C. Meechaiya, U. Ghimire, K. Hosen (2022) Reservoir Assessment Tool 2.0: Stakeholder driven Improvements to Satellite Remote Sensing based Reservoir Monitoring, Environmental Modeling and Software, Vol. 157.
Biswas, N. and F. Hossain (2022) A Multi-decadal Analysis of Reservoir Storage Change in Developing Regions, Journal of Hydrometeorology, Vol. 21(1), pp 71-85.
Biswas, N., F. Hossain, M. Bonnema, H. Lee, F. Chishtie (2021). Towards a Global Reservoir Assessment Tool for Predicting Hydrologic Impacts and Operating Patterns of Existing and Planned Reservoirs, Environmental Modeling and Software, Vol. 140.
Zhou, T., Nijssen, B., Gao, H., & Lettenmaier, D. P. (2016). The Contribution of Reservoirs to Global Land Surface Water Storage Variations. Journal of Hydrometeorology, vol 17(1), pp. 309–325. https://doi.org/10.1175/JHM-D-15-0002.1
I urge the authors to explore the above papers very carefully to reframe their introduction and research questions. It will become clear from these papers that just relying on altimeter data on reservoir levels from DAHITI and a corrected Landsat only reservoir area dataset really do not reflect the true state of the art on what we can do today in capturing reservoir dynamics at sub weekly time scales using multiple sensors, wavelengths and innovative methods. The authors will realize that they are answering that question with the most primitive tools and that too by not really defining what the end goal is to be a useful ‘surrogate’ for GHM model development. Monthly reservoir tracking is déjà vu and not hard to do with or without satellites as at that scale, most reservoirs lag the prevailing hydrology (unless they store multiple years of annual runoff). At monthly or longer time scales, GHMs can work reasonably well with parameterizations based on capacity, embedded rule curves and objective functions. However, today we really do not need to do that as we have 40 years of satellite data with the last 10 years of that being very high frequency, multi-sensor to help us track at a granular level how reservoirs have been operated (to help us model them in GHMs).
My request to the authors therefore is to get rid of question 1, tone down or revise substantially section 3.1 and focus most of the paper on section 3.2 where the key contribution lies. Authors should explore using additional datasets -there is now Hydroweb (multiple altimeters), use Sentinel series from 2016 and even MODIS (for larger surface area reservoirs). For the selected reservoirs, the authors have one advantage that cloud cover is minimum. They also do not need to rely on SRTM DEM as that can be very erroneous and over the US, SRTM captured only the ‘free bathymetry’ above the water level that existed during February 2000 when the Shuttle flew. Perhaps authors could test the validity of SRTM DEM with topo maps and published bathymetry and area-elevation curves (consider checking the RESSED database of USGS). The authors use DAHITI only for elevation but as they point out, multiple sensors can be used to generate surface area which apparently DAHITI has done but is not available in the authors’ reservoirs of interest. So why not generate those reservoir areas from multiple sensors and indices by the authors themselves?
There is also the SWOT mission the authors can talk about in a few sentences in the conclusion section (see http://swot.jpl.nasa.gov). The whole premise of SWOT is to generate simultaneously area and elevation so that we don’t have to jury rig the observation system to derive storage change. There is now plenty of ‘help’ resources to help the community build literacy on SWOT (just click on ‘applications’ of the SWOT website). It is also no surprise that DAHITI (elevation) is generating better results for GHMs in section 3.2 and the GRSADs - this is something we keep seeing all the time as all reservoirs experience storage change via elevation change at levels detectable by altimeters. The same can’t be said about surface area changes unless the reservoir is very large and does not have a complex dendritic shape. A lot depends on the shape, shoreline, climate, surrounding terrain of the reservoirs in how well or poorly a specific satellite data will work in tracking surface area or volume change. That is why I reiterate that the authors should focus more on the GHM model validation part rather than the satellite data assessment part in Section 3.1
I noticed many typos that I tried to note down. One is:
Line 144. I don’t think it’s year 286 to 2020 (although we do have many reservoirs built that early still functional in many places)
Citation: https://doi.org/10.5194/hess-2023-215-RC1
Kedar Otta et al.
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Use of satellite remote sensing to validate reservoir operations in global hydrological models: a case study from the CONUS Kedar Otta, Hannes Müller Schmied, Simon N. Gosling, and Naota Hanasaki https://doi.org/10.5281/zenodo.8291850
Kedar Otta et al.
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