Variations in Discharge Volumes for Hydropower Generation in Switzerland

This study analyses the way climatic variations over the last century impacted the volumes of water available for hydropower production in Switzerland. The analysis relied on virtual intakes located all over Switzerland, which were assumed to be fed by water from mesoscale catchments. Intake capacities were designed using flow duration curves. The results show that the overall warming and increased winter precipitation observed in recent decades have led to more balanced discharge behaviours in rivers and more favourable conditions for electricity production than most periods in the past. In lower-altitude regions of Switzerland, the annual volume of water available for electricity production has not changed significantly; however, significantly more water is available in winters, while less is available during summers. In higher-altitude regions like the Swiss Alps, especially in glaciated catchment areas, significantly more water is available in both seasons; in other words, the annual volume of water available for hydropower production is significantly higher in these areas when compared to earlier periods. Comparison of these results with the actual amount of hydroelectricity produced over the same period reveals that hydrological variations cannot fully explain the variations in power production observed. Plant-specific analyses are needed of the impact of climatic changes on water managemen

Impact of the choice of a reanalysis dataset on statistical downscaling of precipitation

Statistical downscaling techniques based on a perfect prognosis approach often rely on reanalyses to infer the statistical relationship between synoptic predictors and the local variable of interest, here daily precipitation. Nowadays, multiple global reanalysis datasets are available. These are generated by different atmospheric models with different assimilation techniques and present various spatial resolutions. The context of the application of statistical downscaling might drive the choice of an appropriate dataset, for example when the archive length is a leading criterion. However, in many studies a reanalysis dataset is subjectively chosen, according to the user's preferences or the ease of access. The impact of this choice on the results of the downscaling procedure is rarely considered and no comprehensive comparison has been undertaken so far. The present work focused on the analogue method, which is a statistical downscaling technique. It relies on analogy in terms of synoptic-scale predictors with situations in the past to provide a probabilistic prediction for the day of interest. In order to quantify the impact of the datasets, ten different global reanalyses (NCEP Reanalysis I and II, ERA-Interim, NCEP CFSR, JMA JRA-55 and JRA-55C, NASA MERRA-2, NOAA-CIRES 20CR-2c, ERA-20C, and CERA-20C) were compared in seven variants of the analogue method, over 301 precipitation stations in Switzerland. Although all reanalysis datasets are usually assumed very good over Europe, significant differences in terms of performance of precipitation prediction were identified. The choice of the dataset can have a larger impact than the choice of the predictor variables. The impact of the reanalyses was found to increase with the complexity of the analogue method, when local variables are added, such as moisture, as compared to synoptic predictors, such as the geopotential height. As expected, the output spatial resolution of the reanalyses was found to have larger impact on local variables as well. Output resolutions below one degree were found to have generally limited to no benefit. Reanalyses with longer archives allow increasing the pool of potential analogues resulting in higher performances. However, when adding variables affected by errors in a more distant past, the skill decreased again

Using genetic algorithms to explore new predictor variables for statistical precipitation downscaling with analog methods.

Analog methods (AMs) allow for the prediction of local meteorological variables of interest(predictand), such as the daily precipitation, on the basis of synoptic variables (predictors).They rely on the hypothesis that similar situations at the synoptic scale are likely to result insimilar local weather conditions. AMs can rely on outputs of numerical weather predictionmodels in the context of operational forecasting or outputs of climate models in thecontext of climate impact studies. The predictors archive is usually a reanalysisdataset. Different meteorological variables from the NCEP reanalysis 1 were assessed after itsrelease to identify the best predictors for daily precipitation. This former work provided abasis on the top of which more complex methods were developed by adding additionalvariables in a stepwise way. However, the first predictors of the method often remain thesame, and the selection of new predictors is done manually. Nowadays, several newreanalysis datasets are available and were proven more skilful for analog methods than theNCEP reanalysis 1. The accuracy of several variables has significantly improved and morevariables are now available than before. Therefore, the former selection of predictor variablesmight not be optimal anymore. Different variables from various reanalyses shouldbe assessed, which can turn out to be a cumbersome task if done manually andextensively. Genetic algorithms (GAs) were shown to successfully optimize the parameters of theAMs, such as the spatial domain on which the predictors are compared, the selection of thepressure levels and the temporal windows of the predictors, a weighting between predictors,and the number of analog dates to select. GAs can jointly optimize all parameters of AMs andget closer to a global optimum by taking into account the dependencies between parameters.Moreover, GAs can objectively infer parameters that were previously assessed manually, andcan take into account new degrees of freedom. The mutation operator of GAs was identifiedas a key element for this application, and new variants were developed that provedefficient, such as the chromosome of adaptive search radius, which takes no controlparameter. Therefore, we propose using GAs to explore the variables from three reanalyses(MERRA-2, ERA-interim, CFSR) and select the most relevant ones, along with theappropriate analogy criteria. Although the expert’s expertise remains necessary to supervisethe selection of predictors, GAs facilitate the exploration of large datasets. The first testsproved the potential of this approach with the selection of unexpected – but yet relevant –combinations of variables and analogy criteria

Comparison of present global reanalysis datasets in the context of a statistical downscaling method for precipitation prediction

The analogue method is a statistical downscaling method for precipitation prediction. It uses similarity in terms of synoptic-scale predictors with situations in the past in order to provide a probabilistic prediction for the day of interest. It has been used for decades in a context of weather or flood forecasting, and is more recently also applied to climate studies, whether for reconstruction of past weather conditions or future climate impact studies. In order to evaluate the relationship between synoptic scale predictors and the local weather variable of interest, e.g. precipitation, reanalysis datasets are necessary. Nowadays, the number of available reanalysis datasets increases. These are generated by different atmospheric models with different assimilation techniques and offer various spatial and temporal resolutions. A major difference between these datasets is also the length of the archive they provide. While some datasets start at the beginning of the satellite era (1980) and assimilate these data, others aim at homogeneity on a longer period (e.g. 20th century) and only assimilate conventional observations. The context of the application of analogue methods might drive the choice of an appropriate dataset, for example when the archive length is a leading criterion. However, in many studies, a reanalysis dataset is subjectively chosen, according to the user's preferences or the ease of access. The impact of this choice on the results of the downscaling procedure is rarely considered and no comprehensive comparison has been undertaken so far. In order to fill this gap and to advise on the choice of appropriate datasets, nine different global reanalysis datasets were compared in seven distinct versions of analogue methods, over 300 precipitation stations in Switzerland. Significant differences in terms of prediction performance were identified. Although the impact of the reanalysis dataset on the skill score varies according to the chosen predictor, be it atmospheric circulation or thermodynamic variables, some hierarchy between the datasets is often preserved. This work can thus help choosing an appropriate dataset for the analogue method, or raise awareness of the consequences of using a certain dataset

Using genetic algorithms to achieve an automatic and global optimization of analogue methods for statistical downscaling of precipitation

Analogue methods (AMs) rely on the hypothesis that similar situations, in terms of atmospheric circulation, are likely to result in similar local or regional weather conditions. These methods consist of sampling a certain number of past situations, based on different synoptic-scale meteorological variables (predictors), in order to construct a probabilistic prediction for a local weather variable of interest (predictand). They are often used for daily precipitation prediction, either in the context of real-time forecasting, reconstruction of past weather conditions, or future climate impact studies. The relationship between predictors and predictands is defined by several parameters (predictor variable, spatial and temporal windows used for the comparison, analogy criteria, and number of analogues), which are often calibrated by means of a semi-automatic sequential procedure that has strong limitations. AMs may include several subsampling levels (e.g. first sorting a set of analogs in terms of circulation, then restricting to those with similar moisture status). The parameter space of the AMs can be very complex, with substantial co-dependencies between the parameters. Thus, global optimization techniques are likely to be necessary for calibrating most AM variants, as they can optimize all parameters of all analogy levels simultaneously. Genetic algorithms (GAs) were found to be successful in finding optimal values of AM parameters. They allow taking into account parameters inter-dependencies, and selecting objectively some parameters that were manually selected beforehand (such as the pressure levels and the temporal windows of the predictor variables), and thus obviate the need of assessing a high number of combinations. The performance scores of the optimized methods increased compared to reference methods, and this even to a greater extent for days with high precipitation totals. The resulting parameters were found to be relevant and spatially coherent. Moreover, they were obtained automatically and objectively, which reduces efforts invested in exploration attempts when adapting the method to a new region or for a new predictand. In addition, the approach allowed for new degrees of freedom, such as a weighting between the pressure levels, and non overlapping spatial windows. Genetic algorithms were then used further in order to automatically select predictor variables and analogy criteria. This resulted in interesting outputs, providing new predictor-criterion combinations. However, some limitations of the approach were encountered, and the need of the expert input is likely to remain necessary. Nevertheless, letting GAs exploring a dataset for the best predictor for a predictand of interest is certainly a useful tool, particularly when applied for a new predictand or a new region under different climatic characteristics

Automatic and global optimization of the Analogue Method for statistical downscaling of precipitation - Which parameters can be determined by Genetic Algorithms?

The Analogue Method (AM) aims at forecasting a local meteorological variable of interest (the predictand), often the daily precipitation total, on the basis of a statistical relationship with synoptic predictor variables. A certain number of similar situations are sampled in order to establish the empirical conditional distribution which is considered as the prediction for a given date. The method is used in operational medium-range forecasting in several hydropower companies or flood forecasting services, as well as in climate impact studies. The statistical relationship is usually established by means of a semi-automatic sequential procedure that has strong limitations: it is made of successive steps and thus cannot handle parameters dependencies, and it cannot automatically optimize certain parameters, such as the selection of the pressure levels and the temporal windows on which the predictors are compared. A global optimization technique based on Genetic Algorithms was introduced in order to surpass these limitations and to provide a fully automatic and objective determination of the AM parameters. The parameters that were previously assessed manually, such as the selection of the pressure levels and the temporal windows, on which the predictors are compared, are now automatically determined. The next question is: Are Genetic Algorithms able to select the meteorological variable, in a reanalysis dataset, that is the best predictor for the considered predictand, along with the analogy criteria itself? Even though we may not find better predictors for precipitation prediction that the ones often used in Europe, due to numerous other studies which consisted in systematic assessments, the ability of an automatic selection offers new perspectives in order to adapt the AM for new predictands or new regions under different meteorological influences

AtmoSwing, an analog technique model for statistical downscaling and forecasting

Analog methods (AMs) allow predicting local meteorological variables of interest (predictand), such as the daily precipitation, based on synoptic variables (predictors). They rely on the hypothesis that similar atmospheric conditions are likely to result in similar local effects. The statistical relationship is first defined (e.g. which predictors, and how many subsampling steps) and calibrated (e.g. which spatial domain, and how many analogues) before being applied to the target period, may it be for operational forecasting or for climate impact studies. A benefit of AMs is that they are lightweight and can provide valuable results for a negligible cost. AtmoSwing is an open source software that implements different AM variants in a very flexible way, so that they can be easily configured by means of XML files. It is written in C++, is object-oriented and multi-platform. AtmoSwing provides four tools: the Optimizer to establish the relationship between the predictand and predictors, the Downscaler to apply the method for climate impact studies, the Forecaster to perform operational forecasts, and the Viewer to display the results. The Optimizer provides a semi-automatic sequential approach, as well as Monte-Carlo analyses, and a global optimization technique by means of Genetic Algorithms. It calibrates the statistical relationship that can be later applied in a forecasting or climatic context. The Downscaler takes as input the outputs of climate models, either GCMs or RCMs in order to provide a downscaled time series of the predictand of interest at a local scale. The Forecaster automatically downloads and reads operational NWP outputs to provide operational forecasting of the predictand of interest. The processing of a forecast is extremely lightweight in terms of computing resources; it can indeed run on almost any computer. The Viewer displays the forecasts in an interactive GIS environment. It contains several layers of syntheses and details in order to provide a quick overview of the potential critical situations in the coming days, as well as the possibility for the user to go into the details of the forecasted predictand distribution

A Comparison of the Hydrology of the Swiss Alps and the Southern Alps of New Zealand

The hydrology of the Alps in Switzerland and New Zealand is compared. Similarities and differences in topographical features, climate and weather characteristics, precipitation, and streamflow are identified. Precipitation and runoff are much higher in the Southern Alps of New Zealand, whereas the proportion and influence of snow to rainfall is greater in the Swiss Alps. Despite differences related to continental versus island characteristics and different altitudinal ranges, both Alps are important for producing water resources for downstream regions. Swiss evaporation data were used to improve knowledge of evaporation in the Southern Alps. Comparison of water volumes involved in the hydrological cycle highlighted the fact that the Southern Alps are one of the highest wateryielding regions of the world’s temperate zones