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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.

[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]