scholarly journals Adaptive clustering: A method to analyze dynamical similarity and to reduce redundancies in distributed (hydrological) modeling

2020 ◽  
Author(s):  
Uwe Ehret ◽  
Rik van Pruijssen ◽  
Marina Bortoli ◽  
Ralf Loritz ◽  
Elnaz Azmi ◽  
...  
Keyword(s):  
System Analysis ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Model Performance ◽  
Computation Time ◽  
Spatial Proximity ◽  
Hydrological Process ◽  
Distributed Hydrological Model ◽  
Adaptive Clustering ◽  
Wide Range

Abstract. In this paper we propose adaptive clustering as a new way to analyse hydrological systems and to reduce computational efforts of distributed modelling, by dynamically identifying similar model elements, clustering them and inferring dynamics from just a few representatives per cluster. It is based on the observation that while hydrological systems generally exhibit large spatial variability of their properties, requiring distributed approaches for analysis and modelling, there is also redundancy, i.e. there exist typical and recurrent combinations of properties, such that sub systems exist with similar properties, which will exhibit similar internal dynamics and produce similar output when in similar initial states and when exposed to similar forcing. Being dependent on all these factors, similarity is hence a dynamical rather than a static phenomenon, and it is not necessarily a function of spatial proximity. We explain and demonstrate adaptive clustering at the example of a conceptual, yet realistic and distributed hydrological model, fit to the Attert basin in Luxembourg by multi-variate calibration. Based on normalized and binned transformations of model states and fluxes, we first calculated time series of Shannon information entropy to measure dynamical similarity (or redundancy) among sub systems. This revealed that indeed high redundancy exists, that its magnitude differs among variables, that it varies with time, and that for the Attert basin the spatial patterns of similarity are mainly controlled by geology and precipitation. Based on these findings, we integrated adaptive clustering into the hydrological model. It constitutes a shell around the model hydrological process core and comprises: Clustering of model elements, choice of cluster representatives, mapping of results from representatives to recipients, comparison of clusterings over time to decide when re-clustering is advisable. Adaptive clustering, compared to a standard, full-resolution model run used as a virtual reality truth, reduced computation time to one fourth, when accepting a decrease of modelling quality, expressed as Nash–Sutcliffe efficiency of sub catchment runoff, from 1 to 0.84. We suggest that adaptive clustering is a promising tool for both system analysis, and for reducing computation times of distributed models, thus facilitating applications to larger systems and/or longer periods of time. We demonstrate the potential of adaptive clustering at the example of a hydrological system and model, but it should apply to a wide range of systems and models across the earth system sciences. Being dynamical, it goes beyond existing static methods used to increase model performance, such as lumping, and it is compatible with existing dynamical methods such as adaptive time-stepping or adaptive gridding. Unlike the latter, adaptive clustering does not require adjacency of the sub systems to be joined.

scholarly journals Inverse distributed hydrological modelling of Alpine catchments

2006 ◽  
Vol 10 (3) ◽  
pp. 395-412 ◽  
Author(s):  
H. Kunstmann ◽  
J. Krause ◽  
S. Mayr
Keyword(s):  
Parameter Estimation ◽  
Hydrological Model ◽  
Model Performance ◽  
Horizontal Resolution ◽  
Nonlinear Parameter ◽  
Model Complexity ◽  
Distributed Hydrological Model ◽  
Nonlinear Parameter Estimation ◽  
Estimated Parameters ◽  
Physically Based

Abstract. Even in physically based distributed hydrological models, various remaining parameters must be estimated for each sub-catchment. This can involve tremendous effort, especially when the number of sub-catchments is large and the applied hydrological model is computationally expensive. Automatic parameter estimation tools can significantly facilitate the calibration process. Hence, we combined the nonlinear parameter estimation tool PEST with the distributed hydrological model WaSiM. PEST is based on the Gauss-Marquardt-Levenberg method, a gradient-based nonlinear parameter estimation algorithm. WaSiM is a fully distributed hydrological model using physically based algorithms for most of the process descriptions. WaSiM was applied to the alpine/prealpine Ammer River catchment (southern Germany, 710 km2 in a 100×100 m2 horizontal resolution. The catchment is heterogeneous in terms of geology, pedology and land use and shows a complex orography (the difference of elevation is around 1600 m). Using the developed PEST-WaSiM interface, the hydrological model was calibrated by comparing simulated and observed runoff at eight gauges for the hydrologic year 1997 and validated for the hydrologic year 1993. For each sub-catchment four parameters had to be calibrated: the recession constants of direct runoff and interflow, the drainage density, and the hydraulic conductivity of the uppermost aquifer. Additionally, five snowmelt specific parameters were adjusted for the entire catchment. Altogether, 37 parameters had to be calibrated. Additional a priori information (e.g. from flood hydrograph analysis) narrowed the parameter space of the solutions and improved the non-uniqueness of the fitted values. A reasonable quality of fit was achieved. Discrepancies between modelled and observed runoff were also due to the small number of meteorological stations and corresponding interpolation artefacts in the orographically complex terrain. Application of a 2-dimensional numerical groundwater model partly yielded a slight decrease of overall model performance when compared to a simple conceptual groundwater approach. Increased model complexity therefore did not yield in general increased model performance. A detailed covariance analysis was performed allowing to derive confidence bounds for all estimated parameters. The correlation between the estimated parameters was in most cases negligible, showing that parameters were estimated independently from each other.

scholarly journals Diagnostic calibration of a hydrological model in an alpine area

2014 ◽  
Vol 11 (1) ◽  
pp. 1253-1300 ◽  
Author(s):  
Z. He ◽  
F. Tian ◽  
H. C. Hu ◽  
H. V. Gupta ◽  
H. P. Hu
Keyword(s):  
Hydrological Modeling ◽  
Hydrological Model ◽  
Generation Mechanism ◽  
Spatiotemporal Variability ◽  
Model Parameters ◽  
Distributed Hydrological Model ◽  
Runoff Generation ◽  
Alpine Area ◽  
Long Time ◽  
Tailan River Basin

Abstract. Hydrological modeling depends on single- or multiple-objective strategies for parameter calibration using long time sequences of observed streamflow. Here, we demonstrate a diagnostic approach to the calibration of a hydrological model of an alpine area in which we partition the hydrograph based on the dominant runoff generation mechanism (groundwater baseflow, glacier melt, snowmelt, and direct runoff). The partitioning reflects the spatiotemporal variability in snowpack, glaciers, and temperature. Model parameters are grouped by runoff generation mechanism, and each group is calibrated separately via a stepwise approach. This strategy helps to reduce the problem of equifinality and, hence, model uncertainty. We demonstrate the method for the Tailan River basin (1324 km2) in the Tianshan Mountains of China with the help of a semi-distributed hydrological model (THREW).

scholarly journals On the selection of precipitation products for the regionalisation of hydrological model parameters

2021 ◽  
Vol 25 (11) ◽  
pp. 5805-5837
Author(s):  
Oscar M. Baez-Villanueva ◽  
Mauricio Zambrano-Bigiarini ◽  
Pablo A. Mendoza ◽  
Ian McNamara ◽  
Hylke E. Beck ◽  
...  
Keyword(s):  
Hydrological Model ◽  
Model Performance ◽  
Temporal Distribution ◽  
Hydrological Regime ◽  
Hydrologic Model ◽  
Spatial Proximity ◽  
Model Parameters ◽  
Ungauged Catchments ◽  
Water Balance Components ◽  
Feature Similarity

Abstract. Over the past decades, novel parameter regionalisation techniques have been developed to predict streamflow in data-scarce regions. In this paper, we examined how the choice of gridded daily precipitation (P) products affects the relative performance of three well-known parameter regionalisation techniques (spatial proximity, feature similarity, and parameter regression) over 100 near-natural catchments with diverse hydrological regimes across Chile. We set up and calibrated a conceptual semi-distributed HBV-like hydrological model (TUWmodel) for each catchment, using four P products (CR2MET, RF-MEP, ERA5, and MSWEPv2.8). We assessed the ability of these regionalisation techniques to transfer the parameters of a rainfall-runoff model, implementing a leave-one-out cross-validation procedure for each P product. Despite differences in the spatio-temporal distribution of P, all products provided good performance during calibration (median Kling–Gupta efficiencies (KGE′s) > 0.77), two independent verification periods (median KGE′s >0.70 and 0.61, for near-normal and dry conditions, respectively), and regionalisation (median KGE′s for the best method ranging from 0.56 to 0.63). We show how model calibration is able to compensate, to some extent, differences between P forcings by adjusting model parameters and thus the water balance components. Overall, feature similarity provided the best results, followed by spatial proximity, while parameter regression resulted in the worst performance, reinforcing the importance of transferring complete model parameter sets to ungauged catchments. Our results suggest that (i) merging P products and ground-based measurements does not necessarily translate into an improved hydrologic model performance; (ii) the spatial resolution of P products does not substantially affect the regionalisation performance; (iii) a P product that provides the best individual model performance during calibration and verification does not necessarily yield the best performance in terms of parameter regionalisation; and (iv) the model parameters and the performance of regionalisation methods are affected by the hydrological regime, with the best results for spatial proximity and feature similarity obtained for rain-dominated catchments with a minor snowmelt component.

scholarly journals A pragmatic approach for the downscaling and bias correction of regional climate simulations – evaluation in hydrological modeling

2011 ◽  
Vol 4 (1) ◽  
pp. 45-63 ◽  
Author(s):  
T. Marke ◽  
W. Mauser ◽  
A. Pfeiffer ◽  
G. Zängl
Keyword(s):  
Hydrological Modeling ◽  
Hydrological Model ◽  
Climate Model ◽  
Regional Climate ◽  
Model Performance ◽  
Reanalysis Data ◽  
Historical Period ◽  
Climate Simulations ◽  
Meteorological Observations ◽  
Climate Model Simulations

Abstract. The present study investigates a statistical approach for the downscaling of climate simulations focusing on those meteorological parameters most commonly required as input for climate change impact models (temperature, precipitation, air humidity and wind speed), including the option to correct biases in the climate model simulations. The approach is evaluated by the utilization of a hydrometeorological model chain consisting of (i) the regional climate model MM5 (driven by reanalysis data at the boundaries of the model domain), (ii) the downscaling and model interface SCALMET, and (iii) the hydrological model PROMET. The results of four hydrological model runs are compared to discharge recordings at the gauge of the Upper Danube Watershed (Central Europe) for the historical period of 1972–2000 on a daily time basis. The comparison reveals that the presented approaches allow for a more accurate simulation of discharge for the catchment of the Upper Danube Watershed and the considered gauge at the outlet in Achleiten. The correction for subgrid-scale variability is shown to reduce biases in simulated discharge compared to the utilization of bilinear interpolation. Further enhancements in model performance could be achieved by a correction of biases in the RCM data within the downscaling process. Although the presented downscaling approach strongly improves the performance of the hydrological model, deviations from the observed discharge conditions persist that are not found when driving the hydrological model with spatially distributed meteorological observations.

Implications of Ensemble Quantitative Precipitation Forecast Errors on Distributed Streamflow Forecasting

2010 ◽  
Vol 11 (1) ◽  
pp. 69-86 ◽  
Author(s):  
Giuseppe Mascaro ◽  
Enrique R. Vivoni ◽  
Roberto Deidda
Keyword(s):  
Hydrological Model ◽  
Initial Conditions ◽  
Forecast Accuracy ◽  
Precipitation Forecast ◽  
Forecast Errors ◽  
Distributed Hydrological Model ◽  
Streamflow Forecasting ◽  
Ensemble Forecasts ◽  
Wide Range ◽  
Operational Flow

Abstract Evaluating the propagation of errors associated with ensemble quantitative precipitation forecasts (QPFs) into the ensemble streamflow response is important to reduce uncertainty in operational flow forecasting. In this paper, a multifractal rainfall downscaling model is coupled with a fully distributed hydrological model to create, under controlled conditions, an extensive set of synthetic hydrometeorological events, assumed as observations. Subsequently, for each event, flood hindcasts are simulated by the hydrological model using three ensembles of QPFs—one reliable and the other two affected by different kinds of precipitation forecast errors—generated by the downscaling model. Two verification tools based on the verification rank histogram and the continuous ranked probability score are then used to evaluate the characteristics of the correspondent three sets of ensemble streamflow forecasts. Analyses indicate that the best forecast accuracy of the ensemble streamflows is obtained when the reliable ensemble QPFs are used. In addition, results underline (i) the importance of hindcasting to create an adequate set of data that span a wide range of hydrometeorological conditions and (ii) the sensitivity of the ensemble streamflow verification to the effects of basin initial conditions and the properties of the ensemble precipitation distributions. This study provides a contribution to the field of operational flow forecasting by highlighting a series of requirements and challenges that should be considered when hydrologic ensemble forecasts are evaluated.

Modeling the Hydrological Effect on Local Gravity at Moxa, Germany

2006 ◽  
Vol 7 (3) ◽  
pp. 346-354 ◽  
Author(s):  
Shaakeel Hasan ◽  
Peter A. Troch ◽  
J. Boll ◽  
C. Kroner
Keyword(s):  
Transfer Function ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Water Storage ◽  
Superconducting Gravimeter ◽  
Hydrological Processes ◽  
Earth Tides ◽  
Time Averaging ◽  
Distributed Hydrological Model ◽  
High Positive Correlation

Abstract A superconducting gravimeter has observed with high accuracy (to within a few nm s−2) and high frequency (1 Hz) the temporal variations in the earth’s gravity field near Moxa, Germany, since 1999. Hourly gravity residuals are obtained by time averaging and correcting for earth tides, polar motion, barometric pressure variations, and instrumental drift. These gravity residuals are significantly affected by hydrological processes (interception, infiltration, surface runoff, and subsurface redistribution) in the vicinity of the observatory. In this study time series analysis and distributed hydrological modeling techniques are applied to understand the effect of these hydrological processes on observed gravity residuals. It is shown that the short-term response of gravity residuals to medium- to high-rainfall events can be efficiently modeled by means of a linear transfer function. This transfer function exhibits an oscillatory behavior that indicates fast redistribution of stored water in the upper layers (interception store, root zone) of the catchment surrounding the instrument. The relation between groundwater storage and gravity residuals is less clear and varies according to the season. High positive correlation between groundwater and gravity exists during winter months when the freezing of the upper soil layers immobilizes water stored in the unsaturated zone of the catchment. To further explore the spatiotemporal dynamics of the relevant hydrological processes and their relation to observed gravity residuals, a GIS-based distributed hydrological model is applied for the Silberleite catchment. Driven by observed atmospheric forcings (precipitation and potential evapotranspiration), the model allows the authors to compute the variation of water storage in three different layers: the interception store, the snow cover store, and the soil moisture store. These water storage dynamics are then converted to predicted gravity variation at the location of the superconducting gravimeter and compared to observed gravity residuals. During most of the investigated period (January 2000 to January 2004) predictions are in good agreement with the observed patterns of gravity dynamics. However, during some winter months the distributed hydrological model fails to explain the observations, which supports the authors’ conclusion that groundwater variability dominates the hydrological gravity signal in the winter. More hydrogeological research is needed to include groundwater dynamics in the hydrological model.

scholarly journals Effect of length of the observed dataset on the calibration of a distributed hydrological model

2015 ◽  
Vol 368 ◽  
pp. 305-311 ◽  
Author(s):  
X. Cui ◽  
W. Sun ◽  
J. Teng ◽  
H. Song ◽  
X. Yao
Keyword(s):  
Hydrological Model ◽  
Assessment Tool ◽  
Swat Model ◽  
Model Performance ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Discharge Data ◽  
One Year ◽  
Parameter Values ◽  
Discharge Measurements

Abstract. Calibration of hydrological models in ungauged basins is now a hot research topic in the field of hydrology. In addition to the traditional method of parameter regionalization, using discontinuous flow observations to calibrate hydrological models has gradually become popular in recent years. In this study, the possibility of using a limited number of river discharge data to calibrate a distributed hydrological model, the Soil and Water Assessment Tool (SWAT), was explored. The influence of the quantity of discharge measurements on model calibration in the upper Heihe Basin was analysed. Calibration using only one year of daily discharge measurements was compared with calibration using three years of discharge data. The results showed that the parameter values derived from calibration using one year’s data could achieve similar model performance with calibration using three years’ data, indicating that there is a possibility of using limited numbers of discharge data to calibrate the SWAT model effectively in poorly gauged basins.

Dynamical clustering: A new approach to make distributed (hydrological) modeling more efficient by dynamically detecting and removing redundant computations

2020 ◽  
Author(s):  
Uwe Ehret ◽  
Rik van Pruijssen ◽  
Marina Bortoli ◽  
Ralf Loritz ◽  
Elnaz Azmi ◽  
...  
Keyword(s):  
Hydrological Modeling ◽  
Computation Time ◽  
Spatial Proximity ◽  
Full Detail ◽  
Full Resolution ◽  
Adaptive Gridding ◽  
Distributed Hydrological Modeling ◽  
Spatially Distributed ◽  
Resolution Model ◽  
System States

<p>The structural properties of hydrological systems such as topography, soils or land use often show a considerable degree of spatial variability, and so do the drivers of systems dynamics, such as rainfall. Detailed statements about system states and responses therefore generally require spatially distributed and temporally highly resolved hydrological models. This comes at the price of substantial computational costs. However, even if hydrological sub systems potentially behave very differently, in practice we often find groups of these sub systems that behave similarly, but the number, size and characteristics of these groups varies in time. If we have knowledge of such clustered behavior of sub systems while running a model, we can increase computational efficiency by computing in full detail only a few representatives within each cluster, and assign results to the remaining cluster members. Thus, we avoid costly redundant computations. Unlike other methods designed to dynamically remove computational redundancies, such as adaptive gridding, dynamical clustering does not require spatial proximity of the model elements.</p><p>In our contribution, we present and discuss at the example of a distributed, conceptual hydrological model of the Attert basin in Luxembourg, i) a dimensionless approach to express dynamical similarity, ii) the temporal evolution of dynamical similarity in a 5-year period, iii) an approach to dynamically cluster and re-cluster model elements during run time based on an analysis of clustering stability, and iv) the effect of dynamical clustering with respect to computational gains and the associated losses of simulation quality.</p><p>For the Attert model, we found that there indeed exists high redundancy among model elements, that the degree of redundancy varies with time, and that the spatial patterns of similarity are mainly controlled by geology and precipitation. Compared to a standard, full-resolution model run used as a virtual reality ‘truth’, computation time could be reduced to one fourth, when modelling quality, expressed as Nash-Sutcliffe efficiency of discharge, was allowed decreasing from 1 to 0.84. Re-clustering occurred at irregular intervals mainly associated with the onset of precipitation, but on average the patterns of similarity were quite stable, such that during the entire six-year simulation period, only 165 re-clusterings were carried out, i.e. on average once every eleven days.</p>

scholarly journals Assessment of distributed hydrological model performance for simulation of multi-heavy-metal transport in Harrach River, Algeria

2019 ◽  
Vol 80 (1) ◽  
pp. 11-24 ◽  
Author(s):  
Saadia Bouragba ◽  
Katsuaki Komai ◽  
Keisuke Nakayama
Keyword(s):  
Heavy Metals ◽  
Hydrological Model ◽  
Model Performance ◽  
Distributed Hydrological Model ◽  
Data Quality Control ◽  
Object Oriented Design ◽  
Model Based ◽  
Monitoring Strategies ◽  
Geophysical Flow ◽  
Multiple Heavy Metals

Abstract This paper aims to assess the performance of a distributed hydrological model for simulating the transport of various heavy metals in rivers, to enhance and support environmental monitoring strategies for rivers in developing countries. In this context, we evaluated the performance of the Geophysical flow Circulation (GeoCIRC) model based on Object-Oriented Design (OOD) for the simulation of contamination from multiple heavy metals (Pb, Hg, Cr, and Zn) in Harrach River in Algeria. The results of the case study were in good agreement with the observations. Methodology for the assessment of data quality control and the improvement of monitoring procedures was proposed by using the hydrological model to simulate different scenarios. The GeoCIRC-model-based OOD allowed the prediction of the concentrations of heavy metals with minimal input data. Also, various heavy metals could be numerically treated simultaneously because the OOD increases the model's flexibility to allow the handling of many transportable materials. Therefore, the GeoCIRC model is a powerful tool for the monitoring of environmental contamination in rivers by various heavy metals.

scholarly journals Investigation of the transferability of hydrological models and a method to improve model calibration

2005 ◽  
Vol 5 ◽  
pp. 83-87 ◽  
Author(s):  
G. Hartmann ◽  
A. Bárdossy
Keyword(s):  
Model Calibration ◽  
Hydrological Model ◽  
Optimal Parameter ◽  
Model Performance ◽  
Calibration Procedure ◽  
Performance Measure ◽  
Distributed Hydrological Model ◽  
Improve Model ◽  
Spatially Distributed ◽  
Measure Of Performance

Abstract. In order to find a model parameterization such that the hydrological model performs well even under different conditions, appropriate model performance measures have to be determined. A common performance measure is the Nash Sutcliffe efficiency. Usually it is calculated comparing observed and modelled daily values. In this paper a modified version is suggested in order to calibrate a model on different time scales simultaneously (days up to years). A spatially distributed hydrological model based on HBV concept was used. The modelling was applied on the Upper Neckar catchment, a mesoscale river in south western Germany with a basin size of about 4000 km2. The observation period 1961-1990 was divided into four different climatic periods, referred to as "warm", "cold", "wet" and "dry". These sub periods were used to assess the transferability of the model calibration and of the measure of performance. In a first step, the hydrological model was calibrated on a certain period and afterwards applied on the same period. Then, a validation was performed on the climatologically opposite period than the calibration, e.g. the model calibrated on the cold period was applied on the warm period. Optimal parameter sets were identified by an automatic calibration procedure based on Simulated Annealing. The results show, that calibrating a hydrological model that is supposed to handle short as well as long term signals becomes an important task. Especially the objective function has to be chosen very carefully.

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