Abstract. Human-controlled reservoirs have a large influence on the global water cycle. While global hydrological models use generic parametrisations to model human dam operations, the representation of reservoir regulation is often still lacking in Earth System Models. Here we implement and evaluate a widely used reservoir parametrisation in the global river routing model mizuRoute, which operates on a vector-based river network resolving individual lakes and reservoirs, and which is currently being coupled to an Earth System Model. We develop an approach to determine the downstream area over which to aggregate irrigation water demand per reservoir. The implementation of managed reservoirs is evaluated by comparing to simulations ignoring inland waters, and simulations with reservoirs represented as natural lakes, using (i) local simulations for 26 individual reservoirs driven by observed inflows, and (ii) global-scale simulations driven by runoff from the Community Land Model. The local simulations show a clear added value of the reservoir parametrisation, especially for simulating storage for large reservoirs with a multi-year storage capacity. In the global-scale application, the implementation of reservoirs shows an improvement in outflow and storage compared to the no-reservoir simulation, but compared to the natural lake parametrisation, an overall similar performance is found. This lack of impact could be attributed to biases in simulated river discharge, mainly originating from biases in simulated runoff from the Community Land Model. Finally, the comparison of modelled monthly streamflow indices against observations highlights that the inclusion of dam operations improves the streamflow simulation compared to ignoring lakes and reservoirs. This study overall underlines the need to further develop and test water management parametrisations, as well as to improve runoff simulations for advancing the representation of anthropogenic interference with the terrestrial water cycle in Earth System Models.

Despite the proliferation of computer-based research on hydrology and water resources, such research is typically poorly reproducible. Published studies have low reproducibility due to incomplete availability of data and computer code, and a lack of documentation of workflow processes. This leads to a lack of transparency and efficiency because existing code can neither be quality controlled nor reused. Given the commonalities between existing process-based hydrologic models in terms of their required input data and preprocessing steps, open sharing of code can lead to large efficiency gains for the modeling community. Here, we present a model configuration workflow that provides full reproducibility of the resulting model instantiations in a way that separates the model-agnostic preprocessing of specific data sets from the model-specific requirements that models impose on their input files. We use this workflow to create large-domain (global and continental) and local configurations of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) hydrologic model connected to the mizuRoute routing model. These examples show how a relatively complex model setup over a large domain can be organized in a reproducible and structured way that has the potential to accelerate advances in hydrologic modeling for the community as a whole. We provide a tentative blueprint of how community modeling initiatives can be built on top of workflows such as this. We term our workflow the “Community Workflows to Advance Reproducibility in Hydrologic Modeling” (CWARHM; pronounced “swarm”).

Abstract. Human-controlled reservoirs have a large influence on the global water cycle. While global hydrological models use generic parameterizations to model dam operations, the representation of reservoir regulation is still lacking in many Earth system models. Here we implement and evaluate a widely used reservoir parametrization in the global river-routing model mizuRoute, which operates on a vector-based river network resolving individual lakes and reservoirs and is currently being coupled to an Earth system model. We develop an approach to determine the downstream area over which to aggregate irrigation water demand per reservoir. The implementation of managed reservoirs is evaluated by comparing them to simulations ignoring inland waters and simulations with reservoirs represented as natural lakes using (i) local simulations for 26 individual reservoirs driven by observed inflows and (ii) global-domain simulations driven by runoff from the Community Land Model. The local simulations show the clear added value of the reservoir parametrization, especially for simulating storage for large reservoirs with a multi-year storage capacity. In the global-domain application, the implementation of reservoirs shows an improvement in outflow and storage compared to the no-reservoir simulation, but a similar performance is found compared to the natural lake parametrization. The limited impact of reservoirs on skill statistics could be attributed to biases in simulated river discharge, mainly originating from biases in simulated runoff from the Community Land Model. Finally, the comparison of modelled monthly streamflow indices against observations highlights that including dam operations improves the streamflow simulation compared to ignoring lakes and reservoirs. This study overall underlines the need to further develop and test runoff simulations and water management parameterizations in order to improve the representation of anthropogenic interference of the terrestrial water cycle in Earth system models.

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...

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...

Abstract Obtaining reliable water balance estimates remains a major challenge in Canada for large regions with scarce in situ measurements. Various remote sensing products can be used to complement observation-based datasets and provide an estimate of the water balance at river basin or regional scales. This study provides an assessment of the water balance using combinations of various remote sensing and data assimilation-based products and quantifies the non-closure errors for river basins across Canada, ranging from 90,900 to 1,679,100 km 2 , for the period from 2002 to 2015. A water balance equation combines the following to estimate the monthly water balance closure: multiple sources of data for each water budget component, including two precipitation products - the global product WATCH Forcing Data ERA-Interim (WFDEI), and the Canadian Precipitation Analysis (CaPA); two evapotranspiration products - MODIS, and Global Land-surface Evaporation: the Amsterdam Methodology (GLEAM); one source of water storage data - GRACE from three different centers; and observed discharge data from hydrometric stations (HYDAT). The non-closure error is attributed to the different data products using a constrained Kalman filter. Results show that the combination of CaPA, GLEAM, and the JPL mascon GRACE product tended to outperform other combinations across Canadian river basins. Overall, the error attributions of precipitation, evapotranspiration, water storage change, and runoff were 36.7, 33.2, 17.8, and 12.2 percent, which corresponded to 8.1, 7.9, 4.2, and 1.4 mm month -1 , respectively. In particular, non-closure error from precipitation dominated in Western Canada, whereas that from evapotranspiration contributed most in the Mackenzie River basin.

Abstract Obtaining reliable water balance estimates remains a major challenge in Canada for large regions with scarce in situ measurements. Various remote sensing products can be used to complement observation-based datasets and provide an estimate of the water balance at river basin or regional scales. This study provides an assessment of the water balance using combinations of various remote sensing and data assimilation-based products and quantifies the non-closure errors for river basins across Canada, ranging from 90,900 to 1,679,100 km 2 , for the period from 2002 to 2015. A water balance equation combines the following to estimate the monthly water balance closure: multiple sources of data for each water budget component, including two precipitation products - the global product WATCH Forcing Data ERA-Interim (WFDEI), and the Canadian Precipitation Analysis (CaPA); two evapotranspiration products - MODIS, and Global Land-surface Evaporation: the Amsterdam Methodology (GLEAM); one source of water storage data - GRACE from three different centers; and observed discharge data from hydrometric stations (HYDAT). The non-closure error is attributed to the different data products using a constrained Kalman filter. Results show that the combination of CaPA, GLEAM, and the JPL mascon GRACE product tended to outperform other combinations across Canadian river basins. Overall, the error attributions of precipitation, evapotranspiration, water storage change, and runoff were 36.7, 33.2, 17.8, and 12.2 percent, which corresponded to 8.1, 7.9, 4.2, and 1.4 mm month -1 , respectively. In particular, non-closure error from precipitation dominated in Western Canada, whereas that from evapotranspiration contributed most in the Mackenzie River basin.

The goal of this commentary is to critically evaluate the use of popular performance metrics in hydrologic modeling. We focus on the Nash-Sutcliffe Efficiency (NSE) and the Kling-Gupta Efficiency (KGE) metrics, which are both widely used in hydrologic research and practice around the world. Our specific objectives are: (a) to provide tools that quantify the sampling uncertainty in popular performance metrics; (b) to quantify sampling uncertainty in popular performance metrics across a large sample of catchments; and (c) to prescribe the further research that is, needed to improve the estimation, interpretation, and use of popular performance metrics in hydrologic modeling. Our large-sample analysis demonstrates that there is substantial sampling uncertainty in the NSE and KGE estimators. This occurs because the probability distribution of squared errors between model simulations and observations has heavy tails, meaning that performance metrics can be heavily influenced by just a few data points. Our results highlight obvious (yet ignored) abuses of performance metrics that contaminate the conclusions of many hydrologic modeling studies: It is essential to quantify the sampling uncertainty in performance metrics when justifying the use of a model for a specific purpose and when comparing the performance of competing models.

The goal of this commentary is to critically evaluate the use of popular performance metrics in hydrologic modeling. We focus on the Nash-Sutcliffe Efficiency (NSE) and the Kling-Gupta Efficiency (KGE) metrics, which are both widely used in hydrologic research and practice around the world. Our specific objectives are: (a) to provide tools that quantify the sampling uncertainty in popular performance metrics; (b) to quantify sampling uncertainty in popular performance metrics across a large sample of catchments; and (c) to prescribe the further research that is, needed to improve the estimation, interpretation, and use of popular performance metrics in hydrologic modeling. Our large-sample analysis demonstrates that there is substantial sampling uncertainty in the NSE and KGE estimators. This occurs because the probability distribution of squared errors between model simulations and observations has heavy tails, meaning that performance metrics can be heavily influenced by just a few data points. Our results highlight obvious (yet ignored) abuses of performance metrics that contaminate the conclusions of many hydrologic modeling studies: It is essential to quantify the sampling uncertainty in performance metrics when justifying the use of a model for a specific purpose and when comparing the performance of competing models.

A vector‐river network explicitly uses realistic geometries of river reaches and catchments for spatial discretization in a river model. This enables improving the accuracy of the physical properties of the modeled river system, compared to a gridded river network that has been used in Earth System Models. With a finer‐scale river network, resolving smaller‐scale river reaches, there is a need for efficient methods to route streamflow and its constituents throughout the river network. The purpose of this study is twofold: (1) develop a new method to decompose river networks into hydrologically independent tributary domains, where routing computations can be performed in parallel; and (2) perform global river routing simulations with two global river networks, with different scales, to examine the computational efficiency and the differences in discharge simulations at various temporal scales. The new parallelization method uses a hierarchical decomposition strategy, where each decomposed tributary is further decomposed into many sub‐tributary domains, enabling hybrid parallel computing. This parallelization scheme has excellent computational scaling for the global domain where it is straightforward to distribute computations across many independent river basins. However, parallel computing for a single large basin remains challenging. The global routing experiments show that the scale of the vector‐river network has less impact on the discharge simulations than the runoff input that is generated by the combination of land surface model and meteorological forcing. The scale of vector‐river networks needs to consider the scale of local hydrologic features such as lakes that are to be resolved in the network.

A vector‐river network explicitly uses realistic geometries of river reaches and catchments for spatial discretization in a river model. This enables improving the accuracy of the physical properties of the modeled river system, compared to a gridded river network that has been used in Earth System Models. With a finer‐scale river network, resolving smaller‐scale river reaches, there is a need for efficient methods to route streamflow and its constituents throughout the river network. The purpose of this study is twofold: (1) develop a new method to decompose river networks into hydrologically independent tributary domains, where routing computations can be performed in parallel; and (2) perform global river routing simulations with two global river networks, with different scales, to examine the computational efficiency and the differences in discharge simulations at various temporal scales. The new parallelization method uses a hierarchical decomposition strategy, where each decomposed tributary is further decomposed into many sub‐tributary domains, enabling hybrid parallel computing. This parallelization scheme has excellent computational scaling for the global domain where it is straightforward to distribute computations across many independent river basins. However, parallel computing for a single large basin remains challenging. The global routing experiments show that the scale of the vector‐river network has less impact on the discharge simulations than the runoff input that is generated by the combination of land surface model and meteorological forcing. The scale of vector‐river networks needs to consider the scale of local hydrologic features such as lakes that are to be resolved in the network.

Global sensitivity analysis (GSA) has long been recognized as an indispensable tool for model analysis. GSA has been extensively used for model simplification, identifiability analysis, and diagnostic tests. Nevertheless, computationally efficient methodologies are needed for GSA, not only to reduce the computational overhead, but also to improve the quality and robustness of the results. This is especially the case for process-based hydrologic models, as their simulation time typically exceeds the computational resources available for a comprehensive GSA. To overcome this computational barrier, we propose a data-driven method called VISCOUS, variance-based sensitivity analysis using copulas. VISCOUS uses Gaussian mixture copulas to approximate the joint probability density function of a given set of input-output pairs for estimating the variance-based sensitivity indices. Our method identifies dominant hydrologic factors by recycling existing input-output data, and thus can deal with arbitrary sample sets drawn from the input-output space. We used two hydrologic models of increasing complexity (HBV and VIC) to assess the performance of VISCOUS. Our results confirm that VISCOUS and the conventional variance-based method can detect similar important and unimportant factors. Furthermore, the VISCOUS method can substantially reduce the computational cost required for sensitivity analysis. Our proposed method is particularly useful for process-based models with many uncertain parameters, large domain size, and high spatial and temporal resolution.

Global sensitivity analysis (GSA) has long been recognized as an indispensable tool for model analysis. GSA has been extensively used for model simplification, identifiability analysis, and diagnostic tests. Nevertheless, computationally efficient methodologies are needed for GSA, not only to reduce the computational overhead, but also to improve the quality and robustness of the results. This is especially the case for process-based hydrologic models, as their simulation time typically exceeds the computational resources available for a comprehensive GSA. To overcome this computational barrier, we propose a data-driven method called VISCOUS, variance-based sensitivity analysis using copulas. VISCOUS uses Gaussian mixture copulas to approximate the joint probability density function of a given set of input-output pairs for estimating the variance-based sensitivity indices. Our method identifies dominant hydrologic factors by recycling existing input-output data, and thus can deal with arbitrary sample sets drawn from the input-output space. We used two hydrologic models of increasing complexity (HBV and VIC) to assess the performance of VISCOUS. Our results confirm that VISCOUS and the conventional variance-based method can detect similar important and unimportant factors. Furthermore, the VISCOUS method can substantially reduce the computational cost required for sensitivity analysis. Our proposed method is particularly useful for process-based models with many uncertain parameters, large domain size, and high spatial and temporal resolution.

Abstract. Land models are increasingly used in terrestrial hydrology due to their process-oriented representation of water and energy fluxes. Land models can be set up at a range of spatial configurations, often ranging from grid sizes of 0.02 to 2 degrees (approximately 2 to 200 km) and applied at sub-daily temporal resolutions for simulation of energy fluxes. A priori specification of the grid size of the land models typically is derived from forcing resolutions, modeling objectives, available geo-spatial data and computational resources. Typically, the choice of model configuration and grid size is based on modeling convenience and is rarely examined for adequate physical representation in the context of modeling. The variability of the inputs and parameters, forcings, soil types, and vegetation covers, are masked or aggregated based on the a priori chosen grid size. In this study, we propose an alternative to directly set up a land model based on the concept of Group Response Unit (GRU). Each GRU is a unique combination of land cover, soil type, and other desired geographical features that has hydrological significance, such as elevation zone, slope, and aspect. Computational units are defined as GRUs that are forced at a specific forcing resolution; therefore, each computational unit has a unique combination of specific geo-spatial data and forcings. We set up the Variable Infiltration Capacity (VIC) model, based on the GRU concept (VIC-GRU). Utilizing this model setup and its advantages we try to answer the following questions: (1) how well a model configuration simulates an output variable, such as streamflow, for range of computational units, (2) how well a model configuration with fewer computational units, coarser forcing resolution and less geo-spatial information, reproduces a model set up with more computational units, finer forcing resolution and more geo-spatial information, and finally (3) how uncertain the model structure and parameters are for the land model. Our results, although case dependent, show that the models may similarly reproduce output with a lower number of computational units in the context of modeling (streamflow for example). Our results also show that a model configuration with a lower number of computational units may reproduce the simulations from a model configuration with more computational units. Similarly, this can assist faster parameter identification and model diagnostic suites, such as sensitivity and uncertainty, on a less computationally expensive model setup. Finally, we encourage the land model community to adopt flexible approaches that will provide a better understanding of accuracy-performance tradeoff in land models.

This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come.

Abstract Land models are increasingly used and preferred in terrestrial hydrological prediction applications. One reason for selecting land models over simpler models is that their physically based backbone enables wider application under different conditions. This study evaluates the temporal variability in streamflow simulations in land models. Specifically, we evaluate how the subsurface structure and model parameters control the partitioning of water into different flow paths and the temporal variability in streamflow. Moreover, we use a suite of model diagnostics, typically not used in the land modeling community to clarify model weaknesses and identify a path toward model improvement. Our analyses show that the typical land model structure, and their functions for moisture movement between soil layers (an approximation of Richards equation), has a distinctive signature where flashy runoff is superimposed on slow recessions. This hampers the application of land models in simulating flashier basins and headwater catchments where floods are generated. We demonstrate the added value of the preferential flow in the model simulation by including macropores in both a toy model and the Variable Infiltration Capacity model. We argue that including preferential flow in land models is essential to enable their use for multiple applications across a myriad of temporal and spatial scales.

Abstract Hysteresis is a widely reported phenomenon in natural and engineered systems across different temporal and spatial scales. Its definition is non-unique and rather context-dependent, while systems with hysteretic behavior, including hydrological systems, are commonly referred to as path-dependent systems or systems with memory. Despite widespread existence of hysteretic processes, the current generation of hydrologic models do not directly account for hysteresis. In this paper, we review the fundamentals, theories, and general properties of hysteresis in the broad scientific literature and then focus on its representations in hydrological sciences. Through illustrative examples, we show how an incomplete understanding or representation of the underlying processes in a system can lead to considering the system as being path-dependent. We argue that, in most cases, hysteresis is a manifestation of our dimensionality-reducing approach to process understanding and representation. We further explain that modelling hysteresis in an ideal world requires a full-dimensional process representation, based on a perfect understanding of the processes, their heterogeneity, and their spatio-temporal scale dependency. We discuss, however, that the missing dimensions/physics in a hydrologic model may be compensated to some extent by enabling the model with formal hysteretic components. Moreover, we show that the conventional model structure and parameterization may be designed in a way to partially reproduce a desired hysteretic behavior.