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

Abstract. Although elevation data is globally available, and used in many existing hydrological models, its information content is still poorly understood and under-exploited. 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 three 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. It also does not make use of elevation data, but it accounts for input spatial variability. The third model (FLEXT), also semi-distributed, makes explicit use of topography information. The structure of FLEXT 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. All models were calibrated and validated at the catchment outlet. Additionally, the models were evaluated at two nested sub-catchments. FLEXT, performs better than the other models in the nested sub-catchment validation and it is therefore better transferable. This supports the following hypotheses: (1) topography can be used as an integrated indicator to distinguish landscape elements with different hydrological function; (2) the model structure of FLEXT is much better equipped to represent hydrological signatures than a lumped or semi-distributed model, and hence has a more realistic model structure and parameterization; (3) the wetland/terrace and grassland hillslope landscape elements of the Upper Heihe contribute the main part of the fast runoff while the bare soil/rock landscape provides the main contribution to the groundwater. Most of the precipitation on the forested hillslopes is evaporated, thus generating relatively little runoff.

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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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On runoff generation and the distribution of storage deficits

Hydrology Research ◽

10.2166/nh.2013.119 ◽

2013 ◽

Vol 44 (4) ◽

pp. 673-689

Author(s):

A. Wood ◽

K. J. Beven

Keyword(s):

Distribution Function ◽

Beta Function ◽

Water Levels ◽

Distribution Model ◽

Slow Pathway ◽

Hydrological Models ◽

Rainfall Runoff ◽

Runoff Generation ◽

Small Advantage ◽

Better Than

A number of hydrological models use a distribution function to develop the non-linear rainfall–runoff catchment response. In this study the beta function is applied to represent a distribution of soil moisture storages in conjunction with a fast and slow pathway routing. The BETA3 and BETA4 modules, presented in this paper, have a distribution of discrete storage elements that have variable and redistributed water levels at each timestep. The PDM-BETA5 is an analytical solution with a similar structure to the commonly used probability distribution model (PDM). Model testing was performed on three catchments in the Northern Pennine region in England. The performances of the BETA models were compared with a commonly used formulation of the PDM. The BETA models performed marginally better than the PDM in calibration and parameter estimation was better with the BETA models than for the PDM. The BETA models had a small advantage in validation on the hydrologically fast responding test catchments.

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Using NDII pattern for a semi-distributed rainfall-runoff model in tropical nested catchments

10.5194/hess-2020-82 ◽

2020 ◽

Author(s):

Nutchanart Sriwongsitanon ◽

Wasana Jandang ◽

Thienchart Suwawong ◽

Hubert H.~G. Savenije

Keyword(s):

Spatial Distribution ◽

Root Zone ◽

Lag Time ◽

Model Parameters ◽

Model Structure ◽

Rainfall Runoff ◽

Moisture Storage ◽

Rainfall Runoff Model ◽

Runoff Model ◽

Better Than

Abstract. A parsimonious semi-distributed rainfall-runoff model has been developed for flow prediction. In distribution, attention is paid to both timing of runoff and heterogeneity of moisture storage capacities within sub-catchments. This model is based on the lumped FLEXL model structure, which has proven its value in a wide range of catchments. To test the value of distribution, the gauged Upper Ping catchment in Thailand has been divided into 10 sub-catchments, which can be grouped into 5 gauged sub-catchments where internal performance is evaluated. To test the effect of timing, firstly excess rainfall was calculated for each sub-catchment, using the model structure of FLEXL. The excess rainfall was then routed to its outlet using the lag time from storm to peak flow (TlagF) and the lag time of recharge from the root zone to the groundwater (TlagS), as a function of catchment size. Subsequently, the Muskingum equation was used to route sub-catchment runoff to the downstream sub-catchment, before adding to runoff of the downstream sub-catchment, with the delay time parameter of the Muskingum equation being a function of channel length. Other model parameters of this semi-distributed FLEX-SD model were kept the same as in the calibrated FLEXL model of the entire Upper Ping basin, controlled by station P.1 located at the centre of Chiang Mai Province. The outcome of FLEX-SD was compared to: 1) observations at P.1; 2) the results of the calibrated FLEXL model; and 3) the semi-distributed URBS model - another established semi-distributed rainfall-runoff model. FLEX-SD showed better performance than URBS, but a bit lower than the calibrated FLEXL model with NSE of 0.74, 0.71, and 0.76, respectively. Subsequently, at the level of the gauged internal sub-catchments, runoff estimates of FLEX-SD were compared to observations and calibrated FLEXL model results. The results demonstrate that FLEX-SD provides more accurate runoff estimates at P.1, P.67 and P.75 stations which are located along the main Ping River, compared to those provided by the lumped calibrated FLEXL model. The results were less good at 2 tributary stations (P.20 and P.21), where calibrated FLEXL output performed better, while performance was similar at one tributary station (P.4A). Overall, FLEX-SD performed better than URBS at 5 out of 6 stations except at P.21. Subsequently, the effect of distributing moisture storage capacity was tested. Since the FLEX-SD uses the same Sumax value - the maximum moisture holding capacity of the root zone - for all sub-catchments, FLEX-SD-NDII was set-up making use of the spatial distribution of the NDII (the normalized difference infrared index). The readily available NDII appears to be a good proxy for moisture stress in the root zone, particularly during dry periods. The maximum moisture holding capacity in the root zone assumed to be a function of the maximum seasonal range of NDII values. The spatial distribution of this range among sub-catchments was used to calibrate the semi-distributed FLEX-SD-NDII model. The additional constraint by the NDII improved the performance of the model and the realism of the distribution. To test how well the model represents root zone soil moisture, the performance of the FLEX-SD-NDII model was compared to time series of the soil wetness index (SWI). The correlation between the root zone storage and the daily SWI appeared to be very good, even better than the correlation with the NDII, because NDII does not provide good estimates during wet periods. The SWI, which is partly model-based, was not used for calibration, but appeared to be an appropriate index for verification.

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Reproducing different types of changes in hydrological indicators with rainfall-runoff models

Hydrology Research ◽

10.2166/nh.2020.073 ◽

2020 ◽

Vol 51 (2) ◽

pp. 238-256

Author(s):

Carolina Massmann

Keyword(s):

Objective Function ◽

Model Structure ◽

Hydrological Models ◽

Rainfall Runoff ◽

Anthropogenic Changes ◽

Riverine Ecosystems ◽

Different Types ◽

Future Value ◽

The Impact ◽

Flow Reversals

Abstract Hydrological indicators support analyses about the impact of climate and anthropogenic changes on riverine ecosystems. As these studies often rely on hydrological models for estimating the future value of the indicators, it is important to investigate how well, and under which conditions, we can replicate changes in the indicators. This study looks at these questions by investigating the performance that can be achieved depending on the objective function for calibrating the model, the direction of the change in the indicator, the magnitude of this change and the properties of the catchments. The results indicate that, in general, indicators describing the magnitude of discharge (monthly and annual) can be adequately estimated with hydrological models, but that there are difficulties when estimating the characteristics of flow pulses, flow reversals and timing variables. For some of these indicators, it is not even possible to correctly estimate the direction of large changes. The analysis showed further that these problems cannot be resolved by adjusting the calibrated parameters, but that the model structure is unsuitable for modelling these indicators.

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Practical experience and framework for sensitivity analysis of hydrological models: six methods, three models, three criteria

10.5194/hess-2018-78 ◽

2018 ◽

Cited By ~ 1

Author(s):

Anqi Wang ◽

Dimitri P. Solomatine

Keyword(s):

Sensitivity Analysis ◽

Comparative Analysis ◽

Practical Experience ◽

Ease Of Use ◽

Hydrological Models ◽

Rainfall Runoff ◽

Sensitivity Indices ◽

Kolmogorov Smirnov ◽

Better Than

Abstract. Sensitivity Analysis (SA) and Uncertainty Analysis (UA) are important steps for better understanding and evaluation of hydrological models. The aim of this paper is to briefly review main classes of SA methods, and to presents the results of the practical comparative analysis of applying them. Six different global SA methods: Sobol, eFAST, Morris, LH-OAT, RSA and PAWN are tested on three conceptual rainfall-runoff models with varying complexity: (GR4J, Hymod and HBV) applied to the case study of Bagmati basin (Nepal), and also initially tested on the case of Dapoling-Wangjiaba catchment in China. The methods are compared with respect to effectiveness, efficiency and convergence. A practical framework of selecting and using the SA methods is presented. The result shows that, first of all, all the six SA methods are effective. Morris and LH-OAT methods are the most efficient methods in computing SI and ranking. eFAST performs better than Sobol, thus can be seen as its viable alternative for Sobol. PAWN and RSA methods have issues of instability which we think are due to the ways CDFs are built, and using Kolmogorov-Smirnov statistics to compute Sensitivity Indices. All the methods require sufficient number of runs to reach convergence. Difference in efficiency of different methods is an inevitable consequence of the differences in the underlying principles. For SA of hydrological models, it is recommended to apply the presented practical framework assuming the use of several methods, and to explicitly take into account the constraints of effectiveness, efficiency (including convergence), ease of use, as well as availability of software.

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Performance of rainfall–runoff models in reproducing hydrological extremes: a case of the River Malaba sub-catchment

SN Applied Sciences ◽

10.1007/s42452-021-04514-7 ◽

2021 ◽

Vol 3 (4) ◽

Author(s):

Ambrose Mubialiwo ◽

Adane Abebe ◽

Charles Onyutha

Keyword(s):

Goodness Of Fit ◽

Model Performance ◽

Relevant Information ◽

Automatic Calibration ◽

Hydrological Models ◽

Return Periods ◽

Rainfall Runoff ◽

Low Flows ◽

Hydrological Extremes ◽

Better Than

AbstractDaily River Malaba flows recorded from 1999 to 2016 were modelled using seven lumped conceptual rainfall–runoff models including AWBM, SACRAMENTO, TANK, IHACRES, SIMHYD, SMAR and HMSV. Optimal parameters of each model were obtained using an automatic calibration strategy. Mismatches between observed and modelled flows were assessed using a total of nine “goodness-of-fit” metrics. Capacity of the models to reproduce historical hydrological extremes was assessed through comparison of amplitude–duration–frequency (ADF) relationships or curves constructed based on observed and modelled flow quantiles. Generally, most of the hydrological models performed better for high than low flows. ADF curves of both high and low flows for various return periods from 5 to 100 years were well reproduced by AWBM, SAC, TANK and HMSV. ADF curves for high and low flows were poorly reproduced by SIMHYD and SMAR, respectively. Overall, AWBM performed slightly better than other models if both high and low flows are to be considered simultaneously. The deviations of these models were larger for high than low return periods. It was found that the choice of a “goodness-of-fit” metric affects how model performance can be judged. Results from this study also show that when focusing on hydrological extremes, uncertainty due to the choice of a particular model should be taken into consideration. Insights from this study provide relevant information for planning of risk-based water resources applications.

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An algorithm for delineating and extracting hillslopes and hillslope width functions from gridded elevation data

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-8-8865-2011 ◽

2011 ◽

Vol 8 (5) ◽

pp. 8865-8901

Author(s):

P. Noel ◽

A. N. Rousseau ◽

C. Paniconi

Keyword(s):

Three Dimensional ◽

Rainfall Runoff ◽

Dimensional Representation ◽

New Approach ◽

One Dimensional ◽

Digital Elevation ◽

Elevation Data ◽

Average Profile ◽

Elevation Model ◽

Digital Elevation Model Resolution

Abstract. Subdivision of catchment into appropriate hydrological units is essential to represent rainfall-runoff processes in hydrological modelling. The commonest units used for this purpose are hillslopes (e.g. Fan and Bras, 1998; Troch et al., 2003). Hillslope width functions can therefore be utilised as one-dimensional representation of three-dimensional landscapes by introducing profile curvatures and plan shapes. An algorithm was developed to delineate and extract hillslopes and hillslope width functions by introducing a new approach to calculate an average profile curvature and plan shape. This allows the algorithm to be independent of digital elevation model resolution and to associate hillslopes to nine elementary landscapes according to Dikau (1989). This algortihm was tested on two flat and steep catchments of the province of Quebec, Canada. Results showed great area coverage for hillslope width function over individual hillslopes and entire watershed.

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Stepwise improvement of hydrological models using satellite-based evaporation and total water storage estimations

10.5194/egusphere-egu21-796 ◽

2021 ◽

Author(s):

Markus Hrachowitz ◽

Petra Hulsman ◽

Hubert Savenije

Keyword(s):

Model Calibration ◽

Hydrological Model ◽

Model Development ◽

Water Storage ◽

Spatiotemporal Variability ◽

Model Structure ◽

Hydrological Models ◽

Total Water ◽

Spatiotemporal Model ◽

Multiple Variables

<p>Hydrological models are often calibrated with respect to flow observations at the basin outlet. As a result, flow predictions may seem reliable but this is not necessarily the case for the spatiotemporal variability of system-internal processes, especially in large river basins. Satellite observations contain valuable information not only for poorly gauged basins with limited ground observations and spatiotemporal model calibration, but also for stepwise model development. This study explored the value of satellite observations to improve our understanding of hydrological processes through stepwise model structure adaption and to calibrate models both temporally and spatially. More specifically, satellite-based evaporation and total water storage anomaly observations were used to diagnose model deficiencies and to subsequently improve the hydrological model structure and the selection of feasible parameter sets. A distributed, process based hydrological model was developed for the Luangwa river basin in Zambia and calibrated with respect to discharge as benchmark. This model was modified stepwise by testing five alternative hypotheses related to the process of upwelling groundwater in wetlands, which was assumed to be negligible in the benchmark model, and the spatial discretization of the groundwater reservoir. Each model hypothesis was calibrated with respect to 1) discharge and 2) multiple variables simultaneously including discharge and the spatiotemporal variability in the evaporation and total water storage anomalies. The benchmark model calibrated with respect to discharge reproduced this variable well, as also the basin-averaged evaporation and total water storage anomalies. However, the evaporation in wetland dominated areas and the spatial variability in the evaporation and total water storage anomalies were poorly modelled. The model improved the most when introducing upwelling groundwater flow from a distributed groundwater reservoir and calibrating it with respect to multiple variables simultaneously. This study showed satellite-based evaporation and total water storage anomaly observations provide valuable information for improved understanding of hydrological processes through stepwise model development and spatiotemporal model calibration.</p>

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How can expert knowledge increase the realism of conceptual hydrological models? A case study based on the concept of dominant runoff process in the Swiss Pre-Alps

Hydrology and Earth System Sciences ◽

10.5194/hess-22-4425-2018 ◽

2018 ◽

Vol 22 (8) ◽

pp. 4425-4447 ◽

Cited By ~ 9

Author(s):

Manuel Antonetti ◽

Massimiliano Zappa

Keyword(s):

Spatial Distribution ◽

Expert Knowledge ◽

Hydrological Models ◽

Top Down ◽

Bottom Up ◽

Process Maps ◽

Uncertainty Sources ◽

Simulation Results ◽

Set Up ◽

Better Than

Abstract. Both modellers and experimentalists agree that using expert knowledge can improve the realism of conceptual hydrological models. However, their use of expert knowledge differs for each step in the modelling procedure, which involves hydrologically mapping the dominant runoff processes (DRPs) occurring on a given catchment, parameterising these processes within a model, and allocating its parameters. Modellers generally use very simplified mapping approaches, applying their knowledge in constraining the model by defining parameter and process relational rules. In contrast, experimentalists usually prefer to invest all their detailed and qualitative knowledge about processes in obtaining as realistic spatial distribution of DRPs as possible, and in defining narrow value ranges for each model parameter.Runoff simulations are affected by equifinality and numerous other uncertainty sources, which challenge the assumption that the more expert knowledge is used, the better will be the results obtained. To test for the extent to which expert knowledge can improve simulation results under uncertainty, we therefore applied a total of 60 modelling chain combinations forced by five rainfall datasets of increasing accuracy to four nested catchments in the Swiss Pre-Alps. These datasets include hourly precipitation data from automatic stations interpolated with Thiessen polygons and with the inverse distance weighting (IDW) method, as well as different spatial aggregations of Combiprecip, a combination between ground measurements and radar quantitative estimations of precipitation. To map the spatial distribution of the DRPs, three mapping approaches with different levels of involvement of expert knowledge were used to derive so-called process maps. Finally, both a typical modellers' top-down set-up relying on parameter and process constraints and an experimentalists' set-up based on bottom-up thinking and on field expertise were implemented using a newly developed process-based runoff generation module (RGM-PRO). To quantify the uncertainty originating from forcing data, process maps, model parameterisation, and parameter allocation strategy, an analysis of variance (ANOVA) was performed.The simulation results showed that (i) the modelling chains based on the most complex process maps performed slightly better than those based on less expert knowledge; (ii) the bottom-up set-up performed better than the top-down one when simulating short-duration events, but similarly to the top-down set-up when simulating long-duration events; (iii) the differences in performance arising from the different forcing data were due to compensation effects; and (iv) the bottom-up set-up can help identify uncertainty sources, but is prone to overconfidence problems, whereas the top-down set-up seems to accommodate uncertainties in the input data best. Overall, modellers' and experimentalists' concept of model realism differ. This means that the level of detail a model should have to accurately reproduce the DRPs expected must be agreed in advance.

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Hydrological Validation of a Radar-Based Nowcasting Technique

Journal of Hydrometeorology ◽

10.1175/jhm433.1 ◽

2005 ◽

Vol 6 (4) ◽

pp. 532-549 ◽

Cited By ~ 61

Author(s):

Marc Berenguer ◽

Carles Corral ◽

Rafael Sánchez-Diezma ◽

Daniel Sempere-Torres

Keyword(s):

Point Of View ◽

Motion Field ◽

Warning Systems ◽

Rainfall Runoff ◽

Weather Radars ◽

Radar Echoes ◽

Radar Measurements ◽

Rainfall Runoff Model ◽

Runoff Model ◽

Better Than

Abstract Nowcasting precipitation is a key element in the anticipation of floods in warning systems. In this framework, weather radars are very useful because of the high resolution of their measurements both in time and space. The aim of this study is to assess the performance of a recently proposed nowcasting technique (S-PROG) from a hydrological point of view in a Mediterranean environment. S-PROG is based on the advection of weather radar fields according to the motion field derived with an algorithm based on tracking radar echoes by correlation (TREC), and it has the ability of filtering out the most unpredictable scales of these fields as the forecasting time increases. Validation of this nowcasting technique was done from two different perspectives: (i) comparing forecasted precipitation fields against radar measurements, and (ii) by means of a distributed rainfall runoff model, comparing hydrographs simulated with a hydrological model using rainfall fields forecasted by S-PROG against hydrographs generated with the model using the entire series of radar measurements. In both cases, results obtained by a simpler nowcasting technique are used as a reference to evaluate improvements. Validation showed that precipitation fields forecasted with S-PROG seem to be better than fields forecasted using simpler techniques. Additionally, hydrological validation led the authors to point out that the use of radar-based nowcasting techniques allows the anticipation window in which flow estimates are forecasted with enough quality to be sensibly extended.

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