scholarly journals Evaluation of drought propagation in an ensemble mean of large-scale hydrological models

2012 ◽  
Vol 16 (11) ◽  
pp. 4057-4078 ◽  
Author(s):  
A. F. Van Loon ◽  
M. H. J. Van Huijgevoort ◽  
H. A. J. Van Lanen
Keyword(s):  
Large Scale ◽  
Dry Season ◽  
Hydrological Drought ◽  
Hydrological Models ◽  
Drought Characteristics ◽  
Snow Season ◽  
Scale Models ◽  
Ensemble Mean ◽  
Semi Arid

Abstract. Hydrological drought is increasingly studied using large-scale models. It is, however, not sure whether large-scale models reproduce the development of hydrological drought correctly. The pressing question is how well do large-scale models simulate the propagation from meteorological to hydrological drought? To answer this question, we evaluated the simulation of drought propagation in an ensemble mean of ten large-scale models, both land-surface models and global hydrological models, that participated in the model intercomparison project of WATCH (WaterMIP). For a selection of case study areas, we studied drought characteristics (number of droughts, duration, severity), drought propagation features (pooling, attenuation, lag, lengthening), and hydrological drought typology (classical rainfall deficit drought, rain-to-snow-season drought, wet-to-dry-season drought, cold snow season drought, warm snow season drought, composite drought). Drought characteristics simulated by large-scale models clearly reflected drought propagation; i.e. drought events became fewer and longer when moving through the hydrological cycle. However, more differentiation was expected between fast and slowly responding systems, with slowly responding systems having fewer and longer droughts in runoff than fast responding systems. This was not found using large-scale models. Drought propagation features were poorly reproduced by the large-scale models, because runoff reacted immediately to precipitation, in all case study areas. This fast reaction to precipitation, even in cold climates in winter and in semi-arid climates in summer, also greatly influenced the hydrological drought typology as identified by the large-scale models. In general, the large-scale models had the correct representation of drought types, but the percentages of occurrence had some important mismatches, e.g. an overestimation of classical rainfall deficit droughts, and an underestimation of wet-to-dry-season droughts and snow-related droughts. Furthermore, almost no composite droughts were simulated for slowly responding areas, while many multi-year drought events were expected in these systems. We conclude that most drought propagation processes are reasonably well reproduced by the ensemble mean of large-scale models in contrasting catchments in Europe. Challenges, however, remain in catchments with cold and semi-arid climates and catchments with large storage in aquifers or lakes. This leads to a high uncertainty in hydrological drought simulation at large scales. Improvement of drought simulation in large-scale models should focus on a better representation of hydrological processes that are important for drought development, such as evapotranspiration, snow accumulation and melt, and especially storage. Besides the more explicit inclusion of storage in large-scale models, also parametrisation of storage processes requires attention, for example through a global-scale dataset on aquifer characteristics, improved large-scale datasets on other land characteristics (e.g. soils, land cover), and calibration/evaluation of the models against observations of storage (e.g. in snow, groundwater).

scholarly journals Evaluation of drought propagation in an ensemble mean of large-scale hydrological models

2012 ◽  
Vol 9 (7) ◽  
pp. 8375-8424 ◽  
Author(s):  
A. F. Van Loon ◽  
M. H. J. Van Huijgevoort ◽  
H. A. J. Van Lanen
Keyword(s):  
Large Scale ◽  
Dry Season ◽  
Hydrological Drought ◽  
Hydrological Models ◽  
Drought Characteristics ◽  
Snow Season ◽  
Scale Models ◽  
Ensemble Mean ◽  
Semi Arid

Abstract. Hydrological drought is increasingly studied using large-scale models. It is, however, not sure whether large-scale models reproduce the development of hydrological drought correctly. The pressing question is: how well do large-scale models simulate the propagation from meteorological to hydrological drought? To answer this question, we evaluated the simulation of drought propagation in an ensemble mean of ten large-scale models, both land-surface models and global hydrological models, that were part of the model intercomparison project of WATCH (WaterMIP). For a selection of case study areas, we studied drought characteristics (number of droughts, duration, severity), drought propagation features (pooling, attenuation, lag, lengthening), and hydrological drought typology (classical rainfall deficit drought, rain-to-snow-season drought, wet-to-dry-season drought, cold snow season drought, warm snow season drought, composite drought). Drought characteristics simulated by large-scale models clearly reflected drought propagation, i.e. drought events became less and longer when moving through the hydrological cycle. However, more differentiation was expected between fast and slowly responding systems, with slowly responding systems having less and longer droughts in runoff than fast responding systems. This was not found using large-scale models. Drought propagation features were poorly reproduced by the large-scale models, because runoff reacted immediately to precipitation, in all case study areas. This fast reaction to precipitation, even in cold climates in winter and in semi-arid climates in summer, also greatly influenced the hydrological drought typology as identified by the large-scale models. In general, the large-scale models had the correct representation of drought types, but the percentages of occurrence had some important mismatches, e.g. an overestimation of classical rainfall deficit droughts, and an underestimation of wet-to-dry-season droughts and snow-related droughts. Furthermore, almost no composite droughts were simulated for slowly responding areas, while many multi-year drought events were expected in these systems. We conclude that drought propagation processes are reasonably well reproduced by the ensemble mean of large-scale models in contrasting catchments in Europe and that some challenges remain in catchments with cold and semi-arid climates and catchments with large storage in aquifers or lakes. Improvement of drought simulation in large-scale models should focus on a better representation of hydrological processes that are important for drought development, such as evapotranspiration, snow accumulation and melt, and especially storage. Besides the more explicit inclusion of storage (e.g. aquifers) in large-scale models, also parametrisation of storage processes requires attention, for example through a global scale dataset on aquifer characteristics.

scholarly journals Hydrological drought across the world: impact of climate and physical catchment structure

2013 ◽  
Vol 17 (5) ◽  
pp. 1715-1732 ◽  
Author(s):  
H. A. J. Van Lanen ◽  
N. Wanders ◽  
L. M. Tallaksen ◽  
A. F. Van Loon
Keyword(s):  
Large Scale ◽  
Hydrological Drought ◽  
Temperate Climate ◽  
Minor Effect ◽  
Hydrological Models ◽  
Drought Duration ◽  
Groundwater System ◽  
Drought Characteristics ◽  
The World ◽  
Groundwater Systems

Abstract. Large-scale hydrological drought studies have demonstrated spatial and temporal patterns in observed trends, and considerable difference exists among global hydrological models in their ability to reproduce these patterns. In this study a controlled modeling experiment has been set up to systematically explore the role of climate and physical catchment structure (soils and groundwater systems) to better understand underlying drought-generating mechanisms. Daily climate data (1958–2001) of 1495 grid cells across the world were selected that represent Köppen–Geiger major climate types. These data were fed into a conceptual hydrological model. Nine realizations of physical catchment structure were defined for each grid cell, i.e., three soils with different soil moisture supply capacity and three groundwater systems (quickly, intermediately and slowly responding). Hydrological drought characteristics (number, duration and standardized deficit volume) were identified from time series of daily discharge. Summary statistics showed that the equatorial and temperate climate types (A- and C-climates) had about twice as many drought events as the arid and polar types (B- and E-climates), and the durations of more extreme droughts were about half the length. Selected soils under permanent grassland were found to have a minor effect on hydrological drought characteristics, whereas groundwater systems had major impact. Groundwater systems strongly controlled the hydrological drought characteristics of all climate types, but particularly those of the wetter A-, C- and D-climates because of higher recharge. The median number of droughts for quickly responding groundwater systems was about three times higher than for slowly responding systems. Groundwater systems substantially affected the duration, particularly of the more extreme drought events. Bivariate probability distributions of drought duration and standardized deficit for combinations of Köppen–Geiger climate, soil and groundwater system showed that the responsiveness of the groundwater system is as important as climate for hydrological drought development. This urges for an improvement of subsurface modules in global hydrological models to be more useful for water resources assessments. A foreseen higher spatial resolution in large-scale models would enable a better hydrogeological parameterization and thus inclusion of lateral flow.

scholarly journals Calibration and uncertainty issues of a hydrological model (SWAT) applied to West Africa

2006 ◽  
Vol 9 ◽  
pp. 137-143 ◽  
Author(s):  
J. Schuol ◽  
K. C. Abbaspour
Keyword(s):  
West Africa ◽  
Model Calibration ◽  
Large Scale ◽  
Assessment Tool ◽  
Hydrological Models ◽  
Scale Models ◽  
Distributed Hydrological Models ◽  
Water Assessment ◽  
River Niger

Abstract. Distributed hydrological models like SWAT (Soil and Water Assessment Tool) are often highly over-parameterized, making parameter specification and parameter estimation inevitable steps in model calibration. Manual calibration is almost infeasible due to the complexity of large-scale models with many objectives. Therefore we used a multi-site semi-automated inverse modelling routine (SUFI-2) for calibration and uncertainty analysis. Nevertheless, the question of when a model is sufficiently calibrated remains open, and requires a project dependent definition. Due to the non-uniqueness of effective parameter sets, parameter calibration and prediction uncertainty of a model are intimately related. We address some calibration and uncertainty issues using SWAT to model a four million km2 area in West Africa, including mainly the basins of the river Niger, Volta and Senegal. This model is a case study in a larger project with the goal of quantifying the amount of global country-based available freshwater. Annual and monthly simulations with the "calibrated" model for West Africa show promising results in respect of the freshwater quantification but also point out the importance of evaluating the conceptual model uncertainty as well as the parameter uncertainty.

scholarly journals Hydrological drought across the world: impact of climate and physical catchment structure

2012 ◽  
Vol 9 (10) ◽  
pp. 12145-12192 ◽  
Author(s):  
H. A. J. Van Lanen ◽  
N. Wanders ◽  
L. M. Tallaksen ◽  
A. F. Van Loon
Keyword(s):  
Large Scale ◽  
Hydrological Drought ◽  
Temperate Climate ◽  
Minor Effect ◽  
Hydrological Models ◽  
Spatial And Temporal Patterns ◽  
Drought Duration ◽  
Drought Characteristics ◽  
The World ◽  
Groundwater Systems

Abstract. Large-scale hydrological drought studies have demonstrated spatial and temporal patterns in observed trends and considerable difference exists among global hydrological models in their ability to reproduce these patterns. A controlled modeling experiment has been set up to systematically explore the role of climate and physical catchment structure (soils and groundwater systems) to better understand underlying drought-generating mechanisms. Daily climate data (1958–2001) of 1495 grid cells across the world were selected that represent Köppen-Geiger major climate types. These data were fed into a hydrological model. Nine realizations of physical catchment structure were defined for each grid cell, i.e. three soils with different soil moisture supply capacity and three groundwater systems (quickly-, intermediary- and slowly-responding). Hydrological drought characteristics (number, duration and standardized deficit volume) were identified from time series of daily discharge. Summary statistics showed that the equatorial and temperate climate types (A- and C-climates) had about twice as many drought events as the arid and polar types (B- and E-climates) and the duration of more extreme droughts were about half the length. Soils were found to have a minor effect on hydrological drought characteristics, whereas groundwater systems had major impact. Groundwater systems strongly controlled the hydrological drought characteristics of all climate types, but particularly those of the wetter A-, C- and D-climates because of higher recharge. The median number of drought for quickly-responding groundwater systems was about three times higher than for slowly-responding systems, which substantially affected the duration, particularly of the more extreme drought events. Bivariate probability distributions of drought duration and standardized deficit for combinations of Köppen-Geiger climate, soil and groundwater system showed that responsiveness of groundwater systems is as important as climate for hydrological drought development. This urges for an improvement of subsurface modules in global hydrological models to be more useful for water resources assessments. A foreseen higher spatial resolution would enable a better hydrogeological parameterization and inclusion of lateral flow.

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>

Are large scale models useful? A case of nested model domains for assessing current and future stream runoff and sediments

2021 ◽  
Author(s):  
Alena Bartosova ◽  
Berit Arheimer ◽  
Alban de Lavenne ◽  
René Capell ◽  
Johan Strömqvist
Keyword(s):  
Climate Change ◽  
Spatial Patterns ◽  
Large Scale ◽  
Aridity Index ◽  
Global Climate Models ◽  
Hydrological Models ◽  
Nested Model ◽  
Nested Models ◽  
Changing Climate ◽  
Scale Models

<p>Continental and global dynamic hydrological models have emerged recently as tools for e.g. flood forecasting, large-scale climate impact analyses, and estimation of time-dynamic water fluxes into sea basins. One such tool is a dynamic process-based rainfall-runoff and water quality model Hydrological Predictions for Environment (HYPE). We present and compare historical simulations of runoff, soil moisture, aridity, and sediment concentrations for three nested model domains using global, continental (Europe), and national (Sweden) catchment-based HYPE applications. Future impacts on hydrological variables from changing climate were then assessed using the global and continental HYPE applications with ensembles based on 3 CMIP5 global climate models (GCMs).</p><p>Simulated historical sediment concentrations varied considerably among the nested models in spatial patterns while runoff values were more similar. Regardless of the variation, the global model was able to provide information on climate change impacts comparable to those from the continental and national models for hydrological indicators. Output variables that were calibrated, e.g. runoff, were shown to result in more reliable and consistent projected changes among the different model scales than derived variables such as the actual aridity index. The comparison was carried out for ensemble averages as well as individual GCMs to illustrate the variability and the need for robust assessments.</p><p>Global hydrological models are shown to be valuable tools for e.g. first screenings of climate change effects and detection of spatial patterns and can be useful to provide information on current and future hydrological states at various domains. The challenge is (1) in deciding when we should use the large-scale models and (2) in interpreting the results, considering the uncertainty of the model results and quality of data especially at the global scale. Comparison across nested domains demonstrates the significance of scale which needs to be considered when interpreting the impacts alongside with model performance.</p><p>Bartosova et al, 2021: Large-scale hydrological and sediment modeling in nested domains under current and changing climate. Accepted to Special Issue Journal of Hydraulic Engineering.</p>

scholarly journals Improving Meteorological Seasonal Forecasts for Hydrological Modeling in European Winter

2020 ◽  
Vol 59 (2) ◽  
pp. 317-332
Author(s):  
Nicky Stringer ◽  
Jeff Knight ◽  
Hazel Thornton
Keyword(s):  
North Atlantic ◽  
Large Scale ◽  
Hydrological Modeling ◽  
Strongly Correlated ◽  
Hydrological Models ◽  
Seasonal Forecasts ◽  
The North ◽  
Ensemble Mean ◽  
The North Atlantic Oscillation ◽  
Correlation Skill

AbstractRecent advances in the skill of seasonal forecasts in the extratropics during winter mean they could offer improvements to seasonal hydrological forecasts. However, the signal-to-noise paradox, whereby the variability in the ensemble mean signal is lower than would be expected given its correlation skill, prevents their use to force hydrological models directly. We describe a postprocessing method to adjust for this problem, increasing the size of the predicted signal in the large-scale circulation. This reduces the ratio of predictable components in the North Atlantic Oscillation (NAO) from 3 to 1. We then derive a large ensemble of daily sequences of spatially gridded rainfall that are consistent with the seasonal mean NAO prediction by selecting historical observations conditioned on the adjusted NAO forecasts. Over northern and southwestern Europe, where the NAO is strongly correlated with winter mean rainfall, the variability of the predicted signal in the adjusted rainfall forecasts is consistent with the correlation skill (they have a ratio of predictable components of ~1) and are as skillful as the unadjusted forecasts. The adjusted forecasts show larger predicted deviations from climatology and can be used to better assess the risk of extreme seasonal mean precipitation as well as to force hydrological models.

scholarly journals OntCheck: An Ontology-Driven Static Correctness Checking Tool for Component-Based Models

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Xi Lin ◽  
Hehua Zhang ◽  
Ming Gu
Keyword(s):  
Large Scale ◽  
Description Logic ◽  
Early Stage ◽  
Semantic Knowledge ◽  
Unified Framework ◽  
Domain Specific ◽  
Scale Models ◽  
Correctness Checking ◽  
Static Checking

Component-based models are widely used for embedded systems. The models consist of components with input and output ports linked to each other. However, mismatched links or assumptions among components may cause many failures, especially for large scale models. Binding semantic knowledge into models can enable domain-specific checking and help expose modeling errors in the early stage. Ontology is known as the formalization of semantic knowledge. In this paper we propose an ontology-driven tool for static correctness checking of domain-specific errors. two kinds of important static checking, semantic type and domain-restrcted rules, are fulfilled in a unified framework. We first propose a formal way to precisely describe the checking requirements by ontology and then separately check them by a lattice-based constraint solver and a description logic reasoner. Compared with other static checking methods, the ontology-based method we proposed is model-externally configurable and thus flexible and adaptable to the changes of requirements. The case study demonstrates the effectiveness of our method.

scholarly journals Leishmaniasis and Climate Change—Case Study: Argentina

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Oscar Daniel Salomón ◽  
María Gabriela Quintana ◽  
Andrea Verónica Mastrángelo ◽  
María Soledad Fernández
Keyword(s):  
Public Health ◽  
Climate Change ◽  
Large Scale ◽  
Local Level ◽  
Point Of View ◽  
Mitigation Strategies ◽  
Vector Borne Diseases ◽  
Scale Models ◽  
Vector Borne

Vector-borne diseases closely associated with the environment, such as leishmaniases, have been a usual argument about the deleterious impact of climate change on public health. From the biological point of view interaction of different variables has different and even conflicting effects on the survival of vectors and the probability transmission of pathogens. The results on ecoepidemiology of leishmaniasis in Argentina related to climate variables at different scales of space and time are presented. These studies showed that the changes in transmission due to change or increase in frequency and intensity of climatic instability were expressed through changes in the probability of vector-human reservoir effective contacts. These changes of contact in turn are modulated by both direct effects on the biology and ecology of the organisms involved, as by perceptions and changes in the behavior of the human communities at risk. Therefore, from the perspective of public health and state policy, and taking into account the current nonlinear increased velocity of climate change, we concluded that discussing the uncertainties of large-scale models will have lower impact than to develop-validate mitigation strategies to be operative at local level, and compatibles with sustainable development, conservation biodiversity, and respect for cultural diversity.

scholarly journals Large-Scale Water Productivity Assessments with MODIS Images in a Changing Semi-Arid Environment: A Brazilian Case Study

2013 ◽  
Vol 5 (11) ◽  
pp. 5783-5804 ◽  
Author(s):  
Antônio de C. Teixeira ◽  
Morris Scherer-Warren ◽  
Fernando Hernandez ◽  
Ricardo Andrade ◽  
Janice Leivas
Keyword(s):  
Large Scale ◽  
Water Productivity ◽  
Arid Environment ◽  
Semi Arid ◽  
Brazilian Case
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