Global, continental, and other large-scale hydrological research is very important in many different contexts. Examples include; increasing understanding of the climate system and water cycle, assessment of water resources in a changing environment, hydrological forecasting, and water resource management.
We invite contributions from across the atmospheric, meteorological and hydrological communities. In particular, we welcome abstracts that address advances in:
(i) understanding and predicting the current and future state of our global and large scale water resources;
(ii) use of global earth observations and in-situ datasets for large scale hydrology and data assimilation techniques for large scale hydrological models;
(iii) understanding and modelling of extremes: like droughts and floods.
(iv) representing and evaluating different components of the terrestrial water cycle fluxes and storages (e.g. soil moisture, snow, groundwater, lakes, floodplains, evaporation, river discharge) and their impact on current and future water resources and atmospheric modelling.
(v) synthesis studies assembling knowledge gained from smaller scales (e.g. catchments or hillslope) to advance our knowledge on process understanding needed for the further development of large scale models and to identify large scale patterns and trends.
vPICO presentations: Thu, 29 Apr
Developing and applying hyper-resolution models over larger extents has long been a quest in hydrological sciences. With the recent developments of global-scale yet fine data sets and advances in computational power, achieving this goal becomes increasingly feasible.
We here present the development, application, and results of the novel 1 km version of PCR-GLOBWB for the period 1981 until 2020. Even though employing global data sets only, we developed, ran, and evaluated the 1 km model for the continent Europe only. In comparison to past versions of PCR-GLOBWB, input data was replaced with sufficiently fine data, for example the recent SoilGrids and MERIT-DEM data. Preliminary results indicate an improvement of model outcome when evaluating simulated discharge, evaporation, and terrestrial water storage.
Additionally, we aim to answer the question to what extent developing hyper-resolution models is actually needed of whether the run times could be saved by using hyper-resolution state-of-the-art meteorological forcing. Therefore, the relative importance of model resolution and forcing resolution was cross-compared. To that end, the ERA5-Land data set was employed at different resolutions, matching the model resolutions at 1 km, 10 km, and 50 km.
Despite multiple challenges still lying ahead before achieve true hyper-resolution, this application of a 1 km model across an entire continent can form the basis for the next steps to be taken.
How to cite: Hoch, J., Sutanudjaja, E., van Beek, R., and Bierkens, M.: On the influence and limitations of hyper-resolution hydrological modelling – application of the 1 km PCR-GLOBWB model over Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-125, https://doi.org/10.5194/egusphere-egu21-125, 2020.
To meet increasing food demands, irrigated agriculture has expanded into semi-arid areas with limited precipitation and surface water availability. This has greatly intensified the dependence of irrigated crops on groundwater withdrawal and caused a steady increase of non-renewable groundwater use. One of the effects of groundwater pumping is the reduction in streamflow through capture of groundwater recharge, with detrimental effects on aquatic ecosystems. The degree to which groundwater withdrawal affects streamflow or groundwater storage depends on the nature of the groundwater-surface water interaction (GWSI). So far, analytical solutions that have been derived to calculate the impact of groundwater on streamflow depletion involve single wells and streams and do not allow the GWSI to shift from connected to disconnected, i.e. from a situation with two-way interaction to one with a one-way interaction between groundwater and surface water. Including this shift and also analyse the effects of many wells, requires numerical groundwater models that are expensive to setup. Here, we introduce a simple conceptual analytical framework that allows to estimate to what extent groundwater withdrawal affects groundwater heads and streamflow. It allows for a shift in GWSI, calculates at which critical withdrawal rate such a shift is expected and when it is likely to occur after withdrawal commences. It also provides estimates of streamflow depletion and which part of the groundwater withdrawal comes out of groundwater storage and which parts from a reduction in streamflow. The framework is used to provide global maps of critical withdrawal rates and timing, the areas where current withdrawal exceeds critical limits, and maps of groundwater depletion and streamflow depletion rates that result from groundwater withdrawal. The resulting global depletion rates are similar to those obtained from global hydrological models and satellites. The analytical framework is particularly useful for performing first-order sensitivity studies and for supporting hydroeconomic models that require simple relationships between groundwater withdrawal rates and the evolution of pumping costs and environmental externalities.
How to cite: Bierkens, M. F. P., Sutanudjaja, E. H., and Wanders, N.: A conceptual analytical framework to assess the large-scale effects of groundwater withdrawal on groundwater storage and surface water flow, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-684, https://doi.org/10.5194/egusphere-egu21-684, 2021.
The dynamic global water cycle is of ecological and societal importance as it affects the availability of freshwater resources and influences extreme events such as floods and droughts. This work is set in the frame of the GlobalCDA Research Unit, which has the goal of developing a calibration/data assimilation approach (C/DA) to improve the quantification of freshwater resources by combining the global hydrological model WaterGAP with geodetic (GRACE, altimetry) and remote sensing data. This presentation focuses on the validation of the C/DA results using an independent in-situ groundwater data set based on ~1500 monitoring boreholes in France.
The resulting validation data set is applied to independently assess the output of several C/DA experiments: data assimilation using different combinations of the available geodetic and remote sensing data sets and different methods of model calibration, based on either an ensemble Kalman filter approach or a Pareto-optimal calibration algorithm.
To further understand in-situ groundwater and WaterGAP data set, we subtract the coherent signals using Empirical orthogonal function (EOF). Over 85% variances can be explained by the first 3 EOFs for both data sets.
How to cite: Hsu, K.-H., Longuevergne, L., Eicker, A., Hasan, M., Güntner, A., Engels, O., Schulze, K., and Kusche, J.: Using high-resolution groundwater data for the validation of a global hydrological model: evaluating WaterGAP and calibration/data assimilation (C/DA) performance over France, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1317, https://doi.org/10.5194/egusphere-egu21-1317, 2021.
Even though cropland cultivation covers over 40% of the planet’s ice free land surface, most regional and global hydrological models either ignore the representation of cropland or consider crop cultivation in a simplistic way or in abstract terms without any management practices. Yet, the water balance of cultivated areas is strongly influenced by applied management practices (e.g. planting, irrigation, fertilization, harvesting). For instance, the SWAT+ model represents agricultural land by default in a generic way where the timing of the cropping season is driven by accumulated heat units. However, this approach does not work for tropical and sub-tropical regions such as the sub-Saharan Africa where crop growth dynamics are mainly controlled by rainfall rather than temperature.
In this study, we present an approach on how to reasonably incorporate crop phenology using decision tables and global datasets of rainfed and irrigated croplands with the associated cropping calendar and fertilizer applications in a SWAT+ model for North Eastern Africa. We evaluate the influence of the crop phenology representation on simulations of Leaf Area Index (LAI) and Evapotranspiration (ET) using LAI remote sensing data derived from Proba-V satellite and WaPOR ET data respectively. Results show that a representation of crop phenology using global datasets leads to improved temporal patterns of LAI and ET simulations especially for regions with a single cropping cycle. However, for regions with multiple cropping seasons, global phenology datasets need to be complemented with local data or remote sensing data to capture additional cropping seasons. We conclude that regional and global hydrological models can benefit from improved representations of crop phenology and the associated management practices. Future work regarding the incorporation of multiple cropping seasons in global phenology data is needed to better represent cropping cycles in global hydrological models.
How to cite: Nkwasa, A., James Chawanda, C., van Griensven, A., and Jägermeyr, J.: Representation of crop phenology and associated management practices in the SWAT+ model using global datasets for large scale hydrological applications, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2390, https://doi.org/10.5194/egusphere-egu21-2390, 2021.
Climate-induced changes in snow cover, river flow, and freshwater ecosystems will greatly affect the indigenous groups in the Alaska and Yukon River Basin. To support policy-making on climate adaptation and mitigation for these underrepresented groups, an ongoing interdisciplinary effort is being made to combine Indigenous Knowledge with western science (https://www.colorado.edu/research/arctic-rivers/).
A foundational component of this project is a high fidelity representation of the aforementioned land surface processes. To this end, we aim to obtain a set of reliable high-resolution parameters for the Community Territory System Model (CTSM) for the continental scale domain of Alaska and the entire Yukon River Basin, which will be used in climate change simulations. CTSM is a complex, physically based state-of-the-science land surface model that includes complex vegetation and canopy representation, a multi-layer snow model, as well as hydrology and frozen soil physics necessary for the representation of streamflow and permafrost. Two modifications to the default CTSM configuration were made. First, we used CTSM that is implemented with hillslope hydrology to better capture the fine-scale hydrologic spatial heterogeneity in complex terrain. Second, we updated the input soil textures and organic carbon in CTSM using the high-resolution SoilGrid dataset.
In this study, we performed a multi-objective optimization on snow and streamflow metrics using an adaptive surrogate-based modeling optimization (ASMO). ASMO permits optimization of complex land-surface models over large domains through the use of surrogate models to minimize the computational cost of running the full model for every parameter combination. We ran CTSM at a spatial resolution of 1/24th degree and a temporal resolution of one hour using the ERA5 reanalysis data as the meteorological forcings. The ERA5 reanalysis data were bias-corrected to account for the orographic effects. We will discuss the ASMO-CTSM coupling workflow, performance characteristics of the optimization (e.g., computational cost, iterations), and comparisons of the default configuration and optimized model performance.
How to cite: Cheng, Y., Newman, A., Swenson, S., Lawrence, D., Craig, A., and Hamman, J.: A novel application of an adaptive surrogate-based modeling optimization (ASMO) for the Community Territory System Model (CTSM) in Alaska, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3776, https://doi.org/10.5194/egusphere-egu21-3776, 2021.
Global hydrological models (GHM) are a useful tool to investigate the water cycle, to evaluate its sensitivity towards systematic changes, e.g. human impacts, and to project future conditions in river catchments for varying scenarios. They have been successfully applied for decades and there is still room for improvement.
Recently, we revised the Max Planck Institute for Meteorology’s Hydrology model (MPI-HM), which is an established GHM that was used in multiple case studies and inter-comparison projects. While still performing well, its source code (mainly Fortran77) has become increasingly difficult to maintain, thus hampering the implementation of new processes. For this reason, the model was rewritten from scratch based on the MPI-HM process formulations. The new model is mainly written in Python, thereby taking advantage of the highly optimized numpy and xarray libraries, and, hence, is aptly renamed to HydroPy. Using the original formulations, we make sure to preserve or even improve the old model’s skill while the switch to Python allows for much easier debugging and interactive model development.
In our presentation, we will evaluate the performance of the new HydroPy model and demonstrate its skill to simulate river discharge. Furthermore, we compare HydroPy to its predecessor MPI-HM and discuss the reasons of differences between their results.
How to cite: Stacke, T. and Hagemann, S.: Introducing HydroPy, an updated global hydrology model rewritten in Python, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3928, https://doi.org/10.5194/egusphere-egu21-3928, 2021.
Data availability at global scale is increasing exponentially. Although considerable challenges remain regarding the identification of model structure and parameters of continental scale hydrological models, we will soon reach the situation that global scale models could be defined at very high resolutions close to 100 m or less. One of the key challenges is how to make simulations of these ultra-high resolution models tractable ().
Our research contributes by the development of a model building framework that is specifically designed to distribute calculations over multiple cluster nodes. This framework enables domain experts like hydrologists to develop their own large scale models, using a scripting language like Python, without the need to acquire the skills to develop low-level computer code for parallel and distributed computing.
We present the design and implementation of this software framework and illustrate its use with a prototype 100 m, 1 h continental scale hydrological model. Our modelling framework ensures that any model built with it is parallelized. This is made possible by providing the model builder with a set of building blocks of models, which are coded in such a manner that parallelization of calculations occurs within and across these building blocks, for any combination of building blocks. There is thus full flexibility on the side of the modeller, without losing performance.
This breakthrough is made possible by applying a novel approach to the implementation of the model building framework, called asynchronous many-tasks, provided by the HPX C++ software library (). The code in the model building framework expresses spatial operations as large collections of interdependent tasks that can be executed efficiently on individual laptops as well as computer clusters (). Our framework currently includes the most essential operations for building large scale hydrological models, including those for simulating transport of material through a flow direction network. By combining these operations, we rebuilt an existing 100 m, 1 h resolution model, thus far used for simulations of small catchments, requiring limited coding as we only had to replace the computational back end of the existing model. Runs at continental scale on a computer cluster show acceptable strong and weak scaling providing a strong indication that global simulations at this resolution will soon be possible, technically speaking.
Future work will focus on extending the set of modelling operations and adding scalable I/O, after which existing models that are currently limited in their ability to use the computational resources available to them can be ported to this new environment.
More information about our modelling framework is at https://lue.computationalgeography.org.
 M. Bierkens. Global hydrology 2015: State, trends, and directions. Water Resources Research, 51(7):4923–4947, 2015.
 K. de Jong, et al. An environmental modelling framework based on asynchronous many-tasks: scalability and usability. Submitted.
 H. Kaiser, et al. HPX - The C++ standard library for parallelism and concurrency. Journal of Open Source Software, 5(53):2352, 2020.
How to cite: de Jong, K., van Kreveld, M., Panja, D., Schmitz, O., and Karssenberg, D.: Global scale hydrological modelling at 100 m, 1 h resolution, in Python, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7154, https://doi.org/10.5194/egusphere-egu21-7154, 2021.
Global hydrological models simulate water storages and fluxes of the water cycle which is important for e.g. water management decisions and drought/flood predictions. However, the models include many uncertainties due to the model inputs (e.g. climate forcing data), model parameters, and model structure resulting in disagreements with observations. To reduce these uncertainties, the models are typically calibrated against in-situ discharge observations or GRACE-derived total water storage anomalies (TWSA) are integrated into the model by data assimilation.
In this study, we introduce a framework for jointly assimilating multiple observations into the WaterGAP 2.2d model over the Mississippi River Basin for 2003-2018. We do not only assimilate GRACE-derived TWSA but also in-situ discharge observations from gauge stations. In addition, we vary the number as well as the location of the considered discharge stations to derive information about e.g. the influence of assimilating down- or upstream stations.
Our results show a strong influence of the GRACE data and that the assimilation of multiple discharge stations resembles the results of a traditional calibration approach. We expect the most downstream stations to have a larger impact on the assimilation results than the more upstream stations (as the downstream stations already include the information of the upstream stations). The gained insights of this study show a great potential to better assess and understand the global freshwater system and become even more relevant in view of the Surface Water and Ocean Topography (SWOT) satellite. SWOT will be launched in 2022 and is expected to allow the derivation of discharge observations globally for rivers wider than 50-100m.
How to cite: Schulze, K., Engels, O., Kusche, J., Gerdener, H., Müller Schmied, H., Niemann, C., Ackermann, S., and Döll, P.: Joint assimilation of GRACE satellite and in-situ discharge observations into a global hydrological model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7511, https://doi.org/10.5194/egusphere-egu21-7511, 2021.
Accurately identifying the interactions between large-scale land cover and regional climate in the water balance components is crucial for our understanding of how the transformation of native vegetation into agricultural areas impacts the water cycle. Yet the available regional models to access water balance components are often too complex and typically highly dependent on a large number of inputs and parameters. This inadvertently leads to relatively high uncertainty in the model components and their interactions, undermining their use for identifying controlling factor and mechanisms associated with key hydrological processes. In this work, we address the need for a parsimonious model by introducing the Soil Water Balance Modelling Environment (SWaBME). SWaBME is a novel parsimonious hydrological model used to assess the water balance partitioning of typical land cover types in Brazil, a country that is constantly affected by high rates of deforestation and agricultural expansion. The SWaBME model uses a Penman-Monteith formulation to estimate, separately and explicitly, the evapotranspiration (ET) in the three main components (bare soil evaporation, transpiration, and evaporation from canopy interception), which allow it to distinguish the effects of climate and land cover on the ET. The SWaBME model requires only five parameters to be prescribed a priori, and also contains a set of parameters which are directly provided by the recent development of global georeferenced data products. SWaBME is calibrated by following an alternative approach which evaluates hundreds of thousands of randomly generated parameter sets against observed monthly evapotranspiration and soil moisture data (when available) that are ultimately tested at a pre-defined set of soft rules to ensure model consistency. The model calibration were done individually at 10 flux sites in Brazil, but we also investigate whether such preferred parameter combinations produce plausible model performances at the country main land-cover and land-use classes: forests, cerrado/woodlands, pasture/grasslands, and soybean and sugarcane crops. From all the parameters combinations, the model was able to satisfactorily retain about 70 to 90% of the sets for forests and cropland biomes, but appears to constrain much more strongly for pasture/grasslands and cerrado biomes with respectively 30% and 1% of the set retained. Most of the introduced soft rules have low to moderate constraining power, and we found that differences in the calibrated parameters for each biome are more pronounced only when the prior information from literature review was used to constrain specific parameters ranges. The performance with the selected parameters showed Root Mean Squared Error of about 20 to 36 mm/month [RR1] at forest and cropland biomes, 23 to 26 mm/month at the cerrado/woodland and 30 to 36 mm/month at pasture/grasslands; ranking slight better when compared to the more complex (in terms of structure and number of parameters) NOAH/GLDAS model with a RMSE ranging from 30 to 60 mm/month. Overall, SWaBME is a parsimonious model aimed at large-scale application of water balance assessment focusing on assessing the impacts of climate and land-use/land-cover changes primarily in Brazil. However, the structure and approach used here can be widely transferred to other regions of the world.
How to cite: Sabino, M., Rosolem, R., Woods, R., Pacheco de Souza, A., Ribeiro da Rocha, H., and Regina Roberti, D.: SWaBME: A PARSIMONIOUS LARGE-SCALE MODEL TO SIMULATE WATER BALANCE COMPONENTS OF TYPICAL LAND COVER TYPES IN BRAZIL, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9298, https://doi.org/10.5194/egusphere-egu21-9298, 2021.
Large-scale hydrological and water management models (LHMs) require reservoir operations schemes to simulate the influence of human systems on natural earth systems. A typical challenge for representing hundreds of reservoirs in a single LHM is creating consistent scheme capable of simulating realistic water storage and release amidst the diversity in the type of operations. We present a new dataset of observed reservoir operations that includes daily storage, inflow, outflow, evaporation, and elevation level time series for approximately 600 large reservoirs (> 0.1km3 storage) located throughout the conterminous United States. These data have been collected through a combination of mining water agencies databases and formal Freedom of Information (FOI) requests. The newly compiled data are used to derive data-driven simulation schemes, which we compare against contemporary, generic approaches adopted in state-of-the-art LHMs. With release-centric reservoir operations scheme usually more sensitive to biases in hydrologic simulations, we introduce a parsimonious scheme that defines storage targets using harmonic functions that can also be easily extrapolated to un-measured dams. This extrapolation allows us to develop realistic reservoir operations for all 1945 large reservoirs in the conterminous United States currently represented in the Global Reservoir and Dams database.
How to cite: Turner, S., Voisin, N., Steyaert, J., and Condon, L.: Data-driven reservoir storage and release scheme parameterized for all large dams of the United States, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13146, https://doi.org/10.5194/egusphere-egu21-13146, 2021.
Global hydrological models (GHMs) have become an increasingly valuable tool in a range of global impact studies related to water resources. However, glacier parameterization is often overly simplistic or non-existent in GHMs. The representation of glacier dynamics and evolution, including related products such as glacier runoff, can be improved by relying on dedicated global glacier models (GGMs). In this study we test the hypothesis that coupling a GGM to a GHM can lead to increased GHM predictive skills and decreased GHM uncertainty through better glacier parameterization. To this end, the GGM GloGEM is coupled with the GHM PCR-GLOBWB 2 within the eWaterCycle II framework. For the years 2001-2012, the coupled model is evaluated against the uncoupled benchmark in 25 large (>50.000 km2) glacierized basins. Across all basins, the coupled model produces higher runoff throughout the melt season. In July and August, it ranges between 100.07% and 352% of the mean monthly benchmark runoff in lowly and highly glaciated basins respectively. The difference can primarily be explained by the inability of PCR-GLOBWB 2 to simulate snow redistribution and glacier retreat, causing an underestimation of glacier runoff. The coupled model better reproduces basin runoff observations primarily in highly glaciated basins, i.e. where the coupling has the most impact. This study underlines the importance of glacier representation in GHMs and demonstrates the potential of coupling a GHM with a GGM for better glacier representation and runoff predictions in glaciated basins.
How to cite: Wiersma, P., Hut, R., Aerts, J., Drost, N., Zekollari, H., and Hrachowitz, M.: Global glacio-hydrological model coupling for streamflow prediction, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15224, https://doi.org/10.5194/egusphere-egu21-15224, 2021.
Snow processes, with the water stored in the snowpack and released as snowmelt, are very important components of the water balance, in particular in high latitude and mountain regions. The evolution of the snow cover and the timing of the snow melt can have major impact on river discharge. Land surface models are used in Earth System models to compute exchanges of water, energy and momentum between the atmosphere and the surface underneath, and also to compute other components of the hydrological cycle. In order to improve the snow representation, a new multi-layer snow scheme is under development in the HTESSEL land surface model of the European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS), to replace the current single-layer snow scheme used in HTESSEL. The new scheme has already been shown to improve snow and 2‐metre temperature, while in this study, the wider hydrological impact is evaluated and documented.
The analysis is done in the reanalysis context by comparing two ERA5-forced offline HTESSEL experiments. The runoff output of HTESSEL is coupled to the CaMa-Flood hydrodynamic model in order to derive river discharge. The analysis is done globally for the period between 1980-2018. The evaluation was carried out using over 1000 discharge observation time-series with varying catchment size. The hydrological response of the multi-layer snow scheme is generally positive, but in some areas the improvement is not clear and can even be negative with deteriorated signal in river discharge. Further investigation is needed to understand the complex hydrological impact of the new snow scheme, making sure it contributes to an improved description of all hydrological components of the Earth System.
How to cite: Arduini, G., Zsoter, E., Cloke, H., Stephens, E., and Prudhomme, C.: Hydrological impact of the new ECMWF multi-layer snow scheme, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16220, https://doi.org/10.5194/egusphere-egu21-16220, 2021.
Global hydrologic models have become an important research tool in assessing global water resources and hydrologic hazards in a changing environment, and for improving our understanding of how the water cycle is affected by climatic changes worldwide. These complex models have been developed over more than 20 years by multiple research groups, and valuable efforts like ISIMIP (Inter-Sectoral Impact Model Intercomparison Project) contribute to our growing understanding of model uncertainties and differences. However, due to their complexity and vast data outputs, they remain a Blackbox to certain extents. Especially for processes that are poorly constrained by available observations – like groundwater recharge – model results vary largely, and it is unclear what processes dominate where and when. With the inclusion of even more sophisticated implementations e.g., coupled global gradient-based groundwater simulations, it is getting more and more challenging to understand and attribute these models' results.
In this talk, we argue that we need to intensify the efforts in investigating uncertainties within these models, including where they originate and how they propagate. We need to carefully and extensively examine where different processes drive the model results by applying state of the art sensitivity analysis methods. To this end, we discuss development needs and describe pathways to foster the application of sensitivity analysis methods to global hydrological models.
How to cite: Reinecke, R., Pianosi, F., and Wagener, T.: Uncertainty attribution in global hydrological models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2386, https://doi.org/10.5194/egusphere-egu21-2386, 2021.
Thorough evaluation of model fidelity is critical to have faith in a model’s capability to simulate surprise; i.e. in the model’s capability to accurately simulate hydrologic behavior under changing conditions. We define three elements that should support such thorough model evaluation. First, test cases with known solution should be used to isolate the implementation of specific processes in a model and to make sure that the model code can reproduce these known solutions. Second, model simulations of individual processes should be compared to observations of these processes to determine the accuracy and appropriateness of the equations used to represent these processes in the model. Third, benchmarks need to be defined that both provide a lower limit to the accuracy we require the model to have and an upper limit to system’s predictability based on the information contained in the input and output observations. We present progress on all three themes covering (1) the development of test cases with known solutions called “laugh tests”; (2) progress in setting up a continental-scale model for process-based model evaluation; (3) critical notes about the still common use of aggregated efficiency criteria for model evaluation; (4) a summary of existing work on the need of lower and upper benchmarks which will inform further benchmarking work. A guiding principle in our work is to make our code available as open-source, so that the community can reproduce and use our work if desired. As such, we explicitly invite the community to share their own thoughts about these topics with us.
How to cite: Knoben, W., Vionnet, V., and Clark, M.: Progress on a comprehensive earth system model evaluation framework, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3102, https://doi.org/10.5194/egusphere-egu21-3102, 2021.
This paper addresses how large-scale terrestrial water cycling is represented in the land surface schemes of Earth System Models (ESMs). Good representation is essential, for example in regional planning for climate change adaptation and in preparation for hydro-climatic extremes that have recently set records world-wide in devastating consequences for societies and deaths of thousands of people. ESMs provide simulations and projections for the climate system and its interactions with the terrestrial hydrological cycle, and are widely used to study and prepare for associated impacts of climate change. However, the reliability of ESMs is unclear with regard to their representation of large-scale terrestrial hydrology and its changes and interactions between its key variables. Despite being crucial for model realism, analysis of co-variations among terrestrial hydrology variables is still largely missing in ESM performance evaluations. To bridge this research gap, we have studied and identified large-scale co-variation patterns between soil moisture (SM) and the main freshwater fluxes of runoff (R), precipitation (P), and evapotranspiration (ET) from observational data and across 6405 hydrological catchments in different parts and climates of the world. Furthermore, we have compared the identified observation-based relationships with those emerging from ESMs and reanalysis products. Our results show that the most strongly correlated freshwater variables based on observational data are also the most misrepresented hydrological patterns in ESMs and reanalysis simulations. In particular, we find SM and R to have the generally strongest large-scale correlations according to the observation-based data, across the numerous studied catchments with widely different hydroclimatic characteristics. Compared to the SM-R correlation signals, the observation-based correlations are overall weaker for the commonly expected closer dependencies of: R on P; ET on P; SM on P; and ET on SM. Nevertheless, this strongest SM-R correlation and the P-R correlation are the most misrepresented hydrological patterns in reanalysis products and ESMs. Our results also show that ESM outputs can perform relatively well in simulating individual hydrological variables, while exhibiting essential inconsistencies in simulated co-variations between variables. Such investigations of large-scale terrestrial hydrology representation by ESMs can enhance our understanding of fundamental ESM biases and uncertainties while providing important insights for systematic ESM improvement with regard to the large-scale hydrological cycling over the world’s continents and regional land areas.
How to cite: Ghajarnia, N., Kalantari, Z., and Destouni, G.: Is large-scale terrestrial hydrological cycling well represented in Earth System Models?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5332, https://doi.org/10.5194/egusphere-egu21-5332, 2021.
In the Mediterranean, climate change effects are projected to be particularly strong, and with them, the already prominent use of groundwater as a source for drinking water and irrigation is likely to increase. The sustainable amount of water that can be extracted from an aquifer is determined by groundwater recharge. Although important as an indicator for groundwater availability, quantification of this process is not sufficiently accurate at large scales due to feedbacks of processes and mechanisms. It is difficult to measure, and its rainfall based simulation is challenging because the absolute uncertainties of other water balance components accumulate, especially in dry areas.
Global hydrological models (GHMs) have proven to be a valuable tool to assess the impacts of climate change on the global water cycle; however, their simulation of groundwater recharge remains uncertain. In this presentation, we show results of an investigation of groundwater recharge by using an ensemble of eight GHMs and four global circulation models (GCMs). The assessment focuses on the Mediterranean for two evaluation periods 1861-2006 and 2006-2100. Of particular interest are the seasonal patterns of groundwater recharge and whether the models show similar seasonal patterns in the past and under different climate change scenarios. The Mediterranean is versatile in terms of topography and climatic conditions. Thus, the variation of groundwater recharge in both spatial and temporal terms is examined thoroughly.
Further, precipitation characteristics can have significant impacts on recharge amounts. Therefore, the correlation of the GCMs daily precipitation data with the modelled recharge is analyzed. Results show a significant variation within the ensemble. Overall, a declining trend in groundwater recharge is dominant.
How to cite: Kretschmer, D., Reinecke, R., Gerner, A., and Disse, M.: Projections of groundwater recharge changes in the Mediterranean: Uncertainties of simulating groundwater recharge in global hydrological models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6064, https://doi.org/10.5194/egusphere-egu21-6064, 2021.
Continental and global dynamic hydrological models have emerged recently as tools for e.g. flood forecasting, large-scale climate impact analyses, and estimation of time-dynamic water fluxes into sea basins. One such tool is a dynamic process-based rainfall-runoff and water quality model Hydrological Predictions for Environment (HYPE). We present and compare historical simulations of runoff, soil moisture, aridity, and sediment concentrations for three nested model domains using global, continental (Europe), and national (Sweden) catchment-based HYPE applications. Future impacts on hydrological variables from changing climate were then assessed using the global and continental HYPE applications with ensembles based on 3 CMIP5 global climate models (GCMs).
Simulated historical sediment concentrations varied considerably among the nested models in spatial patterns while runoff values were more similar. Regardless of the variation, the global model was able to provide information on climate change impacts comparable to those from the continental and national models for hydrological indicators. Output variables that were calibrated, e.g. runoff, were shown to result in more reliable and consistent projected changes among the different model scales than derived variables such as the actual aridity index. The comparison was carried out for ensemble averages as well as individual GCMs to illustrate the variability and the need for robust assessments.
Global hydrological models are shown to be valuable tools for e.g. first screenings of climate change effects and detection of spatial patterns and can be useful to provide information on current and future hydrological states at various domains. The challenge is (1) in deciding when we should use the large-scale models and (2) in interpreting the results, considering the uncertainty of the model results and quality of data especially at the global scale. Comparison across nested domains demonstrates the significance of scale which needs to be considered when interpreting the impacts alongside with model performance.
Bartosova et al, 2021: Large-scale hydrological and sediment modeling in nested domains under current and changing climate. Accepted to Special Issue Journal of Hydraulic Engineering.
How to cite: Bartosova, A., Arheimer, B., de Lavenne, A., Capell, R., and Strömqvist, J.: Are large scale models useful? A case of nested model domains for assessing current and future stream runoff and sediments, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8852, https://doi.org/10.5194/egusphere-egu21-8852, 2021.
Model comparisons are an important exercise to gain new hydrological insight from the diversity in our communities hydrological models. Current practice in model comparison studies is to have each model be run by the creator/representative of that model and combine the results of all these model runs in a single analysis.
In this work we present the first major model comparison done within the eWaterCycle Open Hydrological Platform. eWaterCycle is a platform for doing hydrological experiments where hydrological models are accessed as objects from an (online) Jupyter notebook experiment environment. Through the use of GRPC4BMI and containers, (pre-existing and newly made) models in any programming language can be used, without diving into the code of those models. This makes eWaterCycle ideally suited to compare (and couple) models with widely different model setups: conceptual versus distributed for example. eWaterCycle is FAIR by design: any eWaterCycle experiment should be reproducible by anyone without the support of the original model developer. This will make it easier for hydrologists to work with each other's models and speed up the cycle of hydrological knowledge generation.
In this comparison we’re looking at the impact of the new ERA5 dataset over the older ERA-Interim dataset as a forcing for hydrological models. A key component in making hydrological experiments reproducible and transparent in eWaterCycle is the use of EMSValTool as a pre-processor for hydrological experiments. Using EMSValTool’s recipes structure ensures that model specific input files based on ERA5 or ERA-Interim are all handled identically where possible and that model specific operations are clearly and transparently defined.
We have run 7 models or model-suites (LISFlood, MARRMoT, WFLOW, HYPE, PCRGlobWB 2.0, SUMMA, HBV) for 6 basins forced with both ERA5 and ERA-Interim and compared model outputs against GRDC discharge observations. From this broad comparison we will conclude what the impact of ERA5 over ERA-Interim will be for hydrological modelling in the foreseeable future.
How to cite: Hut, R., Drost, N., Aerts, J., Bouaziz, L., van Verseveld, W., Jagers, B., Baart, F., Hoch, J., Melsen, L., Bennett, A., Arnal, L., Fenicia, F., Santos, L., Gelati, E., dal Molin, M., Knoben, W., Gharari, S., Hall, C., and Hutton, E. and the the Netherlands eSciencecenter eWaterCycle team: Comparing impact of ERA5 vs ERAInterim on hydrology using the eWaterCycle Open Hydrological Platform, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9452, https://doi.org/10.5194/egusphere-egu21-9452, 2021.
One of the goals of the ERA4CS INDECIS project (http://www.indecis.eu/) is to use available climate datasets at the European scale to derive user-oriented indicators. In this framework, we adapted a methodology to compute the present and future groundwater recharge by precipitation at the European scale. This indicator of groundwater availability aims at supporting water resource management.
The scientific approach partly relies on two indexes related to precipitation infiltration at the watershed scale. The first one is the BaseFlow Index (BFI) which is considered as a fair approximation of the average infiltration coefficient for hydrogeological basins. The second one is the Network Development and Persistence Index (IDPR), a cartographic index calculated from the differences between the real river and the theoretical thalwegs networks. The IDPR provides a qualitative indication of infiltration versus runoff, and is now available at the European scale with a 50 m resolution. We computed the mean interannual BFI over the 1981 – 2010 period for more than 350 gauged and not influenced watersheds distributed over France, with various geological contexts and climates. These BFI values proved to be linearly correlated to the spatial average of the IDPR over these watersheds. The relationship between the two datasets established on these gauged basins was then applied to convert the European IDPR map into an effective precipitation infiltration ratio (EPIR) map.
The modelling process finally consisted in computing the effective precipitation at a daily time step on each cell of a mesh covering the European area. Three different water budget models were applied. The only parameter of these models is the soil water capacity provided by European Soil Data Centre. For the present period, the models were fed with the E-OBS datasets available on a 0.25 degree grid. Resulting time series were time-averaged and multiplied by the spatialized EPIR to provide a European map of annual potential recharge by precipitation infiltration. For the future periods, the same methodology can be applied. Ensemble simulations are in progress using EURO-CORDEX climate projections as input of the hydrological models.
How to cite: Lanini, S., Caballero, Y., Le Cointe, P., Pinson, S., and Desprats, J.-F.: Groundwater Recharge Indicators at the European scale, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2327, https://doi.org/10.5194/egusphere-egu21-2327, 2021.
This study has been run in the context of the European Union research project G3P (Global Gravity-based Groundwater Product) on developing Groundwater storage (GW) as a new product for the EU Copernicus Services. GW variations can be derived on a global scale by subtracting from total water storage (TWS) variations based on the GRACE/GRACE-FO satellite missions variations in other water storage compartments such as soil moisture, snow, surface water bodies, and glaciers. Due to the nature of data acquisition by GRACE and GRACE-FO, the data need filtering in order to reduce North-South-oriented striping errors. However, this also leads to a spatially smoothed TWS signal. For a consistent subtraction of all individual storage compartments from GRACE-based TWS, the individual data sets for all other hydrological compartments need to be filtered in a similar way as GRACE-based TWS.
In order to test different filter methods, we used compartmental water storage data of the global hydrological model WGHM. The decorrelation filter known as DDK filter that is routinely used for GRACE and GRACE-FO data introduced striping artifacts in the smoothed model data. Thus, we can conclude that the DDK filter is not suitable for filtering water storage data sets that do not exhibit GRACE-like correlated error patterns. Alternatively, an isotropic Gaussian filter might be used. The best filter width of the Gaussian filter is determined by minimizing the differences between the empirical spatial correlation functions of each water storage and the spatial correlation function of GRACE-based TWS. We also analyzed time variations of correlation lengths such as seasonal effects. Finally, the selected filter widths are applied to each compartmental storage data set to remove them from TWS and to obtain the GW variations.
This study received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement nº 870353.
How to cite: Sharifi, E., Haas, J., Boergens, E., Dobslaw, H., and Güntner, A.: A global analysis of spatial correlation lengths of water storage anomalies , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3104, https://doi.org/10.5194/egusphere-egu21-3104, 2021.
Understanding how deficit of precipitation impacts the hydrological cycle is of growing interest and is essential for water resource management. It has been recently observed that the relationship between precipitation and runoff during droughts is subjected to a shift in the sense that the predicted runoff is much less than the one expected due to the deficit in precipitation. Unraveling why this occurs requires an accurate knowledge of all the components of the water balance equation. However, large-scale and consistent samples of precipitation, runoff, evapotranspiration, ET and change in storage have always been challenging to collect. Here, we hypothesized that blending ground-based and remote-sensing data products could fill this gap. We present a countrywide dataset of catchment-scale water balance, covering the last 10 water years in Italy. Italy shows a broad variety of climatic and topographic features and faced several droughts over recent years. We use ground-based daily runoff data, interpolated precipitation maps, and a remote-sensed daily evapotranspiration dataset from the LSASAF ET product. The ET dataset is additionally compared with flux towers data across the country, obtaining root mean square errors on the order of 30 mm/month. Lastly, changes in storage are estimated to close the water balance. More than 100 catchments - including the major Italian basins - are selected, according to data availability and reliability. These catchments cover a wide range of size, morphologic and climatic characteristics.
This dataset is a strategic source of information to analyze catchment-scale runoff, ET and storage response to climatic variability across climates and landscapes.
How to cite: Bruno, G., Avanzi, F., Gabellani, S., Ferraris, L., Cremonese, E., Galvagno, M., and Massari, C.: Water-balance response to climate variability, a small-to-large scale Italian dataset, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4674, https://doi.org/10.5194/egusphere-egu21-4674, 2021.
Land surface moisture plays a crucial role in precipitation patterns across the globe. Evapotranspiration (the combination of ground evaporation (E), canopy evaporation (I), and transpiration (T)) from the land surface can influence precipitation through local recycling and the propagation of moisture to downwind regions. However, the role of the land surface and of T, E, and I individually in these two processes are not well understood and limit our understanding of the role of the land surface for both drought onset and intensification. Here we use a version of the Community Earth System Model (CESM1.2 with the Community Atmosphere Model CAM5 and the Community Land Model CLM5) with online water tracers to directly track and quantify the movement of T, E and I moisture across North America for the 1985–2015 period. Initial findings suggest that over 50% of summer precipitation for much of central and northern US and Canada comes from the land surface. The tracers also suggest that, with the exception of the US west coast and desert southwest, 40-60% of land precipitation across the continent comes from the T component. The connection between land surface moisture and drought episodes are examined for different regions of North America. The individual roles of T, E, and I in shaping droughts are also examined.
How to cite: Harrington, T., Skinner, C., and Nusbaumer, J.: Connecting the Land Surface to Droughts: How Transpiration, Canopy Evaporation, and Ground Evaporation Impact Droughts Across the North American Continent, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8856, https://doi.org/10.5194/egusphere-egu21-8856, 2021.
Groundwaters found in aquifers play an important role in the hydrological cycle and are essential for human activities and for natural ecosystems. They account for approximately one third of the human fresh water withdrawals and sustain ecosystems by supplying soil moisture during dry periods. Climate change will impact every components of the climate system and aquifers are no exception. Precipitation is the main driver of groundwater recharge and relatively shallow aquifers respond rather quickly to changes in the precipitation rates. Thus, climate change should have an impact on water table depths and could lead to water scarcity and food insecurity in some regions. Therefore, knowing the response of the aquifers to climate change is important to improve the development of mitigation and adaptation plans in water management.
Here, the response of unconfined shallow aquifers to climate change is assessed at the global scale using the global climate model developed in our institute (CNRM) : CNRM-CM6 and CNRM-ESM2. We analyse simulations conducted for the Coupled Model Intercomparison Project 6 (CMIP6) following four pathways of greenhouse gas concentrations until 2100. The CNRM models are the only global climate models representing the physicals processes involving aquifers. Results show that aquifers should replenish at the global scale on average, which is consistent with the projected global intensification of precipitation. However, the evolution of water table depths is not uniform and presents large regional disparities. Additionally to climate change, anthropogenic impacts like intensive groundwater withdrawals for agricultural, domestic and industrial purposes should exacerbate the depletion in some aquifers basins. In order to identify these regions, the evolution of the water table depths is compared with the population density. This analysis highlights the widening risk of water stress in some already aquifer-dependant regions.
How to cite: Costantini, M., Decharme, B., and Colin, J.: Impact of climate change on groundwater : a global assessment with the CNRM climate models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9634, https://doi.org/10.5194/egusphere-egu21-9634, 2021.
Population growth and climate change is projected to increase the pressure on land and water resources, especially in arid and semi-arid regions. This pressure is expected to affect all driving mechanisms of soil salinization comprising alteration in soil hydrological balance, sea salt intrusion, wet/dry deposition of wind-born saline aerosols — leading to an increase in soil salinity. Soil salinity influences soil stability, bio-diversity, ecosystem functioning and soil water evaporation (1). It can be a long-term threat to agricultural activities and food security. To devise sustainable action plan investments and policy interventions, it is crucial to know when and where salt-affected soils occur. However, current estimates on spatio-temporal variability of salt-affected soils are majorly localized and future projections in response to climate change are rare. Using Machine Learning (ML) algorithms, we related the available measured soil salinity values (represented by electrical conductivity of the saturated paste soil extract, ECe) to some environmental information (or predictors including outputs of Global Circulation Models, soil, crop, topographic, climatic, vegetative, and landscape properties of the sampling locations) to develop a set of data-driven predictive tools to enable the spatio-temporal predictions of soil salinity. The outputs of these tools helped us to estimate the extent and severity of the soil salinity under current and future climatic patterns at different geographical levels and identify the salinization hotspots by the end of the 21st century in response to climate change. Our analysis suggests that a soil area of 11.73 Mkm2 located in non-frigid zones has been salt-affected in at least three-fourths of the 1980 - 2018 period (2). At the country level, Brazil, Peru, Sudan, Colombia, and Namibia were estimated to have the highest rates of annual increase in the total area of soils with an ECe ≥ 4 dS m-1. Additionally, the results indicate that by the end of the 21st century, drylands of South America, southern and Western Australia, Mexico, southwest United States, and South Africa will be the salinization hotspots (compared to the 1961 - 1990 period). The results of this study could inform decision-making and contribute to attaining the United Nation’s Sustainable Development Goals for land and water resources management.
1. Shokri-Kuehni, S.M.S., Raaijmakers, B., Kurz, T., Or, D., Helmig, R., Shokri, N. (2020). Water Table Depth and Soil Salinization: From Pore-Scale Processes to Field-Scale Responses. Water Resour. Res., 56, e2019WR026707. https://doi.org/ 10.1029/2019WR026707
2. Hassani, A., Azapagic, A., Shokri, N. (2020). Predicting Long-term Dynamics of Soil Salinity and Sodicity on a Global Scale, Proc. Nat. Acad. Sci., 117, 52, 33017–33027. https://doi.org/10.1073/pnas.2013771117
How to cite: Shokri, N., Hassani, A., and Azapagic, A.: Soil salinization under different climate change scenarios: A global scale analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13668, https://doi.org/10.5194/egusphere-egu21-13668, 2021.
The benefits of harnessing rivers into human use should not come with a disproportionate expense on the Earth system. Especially, freshwater ecosystems suffer greatly from direct and indirect human impacts, such as excessive water withdrawals and climate change, which are expected to only increase in the near future. Here, we aim for quantifying the extent and degree of considerable flow alterations that threaten the well-being of freshwater ecosystems, across the world.
At the global scale, the ecological status of river systems is often assessed using global hydrological models (GHMs) and hydrological environmental flow (EF) methods. These suffer from substantial uncertainties: 1) the GHMs parameterised with variable climate forcings may give highly dispersed discharge estimates and 2) individual hydrological EF methods capture ecosystem water needs poorly. We tackle these sources of uncertainty by introducing a novel methodology: environmental flow envelopes (EFEs). The EFE is an envelope of safe discharge variability between a lower and an upper bound, defined at the sub-basin scale in monthly time resolution. It is based on pre-industrial (1801-1860) discharge and a large ensemble of EF methods, GHMs, and climate forcings, using ISI-MIP2b data. Using the EFE, we can simultaneously assess the frequency and severity of ecosystem-threatening flow alterations.
Comparing post-industrial (1976-2005) discharge to the EFEs, discharge in 32.7% of the total 3860 sub-basins, covering 28.4% of the global landmass, violates the EFE during more than 10% of all months across four GHMs. These violations are considered as severe threats to freshwater ecosystems. The most impacted regions include areas with high anthropogenic pressure, such as the Middle East, India, Eastern Asia, and Middle America. The violations clearly concentrate on the EFE lower bound during low or intermediate flow seasons. Discharge in 61.4% of sub-basins violates the EFE during more than 10% of low flow season months, average violation being 47.5% below the safe limit denoted by EFE lower bound. Indications of significantly increased flows by violations of the EFE upper bound are fewer and further apart, as well as lower bound violations during high flow season.
Although fractional discharge allocations alone cannot fully capture the ecosystem water needs, this study is a step towards less uncertainty in global EF assessments. The introduced method provides a novel, globally robust way of estimating ecosystem water needs at the sub-basin scale. The results of this study underline the importance of the low flow season, during which EFE violations are the most prevalent. While only preliminary evidence of significantly increased flows emerges in relatively few areas, the EFE upper bound would benefit from further research. The EFE methodology can be used for exploring macro-regional areas where anthropogenic flow alteration threatens freshwater ecosystems the most. However, case-specific studies incorporating factors beyond quantitative flow only are required for practical implications.
How to cite: Virkki, V., Alanärä, E., Porkka, M., Ahopelto, L., Gleeson, T., Mohan, C., and Kummu, M.: Environmental flow envelopes: quantifying ecosystem-threatening flow alterations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14627, https://doi.org/10.5194/egusphere-egu21-14627, 2021.
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