Testing the Efficiency of Parameter Disaggregation for Distributed Rainfall-Runoff Modelling

Sotirios Moustakas; Patrick Willems

doi:10.3390/w13070972

Testing the Efficiency of Parameter Disaggregation for Distributed Rainfall-Runoff Modelling

Water ◽

10.3390/w13070972 ◽

2021 ◽

Vol 13 (7) ◽

pp. 972

Author(s):

Sotirios Moustakas ◽

Patrick Willems

Keyword(s):

Spatial Data ◽

Distributed Model ◽

Model Parameters ◽

Rainfall Runoff ◽

Natural Processes ◽

Reliable Model ◽

Physically Based ◽

Lumped Models ◽

Uniform Model ◽

Modelling Approach

A variety of hydrological models is currently available. Many of those employ physically based formulations to account for the complexity and spatial heterogeneity of natural processes. In turn, they require a substantial amount of spatial data, which may not always be available at sufficient quality. Recently, a top-down approach for distributed rainfall-runoff modelling has been developed, which aims at combining accuracy and simplicity. Essentially, a distributed model with uniform model parameters (base model) is derived from a calibrated lumped conceptual model. Subsequently, selected parameters are disaggregated based on links with the available spatially variable catchment properties. The disaggregation concept is now adjusted to better account for non-linearities and extended to incorporate more model parameters (and, thus, larger catchment heterogeneity). The modelling approach is tested for a catchment including several flow gauging stations. The disaggregated model is shown to outperform the base model with respect to internal catchment dynamics, while performing similarly at the catchment outlet. Moreover, it manages to bridge on average 44% of the Nash–Sutcliffe efficiency difference between the base model and the lumped models calibrated for the internal gauging stations. Nevertheless, the aforementioned improvement is not necessarily sufficient for reliable model results.

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Novel phenomena-based dynamic model of carbon black/composite vapour sensors

Proceedings of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rspa.2006.1776 ◽

2006 ◽

Vol 463 (2078) ◽

pp. 551-568 ◽

Cited By ~ 2

Author(s):

James W.T Yates ◽

Michael J Chappell ◽

Julian W Gardner

Keyword(s):

Mathematical Model ◽

Carbon Black ◽

Ambient Air ◽

Model Parameters ◽

World Systems ◽

Computational Power ◽

Ethanol Vapour ◽

Physically Based ◽

Poly Caprolactone ◽

Modelling Approach

A novel physically based mathematical model of carbon black/polymer vapour sensors is described, which incorporates parameters that have physical meaning. This model has an analytical solution and so requires negligible computational power to analyse a sensor's response to a particular form of input. Another advantage of this modelling approach is that the environmental dependencies of sensor responses may be compensated for and so help in the design of better pattern-recognition algorithms for electronic nose systems. This also means that the underlying chemistry of the sensors may be decoupled from their physical non-analyte specific properties. Experimentally, three different conducting nanocomposite polymers, poly(styrene- co -butadine), poly(ethyl- co -vinyl acetate) and poly(caprolactone), were tested. Each experiment consisted of separate exposures of the sensors to acetone and ethanol vapour in ambient air. A total of 336 such experiments were performed over a two-week period. The model was validated with respect to these data and was then fitted to the two vapour responses simultaneously, demonstrating its applicability to ‘real world’ systems. The temperature dependence of the model parameters was judged to be the most important factor and it needs to be compensated for when applying this type of sensor in practice.

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Role of Infiltration and Saturation Excess in Rainfall-Runoff Modelling in Small Catchments

Selected Scientific Papers - Journal of Civil Engineering ◽

10.2478/sspjce-2013-0001 ◽

2013 ◽

Vol 8 (1) ◽

pp. 5-12 ◽

Cited By ~ 1

Author(s):

Luděk Strouhal ◽

Václav David

Keyword(s):

Mitigation Measures ◽

Distributed Model ◽

Rainfall Runoff ◽

Simple Method ◽

Small Catchment ◽

Physically Based ◽

Long Time ◽

Modelling Experiment ◽

Small Catchments ◽

Saturation Excess

Abstract The paper presents the essential differences in small catchment model behaviour depending on the assumed runoff procedure, i.e. infiltration or saturation excess. It suggests an appropriate model structure and a way to obtain the required boundary conditions. In order to design the flood mitigation measures in small catchments, there is a need of reliable prediction of their behaviour. Long time series of data are rather rare here and the simple models are usually not capable to reflect all the necessary variables and their distribution. However, more comprehensive models are usually very demanding with respect to input data. A model of Bykovicky stream catchment (6.3 km2) was built in the physically based distributed model GSSHA. Out of two years of rainfall-runoff data several events were used for model calibration. Gradually the model was changed in order to explain observed data better. First modelling outcomes suggested a significant influence of saturation excess on flood hydrographs in most of the scenarios. In order to reflect this, the model needs to contain groundwater related processes, but the data on groundwater table position was not available. Therefore a simple method how to obtain it was proposed and tested. The paper discusses the achievements of this modelling experiment.

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A Novel Stacked Long Short-Term Memory Approach of Deep Learning for Streamflow Simulation

Sustainability ◽

10.3390/su132313384 ◽

2021 ◽

Vol 13 (23) ◽

pp. 13384

Author(s):

Majid Mirzaei ◽

Haoxuan Yu ◽

Adnan Dehghani ◽

Hadi Galavi ◽

Vahid Shokri ◽

...

Keyword(s):

Spatial Distribution ◽

Short Term Memory ◽

Data Driven ◽

Rainfall Runoff ◽

Short Term ◽

Runoff Simulation ◽

Physically Based ◽

Long Short Term Memory ◽

Using Data ◽

Modelling Approach

Rainfall-Runoff simulation is the backbone of all hydrological and climate change studies. This study proposes a novel stochastic model for daily rainfall-runoff simulation called Stacked Long Short-Term Memory (SLSTM) relying on machine learning technology. The SLSTM model utilizes only the rainfall-runoff data in its modelling approach and the hydrology system is deemed a blackbox. Conversely, the distributed and physically-based hydrological models, e.g., SWAT (Soil and Water Assessment Tool) preserve the physical aspect of hydrological variables and their inter-relations while taking a wide range of data. The two model types provide specific applications that interest modelers, who can apply them according to their project specification and objectives. However, sparse distribution of point-data may hinder physical models’ performance, which may not be the case in data-driven models. This study proposes a specific SLSTM model and investigates the SLSTM and SWAT models’ data dependency in terms of their spatial distribution. The study was conducted in the two distinct river basins of Samarahan and Trusan, Malaysia, with over 20 years of hydro-climate data. The Trusan basin’s rain gauges are scattered downstream of the basin outlet and Samarahan’s are located around the basin, with one station within each basin’s limits. The SWAT was developed and calibrated following its general modelling approach, however, the SLSTM performance was also tested using data preprocessing with principal component analysis (PCA). Results showed that the SWAT performance for daily streamflow simulation at Samarahan has been superior to that of Trusan. Both the SLSTM and PCA-SLSTM models, however, showed better performance at Trusan with PCA-SLSTM outperforming the SLSTM. This demonstrates that the SWAT model is greatly affected by the spatial distribution of its input data, while data-driven models, irrespective of the spatial distribution of their entry data, can perform well if the data adequacy condition is met. However, considering the structural difference between the two models, each has its specific application in a water resources context. The study of catchments’ response to changes in the hydrology cycle requires a physically-based model like SWAT with proper spatial and temporal distribution of its entry data. However, the study of a specific phenomenon without considering the underlying processes can be done using data-driven models like SLSTM, where improper spatial distribution of data cannot be a restricting factor.

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An investigation of site-similarity approaches to generalisation of a rainfall–runoff model

Hydrology and Earth System Sciences ◽

10.5194/hess-11-500-2007 ◽

2007 ◽

Vol 11 (1) ◽

pp. 500-515 ◽

Cited By ~ 39

Author(s):

A. L. Kay ◽

D. A. Jones ◽

S. M. Crooks ◽

T. R. Kjeldsen ◽

C. F. Fung

Keyword(s):

Frequency Estimation ◽

Flood Frequency ◽

Distributed Model ◽

Model Parameters ◽

Rainfall Runoff ◽

Ungauged Sites ◽

Calibration Uncertainty ◽

Property Space ◽

Rainfall Runoff Model ◽

Runoff Model

Abstract. This paper investigates a new approach to spatial generalisation of rainfall–runoff model parameters – site-similarity with pooling groups – for use in flood frequency estimation at ungauged sites using continuous simulation. The method is developed for the generalisation of a simple conceptual model, the Probability Distributed Model, with four parameters which require specific estimation. The study is based on a relatively large sample of catchments in Great Britain. Various options are investigated within the approach. In the final version, the pooling group comprises the 10 calibrated catchments closest, in catchment property space, to the target site, where the catchment properties used to define the space differ for each parameter of the model. An analysis that, explicitly, takes account of calibration uncertainty as a source of error enables the uncertainty associated with generalised parameter values to be reduced, justifiably. The approach uses calibration uncertainty estimated through jack-knifing and employs a weighting scheme within pooling groups that uses weights which vary both with distance in the catchment property space and with the calibration uncertainty. Models using generalised values from this approach perform relatively well compared with direct calibration. Although performance appears to be better in some areas of the country than others, there are no obvious relationships between catchment properties and performance.

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A complementary modelling approach to manage uncertainty of computationally expensive models

Water Science & Technology ◽

10.2166/wst.2007.599 ◽

2007 ◽

Vol 56 (8) ◽

pp. 1-9 ◽

Cited By ~ 7

Author(s):

Z. Vojinovic

Keyword(s):

Support Vector ◽

Model Parameters ◽

Machine Model ◽

Systematic Analysis ◽

Physically Based Model ◽

Physically Based ◽

Computationally Expensive ◽

Estimation Of Model Parameters ◽

Insight Into ◽

Modelling Approach

The fact that the models applied in the ‘water domain’ are far from reality can be attributed to many reasons. In this context, a systematic analysis of uncertainties reflected by the model error can provide insight into the level of confidence in the model results and how to approach estimation of optimal model parameters. This paper discusses the four commonly used approaches for estimation of model parameters and suggests that an alternative complementary modelling approach should be considered in cases where the traditional model calibration gives limited results and particularly in cases where the computationally expensive models are concerned. It treats uncertainty as modelling the total discrepancy between the model and physical process. The proposed approach combines the results from a physically-based model and Support Vector Machine model into the final solution.

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Water budget, performance of evapotranspiration formulations, and their impact on hydrological modeling of a small boreal peatland-dominated watershed

Canadian Journal of Earth Sciences ◽

10.1139/cjes-2017-0046 ◽

2018 ◽

Vol 55 (2) ◽

pp. 206-220 ◽

Cited By ~ 2

Author(s):

Pierre-Erik Isabelle ◽

Daniel F. Nadeau ◽

Alain N. Rousseau ◽

François Anctil

Keyword(s):

Water Budget ◽

Hydrological Modeling ◽

Distributed Model ◽

Model Parameters ◽

Taylor Models ◽

Physically Based ◽

Model Efficiency ◽

The Impact ◽

Substantial Drop

Peatlands occupy around 13% of the land cover of Canada, and thus they play a key role in the water balance at high latitudes. They are well known for having substantial water loss due to evapotranspiration. Since measurements of evapotranspiration are scarce over these environments, hydrologists generally rely on models of varying complexity to evaluate these water exchanges in the global watershed balance. This study quantifies the water budget of a small boreal peatland-dominated watershed. We assess the performance of three evapotranspiration models in comparison with in situ observations and the impact of using these models in the hydrological modeling of the watershed. The study site (∼1 km2) is located in the eastern James Bay lowlands, Québec, Canada. During summer 2012, an eddy flux tower measured evapotranspiration continuously, while a trapezoidal flume monitored streamflow at the watershed outlet. We estimated evapotranspiration with a combinational model (Penman), a radiation-based model (Priestley–Taylor), and a temperature-based model (Hydro-Québec), and performed the hydrological modeling of the watershed with HYDROTEL, a physically based semi-distributed model. Our results show that the Penman and Priestley–Taylor models reproduce the observations with the highest precision, while a substantial drop in performance occurs with the Hydro-Québec model. However, these discrepancies did not appear to reduce the hydrological model efficiency, at least from what can be concluded from a 3-month modeling period. HYDROTEL appears sensitive to evapotranspiration inputs, but calibration of model parameters can compensate for the differences. These findings still need to be confirmed with longer modeling periods.

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Testing the realism of a topography-driven model (FLEX-Topo) in the nested catchments of the Upper Heihe, China

Hydrology and Earth System Sciences ◽

10.5194/hess-18-1895-2014 ◽

2014 ◽

Vol 18 (5) ◽

pp. 1895-1915 ◽

Cited By ~ 67

Author(s):

H. Gao ◽

M. Hrachowitz ◽

F. Fenicia ◽

S. Gharari ◽

H. H. G. Savenije

Keyword(s):

Realistic Model ◽

Distributed Model ◽

Model Parameters ◽

Model Structure ◽

Rainfall Runoff ◽

Soft Data ◽

Landscape Elements ◽

Elevation Data ◽

Soil Climate ◽

Better Than

Abstract. Although elevation data are globally available and used in many existing hydrological models, their information content is still underexploited. Topography is closely related to geology, soil, climate and land cover. As a result, it may reflect the dominant hydrological processes in a catchment. In this study, we evaluated this hypothesis through four progressively more complex conceptual rainfall-runoff models. The first model (FLEXL) is lumped, and it does not make use of elevation data. The second model (FLEXD) is semi-distributed with different parameter sets for different units. This model uses elevation data indirectly, taking spatially variable drivers into account. The third model (FLEXT0), also semi-distributed, makes explicit use of topography information. The structure of FLEXT0 consists of four parallel components representing the distinct hydrological function of different landscape elements. These elements were determined based on a topography-based landscape classification approach. The fourth model (FLEXT) has the same model structure and parameterization as FLEXT0 but uses realism constraints on parameters and fluxes. All models have been calibrated and validated at the catchment outlet. Additionally, the models were evaluated at two sub-catchments. It was found that FLEXT0 and FLEXT perform better than the other models in nested sub-catchment validation and they are therefore better spatially transferable. Among these two models, FLEXT performs better than FLEXT0 in transferability. This supports the following hypotheses: (1) topography can be used as an integrated indicator to distinguish between landscape elements with different hydrological functions; (2) FLEXT0 and FLEXT are much better equipped to represent the heterogeneity of hydrological functions than a lumped or semi-distributed model, and hence they have a more realistic model structure and parameterization; (3) the soft data used to constrain the model parameters and fluxes in FLEXT are useful for improving model transferability. Most of the precipitation on the forested hillslopes evaporates, thus generating relatively little runoff.

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A Review on Rainfall Runoff Simulation at Ungauged Catchment

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.35.22350 ◽

2018 ◽

Vol 7 (4.35) ◽

pp. 162

Author(s):

W.N.C.W. Zanial ◽

M.A. Malek ◽

M.N.M. Reba

Keyword(s):

Alternative Methods ◽

Model Parameters ◽

Rainfall Runoff ◽

Runoff Simulation ◽

Ungauged Catchments ◽

Ungauged Catchment ◽

Rain Gauges ◽

Other Information ◽

Physically Based ◽

Set Up

Ungauged catchment occurs when no runoff data are available or when very few ground rain gauges are located in a huge catchment. For these catchments, the parameters to be used in rainfall‐runoff models cannot be attained just by adjusting runoff information and thus should be procured by different techniques. Show parameters that require orientation are normally transposed from comparable measured catchments. The rainfall runoff simulation is very important to estimate and predict the flow in ungauged catchment. This investigation reviews ideas to differentiate hydrological comparability for transposing parameters from a gauged to an ungauged catchment. Model parameters that are physically based are generally derived from other information close to the ungauged catchment of intrigue. The primary challenge with rainfall‐runoff demonstrating in ungauged catchments is the absence of neighborhood ground precipitation and streamflow information to be utilized in aligning the proposed show parameters. Parameter alignment is useful since adjustment can represent the impacts of hydrological set up in a specific catchment. Since hydrological models are especially reliant on their limit conditions, the alignment practice directed can modify the predispositions of info information utilized. Parameters' adjustment can fundamentally improve the execution of rainfall‐runoff models since it included media properties of soil and vegetation which are exceptionally heterogeneous and basically are in every case inadequately known. Alternative methods for ungauged catchments are required which are the subject of this study. This study summarizes the important methods used in an ungauged catchments, discusses the issues of using satellite data as a substitute input to rainfall‐runoff models and its comparison with point scale ground data.

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Spatial variability of the parameters of a semi-distributed hydrological model

Proceedings of the International Association of Hydrological Sciences ◽

10.5194/piahs-373-87-2016 ◽

2016 ◽

Vol 373 ◽

pp. 87-94 ◽

Cited By ~ 8

Author(s):

Alban de Lavenne ◽

Guillaume Thirel ◽

Vazken Andréassian ◽

Charles Perrin ◽

Maria-Helena Ramos

Keyword(s):

Temporal Pattern ◽

Flow Parameter ◽

Hydrological Model ◽

Optimal Parameter ◽

Distributed Model ◽

Model Parameters ◽

Distributed Hydrological Model ◽

Hydrologic Models ◽

Resources Management ◽

Lumped Models

Abstract. Ideally, semi-distributed hydrologic models should provide better streamflow simulations than lumped models, along with spatially-relevant water resources management solutions. However, the spatial distribution of model parameters raises issues related to the calibration strategy and to the identifiability of the parameters. To analyse these issues, we propose to base the evaluation of a semi-distributed model not only on its performance at streamflow gauging stations, but also on the spatial and temporal pattern of the optimised value of its parameters. We implemented calibration over 21 rolling periods and 64 catchments, and we analysed how well each parameter is identified in time and space. Performance and parameter identifiability are analysed comparatively to the calibration of the lumped version of the same model. We show that the semi-distributed model faces more difficulties to identify stable optimal parameter sets. The main difficulty lies in the identification of the parameters responsible for the closure of the water balance (i.e. for the particular model investigated, the intercatchment groundwater flow parameter).

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