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

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.

Download Full-text

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

Hydrology and Earth System Sciences ◽

10.5194/hess-15-2963-2011 ◽

2011 ◽

Vol 15 (9) ◽

pp. 2963-2978 ◽

Cited By ~ 41

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.

Download Full-text

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

Journal of Hydrometeorology ◽

10.1175/jhm-d-11-083.1 ◽

2012 ◽

Vol 13 (2) ◽

pp. 604-620 ◽

Cited By ~ 102

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.

Download Full-text

Hunting for Information in Streamflow Signatures to Improve Modelled Drainage

Water ◽

10.3390/w14010110 ◽

2022 ◽

Vol 14 (1) ◽

pp. 110

Author(s):

Raphael Schneider ◽

Simon Stisen ◽

Anker Lajer Højberg

Keyword(s):

Large Scale ◽

Agricultural Land ◽

Hydrological Cycle ◽

Drainage System ◽

Water Model ◽

Generation Process ◽

Hydrological Models ◽

Drainage Patterns ◽

Drain Flow ◽

Set Up

About half of the Danish agricultural land is drained artificially. Those drains, mostly in the form of tile drains, have a significant effect on the hydrological cycle. Consequently, the drainage system must also be represented in hydrological models that are used to simulate, for example, the transport and retention of chemicals. However, representation of drainage in large-scale hydrological models is challenging due to scale issues, lacking data on the distribution of drain infrastructure, and lacking drain flow observations. This calls for more indirect methods to inform such models. Here, we investigate the hypothesis that drain flow leaves a signal in streamflow signatures, as it represents a distinct streamflow generation process. Streamflow signatures are indices characterizing hydrological behaviour based on the hydrograph. Using machine learning regressors, we show that there is a correlation between signatures of simulated streamflow and simulated drain fraction. Based on these insights, signatures relevant to drain flow are incorporated in hydrological model calibration. A distributed coupled groundwater–surface water model of the Norsminde catchment, Denmark (145 km2) is set up. Calibration scenarios are defined with different objective functions; either using conventional stream flow metrics only, or a combination with hydrological signatures. We then evaluate the results from the different scenarios in terms of how well the models reproduce observed drain flow and spatial drainage patterns. Overall, the simulation of drain in the models is satisfactory. However, it remains challenging to find a direct link between signatures and an improvement in representation of drainage. This is likely attributable to model structural issues and lacking flexibility in model parameterization.

Download Full-text

Including hydrologic signatures in the calibration of a groundwater-surface water model to improve representation of artificial drain

10.5194/egusphere-egu2020-9283 ◽

2020 ◽

Author(s):

Simon Stisen ◽

Raphael Schneider ◽

Anker Lajer Højberg

Keyword(s):

Surface Water ◽

Groundwater Flow ◽

Large Scale ◽

Agricultural Land ◽

Water Cycle ◽

Regional Scale ◽

Drainage System ◽

Water Model ◽

Hydrological Models ◽

Drain Flow

About half of the Danish agricultural land is artificially drained to make land arable and increase crop yield. Those artificial drains, mostly in the form on tile drains, have a significant effect on the groundwater flow patterns and the whole water cycle. Consequently, the drainage system must also be represented in hydrological models that are used to understand and simulate, for example, recharge patterns, groundwater flow paths, or the transport and retention of nutrients. However, representation of drain in regional- and large-scale hydrological models is challenging due to i) issues with scale, ii) a lack of data on the distribution of the drain network, and iii) a lack of direct observations of drain flow. This calls for more indirect methods to inform such models.We assume that drain flow leaves a signal in certain hydrograph signatures, as it impacts the generation of streamflow. Based on a dataset of observed discharge covering all of Denmark, and simulation results from regional-scale hydrological models, we use machine learning regressors to shed light on possible correlations between hydrograph signatures and artificial drainage. Building up on this step, we run a series of calibration exercises on a hydrological model of the agriculturally dominated Norsminde catchment, Denmark (~100&#160;km2). The model is set up in the DHI MIKE SHE software, as distributed coupled groundwater-surface water models with a grid size of 100&#160;m. The different calibration exercises differed in the objective functions used: either we only use conventional stream flow metrics (KGE), or also include hydrograph signatures that showed sensitive towards drain flow in our regression analysis. We then evaluate the results from the different calibration exercises, in terms of how well the model reproduces directly observed drain flow, and spatial drainage patterns.Despite including hydrologic signatures in the calibration process, the representation of drain flow in large-scale models remains challenging. Eventually, the insight gained from this and similar studies will be incorporated in the National Water Resources Model for Denmark, to help improving national targeted regulation of nitrate application through fertilizers.

Download Full-text

Streamwater time series coupled to groundwater age tracers informs the hydrologic partitioning of rainfall, the transient age distributions and their associated reactivity

10.5194/egusphere-egu2020-14076 ◽

2020 ◽

Author(s):

Jean Marçais ◽

Jean-Raynald de Dreuzy ◽

Louis A. Derry ◽

Luca Guillaumot ◽

Aurélie Guillou ◽

...

Keyword(s):

Time Series ◽

Large Scale ◽

Groundwater Age ◽

Stream Water ◽

Critical Zone ◽

Low Flow ◽

Scale Model ◽

Spatial And Temporal Variability ◽

Dissolved Silica ◽

Key Characteristics

Intricated variabilities of stream water quality and of stream discharge can provide key insights of integrated processes occurring at the watershed scale. Yet it is difficult to disentangle the effects of hydrologic vs biogeochemical processes occurring in the different compartments of the critical zone, as well as the mixing associated to it. Here we developed a quasi-2D hillslope scale model able to represent the partitioning of precipitation into real evapotranspiration, shallow subsurface lateral flow and deeper groundwater flow circulation. Enhanced with an advective-dispersive particle tracking algorithm, the model delineates the age distributions of the associated flow lines and the resulting transient streamwater transit time distributions (TTDs). To relate geochemical datasets to TTDs, we connected the biogeochemical reactivity, spatially, to the compartment (regolith vs bedrock) and, in time, to the residence time of the different flowpaths.We hypothesized that streamwater time series datasets (discharge and dissolved silica) and in-situ groundwater age tracers (CFCs) would build minimal but orthogonal information upon these partitioning and tracing processes. Applied to 4 different catchments in Brittany, we were able to represent the seasonal dynamics of evapotranspiration, discharge and dissolved silica (DSi) in rivers as well as CFC concentrations in aquifers once key characteristics of the watershed have been informed (evapotranspiration ratio, amount of water stored in the regolith and in the aquifer, bedrock transmissivity, weathering capacity). We found evapotranspiration ratio (ET/P) in average equal to 54% in agreement with independent, large-scale estimates (derived from the French climate Surfex model). The model also provides estimates for typical bedrock transmissivities around 5.10-4 m2/s, mean transit times around 10 years with an important spatial and temporal variability, amount of stored water in average equal to 160 mm (resp. 3.10 m) in the regolith (resp. bedrock) and DSi weathering capacity of 0.3 mg/L/yr, which is in accordance with previous studies carried in crystalline contexts like Brittany [Leray et al. 2012, Kolbe et al. 2016, Mar&#231;ais et al. 2018]. Simplifying the transient behavior of the catchment model with some analytical considerations enabled to directly inform these key characteristics with some properties of the measured datasets (e.g. average low flow rate, mean and standard deviation of the DSi time series, average CFC apparent ages). This shows that these datasets can be used as standalone tracers and provide powerful indicators of critical zone characteristics described above. This also opens new avenues to spatialize the reactivity in the deep critical zone, and to integrate the information provided by different datasets (e.g. climatic forcing, discharge, solute concentrations, groundwater age tracers) measured in streams and in groundwater. Such modeling exercice paves the way toward an interdisciplinary understanding of the critical zone.

Download Full-text

Future hydrological extremes: the uncertainty from multiple global climate and global hydrological models

Earth System Dynamics Discussions ◽

10.5194/esdd-6-1-2015 ◽

2015 ◽

Vol 6 (1) ◽

pp. 1-30 ◽

Cited By ~ 3

Author(s):

I. Giuntoli ◽

J.-P. Vidal ◽

C. Prudhomme ◽

D. M. Hannah

Keyword(s):

Climate Models ◽

Hydrological Cycle ◽

Global Climate ◽

Regional Scale ◽

Global Climate Models ◽

Hydrological Models ◽

Uncertainty Range ◽

Low Flows ◽

Hydrological Extremes ◽

High Flows

Abstract. Projections of changes in the hydrological cycle from Global Hydrological Models (GHMs) driven by Global Climate Models (GCMs) are critical for understanding future occurrence of hydrological extremes. However, uncertainties remain large and need to be better assessed. In particular, recent studies have pointed to a considerable contribution of GHMs that can equal or outweigh the contribution of GCMs to uncertainty in hydrological projections. Using 6 GHMs and 5 GCMs from the ISI-MIP multi-model ensemble, this study aims: (i) to assess future changes in the frequency of both high and low flows at the global scale using control and future (RCP8.5) simulations by the 2080s, and (ii) to quantify, for both ends of the runoff spectrum, GCMs and GHMs contributions to uncertainty using a 2-way ANOVA. Increases are found in high flows for northern latitudes and in low flows for several hotspots. Globally, the largest source of uncertainty is associated with GCMs, but GHMs are the greatest source in snow dominated regions. More specifically, results vary depending on the runoff metric, the temporal (annual and seasonal) and regional scale of analysis. For instance, uncertainty contribution from GHMs is higher for low flows than it is for high flows, partly owing to the different processes driving the onset of the two phenomena (e.g. the more direct effect of the GCMs precipitation variability on high flows). This study provides a comprehensive synthesis of where future hydrological extremes are projected to increase and where the ensemble spread is owed to either GCMs or GHMs. Finally, our results underline the importance of using multiple GCMs and GHMs to envelope the overall uncertainty range and the need for improvements in modeling snowmelt and runoff processes to project future hydrological extremes.

Download Full-text

Objective functions used as performance metrics for hydrological models: state-of-the-art and critical analysis

RBRH ◽

10.1590/2318-0331.252020190155 ◽

2020 ◽

Vol 25 ◽

Author(s):

Paloma Mara de Lima Ferreira ◽

Adriano Rolim da Paz ◽

Juan Martín Bravo

Keyword(s):

Time Series ◽

Model Calibration ◽

Hydrological Model ◽

Performance Metrics ◽

Low Flow ◽

Hydrological Models ◽

Objective Functions ◽

Flow Rates ◽

Daily Streamflow ◽

Synthetic Time Series

ABSTRACT Hydrological models (HMs) can be applied for different purposes, and a key step is model calibration using objective functions (OF) to quantify the agreement between observed and calculated discharges. Fully understanding the OF is important to properly take advantage of model calibration and interpret the results. This study evaluates 36 OF proposed in the literature, considering two watersheds of different hydrological regimes. Daily simulated streamflow time-series, using a distributed hydrological model (MGB-IPH), and ten daily streamflow synthetic time-series, generated from the observed and calculated streamflows, were used in the analysis of each watershed. These synthetic data were used to evaluate how does each metric evaluate hypothetical cases that present isolated very well known error behaviors. Despite of all NSE-derived (Nash-Sutcliffe efficiency) metrics that use the square of the residuals in their formulation have shown higher sensitivity to errors in high flows, the ones that use daily and monthly averages of flow rates in absolute terms were more stringent than the others to assess HMs performance. Low flow errors were better evaluated by metrics that use the flow logarithm. The constant presence of zero flow rates deteriorate them significantly, with the exception of the metrics TRMSE (Transformed root mean square error) did not demonstrate this problem. An observed limitation of the formulations of some metrics was that the errors of overestimation or underestimation are compensated. Our results reassert that each metric should be interpreted specifically thinking about the aspects it has been proposed for, and simultaneously taking into account a set of metrics would lead to a broader evaluation of HM ability (e.g. multiobjective model evaluation). We recommend that the use of synthetic time series as those proposed in this work could be useful as an auxiliary step towards better understanding the evaluation of a calibrated hydrological model for each study case, taking into account model capabilities and observed hydrologic regime characteristics.

Download Full-text

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

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-8-619-2011 ◽

2011 ◽

Vol 8 (1) ◽

pp. 619-652 ◽

Cited By ~ 1

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.

Download Full-text

Large-scale runoff generation – parsimonious parameterisation using high-resolution topography

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-6613-2010 ◽

2010 ◽

Vol 7 (5) ◽

pp. 6613-6646

Author(s):

L. Gong ◽

S. Halldin ◽

C.-Y. Xu

Keyword(s):

High Resolution ◽

Large Scale ◽

Storage Capacity ◽

Hydrological Models ◽

Runoff Generation ◽

Model Framework ◽

Topographic Index ◽

Topographic Analysis ◽

Generation Algorithm ◽

Water Storage Capacity

Abstract. World water resources have primarily been analysed by global-scale hydrological models in the last decades. Runoff generation in many of these models are based on process formulations developed at catchments scales. The division between slow runoff (baseflow) and fast runoff is primarily governed by slope and spatial distribution of effective water storage capacity, both acting a very small scales. Many hydrological models, e.g. VIC, account for the spatial storage variability in terms of statistical distributions; such models are generally proven to perform well. The statistical approaches, however, use the same runoff-generation parameters everywhere in a basin. The TOPMODEL concept, on the other hand, links the effective maximum storage capacity with real-world topography. Recent availability of global high-quality, high-resolution topographic data makes TOPMODEL attractive as a basis for a physically-based runoff-generation algorithm at large scales, even if its assumptions are not valid in flat terrain or for deep groundwater systems. We present a new runoff-generation algorithm for large-scale hydrology based on TOPMODEL concepts intended to overcome these problems. The TRG (topography-derived runoff generation) algorithm relaxes the TOPMODEL equilibrium assumption so baseflow generation is not tied to topography. TGR only uses the topographic index to distribute average storage to each topographic index class. The maximum storage capacity is proportional to the range of topographic index and is scaled by one parameter. The distribution of storage capacity within large-scale grid cells is obtained numerically through topographic analysis. The new topography-derived distribution function is then inserted into a runoff-generation framework similar VIC's. Different basin parts are parameterised by different storage capacities, and different shapes of the storage-distribution curves depend on their topographic characteristics. The TRG algorithm is driven by the HydroSHEDS dataset with a resolution of 3'' (around 90 m at the equator). The TRG algorithm was validated against the VIC algorithm in a common model framework in 3 river basins in different climates. The TRG algorithm performed equally well or marginally better than the VIC algorithm with one less parameter to be calibrated. The TRG algorithm also lacked equifinality problems and offered a realistic spatial pattern for runoff generation and evaporation.

Download Full-text

Cross-scale evaluation of catchment- and global-scale hydrological model simulations of drought characteristics across eight large river catchments

10.21203/rs.3.rs-409757/v1 ◽

2021 ◽

Author(s):

Amit Kumar ◽

Simon N Gosling ◽

Matthew F Johnson ◽

Jamal Zaherpour ◽

Guoyong Leng ◽

...

Keyword(s):

Time Series ◽

Large River ◽

Global Scale ◽

Historical Period ◽

Severe Drought ◽

Hydrological Models ◽

Catchment Scale ◽

Hydrological Extremes ◽

Intercomparison Project ◽

Monthly Runoff

Abstract Although global- and catchment-scale hydrological models are often shown to accurately simulate long-term runoff time-series, far less is known about their suitability for capturing hydrological extremes, such as droughts. Here we evaluated runoff simulations from nine catchment scale hydrological models (CHMs) and eight global scale hydrological models (GHMs) for eight large catchments: Upper Amazon, Lena, Upper Mississippi, Upper Niger, Rhine, Tagus, Upper Yangtze and Upper Yellow. The simulations were conducted within the framework of phase 2a of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). We evaluated the ability of the CHMs, GHMs and their respective ensemble means (Ens-CHM and Ens-GHM) to simulate observed monthly runoff and hydrological droughts over 31 years (1971–2001). Observed and simulated hydrological drought events were identified using the Standardised Runoff Index (SRI) and were classified based on intensity. Our results show that for all eight catchments, CHMs out-performed GHMs in monthly runoff estimation showing a better representation of observed runoff than GHMs. The number of drought events identified under different drought categories (i.e. SRI values of -1 to -1.49, -1.5 to -1.99, and ≤-2) varied significantly between models. All the models, as well as the two ensemble means present limited ability to accurately simulate severe drought events in all eight catchments, in terms of their timing and intensity. By analysing the monthly runoff time-series for several extreme droughts over the historical period, we identify room for improvement in the models so that extreme droughts may ultimately be better represented by both CHMs and GHMs.

Download Full-text