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Hydrologic Prediction in Alpine Environments II

Proposed research: This research programme proposes to analyze the predictability of the hydrologic behaviour of Alpine ecosystems at the spatio-temporal scales relevant for water management, i.e. at spatial scales of between 200 km2 (e.g. a hydropower production catchment) and around 5000 km2 (e.g. flood management of the Swiss Rhone catchment) and at temporal scales ranging from hours to seasons. Research context: Quantitative stream flow predictions are essential for the sustainable management of our natural and man-made environment and for the prevention of natural hazards. Despite of ever better insights into the involved physical processes at the point scale, many existing catchment scale runoff prediction models still show a lack of reliability for stream flow prediction. The present research programme addresses this foremost issue in Alpine environments, which are the source of many major European rivers and play a dominant role for hydropower production and flood protection. Stream flow prediction in such environments is particularly challenging due to the high spatial variability of the meteorological driving forces opposed to notorious data scarcity in remote and high elevation areas. Project context: The present proposal is a follow-up proposal of the Ambizione project Hydrologic Prediction in Alpine Environments. During the main phase of the project (3 years), certain essential research objectives could not be reached, due namely to the maternity leave of the principal investigator (PI), but also due to additional research questions that emerged at the very beginning of this research. The present follow-up project proposes to complete the research programme during a complementary project phase (2 years). Objectives: The main objective of this research programme is to assess under which conditions simple hydrological models can give reliable stream flow predictions in Alpine environments. This objective will be reached based on an analysis of the variability of natural flow generation processes and of the variability of corresponding state-of-the-art hydrological model outputs. During the main phase of the project, the research was concentrated on the analysis of flow generation processes related to snowmelt, which in Alpine areas dominate the hydrological response over a large part of the year. The achieved results include a new hourly snowmelt model combined to a spatially-explicit precipitation-runoff model, an improved snowfall-limit prediction method for hydrological models and a weather generator that produces coupled temperature and prediction scenarios to analyze how these two meteorological variables integrate to the snow-hydrological response.(...)

Snowmelt runoff modelling in mountain environments under changing climate conditions (SnowClim)

Rain-on-snow events with combined snow melting and rainfall is a frequent cause of floods in Europe. Reflecting possible long-term changes in climate conditions, there is the question of climate change impacts on the runoff regime at the regional and local scale. An important part of the research in mountain areas is therefore the issue of possible future changes in snow and glacier melt regimes. The main objective of this project is to contribute to research on processes connected with snow accumulation and melting as a factor of flood risk in the context of changing environment and climate change. The main focus will be possible future changes in snowpack using regional climate models (RCM) and impacts on runoff regime of mountainous basins. The project solution will lean on up-to-date hydrological and geoinformation methods and tools, which are presently applied for modelling the runoff from melting snow. The research will be carried out in selected middle-large basins in Switzerland and in the Czech Republic. Modelling the evolution of the snowpack (snow cover area, snow water equivalent, snowpack duration etc.) will be made by means of energy balance and temperature-index modelling techniques. Simulations using results from RCMs models will be made in order to simulate possible future changes of above mentioned snowpack.

Simulated sensitivity time series and model performance in three German catchments

The data sets contains the major results of the article “Improving information extraction from model data using sensitivity-weighted performance criteria“ written by Guse et al. (2020). In this article, it is analysed how a sensitivity-weighted performance criterion improves parameter identifiability and model performance. More details are given the in article. The files of this dataset are described as follows. Parameter sampling: FAST parameter sampling.xlsx: To estimate the sensitivity, the Fourier Amplitude Sensitivity Test (FAST) was used (R-routine FAST, Reusser, 2013). Each column shows the values of the model parameter of the SWAT model (Arnold et al., 1998). All parameters are explained in detail in Neitsch et al. (2011). The FAST parameter sampling defines the number of model runs. For twelve model parameters as in this case, 579 model runs are required. The same parameter sets were used for all catchments. Daily sensitivity time series: Sensitivity_2000_2005.xlsx: Daily time series of parameter sensitivity for the period 2000-2005 for three catchments in Germany (Treene, Saale, Kinzig). Each column shows the sensitivity of one parameter of the SWAT model. The methodological approach of the temporal dynamics of parameter sensitivity (TEDPAS) was developed by Reusser et al. (2011) and firstly applied to the SWAT model in Guse et al. (2014). As sensitivity index, the first-order partial variance is used that is the ratio of the partial variance of one parameter divided by the total variance. The sensitivity is thus always between 0 and 1. The sum in one row, i.e. the sensitivity of all model parameters on one day, could not be higher than 1. Parameter sampling: LH parameter sampling.xlsx: To calculate parameter identifiability, Latin Hypercube sampling was used to generate 2000 parameter sets (R-package FME, Soetaert and Petzoldt, 2010). Each column shows the values of the model parameter of the SWAT model (Arnold et al., 1998). All parameters are explained in detail in Neitsch et al. (2011). The same parameter sets were used for all catchments. Performance criteria with and without sensitivity weights: RSR_RSRw_cal.xlsx: • Calculation of the RSR once and RSRw separately for each model parameter. • RSR: Typical RSR (RMSE divided by standard deviation) • RSR_w: RSR with weights according to daily sensitivity time series. The calculation was carried out in all three catchments. • The column RSR shows the results of the RSR (RMSE divided by standard deviation) for the different model runs. • The column RSR[_parameter name] shows the calculation of the RSR_w for the specific model parameter. • RSR_w give weights on each day based on the daily parameter sensitivity (as shown in sensitivity_2000_2005.xlsx). This means that days with a higher parameter sensitivity are higher weighted. In the methodological approach the best 25% of the model runs were calculated (best 500 model runs) and the model parameters were constrained to the most appropriate parameter values (see methodological description in the article). Performance criteria for the three catchments: GOFrun_[catchment name]_RSR.xlsx: These three tables are organised identical and are available for the three catchments in Germany (Treene, Saale, Kinzig). In using the different parameter ranges for the catchments as defined in the previous steps, 2000 model simulation were carried out. Therefore, a Latin-Hypercube sampling was used (R-package FME, Soetaert and Petzoldt, 2010). The three tables show the results of 2000 model simulations for ten different performance criteria for the two different methodological approaches (RSR and swRSR) and two periods (calibration: 2000-2005 and validation: 2006-2010). Performance criteria for the three catchments: GOFrun_[catchment name]_MAE.xlsx: The three tables show the results of 2000 model simulations for ten different performance criteria for the two different methodological approaches (MAE and swMAE) and two periods (calibration: 2000-2005 and validation: 2006-2010).

Data and R-scripts for Dynamic Identifiability Analysis of Process-based Hydrological Model Structures

For the article “How to Tailor my Process-based Model? Dynamic Identifiability Analysis of Flexible Model Structures” (Pilz et al., submitted) a flexible simulation environment is coupled with an algorithm for dynamic identifiability analysis to form a diagnostic tool for process-based model building. This associated data description describes first which software is needed (R and the ECHSE model) and how to configure your computer in order to run the model and the analysis. Second, the input data provided within this data set are described. Eventually, the R scripts are described, which were used to initialize and run the model and conduct the subsequent identifiability analysis. This publication comprises the ECHSE model, all R packages in their employed versions, the model setup and results, figures and data presented in the associated research paper, and the R scripts used to initialize and run the model and conduct all analyses.

Regionale Simulationen in der Hydrologie - Quantifizierung der Fehler und der Unsicherheiten

Dieses Projekt soll die im Rahmen des DFG-Schwerpunktprogrammes 'Regionalsierung in der Hydrologie' begonnenen Arbeiten erweitern und vervollständigen. Im SPP wurde von der Arbeitsgruppe der Antragsteller ein durchgängiges Konzept zur Simulation der Wasserflüsse sowohl an Standorten als auch im regionalen Maßstab entwickelt und im Einzugsgebiet der oberen Leine erfolgreich getestet. Die Analyse und Bewertung der mit der regionalen Simulation verbundenen Unsicherheiten (uncertainty analysis) und Fehler (error analysis) konnte nur für einzelne Eigenschaften bzw. Randbedingungen durchgeführt werden. Um eine abschließende Bewertung der entwickelten Regionalisierungsverfahren vornehmen zu können, sind entsprechende Untersuchungen notwendig. Das Ziel dieses Projektes ist es, eine Bewertung der Güte regionaler Simulationen der Wasserflüsse unter Berücksichtigung der skalenabhängigen Unsicherheiten hinsichtlich Boden, Relief, Landnutzung und Klima zu geben. Dieses ist notwendig, um die Anwendbarkeit von Simulations- und Regionalisierungskonzepten zu beurteilen und eine skalenabhängige Bewertung der Ergebnisse zu ermöglichen. - Die Anbindung hydrologischer Modelle an meso- und makroskalige Atmosphärenmodelle hinsichtlich der Hydrologie zu verbessern, ist somit immer noch ein großer Forschungsbedarf vorhanden. Es ist daher geplant, die Arbeiten in dieser Hinsicht auszudehnen und Konzepte zur Kopplung des Regionalisierungsansatzes mit einem Atmosphärenmodell zu entwickeln. Hierbei geht es allerdings nicht um die Realisierung der Kopplung, sondern um die Analyse der notwendigen Vorbereitungsschritte und die Parametrisierung hinsichtlich Boden, Relief und Klima.

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