Since early work on the assessment of global, continental and regional-scale water balance components, many studies use different approaches including global models, as well as data-driven approaches that ingest in-situ or remotely sensed observations or combination of these. They attempted to quantify water fluxes (e.g. evapotranspiration, runoff/discharge, groundwater recharge) and water storages on the terrestrial part of the Earth, either as total estimates (e.g. from GRACE satellites) or in separate compartments (e.g. water bodies, snow, soil, groundwater). In addition, more and more attention is given to uncertainties that stem from forcing datasets, model structure, parameters and combinations of these. Current estimates in literature show that flux and storage calculations differ considerably due to the methodology and datasets used such that a robust assessment of global, continental and regional water balance components is challenging.
This session is seeking for contributions that are focusing on the:
i. past/future assessment of water balance components (fluxes and storages) such as precipitation, river discharge to the oceans (and/or inland sinks), evapotranspiration, groundwater recharge, water use, changes in terrestrial water storage or individual components at global, continental and regional scales,
ii. application of innovative explorative approaches undertaking such assessments – through better use of advanced data driven, statistical approaches and approaches to assimilate (or accommodate) remote sensing datasets for improved estimation of terrestrial water storages/fluxes,
iii. analysis of different sources of uncertainties in estimated water balance components,
iv. examination and attribution of systematic differences in storages/flux estimates between different methodologies, and/or
v. applications/consequences of those findings such as sea level rise and water scarcity.
We encourage submissions using different methodological approaches. Contributions could focus on any of the water balance components or in an integrative manner with focus on global, continental or regional scale applications. Assessments of uncertainty in past/future estimates of water balance components and their implications are highly welcome.
vPICO presentations: Mon, 26 Apr
Earth observations have many missing values. Their complex patterns of missingness can be a significant hurdle for studying Earth system dynamics and climate change impacts. To overcome this issue, missing values are regularly imputed, i.e. infilled, using techniques such as interpolation. However, the common practice to do this for each variable separately can negatively affect the covariance between different data products, resulting in biased estimates. Moreover, relying solely on interpolation for infilling missing values makes only inefficient use of information that may be available from other variables at the same location in space and time.
Here we propose a modular gap-filling algorithm that exploits the multivariate nature of Earth system observations and builds upon the notion that if a value is missing, it is likely that some other variables will be observed at the same location and time and their relationship can be learned. To this end, the algorithm expands upon simple interpolation by additionally applying a statistical imputation method that is designed to account for covariance across variables.
The algorithm is tested using gap-free reanalysis data of relevant variables to land surface processes: ground temperature, precipitation, terrestrial water storage and soil moisture. These variables were masked to match missingness patterns of remote sensing observations. Subsequently, the gap fill estimates can then be compared to the original reanalysis values to assess the merit of the gap fill.
Overall, estimates of the proposed algorithm have lower bias and higher correlation compared to simple interpolation. Furthermore, we demonstrate that the multivariate core of the algorithm improves the physical consistency across the considered variables. In case studies focussing on large-scale droughts, extreme values are correctly reconstructed even in cases of high fraction of missing values. The algorithm can thus be used as a flexible tool for gapfilling remote sensing and in-situ observations commonly used in climate and environmental research and create a coherent observational dataset of a flexible set of observational products.
How to cite: Bessenbacher, V., Gudmundsson, L., and Seneviratne, S. I.: Intelligently Gapfilling Earth Observations: Towards a coherent observational view of Land Hydrology, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8162, https://doi.org/10.5194/egusphere-egu21-8162, 2021.
‘Just drop a catchment and receive reasonable model output’ – still stays as motto and main idea of the ‘Global BROOK90’ project. The open-source R-package is build-up on global land cover, soil, topographical, meteorological datasets and the lumped hydrological model as a core to simulate water balance components on HRU scale all over the world in an automatic mode. First introduced in EGU2020 and followed by GitHub code release including an publication of methodology with few examples we want to continue with the insights on the current state and highlight the future steps of the project.
A global validation of discharge and evapotranspiration components of the model showed promising results. We used 190 small (median size of 64 km2) catchments and FLUXNET data which represent a wide range of relief, vegetation and soil types within various climate zones. The model performance was evaluated with NSE, KGE, KGESS and MAE. In more than 75 % of the cases the framework performed better than the mean of the observed discharge. On a temporal scale the performance is significantly better on a monthly vs daily scale. Cluster analysis revealed that some of the site characteristics have a significant influence on the performance. Additionally, it was found that Global BROOK90 outperforms GloFAS ERA5 discharge reanalysis (for the category with smallest catchments).
A cross-combination of three different BROOK90 setups and three forcing datasets was set up to reveal uncertainties of the Global BROOK90 package using a small catchment in Germany as a case study. Going from local to regional and finally global scale we compared mixtures of model parameterization schemes (original calibrated BROOK90, EXTRUSO and Global BROOK90) and meteorological datasets (local gauges, RaKlida and ERA5). Besides high model performances for a local dataset plus a calibrated model and weaker results for ERA5 and the Global BROOK90, it was found that the ERA5 dataset is still able to provide good results when combined with a regional and local parameterization. On the other side, the combination of a global parameterization with local and regional forcings gives still adequate, but much worse results. Furthermore, a hydrograph separation revealed that the Global BROOK90 parameterization as well as ERA5 discharge data perform weaker especially within low flow periods.
Currently, some new features are added to the original package. First, with the recent release of the ERA5 extension, historical simulations with the package now are expanded to 1950-2021 period. Additionally, an alternative climate reanalysis dataset is included in the framework (Merra-2, 0.5x0.625-degree spatial resolution, starting from 1980). A preliminary validation shows insignificant differences between both meteorological datasets with respect to the discharge based model performance.
Further upgrades of the framework will include the following core milestones: recognition of forecast and climate projections and parameter optimization features. In the nearest future we plan to utilize full power of the Climate Data Store for easy access to seasonal forecasts (i.e. ECMWF, DWD, NCEP) as well as climate projections (CMIP5) to extend the package’s scope to predict near and far future water balance components.
How to cite: Vorobevskii, I. and Kronenberg, R.: Global BROOK90: validation, uncertainties, current progress and future outline., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-742, https://doi.org/10.5194/egusphere-egu21-742, 2021.
The JULES land surface model has a wide ranging application in studying different processes of the earth system including hydrological modeling . Our aim is to tune the existing configuration of the global river routing scheme at 0.5o spatial resolution  and improve river flow simulation performance at finer temporal scales. To do so, we develop a factorial experiment of varying effective river velocity and meander coefficient, components of the Total Runoff Integrating Pathways (TRIP) river routing scheme. We test and adjust best performing configurations at the basin scale based on observations from GRDC 230 stations that exhibiting a variety of hydroclimatic and physiographic conditions. The analysis was focused on watersheds of near-natural conditions  to avoid potential influences of human management on river flow. The HydroATLAS database  was employed to identify basin scale descriptive hydro-environmental indicators that could be associated with the components of the TRIP. These indicators summarize hydrologic and physiographic characteristics of the drainage area of each flow gauge. For each basin we select the best performing set of TRIP parameters per basin resulting to the optimal efficiency of river flow simulation based on the Nash–Sutcliffe and Kling–Gupta efficiency metrics. We find that better performance is driven predominantly by characteristics related to the stream gradient and terrain slope. These indicators can serve as descriptors for extrapolating the adjustment of TRIP parameters for global land configurations at 0.5o spatial resolution using regression models.
 Papadimitriou et al 2017, Hydrol. Earth Syst. Sci., 21, 4379–4401
 Falloon et al 2007. Hadley Centre Tech. Note 72, 42 pp.
 Fang Zhao et al 2017 Environ. Res. Lett. 12 075003
 Linke et al 2019, Scientific Data 6: 283.
How to cite: Koutroulis, A., Grillakis, M., Mathison, C., and Burke, E.: River parametrisation of the JULES land surface model for improved runoff routing at the global scale., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3444, https://doi.org/10.5194/egusphere-egu21-3444, 2021.
Unlike global climate models, hydrological models cannot simulate the feedbacks among atmospheric processes, vegetation, water, and energy exchange at the land surface. This severely limits their ability to quantify the impact of climate change and the concurrent increase of atmospheric CO2 concentrations on evapotranspiration and thus runoff. Hydrological models generally calculate actual evapotranspiration as a fraction of potential evapotranspiration (PET), which is computed as a function of temperature and net radiation and sometimes of humidity and wind speed. Almost no hydrological model takes into account that PET changes because the vegetation responds to changing CO2 and climate. This active vegetation response consists of three components. With higher CO2 concentrations, 1) plant stomata close, reducing transpiration (physiological effect) and 2) plants may grow better, with more leaves, increasing transpiration (structural effect), while 3) climatic changes lead to changes in plants growth and even biome shifts, changing evapotranspiration. Global climate models, which include dynamic vegetation models, simulate all these processes, albeit with a high uncertainty, and take into account the feedbacks to the atmosphere.
Milly and Dunne (2016) (MD) found that in the case of RCP8.5 the change of PET (computed using the Penman-Monteith equation) between 1981- 2000 and 2081-2100 is much higher than the change of non-water-stressed evapotranspiration (NWSET) computed by an ensemble of global climate models. This overestimation is partially due to the neglect of active vegetation response and partially due to the neglected feedbacks between the atmosphere and the land surface.
The objective of this paper is to present a simple approach for hydrological models that enables them to mimic the effect of active vegetation on potential evapotranspiration under climate change, thus improving computation of freshwater-related climate change hazards by hydrological models. MD proposed an alternative approach to estimate changes in PET for impact studies that is only a function of the changes in energy and not of temperature and achieves a good fit to the ensemble mean change of evapotranspiration computed by the ensemble of global climate models in months and grid cells without water stress. We developed an implementation of the MD idea for hydrological models using the Priestley-Taylor equation (PET-PT) to estimate PET as a function of net radiation and temperature. With PET-PT, an increasing temperature trend leads to strong increases in PET. Our proposed methodology (PET-MD) helps to remove this effect, retaining the impact of temperature on PET but not on long-term PET change.
We implemented the PET-MD approach in the global hydrological model WaterGAP2.2d. and computed daily time series of PET between 1981 and 2099 using bias-adjusted climate data of four global climate models for RCP 8.5. We evaluated, computed PET-PT and PET-MD at the grid cell level and globally, comparing also to the results of the Milly-Dunne study. The global analysis suggests that the application of PET-MD reduces the PET change until the end of this century from 3.341 mm/day according to PET-PT to 3.087 mm/day (ensemble mean over the four global climate models).
Milly, P.C.D., Dunne K.A. (2016). DOI:10.1038/nclimate3046.
How to cite: Peiris, T. A. and Döll, P.: A simple approach to mimic the effect of active vegetation in hydrological models to better estimate hydrological variables under climate change, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12025, https://doi.org/10.5194/egusphere-egu21-12025, 2021.
When setting up global scale hydrological models, parameter estimation is a crucial step. Some modellers assign pre-defined parameter values to physical characteristics (such as soil, land cover, etc.) while others estimate parameter values based on observed hydrological data. In both cases, the regionalisation of parameters is a major challenge since both literature values and observed data are often lacking and assumptions are needed. This work aims at identifying suitable parameter regions to perform a regional calibration of the global model World-Wide HYPE (Arheimer et al., 2020) through empirical tests.
The work is organised in two steps. First we compare different ways of taking soil into account when creating hydrological response units. The soil is either considered uniform, indexed to land use or to a simplified soil map. The best soil representation is selected based on the model performance at a global scale. Based on this best representation, the second step aims at evaluating different ways to regionalise the soil parameters of the hydrological model. Previous classifications of hydrological uniform regions are tested for regionalisation of model parameters: hydrobelts (Meybeck et al., 2013), Köppen climate regions (Kottek et al., 2006), soil capacity index (Wang-Erlandsson et al., 2016) and hydroclimatic regions (Knoben et al., 2018).
For the first step, the results show that the best solution is to represent soil by land use. This counterintuitive result is due to the fact that adding information based on a soil map add another calibration step. To avoid increased equifinality, such an effort increases the need for data, which is often lacking at the global scale. For the second step, the creation of parameter regions contributed with minor improvement in terms of model performances, probably because the choice of regions was not suitable for the model approach. Also, the improvement has shown to be higher when available discharge data for calibration were better distributed over the different regions. This work shows that, when calibrating a model at very large scale, a balance should be found between available data and parameter regions resolution.
Arheimer, B., Pimentel, R., Isberg, K., Crochemore, L., Andersson, J. C. M., Hasan, A., and Pineda, L.: Global catchment modelling using World-Wide HYPE (WWH), open data and stepwise parameter estimation, Hydrol. Earth Syst. Sci. 24, 535–559, 2020.
Knoben, W. J., Woods, R. A., and Freer, J. E.: A Quantitative Hydrological Climate Classification Evaluated With Independent Streamflow Data. Water Resources Research, 54(7), 5088-5109, 2018.
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift, 15, 259-263, 2006.
Meybeck, M., Kummu, M., and Dürr, H. H.: Global hydrobelts and hydroregions: improved reporting scale for waterrelated issues? Hydrology and Earth System Sciences, 17(3), 1093-1111, 2013.
Wang-Erlandsson, L., Bastiaanssen, W. G. M., Gao, H., Jägermeyr, J., Senay, G. B., van Dijk, A. I. J. M., Guerschman, J. P., Keys, P. W., Gordon, L. J., and Savenije, H. H. G.: Global root zone storage capacity from satellite-based evaporation, Hydrology Earth System Sciences, 20, 1459-1481, 2016.
How to cite: Santos, L., Andersson, J. C. M., and Arheimer, B.: Global estimation of rainfall-runoff model parameters: an empirical experiment, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13224, https://doi.org/10.5194/egusphere-egu21-13224, 2021.
Although river flow is the best-monitored variable of the terrestrial water cycle, the scarcity of available in situ observations in large portions of the world has until now hindered the development of consistent observational estimates with global coverage. Recently, fusing sparse in-situ river discharge observations with gridded precipitation and temperature using machine learning has shown great potential for developing global monthly runoff estimates (Ghiggi et al., 2019). However, the accuracy of the utilised gridded precipitation and temperature products is variable and the corresponding uncertainty in the resulting runoff and river flow estimates was not yet quantified.
Global-RUNoff ENSEMBLE (G-RUN ENSEMBLE) (Ghiggi et al., in review) provides a multi-forcing global reanalysis of monthly runoff rates at a 0.5° resolution, composed of up to 525 runoff simulations. The G-RUN ENSEMBLE is based on 21 different atmospheric forcing datasets, overall spanning the period 1901-2019. The reconstructions are benchmarked against a comprehensive set of global-scale hydrological models (GHMs) simulations, using a large database of river discharge observations that were not used for model training as a reference.
Overall, the G-RUN ENSEMBLE shows good accuracy compared to the set of GHMs evaluated, especially with respect to the reproduction of the dynamics and seasonality of monthly runoff rates. We found that the spread imposed by the atmospheric forcing data in the G-RUN ENSEMBLE is small compared to the spread observed within the ensemble of GHMs simulations driven with a subset of such forcings. This might occur because GHMs are more impacted by biases in the input meteorological forcing and are more susceptible to accumulate errors over the simulation time than the adopted machine learning approach.
In summary, the multi-forcing nature of the G-RUN ENSEMBLE allows to quantify the uncertainty associated with the currently available atmospheric forcings, thereby paving the way for more robust and reliable water resources assessments, climate change attribution studies, hydro-climatic process studies as well as evaluation, calibration and refinement of GHMs.
Ghiggi, G., Humphrey, V., Seneviratne, S. I., and Gudmundsson, L. 2019: GRUN: an observation-based global gridded runoff dataset from 1902 to 2014, Earth Syst. Sci. Data, 11, 1655–1674, https://doi.org/10.5194/essd-11-1655-2019.
Ghiggi, G., Humphrey, V., Seneviratne, S. I., and Gudmundsson, L.: G-RUN ENSEMBLE: A multi-forcing observation-based global runoff reanalysis, Water Res. Res., in review.
How to cite: Ghiggi, G., Humphrey, V., Seneviratne, S., and Gudmundsson, L.: G-RUN ENSEMBLE: A multi-forcing observation-based global runoff reanalysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2380, https://doi.org/10.5194/egusphere-egu21-2380, 2021.
Validating model representations of land surface processes is crucial for reducing the uncertainty of future projections, especially at high latitudes where climate change is amplified. As part of a regional assessment of the latest version of the Community Land Model (CLM5) in cold environments, we compare simulated grid-scale runoff with discharge measurements in small near-natural catchments in Fennoscandia. CLM5 is the land component of the Norwegian Earth System Model. Evaluating land surface models involves a large set of state and flux variables, for many of which direct measurements are either not available or not representative at the typical modelling spatial scales (100–102 km). In this context, discharge measurements provide valuable information that can be used to assess how well models are able to reproduce the downstream outcome of catchment hydrologic processes. We conduct two CLM5 simulations at 0.25° spatial resolution over Fennoscandia: one forced with the default 3-hourly 0.5° GSWP3v1 product (2000-2014) and another with the hourly 0.25° ERA5 near-surface atmospheric data (2000-2019). To characterise forcing uncertainty, precipitation and temperature forcing data are compared to the daily observational Nordic Gridded Climate Dataset (Norwegian Meteorological Institute), which covers Fennoscandia at 1 km resolution. Daily discharge and catchment information are obtained from the Norwegian Water Resources and Energy Directorate, the Swedish Meteorological and Hydrological Institute, and the Finnish Environment Institute. To avoid uncertainties due to human alterations and model representation of river routing, we select time series of unregulated catchments whose areas are smaller than 103 km2 and thus are compatible with single model grid-cells. Accordingly, we evaluate CLM5 daily total runoff, which is the sum of subsurface and surface runoff prior to channel routing, against observed discharge. We apply the following criteria: (1) bias, variance error and correlation, to assess the reproduction of the overall water balance and of the amplitude and shape of the hydrograph; (2) average seasonal cycles, to evaluate how runoff regimes are simulated; and (3) occurrence and persistence of low and high flow anomalies, to analyse the ability of the model to predict extremes. Further, we investigate whether spatio-temporal patterns of agreement/discrepancy between modelled and measured runoff correlate with atmospheric forcing uncertainties, land surface properties, or climatology. In particular, we try to detect model runoff errors prevailing in specific environmental conditions. This study aims to inform future regional CLM5 experiments that will test atmospheric forcing corrections and alternate parametrisations of hydrologic processes, in the framework of the Land-ATmosphere Interactions in Cold Environments (LATICE) research initiative.
How to cite: Gelati, E., Yilmaz, Y., Jørgensen Bakke, S., and Tallaksen, L. M.: Community Land Model v5 runoff evaluation in small near-natural catchments in Fennoscandia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10248, https://doi.org/10.5194/egusphere-egu21-10248, 2021.
Future sector-specific water withdrawals at a temporal resolution capable of representing patterns in seasonality and a commonly used spatial resolution are an important factor to consider for energy, water, land and environmental research. Projected water withdrawals that are harmonized with assumptions for alternate futures that capture socioeconomic and climatic variation are critical for many modeling studies on future global and regional dynamics. Here we generate a novel global gridded water withdrawals dataset by coupling the Global Change Analysis Model (GCAM) with a land use spatial downscaling model (Demeter), a global hydrologic framework (Xanthos) and a water withdrawal downscaling model (Tethys) for the five Shared Socioeconomic Pathways (SSPs) and four Representative Concentration Pathways (RCPs) scenarios. The dataset provides sectoral monthly data at 0.5° resolution for years 2015 to 2100. The presented dataset will be useful for both global and regional analysis looking at the impacts of socioeconomic, climate and technological futures as well as in characterizing the uncertainties associated with these impacts.
How to cite: Khan, Z., Graham, N., Vernon, C., Wild, T., Chen, M., and Calvin, K.: A global gridded monthly water withdrawal dataset for multiple sectors from 2015 to 2100 at 0.5° resolution under a range of socioeconomic and climate scenarios, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-903, https://doi.org/10.5194/egusphere-egu21-903, 2021.
Flood risk was reported to increase in the future due to climate change and population growth. While recent and earlier studies have derived plausible climate change impacts on global flood risk, dams have never been explicitly implemented into simulation tools. Currently, about half of major river systems worldwide are regulated by dams and more than 3,700 major dams are planned or under construction. Consequently, to realistically assess population exposure to present and future floods, current and future dam landscapes must be integrated into existing flood modeling frameworks.
In this research, the role of dams on future flood risk under climate change is quantified by simulating the global hydrological cycle, including floodplain dynamics, and considering flow regulation by dams.
The global population exposed to historical once-in-100-year floods in our simulation was 9.4 million people, relatively close to the estimate of 5.6 million people indicated in a previous study (Hirabayashi et al., 2013) and the Dartmouth Flood Observatory database which estimated this number as 11.9 million people. Downstream of dams, the number of people exposed to the historical once-in-100-year floods were 7.2 and 13.4 million on average over 2006–2099 given a low and a medium-high greenhouse gas emission trajectory (RCP2.6 and RCP6.0, respectively). By the end of the 21st century, the populations exposed to flooding below dams decreased on average by 20.6% and 12. 9% for the two trajectories compared to simulations not accounting for the flow regulations produced by dams.
At the catchment scale, by considering water regulation in densely populated and heavily water regulated catchments, the occurrence of flood events largely decreases compared to projections not accounting for water regulation. Over the 2070–2099 period and for 14 catchments, the annual flooded area shrank by, on average (first and third quartiles given in bracket), 22.5% (19.8–40.5) and 25.9% (12.1–34.5) for RCP2.6 and RCP6.0 respectively.
To maintain the levels of flood protection that dams have provided, new dam operations will be required to offset the effect of climate change, possibly negatively affecting energy production and water storage. In addition, precise and reliable hydro-meteorological forecasts will be invaluable for enhancing flood protection and avoid excessive outflows. Given the many negative environmental and social impacts of dams, comprehensive assessments that consider both potential benefits and adverse effects are necessary for the sustainable development of water resources.
How to cite: Boulange, J., Hanasaki, N., Yamazaki, D., and Pokhrel, Y.: Quantifying the effect of dams in reducing global flood exposure under climate change, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10453, https://doi.org/10.5194/egusphere-egu21-10453, 2021.
The Nile River Basin (NRB) is a typical example of a transboundary river basin that provides crucial resource for the economy and politics of eleven countries in northeastern Africa. Understanding the reservoir operation in the NRB is crucial to cope with challenges imposed by intense population growth, recurring drought, climate change and increasing competition for water. Data availability to monitor reservoir operation is predominantly an issue, particularly in transboundary basins crossing developing nations, as in the NRB. Such data challenge has been relatively overcome by remote sensing observations that are made available at high spatial and temporal resolutions. Our study implemented a Multi-Sensor Satellite (MSS) approach to understand the reservoir operation in the NRB with the focus on the joint operation of High Aswan Dam (HAD) and Toshka Lakes, located in the south western part of HAD. The MSS approach integrates a suite of satellite observations including Landsat, Sentinel-2, MODIS, satellite altimetry data, and GRACE. The MSS data, along with hydrological model outputs, are used in a water balance model to derive the operation of HAD reservoir and Toshka Lakes. Our study showed that MSS approach has a reasonable skill when modeling the Toshka inflow (i.e., HAD spillway outflow) with an average relative bias -28.5% (averaged for the period 1998-2002) and -6.9% (averaged for the two years 2001-2002). Overall, the MSS approach can potentially assist water managers and dam operators to make more informed decisions in the NRB, especially with the construction of new dams in the upstream countries (e.g., Grand Ethiopian Renaissance Dam; GERD).
How to cite: Abdelmoneim, H., Eldardiry, H., Eladawy, A., and Moghazy, H.: Inferring the Joint Operation of High Aswan Dam and Toshka Lakes using Multi-Sensor Satellite Approach, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14224, https://doi.org/10.5194/egusphere-egu21-14224, 2021.
The spatial resolutions of GRACE solutions (level-2 dampened by truncation and/or filtering and level-3 called MASCON) are generally too coarse (~300km) to estimate regional changes in the terrestrial water storage (TWS) components. Focusing methods such as constrained forward approach and spatial concentration functions could improve the spatial distribution estimates of concentrated masses (e.g. glaciers, lakes). In this study, we apply spatial concentration functions to create high resolution monthly time-series of glaciers mass changes over the Gulf Of Alaska (GOA). Spatial weighting functions are based on heterogeneous glaciers mass distribution maps called a priori. First, we use three a priori of different spatial resolutions and sources to create different spatial functions. Second, we compare the amplitude of glaciers mass variations with others GRACE TWS components over the GOA, using the same spatial functions, to improve the discrimination of glaciers mass distribution. Third, we use a variety of GRACE solutions with different processing assumptions given identical resolution characteristics to estimate the uncertainties associated with our methodological framework. To analyze the accuracy of our assessments, we also compare trends resulting from the spatial concentration functions and the constrained forward approach. Then, we compare our estimates with three released MASCON solutions and published results over: (i) the GOA, (ii) the Saint-Elias Mountains and (iii) the Upper Yukon watershed. The results indicate that the spatial functions are sensitive to glaciers mass distribution. The signal from the glaciers dominate the GRACE TWS over the GOA. All solutions used, provide comparable glaciers mass variations. The two focusing methods give similar trends, but the constrained forward approach is time-consuming. The results obtained here could provide to be useful to further our understanding of the contribution of the GOA’s glaciers to sea-level rise and to river flows at the regional scale.
How to cite: Doumbia, C. and Rousseau, A. N.: High Spatial Resolution Mapping of Glaciers Mass Variations over the Gulf of Alaska Using Spatial Concentration Functions and Monthly GRACE and GRACE-FO Data , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14001, https://doi.org/10.5194/egusphere-egu21-14001, 2021.
Sea level rise (SLR) projections rely on the accurate and precise closure of Earth’s water budget. The Gravity Recovery and Climate Experiment (GRACE) mission has provided global-coverage observations of terrestrial water storage (TWS) anomalies that improve accounting of ice and land hydrology changes and how these changes contribute to sea level rise. The contribution of land hydrology TWS changes to sea level rise is much smaller and less certain than contributions from glacial melt and thermal expansion. Although land hydrology TWS plays a smaller role, it is still important to investigate to improve the precision of the overall global water budget. This study analyzes how data assimilation techniques improve estimates of the land hydrology contribution to sea level rise. To achieve this, three global TWS datasets were analyzed: (1) GRACE TWS observations alone, (2) TWS estimates from the model-only simulation using Catchment Land Surface Model, and (3) TWS estimates from a data assimilation product of (1) and (2). We compared the data assimilation product with the GRACE observations alone and the model-only simulation to isolate the contribution to sea level rise from anthropogenic activities. We assumed a balanced water budget between land hydrology and the ocean, thus changes in global TWS are considered equal and opposite to sea level rise contribution. Over the period of 2003-2016, we found sea level rise contributions from each dataset of +0.35 mm SLR eq/yr for GRACE, -0.34 mm SLR eq/yr for model-only, and a +0.09 mm SLR eq/yr for DA (reported as the mean linear trend). Our results indicate that the model-only simulation is not capturing important hydrologic processes. These are likely anthropogenic driven, indicating direct anthropogenic and climate-driven TWS changes play a substantial role in TWS contribution to SLR.
How to cite: Scheliga, A. and Girotto, M.: Data assimilation of GRACE terrestrial water storage to improve sea level rise estimates and isolate anthropogenic influences, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6380, https://doi.org/10.5194/egusphere-egu21-6380, 2021.
Terrestrial water storage (TWS) strongly modulates the hydrological cycle, and is a key determinant of water resource availability, and an indicator of drought. While historical TWS variations have been extensively studied, the impacts of future climate change on TWS and the linkages to droughts remain unexamined. In this study, we quantify the impacts of climate change on TWS using an ensemble of hydrological simulations and examine the implications on droughts using the TWS drought severity index. Results indicate that climate change is projected to reduce TWS in two-third of global land area; TWS declines are especially severe in the southern hemisphere, leading to clear north-south contrast. Strong agreement across 27 ensemble simulations suggests high confidence in these projections. The declines in TWS translate to substantial increase in the occurrence and frequency of drought by mid- and late-21st century. By the late-21st century global land area and population in extreme-to-exceptional TWS drought could more than double, each increasing from 3% during 1976-2005 to 7% and 8%, respectively. Our findings underscore the need for stringent climate adaptation measures to avoid adverse effects on water resources due to declining TWS and increased droughts.
How to cite: Pokhrel, Y. and the Co-authors: Terrestrial water storage under changing climate and implications on future droughts, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-738, https://doi.org/10.5194/egusphere-egu21-738, 2021.
The Global Runoff Data Centre (GRDC) deploys the data product Global Freshwater Fluxes into the World Oceans since 1996. The quantification of freshwater fluxes into the ocean is an important link to oceanography and climatology, as the salinity of sea water influences ocean currents and is a driver of evaporation and therefore interconnects to the general circulation of the atmosphere.
The first versions of the data product were based on runoff coefficient estimates for ungauged basins. Since 2004, results of the global hydrological model WaterGAP (AG Hydrologie, Goethe-Universität Frankfurt), calibrated with GRDC station data, were used to calculate the freshwater fluxes.
On the basis of the latest WaterGAP 2.2d model, we could now derive the freshwater fluxes in a refined temporal resolution and for a considerably extended time period (1901-2016).
We present a statistical analysis of monthly and annual freshwater input to the oceans within 5° and 10° latitude zones, from 5° cells along the coastlines and from the Global International Water Assessment regions (GIWA).
Beyond that, the GRDC has revised its GIS product Major River Basins of the World, which is now consistent with the WMO Regions and Subregions. The freshwater fluxes have been determined likewise for these catchments.
The provision of integrative data products is one of the objectives of the Global Terrestrial Network Hydrology (GTN-H) corresponding with the commitments of the WMO members at the eighteenth session of the World Meteorological Congress.
How to cite: Recknagel, T., Dornblut, I., Müller Schmied, H., Döll, P., Looser, U., and Plessow, H.: Global Freshwater Fluxes into the World Oceans, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16080, https://doi.org/10.5194/egusphere-egu21-16080, 2021.
Despite the accuracy of GRACE terrestrial water storage estimates and the variety of global hydrological datasets providing precipitations, evapotranspiration, and runoff data, it remains challenging to find datasets satisfying the water budget equation at the global scale.
We select commonly used and widely-assessed datasets. We use several precipitations (CPC, CRU, GPCC, GPCP, GPM, MSWEP, TRMM, ERA5 Land, MERRA2), evapotranspiration (land surface models CLSM, Noah, VIC from GLDAS 2.0, 2.1, and 2.2; GLEAM, MOD16, SSEBop, ERA5 Land, MERRA2), and runoff (land surface models CLSM, Noah, VIC from GLDAS 2.0, 2.1, and 2.2; GRUN, ERA5 Land, MERRA2) datasets to assess the water storage change over more than 150 hydrological basins. Both mascons and spherical harmonics coefficients are used as the reference terrestrial water storage from different centres processing GRACE data. The analysis covers a wide range of climate zones over the globe and is conducted over 2003-2014.
The water budget closure is evaluated with Root Mean Square Deviation (RMSD), Nash-Sutcliffe Efficiency (NSE), and seasonal decomposition. Each dataset is assessed individually across all basins and dataset combinations are also ranked according to their performances. We obtain a total of 1080 combinations, among which several are suitable to close the water budget. Although none of the combinations performs consistently well over all basins, GPCP precipitations provide generally good results, together with GPCC and GPM. A better water budget closure is generally obtained when using evapotranspiration from Catchment Land Surface Models (GLDAS CLSM), while reanalyses ERA5 Land and MERRA2 are especially suitable in cold regions. Concerning runoff, the machine learning GRUN dataset performs remarkably well across climate zones, followed by ERA5 Land and MERRA2 in cold regions. We also highlight highly unrealistic values in evapotranspiration computed with version 2.2 of GLDAS (using data assimilation from GRACE) in most of the cold basins. Our results are robust as changing the GRACE product from one centre to the other does not affect our conclusions.
How to cite: Lehmann, F., Vishwakarma, B. D., and Bamber, J.: Closing the water budget at the global scale using observations, remote sensing, and reanalyses, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10270, https://doi.org/10.5194/egusphere-egu21-10270, 2021.
This study synthesizes the advancements made in the setup of the mesoscale Hydrologic Model (mHM; [1,2,3]) at the global scale. Underlying vegetation and geophysical characteristics are provided at ≈200m, while the mHM simulates water fluxes and states between 10 km and 100 km spatial resolution. The meteorologic forcing data are derived from the readily available, near-real time ERA-5 dataset . The total of 50 global parameters of the Multiscale Parameter Regionalization (MPR) are constrained in two modes: (1) streamflow only across 3054 gauges, and (2) streamflow across 3054 gauges and simultaneously with FLUXNET ET and GRACE TWSA across 258 domains consisting of ≈10° x 10° blocks. Model performance is finally evaluated against a range of observed and reference data since 1985.
The single best parameter set evaluated across 3054 GRDC global streamflow station yield median performance of 0.47 daily KGE (0.55 monthly KGE). This performance varies strongly between continents. For example, median daily KGE across Europe is around 0.55 (N basins=972) and across northern America around 0.5 (N basins=1264). So far, the worst model performance is observed across Africa, with median KGE of 0 (N basins=202), using the same globally constrained parameter set. The deterioration of model performance based on seamless parameterization can be explained by the quality of the underlying data, which corresponds to areas, where water balance closure error is the biggest. Additionally, missed model processes play an important role as well. Finally, there remains a large gap between the onsite calibrations and global calibrations and ongoing research is being done to narrow down these differences. This work is the fundament for building skillful global seasonal forecasting system ULYSSES , which provides hindcasts and operational seasonal forecasts of hydrologic variables using four state of the art hydrologic/land surface models with lead time of 6 months.
-  https://www.ufz.de/mhm
-  https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2008WR007327
-  https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012WR012195
-  https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3803
-  https://www.ufz.de/ulysses
How to cite: Rakovec, O., Kaluza, M., Kumar, R., Schweppe, R., Shrestha, P., Thober, S., Mueller, S., Attinger, S., and Samaniego, L.: Advancements in the mesoscale Hydrologic Model at the global scale, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15869, https://doi.org/10.5194/egusphere-egu21-15869, 2021.
In this work, we suggest a new framework for estimating mean annual runoff, which is a key water balance component. The framework consists of two steps: 1) A process-based hydrological model is used to simulate mean annual runoff on a grid covering the whole study area. 2) Since the parameters of the process-based model are calibrated globally, there are local biases in the runoff estimates relative to the observed runoff. We therefore correct the gridded simulations based on runoff data. Here, step 2 is done by using a Bayesian geostatistical model that treats the process-based simulations as a covariate. The regression coefficient of the covariate is modelled as a spatial field such that the relationship between the covariate (simulations from the process-based model) and the response variable (the observed mean annual runoff) is allowed to vary within the study area. Hence, it is a spatially varying coefficient model. A preprocessing step for including short records in the modelling is also suggested such that we can exploit as much data as possible in the correction procedure. We use state of the art statistical methods such as SPDE and INLA to ensure fast Bayesian inference.
The framework for estimating mean annual runoff is evaluated by predicting mean annual runoff for 1981-2010 for 127 catchments in Norway based on streamflow observations from 411 catchments. Simulations from the process-based HBV model on a 1 km x 1 km grid for the whole country are used as input. We found that on average the proposed approach outperformed a purely process-based approach (HBV) when predicting runoff for ungauged and partially gauged catchments: The reduction in RMSE compared to the HBV model was 20 % for ungauged catchments and 58 % for partially gauged catchments. For ungauged catchments the proposed framework also outperformed a purely geostatistical method with a 10 % reduction in RMSE compared to the geostatistical method. For partially gauged catchments however, purely geostatistical methods performed equally well or slightly better than the proposed two step procedure. In general, we expect the proposed approach to outperform purely geostatistical models in areas where the data availability is low to moderate.
How to cite: Roksvåg, T., Steinsland, I., and Engeland, K.: Estimating mean annual runoff by using a geostatistical spatially varying coefficient model that incorporates process-based simulations and short records, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4233, https://doi.org/10.5194/egusphere-egu21-4233, 2021.
This study explores the influence of groundwater representation on soil moisture, evapotranspiration, total water storage, water table depth and groundwater recharge/discharge through the comparison of multi-model simulations using the stand-alone Community Land Model (CLM3.5) and the ParFlow hydrologic model. ParFlow simulates three-dimensional variably saturated groundwater flow solving Richards equation and overland flow with a two-dimensional kinematic wave approximation, whereas CLM3.5 applies a simple approach to simulate groundwater recharge and discharge processes via the connection of bottom soil layer and an unconfined aquifer. Over Europe with a lateral resolution of 3km, both models were driven with the COSMO-REA6 reanalysis dataset for the time period from 1997 to 2006 at an hourly time step using the same datasets for the static input variables (such as topography, vegetation and soil properties). Evaluation against independent observations including satellite-derived and in-situ soil moisture, evapotranspiration, and total water storage datasets show that both models capture the interannual and seasonal variations well at the regional scale, however ParFlow performs better in simulating surface soil moisture in comparison with in-situ data. Moreover, juxtaposition of both models shows that simulations of water fluxes and sates in both space and time are sensitive to the differences in groundwater representation in the model. For example, simulations with ParFlow have overall wetter soil moisture than CLM, particularly in humid and cold regions and driest soil moisture in the arid and semi-arid regions. Seasonally, ParFlow simulates wetter soil moisture in winter and driest in summer than CLM model. This study helps to understand and quantify uncertainties in groundwater related processes in hydrologic simulations and resulting implications for water resources assessment at regional to continental scales.
How to cite: Naz, B. S., Sharples, W., Goergen, K., and Kollet, S.: Sensitivity of hydrological fluxes and states to groundwater representation in continental scale simulations over Europe., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4341, https://doi.org/10.5194/egusphere-egu21-4341, 2021.
The dynamics of the rainfall-runoff processes are complex and variable both spatially and temporally. There is a rich literature on physical representation of streamflow generation processes, such as saturation excess overland flow, often at small scales. Yet, continental-scale estimations of the streamflow generation processes in zones with shallow groundwater systems are still poor. This has led to inability of earth system models or large-scale hydrologic models to correctly simulate stream flows at (un)gauged basins with high potential for the presence of saturation excess overland flow. Zones with shallow groundwater have a direct impact on the hydrologic response of rainfall events. Depending on the subsurface storage, climate signals and topography, they can enhance the overland flow, or act as a buffer zone to flatten the flood hydrographs.
We have introduced new indices, inspired by the concept of hydrologic function, that include the interactions amongst climatic and geophysical characteristics (soil parameters, topography and lithology) to delineate zones of shallow groundwater over the United States and Canada. We have evaluated and tested the ability of these indices in locating high-resolution zones of shallow groundwater against in-situ observations of water table depth. The knowledge of the spatial pattern of shallow groundwater zones at (un)gauged basins allows an accurate inclusion of hydrologic connectivity in earth system models or large-scale hydrologic models, improving their prediction of stream peak flow. Furthermore, as a significant part of incoming precipitation is transformed to overland flow due to oversaturation, these datasets could be introduced as a useful indicator of areas with flood and erosion susceptibility.
How to cite: Tootchi, A. and Ameli, A.: New functional topographic, lithology and climatic indices to define shallow groundwater systems in (un)gauged basins, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13070, https://doi.org/10.5194/egusphere-egu21-13070, 2021.
The Australian Water Resources Assessment Landscape (AWRA-L) model is a continental gridded, daily time-step, water balance model, developed over the last decade by CSIRO and the Australian Bureau of Meteorology for a range of hydrological applications. The model outputs (including soil moisture, evapotranspiration, runoff and deep drainage; available through www.bom.gov.au/water/landscape) have found wide application for monitoring purposes (e.g. for flood and fire risk, drought monitoring), water reporting (eg. National Water Accounts), and in analysing trends in water balance outputs including streamflow. In addition to these historical/monitoring applications, AWRA-L is being further used for production of 10-day forecasts, seasonal forecasts, and long-term projections of hydrological outputs out to the end of the century.
This study details recent development of AWRA-L for improved performance across the water balance for use in monitoring through to long term projections. Changes are implemented across three broad areas: improved static and dynamic inputs, altered conceptual structure (additional urban component and baseflow ephemerality), and altered calibration approach. In particular, a new spatial calibration approach is applied across the nation using over 300 catchments. To do so model pixel output values are compared against spatially distributed satellite data for soil moisture, evapotranspiration (ET), and two new components including fraction of vegetation (Fveg) and terrestrial water storage (TWS). In the previous versions of the model lumped catchment average values of evapotranspiration and soil moisture were used. In addition to comparing to a wide range of national datasets (streamflow observations, flux tower observations, soil moisture network observations, recharge observations), the model performance was compared for drought analysis (reproducing 2-state rainfall-runoff behaviour observed in parts of Australia) and flood analysis (correlating with operationally used flood forecasting parameters). Overall, the modified AWRA-L outperformed previous versions in terms of water balance estimation according to a wide range of validation data. The successful application of the spatial calibration method can potentially pave the path for more frequent application of complex calibration methods for large scale simulations. Furthermore, consideration of a terrestrial water storage component in the objective function highlights the importance of this factor in capturing more accurate simulation of other water balance components, particularly streamflow. The improved streamflow performance demonstrates the enhanced functionality of the model in capturing intermittency and streamflow shifts in seasonally dry and groundwater dependent catchments, further demonstrated in the drought analysis. Finally, the flood study demonstrates the application and value of the model for real time flood-monitoring and forecasting purposes. This study shows the potential of AWRA-L model and associated spatial calibration approach for accurate simulation of water balance variables for use in continental-scale studies.
How to cite: Shokri, A., Azarnivand, A., Bahramian, K., Keir, G., and Frost, A.: Recent improvements to the Australian Water Resources Assessment Landscape Model (version 7), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15154, https://doi.org/10.5194/egusphere-egu21-15154, 2021.
In the recent past, several studies have demonstrated the ability of deep learning (DL) models, especially based on Long Short-Term Memory (LSTM) networks, for rainfall-runoff modeling. However, almost all of these studies were limited to (multiple) individual catchments or small river networks, consisting of only a few connected catchments.
In this study, we investigate large-scale, spatially distributed rainfall-runoff modeling using DL models. Our setup consists of two independent model components: One model for the runoff-generation process and one for the routing. The former is an LSTM-based model that predicts the discharge contribution of each sub-catchment in a river network. The latter is a Graph Neural Network (GNN) that routes the water along the river network network in hierarchical order. The first part is set up to simulate unimpaired runoff for every sub-catchment. Then, the GNN routes the water through the river network, incorporating human influences such as river regulations through hydropower plants. The main focus is to investigate different model architectures for the GNN that are able to learn the routing task, as well as potentially accounting for human influence. We consider models based on 1D-convolution, attention modules, as well as state-aware time series models.
The decoupled approach with individual models for sub-catchment discharge prediction and routing has several benefits: a) We have an intermediate output of per-basin discharge contributions that we can inspect. b) We can leverage observed streamflow when available. That is, we can optionally substitute the discharge simulations of the first model with observed discharge, to make use of as much observed information as possible. c) We can train the model very efficiently. d) We can simulate any intermediate node in the river network, without requiring discharge observations.
For the experiments, we use a new large-sample dataset called LamaH (Large-sample Data for Hydrology in Central Europe) that covers all of Austria and the foreign upstream areas of the Danube. We consider the entire Danube catchment upstream of Bratislava, a highly diverse region, including large parts of the Alps, that covers a total area of more than 130000km2. Within that area, LamaH contains hourly and daily discharge observations for more than 600 gauge stations. Thus, we investigate DL-based routing models not only for daily discharge, but also for hourly discharge.
Our first results are promising, both daily and hourly discharge simulation. For example, the fully DL-based distributed models capture the dynamics as well as the timing of the devastating 2002 Danube flood. Building upon our work on learning universal, regional, and local hydrological behaviors with machine learning, we try to make the GNN-based routing as universal as possible, striving towards a globally applicable, spatially distributed, fully learned hydrological model.
How to cite: Kratzert, F., Klotz, D., Gauch, M., Klingler, C., Nearing, G., and Hochreiter, S.: Large-scale river network modeling using Graph Neural Networks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13375, https://doi.org/10.5194/egusphere-egu21-13375, 2021.
The Gravity Recovery and Climate Experiment (GRACE) mission, and its successor GRACE Follow-On, have enabled to map on a monthly basis the Terrestrial Water Storage Anomaly (TWSA) since 2002. This unprecedented capability has provided hydrologists with new observations of the spatiotemporal evolution of TWSA, which have been used, among others, to better constrain numerical runoff models, to characterize empirically the relations between runoff and TWSA, or to simply monitor and quantify groundwater depletions. In this study, we explore the possibility to infer from GRACE observations a physically informed and linear dynamical system that models the intrinsic dynamics of TWSA at sub-basin scales.
First, we apply a hexagonal binning over the study area and aggregate the total water volume anomaly derived from GRACE data for each bin. Assuming that each bin exchanges water with the others in proportion to its water content, we then reformulate the mass balance equation of the whole basin as a first order matrix differential equation. All the proportionality coefficients encoding the bin exchanges are gathered in an unknown transition matrix to be determined. Such a transition matrix must satisfy different algebraic properties to be physically consistent and interpretable. In particular, we show that this matrix is necessarily a left stochastic matrix. Finally, we used the time series of total water volume anomaly to estimate this transition matrix by solving an optimization problem on the manifold defined by the aforementioned matrix constraint. This method is applied to the Amazon basin and to mainland Australia respectively, and the predictive performances of the derived dynamical systems are quantified and discussed.
How to cite: Douch, K., Saemian, P., and Sneeuw, N.: Data-driven and physically informed modelling of the Terrestrial Water Storage dynamics , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15253, https://doi.org/10.5194/egusphere-egu21-15253, 2021.
Even though Austria is a water rich country, which uses approximately 3% of its water resources, regional and seasonal challenges to ensure the water supply might occur. To facilitate a long-term, sustainable strategy for water use, detailed information on available water resources and water demand as well as possible changes due to climate change are necessary. In the “Wasserschatz” project the current available groundwater resource and the water use for the following sectors: agriculture, public water supply, industry and selected services (technical snowing and golf courses) were elaborated.
For the Austrian part of the Rhine catchment, the Water Exploitation Index was calculated for the year 2016. Where applicable the abstraction data obtained in the “Wasserschatz” project were directly used in the WEI equation. The data for the WEI equation was obtained from very different data sources (measured data, estimated data, extrapolated data) a differentiated approach was needed for each type of data and for each sector.
A very important part of the WEI are the returns, for which a different method for each sector were developed (agriculture, public water supply, selected services, industry and energy). For agriculture it was assumed that water applied as irrigation was completely transpired into the atmosphere. For cattle, the abstraction data were calculated from the amount cattle, returns were estimated according to the milk production. The abstractions for the drinking water supply were obtained from a model developed by the Institute of Sanitary Engineering and Water Pollution Control at the University of Natural Resources and Life Sciences (Vienna), the returns are assumed to be a fixed factor from the abstractions. For the Industry abstraction data were obtained from the water register(official notices) and from questionnaires (real abstraction data). The responses from the questionnaires were categorized according to company size and NACE codes and the data was extrapolated to other companies. For the returns either data from the water register was used or factors from literature were used.
To obtain the renewable resources the calculated outflow of the Rhine catchment was used. The water use in the WEI is described as the abstractions – returns, where all the water that stays in the catchment is considered a return. For a water rich catchment as the Rhine, the WEI is expected to be very low. In a future step the WEI index for the Austrian part of the Danube will also be calculated. Another planned improvement is to disaggregate the available data and calculate a seasonal WEI+.
How to cite: Broer, M., Schönbauer, A., Lindinger, H., Brielmann, H., and Neunteufel, R.: Developing an approach to calculate the WEI for the Austrian Rhine catchment, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7186, https://doi.org/10.5194/egusphere-egu21-7186, 2021.
Flood diversion canals play a crucial role in assuaging the flood risk by diverting water from the main channel to the nearby rivers, downstream of the same river, or the ocean. For the impact assessment of such canal systems on river discharge worldwide, their explicit inclusion into the global hydrological models (GHMs) is necessary. Despite this fact, such representation is limited due to their complex operations and lack of data. Therefore, we aim to propose a generalized scheme for the flood water diversion in the H08 GHM that ideally requires the universal parameters only. In this scheme, if the discharge exceeds the channel capacity, an amount equivalent to canal capacity is diverted to the canal, which will then flow to the retention ponds, and finally to the destination once the retention ponds get full. A regionalized scheme with site-specific parameters was also considered to evaluate the validity of the simulations.
The proposed scheme was tested in the upper Chao Phraya River basin, which is characterized by four tributaries of Ping, Wang, Yom, and Nan. The government has implemented Yom-Nan canal system to divert water from Yom to Nan River since 2014 to alleviate flooding in the lower Yom basin. The effect of this canal system was analyzed from 1980-2004 using the H08 model with the generalized scheme as well as the regionalized scheme. The simulations showed that the total flood water diverted from the Yom River was around 1.00 km3/year and 1.64 km3/year under the generalized and regionalized schemes, respectively, over the 25 years. This constitutes about 2.62% and 4.29% of the river discharge in the Yom River at the diversion point. In both simulations, nearly 30% of the water has been diverted to the Nan River and the remaining 70% was stored in the retention ponds. To assess the validity of the simulations, we compared the simulation results of the wet water-year 1994 with the observed canal operation data of the wet water-year 2017. The total flood water diverted was around 0.47 km3/year during the year 2017, whereas the same for 1994 was about 0.17 km3/year and 0.48 km3/year under the generalized and regionalized schemes, respectively. This shows that the regionalized simulations are close to the observations, while the generalized simulations reproduced nearly half of the diverted canal flow. The generalized simulations can be further improved by parameterizations.
How to cite: Padiyedath Gopalan, S. and Hanasaki, N.: Implementation of flood diversion canals and retention ponds to the H08 global hydrological model for flood management, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6753, https://doi.org/10.5194/egusphere-egu21-6753, 2021.
The process-based hydrological model Community Water Model (CWatM) is presented simulating the water cycle from regional to global and with different resolutions. Human influences within the water cycle have been further integrated and recent developments to improve the representation of water management and distribution, reservoirs, crops, and groundwater processes are demonstrated. “Water circles" are introduced along with a dashboard to visualise the components of the water cycle and evaluate water availability in the context of water demands. The dashboard along with a set of tutorials and testing suite continue to support the accessibility of model adoption and open-source code development.
How to cite: Smilovic, M., Guillaumot, L., de Bruijn, J., and Burek, P.: Simulating and visualising the continental water cycle with CWatM, the open-source Community Water Model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13471, https://doi.org/10.5194/egusphere-egu21-13471, 2021.
Given the current climate and anthropogenic evolution, water management becomes one of the greatest challenges of the 21st century. Number of studies have analyzed observed hydrologic trends and their connections with the changing climate. Impacts include changes in runoff, river discharge and groundwater recharge. Water quality is also impacted, through its many facets including the water temperature. Despite the important progress made in climate modelling, the impact of the predicted global warming on hydrological processes remains uncertain; particularly, in large hydrosystems. The Seine River basin has a surface of 78,650 km², it includes the Seine River and its 50 tributaries, it is populated by 30% of France inhabitants. The Seine River basin crosses 14 departments and 4 regions, including the Paris metropolitan area. Climate change poses a vulnerability due to its potential political, social, and economic consequences in the Seine basin. The agricultural activities and number industries depend on water resources or are located on the river sides. Our ability to adapt water resource management strategies to the climate change depends on our ability to understand and estimate the actual evolution of water resource.
The terrestrial water budget is now considered as a single continuum. This integrated conceptualisation needs to simulate the spatial and temporal dynamics of water exchanges between the surface and groundwater. Here we propose to improve the representation of the surface water budget with the goal to decrease the uncertainty of the whole water budget of the Seine hydrosystem. We used the process-based physical land surface model ORCHIDEE (tag 2.2) to estimate surface water budget and heat balance for the period 1980-2018. This application takes advantage of high resolution land-use and albedo maps from ESA-CCI database, and various soil map databases. The model was satisfactorily able to reproduce the discharges of each sub-catchment, the actual evapotranspiration fluxes and LAI. With these results, we are able to estimate the the partitioning of the surface water balance of each catchment of the Seine basin. These results have wide ranging implications such as the estimation of energy balance in the basin, the estimation of spatialisation of the aquifer recharge, and the feedback between aquifers and the surface.
How to cite: Kilic, D., Rivière, A., Flipo, N., Ducharne, A., Peylin, P., and Goblet, P.: Integrated modeling of water and heat fluxes in the Seine hydrosystem, France, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4351, https://doi.org/10.5194/egusphere-egu21-4351, 2021.
Changes in the cryosphere caused by global warming are expected to alter the hydrologic system, with inevitable consequences for freshwater availability to humans and ecosystems. Quantitative understandings of the historical hydrologic changes in response to permafrost degradation is essential for projecting future changes with respect to the continuing and possibly intensifying warming. Here we investigate past hydro-climatic changes over three southern Siberian basins with diverse permafrost properties: in the Selenga catchment, all three permafrost types occur, i.e., discontinuous, sporadic and isolated permafrost; the Lena Basin (at gauge Tabaga) is mostly underlain by discontinuous permafrost, while the Aldan is dominated by continuous permafrost.
Based on the reconstruction of terrestrial water storage changes (TWS) from the GRACE satellite mission and hydro-climatic time series over the period 1984-2013, our results show very different change patterns in the TWS among these three basins. There is an unprecedented reduction of TWS (-9.8 km3) in the Selenga basin, but remarkable increases (14.4 km3 and 13.1 km3) in the Lena-Tabaga and Aldan basins, respectively. The diverse changes in TWS, runoff and precipitation over each basin suggest different hydrologic response mechanisms to permafrost degradation under a warming climate. The Selenga, dominated by lateral degradation (i.e., decreasing permafrost extent), suffers severe water loss via deep infiltration of water that was previously stored close to the surface, which induces a drier surface and subsurface drainage system. In contrast, in the Aldan basin, determined by vertical degradation, thicker active layers develop which sustain a water-rich surface and subsurface environment. In the Lena-Tabaga basin finally, which is characterized by both lateral and vertical degradations, the further development of lateral degradation has led to a stronger increase in groundwater storage in comparison to surface runoff during the increased precipitation states, suggesting a potentially groundwater-dominated hydrologic system in this basin. Our findings are of great importance for the regional water management in permafrost-affected regions under ongoing warming.
How to cite: Han, L. and Menzel, L.: Terrestrial hydroclimatic variability in basins of Southern Siberia driven by different states of permafrost degradation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4682, https://doi.org/10.5194/egusphere-egu21-4682, 2021.
In Ethiopia, more than 80% of big freshwater lakes are located in the Rift Valley Lake Basin, which is serving for multipurpose water use of over 30 million people. The basin is one of the most densely populated regions in Ethiopia and it covers an area of 53,035 km2. However, most of the catchments recharging these lakes are ungauged and their water balance is not well quantified, and hence, limiting the development of appropriate water resource management strategies. Prediction for ungauged catchments has demonstrated its effectiveness in hydro-climatic data-rich regions. However, these approaches are not well evaluated in the climatic data-limited condition and the consecutive uncertainty emerging in the small catchments is not adequately quantified. In this study, we use the HBV model to simulate streamflow using global precipitation and potential evapotranspiration products as forcings. We develop and apply a Monte-Carlo scheme to calibrate the model and quantify uncertainty at 16 catchments in the basin where gauging stations are available. Out of these, we use 14 best catchments to derive the best regional regression model by correlating the best calibration parameters, the best validation parameters, and parameters that give the most stable predictions with catchment attributes that are available throughout the basin. A weighting scheme in the regional regression accounts for parameter uncertainty in the calibration. A spatial cross-valuation that is applied 14 times always leaving out one of the gauged catchments provides 14 regional regression functions that express uncertainty regionalization. It also shows that the regionalization procedure that uses the best validation parameters for regionalization provides the most robust results. We then subsequently apply the 14 spatial regression functions of the cross-validation to the remaining 35 ungauged catchments in the Rift Valley Lake Basin to provide regional water balance estimations including quantification of regionalization uncertainty. With these results, our study provides a new procedure to use global precipitation and evapotranspiration products to predict and evaluate streamflow simulation for hydro-climatically data scares regions considering uncertainty. It, therefore, enhances the confidence in the understanding of water balance in those regions and will support the planning and development of appropriate water resource management strategies.
Keywords: Parameters Estimation, Uncertainties, Ungauged Catchment, Weighted Regression, Water Balance
How to cite: Abraham, T., Liu, Y., Tekleab, S., and Hartmann, A.: An Uncertainty Estimation Framework to Quantify the Water Balance of Ethiopian Rift Valley Lake basin, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5014, https://doi.org/10.5194/egusphere-egu21-5014, 2021.
Rapid surface warming in the Arctic region has strong impacts on the Arctic water balance and its individual hydrological components. With the Arctic Ocean being almost entirely surrounded by landmasses and some of the world’s largest rivers draining into it, the link between ocean and surrounding land is remarkably strong. Hence runoff forms one of the key variables in the Arctic freshwater budget and builds the main focus of this study.
Seasonal cycles, as well as annual and seasonal runoff trends are analyzed for the major Arctic watersheds. We first compare river discharge data taken from the reanalysis component from the Global Flood and Awareness System (GloFAS) to available observed river discharge records. GloFAS combines the land surface model from ECMWF’s most recent reanalysis effort ERA5 with a hydrological and channel routing model. Results show that seasonal river discharge peaks are underestimated by GloFAS as well as by direct ERA5 runoff.
Further analysis shows that this discrepancy can be tracked to non-stationary biases in the snow analysis of ERA5, which affect melt and subsequently runoff (Zsoter et al. (2020), https://doi.org/10.21957/p9jrh0xp). It is shown that this bias is substantially improved in ERA5’s downscaled counterpart ERA5-Land. An experimental version of GloFAS that uses ERA5-Land forcing, exhibits improved river discharge values.
Seasonal cycles of ERA5 snow melt show that there is a lag of 1-2 months between the peak in snow melt and observed river discharge, which can be explained by the time it takes for the water to reach the river mouth, but it may also be influenced by water resources management (e.g., Yang et al. (2004), https://doi.org/10.1016/j.jhydrol.2004.03.017 ; Ye et al. (2003), https://doi.org/10.1029/2003WR001991).
In addition, runoff is calculated over the whole pan-arctic region to account for the total freshwater entering the Arctic Ocean from land. Independent mooring-derived estimates of net freshwater flux through the Arctic oceanic gateways show a consistent and strong imprint of the runoff seasonal cycle.
How to cite: Winkelbauer, S., Mayer, M., and Haimberger, L.: Diagnostic evaluation of runoff into the Arctic Ocean and its impact on freshwater transport through Arctic gateways., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7900, https://doi.org/10.5194/egusphere-egu21-7900, 2021.
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