Incorporating Uncertainty Into Multiscale Parameter Regionalization to Evaluate the Performance of Nationally Consistent Parameter Fields for a Hydrological Model

2021 ◽  
Vol 57 (10) ◽  
Author(s):  
Rosanna A. Lane ◽  
Jim E. Freer ◽  
Gemma Coxon ◽  
Thorsten Wagener
Keyword(s):  
Hydrological Model ◽  
Consistent Parameter

Estimation of Regional Parameters in a Macro Scale Hydrological Model

2001 ◽  
Vol 32 (3) ◽  
pp. 161-180 ◽  
Author(s):  
Kolbjørn Engeland ◽  
Lars Gottschalk ◽  
Lena Tallaksen
Keyword(s):  
Additional Data ◽  
Hydrological Model ◽  
Sampling Methods ◽  
Probability Distributions ◽  
Repeated Application ◽  
Landscape Characteristics ◽  
Time Period ◽  
Macro Scale ◽  
Model Element ◽  
Regular Sampling

Macro-scale hydrological modelling implies a repeated application of a model within an area using regional parameters. These parameters are based on climate and landscape characteristics, and they are used to calculate the water balance in ungauged areas. The regional parameters ought to be robust and not too dependent of the catchment and time period used for calibration. The ECOMAG model is applied for the NOPEX-region as a macro-scale hydrological model distributed on a 2×2 km2 grid. Each model element is assigned parameters according to soil and vegetation classes. A Bayesian methodology is followed. An objective function describing the fit between observed and simulated values is used to describe the likelihood of the parameters. Using Baye's theorem these likelihoods are used to update the probability distributions of the parameters using additional data, being it either an additional year of streamflow or an additional streamflow station. Two sampling methods are used, regular sampling and Metropolis-Hastings sampling. The results show that regional parameters exist according to some predefined criteria. The probability distribution of the parameters shows a decreasing variance as data from new catchments are used for updating. A few parameters do, however, not exhibit this property, and they are therefore not suitable in a regional context.

scholarly journals Modelling of the Discharge Response to Climate Change under RCP8.5 Scenario in the Alata River Basin (Mersin, SE Turkey)

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 483
Author(s):  
Ümit Yıldırım ◽  
Cüneyt Güler ◽  
Barış Önol ◽  
Michael Rode ◽  
Seifeddine Jomaa
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Hydrological Model ◽  
Baseline Period ◽  
Change Scenario ◽  
Climate Projections ◽  
Worst Case ◽  
Distributed Process ◽  
In The Beginning ◽  
Se Turkey

This study investigates the impacts of climate change on the hydrological response of a Mediterranean mesoscale catchment using a hydrological model. The effect of climate change on the discharge of the Alata River Basin in Mersin province (Turkey) was assessed under the worst-case climate change scenario (i.e., RCP8.5), using the semi-distributed, process-based hydrological model Hydrological Predictions for the Environment (HYPE). First, the model was evaluated temporally and spatially and has been shown to reproduce the measured discharge consistently. Second, the discharge was predicted under climate projections in three distinct future periods (i.e., 2021–2040, 2046–2065 and 2081–2100, reflecting the beginning, middle and end of the century, respectively). Climate change projections showed that the annual mean temperature in the Alata River Basin rises for the beginning, middle and end of the century, with about 1.35, 2.13 and 4.11 °C, respectively. Besides, the highest discharge timing seems to occur one month earlier (February instead of March) compared to the baseline period (2000–2011) in the beginning and middle of the century. The results show a decrease in precipitation and an increase in temperature in all future projections, resulting in more snowmelt and higher discharge generation in the beginning and middle of the century scenarios. However, at the end of the century, the discharge significantly decreased due to increased evapotranspiration and reduced snow depth in the upstream area. The findings of this study can help develop efficient climate change adaptation options in the Levant’s coastal areas.

scholarly journals Sensitivity and Performance Analyses of the Distributed Hydrology–Soil–Vegetation Model Using Geomorphons for Landform Mapping

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2032
Author(s):  
Pâmela A. Melo ◽  
Lívia A. Alvarenga ◽  
Javier Tomasella ◽  
Carlos R. Mello ◽  
Minella A. Martins ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Overland Flow ◽  
Hydrological Model ◽  
Model Performance ◽  
Soil Parameters ◽  
Vegetation Model ◽  
Landform Classification ◽  
Physically Based ◽  
And Performance ◽  
Streamflow Simulation

Landform classification is important for representing soil physical properties varying continuously across the landscape and for understanding many hydrological processes in watersheds. Considering it, this study aims to use a geomorphology map (Geomorphons) as an input to a physically based hydrological model (Distributed Hydrology Soil Vegetation Model (DHSVM)) in a mountainous headwater watershed. A sensitivity analysis of five soil parameters was evaluated for streamflow simulation in each Geomorphons feature. As infiltration and saturation excess overland flow are important mechanisms for streamflow generation in complex terrain watersheds, the model’s input soil parameters were most sensitive in the “slope”, “hollow”, and “valley” features. Thus, the simulated streamflow was compared with observed data for calibration and validation. The model performance was satisfactory and equivalent to previous simulations in the same watershed using pedological survey and moisture zone maps. Therefore, the results from this study indicate that a geomorphologically based map is applicable and representative for spatially distributing hydrological parameters in the DHSVM.

scholarly journals Regionalisierung hydrologischer Modelle mit Function Space Optimization

2021 ◽  
Author(s):  
Moritz Feigl ◽  
Mathew Herrnegger ◽  
Robert Schweppe ◽  
Stephan Thober ◽  
Daniel Klotz ◽  
...  
Keyword(s):  
Function Space ◽  
Hydrological Model ◽  
Problem Parameter ◽  
Sind Eine

ZusammenfassungDas Schätzen von räumlich verteilten Parametern hydrologischer Modelle ist ein bereits lang erforschtes und anspruchsvolles Problem. Parameter-Transferfunktionen, die einen funktionellen Zusammenhang zwischen Modellparametern und geophysikalischen Gebietseigenschaften herstellen, sind eine potenzielle Möglichkeit, Parameter ohne Kalibrierung zu schätzen. Function Space Optimization (FSO) ist eine symbolische Regressionsmethode, die automatisiert Transferfunktionen aus Daten schätzen kann. Sie basiert auf einem textgenerierenden neuronalen Netzwerk, das die Suche nach einer optimalen Funktion in ein kontinuierliches Optimierungsproblem umwandelt.In diesem Beitrag beschreiben wir die Funktionsweise von FSO und geben ein Beispiel der Anwendung mit dem mesoscale Hydrological Model (mHM). Ziel der Anwendung ist die Schätzung zweier Transferfunktionen für die Parameter KSat (gesättigte hydraulische Leitfähigkeit) und FieldCap (Feldkapazität). Dafür verwenden wir Daten 7 großer deutscher Einzugsgebieten über einen Zeitraum von 5 Jahren zum Schätzen der Transferfunktionen und weiterer numerischer Parameter. Die resultierenden Funktionen und Parameter werden ohne weitere Kalibrierung auf 222 Validierungsgebiete über eine Validierungsperiode von 35 Jahren angewendet. Mit der Anwendung in diesen „unbeobachteten“ Gebieten können wir die Übertragbarkeit und die zumindest regionale Gültigkeit der Transferfunktionen überprüfen.Die Ergebnisse zeigen, dass bei einer Anwendung in unbeobachteten Gebieten die Modellgüte in einem ähnlichen Wertebereich wie in den Trainingsgebieten liegt und somit weiterhin akzeptabel ist. Die Nash-Sutcliffe Efficiency (NSE) in den Trainingsgebieten über den Validierungszeitraum unterscheidet sich mit einem medianen Wert von 0,73 nicht nennenswert von dem der Validierungsgebiete mit einem medianen NSE von 0,65.Zusammengefasst haben Transferfunktionen das Potenzial, die Vorhersagefähigkeiten, Übertragbarkeit auf andere Gebiete sowie physikalische Interpretierbarkeit bestehender hydrologischer Modelle zu verbessern. Mit FSO wurde zum ersten Mal eine objektive, datengetriebene Methode entwickelt, mit der Transferfunktionen geschätzt werden können.

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