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This study focuses on the impacts of climate change on hydrological processes in watersheds and proposes an integrated approach combining a weather generator with a multi-site conditional generative adversarial network (McGAN) model. The weather generator incorporates ensemble GCM predictions to generate regional average synthetic weather series, while McGAN transforms these regional averages into spatially consistent multi-site data. By addressing the spatial consistency problem in generating multi-site synthetic weather series, this approach tackles a key challenge in site-scale climate change impact assessment. Applied to the Jinghe River Basin in west-central China, the approach generated synthetic daily temperature and precipitation data for four stations under different shared socioeconomic pathways (SSP1-26, SSP2-45, SSP3-70, SSP5-85) up to 2100. These data were then used with a long short-term memory (LSTM) network, trained on historical data, to simulate daily river flow from 2021 to 2100. The results show that (1) the approach effectively addresses the spatial correlation problem in multi-site weather data generation; (2) future climate change is likely to increase river flow, particularly under high-emission scenarios; and (3) while the frequency of extreme events may increase, proactive climate policies can mitigate flood and drought risks. This approach offers a new tool for hydrologic–climatic impact assessment in climate change studies. [ABSTRACT FROM AUTHOR]

Operational forecasters desire information about how their reservoir and riverine systems will evolve over monthly to seasonal timescales. Seasonal traces of hydrometeorological variables at a daily or sub-daily resolution are needed to drive hydrological models at this timescale. Operationally available models such as the Climate Forecast System (CFS) provide seasonal precipitation forecasts, but their coarse spatial scale requires further processing for use in local or regional hydrologic models. We focus on three methods to generate such forecasts: (1) a bias-adjustment method, in which the CFS forecasts are bias-corrected by ground-based observations; (2) a weather generator (WG) method, in which historical precipitation data, conditioned on an index of the El Niño–Southern Oscillation, are used to generate synthetic daily precipitation time series; and (3) a historical analog method, in which the CFS forecasts are used to condition the selection of historical satellite-based mean areal precipitation (MAP) traces. The Yaque del Norte River basin in the Dominican Republic is presented herein as a case study, using an independent dataset of rainfall and reservoir inflows to assess the relative performance of the methods. The methods showed seasonal variations in skill, with the MAP historical analog method having the strongest overall performance, but the CFS and WG methods also exhibited strong performance during certain seasons. These results indicate that the strengths of each method may be combined to produce an ensemble forecast product. [ABSTRACT FROM AUTHOR]

The present work presents a methodology based on the use of stochastic weather generators (WGs) for the estimation of high-return-period floods under climate change scenarios. Applying the proposed methodology in a case study, Rambla de la Viuda (Spain), satisfactory results were obtained through the regionalization of the bias-corrected EUROCORDEX climate projections and the integration of this information into the parameterization of the WG. The generated synthetic data series fed a fully distributed hydrological model to obtain the future flood quantiles. The results obtained show a clear increase in the precipitation extreme quantiles for the two analyzed projections. Although slightly reducing the annual amount of precipitation, variations between 4.3% for a return period of 5 years in the mid-term projection and 19.7% for a return period of 100 years in the long-term projection have been observed. In terms of temperatures, the results point to clear increases in the maximum and minimum temperatures for both projections (up to 3.6 °C), these increases being greater for the long-term projection, where the heat waves intensify significantly in both magnitude and frequency. Finally, although rivers may present, in general, with lower flows during the year, flood quantiles experience an increase of 53–58% for high return periods, which reach values of up to 145% when we move to smaller catchments. All this combined translates into substantial shifts in the river flow regimes, increasing the frequency and magnitude of extreme flood events. [ABSTRACT FROM AUTHOR]

This study focuses on the impacts of climate change on hydrological processes in watersheds and proposes an integrated approach combining a weather generator with a multi-site conditional generative adversarial network (McGAN) model. The weather generator incorporates ensemble GCM predictions to generate regional average synthetic weather series, while McGAN transforms these regional averages into spatially consistent multi-site data. By addressing the spatial consistency problem in generating multi-site synthetic weather series, this approach tackles a key challenge in site-scale climate change impact assessment. Applied to the Jinghe River Basin in west-central China, the approach generated synthetic daily temperature and precipitation data for four stations under different shared socioeconomic pathways (SSP1-26, SSP2-45, SSP3-70, SSP5-85) up to 2100. These data were then used with a long short-term memory (LSTM) network, trained on historical data, to simulate daily river flow from 2021 to 2100. The results show that (1) the approach effectively addresses the spatial correlation problem in multi-site weather data generation; (2) future climate change is likely to increase river flow, particularly under high-emission scenarios; and (3) while the frequency of extreme events may increase, proactive climate policies can mitigate flood and drought risks. This approach offers a new tool for hydrologic–climatic impact assessment in climate change studies.

Resampling‐based weather generators simulate new time series of weather variables by reordering the observed values such that the statistics of the simulated data are consistent with the observed ones. These generators are fully data‐driven, easy to implement, and capable of reproducing the dynamics among weather variables. However, although the simulated time series is new, the weather fields produced at arbitrary time steps are replicas of those found in observations, limiting the spatial variability of simulations and preventing the generation of extreme weather fields beyond the range of observed values. To address these limitations, we propose the integration of the Direct Sampling algorithm—a data‐driven method for producing simulations—into resampling‐based weather generators. By incorporating Direct Sampling as a post‐processing step on the outputs of the weather generator, we enhance the spatial variability of the generated weather fields and enable the generation of extreme weather fields. We introduce an approach for generating out‐of‐sample extreme weather fields using Direct Sampling. This method involves utilizing a set of control points in conjunction with Direct Sampling, where the values of these control points are informed by return period analysis. The proposed approach is validated using precipitation, temperature, and cloud cover weather fields in a region of northwest India. The experimental results confirm that Direct Sampling enhances the spatial variability of the weather fields and facilitates the generation of out‐of‐sample precipitation fields that accurately adhere to the spatial statistics provided by return precipitation level maps, as well as the observed precipitation weather field employed in the analysis. Plain Language Summary: Weather generators (WG) are tools for generating artificial weather data. Applications use WG outputs for several tasks, including risk, uncertainty, and climate change analysis. WGs based on "resampling " conforms to a type of WGs that is easy to implement, understand and produce data with properties resembling historical data. However, although those WGs generate new artificial time series, those series are sorted versions of historical weather fields (i.e., weather data values at the spatial domain) Furthermore, those WGs can't generate weather fields with out‐of‐sample data values, that is, extreme weather. In this work, we research the applicability of the Direct Sampling algorithm for creating variations of the simulated weather fields by the WG, and for generating artificial precipitation fields with extreme values. We found that Direct Sampling post‐processing of weather generator outputs is a simple approach to improve the variations of weather fields and for generating extreme precipitation fields conditioned on information provided by an extreme precipitation analysis. The methods exposed in our work show a way to improve the design of those WGs that can benefit several applications like the ones searching the generation of hypothetical extreme weather fields, or seeking better uncertainty quantification or estimates in risk analysis tasks. Key Points: Spatial variability improvement of weather generators based on resampling via the Direct Sampling algorithmDirect Sampling for extreme precipitation fields generation using control points and return periodsEmpirical validation using statistical and connectivity metrics on a data set with precipitation, temperature and cloud cover variables [ABSTRACT FROM AUTHOR]

The study proposes a statistical downscaling procedure for regional rainfall. In the multistage procedure, the spatial downscaling stage using Relevance Vector Machine (RVM) model captures the climate change signals in the monthly scale simulations from the climate models by considering a predictor set that is based on the regional climatic phenomena. The spatially downscaled monthly rainfall is then disaggregated into daily scale using a modified weather generator. In the temporal downscaling stage using a weather generator, the transition matrix is modified to account for the non-stationarities in the rainfall. The proposed methodology is validated in the Bharathapuzha River Basin, India by downscaling rainfall data from climate models, BNU-ESM, CESM1-BGC, CMCC-ESM2, FGOALS-G2, FIO-ESM-2.0, and MIROC4H. The process-based indices specified in the VALUE framework developed from the downscaled rainfall data closely matches the indices developed from the observed rainfall data for the historical period, with absolute PBIAS less than 14% across all the indices considered. The reduction in uncertainty achieved is studied using average band width of the simulations, which is seen to reduce from 186 mm to 52 mm after downscaling. The capability of the downscaling procedure for capturing the non-stationarity in the climate is tested by comparing the performance of the procedure over warm and cold phases of ENSO, and it is found to be satisfactory. Highlights: The study proposes a multi stage, stochastic approach for statistical downscaling of rainfall. It combines the strength of RVM models and weather generators. The statistical downscaling relationship is based on the regional climatic phenomena. Downscaled rainfall series captures the occurrence and distribution characteristics of regional observed rainfall and its non-stationarity. [ABSTRACT FROM AUTHOR]

The combined use of weather generators (WG) and hydrological models (HM) in what is called synthetic continuous simulation (SCS) has become a common practice for carrying out flood studies. However, flood quantile estimations are far from presenting relatively high confidence levels, which mostly relate to the uncertainty of models' input data. The main objective of this paper is to assess how different precipitation regimes, climate extremality, and basin hydrological characteristics impact the uncertainty of daily flood quantile estimates obtained by SCS. A Monte Carlo simulation from 18 synthetic populations encompassing all these scenarios was performed, evaluating the uncertainty of the simulated quantiles. Additionally, the uncertainty propagation of the quantile estimates from the WG to the HM was analyzed. General findings show that integrating the regional precipitation quantile ( X T , P)   in the WG model calibration clearly reduces the uncertainty of flood quantile estimates, especially those near the regional X T , P . Basin size, climate extremality, and the hydrological characteristics of the basin have been proven not to affect flood quantiles' uncertainty substantially. Furthermore, it has been found that uncertainty clearly increases with the aridity of the climate and that the HM is not capable of buffering the uncertainty of flood quantiles, but rather increases it. [ABSTRACT FROM AUTHOR]

Abstract Resampling‐based weather generators simulate new time series of weather variables by reordering the observed values such that the statistics of the simulated data are consistent with the observed ones. These generators are fully data‐driven, easy to implement, and capable of reproducing the dynamics among weather variables. However, although the simulated time series is new, the weather fields produced at arbitrary time steps are replicas of those found in observations, limiting the spatial variability of simulations and preventing the generation of extreme weather fields beyond the range of observed values. To address these limitations, we propose the integration of the Direct Sampling algorithm—a data‐driven method for producing simulations—into resampling‐based weather generators. By incorporating Direct Sampling as a post‐processing step on the outputs of the weather generator, we enhance the spatial variability of the generated weather fields and enable the generation of extreme weather fields. We introduce an approach for generating out‐of‐sample extreme weather fields using Direct Sampling. This method involves utilizing a set of control points in conjunction with Direct Sampling, where the values of these control points are informed by return period analysis. The proposed approach is validated using precipitation, temperature, and cloud cover weather fields in a region of northwest India. The experimental results confirm that Direct Sampling enhances the spatial variability of the weather fields and facilitates the generation of out‐of‐sample precipitation fields that accurately adhere to the spatial statistics provided by return precipitation level maps, as well as the observed precipitation weather field employed in the analysis.

Stochastic weather generators are powerful tools capable of extending the available precipitation records to the desired length. These, however, rely upon the amount of information available, which often is scarce, especially in arid and semi-arid regions. No studies can be found dealing with the uncertainty associated with these estimates related to the amount of information used in the weather generation calibration process, which is precisely the aim of the present study. A Monte Carlo simulation from a synthetic population was performed, evaluating the uncertainty of the simulated quantiles in different practical available information scenarios. The results showed that incorporating a regional study of annual maximum daily precipitation in the model parameterization clearly reduced the uncertainty of all quantile estimates. In addition, it has been proved that the uncertainty of these estimates increases with the population extremality, thus marking the importance of integrating additional information in regions with extreme precipitation patterns. [ABSTRACT FROM AUTHOR]

The Lower Santa Cruz River Basin Study (LSCRB Study) is a collaborative effort of regional and statewide water management stakeholders working with the US Bureau of Reclamation under the auspices of the 2009 SECURE Water Act. The impacts of climate change, land use, and population growth on projected water supply in the LSCRB were evaluated to (1) identify projected water supply and demand imbalances and (2) develop adaptation strategies to proactively respond over the next 40 years. A multi-step hydroclimate modeling and risk assessment process was conducted to assess a range of futures in terms of temperature, precipitation, runoff, soil moisture, and evapotranspiration, with a particular focus on implications for ecosystem health. Key hydroclimate modeling process decisions were informed by ongoing multi-stakeholder engagement. To incorporate the region's highly variable precipitation pattern, the study used a numerical "weather generator" to develop ensembles of precipitation and temperature time series for input to surface hydrology modeling efforts. Hydroclimate modeling outcomes consistently included increasing temperatures, and generated information related to precipitation responses (season length and timing, precipitation amount) considered useful for evaluating potential ecosystem impacts. A range of risks was identified using the hydroclimate modeling outputs that allowed for development of potential adaptation strategies. [ABSTRACT FROM AUTHOR]

Achieving global food security in the face of climate change is a critical challenge, particularly in vulnerable countries like Indonesia. To effectively address this challenge, a systems-based approach utilizing climate-hydrological-crop models has emerged as an integral approach. These models integrate climate, hydrological, and crop components to understand and predict the complex interactions within agricultural systems and their responses to climate variables. By employing this approach, policymakers, researchers, and stakeholders can gain comprehensive insights into the potential consequences of climate change on crop growth, water availability, soil fertility, and overall crop yield. However, challenges exist in the implementation of this approach, including data reliability; scarcity of complete long-term data; lack of experimental information about crop species, especially local varieties; inadequate research resources; lack of expertise concerning modeling approaches; lack of testing; inaccurate testing; calibration; and model uncertainties. Furthermore, to address limitations and challenges in implementing this approach, improving the availability and reliability of data, collection method, and data quality should be conducted to ensure the accuracy of simulation and prediction. Finally, climate-hydrological-crop models, alongside improved data collection and modelling techniques, serve as essential tools for guiding the development of effective adaptation measures to mitigate the impacts of climate change on rice production in Indonesia.

Accurate estimation of evapotranspiration (ET) is required for a good estimation of available water for use in any catchment as ET constitutes major means by which water is lost in any catchment. SWAT model uses climate data in estimating potential evapotranspiration (PET). The PET together with other parameters is then used in estimating actual evapotranspiration (AET). SWAT model provides three different methods in estimating PET which are Penman–Monteith (PM), Hargreaves (H) and Priestly–Taylor (PT). These three PET methods were evaluated in a limited available spatial and temporal data Densu river basin to assess their impact on the resulted model water balance. The missing data in the 8 climate stations that were used in this study were estimated using a WXGEN weather generator. The results showed that the accuracy of the water balance from the SWAT model was defined by how well the PET method selected for the SWAT was able to estimate an accurate spatial and temporal distributed PET in the basin. A discharge at Nsawam, a town within the basin, was used for the model calibration and validation. Hargreaves PET method had the best Nash–Sutcliffe model efficiency coefficient (NSE) values of 0.70 for the calibration and 0.74 for validation period, while the PM PET method had the worse NSE value of 0.66 for calibration and 0.62 for validation. These results were attributed to the limited available data within the basin. The discrepancies in the resultant water balance model could be attributed to the three different PET methods used in relation to the climate data that were available for the calibration and validation of the SWAT model. [ABSTRACT FROM AUTHOR]

This paper presents the evaluation of a multisite statistical weather generator (MulGETS: Multisite weather Generator of École de Technologie Supérieure) based on its simulation effect of precipitation in the Yangtze River Basin. MulGETS effectively generates spatially correlated sequences of precipitation simultaneously, while maintaining their spatial and temporal distribution characteristics. On the spatial scales, the accuracy of the model varies from station to station, and in general, the errors are lower at stations in the middle and lower reaches of the Yangtze River Basin than in the upper reaches. This difference is likely to exist because of the lower amount of rainfall and more complex topography than those of the upper river basins. On the temporal scales, the simulated values are more precise on the annual scale than on the seasonal scale. Large relative errors occur more frequently in winter, ranging from −35% to 25%. MulGETS can consistently produce precipitation by considering the intensity, magnitude, and duration indices with sub-basin varied observations. However, the precipitation maxima were much lower than the observations. This work shows the general reasonability of the model in downscaling precipitation in the Yangtze River Basin. HIGHLIGHTS MulGETS is applied to generate daily precipitation for each subbasin in the Yangtze River Basin.; The performance is evaluated by 9 indices in three categories over seasonal and annual scales.;

Drought risk assessment provides a vital basis for drought relief and prevention. We developed a dynamic agricultural drought risk (DADR) assessment model to predict drought trends and their impacts on crop yield in real time. A weather generator was employed to produce daily meteorological scenarios to simulate drought trends stochastically. Then, it was used to drive a crop model for simulating drought-induced yield loss. The yield loss rate was calculated to assess the DADR, whereas the cumulative yield loss rate was calculated to measure the cumulative impacts of drought on yield. The drought that occurred in the Liaoning Province in 2000 was selected as a case study, and the DADR was assessed weekly during the maize growth period. The statistical parameters of historical meteorological data were used to prove the rationality of meteorological scenarios. The crop data from 1996 to 2012 were used for crop model calibration and verification. The results showed that, on July 3, 2000, the majority of the Liaoning Province experienced severe or moderate DADR, which showed an increasing trend from east to west, while the highest DADR (over 35%) was noted in Fuxin and Chaoyang. The drought during the maize growth period in 2000 caused an average cumulative yield loss rate of 62.4%. The drought in the early seeding and milk maturity stages had a negligible impact on maize yield, contrary to that in the jointing to tasseling period. Our study provides insights into the implementation of drought relief measures and the development of drought monitoring systems. [ABSTRACT FROM AUTHOR]

Globally, between 1950 and 2011 nearly 80,000 debris flow fatalities occurred in densely populated regions in mountainous terrain. Mitigation of these hazards includes the construction of check dams, which limit coarse sediment transport and in the European Alps number in the 100,000s. Check dam functionality depends on periodic, costly maintenance, but maintenance is not always possible and check dams often fail. As such, there is a need to quantify the long-term (10–100 years) geomorphic response of rivers to check dam failures. Here, for the first time, a landscape evolution model (CAESAR-Lisflood) driven by a weather generator is used to replicate check dam failures due to the lack of maintenance, check dam age, and flood occurrence. The model is applied to the Guerbe River, Switzerland, a pre-Alpine catchment containing 73 check dams that undergo simulated failure. Also presented is a novel method to calibrate CAESAR-Lisflood's hydrological component on this ungauged catchment. Using 100-year scenarios of check dam failure, the model indicates that check dam failures can produce 8 m of channel erosion and a 322% increase in sediment yield. The model suggests that after check dam failure, channel erosion is the remobilization of deposits accumulated behind check dams, and, after a single check dam failure channel equilibrium occurs in five years, but after many check dam failures channel equilibrium may not occur until 15 years. Overall, these findings support the continued maintenance of check dams. [Display omitted] • Check dam failure produces 8 m of channel erosion and 322% increase in sediment yield. • In steep mountain channels, bedrock can limit post check dam failure channel erosion. • Spatially distributed rainfall and soil depth improve CAESAR-Lisflood's replication of river discharge. [ABSTRACT FROM AUTHOR]

A major challenge in assessing the impacts of climate change on hydrological processes lies in dealing with large degrees of uncertainty in the future climate projections. Part of the uncertainty is owed to the intrinsic randomness of climate phenomena, which is considered irreducible. Additionally, modelling the response of hydrological processes to the changing climate requires the use of a chain of numerical models, each of which contributes some degree of uncertainty to the final outputs. As a result, hydrological projections, despite the progressive increase in the accuracy of the models along the chain, still display high levels of uncertainty, especially at small temporal and spatial scales. In this work, we present a framework to quantify and partition the uncertainty of hydrological processes emerging from climate models and internal variability, across a broad range of scales. Using the example of two mountainous catchments in Switzerland, we produced high‐resolution ensembles of climate and hydrological data using a two‐dimensional weather generator (AWE‐GEN‐ 2d) and a distributed hydrological model (TOPKAPI‐ETH). We quantified the uncertainty in hydrological projections towards the end of the century through the estimation of the values of signal‐to‐noise ratios (STNR). We found small STNR absolute values (<1) in the projection of annual streamflow for most sub‐catchments in both study sites that are dominated by the large natural variability of precipitation (explains ~70% of total uncertainty). Furthermore, we investigated in detail specific hydrological components that are critical in the model chain. For example, snowmelt and liquid precipitation exhibit robust change signals, which translates into high STNR values for streamflow during warm seasons and at higher elevations, together with a larger contribution of climate model uncertainty. In contrast, projections of extreme high flows show low STNR values due to large internal climate variability across all elevations, which limits the potential for narrowing their estimation uncertainty. [ABSTRACT FROM AUTHOR]

Increasing the share of weather-dependent renewables in the electricity grid is essential to deeply decarbonize the electricity system. Wind and solar "droughts" or low generation days can severely impact grid stability in a renewable-rich grid. This paper analyzes for the first time wind, solar, and hybrid energy-droughts in India using a stochastic weather generator. Available literature analyze the observational data that is of limited duration (30–40 years). Therefore, discussion of low-probability high-impact renewable energy-droughts that have long return periods (in the range of 30 years) is limited in the literature. The present study seeks to address this research gap by exploring the risk of wind, solar, and wind-solar powered energy-droughts based on simulated long time series (5000 years). It is found that the weather generator captures mean, seasonality, and correlation between wind speed and solar irradiance and is therefore used to estimate return periods of extreme wind and solar-droughts. Our analysis shows that wind-droughts are more intense than solar-droughts in India. We examine the role that wind-solar hybridization can play in offsetting low wind energy episodes. The benefits of hybridization are regionally dependent. In South India, hybrid plants have advantages over either wind or solar plants alone. In comparison, for Rajasthan, the benefits of hybridization are limited. When one of the regions (South India or Rajasthan) has a renewable drought, the other region has only a 10% probability of having a similar drought. Our findings highlight the need for having robust inter-regional grid connections to mitigate regional level renewable droughts. • Stochastic weather generator (long timeseries) is used for drought analysis. • Wind-droughts are more intense than solar-droughts over India. • Wind-solar hybridization reduces drought intensity more effectively in South India. • Incentives for hybrid plants should be region specific. • Hybrid energy droughts are complementary between regions (south and west India). [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel stochastic rainfall generator (SRG) that yields unprecedented events. • Two power law tuning parameters of the SRG can be adjusted as per global warming levels. • Revised return periods of Hurricane Harvey rainfall are found to be like that of a previous study. Unprecedented rainfall events are defined as rainfall events that have extremely high magnitude with low probability of occurrence. Although such events have occurred in the recent past, there might be no historical record of unprecedented rainfall. While significant efforts have been made to understand the major drivers of unprecedented events, the use of statistical modelling to estimate the likelihood of unprecedented events has gained little to no attention. The current study proposes a 'serial' type stochastic rainfall generator (SRG) to simulate daily rainfall with an additional focus on unprecedented events. The study introduces resampling of model parameters to yield the non-stationarity in simulated rainfall distribution. The proposed approach is applied to simulate daily rainfall over twenty-six grid points in Southeast Texas, which experienced Hurricane Harvey in 2017. Towards this, the study obtains rainfall accumulation data during Harvey from eight weather stations during. We found that the proposed approach can successfully generate unprecedented events if two power law tail tuning parameters are adjusted with respect to future warming levels. Rainfall magnitudes correspond to 50, 100, 250 and 500-year return periods simulated by the proposed model are found to be substantially higher than historical (i.e., observed) return period. Furthermore, the study found that the estimated return periods of Hurricane Harvey like rainfall is almost the same as the return period estimated by a former study of Emmanuel (2017). The potential benefit of the proposed approach in hydrological risk assessment has also been discussed. [ABSTRACT FROM AUTHOR]

• Weather generator and crop model address low-temperature stress in winter wheat. • Data validation: virtual vs. actual low-temperature stress similarity. • Reduce low-temperature stress-related biases, enhance crop predictions. This study proposes a method for generating virtual samples using a weather generator and crop model to address the scarcity of low-temperature stress samples in winter wheat. Firstly, simulated meteorological data is generated using the weather generator, and then virtual meteorological yield and leaf area index (LAI) from jointing to heading stages are calculated using the crop model. These virtual samples integrate location information from weather stations, low-temperature statistics derived from simulated meteorological data, LAI data, and corresponding simulated meteorological yields. The impact of low-temperature stress on these virtual samples is assessed based on computed statistical data. Validation using real data from Henan Province confirms the similarity between generated low-temperature virtual samples and a low-temperature stress test set. The effectiveness of the created low-temperature virtual samples is evaluated under various low-temperature stress conditions using the random forest method, indicating that increasing the number of low-temperature virtual samples in the training set improves the accuracy of yield prediction models. Specifically, the increase in R2 value and the decrease in RMSE and MAE values validate the efficiency of generating virtual samples. Notably, virtual samples generated by consecutive frost days (CFD) perform excellently under different low-temperature stress and temperature conditions. When all virtual samples in the training set are low-temperature samples, using virtual samples generated based on CFD significantly improves prediction accuracy compared to the case without low-temperature virtual samples. Furthermore, training yield prediction algorithms using generated low-temperature virtual samples can reduce the overestimation of yields from more weather stations, thus enhancing prediction accuracy and reliability. In conclusion, this study effectively addresses the scarcity of low-temperature stress samples in winter wheat, reducing yield overestimation in certain areas. The choice of generation strategy for producing appropriate low-temperature virtual samples should be based on the settings of the training set and accuracy requirements. [ABSTRACT FROM AUTHOR]

Simulation models are a fundamental tool for investigating hydrological processes and for water resource management. In this study, we introduce PyCoSMoS, a Python toolbox that enables researchers to simulate observed univariate time series mimicking hydroclimatic processes. This toolbox preserves arbitrary marginal distribution and autocorrelation functions, while significantly reducing computational burden. PyCoSMoS is built upon the mixed-Uniform CoSMoS method recently proposed by Papalexiou et al. (2023). The toolbox is designed to minimize the user's input, requiring only observed time series, marginal distribution, correlation function, and the number of lags. The output provides both visual and quantitative comparisons between the observed and simulated time series. We evaluate the performance of the package using various synthetic case studies and the results demonstrate satisfactory accuracy. Furthermore, we apply the toolbox to three real case studies: precipitation, temperature, and relative humidity, for which the toolbox can successfully simulate the observed time series in each case. • Python toolbox (PyCoSMoS) that simplifies hydroclimatic process simulation while preserving distribution and autocorrelation functions. • Minimal user input to provide visual and quantitative comparisons between observed and simulated time series. • Accurate simulation of real-world data for precipitation, temperature, and relative humidity. [ABSTRACT FROM AUTHOR]

What elements should a parsimonious model reproduce at a single scale to precisely simulate rainfall at many scales? We posit these elements are: (a) the probability of dry and linear correlation structure of the wet/dry sequence as a proxy reproducing the distribution of wet/dry spells, and (b) the marginal distribution of nonzero rainfall and its correlation structure. We build a two‐state rainfall model, the CoSMoS‐2s, that explicitly reproduces these elements and is easily applicable at any timescale. Additionally, the paper: (a) introduces the Generalized Exponential (GE $\mathcal{G}\mathcal{E}$) distribution system comprising six flexible distributions with desired properties to describe nonzero rainfall and facilitate time series generation; (b) extends the CoSMoS framework to allow simulations with negative correlations; (c) simplifies the generation of binary sequences with any correlation structure by analytical approximations; (d) introduces the rank‐based CoSMoS‐2s that preserves Spearman's correlations, has an analytical formulation, and is also applicable for infinite variance time series, (e) introduces the copula‐based CoSMoS‐2s enabling intermittent times series generation with nonzero values having the dependence structure of any desired copula, and (f) offers conceptual generalizations for rainfall modeling and beyond, with specific ideas for future improvements and extensions. The CoSMoS‐2s is tested using four long hourly rainfall records; the simulations reproduce rainfall properties at multiple scales including the wet/dry spells, probability of dry, characteristics of nonzero rainfall, and the behavior of extremes. Key Points: New flexible system of probability distributions for rainfall intensityAdvanced rainfall generation preserving wet/dry spells, marginal distributions, and copula dependence of nonzero rainfallApplicable to a single time scale and reproducing rainfall characteristics at multiple scales [ABSTRACT FROM AUTHOR]

WXGEN, a weather generator model based on stochastic process theory and mathematical statistics, was widely used in hydrological monitoring models, crop yield estimation models and derived fields of meteorological data. In this study, we used WXGEN to evaluate the simulation accuracy of hydrothermal conditions in the highlands of the Qinghai–Tibet Plateau. Results showed that: (1) The Markovian chain transfer parameters P(W|D) and P(W|W) of each station were between 0.03–0.30 and 0.12–0.74, which was basically consistent with the temporal and spatial distribution of actual precipitation; (2) In the thermal data simulation, more than 96% of the meteorological stations passed the 0.05 level in three different significance tests of monthly mean minimum and maximum temperature and solar radiation, and the measured deviations of simulated annual mean temperature and solar radiation were 0.686 °C and 1.65 MJ/m2, respectively. In all, 94% of the stations in the hydrological simulation passed the monthly precipitation significance test; (3) The simulated vs. measured deviations of annual precipitation, heavy rain days and wet days were 8.04 mm, 1.023 d and 8.374 d, respectively; (4) The simulation of extreme hydrothermal conditions that may affect the yield of highland barley was very close to the measured situation, and the R2 of simulation and measured value was all above 0.85. The simulation of freezing damage was less accurate, but also higher than 0.85. [ABSTRACT FROM AUTHOR]

This article discusses the aspect of modeling weather conditions in marine environment for implementation in the offshore wind farm domain. It is clear that harsh sea weather conditions influence many characteristics of any offshore installation. The accessibility to the infrastructure, maintenance procedures, failure ratios of components, energy provision levels, or utilization of vessels—are the examples of weather-related issues connected to the offshore wind industry. Regarding the growing popularity of digital twin methodology, authors present a novel view to generate weather data with copula-based method. The results obtained are compared to selected historical data and implemented into the maintenance model. The selected indicators of maintenance service are used for usability assessment of proposed copula-based method. [ABSTRACT FROM AUTHOR]

As an essential part of the hydrological cycle, precipitation directly contributes to surface runoff and river runoff formation. Simulation on precipitation variables can effectively solve the adverse effects on hydrological assessment in some areas with insufficient or even no runoff observation. With the widespread use of various weather generators, the traditional stochastic hydrological simulation methods tend to be gradually replaced. To compare these two approaches mentioned above, this paper utilizes the precipitation records from 1958 to 2011 at nine meteorological stations within Huaihe River System in Henan Province to evaluate a stochastic hydrological simulation method, SARIMA model, and two types of weather generators, WeaGETS and LARS-WG, through the comparison of statistical characteristics regarding precipitation variables, such as mean, mean square error, extreme value and coefficient of variation. The results show that (1) on the annual scale, SARIMA has a better performance to reproduce the mean and mean square error as well as the extreme precipitation events than weather generators; (2) regarding the monthly-scale precipitation simulation, SARIMA is good at reproducing the statistical properties of monthly precipitation at the average level, while WeaGETS and LARS-WG work better in simulating monthly precipitation extremes; (3) compared with weather generators, SARIMA is highly constrained by the observed records, and among these two weather generators, WeaGETS scores higher on monthly precipitation simulation under the same sample length conditions. In conclusion, the traditional hydrological simulation method, SARIMA, and weather generators, WeaGETS and LARS-WG, have both benefits and drawbacks. The appropriate choice depends on different research backgrounds and purposes. [ABSTRACT FROM AUTHOR]

A weather generator can be defined as a statistical model for the daily sequences, usually considering the multiple weather variables, such as precipitation, radiation, humidity, and temperature. The conventional weather generator has been mostly used the dry and wet days as random variables. But, the generated time series can contain a relatively large error in the dry and wet spell, due mainly to the key variable of precipitation. Fortunately, a Weather Generator Using Dry and Wet Spells (WGDWS) can be served as a new weather generator, particularly for the dry and wet spells as random variables. In this study, the usability and accuracy of WGDWS were evaluated on the daily weather data from 16 stations located in five major climate regions of China. The statistical values of generated and measured meteorological variables and the statistical values generated by WGDWS and DWSS (a weather generator based on the dry and wet daily transition probability developed by the research group) were compared. A significance test was performed on the monthly values of each climate variable, and the dry- and wet-spell lengths generated by WGDWS. The results showed that there were no significant differences from the measured values. The WGDWS has been widely expected to generate a long series of daily weather data, thereby meeting the climate, moisture, and crop physiological models. Specifically, 93.8% and 96.4% of the absolute errors in the monthly maximum and minimum temperatures were within 0.5°C of the measured values. Some 95.8% of the absolute errors were < 1 d in the monthly rainfall days. Also, 91.7% of monthly rainfall was < 10 mm, where the absolute errors in the monthly rainfall were higher for the tropical and subtropical monsoon climates, exceeding 20 mm. The absolute error in 70.3% of the total monthly solar radiation was within 2 MJ/m2, where the error was larger (> 4 MJ/m2 ) for the subtropical monsoon climate (Tengchong, Yunnan Province). According to the length of the wet and dry spells, the simulated values of the maximum monthly wet, average dry, and wet spell were very consistent with the measurement, with the average absolute errors < 1 d. There was a slightly larger average relative error of the maximum dry spell of the month (4.16 d). In climate type, the WGDWS was used to simulate the temperate and subtropical monsoon climates better than the temperate continental and tropical monsoon climates. A comparison was made on the monthly error distributions of the daily simulation sequences generated by the WGDWS and DWSS (a weather generator using the dry and wet daily transition probability that was previously developed by the research group). There was a consistency in the error distributions for the monthly maximum temperature, monthly minimum temperature, and monthly total solar radiation. The WGDWS simulation of monthly precipitation days was better than that of DWSS, where there was a relatively smaller error of WGDWS under the condition of equal probability. Nevertheless, the DWSS simulation of monthly precipitation was better than that of WGDWS. According to the dry- and wet-spell lengths in the daily series, there was a very close error distribution of the monthly average dry spell of the WGDWS and DWSS. The relative error of WGDWS was smaller than that of DWSS in the maximum dry and wet spell, and the average wet spell. Anyway, the WGDWS simulations were better than those of DWSS in most climate types, except for the average dry spells of the subtropical monsoon and tropical monsoon climates. The WGDWS can be widely expected to better simulate the maximum dry and wet spell, and the average wet spell, compared with the DWSS. Therefore, the WGDWS can accurately reflect the actual conditions in the long-term drought or long-term rainy weather. [ABSTRACT FROM AUTHOR]

In this work, we present a comprehensive evaluation of a stochastic multi‐site, multi‐variate weather generator at the scale of entire Germany and parts of the neighbouring countries covering the major German river basins Elbe, Upper Danube, Rhine, Weser and Ems with a total area of approximately 580,000 km2. The regional weather generator, which is based on a first‐order multi‐variate auto‐regressive model, is setup using 53‐year long daily observational data at 528 locations. The performance is evaluated by investigating the ability of the weather generator to replicate various important statistical properties of the observed variables including precipitation occurrence and dry/wet transition probabilities, mean daily and extreme precipitation, multi‐day precipitation sums, spatial correlation structure, areal precipitation, mean daily and extreme temperature and solar radiation. We explore two marginal distributions for daily precipitation amount: mixed Gamma‐Generalized Pareto and extended Generalized Pareto. Furthermore, we introduce a new procedure to estimate the spatial correlation matrix and model mean daily temperature and solar radiation. The extensive evaluation reveals that the weather generator is greatly capable of capturing most of the crucial properties of the weather variables, particularly of extreme precipitation at individual locations. Some deficiencies are detected in capturing spatial precipitation correlation structure that leads to an overestimation of areal precipitation extremes. Further improvement of the spatial correlation structure is envisaged for future research. The mixed marginal model found to outperform the extended Generalized Pareto in our case. The use of power transformation in combination with normal distribution significantly improves the performance for non‐precipitation variables. The weather generator can be used to generate synthetic event footprints for large‐scale trans‐basin flood risk assessment. [ABSTRACT FROM AUTHOR]

Study region: Peninsular Spain. Study focus: Weather data are the key drivers of hydrological modelling. However, available weather data can present gaps in data sequences and are often limited in their spatial coverage for use in such hydrological models as the Soil and Water Assessment Tool (SWAT). To overcome this limitation, SWAT includes a weather generator algorithm that can complete this data based on long-term weather statistics. This work presents a newly developed weather statistics dataset for Peninsular Spain (PSWG), calculated from national gridded datasets according to the SWAT model format. PSWG provides a higher resolution that stands as a compelling alternative to the statistics calculated from the Climate Forecast System Reanalysis (CFSR) that are available on the SWAT website. New hydrological insights for the region: The dataset has been evaluated using PSWG and CFSR datasets for different data availability scenarios to reconstruct weather series in three watersheds with contrasting weather climates. Results underscore the superiority of the PSWG dataset in reconstructing missing data for hydrological simulations. This approach provides a strong alternative for SWAT applications in Peninsular Spain and the applied methodology can be replicated in other countries that dispose of high-resolution gridded rainfall and temperature datasets.

Individuals living in every region of the world are increasingly vulnerable to negative health outcomes due to extreme heat exposure. Children, in particular, may face long-term consequences associated with heat stress that affect their educational attainment and later life health and well-being. Retrospective individual-level analyses are useful for determining the effects of extreme heat exposure on health outcomes. Typically, future risk is inferred by extrapolating these effects using future warming scenarios that are applied uniformly over space and time without consideration of topographical or climatological gradients. We propose an alternative approach using a stochastic weather generator. This approach employs a 1 °C warming scenario to produce an ensemble of plausible future weather scenarios, and subsequently a distribution of future health risks. We focus on the effect of global warming on fetal development as measured by birth weight in Ethiopia. We demonstrate that predicted changes in birth weight are sensitive to the evolution of temperatures not quantified in a uniform warming scenario. Distributions of predicted changes in birth weight vary in magnitude and variability depending on geographic and socioeconomic region. We present these distributions alongside results from the uniform warming scenario and discuss the spatiotemporal variability of these predicted changes. [ABSTRACT FROM AUTHOR]

Abstract. A method has been developed for assessing the limits of predictability of the frozen soil water content (according to observations at the Nizhnedevitskaya water balance station). The method is based on the analysis of the convergence of a given probabilistic measure (the variance of the calculated soil water content at a given date) to its stable value. The soil water content was simulated by the physically based model of heat and water transfer in a frozen soil column during a autumn-winter seasons. To assess variability of the modelled soil water content at a given date, the boundary meteorological conditions for the autumn-winter period were simulated by the Monte Carlo procedure using a stochastic weather generator. The initial conditions were assigned as the constant soil temperature and soil moisture values over the 1-meter soil column. The predictability of the soil water content in the one-meter layer of the studied soils has occurred to be about 1.5 months; it means that for the forest-steppe conditions, the soil water content before the beginning of soil freezing cannot serve as an indicator of soil water content before spring. Numerical experiments have shown that the soil water content predictability: (1) grows with an increase in the thickness of the considered soil layer and its depth; (2) decreases for coarser soils as compared to finely dispersed soils; (3) is more sensitive to changes in the soil texture than to changes in the climatic norms of precipitation and air temperature

Climate change’s impact on water availability has been widely studied, including its impact on very rare values quantified by return levels using the statistical extreme value theory. However, the application of this theory to estimate extreme low flows is barely justified due to a large temporal dependency and a physically highly bounded lower tail. One possible way of overcoming this difficulty is to simulate a very large sample of river flow time series consistent with the observations or the climate projections in order to enable empirical rare percentile estimations. In this paper, such an approach based on simulation is developed and tested for a small mountainous watershed in the French Alps. A bivariate generator of daily temperature and rainfall, developed in collaboration with Paris-Saclay University and based on hidden Markov models, is used to produce a large number of temperature and rainfall time series, further provided as input to a hydrological model to produce a similarly large sample of river flow time series. This sample is statistically analyzed in terms of low flow occurrence and intensity. This framework is adapted to the analysis of both current climate conditions and projected future climate. To study historical low flow situations, the bivariate temperature and rainfall model is fitted to the observed time series while bias-adjusted climate model outputs are used to calibrate the generator for the projections. The approach seems promising and could be further improved for use in more specific studies dedicated to the climate change impact on local low flow situations.

Climate impact studies often require climate data at a higher space–time resolution than is available from global and regional climate models. Weather generator (WG) models, generally designed for mesoscale applications (e.g., 101–105 km2), are popular and widely used tools to downscale climate data to finer resolution. One advantage of using WGs is their ability to generate the necessary climate variables for impact studies in data sparse regions. In this study, we evaluate the ability of a previously established state of the art WG (the AWE‐GEN‐2d model) to perform in data sparse regions that are beyond the mesoscale, using the Zambezi River basin (106 km2) in southeast Africa as a case study. The AWE‐GEN‐2d model was calibrated using data from satellite retrievals and climate re‐analysis products in place of the absent observational data. An 8‐km climate ensemble at hourly resolution, covering the period of 1976–2099 (present climate and RCP4.5 emission scenario from 2020), was then simulated. Using the simulated 30‐member ensemble, climate indices for both present and future climates were computed. The high‐resolution climate indices allow detailed analysis of the effects of climate change on different areas within the basin. For example, the southwestern area of the basin is predicted to experience the greatest change due to increased temperature, while the southeastern area was found to be already so hot that is less affected (e.g., the number of 'very hot days' per year increase by 18 and 9 days, respectively). Rainfall intensities are found to increase most in the eastern areas of the basin (1 mm·d−1) in comparison to the western region (0.3 mm·d−1). As demonstrated in this study, AWE‐GEN‐2d can be calibrated successfully using data from climate reanalysis products in the absence of ground station data and can be applied at larger scales than the mesoscale. [ABSTRACT FROM AUTHOR]

Weather generators (WGs) are models fitted to a series of observed weather data in order to generate long synthetic series of these data, which is important for risk assessment. WGs are also among the main downscaling tools for climate change impact assessments. Despite their many capabilities, WGs cannot properly reproduce low‐frequency variability (LFV). Moreover, when using WGs for the downscaling of future climate scenarios, a change in rainfall occurrence will have unintended effects on secondary variables such as minimum temperature (Tmin) and maximum temperature (Tmax). This paper presents an approach based on the Quantile Perturbation Method for correcting the LFV of rainfall outputs pertaining to WGs by perturbing the monthly means of the daily generated series. A multivariate monthly generator was used to improve LFV in Tmin and Tmax. An advantage of the proposed method is the preservation of the correlation between daily and monthly timescales as well as the cross‐correlation between variables during the LFV correction. This method further reduces the unintended effects of changes in rainfall occurrence on the secondary variables during downscaling. After applying the method to the LARS‐WG model, the performance of the modified model was tested both directly and indirectly. Concerning the direct validation, the statistical characteristics of the simulated and observed weather data related to the four stations were compared over a diverse range of climatic conditions. Regarding the indirect validation, a comparison was made between the statistical characteristics of the runoffs simulated with a daily hydrologic model fed with the generated and observed weather data. The results showed that the method improved the performance of LARS‐WG regarding the reproduction of some of the observed weather characteristics, particularly LFV. The method was also shown to be well capable of reducing the unintended effects of the changes in rainfall occurrence on secondary variables during downscaling. [ABSTRACT FROM AUTHOR]

Rainfall products can contain significantly different spatiotemporal estimates, depending on their underlying data and final constructed resolution. Commonly used products, such as rain gauges, rain gauge networks, and weather radar, differ in their information content regarding intensities, spatial variability, and natural climatic variability, therefore producing different estimates. Landscape evolution models (LEMs) simulate the geomorphic changes in landscapes, and current models can simulate timeframes from event level to millions of years and some use rainfall inputs to drive them. However, the impact of different rainfall products on LEM outputs has never been considered. This study uses the STREAP rainfall generator, calibrated using commonly used rainfall observation products, to produce longer rainfall records than the observations to drive the CAESAR‐Lisflood LEM to examine how differences in rainfall products affect simulated landscapes. The results show that the simulation of changes to basin geomorphology is sensitive to the differences between rainfall products, with these differences expressed linearly in discharges but non‐linearly in sediment yields. Furthermore, when applied over a 1500‐year period, large differences in the simulated long profiles were observed, with the simulations producing greater sediment yields showing erosion extending further downstream. This suggests that the choice of rainfall product to drive LEMs has a large impact on the final simulated landscapes. The combination of rainfall generator model and LEMs represents a potentially powerful method for assessing the impacts of rainfall product differences on landscapes and their short‐ and long‐term evolution. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd [ABSTRACT FROM AUTHOR]

Climate internal variability (CIV) plays an important role in understanding climate and is one of the principal uncertainties in climate projections. This study aims to estimate CIV and climatological mean (CM) in predictions using different emission scenarios for South Korea. A stochastic weather generator is employed to generate 100 ensembles of 30-year hourly time series for 40 meteorological stations. CIV is then estimated from the detrended method and compared with the noise computed by the two approaches. The extremely high value of the coefficient of determination between CIV values and noise indicates that the methodologies are seamless. The key results of this study include: (1) national average CM and CIV will increase in the future, and that increase will be greater in Representative Concentration Pathway 8.5 and end periods; (2) the nature of future changes in CM and CIV differ according to the indices of interest. Characteristics of three precipitation-quantity indices (total precipitation, totPr; daily maximum precipitation, maxDa; and hourly maximum precipitation, maxHr) and the precipitation-occurrence index (the number of days without precipitation, nonPr) are largely distinct; (3) examining the relationship between factors of changes of CIV and CM reveal a high correlation between them for maxDa and maxHr, but not for other indices; (4) The tail information of distribution for the FOC ratio implies that future changes in total and extreme precipitation are likely to be decoupled for some months or at some locations. The degree of decoupling is more noticeable on the hourly than the daily scale; and (5) the spatial deviation of CIV is also larger during the summer when CIV values are spatially large; this is valid only for totPr and maxDa. Methodologies and results for finer scales help assess the impact of climate change and develop appropriate adaptation and response strategies. [ABSTRACT FROM AUTHOR]

Many 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. Key Points: Bayesian networks are introduced as a novel machine learning methodology for multisite precipitation occurrence generationTheir performance is assessed using several measures in terms of spatial and temporal coherenceThe proposed methodology shows promise, with improvement on several spatiotemporal aspects against existing models [ABSTRACT FROM AUTHOR]

To project local precipitation at the existing meteorological stations in China’s Beijing-Tianjin-Hebei region in the future, local daily precipitation was simulated for three periods (2006–2030, 2031–2050, and 2051–2070) under RCP 4.5 and RCP 8.5 emission scenarios. These projections were statistically downscaled using a weather generator (BCC/RCG-WG) and the output of five global climate models. Based on the downscaled daily precipitation at 174 stations, eight indices describing mean and extreme precipitation climates were calculated. Overall increasing trends in the frequency and intensity of the mean and extreme precipitation were identified for the majority of the stations studied, which is in line with the GCMs’ output. However, the downscaling approach enables more local features to be reflected, adding value to applications at the local scale. Compared with the baseline during 1961–2005, the regional average annual precipitation and its intensity are projected to increase in all three future periods under both RCP 4.5 and RCP 8.5. The projected changes in the number of days with precipitation are relatively small across the Beijing-Tianjin-Hebei region. The regional average annual number of days with precipitation would increase by 0.2~1.0% under both RCP 4.5 and RCP 8.5, except during 2031–2050 under RCP 8.5 when it would decrease by 0.7%. The regional averages of annual days with precipitation ≥25 mm and ≥40 mm, the greatest one-day and five-day precipitation in the Beijing-Tianjin-Hebei region, are projected to increase by 8~30% during all the three periods. The number of days with daily precipitation ≥40 mm was projected to increase most significantly out of the eight indices, indicating the need to consider increased flooding risk in the future. The average annual maximum number of consecutive days without precipitation in the Beijing-Tianjin-Hebei region is projected to decrease, and the drought risk in this area is expected to decrease.

Natural gas customers rely upon utilities to provide gas for heating in the coldest parts of winter. Heating capacity is expensive, so utilities and end users (represented by commissions) must agree on the coldest day on which a utility is expected to meet demand. The return period of such a day is long relative to the amount of weather data that are typically available. This paper develops a weather resampling method called the Surrogate Weather Resampler, which creates a large dataset to support analysis of extremely infrequent events. While most current methods for generating weather data are based on simulation, this method resamples the deviations from typical weather. The paper also shows how extreme temperatures are strongly correlated to the demand for natural gas. The Surrogate Weather Resampler was compared in-sample and out-of-sample to the WeaGETS weather generator using both the Kolmogorov–Smirnov test and an exceedance-based test for cold weather generation. A naïve benchmark was also examined. These methods studied weather data from the National Oceanic and Atmospheric Administration and AccuWeather. Weather data were collected for 33 weather stations across North America, with 69 years of data from each weather station. We show that the Surrogate Weather Resampler can reproduce the cold tail of distribution better than the naïve benchmark and WeaGETS.

The Babolroud watershed has experienced different climate related events such as flooding and drought in recent years that emphasize the urgent need to investigate the impact of climate change on meteorology and hydrology of the watershed. Two general atmospheric circulation softwares were used to predict these changes. However, the large scale computational grid of such software discourages their application to meteorology parameters prediction in small scales. Therefore, an interface tool called the LARS- WG model has been developed to facilitate assessment of such parameters in more manageable scales and at the desired localities. The data generated by the HadCM3 general circulation of the atmosphere software were incorporated into the LARS-WG software under a similar scenario for the 2046-2065 periods. They were downscaled, compared and evaluated the four climatic variables related to the statistical base period of 1982-2011. These data were introduced into the LARS-WG software for calibration and verification. Furthermore, the relationships between the collected data were established between rainfall depth, maximum and minimum temperatures and radiation to simulate the future events. It was observed that the ECHO-G software prediction was lower for precipitation and higher for temperature under the similar scenario as compared to those predicted by the HadCM3 software. Precipitation depth as predicted by this software for the Babolroud watershed ranged from -52 to +17 %. The results indicated a moderate increase in precipitation in the rainy season and an extreme decrease in the summer month. The highest reduction was related to the August rainfall (43 mm) as predicted by the ECHO-G software. The highest increase in precipitation was related to October (27 mm) as predicted by the HadCM3 software.

Downscaling is usually necessary for robust hydrological impact assessments. This may be undertaken using a wide range of methods, including a combination of dynamical and statistical‐stochastic downscaling. This study uses the Spatial–Temporal Neyman‐Scott Rectangular Pulses model—RainSimV3, the precipitation‐conditioned daily weather generator—ICAAM‐WG, and the change factor approach for downscaling synthetic climate scenarios for robust hydrological impact assessment at middle‐sized basins. The ICAAM‐WG was developed based on the concept of the Climate Research Unit daily weather generator (CRU‐WG), motivated by the need for improved representation of heat waves by downscaling methods given the positive feedback between low soil moisture and high air temperature. We demonstrated the validity of the proposed methodology in the 705‐km2 Mediterranean climate basin in southern Portugal. The results show that, for the control period 1980–2010, both RainSimV3 and ICAAM‐WG reproduced not only the mean climatology, but also extreme wet and low precipitation events, as well as the extremes of temperature and heat waves. We found that downscaling with ICAAM‐WG (SIM6), which uses second‐order autoregressive processes for the simulation of temperature during consecutive dry and wet days, outperformed ICAAM‐WG (SIM4), which used only first‐order autoregressive processes, leading to improved simulation of heat waves. ICAAM‐WG (SIM6) well reproduced observed heatwave extremes with return periods of up to 30 years; however, ICAAM‐WG (SIM4) overestimated these extremes substantially. This indicates the importance of incorporating second‐order autoregressive processes in the simulation of heatwave length. In the context of climate warming, the proposed methodology provides a tool to improve downscaled projections of future extremes with confidence intervals for not only wet events but also dry spells and heat waves. [ABSTRACT FROM AUTHOR]

Weather generators are usually used to produce an ensemble of climate time series for vulnerability assessments and impact studies in hydrological and agricultural communities. Multisite and multivariate weather generators (MMWGs) have many advantages over single-site counterparts in terms of coupling with distributed models for the assessment of spatial variability in various impact sectors. However, the existing MMWGs usually suffer from limitations in preserving multisite and multivariate dependencies at multiple time scales, as well as preserving the low-frequency variability of climate variables. This study proposes a new MMWG which preserves low-frequency climate variability by coupling annual, monthly and daily weather generators into a single model. Specifically, the daily precipitation and temperature time series generated by a widely used multisite daily weather generator is adjusted using monthly and annual climate time series generated by a first-order linear autoregressive model. This combination preserves the multisite and multivariate attributes, as well as low-frequency variability at monthly and annual scales. The performance of the proposed MMWG was evaluated by comparing the baseline model against that of its variants with monthly or annual adjustments for climate generation. The performance was also assessed using a hydrological model over two watersheds with different hydroclimatic characteristics. The results show that the proposed weather generator performs well with respect to reproducing the marginal distributional attributes, multisite and multivariate dependencies, and climate variability at the daily, monthly and annual scales. Weather generators with either monthly or annual adjustments (and not both) only improve the simulations in multisite and multivariate dependencies and low-frequency variability at the corresponding time scales. In terms of hydrological modeling, the proposed model consistently performs better than the baseline model and its variants with only monthly or annual adjustments in representing the mean and variance of monthly and annual streamflows. It also performs better in representing the frequency distribution of mean and extreme streamflow events. Overall, the proposed MMWG can effectively produce multisite and multivariate climate time series with low-frequency variability and has a strong potential for use in climate change impact studies. [ABSTRACT FROM AUTHOR]

Ensemble modelling was used to assess the robustness of projected impacts of pumped‐storage (PS) operation and climate change on reservoir ice cover. To this end, three one‐dimensional and a two‐dimensional laterally averaged hydrodynamic model were set up. For the latter, the strength of the impacts with increasing distance from the dam was also investigated. Climate change effects were simulated by forcing the models with 150 years of synthetic meteorological time series created with a weather generator based on available air temperature scenarios for Switzerland. Future climate by the end of the 21st century was projected to shorten the ice‐covered period by ~2 months and decrease ice thicknesses by ~13 cm. Under current climate conditions, the ice cover would already be affected by extended PS operation. For example, the average probability of ice coverage on a specific day was projected to decrease by ~13% for current climate and could further be reduced from ~45% to ~10% for future climate. Overall, the results of all models were consistent. Although the number of winters without ice cover was projected to increase for all one‐dimensional models, studying individual segments of the two‐dimensional model showed that the impact was pronounced for segments close to the PS intake/outlet. In summary, the reservoir's ice cover is expected to partially vanish with higher probability of open water conditions closer to the PS intake/outlet. [ABSTRACT FROM AUTHOR]

Stochastic weather generators are statistical models widely used to produce climate time series with similar statistical properties to observed data. They are also used as downscaling tools to generate climate change scenarios for impact studies. Precipitation is one of the main variables simulated by weather generators and is also a key variable for impact studies, especially for hydrology. Precipitation is usually simulated by multiple precipitation models which have been proposed for simulating site-specific precipitation. However, these models' performance in simulating watershed-averaged extreme precipitation, especially in representing hydrological extremes, has not been well-investigated. Accordingly, this study compares the performance of six probability distributions (exponential, gamma, skewed normal, mixed exponential, hybrid exponential/Pareto, and Weibull distributions) and a polynomial curve-fitting method in generating precipitation for simulating hydrological extremes over three basins using a set of extreme indices. The results show that except for the exponential distribution (EXP), all of the methods produce the distribution of observed precipitation at the daily, monthly and annual scales reasonably well for all three river basins. Although the three-parameter hybrid exponential/Pareto distribution (EXPP) overestimates precipitation extremes, other three-parameter models produce extremes accurately. The three-parameter mixed exponential (MEXP) distribution outperforms other models for simulating precipitation extremes. However, with respect to representing hydrological extremes, the MEXP distribution is not the best model. When simulating extreme streamflows with synthetic weather data, the EXP distribution shows the worst performance, while the curve fitting method (PN) performs the best. The inferiority of the EXPP distribution in generating extreme precipitation does not propagate to extreme flow simulations. Meanwhile, the performance of WEB is moderate in terms of representing hydrological extremes. Overall, finding the model that best reproduces precipitation for simulating hydrological extremes is not as clear-cut, since the performance of each model is extreme-indices dependent. Taking all of the indices into account, the MEXP and the PN appear to be superior in representing extreme precipitation and hydrological extremes. [ABSTRACT FROM AUTHOR]