scholarly journals Comparing Large-Scale Hydrological Model Simulations to Observed Runoff Percentiles in Europe

2012 ◽  
Vol 13 (2) ◽  
pp. 604-620 ◽  
Author(s):  
Lukas Gudmundsson ◽  
Lena M. Tallaksen ◽  
Kerstin Stahl ◽  
Douglas B. Clark ◽  
Egon Dumont ◽  
...  
Keyword(s):  
Time Series ◽  
Interannual Variability ◽  
Large Scale ◽  
System Modeling ◽  
Hydrological Models ◽  
The European Union ◽  
Model Simulations ◽  
Large Spread ◽  
Ensemble Mean ◽  
High Flows

Abstract Large-scale hydrological models describing the terrestrial water balance at continental and global scales are increasingly being used in earth system modeling and climate impact assessments. However, because of incomplete process understanding and limits of the forcing data, model simulations remain uncertain. To quantify this uncertainty a multimodel ensemble of nine large-scale hydrological models was compared to observed runoff from 426 small catchments in Europe. The ensemble was built within the framework of the European Union Water and Global Change (WATCH) project. The models were driven with the same atmospheric forcing data. Models were evaluated with respect to their ability to capture the interannual variability of spatially aggregated annual time series of five runoff percentiles—derived from daily time series—including annual low and high flows. Overall, the models capture the interannual variability of low, mean, and high flows well. However, errors in the mean and standard deviation, as well as differences in performance between the models, became increasingly pronounced for low runoff percentiles, reflecting the uncertainty associated with the representation of hydrological processes, such as the depletion of soil moisture stores. The large spread in model performance implies that any single model should be applied with caution as there is a great risk of biased conclusions. However, this large spread is contrasted by the good overall performance of the ensemble mean. It is concluded that the ensemble mean is a pragmatic and reliable estimator of spatially aggregated time series of annual low, mean, and high flows across Europe.

scholarly journals How Well Do Large-Scale Models Reproduce Regional Hydrological Extremes in Europe?

2011 ◽  
Vol 12 (6) ◽  
pp. 1181-1204 ◽  
Author(s):  
Christel Prudhomme ◽  
Simon Parry ◽  
Jamie Hannaford ◽  
Douglas B. Clark ◽  
Stefan Hagemann ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Storage Capacity ◽  
Spatial Coherence ◽  
Low Flow ◽  
Water Model ◽  
Hydrological Models ◽  
Flow Measurements ◽  
Hydrological Extremes ◽  
High Flows

Abstract This paper presents a new methodology for assessing the ability of gridded hydrological models to reproduce large-scale hydrological high and low flow events (as a proxy for hydrological extremes) as described by catalogues of historical droughts [using the regional deficiency index (RDI)] and high flows [regional flood index (RFI)] previously derived from river flow measurements across Europe. Using the same methods, total runoff simulated by three global hydrological models from the Water Model Intercomparison Project (WaterMIP) [Joint U.K. Land Environment Simulator (JULES), Water Global Assessment and Prognosis (WaterGAP), and Max Planck Institute Hydrological Model (MPI-HM)] run with the same meteorological input (watch forcing data) at the same spatial 0.5° grid was used to calculate simulated RDI and RFI for the period 1963–2001 in the same European regions, directly comparable with the observed catalogues. Observed and simulated RDI and RFI time series were compared using three performance measures: the relative mean error, the ratio between the standard deviation of simulated over observed series, and the Spearman correlation coefficient. Results show that all models can broadly reproduce the spatiotemporal evolution of hydrological extremes in Europe to varying degrees. JULES tends to produce prolonged, highly spatially coherent events for both high and low flows, with events developing more slowly and reaching and sustaining greater spatial coherence than observed—this could be due to runoff being dominated by slow-responding subsurface flow. In contrast, MPI-HM shows very high variability in the simulated RDI and RFI time series and a more rapid onset of extreme events than observed, in particular for regions with significant water storage capacity—this could be due to possible underrepresentation of infiltration and groundwater storage, with soil saturation reached too quickly. WaterGAP shares some of the issues of variability with MPI-HM—also attributed to insufficient soil storage capacity and surplus effective precipitation being generated as surface runoff—and some strong spatial coherence of simulated events with JULES, but neither of these are dominant. Of the three global models considered here, WaterGAP is arguably best suited to reproduce most regional characteristics of large-scale high and low flow events in Europe. Some systematic weaknesses emerge in all models, in particular for high flows, which could be a product of poor spatial resolution of the input climate data (e.g., where extreme precipitation is driven by local convective storms) or topography. Overall, this study has demonstrated that RDI and RFI are powerful tools that can be used to assess how well large-scale hydrological models reproduce large-scale hydrological extremes—an exercise rarely undertaken in model intercomparisons.

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 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 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 five hydrological models across Europe and their suitability for making projections under climate change

2015 ◽  
Vol 12 (10) ◽  
pp. 10289-10330 ◽  
Author(s):  
W. Greuell ◽  
J. C. M. Andersson ◽  
C. Donnelly ◽  
L. Feyen ◽  
D. Gerten ◽  
...  
Keyword(s):  
Climate Change ◽  
Interannual Variability ◽  
Large Scale ◽  
Interannual Variation ◽  
Hydrological Models ◽  
High Discharge ◽  
Annual Cycles ◽  
Model Ranking ◽  
The Mean

Abstract. The main aims of this paper are the evaluation of five large-scale hydrological models across Europe and the assessment of the suitability of the models for making projections under climate change. For the evaluation, 22 years of discharge measurements from 46 large catchments were exploited. In the reference simulations forcing was taken from the E-OBS dataset for precipitation and temperature, and from the WFDEI dataset for other variables. On average across all catchments, biases were small for four of the models, ranging between −29 and +23 mm yr−1 (−9 and +8 %), while one model produced a large negative bias (−117 mm yr−1; −38 %). Despite large differences in e.g. the evapotranspiration schemes, the skill to simulate interannual variability did not differ much between the models, which can be ascribed to the dominant effect of interannual variation in precipitation on interannual variation in discharge. Assuming that the skill of a model to simulate interannual variability provides a measure for the model's ability to make projections under climate change, the skill of future discharge projections will not differ much between models. The quality of the simulation of the mean annual cycles, and low and high discharge was found to be related to the degree of calibration of the models, with the more calibrated models outperforming the crudely and non-calibrated models. The sensitivity to forcing was investigated by carrying out alternative simulations with all forcing variables from WFDEI, which increased biases by between +66 and +85 mm yr−1 (21–28 %), significantly changed the inter-model ranking of the skill to simulate the mean and increased the magnitude of interannual variability by 28 %, on average.

scholarly journals Niger discharge from radar altimetry: Bridging gaps between gauge and altimetry time series

2019 ◽  
Author(s):  
Stefan Schröder ◽  
Anne Springer ◽  
Jürgen Kusche ◽  
Bernd Uebbing ◽  
Luciana Fenoglio-Marc ◽  
...  
Keyword(s):  
Time Series ◽  
Temporal Resolution ◽  
Hydrological Model ◽  
Hydrological Models ◽  
Radar Altimeter ◽  
Model Simulations ◽  
Rating Curves ◽  
Niger River ◽  
Spaceborne Radar ◽  
Lower Temporal Resolution

Abstract. The Niger river represents a challenging target for deriving discharge from spaceborne radar altimeter measurements, in particular since most terrestrial gauges ceased to provide data during the 2000s. Here, we propose to derive altimetric rating curves by bridging gaps between time series from gauge and altimeter measurements using hydrological model simulations. We show that classical pulse-limited altimetry (Jason-1 and-2, Envisat, Saral/Altika) subsequently reproduces discharge well and enables continuing the gauge time series, albeit at lower temporal resolution. Also, SAR altimetry picks up quite well the signal measured by earlier altimeters and allows to build extended time-series of higher quality. However, radar retracking is necessary for pulse-limited altimetry and needs to be further investigated for SAR. Moreover, forcing data for calibrating and running the hydrological models must be chosen carefully. Furthermore, stage-discharge relations must be fitted empirically and may need to allow for breakpoints.

scholarly journals CAMELS-AUS: Hydrometeorological time series and landscape attributes for 222 catchments in Australia

2021 ◽  
Author(s):  
Keirnan J. A. Fowler ◽  
Suwash Chandra Acharya ◽  
Nans Addor ◽  
Chihchung Chou ◽  
Murray C. Peel
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Interannual Variability ◽  
Large Scale ◽  
Arid Zone ◽  
Anthropogenic Influence ◽  
Rating Curve ◽  
Uncertainty Estimates ◽  
The Usa ◽  
Landscape Attributes

Abstract. This paper presents the Australian edition of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) series of datasets. CAMELS-AUS comprises data for 222 unregulated catchments, combining hydrometeorological timeseries (streamflow and 18 climatic variables) with 134 attributes related to geology, soil, topography, land cover, anthropogenic influence, and hydroclimatology. The CAMELS-AUS catchments have been monitored for decades (more than 85 % have streamflow records longer than 40 years) and are relatively free of large scale changes, such as significant changes in landuse. Rating curve uncertainty estimates are provided for most (75 %) of the catchments and multiple atmospheric datasets are included, offering insights into forcing uncertainty. This dataset, the first of its kind in Australia, allows users globally to freely access catchment data drawn from Australia's unique hydroclimatology, particularly notable for its large interannual variability. Combined with arid catchment data from the CAMELS datasets for the USA and Chile, CAMELS-AUS constitutes an unprecedented resource for the study of arid-zone hydrology. CAMELS-AUS is freely downloadable from https://doi.pangaea.de/10.1594/PANGAEA.921850 (Fowler et al., 2020a).

scholarly journals GP-SWAT (v1.0): a two-layer graph-based parallel simulation framework for the SWAT model

2021 ◽  
Author(s):  
Dejian Zhang ◽  
Bingqing Lin ◽  
Jiefeng Wu ◽  
Qiaoying Lin
Keyword(s):  
Large Scale ◽  
Assessment Tool ◽  
Swat Model ◽  
Parallel Simulation ◽  
Hydrological Models ◽  
Input Output ◽  
Flexible Tool ◽  
Model Simulations ◽  
Iterative Model ◽  
Computational Performance

Abstract. High-fidelity and large-scale hydrological models are increasingly used to investigate the impacts of human activities and climate change on water availability and quality. However, the detailed representations of real-world systems and processes contained in these models inevitably lead to prohibitively high execution times, ranging from minutes to days. This becomes computationally prohibitive or even infeasible when large iterative model simulations are involved. In this study, we propose a generic two-layer model parallelization scheme to reduce the run time of computationally expensive model applications through a combination of model spatial decomposition and the graph-parallel Pregel algorithm. Taking the Soil and Water Assessment Tool (SWAT) as an example, we implemented a generic tool named GP-SWAT, enabling model-level and subbasin-level model parallelization on a Spark computer cluster. We then evaluated GP-SWAT in two sets of experiments to demonstrate the potential of GP-SWAT to accelerate single and iterative model simulations and to run in different environments. In each test set, Spark-SWAT was applied for the parallel simulation of eight synthetic hydrological models with different input/output (I/O) burdens and river network characteristics. The experimental results indicate that GP-SWAT can effectively solve high-computational-demand problems of the SWAT model. In addition, as a scalable and flexible tool, it can be run in diverse environments, from a commodity computer running the Microsoft Windows operating system to a Spark cluster consisting of a large number of computational nodes. Moreover, it is possible to apply this generic scheme to other subbasin-based hydrological models or even acyclic models in other domains to alleviate input/output (I/O) demands and optimize model computational performance.

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

2011 ◽  
Vol 15 (9) ◽  
pp. 2963-2978 ◽  
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 ◽  
Spatial Coherence ◽  
Temporal Analysis ◽  
Global Scale ◽  
Hydrological Models ◽  
Context Analysis ◽  
Spatio Temporal

Abstract. The recent concerns for world-wide extreme events related to climate change have motivated the development of large scale models that simulate the global water cycle. In this context, analysis of hydrological extremes is important and requires the adaptation of identification methods used for river basin 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 classified 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 of subsurface runoff (resolution 0.5°) simulated with the WaterGAP model from land points were used. The NCDA and CDA were developed to identify drought events in 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 hydroclimatic regions). Additionally, the CDA provides information on the number of spatially linked areas in drought, maximum spatial event and their geographic location on the globe. Some 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 of drought identified from global hydrological models and spatial event analyses.

scholarly journals GP-SWAT (v1.0): a two-level graph-based parallel simulation tool for the SWAT model

2021 ◽  
Vol 14 (10) ◽  
pp. 5915-5925
Author(s):  
Dejian Zhang ◽  
Bingqing Lin ◽  
Jiefeng Wu ◽  
Qiaoying Lin
Keyword(s):  
Large Scale ◽  
Assessment Tool ◽  
Swat Model ◽  
Parallel Simulation ◽  
Hydrological Models ◽  
Flexible Tool ◽  
Model Simulations ◽  
Iterative Model ◽  
Computational Performance ◽  
Level Model

Abstract. High-fidelity and large-scale hydrological models are increasingly used to investigate the impacts of human activities and climate change on water availability and quality. However, the detailed representations of real-world systems and processes contained in these models inevitably lead to prohibitively high execution times, ranging from minutes to days. Such models become computationally prohibitive or even infeasible when large iterative model simulations are involved. In this study, we propose a generic two-level (i.e., watershed- and subbasin-level) model parallelization schema to reduce the run time of computationally expensive model applications through a combination of model spatial decomposition and the graph-parallel Pregel algorithm. Taking the Soil and Water Assessment Tool (SWAT) as an example, we implemented a generic tool named GP-SWAT, enabling watershed-level and subbasin-level model parallelization on a Spark computer cluster. We then evaluated GP-SWAT in two sets of experiments to demonstrate the ability of GP-SWAT to accelerate single and iterative model simulations and to run in different environments. In each test set, GP-SWAT was applied for the parallel simulation of four synthetic hydrological models with different input/output (I/O) burdens. The single-model parallelization results showed that GP-SWAT can obtain a 2.3–5.8-times speedup. For multiple simulations with subbasin-level parallelization, GP-SWAT yielded a remarkable speedup of 8.34–27.03 times. In both cases, the speedup ratios increased with an increasing computation burden. The experimental results indicate that GP-SWAT can effectively solve the high-computational-demand problems of the SWAT model. In addition, as a scalable and flexible tool, it can be run in diverse environments, from a commodity computer running the Microsoft Windows operating system to a Spark cluster consisting of a large number of computational nodes. Moreover, it is possible to apply this generic tool to other subbasin-based hydrological models or even acyclic models in other domains to alleviate I/O demands and to optimize model computational performance.

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