Simulating catchment scale river discharges and flood events from large scale atmospheric information: Example of the Upper Rhône River (European Alps)

2021 ◽  
Author(s):  
Caroline Legrand ◽  
Benoît Hingray ◽  
Bruno Wilhelm
Keyword(s):  
Time Series ◽  
Extreme Precipitation ◽  
Large Scale ◽  
Climate Models ◽  
Hydrological Model ◽  
European Alps ◽  
Earth System ◽  
Flood Events ◽  
Catchment Scale ◽  
Mountainous Catchments

<p>Floods are highly destructive natural hazards causing widespread impacts on socio-ecosystems. This hazard could be further amplified with the ongoing climate change, which will likely alter magnitude and frequency of floods. Estimating how flood regimes could change in the future is however not straightforward. The classical approach is to estimate future hydrological regimes from hydrological simulations forced by time series scenarii of weather variables for different future climate scenarii. The development of relevant weather scenarii for this is often critical. To be adapted to the critical space and time scales of the considered basins, weather scenarii are thus typically produced from climate models with downscaling models (either dynamic or statistical).</p><p>In this study, we aim to evaluate the capacity of such a simulation chain to reproduce floods observed in the upper Rhône River (10900 km², European Alps) over the last century. The modeling chain is made up of (i) the atmospheric reanalysis ERA-20C (1900-2010), (ii) the statistical downscaling model Analog, and (iii) the glacio-hydrological model GSM-SOCONT (Glacier and Snowmelt SOil CONTribution model; Schaefli et al., 2005). To assess the performance of this modeling chain, the simulated scenarii of mean areal precipitation and temperature are compared to the observed time series over the common period (1961-2010), whereas the discharge scenarii are compared to the reference time series (1920-2010).</p><p>In this presentation, we will discuss (i) the results obtained by the basic Analog method, namely a flood events underestimation due to an underestimation of extreme precipitation values, in particular 3-day and 5-day extreme precipitation, and (ii) the enhanced results obtained by the improved version of Analog SCAMP (Sequential Constructive Atmospheric Analogues for Multivariate weather Predictions; Raynaud et al., 2020) combined to the Schaake Shuffle method.</p><p>References:</p><p>Schaefli, B., Hingray, B., M. Niggli, M., Musy, A. (2005). A conceptual glacio-hydrological model for high mountainous catchments. Hydrology and Earth System Sciences Discussions, European Geosciences Union, 9, 95-109.</p><p>Raynaud, D., Hingray, B., Evin, G., Favre, A.-C., Chardon, J. (2020). Assessment of meteorological extremes using a synoptic weather generator and a downscaling model based on analogues. Hydrology and Earth System Sciences Discussions, European Geosciences Union, 24(9), 4339-4352.</p>

scholarly journals Improving runoff estimates from regional climate models: a performance analysis in Spain

2012 ◽  
Vol 16 (6) ◽  
pp. 1709-1723 ◽  
Author(s):  
D. González-Zeas ◽  
L. Garrote ◽  
A. Iglesias ◽  
A. Sordo-Ward
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Aridity Index ◽  
Regional Climate Models ◽  
Hydrologic Model ◽  
Direct Runoff ◽  
Interpolation Methods ◽  
Basin Scale

Abstract. An important step to assess water availability is to have monthly time series representative of the current situation. In this context, a simple methodology is presented for application in large-scale studies in regions where a properly calibrated hydrologic model is not available, using the output variables simulated by regional climate models (RCMs) of the European project PRUDENCE under current climate conditions (period 1961–1990). The methodology compares different interpolation methods and alternatives to generate annual times series that minimise the bias with respect to observed values. The objective is to identify the best alternative to obtain bias-corrected, monthly runoff time series from the output of RCM simulations. This study uses information from 338 basins in Spain that cover the entire mainland territory and whose observed values of natural runoff have been estimated by the distributed hydrological model SIMPA. Four interpolation methods for downscaling runoff to the basin scale from 10 RCMs are compared with emphasis on the ability of each method to reproduce the observed behaviour of this variable. The alternatives consider the use of the direct runoff of the RCMs and the mean annual runoff calculated using five functional forms of the aridity index, defined as the ratio between potential evapotranspiration and precipitation. In addition, the comparison with respect to the global runoff reference of the UNH/GRDC dataset is evaluated, as a contrast of the "best estimator" of current runoff on a large scale. Results show that the bias is minimised using the direct original interpolation method and the best alternative for bias correction of the monthly direct runoff time series of RCMs is the UNH/GRDC dataset, although the formula proposed by Schreiber (1904) also gives good results.

scholarly journals Downscaling future precipitation extremes to urban hydrology scales using a spatio-temporal Neyman–Scott weather generator

2016 ◽  
Vol 20 (4) ◽  
pp. 1387-1403 ◽  
Author(s):  
Hjalte Jomo Danielsen Sørup ◽  
Ole Bøssing Christensen ◽  
Karsten Arnbjerg-Nielsen ◽  
Peter Steen Mikkelsen
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Regional Climate ◽  
Weather Generator ◽  
Spatial Extent ◽  
Urban Hydrology ◽  
Precipitation Time Series ◽  
Spatio Temporal

Abstract. Spatio-temporal precipitation is modelled for urban application at 1 h temporal resolution on a 2 km grid using a spatio-temporal Neyman–Scott rectangular pulses weather generator (WG). Precipitation time series used as input to the WG are obtained from a network of 60 tipping-bucket rain gauges irregularly placed in a 40 km  ×  60 km model domain. The WG simulates precipitation time series that are comparable to the observations with respect to extreme precipitation statistics. The WG is used for downscaling climate change signals from regional climate models (RCMs) with spatial resolutions of 25 and 8 km, respectively. Six different RCM simulation pairs are used to perturb the WG with climate change signals resulting in six very different perturbation schemes. All perturbed WGs result in more extreme precipitation at the sub-daily to multi-daily level and these extremes exhibit a much more realistic spatial pattern than what is observed in RCM precipitation output. The WG seems to correlate increased extreme intensities with an increased spatial extent of the extremes meaning that the climate-change-perturbed extremes have a larger spatial extent than those of the present climate. Overall, the WG produces robust results and is seen as a reliable procedure for downscaling RCM precipitation output for use in urban hydrology.

scholarly journals Improving climate model coupling through a complete mesh representation: a case study with E3SM (v1) and MOAB (v5.x)

2020 ◽  
Vol 13 (5) ◽  
pp. 2355-2377
Author(s):  
Vijay S. Mahadevan ◽  
Iulian Grindeanu ◽  
Robert Jacob ◽  
Jason Sarich
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Climate Modeling ◽  
System Modeling ◽  
Spectral Element ◽  
Scientific Workflow ◽  
Model Coupling ◽  
Earth System ◽  
Modeling Framework

Abstract. One of the fundamental factors contributing to the spatiotemporal inaccuracy in climate modeling is the mapping of solution field data between different discretizations and numerical grids used in the coupled component models. The typical climate computational workflow involves evaluation and serialization of the remapping weights during the preprocessing step, which is then consumed by the coupled driver infrastructure during simulation to compute field projections. Tools like Earth System Modeling Framework (ESMF) (Hill et al., 2004) and TempestRemap (Ullrich et al., 2013) offer capability to generate conservative remapping weights, while the Model Coupling Toolkit (MCT) (Larson et al., 2001) that is utilized in many production climate models exposes functionality to make use of the operators to solve the coupled problem. However, such multistep processes present several hurdles in terms of the scientific workflow and impede research productivity. In order to overcome these limitations, we present a fully integrated infrastructure based on the Mesh Oriented datABase (MOAB) (Tautges et al., 2004; Mahadevan et al., 2015) library, which allows for a complete description of the numerical grids and solution data used in each submodel. Through a scalable advancing-front intersection algorithm, the supermesh of the source and target grids are computed, which is then used to assemble the high-order, conservative, and monotonicity-preserving remapping weights between discretization specifications. The Fortran-compatible interfaces in MOAB are utilized to directly link the submodels in the Energy Exascale Earth System Model (E3SM) to enable online remapping strategies in order to simplify the coupled workflow process. We demonstrate the superior computational efficiency of the remapping algorithms in comparison with other state-of-the-science tools and present strong scaling results on large-scale machines for computing remapping weights between the spectral element atmosphere and finite volume discretizations on the polygonal ocean grids.

Characterization of extreme meteo-hydrological events in the Alpine Region: historical picture and future scenarios

2020 ◽  
Author(s):  
Matteo Pesce ◽  
Larisa Tarasova ◽  
Ralf Merz ◽  
Jost von Hardenberg ◽  
Alberto Viglione
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Temporal Dynamics ◽  
Regional Scale ◽  
Alpine Region ◽  
Precipitation Extremes ◽  
Climate Indices ◽  
European Alps ◽  
Flood Events ◽  
River Flood

<p>In the European Alps, climate change has determined changes in extreme precipitation and river flood events, which impact the population living downstream with increasing frequency. The objectives of our work are:</p><ol><li>to determine what types of precipitation extremes and river flood events occur in the Alpine Region, based on their generating mechanisms (e.g., frontal convergence storms, convective storms, snow-melt floods, rain-on-snow floods, short and long rain floods, flash floods, ...)</li> <li>to determine the spatial and seasonal distribution of these event types (e.g., their dependence on elevation, geographical location, catchment size, ...) and how precipitation extremes relate to the floods they produce (e.g., the role of snow precipitation and accumulation)</li> <li>to determine whether the event type distribution is changing and will change in the future (e.g., due to climate change).  </li> </ol><p>To these aims, we will compile and analyze historical time series of precipitation and discharge in order to identify events in terms of intensity, duration, and spatial extent. We will use the ETCCDI indices as a measure of the precipitation distribution and hydrograph separation techniques for flow events, following the methodology of Tarasova et al. (2018). We will then characterize each event in terms of generation mechanisms. Furthermore, we will analyze the frequency and magnitude of the different event types in different locations and time of the year and determine whether clusters exist by applying automatic techniques (e.g. K-means clustering algorithm). Finally, we will correlate statistics of precipitation and flood event types with climate indices related to large scale atmospheric circulation, such as Atmospheric Blocking, NAO, etc. (Ciccarelli et al. 2008). Results will be then used for the projection of future storm and flood scenarios.</p><p>We will first apply the methodology in Piedmont by comparing the station-based time series with the NWIOI dataset (ARPA Piemonte) and reanalysis datasets by ECMWF (ERA5, ERA5-Land). We will use a rainfall-runoff model at the daily and sub-daily timescale, through calibration at the regional scale, useful for the simulation of soil saturation and snowpack. We expect to find a statistical correlation between the different datasets, but with changing statistical features over space and time within the single datasets. We aim to provide a detailed picture of the different types of events according to the spatial location and season. The results will be useful, from a scientific perspective, to better understand storm and flood regimes and their change in the Alpine Region, and, from a practical perspective, to better mitigate the risk associated with the occurrence of extreme events.      </p><p>Ciccarelli, N., Von Hardenberg, J., Provenzale, A., Ronchi, C., Vargiu, A., & Pelosini, R. (2008). Climate variability in north-western Italy during the second half of the 20th century. Global and Planetary Change, 63(2-3), 185-195. https://doi.org/10.1016/j.gloplacha.2008.03.006</p><p>Tarasova, L., Basso, S., Zink, M., & Merz, R. (2018). Exploring controls on rainfall-runoff events: 1. Time series-based event separation and temporal dynamics of event runoff response in Germany. Water Resources Research, 54, 7711–7732. https://doi.org/10.1029/2018WR022587</p>

scholarly journals Investigating the Effects of Subgrid Cell Dynamic Heterogeneity on the Large-Scale Modeling of Albedo in Boreal Forests*

2016 ◽  
Vol 20 (5) ◽  
pp. 1-23 ◽  
Author(s):  
Jean-Sébastien Landry ◽  
Navin Ramankutty ◽  
Lael Parrott
Keyword(s):  
Boreal Forests ◽  
Radiative Forcing ◽  
Large Scale ◽  
Climate Models ◽  
Computing Time ◽  
Grid Cell ◽  
Tree Cover ◽  
Similar Amount ◽  
Earth System ◽  
Dynamic Heterogeneity

Abstract Stand-clearing disturbances, which remove most of the tree cover but are followed by forest regrowth, affect extensive areas annually, yet each event is usually much smaller than a typical grid cell in Earth system climate models. This study argues that the approach taken to account for the resulting subgrid cell dynamic heterogeneity substantially affects the computation of land–atmosphere exchanges. The authors investigated in a simplified model the effects of three such approaches on the computation of albedo over boreal forests. It was found that the simplest approach—in which any new disturbance-created patch was immediately merged with the rest of the grid cell—underestimated the annual reflected solar radiation by ~3 W m−2 on average (a relative error of 15%) compared with the most accurate approach—in which albedo computations were performed for each individual subgrid patch. This study also investigated an intermediate approach, in which each patch was tracked individually, but albedo was estimated from a much smaller number of subgrid tiles grouping patches having a similar amount of tree cover. Results from this third approach converged quickly toward the most accurate results as the number of tiles increased and were robust to changes in the thresholds used to assign patches to specific tiles. When computing time prevents implementing the most accurate approach in Earth system climate models, the results advocate for using strategies similar to the intermediate approach in order to avoid biasing the net radiative forcing of stand-clearing disturbances toward a warming impact, at least over boreal forests.

scholarly journals Improving runoff estimates from regional climate models: a performance analysis in Spain

2012 ◽  
Vol 9 (1) ◽  
pp. 175-214
Author(s):  
D. González-Zeas ◽  
L. Garrote ◽  
A. Iglesias ◽  
A. Sordo-Ward
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Hydrologic Model ◽  
Direct Runoff ◽  
Interpolation Methods ◽  
Basin Scale ◽  
The Impact

Abstract. An important aspect to assess the impact of climate change on water availability is to have monthly time series representative of the current situation. In this context, a simple methodology is presented for application in large-scale studies in regions where a properly calibrated hydrologic model is not available, using the output variables simulated by regional climate models (RCMs) of the European project PRUDENCE under current climate conditions (period 1961–1990). The methodology compares different interpolation methods and alternatives to generate annual times series that minimize the bias with respect to observed values. The objective is to identify the best alternative to obtain bias-corrected, monthly runoff time series from the output of RCM simulations. This study uses information from 338 basins in Spain that cover the entire mainland territory and whose observed values of naturalised runoff have been estimated by the distributed hydrological model SIMPA. Four interpolation methods for downscaling runoff to the basin scale from 10 RCMs are compared with emphasis on the ability of each method to reproduce the observed behavior of this variable. The alternatives consider the use of the direct runoff of the RCMs and the mean annual runoff calculated using five functional forms of the aridity index, defined as the ratio between potential evaporation and precipitation. In addition, the comparison with respect to the global runoff reference of the UNH/GRDC dataset is evaluated, as a contrast of the "best estimator" of current runoff on a large scale. Results show that the bias is minimised using the direct original interpolation method and the best alternative for bias correction of the monthly direct runoff time series of RCMs is the UNH/GRDC dataset, although the formula proposed by Schreiber also gives good results.

scholarly journals Snowball Earth Bifurcations in a Fully-Implicit Earth System Model

2021 ◽  
Vol 31 (06) ◽  
pp. 2130017
Author(s):  
Thomas E. Mulder ◽  
Heiko Goelzer ◽  
Fred W. Wubs ◽  
Henk A. Dijkstra
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Ocean Model ◽  
Earth System Model ◽  
System Model ◽  
Snowball Earth ◽  
Earth System ◽  
Geological Evidence ◽  
Fully Implicit ◽  
Additional Support

There is now much geological evidence that the Earth was fully glaciated during several periods in the geological past (about 700[Formula: see text]Myr ago) and attained a so-called Snowball Earth (SBE) state. Additional support for this idea has come from climate models of varying complexity that show transitions to SBE states and undergo hysteresis under changes in solar radiation. In this paper, we apply large-scale bifurcation analyses to a novel, fully-implicit Earth System Model of Intermediate Complexity (I-EMIC) to study SBE transitions. The I-EMIC contains a primitive equation ocean model, a model for atmospheric heat and moisture transport, a sea ice component and formulations for the adjustment of albedo over snow and ice. With the I-EMIC, high-dimensional branches of the SBE bifurcation diagram are obtained through parameter continuation. We are able to identify stable and unstable equilibria and uncover an intricate bifurcation structure associated with the ice-albedo feedback. Moreover, large-scale linear stability analyses are performed near major bifurcations, revealing the spatial nature of destabilizing perturbations.

scholarly journals Brief Communication: Critical Infrastructure impacts of the 2021 mid-July western European flood event

2021 ◽  
Author(s):  
Elco Koks ◽  
Kees Van Ginkel ◽  
Margreet Van Marle ◽  
Anne Lemnitzer
Keyword(s):  
The Netherlands ◽  
Flood Risk ◽  
Extreme Precipitation ◽  
Large Scale ◽  
Critical Infrastructure ◽  
Risk Assessments ◽  
Flood Events ◽  
Infrastructure Systems ◽  
River Flood ◽  
Sewage Systems

Abstract. Germany, Belgium and The Netherlands were hit by extreme precipitation and flooding in July 2021. This Brief Communication provides an overview of the impacts to large-scale critical infrastructure systems and how recovery has progressed during the first six months after the event. The results show that Germany and Belgium were particularly affected, with many infrastructure assets severely damaged or completely destroyed. Impacts range from completely destroyed bridges and sewage systems, to severely damaged schools and hospitals. We find that large-scale risk assessments, often focused on larger (river) flood events, do not find these local, but severe, impacts. This may be the result of limited availability of validation material. As such, this study will not only help to better understand how critical infrastructure can be affected by flooding, but can also be used as validation material for future flood risk assessments.

scholarly journals Copula Bias Correction for Extreme Precipitation in Reanalysis Data over a Greek Catchment

Proceedings ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 4
Author(s):  
Georgia Lazoglou ◽  
Christina Anagnostopoulou ◽  
Charalampos Skoulikaris ◽  
Konstantia Tolika
Keyword(s):  
Bias Correction ◽  
Extreme Precipitation ◽  
Large Scale ◽  
Climate Models ◽  
Reanalysis Data ◽  
Distribution Method ◽  
Maximum Rainfall ◽  
The Real ◽  
Maximum Precipitation ◽  
Extreme Precipitation Events

The projection of extreme precipitation events with higher accuracy and reliability that engender severe socioeconomic impacts more frequently is considered a priority research topic in the scientific community. Although large-scale initiatives for monitoring meteorological and hydrological variables exist, the lack of data is still evident particularly in regions with complex topographic characteristics. The latter results in the use of reanalysis data or data derived from regional climate models, however both datasets are biased to the observations resulting in nonaccurate results in hydrological studies. The current research presents a newly developed statistical method for the bias correction of the maximum rainfall amount at watershed scale. In particular, the proposed approach necessitates the coupling of a spatial distribution method, namely Thiessen polygons, with a multivariate probabilistic distribution method, namely copulas, for the bias correction of the maximum precipitation. The case study area is the Nestos River basin where the several extreme episodes that have been recorded have direct impacts to the regional agricultural economy. Thus, using daily data by three monitoring stations and daily reanalysis precipitation values from the grids closest to these stations, the results demonstrated that the bias corrected maximum precipitation totals (greater than 90%) is much closer to the real max precipitation totals, while the respective reanalysis value underestimates the real precipitation totals. The overall improvement of the output shows that the proposed Thiessen-copula method could constitute a significant asset to hydrologic simulations.

scholarly journals Sensitivity of Heavy Precipitation Forecasts to Small Modifications of Large-Scale Weather Patterns for the Elbe River

2010 ◽  
Vol 11 (3) ◽  
pp. 770-780 ◽  
Author(s):  
Ingo Schlüter ◽  
Gerd Schädler
Keyword(s):  
Extreme Precipitation ◽  
Large Scale ◽  
Spatiotemporal Variability ◽  
Small Scale ◽  
Synoptic Situation ◽  
Flood Events ◽  
Weather Patterns ◽  
Extreme Precipitation Events ◽  
The Impact ◽  
Precipitation Events

Abstract Extreme flood events are caused by long-lasting and/or intensive precipitation. The detailed knowledge of the distribution, intensity, and spatiotemporal variability of precipitation is, therefore, a prerequisite for hydrological flood modeling and flood risk management. For hydrological modeling, temporal and spatial high-resolution precipitation data can be provided by meteorological models. This study deals with the question of how small changes in the synoptic situation affect the characteristics of extreme forecasts. For that purpose, two historic extreme precipitation events were hindcasted using the Consortium for Small Scale Modeling (COSMO) model of the German Weather Service (DWD) with different grid resolutions (28, 7, and 2.8 km), where the domains with finer resolutions were nested into the ones with coarser resolution. The results show that the model is capable of simulating such extreme precipitation events in a satisfactory way. To assess the impact of small changes in the synoptic situations on extreme precipitation events, the large-scale atmospheric fields were shifted to north, south, east, and west with respect to the orography by about 28 and 56 km, respectively, in one series of runs while in another series, the relative humidity and temperature were increased to modify the amount of precipitable water. Both series were performed for the Elbe flood events in August 2002 and January 2003, corresponding to two very different synoptic situations. The results show that the modeled precipitation can be quite sensitive to small changes of the synoptic situation with changes in the order of 20% for the maximum daily precipitation and that the types of synoptic situations play an important role. While van Bebber weather conditions, of Mediterranean origin, were quite sensitive to modifications, more homogeneous weather patterns were less sensitive.

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