Scale effect challenges in urban hydrology highlighted with a distributed hydrological model

Abstract. Nowadays, hydrological models are extensively used in urban water management, future development scenario evaluation and research activities. A growing interest is devoted to the development of fully distributed and grid based models, following the increase of computation capabilities. The availability of high resolution GIS information is needed for such models implementation to understand flooding issues at very small scales. However, some complex issues about scaling effects still remain a serious issue in urban hydrology. The choice of an appropriate spatial resolution is a crucial problem, and the obtained model performance depends highly on the chosen implementation scale. In this paper we propose a two step investigation framework using scaling effects in urban hydrology. In the first step fractal tools are used to highlight the scale dependency observed within distributed data used to describe the catchment heterogeneity, both the structure of the sewer network and the distribution of impervious areas are analyzed. Then an intensive multi-scale modeling work is carried out to understand scaling effects on hydrological model performance. Investigations were conducted using a fully distributed and physically based model, Multi-Hydro, developed at Ecole des Ponts ParisTech (Multi-Hydro (2015)). The model was implemented at 17 spatial resolution ranging from 100 m to 5 m. Results coming out from this work demonstrate scale effect challenges in urban hydrology modeling. In fact, fractal concept highlights the scale dependency observed within distributed data used to implement hydrological models. Patterns of geophysical data change when we change the observation pixel size. The multi-scale modeling investigation performed with Multi-Hydro model at 17 spatial resolutions confirms scaling effect on hydrological model performance. Results were analyzed at three ranges of scales identified in the fractal analysis and confirmed in the modeling work. In the meantime, this work also discussed some issues remaining in urban hydrology modeling such as the availability of high quality data at higher resolutions and, model numerical instabilities as well as the computation time requirements. But still the principal findings of this paper allow replacing traditional methods of model calibration by innovative methods of model resolution alteration based on the spatial data variability and scaling of flows in urban hydrology.

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Scale effect challenges in urban hydrology highlighted with a distributed hydrological model

Hydrology and Earth System Sciences ◽

10.5194/hess-22-331-2018 ◽

2018 ◽

Vol 22 (1) ◽

pp. 331-350 ◽

Cited By ~ 11

Author(s):

Abdellah Ichiba ◽

Auguste Gires ◽

Ioulia Tchiguirinskaia ◽

Daniel Schertzer ◽

Philippe Bompard ◽

...

Keyword(s):

Scale Effect ◽

Hydrological Model ◽

Scale Effects ◽

Model Performance ◽

Scale Dependence ◽

Urban Hydrology ◽

Distributed Data ◽

Hydrological Models ◽

Multi Scale ◽

Scale Modelling

Abstract. Hydrological models are extensively used in urban water management, development and evaluation of future scenarios and research activities. There is a growing interest in the development of fully distributed and grid-based models. However, some complex questions related to scale effects are not yet fully understood and still remain open issues in urban hydrology. In this paper we propose a two-step investigation framework to illustrate the extent of scale effects in urban hydrology. First, fractal tools are used to highlight the scale dependence observed within distributed data input into urban hydrological models. Then an intensive multi-scale modelling work is carried out to understand scale effects on hydrological model performance. Investigations are conducted using a fully distributed and physically based model, Multi-Hydro, developed at Ecole des Ponts ParisTech. The model is implemented at 17 spatial resolutions ranging from 100 to 5 m. Results clearly exhibit scale effect challenges in urban hydrology modelling. The applicability of fractal concepts highlights the scale dependence observed within distributed data. Patterns of geophysical data change when the size of the observation pixel changes. The multi-scale modelling investigation confirms scale effects on hydrological model performance. Results are analysed over three ranges of scales identified in the fractal analysis and confirmed through modelling. This work also discusses some remaining issues in urban hydrology modelling related to the availability of high-quality data at high resolutions, and model numerical instabilities as well as the computation time requirements. The main findings of this paper enable a replacement of traditional methods of “model calibration” by innovative methods of “model resolution alteration” based on the spatial data variability and scaling of flows in urban hydrology.

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Multi-objective calibration of a distributed eco-hydrological model using several remotely sensed information

10.5194/egusphere-egu2020-18880 ◽

2020 ◽

Author(s):

Félix Francés ◽

Carlos Echeverría ◽

Maria Gonzalez-Sanchis ◽

Fernando Rivas

Keyword(s):

Remote Sensing ◽

Model Calibration ◽

Hydrological Model ◽

Model Performance ◽

Temporal Information ◽

Temporal Data ◽

Hydrological Models ◽

Validation Period ◽

Spatio Temporal ◽

Calibration Approach

Calibration of eco-hydrological models is difficult to carry on, even more if observed data sets are scarce. It is known that calibration using traditional trial-and-error approach depends strongly of the knowledge and the subjectivity of the hydrologist, and automatic calibration has a strong dependency of the objective-function and the initial values established to initialize the process.The traditional calibration approach mainly focuses on the temporal variation of the discharge at the catchment outlet point, representing an integrated catchment response and provides thus only limited insight on the lumped behaviour of the catchment. It has been long demonstrated the limited capabilities of such an approach when models are validated at interior points of a river basin. The development of distributed eco-hydrological models and the burst of spatio-temporal data provided by remote sensing appear as key alternative to overcome those limitations. Indeed, remote sensing imagery provides not only temporal information but also valuable information on spatial patterns, which can facilitate a spatial-pattern-oriented model calibration.However, there is still a lack of how to effectively handle spatio-temporal data when included in model calibration and how to evaluate the accuracy of the simulated spatial patterns. Moreover, it is still unclear whether including spatio-temporal data improves model performance in face to an unavoidable more complex and time-demanding calibration procedure. To elucidate in this sense, we performed three different multiobjective calibration configurations: (1) including only temporal information of discharges at the catchment outlet (2) including both temporal and spatio-temporal information and (3) only including spatio-temporal information. In the three approaches, we calibrated the same distributed eco-hydrological model (TETIS) in the same study area: Carraixet Basin, and used the same multi-objective algorithm: MOSCEM-UA. The spatio-temporal information obtained from satellite has been the surface soil moisture (from SMOS-BEC) and the leaf area index (from MODIS).Even though the performance of the first calibration approach (only temporal information included) was slightly better than the others, all calibration approaches provided satisfactory and similar results within the calibration period. To put these results into test, we also validated the model performance by using historical data that was not used to calibrate the model (validation period). Within the validation period, the second calibration approach obtained better performance than the others, pointing out the higher reliability of the obtained parameter values when including spatio-temporal data (in this case, in combination with temporal data) in the model calibration. It is also reliable to mention that the approaches considering only spatio-temporal information provided interesting results in terms of discharges, considering that this variable was not used at all for calibration purposes.

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Calibration of hydrological model parameters for ungauged catchments

Hydrology and Earth System Sciences ◽

10.5194/hess-11-703-2007 ◽

2007 ◽

Vol 11 (2) ◽

pp. 703-710 ◽

Cited By ~ 223

Author(s):

A. Bárdossy

Keyword(s):

Hydrological Model ◽

Model Performance ◽

Model Parameters ◽

Hydrological Models ◽

Rainfall Runoff ◽

Ungauged Catchments ◽

Rainfall Runoff Model ◽

Climate Statistics ◽

Runoff Model ◽

German Part

Abstract. The parameters of hydrological models for catchments with few or no discharge records can be estimated using regional information. One can assume that catchments with similar characteristics show a similar hydrological behaviour and thus can be modeled using similar model parameters. Therefore a regionalisation of the hydrological model parameters on the basis of catchment characteristics is plausible. However, due to the non-uniqueness of the rainfall-runoff model parameters (equifinality), a workflow of regional parameter estimation by model calibration and a subsequent fit of a regional function is not appropriate. In this paper a different approach for the transfer of entire parameter sets from one catchment to another is discussed. Parameter sets are considered as tranferable if the corresponding model performance (defined as the Nash-Sutclife efficiency) on the donor catchment is good and the regional statistics: means and variances of annual discharges estimated from catchment properties and annual climate statistics for the recipient catchment are well reproduced by the model. The methodology is applied to a set of 16 catchments in the German part of the Rhine catchments. Results show that the parameters transfered according to the above criteria perform well on the target catchments.

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

Proceedings of the International Association of Hydrological Sciences ◽

10.5194/piahs-368-305-2015 ◽

2015 ◽

Vol 368 ◽

pp. 305-311 ◽

Cited By ~ 5

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.

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Dynamics of hydrological model parameters: calibration and reliability

10.5194/hess-2019-301 ◽

2019 ◽

Cited By ~ 1

Author(s):

Tian Lan ◽

Kairong Lin ◽

Xuezhi Tan ◽

Chong-Yu Xu ◽

Xiaohong Chen

Keyword(s):

Hydrological Model ◽

Model Performance ◽

Dynamic Parameter ◽

Model Parameters ◽

Hydrological Models ◽

State Variables ◽

Individual Parameter ◽

Calibration Scheme ◽

Response Modes ◽

Abrupt Shifts

Abstract. It has been demonstrated that the dynamics of hydrological model parameters based on dynamic catchment behavior significantly improves the accuracy and robustness of conventional models. However, the calibration for the dynamization of parameter set involves critical components of hydrological models, including parameters, objective functions, state variables, and fluxes, which usually are ignored. Hence, it is essential to design a reliable calibration scheme regarding these components. In this study, we compared and evaluate five calibration schemes with respect to multi-metric evaluation, dynamized parameter values, fluxes, and state variables. Furthermore, a simple and effective tool was designed to assess the reliability of the dynamized parameter set. The tool evaluates the convergence processes for global optimization algorithms using violin plots (ECP-VP), effectively describes the convergence behaviour in individual parameter spaces. The different types of violin plots can well match to all possible properties of fitness landscapes. The results showed that the reasons for poor model performance included time-invariant parameters oversimplifying the dynamic response modes of the model, the high-dimensionality disaster of parameters, the abrupt shifts of the parameter set, and the complicated correlations among parameters. The proposed calibration scheme overcome these issues, characterized the dynamic behaviour of catchments, and improved the model performance. Additionally, the designed ECP-VP tool effectively assessed the reliability of the dynamic parameter set, providing an indication on recognizing the dominant response modes of hydrological models in different sub-periods or catchments with the distinguishing catchment characteristics.

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Information content of stream level class data for hydrological model calibration

Hydrology and Earth System Sciences ◽

10.5194/hess-21-4895-2017 ◽

2017 ◽

Vol 21 (9) ◽

pp. 4895-4905 ◽

Cited By ~ 23

Author(s):

H. J. Ilja van Meerveld ◽

Marc J. P. Vis ◽

Jan Seibert

Keyword(s):

Time Series ◽

Citizen Science ◽

Model Calibration ◽

Hydrological Model ◽

Model Performance ◽

Added Value ◽

Distributed Data ◽

Vertical Resolution ◽

Level Data ◽

Spatially Distributed

Abstract. Citizen science can provide spatially distributed data over large areas, including hydrological data. Stream levels are easier to measure than streamflow and are likely also observed more easily by citizen scientists than streamflow. However, the challenge with crowd based stream level data is that observations are taken at irregular time intervals and with a limited vertical resolution. The latter is especially the case at sites where no staff gauge is available and relative stream levels are observed based on (in)visible features in the stream, such as rocks. In order to assess the potential value of crowd based stream level observations for model calibration, we pretended that stream level observations were available at a limited vertical resolution by transferring streamflow data to stream level classes. A bucket-type hydrological model was calibrated with these hypothetical stream level class data and subsequently evaluated on the observed streamflow records. Our results indicate that stream level data can result in good streamflow simulations, even with a reduced vertical resolution of the observations. Time series of only two stream level classes, e.g. above or below a rock in the stream, were already informative, especially when the class boundary was chosen towards the highest stream levels. There was some added value in using up to five stream level classes, but there was hardly any improvement in model performance when using more level classes. These results are encouraging for citizen science projects and provide a basis for designing observation systems that collect data that are as informative as possible for deriving model based streamflow time series for previously ungauged basins.

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Information content of stream level class data for hydrological model calibration

10.5194/hess-2017-72 ◽

2017 ◽

Author(s):

Ilja van Meerveld ◽

Marc Vis ◽

Jan Seibert

Keyword(s):

Time Series ◽

Citizen Science ◽

Model Calibration ◽

Hydrological Model ◽

Model Performance ◽

Added Value ◽

Data Sets ◽

Distributed Data ◽

Vertical Resolution ◽

Level Data

Abstract. Citizen science can provide spatially distributed data over large areas, including hydrological data. Stream levels are easier to measure than streamflow and can be observed more easily by citizen scientists. However, the challenge with crowd-based stream level data is that observations are taken at irregular time intervals and with a limited vertical resolution. The latter is especially the case at sites where no staff gauge is available and relative stream levels are observed based on (in)visible features in the stream, such as rocks. In order to assess the potential value of crowd-based stream level observations for model calibration, we pretended that stream level observations were available at a limited vertical resolution by transferring streamflow data into stream level classes. A bucket-type hydrological model was calibrated with these hypothetical data sets and subsequently evaluated on the observed streamflow records. Our results indicate that stream level data can result in good streamflow simulations, even with a reduced vertical resolution of the observations. Time series of only two stream level classes, e.g. above or below a rock in the stream, were already informative, especially when the class boundary was chosen towards the highest stream levels. There was some added value in using up to five stream level classes but there was hardly any improvement in model performance when using more level classes. These results are encouraging for citizen science projects and provide a basis for designing observation systems that collect data that are as informative as possible for deriving model-based streamflow time series for previously ungauged basins.

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Learning from one’s errors: A data-driven approach for mimicking an ensemble of hydrological model residuals

10.5194/egusphere-egu21-13244 ◽

2021 ◽

Author(s):

John M. Quilty ◽

Anna E. Sikorska-Senoner

Keyword(s):

Hydrological Model ◽

Model Performance ◽

Data Driven ◽

Gradient Boosting ◽

Hydrological Models ◽

Model Errors ◽

Water Resources Systems ◽

Extreme Gradient Boosting ◽

Data Driven Approach ◽

Input Variables

Despite significant efforts to improve the calibration of hydrological models, when applied to real-world case studies, model errors (residuals) remain. These residuals impair flow estimates and can lead to unreliable design, management, and operation of water resources systems. Since these residuals are auto-correlated, they should be treated with appropriate methods that do not require limiting assumptions (e.g., that the residuals follow a Gaussian distribution).This study introduces a novel data-driven framework to account for residuals of hydrological models. Our framework relies on a conceptual-data-driven approach (CDDA) that integrates two models, i.e., a hydrological model (HM) with a data-driven (i.e., machine learning) model (DDM), to simulate an ensemble of residuals from the HM. In the first part of the CDDA, a HM is used to generate an ensemble of streamflow simulations for different parameter sets. Afterwards, residuals associated with each simulation are computed and a DDM developed to predict the residuals. Finally, the original streamflow simulations are coupled with the DDM predictions to produce the CDDA output, an improved ensemble of streamflow simulations. The proposed CDDA is a useful approach since it respects hydrological processes via the HM and it profits from the DDM&#8217;s ability to simulate the complex (nonlinear) relationship between residuals and input variables.To explore the utility of CDDA, we focus principally on identifying the best DDM and input variables to mimic HM residuals. For this purpose, we have explored eight different DDM variants and multiple input variables (observed precipitation, air temperature, and streamflow) at different lag times prior to the simulation day. Based on a case study involving three Swiss catchments, the proposed CDDA framework is shown to be very promising at improving ensemble streamflow simulations, reducing the mean continuous ranked probability score by 16-29 % when compared to the standalone HM. It was found that eXtreme Gradient Boosting (XGB) and Random Forests (RF), each using 29 input variables, were the strongest predictors of the HM residuals. However, similar performance could be achieved by selecting only the six most important (of the original 29) input variables and re-training the XGB and RF models.Additional experimentation shows that by converting CDDA to a stochastic framework (i.e., to account for important uncertainty sources), significant gains in model performance can be achieved.

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Precipitation Forecast Contribution Assessment in the Coupled Meteo-Hydrological Models

Atmosphere ◽

10.3390/atmos11010034 ◽

2019 ◽

Vol 11 (1) ◽

pp. 34 ◽

Cited By ~ 2

Author(s):

Aida Jabbari ◽

Jae-Min So ◽

Deg-Hyo Bae

Keyword(s):

Lead Time ◽

Hydrological Model ◽

Weather Prediction ◽

Model Performance ◽

Precipitation Forecast ◽

Hydrological Models ◽

Distributed Hydrological Model ◽

Point Scale ◽

Meteorological Model ◽

Temporal And Spatial

A numerical weather prediction and a rainfall-runoff model employed to evaluate precipitation and flood forecast for the Imjin River (South and North Korea). The real-time precipitation at point and catchment scales evaluated to select proper hydrological model to couple with atmospheric model. As a major limitation of previous studies, temporal and spatial resolutions of hydrological model are smaller than those of meteorological model. Here, through high resolution of temporal (10 min) and spatial (1 km × 1 km), the optimal resolution determined. The results showed Weather Research and Forecasting (WRF) model underestimated precipitation in point and catchment assessment and its skill was relatively higher for catchment than point scale, as illustrated by the lower Root Mean Square Error (RMSE) of 59.67, 160.48, 68.49 for the catchment and 84.49, 212.80 and 91.53 for the point scale in the events 2002, 2007 and 2011, respectively. The findings led to choose the semi-distributed hydrological model. The variations in temporal and spatial resolutions illustrated accuracy decrease; additionally, the optimal spatial resolution obtained at 8 km and temporal resolution did not affect the inherent inaccuracy of the results. Lead-time variation demonstrated that lead-time dependency was almost negligible below 36 h. With reference to this study, comparisons of model performance provided quantitative knowledge for understanding credibility and restrictions of meteo-hydrological models.

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Evaluating the Effect of Numerical Schemes on Hydrological Simulations: HYMOD as A Case Study

Water ◽

10.3390/w11020329 ◽

2019 ◽

Vol 11 (2) ◽

pp. 329

Author(s):

Shiyan Zhang ◽

Khalid Al-Asadi

Keyword(s):

Hydrological Model ◽

Optimal Parameter ◽

Model Performance ◽

Hydrological Models ◽

Numerical Schemes ◽

Order Of Accuracy ◽

Explicit Schemes ◽

Implicit Schemes ◽

The Relationship

The importance of numerical schemes in hydrological models has been increasingly recognized in the hydrological community. However, the relationship between model performance and the properties of numerical schemes remains unclear. In this study, we employed two types of numerical schemes (i.e., explicit Runge-Kutta schemes with different orders of accuracy and partially implicit Euler schemes with different implicit factors) in the hydrological model (HYMOD) to simulate the flow hydrograph of the Leaf River basin from 1948 to 1988. Results computed by different numerical schemes were compared and the relationships between model performance and two scheme properties (i.e., the order of accuracy and the implicit factor) were discussed. Results showed that the more explicit schemes generally lead to the overestimation of flow hydrographs, whereas the more implicit schemes lead to underestimation. In addition, the numerical error tended to decrease with increasing orders of accuracy. As a result, the optimal parameter sets found by low-order schemes significantly deviated from those found by the analytical solution. The findings of this study can provide useful implications for designing suitable numerical schemes for hydrological models.

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