Weather Generator Research Articles

Risk Assessment of Future Climate and Land Use/Land Cover Change Impacts on Water Resources

Climate and land use and land cover (LULC) changes will impact watershed-scale water resources. These systemic alterations will have interacting influences on water availability. A probabilistic risk assessment (PRA) framework for water resource impact analysis from future systemic change is described and implemented to examine combined climate and LULC change impacts from 2011–2100 for a study site in west-central Texas. Internally, the PRA framework provides probabilistic simulation of reference and future conditions using weather generator and water balance models in series—one weather generator and water balance model for reference and one of each for future conditions. To quantify future conditions uncertainty, framework results are the magnitude of change in water availability, from the comparison of simulated reference and future conditions, and likelihoods for each change. Inherent advantages of the framework formulation for analyzing future risk are the explicit incorporation of reference conditions to avoid additional scenario-based analysis of reference conditions and climate change emissions scenarios. In the case study application, an increase in impervious area from economic development is the LULC change; it generates a 1.1 times increase in average water availability, relative to future climate trends, from increased runoff and decreased transpiration.

The Vertical City Weather Generator (VCWG v1.3.2)

Abstract. The Vertical City Weather Generator (VCWG) is a computationally efficient urban microclimate model developed to predict temporal and vertical variation of potential temperature, wind speed, specific humidity, and turbulent kinetic energy. It is composed of various sub-models: a rural model, an urban vertical diffusion model, a radiation model, and a building energy model. Forced with weather data from a nearby rural site, the rural model is used to solve for the vertical profiles of potential temperature, specific humidity, and friction velocity at 10 m a.g.l. The rural model also calculates a horizontal pressure gradient. The rural model outputs are applied to a vertical diffusion urban microclimate model that solves vertical transport equations for potential temperature, momentum, specific humidity, and turbulent kinetic energy. The urban vertical diffusion model is also coupled to the radiation and building energy models using two-way interaction. The aerodynamic and thermal effects of urban elements, surface vegetation, and trees are considered. The predictions of the VCWG model are compared to observations of the Basel UrBan Boundary Layer Experiment (BUBBLE) microclimate field campaign for 8 months from December 2001 to July 2002. The model evaluation indicates that the VCWG predicts vertical profiles of meteorological variables in reasonable agreement with the field measurements. The average bias, root mean square error (RMSE), and R2 for potential temperature are 0.25 K, 1.41 K, and 0.82, respectively. The average bias, RMSE, and R2 for wind speed are 0.67 m s−1, 1.06 m s−1, and 0.41, respectively. The average bias, RMSE, and R2 for specific humidity are 0.00057 kg kg−1, 0.0010 kg kg−1, and 0.85, respectively. In addition, the average bias, RMSE, and R2 for the urban heat island (UHI) are 0.36 K, 1.2 K, and 0.35, respectively. Based on the evaluation, the model performance is comparable to the performance of similar models. The performance of the model is further explored to investigate the effects of urban configurations such as plan and frontal area densities, varying levels of vegetation, building energy configuration, radiation configuration, seasonal variations, and different climate zones on the model predictions. The results obtained from the explorations are reasonably consistent with previous studies in the literature, justifying the reliability and computational efficiency of VCWG for operational urban development projects.

Climate benchmarks and input parameters representing locations in 68 countries for a stochastic weather generator, CLIGEN

Abstract. This dataset contains input parameters for 12 703 locations around the world to parameterize a stochastic weather generator called CLIGEN. The parameters are essentially monthly statistics relating to daily precipitation, temperature, and solar radiation. The dataset is separated into three sub-datasets differentiated by having monthly statistics determined from 30-, 20-, and 10-year record lengths. Input parameters related to precipitation were calculated primarily from the NOAA GHCN-Daily network. The remaining input parameters were calculated from various sources including global meteorological and land-surface models that are informed by remote sensing and other methods. The new CLIGEN dataset includes inputs for locations in the US, which were compared to a selection of stations from an existing US CLIGEN dataset representing 2648 locations. This validation showed reasonable agreement between the two datasets, with the majority of parameters showing less than 20 % discrepancy relative to the existing dataset. For the three new datasets, differentiated by the minimum record lengths used for calculations, the validation showed only a small increase in discrepancy going towards shorter record lengths, such that the average discrepancy for all parameters was greater by 5 % for the 10-year dataset. The new CLIGEN dataset has the potential to improve the spatial coverage of analysis for a variety of CLIGEN applications and reduce the effort needed in preparing climate inputs. The dataset is available at the National Agriculture Library Data Commons website at https://data.nal.usda.gov/dataset/international-climate-benchmarks-and-input-parameters-stochastic-weather-generator-cligen (last access: 20 November 2020) and https://doi.org/10.15482/USDA.ADC/1518706 (Fullhart et al., 2020a).

Evaluation of a stochastic weather generator for long-term ensemble streamflow forecasts

ABSTRACT Resampling historical time series remains one of the main approaches used to generate long-term probabilistic streamflow forecasts, while there is a need to develop more flexible approaches taking into account non-stationarities. One possible approach is to use a modelling chain consisting of a stochastic weather generator and a hydrological model. However, the ability of this modelling chain to generate adequate probabilistic streamflows must first be evaluated. The aim of this paper is to compare the performance of a stochastic weather generator against resampling historical meteorological time series in order to produce ensemble streamflow forecasts. The comparison framework is based on 30 years of forecasts for a single Canadian watershed. Forecasts resulting from the two methods are evaluated using the continuous ranked probability score (CRPS) and rank histograms. Results indicate that while there are differences between the methods, they nevertheless perform similarly, thus showing that weather generators can be used as substitutes for resampling the historical past.

Assessing climate change impacts on streamflow and sediment load in the upstream of the Mekong River basin

AbstractAssessments of climate change impacts on streamflow and sediment processes are essential for developing science‐based sustainable watershed management plans. We assessed climate change impacts on streamflow and sediment load in the upstream of the Mekong River Basin, as a case study. Future climate scenarios including an ensemble‐mean climate scenario (EnM scenario) were generated based on 20 GCMs in CMIP5, using a stochastic weather generator (LARS‐WG) coupled with a distribution‐free shuffle procedure. The SWAT model was applied to simulate changes in streamflow and sediment load for the future period 2071–2100 under RCP8.5 with respect to the baseline period 1971–2000. Results show that mean monthly maximum and minimum temperature were projected to increase by all the 20 GCMs, with an ensemble‐mean increase of 4.6–5.7°C and 4.2–5.8°C across the 12 months, respectively. An increase in mean annual precipitation (3.4–55.8%) and streamflow (1.0–72.7%) was also projected by all GCMs. However, projected changes in sediment load were not consistent. One half of the GCMs projected an increase (5.2–53.2%) in annual sediment load while the other half projected a decrease (5.1%–43.2%). In each month, at least three‐quarters of the GCMs projected an increase in monthly streamflow. For monthly sediment load, an increase in May to July was projected by over half of the GCMs, while a decrease was projected by a majority of the GCMs for other months. Our results indicate large uncertainties in streamflow and sediment projections under climate change, demonstrating the need to use multi‐model ensembles in climate change impact studies. Moreover, it was found that the streamflow and sediment loads simulated using the EnM scenario were often close to the ensemble means simulated using the 20 GCMs, which implies that the single EnM scenario has the potential of effectively and efficiently estimating the ensemble means of projections in a multi‐model ensemble.

Optimal water allocation at different levels of climate change to minimize water shortage in arid regions (Case Study: Zayandeh-Rud River Basin, Iran)

Climate change is one of the most important factors influencing the future of the world's environment. The most important impacts of climate change are changes in water supply and demand in different regions of the world. In this study, different climate change patterns in two RCP4.5 and RCP8.5 emission scenarios (RCP: Representative Concentration Pathway), were adopted for the Zayandeh-Rud River Basin, Iran, through weighting of GCMs (General Circulation Models). These climate change patterns are including ideal, medium, and critical patterns. Using the LARS-WG model (Long Ashton Research Station Weather Generator), the outputs of the GCMs were downscaled statistically and the daily temperature and precipitation time series were generated from 2020 to 2044. Then, based on this information, the inflow volume into the Zayandeh-Rud Reservoir was predicted by the IHACRES model (Identification of unit Hydrograph and Component flows from Rainfall, Evaporation and Streamflow) and the agricultural water demand was also estimated based on future evapotranspiration. Finally, using GAMS (General Algebraic Modeling System) software, water resources in this basin were allocated based on the basic management scenario (B) and the water demand management scenario (D). The results showed that the average monthly temperature will increase by 0.6 to 1.3 °C under different climate change patterns. On the other hand, on the annual basis, precipitation will decrease by 6.5 to 31% and inflow volume to the Zayandeh-Rud Reservoir will decrease by 21 to 38%. The results also showed that the water shortages based on the baseline management scenario (B) will be between 334 and 805 MCM (Million Cubic Meters). These range of values varies between 252 and 787 MCM in the water demand management scenario (D). In general, the water shortage can be reduced in the Zayandeh-Rud River Basin with water demand control, but complete resolution of this problem in this region requires more integrated strategies based on a sustainable development, such as a fundamental change in the cropping pattern, prevention of population growth and industrial development.

Simulating compound weather extremes responsible for critical crop failure with stochastic weather generators

Abstract. In 2016, northern France experienced an unprecedented wheat crop loss. The cause of this event is not yet fully understood, and none of the most used crop forecast models were able to predict the event (Ben-Ari et al., 2018). However, this extreme event was likely due to a sequence of particular meteorological conditions, i.e. too few cold days in late autumn–winter and abnormally high precipitation during the spring season. Here we focus on a compound meteorological hazard (warm winter and wet spring) that could lead to a crop loss. This work is motivated by the question of whether the 2016 meteorological conditions were the most extreme possible conditions under current climate, and what the worst-case meteorological scenario would be with respect to warm winters followed by wet springs. To answer these questions, instead of relying on computationally intensive climate model simulations, we use an analogue-based importance sampling algorithm that was recently introduced into this field of research (Yiou and Jézéquel, 2020). This algorithm is a modification of a stochastic weather generator (SWG) that gives more weight to trajectories with more extreme meteorological conditions (here temperature and precipitation). This approach is inspired by importance sampling of complex systems (Ragone et al., 2017). This data-driven technique constructs artificial weather events by combining daily observations in a dynamically realistic manner and in a relatively fast way. This paper explains how an SWG for extreme winter temperature and spring precipitation can be constructed in order to generate large samples of such extremes. We show that with some adjustments both types of weather events can be adequately simulated with SWGs, highlighting the wide applicability of the method. We find that the number of cold days in late autumn 2015 was close to the plausible minimum. However, our simulations of extreme spring precipitation show that considerably wetter springs than what was observed in 2016 are possible. Although the relation of crop loss in 2016 to climate variability is not yet fully understood, these results indicate that similar events with higher impacts could be possible in present-day climate conditions.

Vulnerability of European wheat to extreme heat and drought around flowering under future climate

Identifying the future threats to crop yields from climate change is vital to underpin the continuous production increases needed for global food security. In the present study, the vulnerability of European wheat yield to heat and drought stresses around flowering under climate change was assessed by estimating the 95-percentiles of two indices at flowering under rain-fed conditions: the heat stress index (HSI95) and the drought stress index (DSI95). These two indices represent the relative yield losses due heat stress or drought stress around flowering that could be expected to occur once every 20 years on average. The Sirius wheat model was run under the predicted 2050-climate at 13 selected sites, representing the major wheat-growing regions in Europe. A total of 19 global climate models (GCMs) from the CMIP5 ensemble were used to construct local-scale climate scenarios for 2050 (RCP8.5) by downscaling GCMs climate projections with the LARS-WG weather generator. The mean DSI95 due to extreme drought around flowering under the baseline climate (1981–2010) was large over Europe (DSI95 ∼ 0.28), with wide site variation (DSI95 ∼ 0.0–0.51). A reduction of 12% in the DSI95 was predicted under the 2050-climate; however, vulnerability due to extreme drought around flowering would remain a major constraint to wheat yield (DSI95 ∼ 0–0.57). In contrast, HSI95 under the baseline climate was very small over Europe (HSI95 ∼ 0.0–0.11), but was predicted to increase by 79% (HSI95 ∼ 0.0–0.23) under the 2050-climate, categorising extreme heat stress around flowering as an emergent threat to European wheat production. The development of wheat varieties that are tolerant to drought and heat stresses around flowering, is required, if climate change is not to result in a reduction of wheat yield potential under the future climate in Europe.

A Constrained Stochastic Weather Generator for Daily Mean Air Temperature and Precipitation

A constrained stochastic weather generator (CSWG) for producing daily mean air temperature and precipitation based on annual mean air temperature and precipitation from tree-ring records is developed and tested in this paper. The principle for stochastically generating daily mean air temperature assumes that temperatures in any year can be approximated by a sinusoidal wave function plus a perturbation from the baseline. The CSWG for stochastically producing daily precipitation is based on three additional assumptions: (1) In each month, the total precipitation can be estimated from annual precipitation if there exists a relationship between the annual and monthly precipitations. If that relationship exists, then (2) for each month, the number of dry days and the maximum daily precipitation can be estimated from the total precipitation in that month. Finally, (3) in each month, there exists a probability distribution of daily precipitation amount for each wet day. These assumptions allow the development of a weather generator that constrains statistically relevant daily temperature and precipitation predictions based on a specified annual value, and thus this study presents a unique method that can be used to explore historic (e.g., archeological questions) or future (e.g., climate change) daily weather conditions based upon specified annual values.

Impact of Climate Change on Precipitation Extremes over Ho Chi Minh City, Vietnam

In the context of climate change, the impact of hydro-meteorological extremes, such as floods and droughts, has become one of the most severe issues for the governors of mega-cities. The main purpose of this study is to assess the spatiotemporal changes in extreme precipitation indices over Ho Chi Minh City, Vietnam, between the near (2021–2050) and intermediate (2051–2080) future periods with respect to the baseline period (1980–2009). The historical extreme indices were calculated through observed daily rainfall data at 11 selected meteorological stations across the study area. The future extreme indices were projected based on a stochastic weather generator, the Long Ashton Research Station Weather Generator (LARS-WG), which incorporates climate projections from the Coupled Model Intercomparison Project 5 (CMIP5) ensemble. Eight extreme precipitation indices, such as the consecutive dry days (CDDs), consecutive wet days (CWDs), number of very heavy precipitation days (R20mm), number of extremely heavy precipitation days (R25mm), maximum 1 d precipitation amount (RX1day), maximum 5 d precipitation amount (RX5day), very wet days (R95p), and simple daily intensity index (SDII) were selected to evaluate the multi-model ensemble mean changes of extreme indices in terms of intensity, duration, and frequency. The statistical significance, stability, and averaged magnitude of trends in these changes, thereby, were computed by the Mann-Kendall statistical techniques and Sen’s estimator, and applied to each extreme index. The results indicated a general increasing trend in most extreme indices for the future periods. In comparison with the near future period (2021–2050), the extreme intensity and frequency indices in the intermediate future period (2051–2080) present more statistically significant trends and higher growing rates. Furthermore, an increase in most extreme indices mainly occurs in some parts of the central and southern regions, while a decrease in those indices is often projected in the north of the study area.

Assessment of Surface Water Resources in Case of Muger Sub Basin, Ethiopia

Water resources assessment (WRA) is the process of measuring, collecting and analysing relevant parameters on the quantity and quality of water resources for the purposes of a better development and management of water resources. The aim of this research is Assessment of Surface Water Resource in Case of Muger Sub Basin in Ethiopia. The future possible local climate variables are extracted from Abbay basin RCM and then the bias-corrected with observed meteorological variables which are then used as input to the soil and water assessment tool (SWAT) model as climate data, in addition to climate data soil data, land use land cover, slope of the sub basin and weather Generator together are used to simulate future water yield of Muger sub-basin. Soil and Water Assessment Tool (SWAT) was adopted to perform runoff simulation. The good performance of the SWAT model was confirmed, with a Nash-Sutcliffe efficiency (NSE) and determination coefficients (R2) of 0.76 and 0.99 respectively during calibration for monthly runoff, Nash-Sutcliffe efficiency (NSE) 0.63 and determination coefficients (R2) 0.99 respectively during validation for monthly runoff. The variation of precipitation in Muger sub basin decreased by (2010-2023), (2024-2037) and (2038-2050) from base period (1996-2009) will be 0.36%, 1.076% and 1.74% respectively, Maximum Temperature in sub basin increase from base period (1996-2009) by 0.55%, 2.32% and 4.6% and also minimum temperature in Muger sub basin increase by (2010-2023), (2024-2037) and (2038-2050) from base period (1996-2009) was 0.83%, 2.80 and 8.54% respectively. From this study, it was observed that due to climate change Average annual water yieldin Mugersub basinin (1996-2009), (2010-2023), (2024-2037), (2038-2050) is 4634.07 Mm3, 4525.92 Mm3, 4456.20 Mm3 and 4411.89 Mm3 respectively. Generally as Temperature increase in the study area the amount of rainfall decreases which directly affect the amount of water yield.

Evaluation of Statistical Downscaling Methods for Simulating Daily Precipitation Distribution, Frequency, and Temporal Sequence

HighlightsNine statistical downscaling methods from three downscaling categories were evaluated.Weather generator-based methods had advantages in simulating non-stationary precipitation.Differences in downscaling performance were smaller within each category than between categories.The performance of each downscaling method varied with climate conditions.Abstract. Spatial discrepancy between global climate model (GCM) projections and the climate data input required by hydrological models is a major limitation for assessing the impact of climate change on soil erosion and crop production at local scales. Statistical downscaling techniques are widely used to correct biases of GCM projections. The objective of this study was to evaluate the ability of nine statistical downscaling methods from three available statistical downscaling categories to simulate daily precipitation distribution, frequency, and temporal sequence at four Oklahoma weather stations representing arid to humid climate regions. The three downscaling categories included perfect prognosis (PP), model output statistics (MOS), and stochastic weather generator (SWG). To minimize the effect of GCM projection error on downscaling quality, the National Centers for Environmental Prediction (NCEP) Reanalysis 1 data at a 2.5° grid spacing (treated as observed grid data) were downscaled to the four weather stations (representing arid, semi-arid, sub humid, and humid regions) using the nine downscaling methods. The station observations were divided into calibration and validation periods in a way that maximized the differences in annual precipitation means between the two periods for assessing the ability of each method in downscaling non-stationary climate changes. All methods were ranked with three metrics (Euclidean distance, sum of absolute relative error, and absolute error) for their ability in simulating precipitation amounts at daily, monthly, yearly, and annual maximum scales. After eliminating the poorest two performers in simulating precipitation mean, distribution, frequency, and temporal sequence, the top four remaining methods in ascending order were Distribution-based Bias Correction (DBC), Generator for Point Climate Change (GPCC), SYNthetic weather generaTOR (SYNTOR), and LOCal Intensity scaling (LOCI). DBC and LOCI are bias-correction methods, and GPCC and SYNTOR are generator-based methods. The differences in performances among the downscaling methods were smaller within each downscaling category than between the categories. The performance of each method varied with the climate conditions of each station. Overall results indicated that the SWG methods had certain advantages in simulating daily precipitation distribution, frequency, and temporal sequence for non-stationary climate changes. Keywords: Climate change, Climate downscaling, Downscaling method evaluation, Statistical downscaling.

A Compressive Receding Horizon Approach for Smart Home Energy Management

Current increases in the demand for electricity require sustainable energy management measures and have promoted the adoption of clean and renewable sources, particularly at the residential building level. Active demand management is usually carried out through load shifting based on specific techniques, such as optimisation, heuristics, model-based predictive control and machine learning methodologies. This work addresses the problem of residential load scheduling via optimisation techniques. A compressive receding horizon strategy is proposed for week-ahead load shifting, and the selection is driven by traditional receding horizon and day-ahead allocation strategy misalignment, with weekly household appliance usage patterns. The proposed approach is compared with receding horizon and day-ahead scheduling techniques over 30 different weeks for a prototypical smart home with non-controllable demand, which is representative of a four-resident family and includes micro power generation and battery storage. The simulation results confirm the validity of the proposed strategy in the context of household appliance scheduling problems and show competitive electricity costs and resident discomfort performance compared to state-of-the-art approaches. Furthermore, the proposed compressive receding horizon strategy fully exploits weather and photovoltaic generation forecasts to promote self-consumption and grid demand stress reduction while providing environmental gains and financial benefits to the utility service and consumers, particularly in the case of simultaneously scheduling a huge number of households.

Can we advance individual-level heat-health research through the application of stochastic weather generators?

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.

Climate Change Impacts on Sediment Yield and Debris‐Flow Activity in an Alpine Catchment

AbstractClimate change impacts on sediment production and transfer processes on hillslopes and through channels are governed by possible changes in precipitation, runoff, and air temperature. These hydrological and geomorphological impacts are difficult to predict in temperature‐sensitive Alpine environments. In this study, we combined a stochastic weather generator model with the most current climate change projections to feed a hillslope‐channel sediment cascade model for a major debris‐flow system in the Swiss Alps (the Illgraben). This allowed us to quantify climate change impacts and their uncertainties on sediment yield and the number of debris flows at hourly temporal resolution. We show that projected changes in precipitation and air temperature lead to a reduction in both sediment yield (−48%) and debris‐flow occurrence (−23%). This change is caused by a decrease in sediment supply from hillslopes, which is driven by frost‐weathering. Additionally, we conduct model experiments that show the sensitivity of projected changes in sediment yield and debris‐flow hazard to basin elevation, with important implications for assessing natural hazards and risks in mountain environments. Future changes in hydrological and sediment fluxes are characterized by high uncertainty, mainly due to irreducible internal climate variability. Therefore, this stochastic uncertainty needs to be considered in climate change impact assessments for geomorphic systems.

Temporally downscaling precipitation intensity factors for Köppen climate regions in the United States

Model inputs for prediction of runoff and soil erosion commonly require precipitation intensity information. Intensity is often estimated if precipitation data with high temporal resolution are unavailable. However, when intensity is time-averaged for fixed measurement intervals, estimates become increasingly underestimated with longer intervals due to the assumption that event durations begin and end at specified measurement intervals. In this study, adjustment factors were determined for downscaling the temporal resolution of intensity values derived from selected resolutions within the range of 10 to 1,440 min for Köppen-Geiger climate regions in the United States. In this case, monthly mean maximum 30 min intensity (MX.5P) was downscaled, which is a parameter used to generate stochastic meteorological inputs for models that include the Rangeland Hydrology and Erosion Model (RHEM) and the Water Erosion Prediction Project model (WEPP). The adjustment factors were given by regressions of reference MX.5P values derived from data with 5 min resolution against MX.5P values derived from data with lower temporal resolutions (≥10 min). In addition to using a slope coefficient for intensity in the regression equation, permutations of the equation included use of an elevation coefficient and constants, resulting in four total permutations. For the 143 stations and 17 climate regions analyzed, the four regression equations had roughly equal performance, and all gave statistically significant results. Regressions for adjusting hourly data using only an intensity coefficient in the equation had standard error of the estimate ranging from 1.01 to 2.96 mm h^–1 with an average of 2.04 mm h^–1. When downscaling daily values, the error range was 2.50 to 10.20 mm h^–1 with an average of 5.63 mm h^–1. Average time-to-peak intensity probability distributions for each climate region were also determined. Finally, a stochastic weather generator, CLIGEN, was used to test the effectiveness of applying the climate-based factors as an alternative to using subhourly data.

The Effect of Non-Seasonal Climate Variations on Extreme Rainfall Events in Early Rainy Season Onset in Southest West Java Province

The region of Tasikmalaya, Garut, and Pangandaran (hereafter mentioned as Southeast West Java Province) experienced extreme precipitation that occurred on September 16, 2016, October 6, 2017, and November 5, 2018, which then followed by flood. The characteristics of these extreme rainfall events need to be communicated to the related disaster management agency and the local citizens as a part of understanding the risks and disaster mitigation. This paper aims to determine the relation between extreme rainfall and non-seasonal climate variations such as Madden Julian Oscillation (MJO), El Niño Southern Oscillation (ENSO), tropical storm, and local circulation that occur simultaneously. Atmosphere and ocean data, including daily rainfall, precipitable water, cloud satellite imagery, wind and sea surface temperature were used. Descriptive statistical analysis, atmospheric dynamics, and physical atmosphere were applied to characterize the event, spatially and temporally. The results showed that the MJO was a non-seasonal factor that always exists in these three early rainy season extreme rainfall events in the region. Other non-seasonal factors such as interaction between La Nina and tropical disturbance; La Nina and local circulation; and El Nino and local circulation also affected the extreme rainfall events. We conclude that the intra-seasonal climate variation of MJO and inter-annual climate anomaly of La Nina/ El Nino, tropical storm, and local circulation are among the weather generators for extreme rainfall during early rainy season (September to November) in the Southeast West Java Province.

Identifying weather regimes for regional‐scale stochastic weather generators

AbstractWeather regime based stochastic weather generators (WR‐SWGs) have recently been proposed as a tool to better understand multi‐sector vulnerability to deeply uncertain climate change. WR‐SWGs can distinguish and simulate different types of climate change that have varying degrees of uncertainty in future projections, including thermodynamic changes (e.g., rising temperatures, Clausius‐Clapeyron scaling of extreme precipitation) and dynamic changes (e.g., shifting circulation and storm tracks). These models require the accurate identification of WRs that are representative of both historical and plausible future patterns of atmospheric circulation, while preserving the complex space–time variability of weather processes. This study proposes a novel framework to identify such WRs based on WR‐SWG performance over a broad geographic area and applies this framework to a case study in California. We test two components of WR‐SWG design, including the method used for WR identification (Hidden Markov Models (HMMs) vs. K‐means clustering) and the number of WRs. For different combinations of these components, we assess performance of a multi‐site WR‐SWG using 14 metrics across 13 major California river basins during the cold season. Results show that performance is best using a small number of WRs (4–5) identified using an HMM. We then juxtapose the number of WRs selected based on WR‐SWG performance against the number of regimes identified using metastability analysis of atmospheric fields. Results show strong agreement in the number of regimes between the two approaches, suggesting that the use of metastable regimes could inform WR‐SWG design. We conclude with a discussion of the potential to expand this framework for additional WR‐SWG design parameters and spatial scales.

Coupling large-scale climate indices with a stochastic weather generator to improve long-term streamflow forecasts in a Canadian watershed

This paper aims at improving long-term streamflow forecasts by implementing a novel technique based on conditioning the parameters of a stochastic weather generator on large-scale climate indices, with varying lengths of training periods during the establishment of correlations. The most important climate indices are identified by looking at yearly correlations between a set of 40 indices and meteorological data (precipitation and temperature) at the watershed scale. A linear model is then constructed to identify precipitation and temperature anomalies to induce perturbations in the stochastic weather generator. Time windows of 5, 10, 15, 20 and 30 years are used in determining the optimal linear model. The performance of the proposed approach is assessed against that of a resampling of past climatology and using the same stochastic weather generator unconditioned on climate indices. Each member of the ensemble weather forecast is then fed to a hydrological model to create the Ensemble Streamflow Forecasts (ESF) with a one-year forecasting horizon. The three approaches are tested in hindcast mode over a 30-year period at 12 forecast dates. Results show that temperatures are significantly correlated with large-scale climate indices, whereas precipitation is only weakly related to the same indices. The length of the time window has a considerable impact on the prediction ability of the linear models. The precipitation models based on short duration time windows performed better than those based on longer windows, while the reverse was found for the temperature models. A comparison between all three Ensemble Streamflow Forecast approaches is assessed using the Continuous Ranked Probability Score (CRPS) metric. Results show that the proposed method improves long-term streamflow forecasting, particularly the volumetric bias and the peak flows during the spring flood.

Watershed-Scale, Probabilistic Risk Assessment of Water Resources Impacts from Climate Change

A framework for the assessment of relative risk to watershed-scale water resources from systemic changes is presented. It is composed of two experiments, or pathways, within a Monte Carlo structure and provides quantification of prediction uncertainty. One simulation pathway is the no change, or null hypothesis, experiment, and the other provides simulation of the hypothesized system change. Each pathway uses a stochastic weather generator and a deterministic water balance model. For climate change impact analysis, the framework is calibrated so that the differences between thirty-year average precipitation and temperature pathway values reproduce climate trends. Simulated weather provides forcing for identical water balance models. Probabilistic time histories of differences in actual evapotranspiration, runoff, and recharge provide likelihood per magnitude change to water resources availability. The framework is applied to a semi-arid watershed in Texas. Projected climate trends for the site are a 3 °C increase in average temperature and corresponding increase in potential evapotranspiration, no significant change in average annual precipitation, and a semi-arid classification from 2011–2100. Two types of water balance model are used in separate applications: (1) monthly water balance and (2) daily distributed parameter. Both implementations predict no significant change, on average, to actual evapotranspiration, runoff, or recharge from 2011–2100 because precipitation is unchanged on average. Increases in extreme event intensity are represented for future conditions producing increased water availability during infrequent events.