Displays

Groundwater remains one of the least comprehensively monitored storage components in the hydrological cycle, because it's flow and storage processes are strongly linked to geology of the underground and because direct observations from well sites provide only point observations of complex and partly deep aquifer systems.

In recent years, geodetic methods have become increasingly available to complement ground-based observations and to expand investigations of the impact of climate extremes or human water use on groundwater storage variability. Satellite gravimetry from the Gravity Recovery And Climate Experiment (GRACE/FO) has been shown to be sensitive to groundwater depletion at large spatial scales (> 300km) and relatively high temporal resolution (monthly). These data provide a valuable boundary condition for regional studies, and they have been applied widely to improve parameter and structure of hydrological models.

Moreover, changes in groundwater stocks cause surface deformation associated with regional elastic loading of the Earth’s crust and localized poroelastic compaction of the aquifer skeleton, which are detectable by GPS and InSAR. The loading signal is typically much smaller than the land subsidence due to poroelastic compaction and thus masks out the loading signal adjacent to the aquifer system. However, the poroelastic signal can be used to estimate groundwater volume change in confined aquifer units and provides insight into the mechanical properties of the aquifer system. Also, the deformation sensors provide spatial resolutions of tens of meters (e.g., InSAR) to several kilometers (e.g., GPS) that can be used to solve for the volume of fluid removed from the aquifer system.

In this presentation, we demonstrate and discuss the applicability of poroelastic modeling, by applying GPS and InSAR based observations of vertical land motion, to quantify groundwater storage changes. Using the Central Valley in California as an example, we will show when this approach is applicable and when it is not, depending on the type of aquifer and observed deformation compared to water level changes. Using a 1-D poroelastic calculation based on deformation data, we find a groundwater loss of 21.3±7.2 km³ for the entire Central Valley during 2007-2010 and of 29.3±8.7 km³ for the San Joaquin Valley during 2012-2015. These loss estimates during drought are consistent with that of GRACE-based estimates considering uncertainty ranges.

Finally, we will discuss the increased availability of high-resolution radar data from Sentinel 1A/B as well as the upcoming radar mission NASA-ISRO SAR Mission (NISAR), to be launched in 2022, and how this will allow for high-resolution monitoring of vertical land motion and with that of compaction in confined aquifers around the world. The availability of these datasets increases the capability of geodetic methods for groundwater monitoring at higher spatial resolution than GRACE data, hence, providing the potential to apply these datasets to further improve parameterization and formulation of groundwater routines in regional to large-scale hydrological models.

How to cite: Werth, S., Shirzaei, M., Carlson, G., and Ojha, C.: Remote sensing of groundwater storage change - past, present and future, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12302, https://doi.org/10.5194/egusphere-egu2020-12302, 2020.

The Arkavathy river was once a major water supply source to the city of Bangalore, India, till 1970s but has completely dried up post the 1990s. The study re-invigorates on the socio-hydro dynamics in the Upper Arkavathy Catchment (UAC), covering 1432 km², through the combination of latest remote sensing products (namely Gravity Recovery and Climate Experiment (GRACE), Global Land Data Assimilation System (GLDAS), Landsat derived NDVI). The parameters of remotely sensed long-term precipitation and temperature from corresponded well with in-situ data. Seasonal trend analysis helped re-instate no evidence of climatic driven drought to explain the decline of flows in the river. To investigate the anthropogenic proximate drivers of change - mainly groundwater exploitation and increase in water intensive cropping in the catchment - a spatio-temporal assimilation of GRACE TWS, GLDAS state variables and LandSAT-NDVI with in-situ well observations is incorporated into the water balance equation. While, studies have shown high correlation in quantifying groundwater storage changes (GWSC) and attempted downscaling with this GRACE-GLDAS-GWL-NDVI assimilation in natural catchments, this did not seem to be very skilful in human-altered fractured rock aquifers of south India for the following reasons. Firstly, the GRACE-TWS (RL-06) for the grid showed a meagre declining trend of -.033mm/year (2002-2018) and did not seem to capture the deeper groundwater extraction as compared to the social narrative in shift of hundreds of metres decline in static water levels. Secondly, the disaggregation through the GLDAS-NOAH soil moisture which corresponded well with rainfall patterns, assigns inclusion of only the shallow storage fluxes in the sub-surficial aquifer showing -5.3mm/year, which explains no overland flows in the river, but neglects the modelling of the GW aquifer and showed a faulty +47.4mm/year (2002-2018). Thirdly, the simple addition of groundwater observation well trends showed a decrease of -106.6mm/year in GWSC (2001-2017) as compared to the -656.6mm/year (1970-2000) of field scale models by Srinivasan et.al (2015). This is attributed to the fact that data used in such studies from the governmental groundwater authority boards are generally of shallower wells (up to 70m below surface) and cannot be representative of the on-ground reality of shift to deeper exploitation of GW (up to 350m) by privatised borewells. Finally, cloud-cover and scan line error corrected NDVI pixels showed an increase of irrigated area in the UAC by 31% (1972-2018). However, we observed long term data gaps (1998-2003) in images and higher uncertainties during the crucial cropping season due to monsoonal cloud cover (JJASO months) in the images to effectively understand the agricultural dynamics. Hence, it is concluded that this  procedure coupled with this period receiving higher rainfall with an average of1000mm/year (2001-2019) as compared to 800mm/year (1901-2000) makes it an unreliable method to disassociate the human interventions in modifying hydro-geologic fluxes or patterns accurately in the UAC.

How to cite: Kulkarni, T., Gassmann, M., and Vajedian, S.: On the difficulties in estimating water balance components from remote sensing in an anthropogenically modified catchment in southern India, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2197, https://doi.org/10.5194/egusphere-egu2020-2197, 2020.

Global hydrological models often ingest remotely-sensed observations supported by ground-truthed data in attempts to better quantify water balance components, e.g. soil water content, evapotranspiration, runoff/discharge, groundwater recharge. However, the scaling up process from local observations to that global, coarse, scale contains large uncertainty, often undermining the relevance of water balance calculations.

With recent more advanced high-resolution satellite data, freely available at 10m spatial resolution and (sub-) weekly temporal resolution, there is a possibility to reduce uncertainty in that upscaling. However, there are two challenges in doing so when working with global models: exponential increase of computational effort, and the need for quantifying the yet unknown uncertainty of assumptions that coarse global model cells and their underlying equations bring.

This study hypothesises that a bottom-up approach with high-resolution satellite data and in situ observations will better constrain and quantify uncertainty. By using these more spatially-explicit data, we make the case that the estimation of water balance components should become more data-driven. We propose a more data-driven model that improves uncertainty of estimation and scalability by using more sophisticated, remotely-sensed interpolation layers.

Our case study shows New Zealand-wide estimates of evapotranspiration and groundwater recharge at two resolutions: 1km x 1km, using an earlier developed model and MODIS satellite data; and a novel approach at 10m x 10m using Sentinel-1 and Sentinel-2 data to better incorporate impervious areas (e.g., anthropogenic urbanised land covers) and small land patches (e.g., small forestry areas). We then study the implications of using different spatial scales and quantify the differences between 10m x 10m versus 1km x 1km model pixel estimates. Our method is applied in the Google Earth Engine, a free-for-research high performance cloud computing facility, hence providing powerful computational resources and making our approach easily scalable to global, regional and catchment scales. 

Finally, we discuss what underlying model assumptions in traditional models could be changed to facilitate a method that works consistently at these different scales.

How to cite: Westerhoff, R., Mourot, F., and Tschritter, C.: Improvement of uncertainty estimation in global hydrological models by using high resolution satellite data as an interpolator, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4323, https://doi.org/10.5194/egusphere-egu2020-4323, 2020.

The knowledge of global precipitation is of crucial importance to the study of climate dynamics and the global water cycle in general. Although global precipitation climatologies have existed for some time, and their understanding has improved dramatically due to the vast amount of different data sources, their information has not been comprehensive enough due to precipitation spatial-temporal variability. Thus, ground station reports are, in some cases, not representative of the surrounding areas. Remote sensing data and model simulations complemented the traditional surface measurements and offered unprecedented coverage on a global scale. It is important to note that satellite data records are now of sufficient time frame lengths and with methods “mature” enough to develop meaningful precipitation climatologies that are able to provide information on precipitation patterns and intensities on a global scale. While data (and in some cases exploration/visualization tools as well) are widely available, each dataset comes with different spatial resolution, temporal resolution, and biases.

Consequently, this unique opportunity to obtain a robust quantification of global precipitation has been hindered by the uncertainty, already revealed in the first attempts of the unification of different data products. Herein, we present a multi-source quantification of global precipitation, focusing on the description of the underlying uncertainties. Our approach combines station (CRU, GHCN-M, PRECL, UDEL, and CPC Global), remote sensing (PERSIANN, PERSIANN-CCS, PERSIANN-CDR, GPCP, GPCP_PEN_v2.2, CMAP, and CPC-Global) and reanalysis (NCEP1, NCEP2, and 20CRv2) data products, providing an updated overview of the role of precipitation in global water cycle.

How to cite: Vargas Godoy, M. R., Pradhan, R. K., Pratap, S., Rahim, A., and Markonis, Y.: Multi-source quantification of precipitation in the global water cycle, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10920, https://doi.org/10.5194/egusphere-egu2020-10920, 2020.

Satellite-based remote sensing offers potential pathways for accurately closing the water and energy balance of watersheds from observations, a fundamental challenge in hydrology. However, previous attempts based on purely satellite-based estimates have been hindered by large data uncertainties and lack of estimates for key components, such as runoff. Here, we use a novel approach based on the Budyko hypothesis to quantify both the degree of closure and its uncertainties in watershed-scale water and energy balance closure arising from an ensemble of 56 global satellite datasets for precipitation (P), terrestrial evaporation (ET), and net radiation (Rn). We use 7 quasi-global precipitation datasets which include CHIRPS, CMORPH, PERSIANN, PERSIANN-CCS, PERSIANN-CDR, TRMM 3B42RT, TRMM 3B43. For ET, we use 8 datasets - AVHRR, SSEBOp, MOD16A3, GLEAM v3.3a, GLEAM v3.3b, CSIRO-PML, BESS, and FluxCom. For Rn, we use the CERES dataset. We find large spatial variability along with aridity, elevation and other gradients. Results show that errors in water and energy balance closure can be attributed primarily to uncertainties in terrestrial evaporation data. These findings have implications for improving the understanding of global hydrology and regional water management and can guide the development of satellite remote sensing datasets and earth system models. In addition, we rank the P and ET datasets that perform the best in closing the combined water and energy balance of global catchments. For P, we see that gauge-calibrated datasets such as PERSIANN-CDR, TRMM 3B43 perform the best. In terms of ET, we see that BESS performs the best in the northern boreal forests and GLEAM performs the best in drylands.

How to cite: Alam, S., Koppa, A., Miralles, D. G., and Gebremichael, M.: Closing the Combined Water and Energy Balance of Global Watersheds Based on Satellite Data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12736, https://doi.org/10.5194/egusphere-egu2020-12736, 2020.

Water is at the centre of economic and social development; it is vital to maintain health, grow food, manage the environment, produce renewable energy, support industrial processes and create jobs. Despite the importance of water, to date over one third of the world's population still lacks access to drinking water resources and this number is expected to increase due to climate change and outdated water management. As over half of the world’s potable water supply is extracted from rivers, either directly or from reservoirs, understanding the variability of the stored water on and below landmasses, i.e., runoff, is of primary importance. Apart from river discharge observation networks that suffer from many known limitations (e.g., low station density and often incomplete temporal coverage, substantial delay in data access and large decline in monitoring capacity), runoff can be estimated through model-based or observation-based approaches whose outputs can be highly model or data dependent and characterised by large uncertainties.

On this basis, developing innovative methods able to maximize the recovery of information on runoff contained in current satellite observations of climatic and environmental variables (i.e., precipitation, soil moisture, terrestrial water storage anomalies and land cover) becomes mandatory and urgent. In this respect, within the European Space Agency (ESA) STREAM Project (SaTellite based Runoff Evaluation And Mapping), a solid “observational” approach, exploiting space-only observations of Precipitation (P), Soil Moisture (SM) and Terrestrial Water Storage Anomalies (TWSA) to derive total runoff has been developed and validated. Different P and SM products have been considered. For P, both in situ and satellite-based (e.g., Tropical Rainfall Measuring Mission, TRMM 3B42) datasets have been collected; for SM, Advanced SCATterometer, ASCAT, and ESA Climate Change Initiative, ESA CCI, soil moisture products have been extracted. TWSA time series are obtained from the latest Goddard Space Flight Center’s global mascon model, which provides storage anomalies and their uncertainties in the form of monthly surface mass densities per approximately 1°x1° blocks.

Total runoff estimates have been simulated for the period 2003-2017 at 5 pilot basins across the world (Mississippi, Amazon, Niger, Danube and Murray Darling) characterised by different physiographic/climatic features. Results proved the potentiality of satellite observations to estimate runoff at daily time scale and at spatial resolution better than GRACE spatial sampling. In particular, by using satellite TRMM 3B42 rainfall data and ESA CCI soil moisture data, very good runoff estimates have been obtained over Amazon basin, with a Kling-Gupta efficiency (KGE) index greater than 0.92 both at the closure and over several inner stations in the basin. Good results found for Mississippi and Danube are also encouraging with KGE index greater than 0.75 for both the basins.

How to cite: Camici, S., Brocca, L., Massari, C., Giuliani, G., Sneeuw, N., Farahani, H. H., Restano, M., and Benveniste, J.: An observation-based approach for global runoff estimation: exploiting satellite soil moisture and Grace, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13718, https://doi.org/10.5194/egusphere-egu2020-13718, 2020.

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. Its goal consists of developing a calibration/data assimilation approach (C/DA) to improve the tracking and predicting of freshwater availability by combining data from the global hydrological model WaterGAP with geodetic (GRACE, altimetry) and remote sensing data using an ensemble Kalman filter. The aim of this study is focused on the validation of C/DA results using independent datasets. We propose a double strategy: (1) we use regional models. We apply the high-resolution regional model AquiFR, a platform coupling the SURFEX land surface model with a set of hydrogeological models, providing storage changes in each individual compartments at daily time steps with a resolution of 8 km. (2) We built a large dataset of ~3000 monitoring boreholes in France. In order to compare the irregularly sampled borehole data with C/DA results, several interpolation methods are tested.

How to cite: Hsu, K.-H., Longuevergne, L., and Eicker, A.: How to validate calibrated/assimilated global hydrological outputs with high resolution groundwater data and regional models: an investigation of WaterGAP performance over France , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6587, https://doi.org/10.5194/egusphere-egu2020-6587, 2020.

The predictive ability of a hydrological model depends among others on how well the model is calibrated by model parameter adjustment. When calibrating spatially distributed models such as global hydrological models in which river basins are represented by laterally connected grid cells of mostly 0.5° latitude by 0.5° longitude, it is not appropriate and possible to adjust the parameters of each grid cell individually. This is mainly due to the lack of high-resolution observations but also due to the required computational effort. It needs to be investigated which spatial extent of calibration units for which parameters are uniformly adjusted, is optimal given the available observations and the characteristics of the region or river basin. To explore the effect of size and number of calibration units, the WaterGAP Global Hydrological Model (WGHM) was calibrated for a large river basin in North America, the Mississippi basin, successively dividing the basin into smaller calibration units, i.e., sub-basins, in order to examine the feasibility and value of reducing the size of calibration units for the given set of observations. Total water storage anomalies from GRACE satellites, snow cover from MODIS and in-situ streamflow were used as observations in an ensemble-based multi-criterial Pareto Optimization Calibration (POC) framework using the Borg-MOEA optimization algorithm.

How to cite: Hasan, H. M., Güntner, A., Shadkam, S., and Döll, P.: Multi-criterial calibration of a global hydrological model for the Mississippi basin: Exploring the effect of the number of calibration units, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7107, https://doi.org/10.5194/egusphere-egu2020-7107, 2020.

The representation of the water and energy cycle in environmental models is closely linked to the parameter values used in the process parametrizations. The dimension of the parameter space in spatially distributed environmental models corresponds to the number of grid cells multiplied by the number of parameters per grid cell. For large-scale simulations on national and continental scales, the dimensionality of the parameter space is too high for efficient parameter estimation using inverse estimation methods. A regularization of the parameter space is necessary to reduce its dimensionality. The Multiscale Parameter Regionalization (MPR) is one approach to achieve this.

MPR translates local geophysical properties into model parameters. It consists of two steps: 1) local high-resolution geophysical data sets (e.g. soil maps) are translated into model parameters using a transfer function. 2) the high-resolution model parameters are scaled to the model resolution using suitable upscaling operators (e.g., harmonic mean). The MPR technique was introduced into the mesoscale hydrologic model (mHM, Samaniego et al. 2010, Kumar et al. 2013) and it is key factor for its success on transferring parameters across scales and locations.  

In this study, we apply MPR to vegetation and soil parameters in the land surface model HTESSEL. This model is the land-surface component of the European Centre for Medium-Range Weather Forecasting seasonal forecasting system. About 100 hard-coded parameters have been extracted to allow for a comprehensive sensitivity analysis and parameter estimation.

We analyze simulated evaporation and runoff fluxes by HTESSEL using parameters estimated by MPR in comparison to a default HTESSEL setup over Europe. The magnitude of simulated long-term fluxes deviates the most (up to 10% and 20% for evapotranspiration and runoff, respectively) in regions with a large subgrid variability in geophysical attributes (e.g., soil texture). The choice of transfer functions and upscaling operators influences the magnitude of these differences and governs model performance assessed after calibration against observations (e.g. streamflow).

References:

Samaniego L., et al.  https://doi.org/10.1029/2008WR007327

Kumar, R., et al.  https://doi.org/10.1029/2012WR012195

How to cite: Thober, S., Kelbling, M., Pappenberger, F., Prudhomme, C., Balsamo, G., Schweppe, R., Attinger, S., and Samaniego, L.: Improvement of the simulation of the water and energy cycle using Multiscale Parameter Regionalization (MPR), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11596, https://doi.org/10.5194/egusphere-egu2020-11596, 2020.

Hydrological forecasting benefits substantially from good initial conditions, which translate information into the forecast. It is therefore important to perform frequent updates of the initial state of the model before the forecast, which demands good meteorological forcing data. For a continental or global hydrological model, it is difficult to find observational data sets which fulfill the requirements of (i) long time series for calibration and spin up, (ii) consistent quality, (iii) at least daily time steps, and (iv) at least data for temperature and precipitation. HydroGFD3 is a new data set that fulfills all the criteria and provides real-time updated data.

HydroGFD3 builds upon the ERA5 reanalysis data set, and performs a bias correction for each new produced month. In contrast to earlier versions (Berg et al., 2018), HydroGFD3 is based on a multi-source climatological background, upon which individual days are produced by adding anomalies from different freely available monthly global observational data sets. These are then disaggregated based on the ERA5 reanalysis. For production redundancy and local tailoring, HydroGFD3 is produced in several tiers, each using different observational data sets originating from GPCC and CPC. Further, intermediate daily updates of the reanalysis through the source ERA5T allow the data set to be updated to within a few days of real-time.

To reach actual real-time, one tier is based on a bias correction method calibrated on the period 1980-2009, which is applied on ERA5T, and further prolonged to current day using the ECMWF deterministic forecasts. The assumption for this to work is that the forecasts have a similar bias as the reanalysis model, which is currently the case. The method also allows bias correction of the forecasts themselves; solving the issue of “drift” in the forecasts as the hydrological model adjusts to the (biased) climatological state of the forcing data.

Berg, Peter, Chantal Donnelly, and David Gustafsson. "Near-real-time adjusted reanalysis forcing data for hydrology." Hydrology and Earth System Sciences 22.2 (2018): 989-1000.

How to cite: Berg, P., Almén, F., Bozhinova, D., and Mook, R.: HydroGFD3: a climatological and real-time updated hydrological forcing dataset, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8849, https://doi.org/10.5194/egusphere-egu2020-8849, 2020.

We introduce a set of global high-resolution (0.05^◦) precipitation (P) climatologies corrected for bias using streamflow (Q) observations from 9372 stations worldwide. For each station, we inferred the “true” long-term P using a Budyko curve, an empirical equation relating long-term P, Q, and potential evaporation. We subsequently calculated long-term bias correction factors for three state-of-the-art P climatologies (WorldClim V2, CHELSA V1.2, and CHPclim V1), after which we used random forest regression to produce global gap-free bias correction maps for the climatologies. Monthly climatological bias correction factors were calculated by disaggregating the long-term bias correction factors based on gauge catch efficiencies. We found that all three climatologies systematically underestimate P over parts of all major mountain ranges globally, despite the explicit consideration of orography in the production of each climatology. Additionally, all climatologies underestimate P at latitudes > 60^◦N, likely due to gauge under-catch. Exceptionally high long-term correction factors (> 1.5) were obtained for all three climatologies in Alaska, High Mountain Asia, and Chile — regions characterized by marked elevation gradients, sparse gauge networks, and significant snowfall. Using the bias-corrected WorldClim V2, we demonstrated that other widely used P datasets (GPCC V2015, GPCP V2.3, and MERRA-2) severely underestimate P over Chile, the Himalayas, and along the Pacific coast of North America. Mean P for the global land surface based on the bias-corrected World-Clim V2 is 862 mm yr−1 (a 9.4 % increase over the original WorldClim V2). The annual and monthly bias-corrected P climatologies have been released as the Precipitation Bias CORrection (PBCOR) dataset — downloadable via www.gloh2o.org/pbcor.

How to cite: Alvarez-Garreton, C., Beck, H., Wood, E., McVicar, T. R., Zambrano-Bigiarini, M., Baez-Villanueva, O. M., Sheffield, J., and Karger, D. N.: Bias correction of global high-resolution precipitation climatologies using streamflow observations from 9372 catchments , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10699, https://doi.org/10.5194/egusphere-egu2020-10699, 2020.

Stable isotope ratios of H and O in water are powerful tracers that have supported estimation and partitioning of hydrological fluxes at scales from individual catchments to the globe. Most studies, however, assume for simplicity and lack of constraints that isotopic fluxes associated with groundwater recharge and abstraction are in balance. We present a critical assessment of this assumption based on new gridded, 3-dimensional maps of the isotopic composition of groundwaters across the contiguous United States. These show that 1) the isotopic composition of shallow (recently recharged) groundwater differs from that of incident or basinally-integrated precipitation across much of the study area, implying selective recharge of precipitation, and 2) the approximate production-weighted isotope ratios of groundwater differ substantially from recently recharged water in many regions, implying an imbalance in isotope fluxes to/from the subsurface. Accounting for these imbalances leads us to revised estimates of the relative roles of various runoff generation processes and evapotranspiration sources in US-wide isotope mass balances.

How to cite: Bowen, G., Allen, S., and Good, S.: Incorporating groundwater disequilibrium in large-scale, isotopically-constrained water budgets, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12491, https://doi.org/10.5194/egusphere-egu2020-12491, 2020.

The Southern Ice Field (CHS) corresponds to one of the largest continental ice plains, representing a water source for the entire globe. It extends from 40°20' S to 51°30' S, covering an area of approximately 16.800 km² and consisting of 49 glaciers distributed in the southern territory of Chile and part of the Argentine Patagonia. Due to climatic change, the CHS has been affected, like all the ecosystems that compose the planet, generating disturbances in their natural state, consequently, the systems that constitute the CHS tend to look for a new balance. However, the new state(s) of equilibrium can present a great deal of variability, which is why the Intergovernmental Panel on Climate Change (IPCC) has drawn up the Representative Concentration Pathways (RCP), which aim to account for the effects of climate change by representing the total radiative forcing calculated for the year 2100 and including the net effect of Greenhouse Gases (GHG), in addition to other anthropogenic forcing. Based on this, the main objective of the present study is to give an account of a projection and simulation of the water balance in the CHS, informing about the physical processes occurred in the historical period (1970 - 2005), the current period considering a near past and future (2006 - 2050) and a projection to the distant future (2051 - 2100). The simulation of the water balance considers two General Circulation Models (GCMs: MPI-ESM and CSIRO-Mk3-6-0), which are numerical models frequently implemented to simulate the effects of climate change. These models are evaluated under two RCP scenarios 4.5 and 8.5, giving the most unfavorable results under the latter scenario when evaluating the CSIRO-Mk3-6-0 model, since temperature increases of up to 8°C and an oscillating precipitation regime are observed. On the other hand, the MPI-ESM model indicates increases of 1.5°C and 2.5°C accordingly to each scenario and decreases of ± 1/3 of the current observed precipitation. Both models, when evaluated in the previously mentioned scenarios, indicate that the basins that make up the CHS present an emptying to a greater or lesser degree according to the scenario, for which reason, the ice mass that makes up the CHS will follow the behavior it has experienced up to now and will continue to detach itself. To this last, we must add the effect of the decreases in precipitations that reach an average deficit of 30 mm by year 2050 and increases in temperature that exceed the values reported by the IPCC (2019), which they look for to control the effects of the climatic change in the present situation.

How to cite: Jerez Toledo, C. and Vargas Mesa, X.: Projections and simulation of water balance in the Southern Ice Field, Patagonia, Chile, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11888, https://doi.org/10.5194/egusphere-egu2020-11888, 2020.

Vegetation structure and activity are the crucial links between the water, carbon and energy cycles. However, their representation remains a major source of uncertainty in large-scale models. Hydrological models not only vary in the way they include vegetation and its interaction with water, but also become less tangible when their complexity increases. This poses a challenge in validating these models, as shown by several comparisons of dynamic vegetation models.

In this context, the increasing availability and quality of Earth observation-based data provides a new avenue and valuable information to improve model simulations and gain insights into the role of vegetation within the global water cycle. On the one hand, such observations can be used to calibrate model parameters. On the other hand, more vegetation related data allows new approaches to describe in-model vegetation characteristics beyond the values that are traditionally defined for plant functional types.

In this study, we use a simple and highly transparent global hydrological model and constrain its vegetation related parameters against diverse Earth observation-based data. We include GRACE terrestrial water storage anomalies, GlobSnow snow water equivalent, ESA CCI soil moisture as well as estimates of evapotranspiration from FLUXCOM and gridded runoff from GRUN in a multi-criteria calibration approach that considers the strengths and uncertainties of each data stream.

Further, we conduct several factorial experiments to test alternative approaches for representing vegetation characteristics that influence processes like infiltration, root water uptake and transpiration. The approaches range from the simple differentiation of vegetated and non-vegetated areas over applying plant functional type-specific parameters to defining vegetation characteristics as functions of Earth observation-based data such as EVI, tree cover and estimates of plant rooting depth.

For each of the experiments, the model is calibrated and the results are finally compared with each other and against observations to quantify the ability to reproduce observational patterns and to assess the effects of vegetation on simulated hydrological processes across spatio-temporal scales.

How to cite: Trautmann, T., Koirala, S., Carvailhais, N., Güntner, A., Kim, H., and Jung, M.: Using Earth observation data of vegetation to improve global hydrological simulations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9951, https://doi.org/10.5194/egusphere-egu2020-9951, 2020.

State of the art global hydrological models (GHMs) are able to assess continental water storages and fluxes. Different current GHMs provide conflicting estimates of e.g. evapotranspiration or discharge, resulting in differing water availability or climate change impact estimates. The Global Calibration and Data Assimiliation project (GlobalCDA) aims at enhancing our understanding of global freshwater resources by combining state-of-the-art hydrological modelling with new data assimilition and calibration methods using multiple geodetic and remote sensing data.

This study is part of the hydrological model development efforts within GlobalCDA and analyzes the effect of the adaptation and implementation of an existing dynamic floodplain model (Adam, 2017) into WaterGAP2.2d, a state-of-the-art GHM. The implemented floodplain model approach combines the modeling of a two-way river-floodplain interaction, downstream water transport within river and floodplain and flood-plain-groundwater interactions.

The effect of information on the water level of surface water bodies on the model results is assessed using the Amazon basin as study area. Observed river discharge is used to assess the changes in model efficiency as floodplains and other wetlands have a strong impact on river discharge dynamics. This study shows the value of the modeling of large floodplains and wetlands for an improved estimation of terrestrial water cycle components.

How to cite: Niemann, C., Wissenbach, S., and Döll, P.: Assessment of the effect of including information on the water level of surface water bodies into large scale hydrological modelling – Case study Amazon basin, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21834, https://doi.org/10.5194/egusphere-egu2020-21834, 2020.