Weather Generator Research Articles

Urban Heat Island intensity and buildings’ energy needs in Duran, Ecuador: Simulation studies and proposal of mitigation strategies

Urban Heat Island (UHI) is a phenomenon that is affecting cities across the world. In tropical climates, an increased UHI intensity can cause a significant rise in heat stress probability, both indoors and outdoors. A general increase in the air-conditioning energy consumption of buildings should also be expected as a consequence. This paper evaluates the case of Duran, Ecuador. It estimated, through a spatial distribution analysis, urban morphology parameters, properties of materials, and anthropogenic heat emissions in the streets. Twenty-eight urban, randomly selected samples were classified using the k-means clustering technique, which resulted in four preliminary clusters. Exploratory UHI simulations were carried out in the Urban Weather Generator tool (UWG) using the four clusters to investigate the relevance of the main UHI driving factors through a sensitivity analysis. Urban samples were weighted, considering the sensitivity analysis, for better classifying the driving factors. Results indicate that Duran city is strongly affected by UHI, especially in informal settlements with high anthropogenic heat release. The urban temperatures obtained by the model were compared to the experimental measurements in different locations in the city. Besides, the building cooling needs were simulated by using the TRNSYS v. 17 tool for residential and commercial buildings of the city, showing that UHI is increasing cooling-related energy consumption in a 30–70 % for residential buildings and a 10–20 % for commercial buildings. Finally, general mitigation strategies and policy implications to reduce the UHI intensity are proposed, and a set of scenarios is tested for the cluster with the highest UHI intensity.

Simulating precipitation in the Northeast United States using a climate‐informed K‐nearest neighbour algorithm

AbstractDecadal prediction using climate models faces long‐standing challenges. While global climate models may reproduce long‐term shifts in climate due to external forcing, in the near term, they often fail to accurately simulate interannual climate variability, as well as seasonal variability, wet and dry spells, and persistence, which are essential for water resources management. We developed a new climate‐informed K‐nearest neighbour (K‐NN)‐based stochastic modelling approach to capture the long‐term trend and variability while replicating intra‐annual statistics. The climate‐informed K‐NN stochastic model utilizes historical data along with climate state information to provide improved simulations of weather for near‐term regional projections. Daily precipitation and temperature simulations are based on analogue weather days that belong to years similar to the current year's climate state. The climate‐informed K‐NN stochastic model is tested using 53 weather stations in the Northeast United States with an evident monotonic trend in annual precipitation. The model is also compared to the original K‐NN weather generator and ISIMIP‐2b GFDL general circulation model bias‐corrected output in a cross‐validation mode. Results indicate that the climate‐informed K‐NN model provides improved simulations for dry and wet regimes, and better uncertainty bounds for annual average precipitation. The model also replicates the within‐year rainfall statistics. For the 1961–1970 dry regime, the model captures annual average precipitation and the intra‐annual coefficient of variation. For the 2005–2014 wet regime, the model replicates the monotonic trend and daily persistence in precipitation. These improved modelled precipitation time series can be used for accurately simulating near‐term streamflow, which in turn can be used for short‐term water resources planning and management.

Ensemble Modeling of the Impact of Climate Warming and Increased Frequency of Extreme Climatic Events on the Thermal Characteristics of a Sub-Tropical Lake

Lake ecosystems are impacted by changes in climatic conditions. Climate changes forecasted to occur are reflected in models by slow gradual changes over extended periods of time. Output from weather generators, on the other hand, can simulate short-term extreme conditions and weather patterns. In order to evaluate the likely impact of climate changes on a large sub-tropical lake, specifically the thermal regime of the lake, we constructed climate scenarios using a weather generator. The 30-year scenarios included no change in climate conditions, a gradual change, increased frequency of heat waves and a merging of the latter two. The projected impact on the lake’s physical properties was evaluated using an ensemble of 1-D hydrodynamic lake models. The gradual increase scenario had the largest impact on annual temperatures and stratification period; however, increased heat waves had a large effect on the summer lake conditions and introduced a larger degree of variability in water temperature. The use of the ensemble of models resulted in variability in the projected impacts; yet, the large degree of similarity between projected trends and patterns increased confidence in the results. The projected effect the heat waves will have on the lake conditions highlights the need to include heat waves in climate studies and the need for impact studies in order to better understand possible consequences for lake ecosystems.

Microclimate and urban morphology effects on building energy demand in different European cities

In the building sector, initiatives for Nearly Zero Energy Buildings (NZEBs) are gaining importance to tackle climate change and decrease energy consumption. Ever-greater efforts from developers are required to achieve such a goal, and energy audits are becoming increasingly sophisticated. Nevertheless, Building Energy Models (BEMs) are still often employed considering the building as self-standing, neglecting the urban morphology and the local climate conditions. In this study, the Urban Heat Island (UHI) phenomenon has been investigated using the Urban Weather Generator tool (UWG). The UHI intensity (i.e. the increase in urban air temperature compared to the rural one) has been analysed comparing two different urban configurations: the reference district is conceived referring to existing European suburbs whereas the enhanced district is characterized by high-efficiency buildings and UHI mitigation strategies. Urban canyon air temperatures have been calculated as a function of the above district configurations, city locations corresponding to 3 European climate zones, and district geometries (6 cases, differing in building spacing and height). The results show an increase in urban air temperatures ranging from 0.8 °C in winter to 2.0 °C in summer. In a second stage, the heating and cooling consumption of a residential building in EnergyPlus environment has been tested under the different urban weather conditions and district characteristics. When rural weather data are used as input, the yearly air-conditioning needs are underestimated by 10%. Concerning the two urban configurations, whilst the annual energy consumption of the enhanced design is only 2.2% less than the reference case, splitting the heating and the cooling load, the difference is closer to 8%, with opposite trends for heating and cooling.

Severe winter chill decline impacts Tunisian fruit and nut orchards

Future warming predicted for Tunisia may reduce winter chill and affect the production of tree fruits and nuts, one of the key sectors of the country’s economy. To assess past and future chill availability, we used weather records from 1973 to 2019 to simulate historic and future chill for 20 weather stations (16 from Tunisia, 3 from Algeria and 1 from Libya) by generating 6 past scenarios and 72 future scenarios for 2041–2070 and for 2071–2100, for two Representative Concentration Pathways (RCP4.5 and RCP8.5). We selected three future scenarios representing optimistic, intermediate, and pessimistic outlooks. Safe Winter Chill (SWC; in Chill Portions) was calculated using the Dynamic Model based on data for 100 winter seasons produced with a weather generator. SWC was then mapped for the whole country. Chilling requirements for typical Mediterranean fruit species (almond, pistachio, and peach cultivars) were compared with future SWC to select the best adapted fruit species, and cultivars within species, for each region. Our findings revealed severe SWC decline in the recent past. This decline will continue in the course of the twenty-first century. At the end of the century, we expect few of the studied species and cultivars to be able to fulfill their chilling requirements. By that time, only almonds may remain viable, whereas pistachios and peaches may experience alarming chill shortfalls. While our findings paint a grim picture for Tunisia’s orchards, they also allow insights for many other important fruit-growing regions with Mediterranean climate.

Multisite Weather Generators Using Bayesian Networks: An Illustrative Case Study for Precipitation Occurrence

AbstractMany existing approaches for multisite weather generation try to capture several statistics of the observed data (such as pairwise correlations) in order to generate spatially and temporarily consistent series. In this work, we analyze the application of Bayesian networks to this problem, focusing on precipitation occurrence and considering a simple case study to illustrate the potential of this new approach. We use Bayesian networks to approximate the multivariate (multisite) probability distribution of observed gauge data, which is factorized according to the relevant (marginal and conditional) dependencies. This factorization allows the simulation of synthetic samples from the multivariate distribution, thus providing a sound and promising methodology for multisite precipitation series generation.

Assessing impacts of future climate change on hydrological processes in an urbanizing watershed with a multimodel approach

Abstract The sensitivity of hydrological processes to the changed environment is of great concern. The integrated impacts of climate change and urbanization in the future have been assessed in a watershed in Northwest China through a multimodel approach based on the combined application of Generalized Watershed Loading Functions, the Long Ashton Research Station Weather Generator, and the Land Change Modeler. The results showed that both climate change and urbanization would lead to more watershed streamflow, and their combination would have synergistic effects on additional increases. In addition, there would be different seasonal distributions of streamflow with a greater proportion of runoff. These study results are helpful in supporting projects and/or decision-making processes for managers by providing more insights into the regional hydrological changes affected by climate change and urbanization. The proposed methodology of the combined multimodel approach may be applicable in other areas with similar conditions.

Variability assessment of simulated recharge resulted from precipitation using different GCMs, case study: west shore of Lake Urmia, Iran

Variations associated with recharge rate prediction are studied by a comparison between the simulated recharge based on weather data generated by Long Ashton Research Station Weather Generator (LARS-WG), which is a downscaling model with spectra of global circulation models (GCMs). The study area included aquifer of Urmia Plain (West Shore of Lake Urmia) located in Iran, Middle East. The aquifer area was classified based on soil composition gradation. Hydrologic Evaluation of Landfill Performance (HELP) model and percolation into vertical column were used for 1-dimensional modelling (pseudo-2-D modelling) of the recharge of various zones of Urmia Plain. The data required for modelling of weather parameter and recharge include average daily temperature, daily rainfall, and solar radiation. The recharge modelling was performed for time periods of 2020–2040, 2041–2060, 2061–2080, and 2081–2100 and five GCMs based on the 5th report issued by Intergovernmental Panel on Climate Change (IPCC), namely, EC-EARTH, GFDL-CM3, HadGEM2, MIROC5, and MPI-ESM-MR, under RCP 8.5 and RCP 4.5 scenarios. The recharge results of periods were compared between the GCMs. This variability in recharge prediction indicated that GCMs are highly important tools for water management planning.

A Statistical Tool to Generate Potential Future Climate Scenarios for Hydrology Applications

Global warming associated with greenhouse emissions will modify the availability of water resources in the future. Methodologies and tools to assess the impacts of climate change are useful for policy making. In this work, a new tool to generate potential future climate scenarios in a water resources system from historical and regional climate models’ information has been developed. The GROUNDS tool allows generation of the future series of precipitation, temperature (minimum, mean, and maximum), and potential evapotranspiration. It is a valuable tool for assessing the impacts of climate change in hydrological applications since these variables play a significant role in the water cycle, and it can be applicable to any case study. The tool uses different approaches and statistical correction techniques to generate individual local projections and ensembles of them. The non-equifeasible ensembles are created by combining the individual projections whose control or corrected control simulation has a better fit to the historical series in terms of basic and droughts statistics. In this work, the tool is presented, and the methodology implemented is described. It is also applied to a case study to illustrate how the tool works. The tool was previously tested in different typologies of water resources systems that cover different spatial scales (river basin, aquifer, mountain range, and country), obtaining satisfactory results. The local future scenarios can be propagated through appropriate hydrological models to study the impacts on other variables (e.g., aquifer recharge, chloride concentration in coastal aquifers, streamflow, snow cover area, and snow depth). The tool is also useful in quantifying the uncertainties of the future scenarios by combining them with stochastic weather generators.

Variability in spatial–temporal recharge under the observed and projected climate: A site-specific simulation in the black soil region of Russia

The main purpose of this work is to evaluate diffuse groundwater recharge and its temporal dynamics at sites with different landscapes and soil profiles under observed and projected climate variability. Three typical sites with different landscapes and topsoil profiles were chosen for the field study. Field work consisted of pit development and soil sample collection for laboratory study using the centrifuge method. Soil hydraulic parameters were obtained by the RETC code using the observed values of volumetric water content vs. pressure head. HYDRUS-1D code was used to estimate groundwater recharge based on a 70-year (1945–2015) meteorological dataset with daily resolution (including values of precipitation, air temperature, wind speed, and humidity). This dataset was preprocessed using our SURFBAL code to calculate the surface and topsoil water and energy balance. Retrospective historical simulation results showed that there was a considerable variation in the mean annual groundwater recharge values (49–104 mm/year) for the three studied profiles. The temporal change in groundwater recharge occurred relatively synchronously at all sites and was governed by the change in the current annual aridity index. The results of this historical simulation did not reveal any traces of climate change in the groundwater recharge of the studied region in the last 30–40 years. To predict the recharge variations in the second half of the 21st century, the LARSWG forecast weather generator with the climate projections of the 5th General Circulation Models (GCMs) from the Coupled Model Intercomparison Project 5 (CMIP5) family was used. On average in the case of climate development under the maximum greenhouse gas emissions RCP8.5 scenario, there will be a reduction in groundwater recharge in the studied region in 2060–2080, caused by two factors: change of aridity index, which determines evapotranspiration during a warm season and the accumulation of snow and subsequent meltwater infiltration during the snowmelt periods.

Climate change impact assessment on the hydrology of a large river basin in Ethiopia using a local-scale climate modelling approach

Local-scale climate change adaptation is receiving more attention to reduce the adverse effects of climate change. The process of developing adaptation measures at local-scale (e.g., river basins) requires high-quality climate information with higher resolution. Climate projections are available at a coarser spatial resolution from Global Climate Models (GCMs) and require spatial downscaling and bias correction to drive hydrological models. We used the hybrid multiple linear regression and stochastic weather generator model (Statistical Down-Scaling Model, SDSM) to develop a location-based climate projection, equivalent to future station data, from GCMs. Meteorological data from 24 ground stations and the most accurate satellite and reanalysis products identified for the region, such as Climate Hazards Group InfraRed Precipitation with Station Data were used. The Soil Water Assessment Tool (SWAT) was used to assess the impacts of the projected climate on hydrology. Both SDSM and SWAT were calibrated and validated using the observed climate and streamflow data, respectively. Climate projection based on SDSM, in one of the large and agricultural intensive basins in Ethiopia (i.e., Awash), show high variability in precipitation but an increase in maximum (Tmax) and minimum (Tmin) temperature, which agrees with global warming. On average, the projection shows an increase in annual precipitation (>10%), Tmax (>0.4 °C), Tmin (>0.2 °C) and streamflow (>34%) in the 2020s (2011–2040), 2050s (2041–2070), and 2080s (2071–2100) under RCP2.6-RCP8.5. Although no significant trend in precipitation is found, streamflow during March–May and June–September is projected to increase throughout the 21 century by an average of more than 1.1% and 24%, respectively. However, streamflow is projected to decrease during January–February and October–November by more than 6%. Overall, considering the projected warming and changes in seasonal flow, local-scale adaptation measures to limit the impact on agriculture, water and energy sectors are required.

Quantifying the Building Energy Dynamics of Manhattan, New York City, Using an Urban Building Energy Model and Localized Weather Data

Building sectors account for major energy use and greenhouse gas emissions in the US. While urban building energy-use modeling has been widely applied in many studies, limited studies have been conducted for Manhattan, New York City (NYC). Since the release of the new “80-by-50” law, the NYC government has committed to reducing carbon emissions by 80% by 2050; indeed, the government is facing a big challenge for reducing the energy use and carbon emissions. Therefore, understanding the building energy use of NYC with a high spatial and temporal resolution is essential for the government and local citizens in managing building energy use. This study quantified the building energy use of Manhattan in NYC with consideration of the local microclimate by integrating two popular modeling platforms, the Urban Weather Generator (UWG) and Urban Building Energy Modeling (UBEM). The research results suggest that (1) the largest building energy use is in central Manhattan, which is composed of large numbers of commercial buildings; (2) a similar seasonal electricity-use pattern and significantly different seasonal gas-use patterns could be found in Manhattan, NYC, due to the varied seasonal cooling and heating demand; and (3) the hourly energy-use profiles suggest only one electricity-use peak in the summer and two gas-use peaks in the winter.

Using Statistical and Dynamical Downscaling to Assess Climate Change Impacts on Mine Reclamation Cover Water Balances

The oil sands industry in Canada uses soil–vegetation–atmosphere-transfer (SVAT) water balance models, calibrated against short-term (<≈ 10 years) field monitoring data, to evaluate long-term (≈60 years) reclamation cover design performance. These evaluations use long-term historical climate data; however, the effects of climate change should also be incorporated in these analyses. Although statistical downscaling of global climate change projections is commonly used to obtain local, site-specific climate, high resolution dynamical downscaling can also be used. The value of this latter approach to obtain local site-specific projections for mine reclamation covers has not been evaluated previously. This study explored the differences in key water balance components of three reclamation covers and three natural sites in northern Alberta, Canada, under future, site-specific, statistical, and dynamical climate change projections. Historical meteorological records were used to establish baseline periods. Temperature datasets were used to calculate potential evapotranspiration (PET) using the Hargreaves–Samani method. Statistical downscaling uses the Long Ashton Research Station Weather Generator (LARS-WG) and global circulation model (GCM) projections of temperature and precipitation. Dynamical climate change projections were generated on a 4 km grid using the weather research and forecasting (WRF) model. These climate projections were applied to a physically-based water balance model (i.e. Hydrus-1D) to simulate actual evapotranspiration (AET) and net percolation (NP) for the baseline and future periods. The key findings were: (a) LARS-WG outperformed WRF in simulating baseline temperatures and precipitation; (b) both downscaling methods showed similar directional shifts in the future temperatures and precipitation; (c) this, in turn, created similar directional shifts in future growing season median AET and NP, although the increase in future NP for LARS-WG was higher than that for WRF. The relative increases in future NP were much higher than the relative increases in future AET, particularly for the reclamation covers.

Model simulations of the ecological dynamics induced by climate and nutrient load changes for deep subalpine Lake Maggiore (Italy/Switzerland)

Climate warming affects lake ecosystems both through its direct effect on the phenology of species and through the alteration of the physical and chemical environments, which in turn affect community composition. In deep lakes, stratification enhancement and mixing reduction have already been observed, leading to hypolimnetic anoxia and to the rise of cyanophytes. The increase in stability depends on the rise of air temperature due to global emissions of greenhouse gases (GHG). Primary production could then either increase with rising epilimnetic temperature and buoyancy or decrease as fewer nutrients are upwelled from deep layers. The prevailing outcome, as well as the quantitative and temporal dynamics of all climate-induced modifications, depend on the specific lake characteristics. Individual analyses are then needed, one-dimensional coupled hydrodynamic-ecological numerical models being suitable tools for such predictions. Here, we simulated with GLM-AED2 (General Lake Model – Aquatic EcoDynamics) the 2020-2085 dynamics of the oligomictic and oligotrophic deep subalpine Lake Maggiore (Italy/Switzerland), according to the Swiss Climate Change Scenarios CH2011. Multiple realisations were performed for each scenario with random meteorological series obtained from the Vector-Autoregressive Weather Generator (VG), highlighting the uncertainties related to meteorology. Increase and decrease of nutrient loads were also tested. Results show that anoxia would occur in the hypolimnion regardless of nutrient input reduction, unless global GHG emissions were immediately reduced. Total phytoplankton biomass would be weakly affected by climate change, strongly depending on nutrient input, yet water warming would cause cyanophytes to compete with diatoms. Therefore, the fate of Lake Maggiore would be tied to both global and local environmental policies.

CLIGEN as a weather generator for predicting rainfall erosion using USLE based modelling systems

CLIGEN is a stochastic weather generator that has been used as input to WEPP. Normally, USLE based models predict erosion using parameter values that are long-term averages but RUSLE2 has a facility to predict erosion for single storms through user entered data. This enables CLIGEN to be used as a weather generator for RUSLE2 when EI30 values for CLIGEN generated rainfall are determined separately. This can be achieved using daily erosivity density (EI30 per unit quantity of rain) data generated by RUSLE2 for each location or by other methods that have the capacity to determine daily EI30 values independently of RUSLE2. One such method developed by Yu was compared with the RUSLE2 based method in terms of its ability to predict temporal variations in soil loss during the calendar year from bare fallow and cropped areas. The process of determining EI30 values by the Yu method involves generating EI30 values so as to match R-factor values used by RUSLE2. This enables CLIGEN to predict soil loss values that are as useful those generated using RUSLE2 erosivity densities in terms of predicting long-term variations in soil loss during the year. However, CLIGEN does not necessarily produce stochastic rainfall data evenly over decades. Consequently, the process of matching R-factors associated with RUSLE2 with those generated by using CLIGEN should be undertaken using the same time frame as used for obtaining the long-term mean soil loss amounts.

Event generation for probabilistic flood risk modelling: multi-site peak flow dependence model vs. weather-generator-based approach

Abstract. Flood risk assessment is an important prerequisite for risk management decisions. To estimate the risk, i.e. the probability of damage, flood damage needs to be either systematically recorded over a long period or modelled for a series of synthetically generated flood events. Since damage records are typically rare, time series of plausible, spatially coherent event precipitation or peak discharges need to be generated to drive the chain of process models. In the present study, synthetic flood events are generated by two different approaches to modelling flood risk in a meso-scale alpine study area (Vorarlberg, Austria). The first approach is based on the semi-conditional multi-variate dependence model applied to discharge series. The second approach relies on the continuous hydrological modelling of synthetic meteorological fields generated by a multi-site weather generator and using an hourly disaggregation scheme. The results of the two approaches are compared in terms of simulated spatial patterns of peak discharges and overall flood risk estimates. It could be demonstrated that both methods are valid approaches for risk assessment with specific advantages and disadvantages. Both methods are superior to the traditional assumption of a uniform return period, where risk is computed by assuming a homogeneous return period (e.g. 100-year flood) across the entire study area.

The importance of chill model selection — a multi-site analysis

Abstract Winter chill, which temperate trees require in order to overcome dormancy, is expected to decrease substantially in the future in most deciduous fruit tree growing areas. Several mathematical models have been developed in different regions to quantify chill requirements of tree species and cultivars. The Dynamic model has emerged as the most plausible and reliable model, yet all chill models have been found inadequate in at least some growing regions. Accurate models are crucial for the development of quantitatively appropriate climate change adaptation strategies for temperate orchards. To demonstrate the importance of model choice we compared the outputs from 13 agricultural and forest chill models using past and projected future weather data for nine sites in Chile, Tunisia and Germany. To evaluate chill risk, we used a weather generator calibrated with 45 years of temperature data to generate 100 years of synthetic temperature records per scenario for multiple climate scenarios. Chill was computed for 10 past scenarios and projected for 60 future scenarios (for 2050 and 2085 according to greenhouse gas concentration scenarios RCP4.5 and RCP8.5, using projections from 15 climate models). Results show that estimations differ substantially across chill models, even for the same sites and scenarios. The “Chilling Hours” model and the “Chilling Rate” function showed high sensitivity across regions in future scenarios. The “North Carolina”, “Utah”, “Modified Utah” and “Low Chill” models all suggest negative chill levels for past and future scenarios in Tunisia (despite the thriving fruit tree industry there). Only two models projected chill decreases in all sites. In Mediterranean climate areas (central Chile and Tunisia) the “Dynamic” and “Positive Utah” models forecasted similar chill reductions for future scenarios, whereas in temperate locations (Germany) the “Dynamic” model forecasted lower chill increase compared with the “Utah” and “Positive Utah” models. Despite the “Dynamic” and the “Positive Utah” models showing similar performance among climates, the “Dynamic” model appears to be the best current option, due its more physiologically credible structure. However, further research is needed to develop or identify models that are valid across wide climatic gradients. Our results show that a major source of variation and inaccuracy in chilling assessments is the choice of the chill model used to make the assessment.

Climate change impacts on agriculture's southern frontier – Perspectives for farming in North Patagonia

AbstractWinter chill is expected to decrease in many of the suitable growing regions for deciduous trees. Argentinean North Patagonia hosts extensive fruit tree cultivation, which provides an important contribution to both local and global food security. Using historic records from 11 weather stations from North Patagonia, we evaluate the possible impacts of climate change on fruit tree cultivation. We assess winter chill and seasonal heat availability, and the risk of spring frost events based on outputs from 15 Global Climate Models (GCMs) for two Representative Concentration Pathway (RCP) scenarios and two future time periods (represented by central years 2050 and 2085). Metrics were estimated for 47 years of records from the weather stations, as well as typical conditions for 10 past scenarios and 60 future GCM and RCP projections. Scenarios consisted of 100 plausible annual temperature records produced by a weather generator. Results suggest that fruit tree dormancy in Argentinean North Patagonia will not be strongly affected by climate change. Compared to the past, winter chill may only decrease by 9% in the RCP4.5 scenario by 2050 in the northeastern and eastern subregion, while in the central‐south and west the reduction seems unlikely to exceed 6% by the same RCP scenario and year. Our models project stable high growing season heat in the northeastern and eastern regions, and major increases in the south by 2085 in both RCP scenarios. Projections of spring frost events varied between 0 and about 25 hours below 0°C depending on the site. Increasing heat availability may create opportunities for fruit and nut growers to introduce new species and cultivars to the region. Our results provide a basis for planning such introductions and for enabling growers to exploit new opportunities for producing temperate orchard crops beyond their traditional ranges.

Stochastic weather generator for the design and reliability evaluation of desalination systems with Renewable Energy Sources

Abstract The operation of Renewable Energy Sources (RES) systems is highly affected by the continuously changing meteorological conditions and the design of a RES system has to be robust to the unknown weather conditions that it will encounter during its lifetime. In this paper, the use of Stochastic Weather Generators (SWGENs) is introduced for the optimal design and reliability evaluation of hybrid Photovoltaic/Wind-Generator systems providing energy to desalination plants. A SWGEN is proposed, which is based on parametric Markov-Switching Auto-Regressive (MSAR) models and is capable to simulate realistic hourly multivariate time series of solar irradiance, temperature and wind speed of the target installation site. Numerical results are presented, demonstrating that: (i) SWGENs enable to evaluate the reliability of RES-based desalination plants during their operation over a 20 years lifetime period and (ii) using an appropriate time series simulated with a SWGEN as input to the design optimization process results in a RES-based desalination plant configuration with higher reliability compared to the configurations derived when the other types of meteorological datasets are used as input to the design optimization process.

Nonstationary stochastic rain type generation: accounting for climate drivers

Abstract. At subdaily resolution, rain intensity exhibits a strong variability in space and time, which is favorably modeled using stochastic approaches. This strong variability is further enhanced because of the diversity of processes that produce rain (e.g., frontal storms, mesoscale convective systems and local convection), which results in a multiplicity of space–time patterns embedded into rain fields and in turn leads to the nonstationarity of rain statistics. To account for this nonstationarity in the context of stochastic weather generators and therefore preserve the relationships between rainfall properties and climatic drivers, we propose to resort to rain type simulation. In this paper, we develop a new approach based on multiple-point statistics to simulate rain type time series conditional to meteorological covariates. The rain type simulation method is tested by a cross-validation procedure using a 17-year-long rain type time series defined over central Germany. Evaluation results indicate that the proposed approach successfully captures the relationships between rain types and meteorological covariates. This leads to a proper simulation of rain type occurrence, persistence and transitions. After validation, the proposed approach is applied to generate rain type time series conditional to meteorological covariates simulated by a regional climate model under an RCP8.5 (Representative Concentration Pathway) emission scenario. Results indicate that, by the end of the century, the distribution of rain types could be modified over the area of interest, with an increased frequency of convective- and frontal-like rains at the expense of more stratiform events.