scholarly journals GMD perspective: The quest to improve the evaluation of groundwater representation in continental- to global-scale models

2021 ◽  
Vol 14 (12) ◽  
pp. 7545-7571
Author(s):  
Tom Gleeson ◽  
Thorsten Wagener ◽  
Petra Döll ◽  
Samuel C. Zipper ◽  
Charles West ◽  
...  
Keyword(s):  
Model Evaluation ◽  
Land Surface ◽  
Large Scale ◽  
Groundwater Resources ◽  
Regional Scale ◽  
Global Scale ◽  
Expert Elicitation ◽  
Data Availability ◽  
Scale Model ◽  
Scale Models

Abstract. Continental- to global-scale hydrologic and land surface models increasingly include representations of the groundwater system. Such large-scale models are essential for examining, communicating, and understanding the dynamic interactions between the Earth system above and below the land surface as well as the opportunities and limits of groundwater resources. We argue that both large-scale and regional-scale groundwater models have utility, strengths, and limitations, so continued modeling at both scales is essential and mutually beneficial. A crucial quest is how to evaluate the realism, capabilities, and performance of large-scale groundwater models given their modeling purpose of addressing large-scale science or sustainability questions as well as limitations in data availability and commensurability. Evaluation should identify if, when, or where large-scale models achieve their purpose or where opportunities for improvements exist so that such models better achieve their purpose. We suggest that reproducing the spatiotemporal details of regional-scale models and matching local data are not relevant goals. Instead, it is important to decide on reasonable model expectations regarding when a large-scale model is performing “well enough” in the context of its specific purpose. The decision of reasonable expectations is necessarily subjective even if the evaluation criteria are quantitative. Our objective is to provide recommendations for improving the evaluation of groundwater representation in continental- to global-scale models. We describe current modeling strategies and evaluation practices, and we subsequently discuss the value of three evaluation strategies: (1) comparing model outputs with available observations of groundwater levels or other state or flux variables (observation-based evaluation), (2) comparing several models with each other with or without reference to actual observations (model-based evaluation), and (3) comparing model behavior with expert expectations of hydrologic behaviors in particular regions or at particular times (expert-based evaluation). Based on evolving practices in model evaluation as well as innovations in observations, machine learning, and expert elicitation, we argue that combining observation-, model-, and expert-based model evaluation approaches, while accounting for commensurability issues, may significantly improve the realism of groundwater representation in large-scale models, thus advancing our ability for quantification, understanding, and prediction of crucial Earth science and sustainability problems. We encourage greater community-level communication and cooperation on this quest, including among global hydrology and land surface modelers, local to regional hydrogeologists, and hydrologists focused on model development and evaluation.

scholarly journals GMD Perspective: the quest to improve the evaluation of groundwater representation in continental to global scale models

2021 ◽  
Author(s):  
Tom Gleeson ◽  
Thorsten Wagener ◽  
Petra Döll ◽  
Samuel C. Zipper ◽  
Charles West ◽  
...  
Keyword(s):  
Model Evaluation ◽  
Land Surface ◽  
Large Scale ◽  
Groundwater Resources ◽  
Regional Scale ◽  
Global Scale ◽  
Expert Elicitation ◽  
Data Availability ◽  
Scale Model ◽  
Scale Models

Abstract. Continental- to global-scale hydrologic and land surface models increasingly include representations of the groundwater system. Such large-scale models are essential for examining, communicating, and understanding the dynamic interactions between the Earth System above and below the land surface as well as the opportunities and limits of groundwater resources. We argue that both large-scale and regional-scale groundwater models have utility, strengths and limitations so continued modeling at both scales is essential and mutually beneficial. A crucial quest is how to evaluate the realism, capabilities and performance of large-scale groundwater models given their modeling purpose of addressing large-scale science or sustainability questions as well as limitations in data availability and commensurability. Evaluation should identify if, when or where large-scale models achieve their purpose or where opportunities for improvements exists so that such models better achieve their purpose. We suggest that reproducing the spatio-temporal details of regional-scale models and matching local data is not a relevant goal. Instead, it is important to decide on reasonable model expectations regarding when a large scale model is performing “well enough” in the context of its specific purpose. The decision of reasonable expectations is necessarily subjective even if the evaluation criteria is quantitative. Our objective is to provide recommendations for improving the evaluation of groundwater representation in continental- to global-scale models. We describe current modeling strategies and evaluation practices, and subsequently discuss the value of three evaluation strategies: 1) comparing model outputs with available observations of groundwater levels or other state or flux variables (observation-based evaluation); 2) comparing several models with each other with or without reference to actual observations (model-based evaluation); and 3) comparing model behavior with expert expectations of hydrologic behaviors in particular regions or at particular times (expert-based evaluation). Based on evolving practices in model evaluation as well as innovations in observations, machine learning and expert elicitation, we argue that combining observation-, model-, and expert-based model evaluation approaches, while accounting for commensurability issues, may significantly improve the realism of groundwater representation in large-scale models. Thus advancing our ability for quantification, understanding, and prediction of crucial Earth science and sustainability problems. We encourage greater community-level communication and cooperation on this quest, including among global hydrology and land surface modelers, local to regional hydrogeologists, and hydrologists focused on model development and evaluation.

scholarly journals HESS Opinions: Improving the evaluation of groundwater representation in continental to global scale models

2020 ◽  
Author(s):  
Tom Gleeson ◽  
Thorsten Wagener ◽  
Petra Döll ◽  
Samuel C. Zipper ◽  
Charles West ◽  
...  
Keyword(s):  
Model Evaluation ◽  
Land Surface ◽  
Large Scale ◽  
Earth Science ◽  
Groundwater Resources ◽  
Global Scale ◽  
Data Availability ◽  
Scale Models ◽  
Sustainability Problems ◽  
Evaluation Approaches

Abstract. Continental- to global-scale hydrologic and land surface models increasingly include representations of the groundwater system, driven by crucial Earth science and sustainability problems. These models are essential for examining, communicating, and understanding the dynamic interactions between the Earth System above and below the land surface as well as the opportunities and limits of groundwater resources. A key question for this nascent and rapidly developing field is how to evaluate the realism and performance of such large-scale groundwater models given limitations in data availability and commensurability. Our objective is to provide clear recommendations for improving the evaluation of groundwater representation in continental- to global-scale models. We identify three evaluation approaches, including comparing model outputs with available observations of groundwater levels or other state or flux variables (observation-based evaluation); comparing several models with each other with or without reference to actual observations (model-based evaluation); and comparing model behavior with expert expectations of hydrologic behaviors that we expect to see in particular regions or at particular times (expert-based evaluation). Based on current and evolving practices in model evaluation as well as innovations in observations, machine learning and expert elicitation, we argue that combining observation-, model-, and expert-based model evaluation approaches may significantly improve the realism of groundwater representation in large-scale models, and thus our quantification, understanding, and prediction of crucial Earth science and sustainability problems. We encourage greater community-level communication and cooperation on these challenges, including among global hydrology and land surface modelers, local to regional hydrogeologists, and hydrologists focused on model development and evaluation.

Global scale hydrological modelling at 100 m, 1 h resolution, in Python

2021 ◽  
Author(s):  
Kor de Jong ◽  
Marc van Kreveld ◽  
Debabrata Panja ◽  
Oliver Schmitz ◽  
Derek Karssenberg
Keyword(s):  
Large Scale ◽  
Model Building ◽  
Flow Direction ◽  
Building Blocks ◽  
Global Scale ◽  
Data Availability ◽  
Hydrological Models ◽  
Continental Scale ◽  
Modelling Framework ◽  
Scale Models

<p>Data availability at global scale is increasing exponentially. Although considerable challenges remain regarding the identification of model structure and parameters of continental scale hydrological models, we will soon reach the situation that global scale models could be defined at very high resolutions close to 100 m or less. One of the key challenges is how to make simulations of these ultra-high resolution models tractable ([1]).</p><p>Our research contributes by the development of a model building framework that is specifically designed to distribute calculations over multiple cluster nodes. This framework enables domain experts like hydrologists to develop their own large scale models, using a scripting language like Python, without the need to acquire the skills to develop low-level computer code for parallel and distributed computing.</p><p>We present the design and implementation of this software framework and illustrate its use with a prototype 100 m, 1 h continental scale hydrological model. Our modelling framework ensures that any model built with it is parallelized. This is made possible by providing the model builder with a set of building blocks of models, which are coded in such a manner that parallelization of calculations occurs within and across these building blocks, for any combination of building blocks. There is thus full flexibility on the side of the modeller, without losing performance.</p><p>This breakthrough is made possible by applying a novel approach to the implementation of the model building framework, called asynchronous many-tasks, provided by the HPX C++ software library ([3]). The code in the model building framework expresses spatial operations as large collections of interdependent tasks that can be executed efficiently on individual laptops as well as computer clusters ([2]). Our framework currently includes the most essential operations for building large scale hydrological models, including those for simulating transport of material through a flow direction network. By combining these operations, we rebuilt an existing 100 m, 1 h resolution model, thus far used for simulations of small catchments, requiring limited coding as we only had to replace the computational back end of the existing model. Runs at continental scale on a computer cluster show acceptable strong and weak scaling providing a strong indication that global simulations at this resolution will soon be possible, technically speaking.</p><p>Future work will focus on extending the set of modelling operations and adding scalable I/O, after which existing models that are currently limited in their ability to use the computational resources available to them can be ported to this new environment.</p><p>More information about our modelling framework is at https://lue.computationalgeography.org.</p><p><strong>References</strong></p><p>[1] M. Bierkens. Global hydrology 2015: State, trends, and directions. Water Resources Research, 51(7):4923–4947, 2015.<br>[2] K. de Jong, et al. An environmental modelling framework based on asynchronous many-tasks: scalability and usability. Submitted.<br>[3] H. Kaiser, et al. HPX - The C++ standard library for parallelism and concurrency. Journal of Open Source Software, 5(53):2352, 2020.</p>

scholarly journals Application of global models and satellite data for smaller-scale groundwater recharge studies

2016 ◽  
Author(s):  
Rogier Westerhoff ◽  
Paul White ◽  
Zara Rawlinson
Keyword(s):  
New Zealand ◽  
Satellite Data ◽  
Input Data ◽  
Large Scale ◽  
Regional Scale ◽  
Global Scale ◽  
Global Models ◽  
Scale Models ◽  
Boundary Issues ◽  
Rainfall Recharge

Abstract. Large-scale models and satellite data are increasingly used to characterise groundwater and its recharge at the global scale. Although these models have the potential to fill in data gaps and solve trans-boundary issues, they are often neglected in smaller-scale studies, since data are often coarse or uncertain. Large-scale models and satellite data could play a more important role in smaller-scale (i.e., national or regional) studies, if they could be adjusted to fit that scale. In New Zealand, large-scale models and satellite data are not used for groundwater recharge estimation at the national scale, since regional councils (i.e., the water managers) have varying water policy and models are calibrated at the local scale. Also, some regions have many localised ground observations (but poor record coverage), whereas others are data-sparse. Therefore, estimation of recharge is inconsistent at the national scale. This paper presents an approach to apply large-scale, global, models and satellite data to estimate rainfall recharge at the national to regional scale across New Zealand. We present a model, NGRM, that is largely inspired by the global-scale WaterGAP recharge model, but is improved and adjusted using national data. The NGRM model uses MODIS-derived ET and vegetation satellite data, and the available nation-wide datasets on rainfall, elevation, soil and geology. A valuable addition to the recharge estimation is the model uncertainty estimate, based on variance, covariance and sensitivity of all input data components in the model environment. This research shows that, with minor model adjustments and use of improved input data, large-scale models and satellite data can be used to derive rainfall recharge estimates, including their uncertainty, at the smaller scale, i.e., national and regional scale of New Zealand. The estimated New Zealand recharge of the NGRM model compare well to most local and regional lysimeter data and recharge models. The NGRM is therefore assumed to be capable to fill in gaps in data-sparse areas and to create more consistency between datasets from different regions, i.e., to solve trans-boundary issues. This research also shows that smaller-scale recharge studies in New Zealand should include larger boundaries than only a (sub-)aquifer, and preferably the whole catchment. This research points out the need for improved collaboration on the international to national to regional levels to further merge large-scale (global) models to smaller (i.e., national or regional) scales. Future research topics should, collaboratively, focus on: improvement of rainfall-runoff and snowmelt methods; inclusion of river recharge; further improvement of input data (rainfall, evapotranspiration, soil and geology); and the impact of recharge uncertainty in mountainous and irrigated areas.

scholarly journals An application of a large scale conceptual hydrological model over the Elbe region

1999 ◽  
Vol 3 (3) ◽  
pp. 363-374 ◽  
Author(s):  
M. Lobmeyr ◽  
D. Lohmann ◽  
C. Ruhe
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Sunshine Duration ◽  
Regional Scale ◽  
Scale Model ◽  
Surface Parameterization ◽  
Extreme Flood ◽  
Daily Sunshine ◽  
Surface Scheme ◽  
The Baltic

Abstract. This paper investigates the ability of the VIC-2L model coupled to a routing model to reproduce streamflow in the catchment of the lower Elbe River, Germany. The VIC-2L model, a hydrologically-based land surface scheme (LSS) which has been tested extensively in the Project for Intercomparison of Land-surface Parameterization Schemes (PILPS), is put up on the rotated grid of 1/6 degree of the atmospheric regional scale model (REMO) used in the Baltic Sea Experiment (BALTEX). For a 10 year period, the VIC-2L model is forced in daily time steps with measured daily means of precipitation, air temperature, pressure, wind speed, air humidity and daily sunshine duration. VIC-2L model output of surface runoff and baseflow is used as input for the routing model, which transforms modelled runoff into streamflow, which is compared to measured streamflow at selected gauge stations. The water balance of the basin is investigated and the model results on daily, monthly and annual time scales are discussed. Discrepancies appear in time periods where snow and ice processes are important. Extreme flood events are analyzed in more dital. The influence of calibration with respect to runoff is examined.

scholarly journals How evaluation of hydrological models influences results of climate impact assessment—an editorial

2020 ◽  
Vol 163 (3) ◽  
pp. 1121-1141
Author(s):  
Valentina Krysanova ◽  
Fred F. Hattermann ◽  
Zbigniew W. Kundzewicz
Keyword(s):  
Impact Assessment ◽  
Model Evaluation ◽  
Regional Scale ◽  
Global Scale ◽  
Climate Impact ◽  
Climate Impacts ◽  
Hydrological Models ◽  
Calibration And Validation ◽  
Climate Impact Assessment ◽  
Scale Models

AbstractThis paper introduces the Special Issue (SI) “How evaluation of hydrological models influences results of climate impact assessment.” The main objectives were as follows: (a) to test a comprehensive model calibration/validation procedure, consisting of five steps, for regional-scale hydrological models; (b) to evaluate performance of global-scale hydrological models; and (c) to reveal whether the calibration/validation methods and the model evaluation results influence climate impacts in terms of the magnitude of the change signal and the uncertainty range. Here, we shortly describe the river basins and large regions used as case studies; the hydrological models, data, and climate scenarios used in the studies; and the applied approaches for model evaluation and for analysis of projections for the future. After that, we summarize the main findings. The following general conclusions could be drawn. After successful comprehensive calibration and validation, the regional-scale models are more robust and their projections for the future differ from those of the model versions after the conventional calibration and validation. Therefore, climate impacts based on the former models are more trustworthy than those simulated by the latter models. Regarding the global-scale models, using only models with satisfactory or good performance on historical data and weighting them based on model evaluation results is a more reliable approach for impact assessment compared to the ensemble mean approach that is commonly used. The former method provides impact results with higher credibility and reduced spreads in comparison to the latter approach. The studies for this SI were performed in the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP).

scholarly journals On the spatio-temporal analysis of hydrological droughts from global hydrological models

2011 ◽  
Vol 8 (1) ◽  
pp. 619-652 ◽  
Author(s):  
G. A. Corzo Perez ◽  
M. H. J. van Huijgevoort ◽  
F. Voß ◽  
H. A. J. van Lanen
Keyword(s):  
Time Series ◽  
20Th Century ◽  
Large Scale ◽  
Southern Oscillation ◽  
Regional Scale ◽  
Spatial Coherence ◽  
Temporal Analysis ◽  
Global Scale ◽  
Scale Models ◽  
Spatio Temporal

Abstract. The recent concerns for world-wide extreme events related to climate change phenomena have motivated the development of large scale models that simulate the global water cycle. In this context, analyses of extremes is an important topic that requires the adaptation of methods used for river basin and regional scale models. This paper presents two methodologies that extend the tools to analyze spatio-temporal drought development and characteristics using large scale gridded time series of hydrometeorological data. The methodologies are distinguished and defined as non-contiguous and contiguous drought area analyses (i.e. NCDA and CDA). The NCDA presents time series of percentages of areas in drought at the global scale and for pre-defined regions of known hydroclimatology. The CDA is introduced as a complementary method that generates information on the spatial coherence of drought events at the global scale. Spatial drought events are found through CDA by clustering patterns (contiguous areas). In this study the global hydrological model WaterGAP was used to illustrate the methodology development. Global gridded time series (resolution 0.5°) simulated with the WaterGAP model from land points were used. The NCDA and CDA were applied to identify drought events in subsurface runoff. The percentages of area in drought calculated with both methods show complementary information on the spatial and temporal events for the last decades of the 20th century. The NCDA provides relevant information on the average number of droughts, duration and severity (deficit volume) for pre-defined regions (globe, 2 selected climate regions). Additionally, the CDA provides information on the number of spatially linked areas in drought as well as their geographic location on the globe. An explorative validation process shows that the NCDA results capture the overall spatio-temporal drought extremes over the last decades of the 20th century. Events like the El Niño Southern Oscillation (ENSO) in South America and the pan-European drought in 1976 appeared clearly in both analyses. The methodologies introduced provide an important basis for the global characterization of droughts, model inter-comparison, and spatial events validation.

scholarly journals Extending a Large-Scale Model to Better Represent Water Resources without Increasing the Model’s Complexity

Water ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3067
Author(s):  
Robyn Horan ◽  
Nathan J. Rickards ◽  
Alexandra Kaelin ◽  
Helen E. Baron ◽  
Thomas Thomas ◽  
...  
Keyword(s):  
Large Scale ◽  
Model Performance ◽  
Data Availability ◽  
Model Complexity ◽  
Low Flow ◽  
Scale Model ◽  
Water Balance Components ◽  
Large Scale Model ◽  
Scale Models ◽  
Availability Assessment

The increasing impact of anthropogenic interference on river basins has facilitated the development of the representation of human influences in large-scale models. The representation of groundwater and large reservoirs have realised significant developments recently. Groundwater and reservoir representation in the Global Water Availability Assessment (GWAVA) model have been improved, critically, with a minimal increase in model complexity and data input requirements, in keeping with the model’s applicability to regions with low-data availability. The increased functionality was assessed in two highly anthropogenically influenced basins. A revised groundwater routine was incorporated into GWAVA, which is fundamentally driven by three input parameters, and improved the simulation of streamflow and baseflow in the headwater catchments such that low-flow model skill increased 33–67% in the Cauvery and 66–100% in the Narmada. The existing reservoir routine was extended and improved the simulation of streamflow in catchments downstream of major reservoirs, using two calibratable parameters. The model performance was improved between 15% and 30% in the Cauvery and 7–30% in the Narmada, with the daily reservoir releases in the Cauvery improving significantly between 26% and 164%. The improvement of the groundwater and reservoir routines in GWAVA proved successful in improving the model performance, and the inclusions allowed for improved traceability of simulated water balance components. This study illustrates that improvement in the representation of human–water interactions in large-scale models is possible, without excessively increasing the model complexity and input data requirements.

scholarly journals Application of an improved global-scale groundwater model for water table estimation across New Zealand

2018 ◽  
Author(s):  
Rogier Westerhoff ◽  
Paul White ◽  
Gonzalo Miguez-Macho
Keyword(s):  
New Zealand ◽  
Water Table ◽  
Input Data ◽  
Large Scale ◽  
Hydraulic Head ◽  
Global Scale ◽  
Scale Model ◽  
Catchment Scale ◽  
Scale Models ◽  
Model Equations

Abstract. Many studies underline the importance of groundwater assessment at the larger, i.e., global, scale. The large-scale models used for these assessments are often simplified and typically not used for smaller-scale, i.e., catchment-scale, studies, because hydrology and water policy are traditionally best constrained at the catchment scale, and because large-scale models are too uncertain for that scale. However, smaller-scale groundwater models can still have considerable uncertainty, especially in data-sparse areas. There is a potential for larger-scale models to constrain the uncertainty for small-scale models. That is because they can provide an extra source of information in data-sparse areas, such as the initial estimate of hydraulic head. Large-scale models, often quick and simple, can thus take away some of the computational burden of local and more sophisticated applications. The problem of this approach is that model uncertainty of large-scale models is often too large, because the quality of their, coarse and global-scale, input data is large, and often inconsistent with the input data of local models. What is needed is an approach where large-scale and local models can meet in the middle. This study uses an existing, global-scale, groundwater flow model. It feeds that model with national input data of New Zealand terrain, geology, and recharge. It then builds the first New-Zealand national-scale groundwater model. The resulting nationwide maps of hydraulic head and water table depths show that the model points out the main alluvial aquifers with fine spatial detail (200m grid resolution). The national input data and finer spatial detail result in better and more realistic variations of water table depth than the original, global-scale, model outputs. In two regional case studies in New Zealand, the hydraulic head matches the available groundwater level data well. The nationwide water tables show that the model is mostly driven by the elevation (gravity) and impeded by the geology (permeability). The use of this first New Zealand-wide model can aid in provision of water table estimates in data-sparse regions. The national model can also be used to solve inconsistency of models in areas of trans-boundary aquifers, i.e., aquifers that cover more than one region in New Zealand. Shortcomings of the model are caused by the simplified model properties, but also by the accuracy of input data. Future research should therefore not only focus on further improvements of model equations, but also improved estimation of hydraulic conductivity and the digital elevation model, especially in areas of shallow groundwater level. We further surmise that the findings of this study, i.e., application of a global-scale models at smaller-scales, will lead to subsequent improvement of the global-scale model equations.

scholarly journals Impacts of spatial resolution and representation of flow connectivity on large-scale simulation of floods

2017 ◽  
Author(s):  
Cherry May R. Mateo ◽  
Dai Yamazaki ◽  
Hyungjun Kim ◽  
Adisorn Champathong ◽  
Jai Vaze ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Large Scale ◽  
Flood Hazard ◽  
Regional Scale ◽  
Global Scale ◽  
Test Case ◽  
Efficiency Coefficient ◽  
Computational Power ◽  
Extreme Flood ◽  
Simulation Results

Abstract. Global-scale River Models (GRMs) are core tools for providing consistent estimates of global flood hazard, especially in data-scarce regions. Due to former limitations in computational power and input datasets, most GRMs have been developed to use simplified representation of flow physics and run at coarse spatial resolutions. With increasing computational power and improved datasets, the application of GRMs to finer resolutions is becoming a reality. To support development in this direction, the suitability of GRMs for application to finer resolutions needs to be assessed. This study investigates the impacts of spatial resolution and flow connectivity representation on the predictive capability of a GRM, CaMa-Flood, in simulating the 2011 extreme flood in Thailand. Analyses show that when single downstream connectivity (SDC) is assumed, simulation results deteriorate with finer spatial resolution; Nash–Sutcliffe Efficiency coefficient decreased by more than 35 % between simulation results at 10 km resolution and 1 km resolution. When multiple downstream connectivity (MDC) is represented, simulation results slightly improve with finer spatial resolution. The SDC simulations result in excessive backflows on very flat floodplains due to the restrictive flow directions in finer resolutions. MDC channels attenuated these effects by maintaining flow connectivity and flow capacity between floodplains in varying spatial resolutions. While a regional-scale flood was chosen as a test case, these findings are universal and can be extended to global-scale simulations. These results demonstrate that a GRM can be used for higher resolution simulations of large-scale floods, provided that MDC in rivers and floodplains is adequately represented in the model structure.

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