Weather Generator Research Articles

A Web API for weather generation and pest development simulation in North America

Climate is an essential component of environmental models. Over the last two decades, many weather generators have been presented in the literature. Although their implementation into software has been of great help to environmental modellers, their lack of integration into modelling frameworks still represents a challenge for end users. In many cases, end users have to retrieve the climate variables by themselves in order to use an environmental model. In some other cases, the weather generator software is embedded into the modelling framework, but this increases the maintenance effort.In this paper, we present a different approach: the deployment of a weather generator as a Web API. A few application examples are provided to illustrate the benefits of this implementation. In summary, a Web API facilitates the integration into modelling frameworks, decreases the maintenance effort and avoid interoperability issues due to different programming languages.

Water Savings with Irrigation Water Management at Multi-week Lead Time Using Extended Range Predictions

The Extended Range Predictions (ERP) provide Sub-seasonal to Seasonal (S2S) prediction of meteorological variables at a lead time of multiple weeks. Despite the considerable improvements in the ERP system, their applications in real-time irrigation water management are limited. Such limited use of ERP is due to uncertainty in predictions and a spatial mismatch in scales between the model grids and farms. Here, we develop a chance-constrained optimization framework for farm-scale irrigation water management planning for multiple weeks in advance. The degrading model performance with time and the related uncertainty are addressed by generating large ensembles using weather generators. Working with grape farmers in India has revealed that such an advance planning could save water by 3–25 % at a farm-scale without losing yield, compared with the existing approach requiring near real-time decisions. Moreover, the co-development of such irrigation tools with farmers increases the use and usability of S2S predictions.

The effect of spatial–temporal characteristics of rainfall on urban inundation processes

AbstractThe spatial–temporal characteristics of rainfall is one of the important elements affecting urban inundation. In this work, the effect of spatial–temporal characteristics of rainfall on urban inundation processes was investigated. To solve the problem that it is difficult to acquire the spatial–temporal characteristics of rainfall, the weather generator model was used to generate rainfall data in accordance with the climatic conditions of the study area. Then, precise rainfall data with 1 km2 spatial resolution and 5 min temporal resolution were obtained through the downscaling method. Finally, the GAST hydrodynamic model was used to simulate the urban inundation processes under different rainfall scenarios. The simulated results show that the inundation area and the inundation water volume increase with the rainfall spatial–temporal heterogeneity, and the inundation peak time lags behind. The impact of rainfall spatial–temporal heterogeneity on inundation weakens as the rainfall return period prolongs. When the rainfall spatial heterogeneity increases, the high‐risk inundation area enlarges, while the low‐risk inundation area shrinks. The temporal distribution of rainfall exerts the greatest impact on the inundation peak time. The research results can help researchers understand the key effects of the spatial–temporal characteristics of rainfall on urban inundation, and obtain relatively reliable simulated inundation results even without detailed information of the spatial–temporal characteristics of rainfall.

System Thinking Approach toward Reclamation of Regional Water Management under Changing Climate Conditions

This paper represents a streamflow prediction model with the approach of ensemble multi-GCM downscaling and system dynamics (SD) for the Aji-Chay watershed located in northwest Iran. In this study, firstly, the precipitation and temperature projection for the future was assessed according to the climate change impact using a statistical downscaling technique, i.e., Long Ashton Research Station Weather Generator (LARS-WG); secondly, a rainfall-runoff model for future horizons was developed according to artificial neural networks (ANN); finally, an SD model was developed according to plausible reclamation scenarios, i.e., cloud seeding, increasing the irrigation efficiency and reducing agricultural production, controlling policies on groundwater withdrawal as well as environmental awareness, and cultivation to reduce domestic consumption to achieve sustainable development. For downscaling purposes, the outputs of four general circulation models (GCMs) including EC-EARTH, HadGEM2, MIROC5, MPI-ESM from Coupled Model Intercomparison Project 5 (CMIP5) were applied. The results of multi-GCM downscaling indicated an ascending trend of 0.1 °C to +1.3 °C for temperature and a descending trend of 17 to 23% for precipitation by 2040 under representative concentration pathways (RCPs) of 4.5 and 8.5, respectively. Moreover, the results of the SD model revealed that none of the individual reclamation scenarios were impressive on water balance sustainable conditions; instead, the simultaneous implementation of all plausible scenarios managed to meet the requirements of socio-environment aspects as well as sustainability approaches.

Improvements, extensions, and validation of the Urban Weather Generator (UWG) for performance-oriented neighborhood planning

Policymakers have to assess and optimize neighborhood-scale planning aspects to profit from climate mitigation and adaptation measures due to various thermal environmental concerns. There are many models to evaluate the local thermal environment, but their outputs or complexity limit their application in performance-oriented neighborhood planning. In this study, the Urban Weather Generator (UWG) was updated to meet the needs of neighborhood planners. The model improves the radiation, vegetation, and convective heat transfer calculation processes to better match the actual physical representation of the neighborhood. Besides, the functions of the model are extended to calculate the mean radiant temperature (MRT) and water temperature for the evaluation of outdoor human thermal comfort and blue infrastructure effects. A new graphical interface also makes it easier for planning decision-makers to start quickly. Then, the prediction results of the updated model are evaluated with the neighborhood observations of the hot and humid regions of Guangzhou and Nanning. The air temperature, relative humidity (RH), and MRT results revealed that this novel model performs quite consistently and demonstrated the reliability and computational efficiency of the UWG model. In the early stages of thermal environment evaluation, the UWG model can be used for performance-oriented neighborhood planning.

ClimaSG: A weather generator for crop modelling and water requirements studies

A new stochastic weather generator, ClimaSG, is presented and evaluated under a wide range of climates. The model requires monthly means data as inputs to generate daily weather data. Validation tests revealed that ClimaSG performed well in capturing monthly rainfall variability but showed some limitations for generating extreme temperatures. Solar radiation, air humidity, and wind speed were reproduced acceptably. The weather generator was particularly accurate in predicting mean and extreme values of ET0, which indicates that ClimaSG may be used as the basis for calculating crop water requirements and for irrigation design and planning purposes. The suitability of synthetic weather series generated with ClimaSG for crop modelling applications was further assessed by running the model OliveCan for low density rainfed (LD) and super high density irrigated (SHD) olive orchards for several locations in Spain. Yield distributions simulated with synthetic weather rarely differed significantly from those obtained with actual weather data, although the coefficient of variation (CV) was usually underestimated. Considering its low input requirements and satisfactory performance, ClimaSG represents a promising source of weather data for different practical applications.

Assessment of stochastic weather forecast of precipitation near European cities, based on analogs of circulation

Abstract. In this study, we assess the skill of a stochastic weather generator (SWG) to forecast precipitation in several cities in western Europe. The SWG is based on a random sampling of analogs of the geopotential height at 500 hPa (Z500). The SWG is evaluated for two reanalyses (NCEP and ERA5). We simulate 100-member ensemble forecasts on a daily time increment. We evaluate the performance of SWG with forecast skill scores and we compare it to ECMWF forecasts. Results show significant positive skill score (continuous rank probability skill score and correlation) compared with persistence and climatology forecasts for lead times of 5 and 10 d for different areas in Europe. We find that the low predictability episodes of our model are related to specific weather regimes, depending on the European region. Comparing the SWG forecasts to ECMWF forecasts, we find that the SWG shows a good performance for 5 d. This performance varies from one region to another. This paper is a proof of concept for a stochastic regional ensemble precipitation forecast. Its parameters (e.g., region for analogs) must be tuned for each region in order to optimize its performance.

Regional sub-daily stochastic weather generator based on reanalyses for surface water stress estimation in central Tunisia

We present MetGen: a sub-daily multi-variable stochastic weather generator implemented as an R library that can be used to perform gap-filling and to extend in time meteorological observation series. MetGen is tailored to provide surrogate series of air temperature, relative air humidity, global radiation and wind speed needed for surface water stress estimation that requires sub-daily resolution. Multiple gauged stations can be used to increase the calibration data although spatial dependence is not modeled. The approach relies on Generalized Linear Models that use, among their covariates, large-scale variables derived from ERA5 reanalyses. MetGen aims at preserving key features of the meteorological variables along with inter-variable dependencies. We illustrate the abilities of MetGen using a case study with three stations in central Tunisia. We consider as alternatives a univariate and a multivariate bias correction techniques along with the un-processed large-scale variables.

The effect of climate change on malaria transmission in the southeast of Iran.

Malaria is a vector-borne disease, likely to be affected by climate change. In this study, general circulation model (GCM)-based scenarios were used for projecting future climate patterns and malaria incidence by artificial neural networks (ANN) in Zahedan district, Iran. Daily malaria incidence data of Zahedan district from 2000 to 2019 were inquired. The gamma test was used to select the appropriate combination of parameters for nonlinear modeling. The future climate pattern projections were obtained from HadGEM2-ES. The output was downscaled using LARS-WG stochastic weather generator under two Representative Concentration Pathway (RCP2.6 and RCP8.5) scenarios. The effect of climate change on malaria transmission for 2021-2060 was simulated by ANN. The designed model indicated that the future climate in Zahedan district will be warmer, more humid, and with more precipitation. Assessment of the potential impact of climate change on the incidence of malaria by ANN showed the number of malaria cases in Zahedan under both scenarios (RCP2.6 and RCP 8.5). It should be noted that due to the lack of daily malaria data before 2013, monthly data from 2000 were used only for initial analysis; and in preprocessing and simulation analyses, the daily malaria data from 2013 to 2019 were used. Therefore, if proper interventions are not implemented, malaria will continue to be a health issue in this region.

Characterizing Hydrologic Vulnerability under Nonstationary Climate and Antecedent Conditions Using a Process-Informed Stochastic Weather Generator

Characterizing Hydrologic Vulnerability under Nonstationary Climate and Antecedent Conditions Using a Process-Informed Stochastic Weather Generator

Future Climate Projections Using SDSM and LARS-WG Downscaling Methods for CMIP5 GCMs over the Transboundary Jhelum River Basin of the Himalayas Region

Climate change is one of the leading issues affecting river basins due to its direct impacts on the cryosphere and hydrosphere. General circulation models (GCMs) are widely applied tools to assess climate change but the coarse spatial resolution of GCMs limit their direct application for local studies. This study selected five CMIP5 GCMs (CCSM4, HadCM3, GFDL-CM3, MRI-CGCM3 and CanESM2) for performance evaluation ranked by Nash–Sutcliffe coefficient (NSE) and Kling–Gupta Efficiency (KGE). CCSM4 and HadCM3 large-scale predictors were favored based on ranks (0.71 and 0.68, respectively) for statistical downscaling techniques to downscale the climatic indicators Tmax, Tmin and precipitation. The performance of two downscaling techniques, Statistical Downscaling Methods (SDSM) and Long Ashton Research Station Weather Generator (LARS-WG), were examined using the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), bias, NSE and KGE with weights (Wi) for the validation period. The results of statistical measures proved SDSM more efficient (0.67) in comparison to the LARS-WG (0.51) for the validation time for the Jhelum River basin. The findings revealed that the SDSM simulation for Tmax and Tmin are more comparable to the reference data for the validation period except simulation of extreme events by precipitation. The 21st century climatic projections exhibited a significant rise in Tmax (2.37–4.66 °C), Tmin (2.47–4.52 °C) and precipitation (7.4–11.54%) for RCP-4.5 and RCP-8.5, respectively. Overall, the results depicted that winter and pre-monsoon seasons were potentially most affected in terms of warming and precipitation, which has the potential to alter the cryosphere and runoff of the Jhelum River basin.

The role of wind-solar hybrid plants in mitigating renewable energy-droughts

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.

Downscaling of climate change scenarios for a high-resolution, site-specific assessment of drought stress risk for two viticultural regions with heterogeneous landscapes

Abstract. Extended periods without precipitation, observed for example in central Europe including Germany during the seasons from 2018 to 2020, can lead to water deficit and yield and quality losses for grape and wine production. Irrigation infrastructure in these regions to possibly overcome negative effects is largely non-existent. Regional climate models project changes in precipitation amounts and patterns, indicating an increase in frequency of the occurrence of comparable situations in the future. In order to assess possible impacts of climate change on the water budget of grapevines, a water balance model was developed, which accounts for the large heterogeneity of vineyards with respect to their soil water storage capacity, evapotranspiration as a function of slope and aspect, and viticultural management practices. The model was fed with data from soil maps (soil type and plant-available water capacity), a digital elevation model, the European Union (EU) vineyard-register, observed weather data, and future weather data simulated by regional climate models and downscaled by a stochastic weather generator. This allowed conducting a risk assessment of the drought stress occurrence for the wine-producing regions Rheingau and Hessische Bergstraße in Germany on the scale of individual vineyard plots. The simulations showed that the risk for drought stress varies substantially between vineyard sites but might increase for steep-slope regions in the future. Possible adaptation measures depend highly on local conditions and are needed to make targeted use of water resources, while an intense interplay of different wine-industry stakeholders, research, knowledge transfer, and local authorities will be required.

Simulation and Evaluation of Hydrothermal Conditions in Crop Growth Period: A Case Study of Highland Barley in the Qinghai-Tibet Plateau

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.

Proposing a combined method for the estimation of spatial and temporal variation of crop water productivity under deficit irrigation scenarios based on the AquaCrop model

Deficit irrigation is a management strategy to improve crop water productivity, especially in arid and semi-arid regions. Soil characteristics and weather parameters are among the factors affecting crop water productivity in water stress conditions. Due to spatial changes in soil characteristics and temporal and spatial variations in meteorological parameters, it can be expected that crop water productivity will also have temporal and spatial variations. In this study, by combining the Geographic Information System (GIS) with the grid weather generation tools from the Crop Growth Monitoring System (CGMS) and the plug-in version of the AquaCrop, a combined method was developed to investigate the spatial and temporal variation of crop yield, seasonal crop evapotranspiration, and water productivity of maize under various irrigation scenarios. The proposed model was implemented in a case study in the west of Iran. The study area was divided into 37 grid weather with 5 * 5 km and 19 soil units. By overlaying soil units and grid weathers, 94 homogeneous units were created. The model was executed for 94 homogeneous areas, using calibrated crop file of grain maize under four irrigation scenarios of 40, 60, 80, and 100% of potential irrigation requirement (S40, S60, S80, and S100, respectively) for 28 years (1988–2015) of weather data (10,528 runs). The results showed that by increasing water stress, the percentage of spatial and temporal variation of the studied parameters (crop yield, seasonal crop water requirement, and water productivity) would be increased. The percentage of spatial changes in crop yield and crop water productivity was more significant than temporal changes. The average of crop water productivity in the scenarios of S100, S80, S60, and S40 was determined as 1.5, 1.4, 1.2, and 0.5 kg m−3, respectively.

Modelling the long-term geomorphic response to check dam failures in an alpine channel with CAESAR-Lisflood

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.

Modelling climate change impact on soil loss and erosion vulnerability in a watershed of Shiwalik Himalayas

Understanding the impact of climate change on soil erosion is required to plan effective soil conservation and management practices in hilly and mountainous landscape. Though, few studies have been executed in Indian Himalayas, much less in Shiwalik Himalayas. Thus, keeping this in view, the aim of the study is to estimate the possible impact of projected climate change scenarios on soil loss and erosion vulnerability using Statistical Downscaling Model (SDSM), MarkSim Weather Generator, Agricultural Policy Environmental eXtender (APEX) model and AHP method. The present study downscaled four climate scenarios on the near-future, noted the 2020 s (2011–2040), mid-future, noted 2050 s (2011–2070) and far-future, noted 2080 s (2071–2100/2095) under two Special Report Emission Scenarios (SRES), A2 and B2 with two Representative Concentration Pathway (RCP), 4.5 and 8.5 scenarios. Both SRES and RCP scenarios predicted an increase in temperature and annual rainfall depth during 21st century. Calibrated APEX model was used to simulate future soil loss. The study exposed that the average annual soil loss might increase up to 46.94%, 38.80%, 35.34% and 25.93% in the A2, B2, RCP 4.5 and RCP 8.5 scenarios respectively from the base period (1985–2014). The vulnerability assessment indicates, most of the areas might be under risk from slightly to highly vulnerable and was found that forest land cover could resist this erosion vulnerability to a certain extent. The findings of the study showed a possibility for climate change to increase the rate of soil loss unless conservation strategies or proper land use plans are remunerated. This study offers scientists and policymakers to a detailed understanding of the future impact of rainfall on soil loss and erosion vulnerability in the Shiwalik Himalayas.

Numerical Investigation of Sediment‐Yield Underestimation in Supply‐Limited Mountain Basins With Short Records

AbstractClimate and sediment supply are critical for geomorphic systems. It is known that complex relations between such forcings and sediment mobilization may lead to dampened or erased environmental signals in sediment records. But it is unclear under which circumstances environmental signals are transmitted and measurable downstream. We used a numerical approach consisting of a sediment cascade model and a stochastic weather generator to quantify climate forcing effects under a range of sediment supply regimes in a debris‐flow catchment in the Swiss Alps (Illgraben). We show that sediment yields estimated from short records are highly uncertain both in terms of mean and interannual variability. Furthermore, sediment yields tend to be underestimated in supply‐limited systems, where also long‐term memory effects, driven by the history of sediment storage, are evident. Consequently, determining geomorphic system response from short records may be grossly inaccurate and should be extended with sediment supply detection and uncertainty analysis.

Creation and application of future typical weather files in the evaluation of indoor overheating in free-floating buildings

Expected Global warming and heatwaves coupled with the urban heat island effect (UHI) can overheat indoor environments of free-floating buildings in temperate climate regions. Overheating assessment requires practitioners to use appropriate climate data and suitable measurement indices. The aim of this article is first, to propose a practical approach to generate yearly and typical ready-to-use future typical weather datasets (FTWY) using high-resolution Regional Climate Model (RCM) data from Coordinated Regional Climate Downscaling Experiment (CORDEX), and second, investigate the potential of FTWYs in the assessment of indoor overheating, considering UHI effect.To achieve these objectives, three dynamically downscaled (DDS) FTWYs generated from RCMs (IPSL-SMHI, CNRM-ALADIN, MPI-REMO) were compared with one statistically downscaled (ESD) FTWY from Meteonorm, and observed heatwave weather data of 2003. Comparative analysis was performed in two stages: comparison of monthly statistical distribution of climate variables, and analysis of heatwave presence. Urban weather generator (UWG) was used to project UHI effect on two weather files for two buildings, and three overheating measurement indices were used to exploit results. Comparative analysis of weather files show that temperature in a FTWY in the medium future (2040–2070) is likely not as intense as the heatwave of 2003 for Nantes. Results also confirm that it is better to use two weather files, and at least two overheating indices to obtain reliable outputs. This study also revealed that indoor overheating is not limited to densely built areas where impact of UHI is highest; buildings located in sparsely built neighbourhoods are also at risk.

Analysis of the Potential Impact of Climate Change on Climatic Droughts, Snow Dynamics, and the Correlation between Them

Climate change is expected to increase the occurrence of droughts, with the hydrology in alpine systems being largely determined by snow dynamics. In this paper, we propose a methodology to assess the impact of climate change on both meteorological and hydrological droughts, taking into account the dynamics of the snow cover area (SCA). We also analyze the correlation between these types of droughts. We generated ensembles of local climate scenarios based on regional climate models (RCMs) representative of potential future conditions. We considered several sources of uncertainty: different historical climate databases, simulations obtained with several RCMs, and some statistical downscaling techniques. We then used a stochastic weather generator (SWG) to generate multiple climatic series preserving the characteristics of the ensemble scenario. These were simulated within a cellular automata (CA) model to generate multiple SCA future series. They were used to calculate multiple series of meteorological drought indices, the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and a novel hydrological drought index (Standardized Snow Cover Index (SSCI)). Linear correlation analysis was applied to both types of drought to analyze how they propagate and the time delay between them. We applied the proposed methodology to the Sierra Nevada (southern Spain), where we estimated a general increase in meteorological and hydrological drought magnitude and duration for the horizon 2071–2100 under the RCP 8.5 emission scenario. The SCA droughts also revealed a significant increase in drought intensity. The meteorological drought propagation to SCA droughts was reflected in an immediate or short time (1 month), obtaining significant correlations in lower accumulation periods of drought indices (3 and 6 months). This allowed us to obtain information about meteorological drought from SCA deficits and vice versa.