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HS2.5.2

EDI
Recent advancement in estimating global, continental and regional scale water balance components

Since early work on the assessment of global, continental and regional-scale water balance components, many studies use different approaches including global models, as well as data-driven approaches that ingest in-situ or remotely sensed observations or combinations of these. They attempted to quantify water fluxes (e.g. evapotranspiration, streamflow, groundwater recharge) and water storage on the terrestrial part of the Earth, either as total estimates (e.g. from GRACE satellites) or in separate compartments (e.g. water bodies, snow, soil, groundwater). In addition, increasing attention is given to uncertainties that stem from forcing datasets, model structure, parameters and combinations of these. Current estimates in literature show that flux and storage estimates differ considerably due to the methodology and datasets used such that a robust assessment of global, continental and regional water balance components remains challenging.

This session is seeking for contributions focusing on:
i. past/future assessment of water balance components (fluxes and storages) such as precipitation, freshwater fluxes to the oceans (and/or inland sinks), evapotranspiration, groundwater recharge, water use, changes in terrestrial water storage or individual components at global, continental and regional scales,
ii. application of innovative explorative approaches undertaking such assessments – through better use of advanced data driven, statistical approaches and approaches to assimilate (or accommodate) remote sensing datasets for improved estimation of terrestrial water storages/fluxes,
iii. analysis of different sources of uncertainties in estimated water balance components,
iv. examination and attribution of systematic differences in storages/flux estimates between different methodologies, and/or
v. applications/consequences of those findings such as sea level rise and water scarcity.

We encourage submissions using different methodological approaches. Contributions could focus on any of the water balance components or in an integrative manner with focus on global, continental or regional scale applications. Assessments of uncertainty in past/future estimates of water balance components and their implications are highly welcome.

Convener: Stephanie EisnerECSECS | Co-conveners: Hannes Müller SchmiedECSECS, Lukas Gudmundsson, Rohini Kumar, Robert ReineckeECSECS
Presentations
| Wed, 25 May, 15:10–16:33 (CEST)
 
Room 2.17

Wed, 25 May, 15:10–16:40

Chairpersons: Robert Reinecke, Stephan Thober

15:10–15:13
Introduction

15:13–15:23
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EGU22-6085
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ECS
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solicited
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On-site presentation
Basil Kraft et al.

While deep learning models are capable of representing complex temporal processes in a data-adaptive way, they lack physical consistency and interpretability. Thus, the combination of machine learning and physically-based approaches in so-called hybrid modeling has been proposed recently [1]. Gathering insights into complex Earth system processes in a data-driven way has, arguably, a large potential for hydrology. This is, on the one hand, due to the richness of Earth observations of hydrological quantities, such as terrestrial water storage, runoff, snow cover, or evapotranspiration. On the other hand, the large uncertainties in current global hydrological models across spatial and temporal scales motivate the exploration of alternative, complementary approaches.

In this work [2], we evaluate an experimental approach for the global, data-driven decomposition of terrestrial water storage. Therefore, we developed a dynamic hybrid model which represents the main terrestrial water storage components of groundwater, soil moisture, and snowpack. The model consists of a recurrent neural network that estimates spatiotemporally varying and physically interpretable quantities, which are used as coefficients in a set of hydrological balance equations. The hybrid model is fed with meteorological variables and gridcell-level landscape properties and is optimized end-to-end using gridded evapotranspiration, runoff, terrestrial water storage, and snow water equivalent.

By outsourcing the estimation of coefficients to a neural network, we achieve improved local data adaptivity. The simulated fluxes and storage components are realistic and plausible overall, and our approach yields a larger contribution of soil moisture to the terrestrial water storage variations compared to physically-based hydrological models, especially in tropical savanna regions. The presented approach is a proof of concept of the hybrid modeling approach for the global terrestrial water cycle and we acknowledge uncertainties due to data and physical constraints that can be further improved. The presented work is a first step toward the data-driven yet physically constrained estimation of global water storage components and could find broad application in the Earth sciences.

[1] Reichstein et al. (2019, Nature) https://www.nature.com/articles/s41586-019-0912-1

[2] Kraft et al. (2021, HESSD) https://hess.copernicus.org/preprints/hess-2021-211/

How to cite: Kraft, B., Jung, M., Körner, M., Koirala, S., and Reichstein, M.: Estimating global terrestrial water storage components by a physically constrained recurrent neural network, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6085, https://doi.org/10.5194/egusphere-egu22-6085, 2022.

15:23–15:28
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EGU22-8759
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ECS
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Virtual presentation
Emad Hasan et al.

Anthropogenic climate change (ACC) has led to a significant shift in the hydrological water budget natural balance. The human-induced modifications in water systems, land cover, and land use have introduced the notion of “nonstationarity” in the hydrologic system. The concept implies significant changes in the hydrologic systems’ intra-annual and interannual variabilities with a time-variant mean, variance, and non-uniform density distribution. Under nonstationary conditions, extreme weather and climate events became frequent. Their magnitudes, durations, and frequencies are outside the historically observed ranges. A nonstationary system displays a volatile memory that hinders any reliable future projections. We revaluate the nonstationarity in global hydrological systems using gravity measurements from the GRACE (Gravity Recovery and Climate Experiment) mission. We utilized GRACE mascons (mass concentration blocks) solutions of RL06 from the Center for Space Research (CSR) between April 2002 to June 2017. We employed the KPSS and the ADF tests for stationarity in deterministic and periodic components, respectively. The KPSS test identified 25 hotspots globally that are nonstationary around the deterministic trend. These hotspot locations have undergone extensive anthropogenic activities on the available freshwater resources. The ADF test mapped the nonstationary systems around the mean and the variance components in 11 hotspot locations. These locations are noted by the KPSS test as nonstationary systems around the trend as well. Understanding the nonstationary state in hydrologic systems will enhance our awareness and preparedness to mitigate future extremes.

How to cite: Hasan, E., Save, H., Tamisiea, M., and Bettadpur, S.: Nonstationarity in Global Hydrological Water Budget, Evidence-based on GRACE Satellite Mission, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8759, https://doi.org/10.5194/egusphere-egu22-8759, 2022.

15:28–15:33
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EGU22-7708
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ECS
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On-site presentation
Tina Trautmann et al.

Over the last decade Terrestrial Water Storage (TWS) variations from GRACE and GRACE-FO satellite gravimetry have provided valuable observations for validation and calibration of hydrological models, and for data assimilation. While GRACE estimates represent the vertically integrated variations of all water storages, previous studies have shown the regional relevance of surface water and flood plain storage for TWS variations, and the inability to reproduce observed TWS by global hydrological models is often attributed to neglecting processes of river routing and floodplain dynamics. However, it is unclear if these processes need to be considered by computationally expensive river routing schemes in hydrologic model calibration and validation at large to global scales.  

In this exploratory analysis, we assess the effect of river water storage that is included in the vertically integrated GRACE TWS variations on the calibration of a global hydrological model, and its relevance for model validation. For this purpose, we first determine an observation-based estimate of river storage by applying a routing scheme on GRUN runoff data, considering different effective flow velocities. Obtained river storage is then removed from GRACE TWS, and the TWS variations either with or without river storage are used along with other observational based data of evapotranspiration, soil moisture and runoff to constrain parameters of a simple global hydrological model that does not encompass a river routing module in a multi-criteria calibration approach. 

While the removal of river storage changes the TWS constraint itself, especially its amplitude, at regional and global scale, we do not find a significant influence on calibrated parameters and thus model simulations. Instead, issues related to data uncertainty and inconsistency, as well as hydrological processes neglected by the model impose greater limitations than the rather local to regional relevance of river water storage. Furthermore, additional constraints from other data streams seem to not allow for adjustment to the changed TWS constraint in the calibration approach. However, simulating and adding river storage to modelled TWS after model calibration improves model validation relative to GRACE TWS globally and regional. Largest improvement is obtained in tropics and Northern low- and wetlands, where a substantial volume of water accumulates in major rivers, highlighting the importance of considering river water in these regions. Difficulties to reproduce TWS variations are mainly apparent in semi-arid regions where a generally lower volume of water is stored on land surface and neglected processes (e.g. evaporation and percolation from the flow channel) play a role. 

While it’s arguable that the presented results are quite specific to the used data and model structure, the key issues are shared among global hydrological modelling studies. Therefore, our findings suggest that omitting routing for large-scale model calibration against GRACE TWS is a valid option, considering limited computational and temporal resources. Nonetheless, the findings encourage the inclusion of river storage dynamics for validation of large-scale hydrological studies.

How to cite: Trautmann, T., Koirala, S., Güntner, A., Kim, H., and Jung, M.: Implications of river storage for integrating GRACE TWS observations into a global hydrological model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7708, https://doi.org/10.5194/egusphere-egu22-7708, 2022.

15:33–15:38
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EGU22-6789
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ECS
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Virtual presentation
Amirhossein Shadmehri Toosi et al.

Climate change has a significant impact on the environment by increasing the frequency of extreme precipitation events. Underestimating the potential risks of such events and lack of climate resilience will result in a substantial crisis in terms of water security. Understanding the hydrological consequences is difficult due to complexities and additional environmental feedbacks, depending on landuse/landcover, soil and climate.

The Gravity Recovery and Climate Experiment (GRACE) has provided an unprecedented perspective on global fluctuations in terrestrial water storage (TWS) over the past decade. While numerous studies have correlated different hydrological variables against TWS, no study has tested different rainfall thresholds (intensity) impacting TWS. Existing studies mostly have explored the relationship between TWS anomalies and hydrological variables using individual responses, while few have looked at multi-variable interaction. Single indicators (e.g., standardized precipitation index) may limit ecohydrological understanding of soil-vegetation-atmosphere water transfer, as many factors play essential roles in land-atmosphere interactions. In particular, rainfall characteristics can significantly impact the interaction between hydrological factors by accelerating or slowing processes. Hence, including appropriate temporal resolution of precipitation in analyses is essential; e.g., monthly data are not a good indicator for understanding ecohydrological interactions. Therefore, this research aims to improve our understanding of the spatiotemporal response of TWS to climate change impacts on rainfall characteristics. Monthly GRACE TWS time series anomalies are analyzed against aggregated monthly rainfall with different daily thresholds (intensities). The obtained results are used to find explanatory variables such as land use/land cover, soil type, and climatic zones that determine the significance between TWS and various variables. The methodology provides a valuable insight into the mechanisms in which TWS is affected by rainfall characteristics at different spatiotemporal scales across various hydrological contexts across Australia.

How to cite: Shadmehri Toosi, A., Batelaan, O., Shanafield, M., and Guan, H.: Impact of rainfall intensity on GRACE total water storage across Australia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6789, https://doi.org/10.5194/egusphere-egu22-6789, 2022.

15:38–15:43
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EGU22-2117
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ECS
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Virtual presentation
Abhishek Abhishek and Tsuyoshi Kinouchi

Despite receiving an annual rainfall of about 1718 mm (twice as the global average of 810 mm), Japan has erratically faced water shortages at various levels. Since most of these events last less than six months, their detailed assessment remains largely unexplored. Here, firstly we correct the GRACE- and GRACE-FO-derived terrestrial water storage anomaly (TWSA) for co- and post-seismic corrections corresponding to the Tohoku-Oki earthquake (Mw=9.1) that occurred on March 11th, 2011. Secondly, we fill the 34 missing values (23 due to battery management and 11 between the two missions) in the TWSA time series using the ANN and LSTM models. Lastly, we employ the Drought Potential Index (DPI) recently devised by Abhishek et al. (Journal of Hydrology, Volume 603, Part A, 2021, 126868) to quantity the drought potential of the region. The seismic correction using the least square fitting of the TWSA in the spectral domain results in a 76% increase (raw: -3.50 mm yr-1 vs. corrected: -0.83 mm yr-1) in linear trends from May 2002 to April 2020. The seismic correction accounts for an increase of 54.45 mm of TWSA during March 2011, with continually decreasing post-seismic relaxations until 2017. Both ANN and LSTM performed reasonably well (r>0.85, NSE>0.70) during calibration and validation phases, and therefore, an average of the two modeled TWSA was used during the data gaps. The maximum water storage deficit (DPI = 1) was observed during July 2014, followed by September 2016 and October 2012 (DPI≈0.85 for both). Some other years of significant water-stressed conditions include 2005, 2007, 2008, and 2013. The crux of this effective water storage-based DPI is that, unlike traditional assessment of water deficit, it considers the monthly potential water deficit and is therefore capable of capturing the droughts that evolve during dry and wet seasons. DPI can also indicate the long-term tendency and transition of the study region to a drought-prone area.

How to cite: Abhishek, A. and Kinouchi, T.: Evaluation of the novel Drought Potential Index (DPI) over Japan, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2117, https://doi.org/10.5194/egusphere-egu22-2117, 2022.

15:43–15:48
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EGU22-4372
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On-site presentation
Luis Samaniego et al.

Global hydrological models (GHMs) are a fundamental component of the Earth System Modeling  initiative that aims to realize a Digital Twin in the next five to ten years [1]. Recent model evaluations of the state-of-the-art global hydrological models [2,3], however, indicate that existing models have several deficiencies that lead to poor model efficiencies of key terrestrial environmental variables such as runoff, evapotranspiration, and soil moisture, among others. 

In this study, we evaluate four hydrological models: JULES, HTESSEL, mHM, and PCR-GLOBWB. These models are part of the modelling chain of the Copernicus Climate Change Service project ULYSSES [4], which aims to deliver global operational hydrological forecasts at a spatial resolution of 0.1$^\circ$. The operational service started in July 2020 and the data will be provided every month through the Copernicus Data Store.

The initial conditions of the GHMs for the hindcast skill assessment are obtained with the ERA5-land reanalysis [5]. This global dataset provides meteorological forcings (e.g., precipitation and temperature) since 1950 with daily time steps. For this reason, historical simulations of streamflow, obtained with these GHMs from 1981 until 2020 will be cross-evaluated against observed streamflow provided by 2850 GRDC gauging stations. Simulations of evapotranspiration and terrestrial water storage anomalies were evaluated against GRACE and FLUXNET datasets, respectively.

During the model calibration phase, models were evaluated in a stratified sample of size 120 basins (i.e., considering hydroclimatic regions and locations around the world). The results of the evaluation indicate that the median value of the Nash-Sutcliffe efficiency obtained with daily streamflow for these models varies from 0.20 to 0.50. The mean Kling-Gupta efficiency (KGE) metric ranges from 0.45 to 0.63. The maximum KGE value corresponds to the mHM model, while the other models are clustered around 0.45.

This result alone is quite promising considering the results presented in Beck et al. [2]. One reason for these good results is the relation between the standardization of the input data sets and the common routing model (mRM [6]) with a very detailed river network [7]. The considerable difference in performance between mHM and the other GHMs can be attributed to the parameterization of the models and model structure. mHM is the only GHM that employs the MPR technique [8] and includes fast and slow interflow components. Evaluation metrics obtained with the ILAMB [8] tool indicate that all models have exhibited satisfactory efficiencies (> 0.5 variable score) for monthly climatologies of latent heat, evapotranspiration and runoff. mHM, JULES, and PCR-GLOBWB, perform relatively well, representing the terrestrial water storage anomaly, although any of these models have explicit a detailed representation of the groundwater aquifers.

In this presentation, specific results of the model cross-validation, per geographic region will be presented. Finally, recommendations for further GHM model improvement will be discussed.

References:
[1] Bauer, P. et al. https://doi.org/10.1038/s43588-021-00023-0  2021
[2] Beck, H.E., et al.  https://doi.org/10.1002/2015WR018247, 2016.
[3] Harrigan, S et al. https://doi.org/doi:10.5194/essd-12-2043-2020 2020. 
[4] https://www.ufz.de/ulysses, ECMWF/COPERNICUS/2019/C3S\_432\_Lot3\_UFZ
[5] https://www.ecmwf.int/en/era5-land
[6] Thober et al. https://doi.org/10.5194/gmd-12-2501-2019, 2019
[7] http://hydro.iis.u-tokyo.ac.jp/~yamadai/cama-flood/index.html
[8] Samaniego et al. https://doi.org/10.1029/2008WR007327, 2010
[9] https://www.ilamb.org. 

How to cite: Samaniego, L., Rakovec, O., Martinez de la Torre, A., Sutanudjaja, E., Shrestha, P. K., Blyth, E., Wanders, N., Kelbling, M., and Thober, S.: Multivariate evaluation of four high-resolution hydrological models at  global scale, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4372, https://doi.org/10.5194/egusphere-egu22-4372, 2022.

15:48–15:53
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EGU22-9614
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ECS
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On-site presentation
Elisie Kåresdotter et al.

Growing populations contribute to increased pressures on water resource availability. Understanding the impacts of various human pressures on terrestrial water flows is important to meet the challenges of sustainable water resource management. For useful assessment of and planning for societal water-availability impacts, it is also imperative to disentangle the direct influences of human activities in the landscape from external climate-driven influences on water flows and their variation and change. One approach to such disentanglement is to use a distributed global hydrological model that can realistically represent climate and direct anthropogenic modifications of the water system. This study uses this approach to quantify and separate the climate-driven change components of key hydrological variables (evapotranspiration, runoff, soil moisture, and storage change) from the human-driven change components that are modified by interventions such as dams, water reservoirs, and water withdrawals for irrigation, industry, and households. Using a global hydrological model in two different modes, one with and one without the inclusion of human activities, the result differences indicate the direct anthropogenic influences. Human activities are found to drive changes to all hydrological variables with different magnitudes and directions depending on geographic location. The largest differences between the pristine and the human-activity model runs are seen in regions with the highest population density. In such regions, which also tend to have relatively large numbers of dams used for irrigation, water storage is largely decreasing and feeding into increased runoff and evapotranspiration. Our findings provide new knowledge of how humans affect different hydrological fluxes and storages globally, including a more complete set of hydrological variables than in previous studies. This enables closure of hydrological balances and informs further research on historic and future hydrological trends, which is of special interest for areas lacking historic data and being particularly vulnerable to water availability changes.

Keywords: Hydrological variability and change; Global hydrological modeling; Anthropogenic change; Climate-driven change; Water fluxes and storages

How to cite: Kåresdotter, E., Kalantari, Z., and Destouni, G.: Distinguishing Direct Human-driven Changes in the Global Terrestrial Water Cycle, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9614, https://doi.org/10.5194/egusphere-egu22-9614, 2022.

15:53–15:58
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EGU22-3078
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ECS
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Virtual presentation
Denise Cáceres and Petra Döll

Glaciers are important contributors to streamflow (Q), acting as water storage units during the accumulation season of a glaciological year and releasing water during its melting season. Glaciers often play an essential role in semiarid regions where the anthropogenic pressure on surface water resources is very high (e.g., Indus basin). The climate-driven glacier retreat observed worldwide is having major consequences on surface water supply. Here, we present a model-based approach to estimate the contribution of glaciers to streamflow and surface water supply over the global continental area (except Greenland and Antarctica). We refer to this contribution, i.e. the fraction of Q that can be explained by the presence of glaciers as opposed to their absence, as glacier-dependent streamflow (GDS). GDS is derived from the global hydrology model WaterGAP 2.2d at 0.5° resolution; it is equal to the difference between Q computed with the standard version of the model, which does not include glaciers, and Q computed with a non-standard version that includes glaciers (Cáceres et al., 2020). Global maps of mean yearly and mean monthly GDS are given in absolute values (m3/s), and in percentage of Q and of consumptive water use from surface water over two 30-year periods, 1951-1980 and 1981-2010. The model performance is evaluated by comparing Q simulated with WaterGAP 2.2d including glaciers to observations from the Global Streamflow Indices and Metadata archive (GSIM) downstream from glaciers. With this study, we aim (1) to identify the regions that rely the most on GDS for surface water supply and are therefore most vulnerable to water scarcity problems related to glacier retreat, (2) to identify spatial and temporal changes (e.g., shifts in seasonality, long-term trend) in GDS between 1951-1980 and 1981-2010, and (3) to evaluate the performance of WaterGAP 2.2d including glaciers in terms of Q.

Cáceres, D., Marzeion, B., Malles, J. H., Gutknecht, B. D., Müller Schmied, H., and Döll, P.: Assessing global water mass transfers from continents to oceans over the period 1948–2016, 24, 4831–4851, https://doi.org/10.5194/hess-24-4831-2020, 2020.

How to cite: Cáceres, D. and Döll, P.: The contribution of glaciers to streamflow and surface water supply: a global-scale analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3078, https://doi.org/10.5194/egusphere-egu22-3078, 2022.

15:58–16:03
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EGU22-9061
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ECS
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On-site presentation
Lucia Rinchiuso et al.

The evolution and possible limitation of water resources under climate change will become a crucial problem over the next decades and accurate hydrological projections are fundamental tools to assess the problem. The goal of this study is to improve the simulation of both river discharges and evaporation with the ORCHIDEE (Organising Carbon and Hydrology in Dynamic Ecosystems) land surface model by accounting for a high-resolution river network and water management influence.

This work will allow us to produce long-term projections of river discharge in France under different regional-scale climate change scenarios for the national project Explore2 and the French climate services.

To this end, we present here the evaluation and calibration of an improved version of ORCHIDEE, run off-line over France with atmospheric forcing from the SAFRAN reanalysis at an 8-km resolution and 1-hourly time step. First, we implement a high-resolution river routing scheme recently developed to better reproduce the water flow through the river network from the source to the outlet. It relies on topographical and hydrological information from the MERIT Hydro (Multi-Error-Removed Improved-Terrain) digital elevation model scaled at a 2km resolution, which allows us to define sub-basins at a higher resolution than the atmospheric forcing and to correctly position a majority of French gauging stations along the reconstructed rivers.

By comparing the discharge simulations to observations from the French hydrometric database (http://hydro.eaufrance.fr/) on about 800 stations with variable upstream areas, selected for their long and good-quality record, and medium-to-low human pressures, we find a very general overestimation of river discharge by the model, except in mountainous areas where earlier studies showed that the SAFRAN reanalysis was underestimating precipitation. The comparison of the simulated evapotranspiration to the data-driven FLUXCOM gridded product, over the upstream area of each selected station, shows a systematic underestimation, which can be explained by the underestimation of precipitation over mountains, and is elsewhere consistent with the overestimation of river discharge.

Further comparison to water withdrawals and consumption data from the national database BNPE (http://bnpe.eaufrance.fr/) suggests that both river discharge overestimate and evapotranspiration underestimate can be partly attributed to the neglect of water management in ORCHIDEE, although the studied stations have been selected for their weak human influence. We will thus incorporate water management information in ORCHIDEE in two ways: by activating an irrigation parametrization to consistently describe the impact of this human pressure on both river discharge and evapotranspiration, and by reducing river discharge from the other abstraction sources. The related parameters will finally be calibrated such as to best reproduce the observed discharge, evapotranspiration, and irrigation withdrawals.

How to cite: Rinchiuso, L., Ducharne, A., Polcher, J., Peylin, P., Arboleda Obando, P., Schrapffer, A., and Sauquet, E.: Improving land surface hydrological simulations over France using a high resolution river network and a description of anthropocentric pressures, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9061, https://doi.org/10.5194/egusphere-egu22-9061, 2022.

16:03–16:08
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EGU22-5336
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ECS
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Virtual presentation
Saritha Padiyedath Gopalan and Naota Hanasaki

The countries of Southeast Asia are projected to experience severe flood damage and economic impacts from climate change, compared with the global average. Thailand is the second-largest economy in Southeast Asia and future flood damage is likely to hinder the economic growth of Thailand because Bangkok City (the commercial hub of the country) is located in the Chao Phraya River delta, where floods are frequent. Despite this fact, thus far, comparatively little research has been conducted to investigate the combined effects of climate change, human activities, and adaptation measures on flood risk reduction in the Chao Phraya River Basin (CPRB). Therefore, this study was conducted in the CPRB to examine the adaptation potential of (i) existing structural and non-structural measures that include reservoir and diversion dams, diversion canals, and water retention areas, and (ii) a combination of alterations made to the existing diversion canals and retention areas (combined adaptation) on reducing future floods using the H08 global hydrological model.

Future flood risk was analyzed using various flood risk indicators including flood frequency, number of flooding days, and annual maximum daily discharge. The results revealed that the impact of existing measures on the future flood reduction was smaller than the increase caused by warming in the upper and lower CPRB. Although the combined adaptation measures had considerable potential to reduce the magnitude and duration of future floods in the CPRB, extreme floods may continue to occur in the basin and further strategies are needed to alleviate the flood risk. Our findings emphasize that the integration of various existing structural and non-structural measures along with adaptation measures will be insufficient to completely mitigate future flood risk in the CPRB although the considered measures can greatly reduce future flooding. This study highlights the areas of the CPRB that are vulnerable to extreme flooding in the future and thus require area-based prioritization for flood management. Moreover, this study clearly indicated that GHMs can be effectively implemented for the design of regional adaptation measures.

How to cite: Padiyedath Gopalan, S. and Hanasaki, N.: Countermeasures against flood in the Chao Phraya River Basin, Thailand - Assessment and adaptation to combat climate change , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5336, https://doi.org/10.5194/egusphere-egu22-5336, 2022.

16:08–16:13
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EGU22-8292
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On-site presentation
Miriam Kosmale et al.

Sufficient groundwater resources are unarguably essential for human populations all over the world. With a contribution of over 30% to freshwater reserves in the global hydrological cycle, it is important to increase the capacity of the currently sparse groundwater monitoring network. Spatially and temporally continuous monitoring of groundwater as an Essential Climate Variable (ECV) can be realized with remote sensing techniques. Within the “Global Gravity-Based Groundwater Product” G3P-project (www.g3p.eu), gravimetric satellite missions GRACE and GRACE-FO are applied for global groundwater monitoring. Groundwater derived from gravimetric measurements require detailed knowledge of all continental water compartments, which are contributing to the total water storage variations.

Within the G3P project the Finnish Meteorological Institute is producing a global gap-filled Snow Water Equivalent (SWE) product that describes the snow compartment for global groundwater estimation. The product complements remote sensing-based information with model-based data for regions where remote sensing can’t observe SWE on global scale.

The production of SWE from long-term satellite observations covering the full GRACE and GRACE-FO mission period from 2002 to 2021 are investigated. The Finnish Meteorological Institute efforts within the Copernicus Land monitoring service and ESA frameworks ensure operational Near-Real-Time information on SWE for the Northern hemisphere. Microwave and optical remote sensing sensor techniques are the basis for the SWE monitoring services. Validation with in-situ reference data is important in understanding product accuracy. Pixel-level uncertainties provided with the snow product support efforts on groundwater estimation. Model- and remote sensing-based SWE are evaluated on various regional scales.  As part of the new Gravity-Based Groundwater Product G3P, global Snow Water Equivalent products will be presented and discussed.

How to cite: Kosmale, M., Luojus, K., Ikonen, J., and Venäläinen, P.: Global Snow Water Equivalent for continuous groundwater monitoring from space: uncertainties, evaluation, and application, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8292, https://doi.org/10.5194/egusphere-egu22-8292, 2022.

16:13–16:18
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EGU22-6409
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ECS
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Virtual presentation
Emmanuel Dubois et al.

Although the long-term impacts of climate change on the different water budget components have been widely studied, numerous challenges in accounting for land use (LU) changes in regional scale hydrologic simulations remain. As it can be challenging to map the evolution of LU and incorporate the changes in models, models are often calibrated with the hypothesis that LU is constant through time. Therefore, little is known about the quantitative impact of LU change on the water budget components and more specifically on the regional-scale groundwater recharge (GWR), although it is widely accepted that GWR depends on LU. The objective of this work was to assess the impact of LU changes on the simulation of regional-scale GWR and identify the magnitude of changes to produce significant changes in GWR. GWR was simulated with a transient-state spatialized superficial water budget over three regional-scale watersheds (>2 000 km2) in the cold and humid climate of southern Quebec (Canada). The model computes snow accumulation and snowmelt, as well as soil freezing to provide spatially distributed runoff, actual evapotranspiration, and GWR fluxes with a monthly time step on a 500 m x 500 m grid. Four versions of the model are calibrated over the 1990-2017 period considering constant LU, constant LU and rainfall interception in forested areas, transient LU (annual time step, two data sources), and transient land use and rainfall interception in forested areas. The model versions with transient LU performed better and were calibrated with sets of statistically different parameters while the model versions with rainfall interception did not systematically enhanced the calibration results. Because the observed LU changes were relatively limited during the 1990-2017 period in the study areas, the simulated variables with the four versions were not significantly different. To assess the joint effects of LU change and climate change on GWR, two scenarios of future LU changes were developed and combined with climate change scenarios to simulate future GWR. Results were analyzed to identify the type and intensity of LU changes necessary to produce significant changes in GWR.

How to cite: Dubois, E., Larocque, M., Brunner, P., and Gagné, S.: Long-term impact of land use change on the simulation of distributed regional-scale groundwater recharge in cold and humid climates, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6409, https://doi.org/10.5194/egusphere-egu22-6409, 2022.

16:18–16:23
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EGU22-8714
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ECS
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On-site presentation
Verena Bessenbacher et al.

With a rapidly warming climate, future droughts are predicted to increase in frequency, duration, extent, and severity for many regions, whilst uncertainty of drought predictions in CMIP6 ensembles remains high. Monitoring the occurrence of agricultural and ecological droughts (i.e. soil moisture droughts), in present and future climate is therefore vital. However, available drought monitoring products do not use information from soil moisture ground observations, although those are the only observations available that extend into the vegetation-relevant root zone.

A central challenge of these ground observations (included in the international soil moisture network ISMN) is that they are not evenly distributed across the globe, favoring Europe and the US. Upscaling these observations to global soil moisture estimates for drought monitoring can lead to underrepresented areas suffering from misrepresentation of drought occurrences. Installing new measurement stations is costly, therefore placing them should focus on alleviating the problem of these underrepresented regions and ecosystems. 

We apply a statistical learning method to identify under-represented ecosystems and environmental conditions to inform future station placement. We overlay these maps with future drought occurrence maps and drought uncertainty maps to scan for regions that are especially vulnerable in the future given the current station net. The analysis is built around an up-scaling approach where the model is trained to predict station-level soil moisture as a function of gridded atmospheric precipitation and temperature. The resulting model can be used to estimate soil moisture at locations without observations. For doing so we rely on the CMIP6 ensemble as a laboratory, which enables us to create virtual soil moisture stations based on continuously available soil moisture simulations. 

The first results show that strategically placing new soil moisture observation stations where the climate space is most under-sampled leads to an increase of drought estimation accuracy. We are planning to further investigate hypothetical station configurations and follow up with the question of where future “station measurement years” should optimally be distributed around the globe to increase drought monitoring from ground observation in areas with low station coverage but high drought risk and high uncertainty in future projections. 

How to cite: Bessenbacher, V., Gudmundsson, L., and Seneviratne, S. I.: Capturing future soil-moisture droughts from irregularly distributed ground observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8714, https://doi.org/10.5194/egusphere-egu22-8714, 2022.

16:23–16:28
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EGU22-4269
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Virtual presentation
Peter Lehmann et al.

Evapotranspiration (ET) modeling is central to resolving water and energy balances and linking the terrestrial water and carbon cycles. An important challenge remains how to partition the ET flux into transpiration (T) and soil evaporation (E). We expand the surface evaporation capacitor model of Or and Lehmann (2019) by considering concurrent root water uptake. The original capacitor model simulates surface evaporation (focusing on stage-1) and internal redistribution following rainfall events. The thickness of the evaporation-active soil layer is defined by an intrinsic soil property termed the evaporation characteristic length (deduced from soil water characteristics and hydraulic conductivity functions). The modified model considers water extraction by plant roots  from the capacitor layer as well as water redistributed into deeper layers (sheltered from soil evaporation but accessible for root water uptake). Depending on the amount of water leaking below the capacitor depth, vegetation can take up this natural storage at rates limited by the hydraulic properties of the rhizosphere. To model evapotranspiration and its partitioning at the global scale, we use spatial information on (i) maximum root depth and (ii) soil hydraulic properties defining the depth of the capacitor layer. We assess the performance of the ET capacitor model in comparison with results from available land surface models and estimates based on remote sensing products.

How to cite: Lehmann, P., Gupta, S., Or, D., and Carminati, A.: Estimating global scale evapotranspiration using soil-based evaporation characteristic length and root zone depth distribution, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4269, https://doi.org/10.5194/egusphere-egu22-4269, 2022.

16:28–16:33
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EGU22-12034
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On-site presentation
Kolbjorn Engeland et al.

We present a new initiative, LATICE MIP-ET, with the aim to compare model estimates of evapotranspiration (ET) in a high latitude environment. The study is part of the LATICE (Land-ATmosphere Interactions in Cold Environments) strategic research initiative at the University of Oslo.

The main motivation for LATICE MIP-ET is the need to improve knowledge about the actual evapotranspiration in cold environments. Recent estimates of mean annual evapotranspiration for Norway summarized in Erlandsen et al (2021) range from 175 – 500 mm/year, i.e. between 13 and 31% of mean annual precipitation. These estimates are based on different gridded versions of the hydrological, water balance model HBV, where the estimated evapotranspiration depends on precipitation inputs and streamflow measurements included in the model calibration. No reference measurements of evapotranspiration are used to benchmark the model estimates.

The aim of this MIP is to constrain the range of the estimated mean annual evapotranspiration by (i) introducing local observations of evapotranspiration and (ii) compare model estimates from two land surface models (CLM and SURFEX) and two hydrological models (SHYFT and HBV). Model estimates are compared at three scales, namely point, catchment, and regional. At the point scale, field observations of evapotranspiration are available at five eddy covariance flux sites covering a gradient in climate across Norway, from low altitude forested and grassland sites to high mountain and high latitude sites. At these sites we compare the models’ ability to capture diurnal and seasonal variations in evapotranspiration and compare to the observations. We will also compare how models simulate the relationship between potential and actual evapotranspiration and assess the models’ sensitivity to the choice of vegetation-and soil parameters. The second scale is the river catchment scale. For a selected set of catchments, we compare simulated water balance to observed discharge and evaluate the sensitivity to atmospheric forcing and land cover. The third scale is the regional scale. At this scale we compare mean annual estimates of evapotranspiration for the whole of Norway. The comparison will include mapping the spatial and temporal distribution of evapotranspiration fractions (transpiration, soil evaporation, and canopy evaporation).

The presentation will focus on the design of the LATICE MIP-ET, including the choice of regional and local forcing and land cover data, and the first results of the model intercomparison at the local scale. In a follow-up study we aim to invite the scientific community to join the MIP.

This work is a contribution to the Strategic Research Initiative ‘Land Atmosphere Interaction in Cold Environments’ (LATICE) of the University of Oslo and the EMERALD research project. 

References:

Erlandsen, H.B., Beldring, S., Eisner, S., Hisdal, H., Huang, S., Tallaksen, L.M. (2021) Constraining the HBV model for robust water balance assessments in a cold climate. Hydrology Research 2021; nh2021132. doi: https://doi.org/10.2166/nh.2021.132

How to cite: Engeland, K., Aas, K. S., Erlandsen, H. B., Gelati, E., Huang, S., Narayanappa, D., Pirk, N., Silantyeva, O., Tallaksen, L. M., Vatne, A., and Yilmaz, Y.: LATICE MIP evapotranspiration – A model intercomparison project for evapotranspiration estimates at high latitudes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12034, https://doi.org/10.5194/egusphere-egu22-12034, 2022.