Intercomparison studies play an important, but limited role in understanding the usefulness and limitations of currently available hydrological models. Comparison studies are often limited to well-behaved hydrological regimes, where rainfall-runoff processes dominate the hydrological response. These efforts have not covered western Canada due to the difficulty in simulating that region’s complex cold region hydrology with varying spatiotemporal contributing areas. This intercomparison study is the first of a series of studies under the intercomparison project of the international and interprovincial transboundary Nelson-Churchill River Basin (NCRB) in North America (Nelson-MIP), which encompasses different ecozones with major areas of the non-contributing Prairie potholes, forests, glaciers, mountains, and permafrost. The performance of eight hydrological and land surface models is compared at different unregulated watersheds within the NCRB. This is done to assess the models’ streamflow performance and overall fidelity without and with calibration, to capture the underlying physics of the region and to better understand why models struggle to accurately simulate its hydrology. Results show that some of the participating models have difficulties in simulating streamflow and/or internal hydrological variables (e.g., evapotranspiration) over Prairie watersheds but most models performed well elsewhere. This stems from model structural deficiencies, despite the various models being well calibrated to observed streamflow. Some model structural changes are identified for the participating models for future improvement. The outcomes of this study offer guidance for practitioners for the accurate prediction of NCRB streamflow, and for increasing confidence in future projections of water resources supply and management.

Model calibration and validation are critical in hydrological model robustness assessment. Unfortunately, the commonly used split‐sample test (SST) framework for data splitting requires modelers to make subjective decisions without clear guidelines. This large‐sample SST assessment study empirically assesses how different data splitting methods influence post‐validation model testing period performance, thereby identifying optimal data splitting methods under different conditions. This study investigates the performance of two lumped conceptual hydrological models calibrated and tested in 463 catchments across the United States using 50 different data splitting schemes. These schemes are established regarding the data availability, length and data recentness of continuous calibration sub‐periods (CSPs). A full‐period CSP is also included in the experiment, which skips model validation. The assessment approach is novel in multiple ways including how model building decisions are framed as a decision tree problem and viewing the model building process as a formal testing period classification problem, aiming to accurately predict model success/failure in the testing period. Results span different climate and catchment conditions across a 35‐year period with available data, making conclusions quite generalizable. Calibrating to older data and then validating models on newer data produces inferior model testing period performance in every single analysis conducted and should be avoided. Calibrating to the full available data and skipping model validation entirely is the most robust split‐sample decision. Experimental findings remain consistent no matter how model building factors (i.e., catchments, model types, data availability, and testing periods) are varied. Results strongly support revising the traditional split‐sample approach in hydrological modeling.

Abstract. Model intercomparison studies are carried out to test and compare the simulated outputs of various model setups over the same study domain. The Great Lakes region is such a domain of high public interest as it not only resembles a challenging region to model with its trans-boundary location, strong lake effects, and regions of strong human impact but is also one of the most densely populated areas in the United States and Canada. This study brought together a wide range of researchers setting up their models of choice in a highly standardized experimental setup using the same geophysical datasets, forcings, common routing product, and locations of performance evaluation across the 1 million square kilometer study domain. The study comprises 13 models covering a wide range of model types from Machine Learning based, basin-wise, subbasin-based, and gridded models that are either locally or globally calibrated or calibrated for one of each of six predefined regions of the watershed. Unlike most hydrologically focused model intercomparisons, this study not only compares models regarding their capability to simulated streamflow (Q) but also evaluates the quality of simulated actual evapotranspiration (AET), surface soil moisture (SSM), and snow water equivalent (SWE). The latter three outputs are compared against gridded reference datasets. The comparisons are performed in two ways: either by aggregating model outputs and the reference to basin-level or by regridding all model outputs to the reference grid and comparing the model simulations at each grid-cell. The main results of this study are: (1) The comparison of models regarding streamflow reveals the superior quality of the Machine Learning based model in all experiments performance; even for the most challenging spatio-temporal validation the ML model outperforms any other physically based model. (2) While the locally calibrated models lead to good performance in calibration and temporal validation (even outperforming several regionally calibrated models), they lose performance when they are transferred to locations the model has not been calibrated on. This is likely to be improved with more advanced strategies to transfer these models in space. (3) The regionally calibrated models – while losing less performance in spatial and spatio-temporal validation than locally calibrated models – exhibit low performances in highly regulated and urban areas as well as agricultural regions in the US. (4) Comparisons of additional model outputs (AET, SSM, SWE) against gridded reference datasets show that aggregating model outputs and the reference dataset to basin scale can lead to different conclusions than a comparison at the native grid scale. This is especially true for variables with large spatial variability such as SWE. (5) A multi-objective-based analysis of the model performances across all variables (Q, AET, SSM, SWE) reveals overall excellent performing locally calibrated models (i.e., HYMOD2-lumped) as well as regionally calibrated models (i.e., MESH-SVS-Raven and GEM-Hydro-Watroute) due to varying reasons. The Machine Learning based model was not included here as is not setup to simulate AET, SSM, and SWE. (6) All basin-aggregated model outputs and observations for the model variables evaluated in this study are available on an interactive website that enables users to visualize results and download data and model outputs.

A simple algorithm is provided for randomly sampling a set of N +1 weights such that their sum is constrained to be equal to one, analogous to randomly subdividing a pie into N +1 slices where the probability distribution of slice volumes are identically distributed. The cumulative density and probability density functions of the random weights are provided. The algorithmic implementation for the random number sampling are made available. This algorithm has potential applications in calibration, uncertainty analysis, and sensitivity analysis of environmental models. Three example applications are provided to demonstrate the efficiency and superiority of the proposed method compared to alternative sampling methods. • Present unbiased method to sample weights that sum up to 1. • Examples demonstrating the benefit of unbiased sampling. • Code made available in multiple languages.

Abstract. Model intercomparison studies are carried out to test and compare the simulated outputs of various model setups over the same study domain. The Great Lakes region is such a domain of high public interest as it not only resembles a challenging region to model with its transboundary location, strong lake effects, and regions of strong human impact but is also one of the most densely populated areas in the USA and Canada. This study brought together a wide range of researchers setting up their models of choice in a highly standardized experimental setup using the same geophysical datasets, forcings, common routing product, and locations of performance evaluation across the 1×106 km2 study domain. The study comprises 13 models covering a wide range of model types from machine-learning-based, basin-wise, subbasin-based, and gridded models that are either locally or globally calibrated or calibrated for one of each of the six predefined regions of the watershed. Unlike most hydrologically focused model intercomparisons, this study not only compares models regarding their capability to simulate streamflow (Q) but also evaluates the quality of simulated actual evapotranspiration (AET), surface soil moisture (SSM), and snow water equivalent (SWE). The latter three outputs are compared against gridded reference datasets. The comparisons are performed in two ways – either by aggregating model outputs and the reference to basin level or by regridding all model outputs to the reference grid and comparing the model simulations at each grid-cell. The main results of this study are as follows: The comparison of models regarding streamflow reveals the superior quality of the machine-learning-based model in the performance of all experiments; even for the most challenging spatiotemporal validation, the machine learning (ML) model outperforms any other physically based model. While the locally calibrated models lead to good performance in calibration and temporal validation (even outperforming several regionally calibrated models), they lose performance when they are transferred to locations that the model has not been calibrated on. This is likely to be improved with more advanced strategies to transfer these models in space. The regionally calibrated models – while losing less performance in spatial and spatiotemporal validation than locally calibrated models – exhibit low performances in highly regulated and urban areas and agricultural regions in the USA. Comparisons of additional model outputs (AET, SSM, and SWE) against gridded reference datasets show that aggregating model outputs and the reference dataset to the basin scale can lead to different conclusions than a comparison at the native grid scale. The latter is deemed preferable, especially for variables with large spatial variability such as SWE. A multi-objective-based analysis of the model performances across all variables (Q, AET, SSM, and SWE) reveals overall well-performing locally calibrated models (i.e., HYMOD2-lumped) and regionally calibrated models (i.e., MESH-SVS-Raven and GEM-Hydro-Watroute) due to varying reasons. The machine-learning-based model was not included here as it is not set up to simulate AET, SSM, and SWE. All basin-aggregated model outputs and observations for the model variables evaluated in this study are available on an interactive website that enables users to visualize results and download the data and model outputs.

As continental to global scale high-resolution meteorological datasets continue to be developed, there are sufficient meteorological datasets available now for modellers to construct a historical forcing ensemble. The forcing ensemble can be a collection of multiple deterministic meteorological datasets or come from an ensemble meteorological dataset. In hydrological model calibration, the forcing ensemble can be used to represent forcing data uncertainty. This study examines the potential of using the forcing ensemble to identify more robust parameters through model calibration. Specifically, we compare an ensemble forcing-based calibration with two deterministic forcing-based calibrations and investigate their flow simulation and parameter estimation properties and the ability to resist poor-quality forcings. The comparison experiment is conducted with a six-parameter hydrological model for 30 synthetic studies and 20 real data studies to provide a better assessment of the average performance of the deterministic and ensemble forcing-based calibrations. Results show that the ensemble forcing-based calibration generates parameter estimates that are less biased and have higher frequency of covering the true parameter values than the deterministic forcing-based calibration does. Using a forcing ensemble in model calibration reduces the risk of inaccurate flow simulation caused by poor-quality meteorological inputs, and improves the reliability and overall simulation skill of ensemble simulation results. The poor-quality meteorological inputs can be effectively filtered out via our ensemble forcing-based calibration methodology and thus discarded in any post-calibration model applications. The proposed ensemble forcing-based calibration method can be considered as a more generalized framework to include parameter and forcing uncertainties in model calibration.

AbstractHydrologic model intercomparison studies help to evaluate the agility of models to simulate variables such as streamflow, evaporation, and soil moisture. This study is the third in a sequen...

Lakes and reservoirs have critical impacts on hydrological, biogeochemical, and ecological processes, and they should be an essential component of regional-scale hydrological and eco-hydrological m...

Environmental models are used extensively to evaluate the effectiveness of a range of design, planning, operational, management and policy options. However, the number of options that can be evaluated manually is generally limited, making it difficult to identify the most suitable options to consider in decision-making processes. By linking environmental models with evolutionary and other metaheuristic optimization algorithms, the decision options that make best use of scarce resources, achieve the best environmental outcomes for a given budget or provide the best trade-offs between competing objectives can be identified. This Introductory Overview presents reasons for embedding formal optimization approaches in environmental decision-making processes, details how environmental problems are formulated as optimization problems and outlines how single- and multi-objective optimization approaches find good solutions to environmental problems. Practical guidance and potential challenges are also provided.