Validation and Intercomparison of Different Updating Procedures for Real-Time Forecasting

This work addresses the role of accurate input data in hydrological model simulations and explores the often-overlooked errors associated with evapotranspiration (ET). While existing literature primarily focuses on uncertainties in rainfall, this study underscores the necessity of considering errors in ET, as evidenced by some studies suggesting their substantial impact on hydrological model responses. A comprehensive exploration of uncertainty quantification resulting from errors in ET in hydrological model simulations is presented, highlighting the imperative to scrutinize this facet amidst diverse uncertainties. There are two approaches for addressing uncertainty in potential evapotranspiration (PET) inputs as discussed: directly considering uncertainty in PET data series or accounting for uncertainty in the parameters used for PET estimation. Furthermore, details are provided about the existing error models for PET measurements, revealing a limited number of studies that specifically account for ET-related uncertainties. Researchers commonly address ET errors by considering both systematic and random errors; some studies suggest that systematic errors in PET have a more substantial impact compared to random errors on hydrological model responses. In summary, the objective of this paper is to offer an in-depth exploration of uncertainty associated with PET inputs and their influence on hydrological modeling.

Various methods are available for assessing uncertainties in climate impact studies. Among such methods, model weighting by expert elicitation is a practical way to provide a weighted ensemble of models for specific real-world impacts. The aim is to decrease the influence of improbable models in the results and easing the decision-making process. In this study both climate and hydrological models are analyzed and the result of a research experiment is presented using model weighting with the participation of 6 climate model experts and 6 hydrological model experts. For the experiment, seven climate models are a-priori selected from a larger Euro-CORDEX ensemble of climate models and three different hydrological models are chosen for each of the three European river basins. The model weighting is based on qualitative evaluation by the experts for each of the selected models based on a training material that describes the overall model structure and literature about climate models and the performance of hydrological models for the present period. The expert elicitation process follows a three-stage approach, with two individual elicitations of probabilities and a final group consensus, where the experts are separated into two different community groups: a climate and a hydrological modeller group. The dialogue reveals that under the conditions of the study, most climate modellers prefer the equal weighting of ensemble members, whereas hydrological impact modellers in general are more open for assigning weights to different models in a multi model ensemble, based on model performance and model structure. Climate experts are more open to exclude models, if obviously flawed, than to put weights on selected models in a relatively small ensemble. The study shows that expert elicitation can be an efficient way to assign weights to different hydrological models, and thereby reduce the uncertainty in climate impact. However, for the climate model ensemble, comprising seven models, the elicitation in the format of this study could only reestablish a uniform weight between climate models.

The resolution of digital elevation model (DEM) used for watershed delineation and data extracted from the delineated watershed relevant to hydrography plays an important role in the calibration of hydrological models. The effect of DEM resolution on the drainage length, watershed area and average slope are studied in many earlier works. DEM resolution can have impact on hydrologic modeling parameters, water quality simulation results. The impacts of DEM resolution on results of the watershed response and water quality modeling are studied using HSPF (Hydrologic Simulation Program Fortran) model applied to a watershed in Kentucky in this study. Three major streams in this watershed are impaired due to pathogen pollution according to the current state of Kentucky water quality standard set for recreation. The effects of DEM resolution on water quality modeling and calibration for fecal coliform bacteria are studied using a HSPF model. Results from this study indicate that the DEM resolution affects hydrography parameters of the delineated watershed, influence the calibration process, and the non-point and point source loadings to the streams in the watershed.

GIS based generation of dynamic hydrological and land patch simulation models for rural watershed areas

This study presents a method to improve the reconstruction of historical flows on gauged and ungauged basins. To do so, a multi-model weighted averaging of hydrological model simulations for a large spatial domain in the province of Quebec, Canada, is used. The distributed hydrological model HYDROTEL was implemented over the region and covered 95 gauged basins. An optimal interpolation (OI) assimilation method was first implemented as a baseline to improve the HYDROTEL flow simulations over the 95 basins. Then, a series of multi-model averaging techniques were applied to an ensemble of 144 HYDROTEL simulations that were generated by modifying parameter sets, driving weather datasets and evapotranspiration modules. The averaging methods were applied in a leave-one-out cross-validation scheme, where all 94 gauged basins were pooled together to compute the weights, and those weights were applied to the 95th basin. All basins were evaluated in such a manner and compared to the OI method. Implementing a year-by-year (or shorter period) weighting scheme instead of computing weights over all available data significantly improved the results. This allowed the weights to better reflect each year’s hydrological characteristics rather than compromising to improve the overall average. The Kling-Gupta Efficiency (KGE) and peak flow metrics showed that the Granger-Ramanathan variant “A” (GRA) was similar in performance to the OI method but did not have the drawbacks that OI can typically introduce. The multi-model application can also be further improved by adding more simulations from other hydrological models, whereas the OI method cannot make use of such additional information, thus hitting a performance plateau. This study shows that it is possible to improve regional hydrological model simulations, both for overall flows and peak flows, and on the historical period for both gauged and ungauged basins. This can then be used to better estimate risk in flood frequency analysis and other statistical analyses.

Flow prediction in ungauged catchments is a major unresolved challenge in scientific and engineering hydrology. This study attacks the prediction in ungauged catchment problem by exploiting advances in flow index selection and regionalization in Bayesian inference and by developing new statistical tests of model performance in ungauged catchments. First, an extensive set of available flow indices is reduced using principal component (PC) analysis to a compact orthogonal set of “flow index PCs.” These flow index PCs are regionalized under minimal assumptions using random forests regression augmented with a residual error model and used to condition hydrological model parameters using a Bayesian scheme. Second, “adequacy” tests are proposed to evaluate a priori the hydrological and regionalization model performance in the space of flow index PCs. The proposed regionalization approach is applied to 92 northern Spain catchments, with 16 catchments treated as ungauged. It is shown that (1) a small number of PCs capture approximately 87% of variability in the flow indices and (2) adequacy tests with respect to regionalized information are indicative of (but do not guarantee) the ability of a hydrological model to predict flow time series and are hence proposed as a prerequisite for flow prediction in ungauged catchments. The adequacy tests identify the regionalization of flow index PCs as adequate in 12 of 16 catchments but the hydrological model as adequate in only 1 of 16 catchments. Hence, a focus on improving hydrological model structure and input data (the effects of which are not disaggregated in this work) is recommended.

How rapidly advancing climate change will impact the water cycle and its extremes remains poorly understood and is the origin of much uncertainty. This uncertainty limits our ability to build societal and ecosystem resilience – contributing to policy challenges for adaptation to water scarcity and other hydro-climatic risks. In this context, we rely on hydrologic simulation models to provide robust short-term predictions as well as long-term projections of water cycle dynamics across scales. Even though advancements in observational systems and increasingly detailed simulation models enable us to observe and simulate the water cycle at unprecedent resolutions over large domains, intercomparison studies still reveal inconsistent emergent model behavior. These large domain models are difficult to constrain using current observational datasets given that these are often highly imbalanced, while available theory provides only limited guidance regarding which hydrologic processes we can expect to dominate in diverse climates and landscapes. At the same time, the performance of machine learning models improves rapidly, bypassing process knowledge and therefore questioning the basic need for scientific understanding.In this talk, I argue that process-based evaluation is an important bridge between hydrologic theory, observations and simulation models – even in the presence of high model complexity and significant data imbalances. I will discuss examples of how process-based evaluation can elicit controlling factors on hydrologic processes by utilizing hydrologically relevant gradients in both simulated and observed data. Thus, demonstrating how this approach can provide a pathway towards assessing and ultimately improving the consistency between perceived and simulated hydrologic process controls over large domains.

This paper presents a new flood modelling tool developed by coupling a full 2D hydrodynamic model with hydrological models. The coupled model overcomes the main limitations of the individual modelling approaches, i.e. high computational costs associated with the hydrodynamic models and less detailed representation of the underlying physical processes related to the hydrological models. When conducting a simulation using the coupled model, the computational domain (e.g. a catchment) is first divided into hydraulic and hydrological zones. In the hydrological zones that have high ground elevations and relatively homogeneous land cover or topographic features, a conceptual lumped model is applied to obtain runoff/net rainfall, which is then routed by a group of pre-acquired ‘unit hydrographs’ to the zone borders. These translated hydrographs will then be used to drive the full 2D hydrodynamic model to predict flood dynamics at high resolution in the hydraulic zones that are featured with complex topographic settings, including roads, buildings, etc. The new coupled flood model is applied to reproduce a major flood event that occurred in Morpeth, northeast England in September 2008. While producing similar results, the new coupled model is shown to be computationally much more efficient than the full hydrodynamic model.

Hydrological models are an effective tool for the estimation of peak floods and runoff in planning water development and flood mitigation/adaptation. Continuous hydrologic modeling can determine the relationships between hydrologic processes and environmental changes over long time periods. Therefore, the selection of a theoretically robust and functionally reliable hydrological model is crucial for the effective management of flood risk within a basin. The Soil and Water Assessment Tool (SWAT) is one of the most widely used hydrological models for assessing the impacts of climate change on discharge in large basins. In contrast, the Hydrologic Engineering Centers’ Hydrologic Modeling System (HEC-HMS) has been one of the most rapidly evolving and promising hydrological models, with its latest version (v4.9) supporting both fully distributed and semidistributed hydrological modeling approaches. This study was conducted to compare the performances of SWAT and HEC-HMS in long-term continuous simulations in the Pearl River Basin, which is the second largest river basin in terms of discharge in China. For purposes of comparison, both models employed identical input data and comparable configurations. The results demonstrate that both models are capable of predicting river discharge at designated station satisfactories, with the Nash-Sutciffe coefficient exceeding 0.7. Benefiting from its elaborate use of the modified Soil Conservation Service (SCS) loss model and more advanced automatic calibration program, SWAT obtained more accurate results than HEC-HMS in the validation period. However, HEC-HMS is distinguished by its customizable options for constructing hydrological models, and it exhibits considerable potential for application in large-scale river basins such as the Pearl River Basin, enabling long-term, continuous hydrological simulations.

Providing online access to hydrological model simulations through interactive geospatial animations

The assessment of climate change impacts on water resources involves several methodological decisions, including choices of global climate models (GCMs), emission scenarios, downscaling techniques, and hydrologic modeling approaches. Among these, hydrologic model structure selection and parameter calibration are particularly relevant and usually have a strong subjective component. The goal of this research is to improve understanding of the role of these decisions on the assessment of the effects of climate change on hydrologic processes. The study is conducted in three basins located in the Colorado headwaters region, using four different hydrologic model structures [PRMS, VIC, Noah LSM, and Noah LSM with multiparameterization options (Noah-MP)]. To better understand the role of parameter estimation, model performance and projected hydrologic changes (i.e., changes in the hydrology obtained from hydrologic models due to climate change) are compared before and after calibration with the University of Arizona shuffled complex evolution (SCE-UA) algorithm. Hydrologic changes are examined via a climate change scenario where the Community Climate System Model (CCSM) change signal is used to perturb the boundary conditions of the Weather Research and Forecasting (WRF) Model configured at 4-km resolution. Substantial intermodel differences (i.e., discrepancies between hydrologic models) in the portrayal of climate change impacts on water resources are demonstrated. Specifically, intermodel differences are larger than the mean signal from the CCSM–WRF climate scenario examined, even after the calibration process. Importantly, traditional single-objective calibration techniques aimed to reduce errors in runoff simulations do not necessarily improve intermodel agreement (i.e., same outputs from different hydrologic models) in projected changes of some hydrological processes such as evapotranspiration or snowpack.

Parameter uncertainty in hydrologic modeling is commonly evaluated, but assessing the impact of spatial input data uncertainty in spatially descriptive ‘distributed’ models is not common. This study compares the significance of uncertainty in spatial input data and model parameters on the output uncertainty of a distributed hydrology and sediment transport model, HYdrology Simulation using Time-ARea method (HYSTAR). The Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm was used to quantify parameter uncertainty of the model. Errors in elevation and land cover layers were simulated using the Sequential Gaussian/Indicator Simulation (SGS/SIS) techniques and then incorporated into the model to evaluate their impact on the outputs relative to those of the parameter uncertainty. This study demonstrated that parameter uncertainty had a greater impact on model output than did errors in the spatial input data. In addition, errors in elevation data had a greater impact on model output than did errors in land cover data. Thus, for the HYSTAR distributed hydrologic model, accuracy and reliability can be improved more effectively by refining parameters rather than further improving the accuracy of spatial input data and by emphasizing the topographic data over the land cover data.

This paper presents the results of an investigation into the problems associated with using downscaled meteorological data for hydrological simulations of climate scenarios. The influence of both the hydrological models and the meteorological inputs driving these models on climate scenario simulation studies are investigated. A regression‐based statistical tool (SDSM) is used to downscale the daily precipitation and temperature data based on climate predictors derived from the Canadian global climate model (CGCM1), and two types of hydrological model, namely the physically based watershed model WatFlood and the lumped‐conceptual modelling system HBV‐96, are used to simulate the flow regimes in the major rivers of the Saguenay watershed in Quebec. The models are validated with meteorological inputs from both the historical records and the statistically downscaled outputs. Although the two hydrological models demonstrated satisfactory performances in simulating stream flows in most of the rivers when provided with historic precipitation and temperature records, both performed less well and responded differently when provided with downscaled precipitation and temperature data. By demonstrating the problems in accurately simulating river flows based on downscaled data for the current climate, we discuss the difficulties associated with downscaling and hydrological models used in estimating the possible hydrological impact of climate change scenarios. Copyright © 2007 John Wiley & Sons, Ltd.

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High‐resolution atmospheric forecasting systems are characterized by an increasing complexity which renders validation and interpretation of the model results more and more demanding. This is especially evident in complex terrain. In such areas, the limited density of meteorological observing stations does not allow an accurate evaluation of high‐resolution weather forecasts. Accordingly, advanced tools for the validation of atmospheric models are necessary and of great interest to forecasters. In this paper we address this issue, and present the possibilities and advantages offered by coupling atmospheric and hydrological models. A distributed hydrological model has been used to estimate the forecast quality of two different weather prediction models at different resolutions for the mesoscale Ticino–Verzasca–Maggia watershed, a high catchment located in the southern European Alps draining from the north into the Lago Maggiore. For this area, several hydrological forecast‐driven model experiments have been carried out as one‐way nested simulations using hourly time steps and 0.5 × 0.5 km grid resolution. The availability of meteorological forecast sequences allowed continuous atmospheric– hydrological simulations covering non‐overlapping periods of 3.5 years and 2.5 months. The model results have been analysed using multiple comparisons including rainfall and runoff measurements, as well as observation‐driven model results of river runoff and water balances at the catchment‐scale. It is shown that the hydrological model is sufficiently sensitive to provide substantial information for the validation of atmospheric models. Copyright © 2003 Royal Meteorological Society.