Catchment Scale Evaluation of Multiple Global Hydrological Models from ISIMIP2a over North America

A satisfactory performance of hydrological models under historical climate conditions is considered a prerequisite step in any hydrological climate change impact study. Despite the significant interest in global hydrological modeling, few systematic evaluations of global hydrological models (gHMs) at the catchment scale have been carried out. This study investigates the performance of 4 gHMs driven by 4 global observation-based meteorological inputs at simulating weekly discharges over 198 large-sized North American catchments for the 1971–2010 period. The 16 discharge simulations serve as the basis for evaluating gHM accuracy at the catchment scale within the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). The simulated discharges by the four gHMs are compared against observed and simulated weekly discharge values by two regional hydrological models (rHMs) driven by a global meteorological dataset for the same period. We discuss the implications of both modeling approaches as well as the influence of catchment characteristics and global meteorological forcing in terms of model performance through statistical criteria and visual hydrograph comparison for catchment-scale hydrological studies. Overall, the gHM discharge statistics exhibit poor agreement with observations at the catchment scale and manifest considerable bias and errors in seasonal flow simulations. We confirm that the gHM approach, as experimentally implemented through the ISIMIP2a, must be used with caution for regional studies. We find the rHM approach to be more trustworthy and recommend using it for hydrological studies, especially if findings are intended to support operational decision-making.

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Comparison between dynamic and static sensitivity analysis approaches for impact assessment of different potential evapotranspiration methods on hydrological models performance

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0192.1 ◽

2021 ◽

Author(s):

Rodric Mérimé Nonki ◽

André Lenouo ◽

Christopher J. Lennard ◽

Raphael M. Tshimanga ◽

Clément Tchawoua

Keyword(s):

Sensitivity Analysis ◽

Impact Assessment ◽

Hydrological Modeling ◽

Potential Evapotranspiration ◽

Model Performance ◽

Estimation Methods ◽

Model Parameters ◽

Hydrological Models ◽

Sensitivity Approach ◽

Static Sensitivity

AbstractPotential Evapotranspiration (PET) plays a crucial role in water management, including irrigation systems design and management. It is an essential input to hydrological models. Direct measurement of PET is difficult, time-consuming and costly, therefore a number of different methods are used to compute this variable. This study compares the two sensitivity analysis approaches generally used for PET impact assessment on hydrological model performance. We conducted the study in the Upper Benue River Basin (UBRB) located in northern Cameroon using two lumped-conceptual rainfall-runoff models and nineteen PET estimation methods. A Monte-Carlo procedure was implemented to calibrate the hydrological models for each PET input while considering similar objective functions. Although there were notable differences between PET estimation methods, the hydrological models performance was satisfactory for each PET input in the calibration and validation periods. The optimized model parameters were significantly affected by the PET-inputs, especially the parameter responsible to transform PET into actual ET. The hydrological models performance was insensitive to the PET input using a dynamic sensitivity approach, while he was significantly affected using a static sensitivity approach. This means that the over-or under-estimation of PET is compensated by the model parameters during the model recalibration. The model performance was insensitive to the rescaling PET input for both dynamic and static sensitivities approaches. These results demonstrate that the effect of PET input to model performance is necessarily dependent on the sensitivity analysis approach used and suggest that the dynamic approach is more effective for hydrological modeling perspectives.

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Comparing model performance of two rainfall-runoff models in the Rhine basin using different atmospheric forcing data sets

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-4-4325-2007 ◽

2007 ◽

Vol 4 (6) ◽

pp. 4325-4360 ◽

Cited By ~ 2

Author(s):

A. H. te Linde ◽

J. C. J. H. Aerts ◽

R. T. W. L. Hurkmans ◽

M. Eberle

Keyword(s):

Climate Change ◽

Model Performance ◽

Change Scenario ◽

Hydrological Models ◽

Rainfall Runoff ◽

Meteorological Forcing ◽

Lumped Model ◽

Distributed Models ◽

Rhine Basin ◽

Physically Based

Abstract. Due to the growing wish and necessity to simulate the possible effects of climate change on the discharge regime on large rivers such as the Rhine in Europe, there is a need for well performing hydrological models that can be applied in climate change scenario studies. There exists large variety in available models and there is an ongoing debate in research on rainfall-runoff modelling on whether or not physically based distributed models better represent observed discharges than conceptual lumped model approaches do. In this paper, the hydrological models HBV and VIC were compared for the Rhine basin by testing their performance in simulating discharge. Overall, the semi-distributed conceptual HBV model performed much better than the distributed physically based VIC model (E=0.62, r2=0.65 vs. E=0.31, r2=0.54 at Lobith). It is argued here that even for a well-documented river basin such as the Rhine, more complex modelling does not automatically lead to better results. Moreover, it is concluded that meteorological forcing data has a considerable influence on model performance, irrespectively to the type of model structure and the need for ground-based meteorological measurements is emphasized.

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Investigating the reasons for poor model performance in a changing climate

10.5194/egusphere-egu2020-487 ◽

2020 ◽

Author(s):

Doris Duethmann ◽

Günter Blöschl ◽

Juraj Parajka

Keyword(s):

Climate Change ◽

Vegetation Dynamics ◽

Vegetation Index ◽

Model Performance ◽

Model Structure ◽

Precipitation Data ◽

Hydrological Models ◽

Discharge Data ◽

Climate Conditions ◽

Changing Climate

Hydrological models are often applied to estimate climate change impacts on hydrology. However, several studies demonstrated that hydrological models do not perform well when applied under changing climate conditions. In order to decide on the way forward for improving hydrological modelling in climate change contexts, it is important to understand the reasons for poor performance in a changing climate, but there are only a few studies on this topic.Here we revisit a study in Austria that demonstrated the inability of a conceptual model to simulate the discharge response to increases in precipitation and air temperature. We set up hypotheses for the differences between the observed and simulated changes in discharge and test these using simulations with various modifications of the model (including modifications of the input data, model calibration, and model structure).The baseline model overestimates discharge trends over 1978&#8722;2013, on average over all 156 catchments, by 93 &#177; 50 mm yr&#8722;1 per 35 years. Accounting for vegetation dynamics in the calculation of reference evaporation based on a satellite-derived vegetation index, reduces the difference between simulated and observed discharge by 35 &#177; 9 mm yr&#8722;1 per 35 years. Inhomogeneities in the precipitation data, caused by a variable number of stations and, to a lesser degree, climate variability effects on the undercatch error, can explain 44 &#177; 28 mm yr&#8722;1 per 35 years of this difference. Extending the calibration period from 5 to 25 years, varying the objective function by including annually aggregated discharge data, or estimating evaporation with the Penman-Monteith instead of the Blaney-Criddle approach has little influence on the simulated discharge trends. The model structure problem with respect to vegetation dynamics has important implications for studies in a climate change context. Our results furthermore highlight the importance of using precipitation data based on a stationary input station network for studying observed hydrologic changes.

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Evaluation of three semi-distributed hydrological models in simulating discharge from a small forest and arable dominated catchment

Biologia ◽

10.1515/biolog-2017-0108 ◽

2017 ◽

Vol 72 (9) ◽

Author(s):

Ilona Kása ◽

Györgyi Gelybó ◽

Ágota Horel ◽

Zsófia Bakacsi ◽

Eszter Tóth ◽

...

Keyword(s):

Model Performance ◽

Coefficient Of Determination ◽

Lake Balaton ◽

Hydrological Models ◽

Catchment Scale ◽

Discharge Data ◽

Flow Duration Curves ◽

Distributed Hydrological Models ◽

Intercomparison Study ◽

Light Precipitation

AbstractCatchment scale hydrological models are promising tools for simulating the effect of catchment-specific processes and management on soil and water resources. Here, we present a model intercomparison study of runoff simulations using three different semi-distributed rainfall-runoff catchment models. The objective of this study was to demonstrate the applicability of the Hydrologiska Byrans Vattenavdelning (HBV-Light); Precipitation, Evapotranspiration and Runoff Simulator for Solute Transport (PERSiST); and INtegrated CAtchment (INCA) models on Somogybabod Catchment, near Lake Balaton, Hungary.The models were calibrated and validated against observed discharge data at the outlet of the catchment for the period of January 1, 2006 –July 12, 2015. Model performance was evaluated using graphical representations, e.g. daily and monthly hydrographs and Flow Duration Curves (FDC) and model evaluation statistic; Nash–Sutcliffe efficiency (NSE) and coefficient of determination (

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Multimodel evaluation of twenty lumped hydrological models under contrasted climate conditions

Hydrology and Earth System Sciences ◽

10.5194/hess-16-1171-2012 ◽

2012 ◽

Vol 16 (4) ◽

pp. 1171-1189 ◽

Cited By ~ 95

Author(s):

G. Seiller ◽

F. Anctil ◽

C. Perrin

Keyword(s):

Test Procedure ◽

Model Performance ◽

Flow Simulation ◽

Added Value ◽

Hydrological Models ◽

Model Ensemble ◽

Climate Conditions ◽

Sample Test ◽

Improved Performance ◽

The Individual

Abstract. This paper investigates the temporal transposability of hydrological models under contrasted climate conditions and evaluates the added value of using an ensemble of model structures for flow simulation. This is achieved by applying the Differential Split Sample Test procedure to twenty lumped conceptual models on a catchment in the Province of Québec (Canada) and another one in the State of Bavaria (Germany). First, a calibration/validation procedure was applied on four historical non-continuous periods with contrasted climate conditions. Then, model efficiency was quantified individually (for each model) and collectively (for the model ensemble). The individual analysis evaluated model performance and robustness. The ensemble investigation, based on the average of simulated discharges, focused on the twenty-member ensemble and all possible model subsets. Results showed that using a single model may provide hazardous results when the model is to be applied in contrasted conditions. Overall, some models turned out as a good compromise in terms of performance and robustness, but generally not as much as the twenty-model ensemble. Model subsets offered yet improved performance over the twenty-model ensemble, but at the expanse of spatial transposability (i.e. need of site-specific analysis).

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GRACE-REC: a reconstruction of climate-driven water storage changes over the last century

Earth System Science Data ◽

10.5194/essd-11-1153-2019 ◽

2019 ◽

Vol 11 (3) ◽

pp. 1153-1170 ◽

Cited By ~ 19

Author(s):

Vincent Humphrey ◽

Lukas Gudmundsson

Keyword(s):

Sea Level ◽

Large Scale ◽

Hydrological Modeling ◽

Water Storage ◽

Hydrological Models ◽

Meteorological Forcing ◽

Terrestrial Water ◽

User Groups ◽

Data Gap ◽

Gravity Recovery

Abstract. The amount of water stored on continents is an important constraint for water mass and energy exchanges in the Earth system and exhibits large inter-annual variability at both local and continental scales. From 2002 to 2017, the satellites of the Gravity Recovery and Climate Experiment (GRACE) mission have observed changes in terrestrial water storage (TWS) with an unprecedented level of accuracy. In this paper, we use a statistical model trained with GRACE observations to reconstruct past climate-driven changes in TWS from historical and near-real-time meteorological datasets at daily and monthly scales. Unlike most hydrological models which represent water reservoirs individually (e.g., snow, soil moisture) and usually provide a single model run, the presented approach directly reconstructs total TWS changes and includes hundreds of ensemble members which can be used to quantify predictive uncertainty. We compare these data-driven TWS estimates with other independent evaluation datasets such as the sea level budget, large-scale water balance from atmospheric reanalysis, and in situ streamflow measurements. We find that the presented approach performs overall as well or better than a set of state-of-the-art global hydrological models (Water Resources Reanalysis version 2). We provide reconstructed TWS anomalies at a spatial resolution of 0.5∘, at both daily and monthly scales over the period 1901 to present, based on two different GRACE products and three different meteorological forcing datasets, resulting in six reconstructed TWS datasets of 100 ensemble members each. Possible user groups and applications include hydrological modeling and model benchmarking, sea level budget studies, assessments of long-term changes in the frequency of droughts, the analysis of climate signals in geodetic time series, and the interpretation of the data gap between the GRACE and GRACE Follow-On missions. The presented dataset is published at https://doi.org/10.6084/m9.figshare.7670849 (Humphrey and Gudmundsson, 2019) and updates will be published regularly.

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Finding the Optimal Multimodel Averaging Method for Global Hydrological Simulations

Remote Sensing ◽

10.3390/rs13132574 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2574

Author(s):

Wenyan Qi ◽

Jie Chen ◽

Chongyu Xu ◽

Yongjing Wan

Keyword(s):

Hydrological Modeling ◽

Averaging Method ◽

Hydrological Models ◽

Hydrological Simulation ◽

Climate Conditions ◽

Averaging Methods ◽

Model Driven ◽

Climatic Regions ◽

Arctic Regions ◽

The World

Global gridded precipitations have been extensively considered as the input of hydrological models for runoff simulations around the world. However, the limitations of hydrologic models and the inaccuracies of the precipitation datasets could result in large uncertainty in hydrological forecasts and water resource estimations. Therefore, it is of great importance to investigate the hydrological value of a weighted combination of hydrological models driven by different precipitation datasets. In addition, due to the diversities of combination members and climate conditions, hydrological simulation for watersheds under different climate conditions may show various sensitivities to the weighted combinations. This study undertakes a comprehensive analysis of various multimodel averaging methods and schemes (i.e., the combination of the members in averaging) to identify the most skillful and reliable multimodel averaging application. To achieve this, four hydrological models driven by six precipitation datasets were used as averaging members. The behaviors of 9 averaging methods and 11 averaging schemes in hydrological simulations were tested over 2277 watersheds distributed in different climate regions in the world. The results show the following: (1) The multi-input averaging schemes (i.e., members consist of one model driven by multiple precipitation datasets) generally perform better than the multimodel averaging schemes (i.e., members consist of multiple models driven by the same precipitation dataset) for each averaging method; (2) The use of multiple members can improve the averaging performances. Six averaging members are found to be necessary and advisable, since using more than six members only imrpoves the estimation results slightly, as compared with using all 24 members; (3) The advantage of using averaging methods for hydrological modeling is region dependent. The averaging methods, in general, produced the best results in the warm temperate region, followed by the snow and equatorial regions, while a large difference among various averaging methods is found in arid and arctic regions. This is mainly due to the different averaging methods being affected to a different extent by the poorly performed members in the arid and arctic regions; (4) the multimodel superensemble method (MMSE) is recommended for its robust and outstanding performance among various climatic regions.

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Spatio-Temporal Hydrological Model Structure and Parametrization Analysis

Journal of Marine Science and Engineering ◽

10.3390/jmse9050467 ◽

2021 ◽

Vol 9 (5) ◽

pp. 467

Author(s):

Mostafa Farrag ◽

Gerald Corzo Perez ◽

Dimitri Solomatine

Keyword(s):

Hydrological Modeling ◽

Model Building ◽

Model Performance ◽

Model Description ◽

Generation Model ◽

Model Structure ◽

Runoff Generation ◽

Spatial Sensitivity ◽

Spatio Temporal ◽

Set Up

Many grid-based spatial hydrological models suffer from the complexity of setting up a coherent spatial structure to calibrate such a complex, highly parameterized system. There are essential aspects of model-building to be taken into account: spatial resolution, the routing equation limitations, and calibration of spatial parameters, and their influence on modeling results, all are decisions that are often made without adequate analysis. In this research, an experimental analysis of grid discretization level, an analysis of processes integration, and the routing concepts are analyzed. The HBV-96 model is set up for each cell, and later on, cells are integrated into an interlinked modeling system (Hapi). The Jiboa River Basin in El Salvador is used as a case study. The first concept tested is the model structure temporal responses, which are highly linked to the runoff dynamics. By changing the runoff generation model description, we explore the responses to events. Two routing models are considered: Muskingum, which routes the runoff from each cell following the river network, and Maxbas, which routes the runoff directly to the outlet. The second concept is the spatial representation, where the model is built and tested for different spatial resolutions (500 m, 1 km, 2 km, and 4 km). The results show that the spatial sensitivity of the resolution is highly linked to the routing method, and it was found that routing sensitivity influenced the model performance more than the spatial discretization, and allowing for coarser discretization makes the model simpler and computationally faster. Slight performance improvement is gained by using different parameters’ values for each cell. It was found that the 2 km cell size corresponds to the least model error values. The proposed hydrological modeling codes have been published as open-source.

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Catchment-scale evaluation of pollution potential of urban snow at two residential catchments in southern Finland

Water Science & Technology ◽

10.2166/wst.2013.466 ◽

2013 ◽

Vol 68 (10) ◽

pp. 2164-2170 ◽

Cited By ~ 10

Author(s):

Nora Sillanpää ◽

Harri Koivusalo

Keyword(s):

Snow Water Equivalent ◽

Hydrological Cycle ◽

Cold Climate ◽

Road Maintenance ◽

Catchment Scale ◽

Small Surface ◽

Climate Conditions ◽

Snow Water ◽

High Level ◽

Mass Loads

Despite the crucial role of snow in the hydrological cycle in cold climate conditions, monitoring studies of urban snow quality often lack discussions about the relevance of snow in the catchment-scale runoff management. In this study, measurements of snow quality were conducted at two residential catchments in Espoo, Finland, simultaneously with continuous runoff measurements. The results of the snow quality were used to produce catchment-scale estimates of areal snow mass loads (SML). Based on the results, urbanization reduced areal snow water equivalent but increased pollutant accumulation in snow: SMLs in a medium-density residential catchment were two- to four-fold higher in comparison with a low-density residential catchment. The main sources of pollutants were related to vehicular traffic and road maintenance, but also pet excrement increased concentrations to a high level. Ploughed snow can contain 50% of the areal pollutant mass stored in snow despite its small surface area within a catchment.

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Development of dynamic system response curve method for estimating initial conditions of conceptual hydrological models

Journal of Hydroinformatics ◽

10.2166/hydro.2018.022 ◽

2018 ◽

Vol 20 (6) ◽

pp. 1387-1400

Author(s):

Yiqun Sun ◽

Weimin Bao ◽

Peng Jiang ◽

Xuying Wang ◽

Chengmin He ◽

...

Keyword(s):

Dynamic System ◽

Response Curve ◽

Initial Conditions ◽

Model Performance ◽

System Response ◽

Hydrological Models ◽

Flood Prediction ◽

Ill Posed ◽

Value Decomposition

Abstract The dynamic system response curve (DSRC) has its origin in correcting model variables of hydrologic models to improve the accuracy of flood prediction. The DSRC method can lead to unstable performance since the least squares (LS) method, employed by DSRC to estimate the errors, often breaks down for ill-posed problems. A previous study has shown that under certain assumptions the DSRC method can be regarded as a specific form of the numerical solution of the Fredholm equation of the first kind, which is a typical ill-posed problem. This paper introduces the truncated singular value decomposition (TSVD) to propose an improved version of the DSRC method (TSVD-DSRC). The proposed method is extended to correct the initial conditions of a conceptual hydrological model. The usefulness of the proposed method is first demonstrated via a synthetic case study where both the perturbed initial conditions, the true initial conditions, and the corrected initial conditions are precisely known. Then the proposed method is used in two real basins. The results measured by two different criteria clearly demonstrate that correcting the initial conditions of hydrological models has significantly improved the model performance. Similar good results are obtained for the real case study.

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