HS2.2.1

Incorporating novel data and hydrological process understanding into catchment models across spatio-temporal scales

Understanding and representing hydrological processes is the basis for developing and improving hydrological and Earth system models. Relevant hydrological data are becoming globally available at an unprecedented rate, opening new avenues for modelling (model parametrization, evaluation, and application) and process representation. As a result, a variety of models are developed and trained by new quantitative and qualitative data at various temporal and spatial scales.
In this session, we welcome contributions on novel frameworks for model development, evaluation and parametrization across spatio-temporal scales.

Potential contributions could (but are not limited to):
(1) introduce new global and regional data products into the modeling process;
(2) upscale experimental knowledge from smaller to larger scale for better usage in catchment models;
(3) advance seamless modeling of spatial patterns in hydrology and land models using distributed earth observations;
(4) improve model structure by representing often neglected processes in hydrological models such as human impacts, river regulations, irrigation, as well as vegetation dynamics;
(5) provide novel concepts for improving the characterization of internal and external model fluxes and their spatio-temporal dynamics;
(6) introduce new approaches for model calibration and evaluation, especially to improve process representation, and/or to improve model predictions under changing conditions;
(7) develop novel approaches and performance metrics for evaluating and constraining models in space and time

This session is organized as part of the grass-root modelling initiative on "Improving the Theoretical Underpinnings of Hydrologic Models" launched in 2016.

Convener: Simon Stisen | Co-conveners: Luis Samaniego, Sina KhatamiECSECS, Shervan Gharari, Björn Guse
Presentations
| Tue, 24 May, 15:10–18:18 (CEST)
 
Room B

Presentations: Tue, 24 May | Room B

Chairpersons: Simon Stisen, Luis Samaniego
15:10–15:16
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EGU22-8451
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Presentation form not yet defined
Martyn Clark, Louise Arnal, Andrew Bennett, Dave Casson, Shervan Gharari, Janine Hay, Jim Freer, Wouter Knoben, Hongli Liu, Naoki Mizukami, Bart Nijssen, Simon Papalexiou, Raymond Spiteri, Guoqiang Tang, Ashley Van Beusekom, and Andy Wood

Many hydrologic modelling groups face similar challenges, with untapped opportunities to share code and concepts across different model development groups. An active community of practice is emerging, where the focus is not so much on developing a community hydrologic model, but more on advancing the science and practice of community hydrologic modeling. This presentation will summarize our recent efforts to develop open-source models, methods, and datasets to enable process-based hydrologic prediction across North America (and beyond). The contributions include (1) developing ensemble meteorological datasets for North America and the globe; (2) developing modular approaches to hydrologic modeling through a hierarchal approach that separates different model sub-domains (vegetation, snow, soil, groundwater) and separates the physical representations from the numerical solution; (3) implementing third-party numerical solvers (sundials) to improve the robustness and efficiency of the numerical solutions; (4) developing agile parallelization methods capable of handling heterogeneous computing loads and bottlenecks in the downstream reaches of large river networks; (5) implementing flexible model configuration toolbox to accelerate the implementation of large-domain hydrologic models; (6) advancing methods for river lake routing, including development of integrated river-lake hydrography datasets and development of large-domain reservoir management models; (7) advancing methods for large-domain parameter estimation; (8) advancing methods for ensemble data assimilation; and (9) advancing methods for probabilistic hydrologic prediction on time scales from seconds to seasons. We will discuss some of the major challenges encountered and the high-priority research that is necessary to advance capabilities in large-domain hydrologic prediction.

How to cite: Clark, M., Arnal, L., Bennett, A., Casson, D., Gharari, S., Hay, J., Freer, J., Knoben, W., Liu, H., Mizukami, N., Nijssen, B., Papalexiou, S., Spiteri, R., Tang, G., Van Beusekom, A., and Wood, A.: Advancing the science and practice of community hydrologic modeling: Development of open-source models, methods, and datasets to enable process-based hydrologic prediction across North America (and beyond), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8451, https://doi.org/10.5194/egusphere-egu22-8451, 2022.

15:16–15:22
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EGU22-7786
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ECS
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Virtual presentation
Louisa Oldham, Jim Freer, Gemma Coxon, Christopher Jackson, John Bloomfield, and Nicholas Howden

Groundwater-dominated catchments are often critical for nationally-important water resources. Many conceptual rainfall-runoff models used for the simulation of river flows tend to degrade in their model performance in groundwater-dominated catchments as they are rarely designed to simulate spatial groundwater behaviours or interactions with surface waters. Intercatchment groundwater flow is one such neglected variable. Efforts have been made to incorporate this process into existing models, but there is a need for greater emphasis on improving our perceptual models of groundwater-surface water interactions prior to any model edits.

In this study, national meteorological, hydrological, hydrogeological, geological and artificial influence (characterising abstractions and return flows) datasets are used to develop a perceptual model of intercatchment groundwater flow and how it varies spatially and temporally across the River Thames. We characterise the water balance, presence of gaining/losing river reaches and intra-annual dynamics in 80 subcatchments of the River Thames in the UK, taking advantage of its wealth of data, densely gauged river network and geological variability.

We show the prevalence of non-conservative river reaches across the study area, with heterogeneity both between, and within, geological units giving rise to a complex distribution of recharge and discharge points along the river network. We identify where non-conservative reaches can be attributed to intercatchment groundwater flow, and where other processes (e.g. human abstractions and discharge uncertainty) are likely the cause. Escarpments of Chalk and Jurassic Limestone show evidence of intercatchment groundwater flow both from headwater to downstream reaches, and out-of-catchment via springlines. We found temporal as well as spatial variability across the study area, with more seasonality and variability in river catchments on Jurassic Limestone outcrops and less on Chalk and Lower Greensand outcrops. Our results show the need for a degree of local investigation and hydrogeological perceptualisation within regional analysis, which we show to be achievable given relatively simple geological interpretation and data requirements.  We then discuss the inclusion of external flow fluxes within existing models to enable calibration improvements in groundwater-dominated catchments, and, importantly, the characterisation of these fluxes given the temporal and spatial variability of intercatchment groundwater flow that our perceptual model has shown.

How to cite: Oldham, L., Freer, J., Coxon, G., Jackson, C., Bloomfield, J., and Howden, N.: Evidence-based requirements for perceptualising intercatchment groundwater flow in hydrological models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7786, https://doi.org/10.5194/egusphere-egu22-7786, 2022.

15:22–15:28
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EGU22-5695
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ECS
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On-site presentation
Mirjam Scheller, Ilja van Meerveld, Eric Sauquet, Jan Seibert, and Marc Vis

Since 2012 the Observatoire National des Étiages (ONDE) has collected observations about the state of headwater streams across France. Streams that occasionally dry up (also called temporary or intermittent streams) are visited at least five times a year by trained staff and the flow state is visually assessed (dry stream, standing water, flowing water). These data have been used to calculate regional probabilities of drying with statistical models. However, the usefulness of this kind of data for the calibration or validation of bucket-type hydrological models has so far not been assessed.

We, therefore, used the ONDE dataset for the calibration of the HBV model to evaluate the potential of temporary stream observations to improve model predictions in data-scarce regions. The model was calibrated for almost 90 catchments throughout France. We used the information on the flow state of temporary streams, either alone or in combination with limited observations of discharge or water level at the outlet of the catchment for model calibration and evaluated the simulations based on the measured discharge. Because the ONDE data set is large, we could do an extensive analysis of the value of temporary stream observations and the way to optimize their use in hydrological models. While the study focuses on catchments in France, visual observations of temporary streams can - with the help of citizen scientists - be collected at any place in the world. Thus, the method has the potential to be applied in truly data scarce regions.

How to cite: Scheller, M., van Meerveld, I., Sauquet, E., Seibert, J., and Vis, M.: Are temporary stream observations useful for the calibration of a lumped hydrological model?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5695, https://doi.org/10.5194/egusphere-egu22-5695, 2022.

15:28–15:34
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EGU22-8487
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ECS
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Virtual presentation
Annika Künne, Louise Mimeau, Flora Branger, and Sven Kralisch

Intermittent rivers and ephemeral streams (IRES) account for about half of the world’s river
networks and are considered to increase under climate change and growing anthropogenic water
use. However, the hydrological mechanisms that control the spatio-temporal flow patterns in IRES
and their effects on the expansion and contraction of stream segments are not fully understood.
Discharge measurements mainly exist for gauging stations, which are often located downstream and
in the rivers’ main stems. They are often less impacted by flow intermittence than headwaters and
smaller river channels. In consequence, impacts of climate change and anthropogenic alterations on
hydrological process dynamics in IRES cannot easily be analysed, neither the influences of climate
change and human water use on IRES be quantified.
Within the framework of the Horizon 2020 project DRYvER on Drying River Networks and
Climate Change, we try to tackle this challenge by developing methods and tools using the JAMS
modelling framework and J2K model family to assess hydrological process interactions at high
spatial and temporal resolutions, which include the scale of small reaches (about 50 ha catchment
size). For that purpose, we developed process-based hydrological models for six mesoscaled river
basins between 200 km² and 350 km² in different European countries (Croatia, Czech Republic,
Finland, France, Hungary, Spain). At the same time, we used data from field measurements
and a citizens science application to validate our models at the reach scale. In this study we
analyse the ability of our hydrological model to represent observed temporal and spatial dynamics
of flow intermittence at high resolution, and develop adaptations that allows using these models
in an upscaling step to estimate the impacts of future climatic changes and anthropogenic water
consumption on flow intermittence all over Europe.

How to cite: Künne, A., Mimeau, L., Branger, F., and Kralisch, S.: Prediction of flow intermittence in Drying River Networks using a process-based hydrological model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8487, https://doi.org/10.5194/egusphere-egu22-8487, 2022.

15:34–15:40
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EGU22-4015
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ECS
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On-site presentation
Andrea Betterle and Gianluca Botter

This contribution analyses how the topological relationship between two river basins affects the correlation of the flows at their outlets. A pair of river basins can have two distinct topological configurations. They can either be nested (if the smaller catchment is part of a larger one), or they can be disjointed (or non-nested), if their contributing areas are not overlapping.  Nested catchments tend to be considered as hydrologically more similar as they share a fraction of their contributing area and, consequently, a fraction of their streamflows (i.e. they are hydrologically connected).  Nonetheless, using a large dataset of catchments spanning a wide range of scales and geomorphoclimatic conditions, we show that – as inter-catchment distance increases – the correlation between daily flows at the outlet of nested sites experiences a faster decline as compared to non-nested sites. By using a recently developed analytical model we are able to highlight that the enhanced loss of streamflow correlation in nested sites is primarily due to a sharp decrease in the frequency of simultaneous runoff-generating rainfall in the two contributing areas, and to a larger loss of correlation between their magnitudes. This surprising effect can be explained by the fact that, as distance increase, nested catchments tend to become systematically more heterogeneous in hydrologically-critical features such as in their size, elevation and slope. Acknowledging and understanding the enhanced hydrological variability of nested catchments across scales can help to better capture the spatial variability of river flows, with benefits for streamflow regionalization, ecological modelling and processes interpretation.

How to cite: Betterle, A. and Botter, G.: Does catchment nestedness enhance hydrological similarity?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4015, https://doi.org/10.5194/egusphere-egu22-4015, 2022.

15:40–15:46
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EGU22-6534
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ECS
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Virtual presentation
Tibebe Tigabu, Paul Wagner, Balaji Narasimhan, and Nicola Fohrer

Abstract: Parametrization is an important step to construct a reasonable hydrologic model for a catchment. However, selecting appropriate model parameters can be challenging, particularly in data scarce regions. Conventionally, hydrologic model parameters are selected through calibration assuming that catchment processes and model parameters are stationary over time. However, the assumption of stationarity may not be valid all the time due to temporal changes in the behaviors of catchments. Therefore, the purpose of this study is to investigate the influence of temporal variability on the SWAT model parametrization using different calibration periods. To this end, we calibrated the SWAT model based on daily and monthly streamflow data in the Adyar catchment, Chennai. Results showed that the SWAT model performance and parameter values differed when the calibration periods were shifted by one year. This is reflected in the KGE (Kling Gupta Efficiency) values that varied between 0.38 to 0.68 for calibration periods of 2004-2007,2005-2008, 2006-2009, 2007-2010, 2008-2011, 2009-2012 and 2010-2013. Likewise, the selection of values for sensitive model parameters varied even though the parameter values were chosen in the same ranges. Moreover, independent model evaluation for wet and dry years showed significantly different performance indices and model parameter values. The model efficiency of wet years (NSE = 0.59 and KGE= 0.68) was by far better than the model efficiency of dry years (NSE = -0.59 and KGE = 0.1). In general, this study provides a good insight into hydrologic model calibration under non-stationarity conditions.

How to cite: Tigabu, T., Wagner, P., Narasimhan, B., and Fohrer, N.: Influences of temporal variability on the calibration of a hydrologic model  , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6534, https://doi.org/10.5194/egusphere-egu22-6534, 2022.

15:46–15:52
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EGU22-489
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ECS
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Virtual presentation
Akshay Kadu and Basudev Biswal

One of the compelling problems in hydrology is to predict discharge at a point in a river network without any historical data. Any solution to such a problem requires knowledge of how a drainage basin functions, thus linking its response to precipitation inputs with its physiological characteristics. In this regard, considerable efforts have been made to understand the link between basin size and peak discharge. The present study extends the discharge-area analysis to the recession domain. We assumed two dominant flow processes to exist in natural basins viz; Pure Surface Flow (PSF) and Mixed Surface Sub-surface Flow (MSSF). As the recession progresses, MSSF is expected to become the dominant flow generation mechanism in the basin. The MSSF is directly proportional to the total length of the saturated channel network or active drainage network (ADN) in a basin, which is directly proportional to the basin area. Thus, during the late recession period, the discharge is supposed to be directly proportional to the basin area. Using a geomorphological hydrologic response model, we tried to prove this direct relationship between discharge and basin area during the recession period. The results obtained are in agreement with our assumed hypothesis.

How to cite: Kadu, A. and Biswal, B.: A General Perspective of Discharge-Area Relationship during Recession , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-489, https://doi.org/10.5194/egusphere-egu22-489, 2022.

15:52–15:58
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EGU22-6003
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ECS
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On-site presentation
Ioannis Sofokleous, Adriana Bruggeman, Marinos Eliades, and Corrado Camera

The accurate simulation and partitioning of the water balance components is important for the simulation of hydrological processes and the interaction between the land and the atmosphere. The objective of this study is to identify the model parameters and parameterization options that impact the most the water balance components modelled with the Noah-MP land surface model coupled to the WRF-Hydro hydrological model. The water balance components are runoff and total evapotranspiration (ET), comprised of evaporation, transpiration and interception. Three types of different parameterization options and 12 model parameters were tested and the sensitivity of the model output was investigated for two consecutive hydrological years and for 31 watersheds in the Troodos mountains of Cyprus, in the Eastern Mediterranean. A baseline configuration, based on initial estimates of model parameters from previous literature and suggested default values, of the Noah-MP and WRF-Hydro model system was found to systematically overestimate the total streamflow of the 31 watersheds, with the median runoff coefficient equal to 0.4 compared to the observed value of 0.2. Consistent with the streamflow overestimation, the ratio of total ET to total precipitation was underestimated, with a value of 0.5 compared to the value of 0.8 from local observations. The sensitivity analysis revealed that specific parameters can substantially modify the amount of simulated streamflow and ET. The bedrock drainage parameter, hydraulic conductivity and soil porosity can each reduce or increase streamflow and ET up to 20% on average. Among the vegetation parameters and model parameterization options, the change of the dynamic vegetation option, the use of the Jarvis-based stomatal conductance model, instead of the Ball-Berry model, and the simulation of nocturnal transpiration can each increase ET by about 20%, and thus reduce the overestimation of total streamflow. The findings of this sensitivity analysis can be used to configure the Noah-MP and WRF-Hydro models in order to improve the simulation of the water balance of the studied area and other areas with similar hydroclimatic characteristics.

How to cite: Sofokleous, I., Bruggeman, A., Eliades, M., and Camera, C.: Sensitivity analysis of water balance components with the Noah-MP and WRF-Hydro models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6003, https://doi.org/10.5194/egusphere-egu22-6003, 2022.

15:58–16:04
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EGU22-3264
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ECS
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Presentation form not yet defined
E. Andres Quichimbo, Michael Bliss Singer, Katerina Michaelides, Rafael Rosolem, and Mark Cuthbert

Dryland regions cover around one-third of the global land surface and are naturally prone to water scarcity. Drylands typically experience highly variable precipitation, both spatially and temporally, with potential evapotranspiration (PET) greatly exceeding annual rates of precipitation. However, our ability to accurately quantify the key components of the water partitioning in these regions is hampered by the scarcity of data and the highly dynamic nature of the hydrological processes. In this context, assessing the ability of models to represent the key hydrological processes at different spatial and temporal scales is of key importance to enhance our understanding and quantification of the water balance in dryland regions. Here, we have assessed the impact of the model grid size and the temporal scale of climatic forcing in combination with variations in model structure in the description and representation of key hydrological processes that influence the water partitioning in dryland regions. The analysis was performed in the Walnut Gulch Experimental Watershed where a dense network of hydrological measurements is readily available for model evaluation. We show that our parsimonious model, DRYP, can describe well the water partitioning across a range of different temporal and spatial scales. However, we find that sub-daily time steps of precipitation and PET, combined with a fine spatial resolution of less than or equal to 1km grid size, are needed for robust quantification of the water partitioning, which is also very sensitive to the choice of infiltration model. The results highlight the important role of channel losses through the streambed of ephemeral streams (~7% of the precipitation), and the impact of the underlying alluvial riparian area in the partitioning of water fluxes between riparian vegetation evapotranspiration (~60 % of transmission losses) and the production of focused groundwater recharge (~3 % of the precipitation). These results have important implications for the potential for improving the performance of large scale models in dryland regions by indicating the appropriate temporal and spatial scales required for the proper representation of dryland hydrological processes.

How to cite: Quichimbo, E. A., Singer, M. B., Michaelides, K., Rosolem, R., and Cuthbert, M.: Sensitivity to spatial and temporal resolution in the performance of a parsimonious hydrological model for quantifying water balance partitioning in dryland regions., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3264, https://doi.org/10.5194/egusphere-egu22-3264, 2022.

16:04–16:10
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EGU22-12561
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ECS
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On-site presentation
Martin Morlot, Riccardo Rigon, and Giuseppe Frometta

River Adige is the second longest in Italy and affects the population living in the Trentino Alto Adige and the Venetian plain for irrigation. Having an area relatively small (~11000 square kilometers), it is however affected by a complexity of issues including: high anthropization causing intensive and often conflicting water uses, displacement of water resources from one sub-catchment to another, presence of seasonal snow cover with runoff delayed from snow falling season to late Spring and Summer, glaciers depletion under the climate change impulse. All those issues make the modeling of the water cycle of the river area challenging and, at the same time urgent.

This contribution has the objective to illustrate an effort to model the basin at high resolution with the aim to search for the closure of the water and energy budgets for the years of 1980-2018. Within this budgets simulation, we want to address a quantitative assessment of the effects of recent climate changes on the availability of the resource and, for what concern, the basin area evaluate the regional variability of the resource up to the scale of sub-catchments of area of around 5km². This is done with the help of the GEOframe modeling system (Formetta et al, 2014), an open-source, semi-distributed, component-based hydrological modeling system. The different components of the system enable to model different processes of the hydrological cycle: geomorphology, radiation, evapotranspiration, rainfall-snowmelt separation, discharge calculation and the try of different hypothesis on the work of the elementary hydrological components. The results are also compared with those of the analysis conducted in Thedoros et al., 2020.

References:

Formetta, G., A. Antonello, S. Franceschi, O. David, and R. Rigon. 2014. “Hydrological Modelling with Components: A GIS-Based Open-Source Framework.” Environmental Modelling & Software 55 (May): 190–200.

Mastrotheodoros, Theodoros, Christoforos Pappas, Peter Molnar, Paolo Burlando, Gabriele Manoli, Juraj Parajka, Riccardo Rigon, et al. 2020. “More Green and Less Blue Water in the Alps during Warmer Summers.” Nature Climate Change 10 (2): 155–61.

How to cite: Morlot, M., Rigon, R., and Frometta, G.: Modeling the hydrological cycle of the Adige using the GEOframe system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12561, https://doi.org/10.5194/egusphere-egu22-12561, 2022.

16:10–16:16
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EGU22-13237
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ECS
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On-site presentation
Qinuo Zhang, Ke Zhang, Linjun Chao, Xinyu Chen, Jiayi Wang, and Nan Wu

Runoff generation in semi-humid regions is always characterized by a complex nonlinear process influenced by both saturation excess mechanism and infiltration excess mechanism. A hybrid runoff generation module is proposed in this study to delineate the mixed rainfall-runoff process by integrating an infiltration module, based on a modified Horton equation, with the saturation excess runoff generation module of Xinanjiang model at grid scale. A new distributed hydrological model, termed grid-Xinanjiang-infiltration-excess (GXAJ-IE) model, is subsequently developed in the context of grid-Xinanjiang model. Not only in semi-humid regions, but GXAJ-IE model is also expected to achieve acceptable performance in other hydrometeorological zones due to its superimposed runoff generation structure. Thus GXAJ-IE model is tested in four watersheds across different hydrometeorological zones (humid, semi-humid, semi-arid and arid) of China, and two models with single runoff generation mode, grid-Xinanjiang (GXAJ) model and grid-infiltration-excess (GIE) model, are set as benchmarks for comparison purpose. The results indicate that compared with the two benchmark models, GXAJ-IE model has higher flexibility and robustness in reproducing the flood hydrographs, especially the flood peaks, driven by various rainfall patterns in the semi-humid Dongwan and Maduwang watersheds. Furthermore, GXAJ-IE model could well capture the spatiotemporal characteristic of the saturation and infiltration excess runoff components, and delineate the evolution of their contributing areas within a flood event. Yet rainfall input with low spatiotemporal resolution still remains a limitation to give full play to the advantage of GXAJ-IE model. None of the models performs well in the arid and semi-arid Suide watershed, even though, GXAJ-IE model shows comparable simulation accuracy with GIE model whereas GXAJ model absolutely loses its edge. In the humid Tunxi watershed, GXAJ-IE model produces comparably good performance with GXAJ model while GIE model is slightly inferior. Overall, GXAJ-IE model is fairly adaptable to different hydrometeorological regions in China and shows great potential for universal application, with an especially promising prospect in improving the flood forecasting accuracy for the semi-humid watersheds.

How to cite: Zhang, Q., Zhang, K., Chao, L., Chen, X., Wang, J., and Wu, N.: Testing a new hybrid runoff generation module in four typical watersheds across different hydrometeorological zones in China, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13237, https://doi.org/10.5194/egusphere-egu22-13237, 2022.

16:16–16:22
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EGU22-5206
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On-site presentation
Daniel Moraetis, Kosmas Palvopoulos, Charalambos Fassoulas, Andreas Scharf, Frank Mattern, Xuan Yu, Christos Pennos, Kostas Adamopoulos, Stylianos Zacharias, Hamdan Hamdan, and Nikolaos Nikolaidis

As part of the International Geoscience IGCP-715 project, we present the core objective and preliminary analyses on the karst development of the study areas. Our aim is to further characterize the geomorphologic features of the extended karst of Koiliaris Critical Zone Observatory (KCZO), Crete, Greece. Simultaneously to better understand the main drivers of karst development we compare the CKZO karst system with other areas in different tectonic contexts such as Oman (Salma Plateau), the northern UAE and southern China (Guilin karst area).

Hydrological studies and previous geomorphologic analysis of KCZO suggest that 27% of the total water budget is coming from the adjacent watershed in the east where an extensive karst system with two explored super-deep caves is situated (Liontari Cave - 1100 m, Gourgouthakas Cave - 1200 m). The area is build up by a continuous carbonate succession exceeding 5 km in depth, lying on top of the Hellenic subduction zone. Field work and Google Earth mapping show two dominantly striking directions of failures (fault, fracture surfaces), trending E-W to ESE-WNW (90-120°) and N-S to NNE-SSE (0-22.5°). The N-S surfaces are mainly fractures while the E-W ones are mainly thrusts and/or strike-slip faults with obvious large displacements of hundreds of meters. The karst development in a subduction zone with dramatic thrusting on the overriding plate has created super-deep caves which are controlled by the vertical bedding and a series of faults and fractures. The area exhibits two layers with different hydraulic properties, a fast water-transferring zone and a slower one which is consistent and supports the hypothesis of the hydrologic model.  

At the Salma Plateau, in Oman, the karstic system is related to rapid uplifted Eocene limestones that overlay the Semail Ophiolite. There is a large cave (Majilis Al Jinn) at an area of interconnected fractures (and/or faults?). It is the only karstic system presented inhere which has similarities with the karstic system in the KCZO.

At the UAE and northern Oman (Musandam) is an active collision zone between Arabia and Eurasia with 2000-m-thick allochthonous Mesozoic limestones. The area lacks a subsurface karst system, and the only karst has developed in steep wadis.

Finally, the Guilin area in China represents a former passive margin with a Devonian limestone. It features a spectacular karst of conical peaks (fengcong) and tower peaks (fenglin). Caves exhibit mainly a horizontal development and there is no similarities to the KCZO.

How to cite: Moraetis, D., Palvopoulos, K., Fassoulas, C., Scharf, A., Mattern, F., Yu, X., Pennos, C., Adamopoulos, K., Zacharias, S., Hamdan, H., and Nikolaidis, N.: Karst development in different tectonic settings (Middle East, Greece, South China), concept analysis and first findings towards hydrology modeling reconsideration, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5206, https://doi.org/10.5194/egusphere-egu22-5206, 2022.

16:22–16:28
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EGU22-12250
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ECS
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On-site presentation
Judith Meyer, Ralf Loritz, Laurent Pfister, and Erwin Zehe

In recent years, major floods triggered by convective events during the summer months repeatedly occurred in central western Europe, where flood regimes had previously been characterised by slowly developing inundations in winter. This imposes a great challenge on conceptual hydrological models, as other runoff mechanisms seem to dominate during convective storms than the storage-driven runoff production, which dominates flood formation in the wet season. Hence, most hydrological models that are used for operational flood forecasting struggle when applied to convective events and show deficiencies in capturing peak flows and timing flood volumes. It is thereby unclear to which extent the uncertainty in precipitation input and discharge observations, the influence of the catchment state, the model structure itself, or a combination of several or all of these factors, compromise successful predictions. To shed light on this question, we will compare how different model structures perform during high flows across a range of catchment physiographic settings. We will analyse the performance of a conceptual hydrological water balance model and identify its deficiencies by comparing it to a data driven model. This comparison will reveal whether uncertainties in the input and output data or model structural deficiencies are the major source of error. This will allow us to identify systematic errors, compare them and improve the model structure.

How to cite: Meyer, J., Loritz, R., Pfister, L., and Zehe, E.: Learning from differing errors between machine learning and a conceptual hydrological model - the case of convective storms and flash floods, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12250, https://doi.org/10.5194/egusphere-egu22-12250, 2022.

16:28–16:34
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EGU22-6239
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ECS
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On-site presentation
Mahkameh Taheri and James Craig

Runoff characteristics of wetland-dominated landscapes are highly influenced by the heterogeneity of wetland properties such as contributing areas, local storage deficits, and degree of connection to the wetland network. Field observations indicate the fill-and-spill process is a common and controlling process in wetland-dominated landscapes in which wetlands receive water until a threshold is satisfied and then release water to the next water bodies in a basin. The upscaled probabilistic fill-and-spill algorithm provided here aims to take the understanding from observation of the fill-and-spill process in individual wetlands at local scales to estimate the response of thousands of wetlands to precipitation or snowmelt events at a larger scale. For this purpose, a novel derived distribution approach is implemented using the probabilistic characteristics of wetlands, i.e., deficit and concentrating factor probability distribution functions. The method proposed here is a generalization of the Probability Distributed Model (PDM) and Xinanjiang probabilistic runoff models with the inclusion of the effects of wetland contributing area and cascading networks. The analytical solution of this upscaled fill-and-spill processes has been compared with a Monte Carlo solution and then implemented in RAVEN, a semi distributed hydrological modelling framework. The accuracy and ability of the model simulation has been tested on ten watersheds in the Qu'Appelle River Basin in Saskatchewan, Canada. The promising simulation results obtained from manual and automated calibration shows strength of the proposed upscaled fill-and-spill algorithm in simulation of low gradient landscapes.

How to cite: Taheri, M. and Craig, J.: Implementation of an upscaled probabilistic fill-and-spill method to simulate wetland-dominated landscapes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6239, https://doi.org/10.5194/egusphere-egu22-6239, 2022.

16:34–16:40
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EGU22-7813
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ECS
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Virtual presentation
Andrea Galletti, Diego Avesani, Alberto Bellin, and Bruno Majone

Natural streamflow of most mountain catchments worldwide is altered as a consequence of hydropower exploitation and other water uses. Hydrological modelling in these watersheds represents a challenging task, as the streamflow alteration caused by hydropower production is linked to operational schedules as well as to geometrical and technical constraints, which are system specific. Key parameters controlling hydropower functioning are difficult to acquire, because protected by producers, hence modelling hydropower systems in large domains often resorts to simplified (and less realistic) approaches, in order to cope with the lack of information. However, the accuracy of the simulations depends critically on the reliability of the simplified assumptions, which varies among the proposed approaches. In this work we analyzed the impact of the simplifications typically introduced in modelling hydropower at the catchment and larger scales by assuming as reference HYPERstreamHS, a coupled hydrological and hydraulic model exploiting the information publicly available on single hydropower systems. We present an application of the proposed framework to the Adige river basin, a large watershed located in the south-eastern portion of the Alps, in which the presence of 39 large hydropower systems characterized by complex infrastructures, 22 of which connected to storage reservoirs, causes significant alterations of streamflow timing and magnitude. We demonstrate the benefit of accurately representing hydropower-related water diversions by analyzing how the model represents the observed streamflows at impacted sites and hydropower production at the regional scale. We also provide insights on how a simplified representation of large hydropower systems can lead to a biased evaluation of streamflow alterations at impacted sections and of hydropower production at several sites. Our results show that the effects of different simplifications that may be adopted in the modelling framework combine in a non-linear manner, thus complicating the overall evaluation of the associated impacts.

How to cite: Galletti, A., Avesani, D., Bellin, A., and Majone, B.: On the accurate representation of hydropower systems in large-scale hydrological models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7813, https://doi.org/10.5194/egusphere-egu22-7813, 2022.

Coffee break
Chairpersons: Björn Guse, Sina Khatami, Simon Stisen
17:00–17:06
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EGU22-11728
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ECS
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On-site presentation
Florence Tan, Pasquale Borrelli, Gert Verstraeten, Anatoly Tsyplenkov, Benjamin Campforts, Valentin Golosov, Bernhard Lehner, Jean Poesen, and Matthias Vanmaercke

For many decades, sediment yield (SY) observations have been collected around the world to analyze, monitor, and better understand the state and dynamics of various Earth system processes. These records are highly relevant for a wide variety of research applications, yet they remain poorly accessible, especially for large-scale studies. A main reason for this is the fact that many of these measurements are collected on an isolated basis, leading to inconsistencies across data sets. SY observations also suffer from large uncertainties in data quality: key factors such as location accuracy, sampling method and frequency, measuring period, and others vary greatly but are not systematically reported.

To address these shortcomings and provide a standardized global reference for SY data, we are developing an extensive, coherent and georeferenced global database of contemporary SY observations. Through an extensive review of (grey) literature and contacts with numerous research groups, we already compiled SY observations for >8,000 catchments worldwide (comprising a total of >80,000 catchment years of observations). These observations are either derived from gauging station measurements or reservoir sedimentation rates. We assess the reliability of SY records and provide data quality indices based on available information such as measuring location, reported catchment area, sampling method and frequency, and measuring period. We further link the SY observations to the HydroSHEDS global river network, making them readily accessible and consistent with a wide array of hydro-environmental catchment variables also connected to the HydroSHEDS network. 

This new global SY database creates untouched opportunities for large-scale model development and statistical analyses of sediment-related factors and processes, such as soil erosion, sediment budgets, land cover and land use change impacts, or hydrological and sediment connectivity. Here we present a first overview of the data collected so far, its spatial patterns and its research potential. 

How to cite: Tan, F., Borrelli, P., Verstraeten, G., Tsyplenkov, A., Campforts, B., Golosov, V., Lehner, B., Poesen, J., and Vanmaercke, M.: Developing a global database of contemporary sediment yield observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11728, https://doi.org/10.5194/egusphere-egu22-11728, 2022.

17:06–17:12
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EGU22-2828
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ECS
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On-site presentation
Samira Sadat Soltani, Marwan Fahs, Ahmad Al Bitar, and Behzad Ataie-Ashtiani

The flow conditions (e.g., river network) for rivers and open channels are often forced into hydrogeological models that use a constant horizontal grid resolution without correction for grid mismatching. As a result, the flow velocity will be significantly underestimated if the width of rivers is substantially narrower than the grid size of these models. Furthermore, the exchange between the river and the subsurface is overestimated, resulting in an erroneously large vertical exchange.

In response to this challenge, in this work, the subscale channel flow is approximated in the kinematic wave equation by a scaled roughness coefficient. A relationship between grid cell size and river width is used for this purpose, which follows a simplified modification of the Manning-Strickler equation. In addition, the exchange between the subsurface and the river, as well as the rate of ex- and in-filtration, are scaled across river beds based on grid resolution. As a result, even though the grid size is relatively large, the exchange rates are corrected across river beds. The effectiveness of the scaling of river parametrization is validated against groundwater gauges and remote sensing-based surface soil moisture in a fully coupled subsurface-land surface ParFlow-CLM at a spatial resolution of 0.055° (~6 km) over the Upper Rhine Basin. The validity of the results is examined through an innovative application of the First Order Reliability Method (FORM) for the time period 2012-2014. Results indicate that the scaling approach improves the estimates of soil moisture, particularly in the summer and autumn seasons when cross-validated with independent CCI-SM observations. This improvement is achieved (SM RMSE reduction from 0.03 to 0.005) due to the effective impacts of the scaling river parametrization on SM estimation. FORM results show that the accuracy of ParFlow-CLM soil moisture simulations by using scaling approach is more than 95, 89, 85 and 92 percent for Autumn, Winter, April and Summer, respectively. The scaling river parametrization also shows overall improvements in groundwater level estimation, particularly over the central and northern regions where the groundwater level is shallow.

How to cite: Soltani, S. S., Fahs, M., Al Bitar, A., and Ataie-Ashtiani, B.:  Fully coupled subsurface-land surface hydrological models: A scaling approach to improve subsurface storage predictions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2828, https://doi.org/10.5194/egusphere-egu22-2828, 2022.

17:12–17:18
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EGU22-6315
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ECS
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On-site presentation
Hafsa Mahmood, Raphael Schneider, Rasmus Frederiksen, Anders Christiansen, and Simon Stisen

Almost 50% of the agricultural land in Denmark is tile drained, and it includes a wide range of hydrogeological and topographical settings. These drains in the shallow groundwater system influence the hydrology and nutrient transport in subsurface and surface waters significantly. Therefore, it is critical to understand the share of drainage with respect to the recharge in shallow groundwater systems to get a holistic picture of drain flow dynamics in varied topographical and hydrogeological settings. To address these issues, multiple tile-drain catchments (28 sites, with measured drain flow timeseries) across Denmark are used to test the response of tile drains in varied topographical and hydrogeological settings on field scale. Using the national hydrological model of Denmark (DK-model) in MIKE-SHE as a basis, 10m resolution groundwater flow models for all the drain catchments are established. Combined calibration for all drain catchments is conducted by evaluating percent bias (PBIAS) and Kling-Gupta Efficiency (KGE) of simulated and observed discharge data using the Pareto Archived Dynamically Dimensioned Search (PADDS) of the OSTRICH optimization tool. Principal component analysis (PCA) on independent physical explanatory variables (and indexes) representing topography and hydrogeology is used to reduce all collected variables to significant variables only. Linear polynomial ridge regression is used to study whether independent explanatory variables are sufficient to represent drain flow distribution or whether additional information derived from the groundwater flow models is needed. In this presentation, we will show if the independent topographical and geological variables can predict drain flow fraction and among all explanatory variables, which variables play the most significant role. Moreover, the resulting groundwater flow model of Denmark will serve to produce a training dataset of drain flow fraction that can be used further with machine learning approaches to predict drain flow dynamics for all of Denmark. The results of the study will contribute to improved drain flow predictions across all of Denmark by improving the understanding of controls on drain flow behaviour.

How to cite: Mahmood, H., Schneider, R., Frederiksen, R., Christiansen, A., and Stisen, S.: Assessing the physical controls of simulated drain flow dynamics , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6315, https://doi.org/10.5194/egusphere-egu22-6315, 2022.

17:18–17:24
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EGU22-8452
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ECS
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On-site presentation
Adnan Moussa, Mauro Sulis, and Julian Klaus

Integrated hydrological models (IHM) are often used for a better understanding of the hydrological fluxes at the surface and in the subsurface. IHMs can also be coupled to land surface models to study the interactions between vegetation and hydrological processes. At our study site, the Weierbach catchment (43 ha), Luxembourg, sap flux observations showed high spatial variability in observed transpiration due to many factors such as DBH, landscape characteristics and position, and tree species. However, this spatial variability is often not captured by land surface models at small scale and transpiration fluxes are usually treated as an integrated flux. In our study, we employ the coupled integrated surface-subsurface hydrological model Parflow coupled with the land surface model (CLM) to simulate water and energy fluxes in the forested Weierbach catchment in Luxembourg. Our objectives are twofold. First, we evaluate the coupled physically-based model and land surface model with discharge, groundwater level and soil moisture, and spatiotemporal sap flow data. In addition to that, we are exploring whether simulated and observed transpiration for three different hillslope positions (plateau, midslope, hillfoot) are driven by atmospheric demand or water availability in the subsurface. Our main result was that model has captured discharge, groundwater fluxes and the average transpiration well. However, the modelled transpiration showed a much smaller spatial variability compared to the spatial variability derived from sapflow observation. For the three different hillslope positions, we found that the fluxes were mainly driven by atmospheric demand and the model captured this dominance well. Our results demonstrate that there is a limitation of the model in reproducing the spatial variability of transpiration in the heterogeneous forest and future modelling work at small scale needs to better parameterize the spatial characteristics of vegetation.

How to cite: Moussa, A., Sulis, M., and Klaus, J.: On the value of benchmarking a fully coupled surface-subsurface model with spatially distributed sap flow measurements, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8452, https://doi.org/10.5194/egusphere-egu22-8452, 2022.

17:24–17:30
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EGU22-8027
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ECS
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Virtual presentation
Mathias Jackel, Markus Casper, and Hadis Mohajerani

Pedotransferfunctions (PTF) play a major role in physically based hydrological modeling, as they establish the relationship between soil properties and the water stress curve. The selection of the PTF thus has a great influence on the water balance of a catchment in general, as well as on the spatial distribution and intensity of runoff processes. In this study, these very influences of PTF's on the runoff processes will be investigated. The hydrological model "WaSiM-ETH", which was calibrated and validated, is used for the investigations. On the basis of this modeling, 11 further scenarios were created, which differ only in their PTFs. In all scenarios an artificial weather event is applied, which includes a constant heavy precipitation of 100 mm as well as input data, which excludes the generation of interfering processes e.g., evaporation or snowfall. In addition, these scenarios will be applied to different system conditions to determine the differences of dry, humid, and wet system preconditions. Ultimately, the precipitation intensity but not the amount of the artificial weather event will also be varied to be able to determine any change in the dominant runoff processes due to precipitation intensity. The results of the modeling will then be compared using the generated surface runoff, interflow, and deep infiltration. In addition, a check of the modeling with a runoff process map available for the catchment area as well as a pattern comparison using the spatial efficiency metric (SPEAF) will be performed. It is expected that the different PTF´s per se, as well as depending on the system precondition and precipitation intensity, will result in very different spatial distributions and dominance of the individual runoff processes. Thus, one goal is to find the PTF´s that provide comprehensible distributions and intensities of the considered runoff processes.

How to cite: Jackel, M., Casper, M., and Mohajerani, H.: The effect of different pedotransferfunctions on the spatial distribution and intensity of runoff processes using the hydrological model WaSiM-ETH., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8027, https://doi.org/10.5194/egusphere-egu22-8027, 2022.

17:30–17:36
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EGU22-9950
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ECS
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On-site presentation
Matevz Vremec, Veronika Forstner, Markus Herndl, Luca Guillaumot, Peter Burek, and Steffen Birk

Warming and elevated CO2 concentrations are expected to alter catchment hydrology through changes in precipitation and evapotranspiration. In particular, warming is expected to enhance evapotranspiration, whereas elevated CO2 tends to decrease root-water uptake, thus reducing evapotranspiration. Plot-scale Lysimeter Temperature Free Air Carbon Enrichment (Lysi-T-FACE) systems provide in-depth information on the response of soil water fluxes to future climate conditions, particularly evapotranspiration and seepage. Hydrological models of different complexity can be used to extend the findings from the plot scale to the catchment level, allowing the assessment of the discharge response to climate change.

We run a climate change experiment by using lysimeters to study the effect of elevated CO2 and warming on alpine grassland soil water fluxes. The experiment includes six lysimeters, with a reference lysimeter operating under ambient conditions, two lysimeters are treated with elevated CO2 concentration of +300 ppm, two lysimeters are operating under constant warming of +3 K, and one operating under a combination of warming and elevated CO2. Soil water fluxes within each lysimeter were modelled with the process-based hydrological model Hydrus-1D. We observed differences in seepage between the six lysimeters at both the event-based and annual time scale. For some individual events, such as the heavy rainfall event following a dry period in summer 2018, more remarkable differences between the experiments were observed.

To upscale the effects of the lysimeter-based approach to catchment scale, a conceptual lumped-parameter model (GR4J-Cemaneige) was used to model the discharge of a nearby alpine grassland catchment. The GR4J model reproduced discharge well when using lysimeter ET at ambient conditions (NSE>0.75). Evapotranspiration (ET) as input was modified based on the lysimeter ET fluxes representing possible future climate conditions. The effects of different ET inputs on simulated catchment discharge were similar to those on seepage at the plot level on an annual basis. However, no significant effects of different ET input on discharge were observed at individual events, such as the one in 2018. A comparison between a process-based hydrological model and the conceptual lumped-parameter model is planned to further investigate the effect of the hydrological response to climate change at the catchment scale.

How to cite: Vremec, M., Forstner, V., Herndl, M., Guillaumot, L., Burek, P., and Birk, S.: Alpine grassland hydrologic response to climate change from plot to catchment scale, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9950, https://doi.org/10.5194/egusphere-egu22-9950, 2022.

17:36–17:42
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EGU22-9791
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ECS
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On-site presentation
Olivier Grandjouan, Flora Branger, Matthieu Masson, Benoit Cournoyer, and Marina Coquery

The origin and transport of water in peri-urban catchments is complex to model as they are affected by multiple anthropogenic modifications of water pathways (surface imperviousness, sewer overflow releases…), especially in a context of fast growing urbanization. The hydrological dynamics are also  impacted by natural and agricultural land use patterns. Perceptual models aim at reproducing our understanding of a catchment behaviour and can be useful to illustrate the impact of such spatial contrast and human-induced modifications on a catchment hydrological dynamics. Conservative geochemical and microbiological tracers can be linked to the hydrological processes and water pathways to enhance this understanding and to build-up the hydrological perceptual model of a catchment.

From 2017 to 2019, a monthly monitoring of geochemical and microbiological tracers was conducted at the Ratier catchment (19 km²) near Lyon (France). Surface waters were collected and analysed for major chemical parameters (cations, anions, dissolved organic carbon and conductivity), dissolved metals, stable isotopes (2H et 18O), and microbial parameters (total bacterial counts, microbial source tracking DNA datasets, species – specific DNA trackings). Using these datasets, a step-by-step statistical approach was undertaken, and used to build-up the perceptual hydrological model. The main steps were: (1) group correlated biochemical parameters to reduce redundancy in the dataset, (2) compute the main indicators illustrating the hydro-climatologic dynamics during the sampling campaigns (e.g. antecedent index precipitation, average daily flow) based on the hypothesis of a two-component catchment (groundwater and subsurface flow), and (3) perform a principal component analysis to link the biogeochemical dataset to the computed hydro-climatologic indicators and the runoff processes.

Results revealed a differentiation of the datasets in two groups matching groundwaters and subsurface waters. Groundwaters showed two geochemical profiles linked to the two main geological formations of the catchment. Subsurface waters showed more variable biogeochemical patterns highly influenced by land use and soil properties. This step-by-step statistical approach led to a better understanding of the dynamics of the water pathways and these insights were then used to build-up the hydrological perceptual model of the catchment. As a next step, such a model should help in the evaluation and improvement of a distributed hydrological model.

How to cite: Grandjouan, O., Branger, F., Masson, M., Cournoyer, B., and Coquery, M.: A biogeochemical approach to build a perceptual hydrological model for a small peri-urban catchment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9791, https://doi.org/10.5194/egusphere-egu22-9791, 2022.

17:42–17:48
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EGU22-11393
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ECS
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Virtual presentation
Leveraging sap flow data in a catchment-scale hybrid model to improve soil moisture and transpiration estimates
(withdrawn)
Ralf Loritz, Maoya Bassiouni, Anke Hildebrandt, Sibylle Hassler, and Erwin Zehe
17:48–17:54
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EGU22-9172
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ECS
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On-site presentation
José Gomis-Cebolla, Alicia Garcia-Arias, Martí Perpinyà-Vallès, and Félix Francés

Calibration of distributed hydrological models needs to include spatial information of the hydrological processes in order to guarantee a robust spatial representation of the model state variables. Satellite remote sensing monitoring the Earth in a temporal and spatial comprehensive way stands out as a valuable resource of this kind of information. Surface soil moisture (SSM) plays a key role in the description of the hydrological cycle, especially in semi-arid areas. Nevertheless, the coarse resolution of available SSM products has restricted the use of SSM in the calibration of hydrological models to only the temporal approach. The current operational SSM estimates (1km) resulting from new sensor estimates or the application of downscaling methodologies pave the way for this spatial calibration approach. The present study explores the applicability of these spatially enhanced SSM estimates for distributed eco-hydrological modelling in Mediterranean forest basins. On one hand, it contributes to fill the existing research gap on the use of remote sensing SSM spatial patterns within the distributed hydrological modelling framework, in particular in medium/small basins. On the other hand, it serves as an indirect validation method for the spatial performance of satellite SSM products. TETIS eco-hydrological distributed model was implemented in three case studies, named Carraixet (eastern Spain), Hozgarganta (southern Spain), and Ceira (western Portugal), which were strategically selected to perform this research in the Mediterranean Region. The SSM estimates selected for evaluation were: Sentinel-1 SSM provided by the Copernicus Global Land Services (CGLS), SMAP SSM disaggregated using Sentinel-1 provided by the National Aeronautics and Space Administration (NASA), SMOS SSM provided by the Barcelona Expert Center (BEC), and SMOS and SMAP SSM disaggregated using the Dispatch algorithm provided by Lobelia Earth. The methodology employed involved a multi-objective and multi-variable calibration using the considering remote sensing SSM spatial patterns and in-situ streamflow, using the Spatial Efficiency Metric (SPAEF) and the Nash-Sutcliffe efficiency index (NSE) respectively. In spite of the spatial and temporal differences amongst products, the multi-objective calibration approach proposed increased the robustness of the hydrological modelling. Spatial and temporal agreement depends on the selection of the SSM product. The disaggregating methodology determined the spatial agreement to a greater degree than the sensor itself.

How to cite: Gomis-Cebolla, J., Garcia-Arias, A., Perpinyà-Vallès, M., and Francés, F.: Potential of satellite surface soil moisture products for spatially calibrating distributed eco-hydrological models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9172, https://doi.org/10.5194/egusphere-egu22-9172, 2022.

17:54–18:00
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EGU22-10770
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ECS
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On-site presentation
Nathaniel Chaney, Laura Torres Rojas, Jiaxuan Cai, and Noemi Vergopolan

Emerging field-scale resolving land surface models (LSMs), such as HydroBlocks, aim to model the water, energy, and biogeochemical cycles (e.g., surface energy partitioning) at 10-100 meter spatial scales over continental extents. However, there has yet to be a concerted effort to evaluate the realism of the simulated field-scale spatial patterns. This presentation challenges the scientific community to evaluate the modeled multi-scale spatial patterns of contemporary land surface models more critically. Here, we present an approach to evaluate the modeled multi-scale spatial patterns of land surface temperature (a linchpin state variable in the land surface energy and water cycles) of the HydroBlocks LSM using GOES-16 land surface temperature over the contiguous United States (CONUS).

To perform this evaluation, HydroBlocks is run at an effective 30-meter spatial resolution over CONUS at an hourly time step between 2015 and 2020. The domain is then split into 0.5 arcdegree grid cells (~50 km) and a series of spatial statistics are computed (e.g., spatial variance and correlation length) at hourly, daily, monthly, and annual time steps. These spatial statistics are also calculated using the GOES-16 land surface temperature product at the available time steps (with 80%+ spatial coverage per 0.5 degree grid cell). GOES-16 provides hourly observations of land surface temperature over CONUS at a 2 km spatial resolution. The simulated and observed spatial statistics are then compared between 2017 and 2020 for each macroscale grid cell over CONUS. The results show a poor correlation between the two at hourly time scales but show marked improvement over larger time scales. In any case, the surprisingly weak correlation between the observed and simulated spatial statistics reinforce the need to think more critically about the spatial uncertainty chain in land surface models. More importantly, this work reemphasizes the need to make simulated spatial patterns an integral part of the evaluation and calibration of macroscale land surface and hydrologic models moving forward.

How to cite: Chaney, N., Torres Rojas, L., Cai, J., and Vergopolan, N.: How realistic are the spatial patterns simulated from field-scale resolving land surface models?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10770, https://doi.org/10.5194/egusphere-egu22-10770, 2022.

18:00–18:06
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EGU22-7297
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ECS
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Virtual presentation
Jari-Pekka Nousu, Kersti Leppä, Hannu Marttila, Pertti Ala-aho, Mika Aurela, Annalea Lohila, and Samuli Launiainen

Surface soil parameters, especially soil moisture has a key role in soil nutrient cycling, greenhouse gas emissions, vegetation water use as well as energy and water exchanges between land and the atmosphere. In this study, we model soil moisture with three different model conceptualizations developed in Spatial Forest Hydrology model (SpaFHy) in a subarctic Pallas catchment in Northern Finland, covered by coniferous forests and peatlands. The model versions differ in how the groundwater flow is treated, which is shown to have a clear impact on the spatiotemporal soil moisture dynamics within the catchment. The conceptualizations range from i) neglecting groundwater storage, to ii) TOPMODEL approach, and to iii) spatially distributed groundwater flow model. By comparing these scenarios, we are able to assess when and where solving the 2D ground water flow is prerequisite for accurate predictions of soil moisture, and in which conditions soil moisture variability is driven more by local processes. The model results are compared against continuous point-scale measurements, and spatially against distributed measurement campaigns conducted in the study area. In addition, we compare the spatiotemporal soil moisture simulations with novel SAR-based soil moisture maps. SAR signal is well suited to estimate topsoil moisture thanks to its high sensitivity to water. However, different topographic and vegetation settings create challenges for SAR signals to capture the properties of soil, and thus, SAR soil moisture estimates have not been as widely used in forested areas. Remote sensing products such as SAR-based soil moisture maps possess a major potential to further develop spatially distributed land surface and hydrological models.

How to cite: Nousu, J.-P., Leppä, K., Marttila, H., Ala-aho, P., Aurela, M., Lohila, A., and Launiainen, S.: Exploring spatiotemporal dynamics of soil moisture: three model conceptualizations in a subarctic catchment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7297, https://doi.org/10.5194/egusphere-egu22-7297, 2022.

18:06–18:12
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EGU22-1570
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ECS
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On-site presentation
Charles Whittaker and Robert Leconte

Flood forecasting agencies and hydropower companies require cost effective approaches for accurate estimation of snow water equivalent (SWE) to improve spring flow forecast and to make informed decision about reservoir operation.  The lack of accurate SWE estimation at the watershed scale is an issue in northern watersheds, as snow surveys are either absent, or sparsely distributed and infrequent (monthly to bi-weekly). Remotely sensed SWE data sets retrieved from passive microwave satellites, such as GlobSnow, offers the advantage of high frequent coverage of the Northern Hemisphere at the watershed scale.  The main issue is that SWE is typically underestimated because of vegetation. Also, the signal saturates for deep snowpacks.  An approach is therefore required to correct GlobSnow which does not resort to local SWE measurements.

A correction factor approach which focuses on improving the Maximum Snow Water Equivalent (MSWE) estimate for a watershed produced by publicly available regional databases, such as GlobSnow, has been developed. The method does not require point SWE measurements and assumes that the spring runoff volume calculated from historical streamflow observations equals the total snow melt volume retrieved from GlobSnow’s MSWE, less infiltration into frozen ground. The latter is calculated from freely available hydro-meteorological information. The method presented below introduces a cost-effective approach which can bridge the temporal and spatial sparsity that is often associated with the snow survey programs.

The results from applying this approach to the regional GlobSnow database to northern watersheds in Quebec show that the Corrected GlobSnow (C-Glob) more accurately correlates to the manual snow surveys, compared to the uncorrected GlobSnow data source. The corrected database may prove especially useful for watersheds where no SWE measurements are available, may serve as a supplementary source of information to better understand what takes place over the entire watershed by filling gaps of manual surveys.

How to cite: Whittaker, C. and Leconte, R.: A novel watershed scale Snow Water Equivalent (SWE) correction approach, using stream flow and remote sensing data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1570, https://doi.org/10.5194/egusphere-egu22-1570, 2022.

18:12–18:18
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EGU22-7298
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Virtual presentation
Abolfazl Jalali Shahrood and Ali Torabi Haghighi

River ice has a significant impact on nearly 60% of rivers in the northern hemisphere where ice jams due to river ice are responsible for some severe and recurrent floods in the northern rivers. The ice formation (i.e., freeze-up event) occurs during the wintertime when the temperature drops to freezing point and when the flow gradually reaches the lowest value for a while, and it keeps the river frozen with low flow occurring close to the period of the maximum ice cover before the spring melt is initiated.  In this research, we focus on detecting the periods with low flow (i.e., freezing period). Daily discharge time series were used to derive the annual freezing periods as well as extreme discharge values over a century in Tana and Tornio Rivers in the Finnish borders of Sweden and Norway.  Therefore, the timing characteristics such as duration, probable shifts through time, and overall flow extremes including the average low and high flow in a period of 90 days in each water year were quantified. The study showed that both low and high flows in two rivers had a significant negative trend in their occurrence date by a confidence level of 95%. In addition, it was observed that the seasonal 90-day low and high flow periods happened earlier in recent years. On the other hand, Tana River showed a negative trend in its annual minimum flow over the century which is an opposite event in comparison with Tornio River. The duration of low flow in the Tana River has been significantly increased by the confidence level of 95% from a range of 50-70 days to a range of 100-140 days. In Tornio River, the duration has been significantly decreased by the confidence level of 95%. In the first ten years, the duration is about 120 days on average, while the duration in the last ten years is about 50 days.

How to cite: Jalali Shahrood, A. and Torabi Haghighi, A.: 100 years of river flow timing characteristics and extreme flow analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7298, https://doi.org/10.5194/egusphere-egu22-7298, 2022.