Accounting for precipitation asymmetry in a multiplicative random cascade disaggregation model

Analytical multiplicative random cascades (MRCs) are widely used for the temporal disaggregation of coarse-resolution precipitation time series. This class of models applies scaling models to represent the dependence of the cascade generator on the temporal scale and the precipitation intensity. Although determinant, the dependence on the external precipitation pattern is usually disregarded in the analytical scaling models. Our work presents a unified MRC modelling framework that allows the cascade generator to depend in a continuous way on the temporal scale, precipitation intensity and a so-called precipitation asymmetry index. Different MRC configurations are compared for 81 locations in Switzerland with contrasted climates. The added value of the dependence of the MRC on the temporal scale appears to be unclear, unlike what was suggested in previous works. Introducing the precipitation asymmetry dependence into the model leads to a drastic improvement in model performance for all statistics related to precipitation temporal persistence (wet–dry transition probabilities, lag-n autocorrelation coefficients, lengths of dry–wet spells). Accounting for precipitation asymmetry seems to solve this important limitation of previous MRCs. The model configuration that only accounts for the dependence on precipitation intensity and asymmetry is highly parsimonious, with only five parameters, and provides adequate performances for all locations, seasons and temporal resolutions. The spatial coherency of the parameter estimates indicates a real potential for regionalisation and for further application to any location in Switzerland.</p

Effects of Increased Wind Power Generation on Mid-Norway’s Energy Balance under Climate Change: A Market Based Approach

Thanks to its huge water storage capacity, Norway has an excess of energy generation at annual scale, although significant regional disparity exists. On average, the Mid-Norway region has an energy deficit and needs to import more electricity than it exports. We show that this energy deficit can be reduced with an increase in wind generation and transmission line capacity, even in future climate scenarios where both mean annual temperature and precipitation are changed. For the considered scenarios, the deficit observed in winter disappears, i.e., when electricity consumption and prices are high. At the annual scale, the deficit behaviour depends more on future changes in precipitation. Another consequence of changes in wind production and transmission capacity is the modification of electricity exchanges with neighbouring regions which are also modified both in terms of average, variability and seasonality

Comprehensive space–time hydrometeorological simulations for estimating very rare floods at multiple sites in a large river basin

Estimates for rare to very rare floods are limited by the relatively short streamflow records available. Often, pragmatic conversion factors are used to quantify such events based on extrapolated observations, or simplifying assumptions are made about extreme precipitation and resulting flood peaks. Continuous simulation (CS) is an alternative approach that better links flood estimation with physical processes and avoids assumptions about antecedent conditions. However, long-term CS has hardly been implemented to estimate rare floods (i.e. return periods considerably larger than 100 years) at multiple sites in a large river basin to date. Here we explore the feasibility and reliability of the CS approach for 19 sites in the Aare River basin in Switzerland (area: 17 700 km2) with exceedingly long simulations in a hydrometeorological model chain. The chain starts with a multi-site stochastic weather generator used to generate 30 realizations of hourly precipitation and temperature scenarios of 10 000 years each. These realizations were then run through a bucket-type hydrological model for 80 sub-catchments and finally routed downstream with a simplified representation of main river channels, major lakes and relevant floodplains in a hydrologic routing system. Comprehensive evaluation over different temporal and spatial scales showed that the main features of the meteorological and hydrological observations are well represented and that meaningful information on low-probability floods can be inferred. Although uncertainties are still considerable, the explicit consideration of important processes of flood generation and routing (snow accumulation, snowmelt, soil moisture storage, bank overflow, lake and floodplain retention) is a substantial advantage. The approach allows for comprehensively exploring possible but unobserved spatial and temporal patterns of hydrometeorological behaviour. This is of particular value in a large river basin where the complex interaction of flows from individual tributaries and lake regulations are typically not well represented in the streamflow observations. The framework is also suitable for estimating more frequent floods, as often required in engineering and hazard mapping

Comprehensive space-time hydrometeorological simulations for estimating very rare floods at multiple sites in a large river basin

Estimates for rare to very rare floods are limited by the relatively short streamflow records available. Often, pragmatic conversion factors are used to quantify such events based on extrapolated observations, or simplifying assumptions are made about extreme precipitation and resulting flood peaks. Continuous simulation (CS) is an alternative approach that better links flood estimation with physical processes and avoids assumptions about antecedent conditions. However, long-term CS has hardly been implemented to estimate rare floods (i.e. return periods considerably larger than 100 years) at multiple sites in a large river basin to date. Here we explore the feasibility and reliability of the CS approach for 19 sites in the Aare River basin in Switzerland (area: 17 700 km2) with exceedingly long simulations in a hydrometeorological model chain. The chain starts with a multi-site stochastic weather generator used to generate 30 realizations of hourly precipitation and temperature scenarios of 10 000 years each. These realizations were then run through a bucket-type hydrological model for 80 sub-catchments and finally routed downstream with a simplified representation of main river channels, major lakes and relevant floodplains in a hydrologic routing system. Comprehensive evaluation over different temporal and spatial scales showed that the main features of the meteorological and hydrological observations are well represented and that meaningful information on low-probability floods can be inferred. Although uncertainties are still considerable, the explicit consideration of important processes of flood generation and routing (snow accumulation, snowmelt, soil moisture storage, bank overflow, lake and floodplain retention) is a substantial advantage. The approach allows for comprehensively exploring possible but unobserved spatial and temporal patterns of hydrometeorological behaviour. This is of particular value in a large river basin where the complex interaction of flows from individual tributaries and lake regulations are typically not well represented in the streamflow observations. The framework is also suitable for estimating more frequent floods, as often required in engineering and hazard mapping

Uncertainty components estimates in transient climate projections. Bias of moment-based estimators in the single time and time series approaches.

In most climate impact studies, model uncertainty in projections is estimatedas the empirical variance of the climate responses for the different modelingchains. These estimates are however biased. We explore the importance ofthe bias for a simple but classical configuration where uncertainties in projectionsare composed of two sources: model uncertainty and internal climatevariability. We derive exact formulation of the bias. It is positive, i.e. theempirical variance tends to overestimate the true model uncertainty variance.It can be especially high when a single time ANOVA is used for the analysis.In the most critical configurations, when the number of members availablefor each modeling chain is small (≤ 3) and when internal variability explainsmost of total uncertainty variance (75% or more), the overestimation is higherthan 100% of the true model uncertainty variance. The bias is considerablysmaller with a time series ANOVA approach, owing to the multiple time stepsaccounted for. The longer the transient time period used for the analysis,the smaller the bias. When a quasi-ergodic ANOVA approach is applied todecadal data for the whole 1980-2100 period, the bias is up to 2.5 to 20 timessmaller than that obtained with a single time approach, depending on the projectionlead time. Whatever the approach, the bias is likely to be not negligiblefor a large number of climate impact studies resulting in a likely large overestimationof the contribution of model uncertainty to total variance. In manycases, classical empirical estimators of model uncertainty should be thus biascorrected

Generation of meteorological scenarios from NCEP reanalyses. Application for the generation of flood scenarios for the Rhone upstream to Lake Leman

Optimal water resources management requires hydrological scenarios for the climate situation under consideration. These scenarios may be produced from meteorological scenarios thanks to an appropriate hydrological model. The Laboratory "Hydrology and Land Improvement" (HYDRAM) developed a combined downscaling model for the multisite stochastic generation of the meteorological variables required for the generation of such scenarios. The model combines a statistical downscaling model and a k-nearest neighbour resampling approach to generate hourly precipitation and temperature series from NCEP reanalyses. It was applied for the upper Rhone catchment. Observed statistics are well reproduced for both meteorological variables. Then it was used for the generation of a suite of flood scenarios at different hydrological stations of the studied catchment. The stochastic generator can also be applied to downscale climate experiments from global and/or regional climate models for future climate conditions

Generation of meteorological scenarios from NCEP reanalyses. Application for the generation of flood scenarios for the Rhone upstream to Lake Leman

International audienceOptimal water resources management requires hydrological scenarios for the climate situation under consideration. These scenarios may be produced from meteorological scenarios thanks to an appropriate hydrological model. The Laboratory « Hydrology and Land Improvement » (HYDRAM) developed a combined downscaling model for the multisite stochastic generation of the meteorological variables required for the generation of such scenarios. The model combines a statistical downscaling model and a k-nearest neighbour resampling approach to generate hourly precipitation and temperature series from NCEP reanalyses. It was applied for the upper Rhone catchment. Observed statistics are well reproduced for both meteorological variables. Then it was used for the generation of a suite of flood scenarios at different hydrological stations of the studied catchment. The stochastic generator can also be applied to downscale climate experiments from global and / or regional climate models for future climate conditions

Choix des pas de temps et d'espace pour des modélisations parcimonieuses en hydrologie des crues - Optimal space and time scales for parsimonious rainfall-runoff models

International audienceThe proposed methodology allows for the identification of time and space scales to be satisfied for the development of a parsimonious rainfall-runoff model. The maximum acceptable time step (MATS) is first determined so that the dynamical hydrological response of the basin can be properly represented. A characteristic time of the basin hydrological response is used therefore. The MATS allows next to estimate the maximum acceptable space scale with respect to the geostatistical properties of the precipitation cumulated over the MATS. As a result, it is possible to define if the basin can be represented for the MATS by a lumped hydrological model or, if not, the minimum number of sub-basins needed for a reasonable representation of the precipitation spatial variability

Balanced estimate and uncertainty assessment of European climate change using the large EURO-CORDEX regional climate model ensemble

Large Multiscenarios Multimodel Ensembles (MMEs) of regional climate model (RCM) experiments driven by Global Climate Models (GCMs) are made available worldwide, and aim at providing robust estimates of climate changes and associated uncertainties. Due to many missing combinations of emission scenarios and climate models leading to sparse Scenario-GCM-RCM matrices, these large ensembles are however very unbalanced, which makes uncertainty analyses impossible with standard approaches. In this paper, the uncertainty assessment is carried out by applying an advanced statistical approach, called QUALYPSO, to a very large ensemble of 87 EURO-CORDEX climate projections, the largest MME based on regional climate models ever produced in Europe. This analysis provides a detailed description of this MME, including i) balanced estimates of mean changes for near-surface temperature and precipitation in Europe, ii) the total uncertainty of projections and its partition as a function of time, and iii) the list of the most important contributors to the model uncertainty. For changes of total precipitation and mean temperature in winter (DJF) and summer (JJA), the uncertainty due to RCMs can be as large as the uncertainty due to GCMs at the end of the century (2071-2099). Both uncertainty sources are mainly due to a small number of individual models clearly identified. Due to the highly unbalanced character of the MME, mean estimated changes can drastically differ from standard average estimates based on the raw ensemble of opportunity. For the RCP4.5 emission scenario in Central-Eastern Europe for instance, the difference between balanced and direct estimates are up to 0.8 • C for summer temperature changes and up to 20% for summer precipitation changes at the end of the century

Generating hourly mean areal precipitation times series with an at-site weather generator in Switzerland

International audienceContinuous hydrological simulation is a powerful approach for generating longterm series of river discharges used for hydrological analyses. This approach requires as inputs precipitation time series generated by a stochastic weather generator (WGEN) to simulate discharge time series. For small catchments where a lumped hydrological model is suitable, the weather generator needs to generate time series of mean areal precipitation (MAP). Here we assess the ability of an at-site hybrid WGEN to generate time series of MAP for a set of test areas ranging from 9 to 1,089 km 2 . The generator is composed of a model based on a Markov chain model used to generate time series of daily MAP, and a multiplicative random cascade used to disaggregate them to an hourly resolution. The work is carried out at several test locations in Switzerland with different precipitation regimes. The parameters of the model are estimated on the observed MAP time series extracted from CombiPrecip, a 1 km 2 resolution radar-gauge product of precipitation assimilating rain gauges and radar data. For each test location and each test area, 100-year time series are generated and compared with the observed MAP time series. Whatever the location and spatial scale considered, the performance of the WGEN is satisfactory. The model reproduces the observed standard statistics and extreme precipitation of observed MAP very well. At an hourly resolution, better results are obtained at larger spatial scales, while no difference is noticed at a daily resolution. The study shows that using this hybrid WGEN is possible to model and generate MAP for areas ranging from 9 to 1,089 km 2 . Moreover, this particular WGEN is easy to implement for end-user applications. The modelling approach is even more promising as high-resolution gridded precipitation data are expected to become increasingly available worldwide, offering a source of data to calibrate the hybrid model