Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied ...Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied to seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society （IRI）. In conjunction with the GLM （generalized linear modeling） weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts （the IRI format only specifies probabilities for three categories： below normal, near normal, and above normal） into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and minimum and maximum temperature, as well as more meaningful daily weather statistics for crop yields, such as the number of dry days and the amount of precipitation on wet days. The approach is extended to the case of climate change scenarios, treating a hypothetical return to a previously observed drier regime in the Pampas.展开更多
By adopting various stochastic weather generators, different research groups in their recent studies have realized the importance of the effects of climatic variability on crop growth and development. The conventional...By adopting various stochastic weather generators, different research groups in their recent studies have realized the importance of the effects of climatic variability on crop growth and development. The conventional assessments derived climate change scenarios from General Circulation Models (GCMs) experiments, however, are incapable of helping to understand this importance. The particular interest here is to review the general methodological scheme to incorporate stochastic weather generator into climate impact studies and the specific approaches in our studies, and put forward uncertainties that still exist. A variety of approaches have been taken to develop the parameterization program and stochastic experiment, and adjust the parameters of atypical stochastic weather generator called WGEN. Usually, the changes in monthly means and variances of weather variables between controlled and changed climate are used to perturb the parameters to generate the intended daily climate scenarios. We establish a parameterization program and methods for stochastic experiment of WGEN in the light of outputs of short-term climate prediction models, and evaluate its simulations on both temporal and spatial scales. Also, we manipulated parameters in relation to the changes in precipitation to produce the intended types and qualitative magnitudes of climatic variability. These adjustments yield various changes in climatic variability for sensitivity analyses. The impacts of changes in climatic variability on maize growth, final yield, and agro-climatic resources in Northeast China are assessed and presented as the case studies through the above methods. However, this corporation is still equivocal due to deficiencies of the generator and unsophisticated manipulation of parameters. To detect and simulate the changes in climatic variability is one of the indispensable ways to reduce the uncertainties in this aspect.展开更多
Recent years have seen a surge in assessment of potential impacts of climate change. As one of the most important tools for generating synthetic hydrological model inputs, weather generators have played an important r...Recent years have seen a surge in assessment of potential impacts of climate change. As one of the most important tools for generating synthetic hydrological model inputs, weather generators have played an important role in climate change impact analysis of water management. However, most weather generators like statistical downscaling model （SDSM） and long Ashton research station weather generator （LARS-WG） are designed for single site data generation. Considering the significance of spatial correlations of hydro-meteorological data, multi-site weather data generation becomes a necessity. In this study we aim to evaluate the performance of a new multi-site stochastic model, geo-spatial temporal weather generator （GIST）, in simulating precipitation in the Qiantang River Basin, East China. The correlation matrix, precipitation amount and occurrence of observed and GiST-generated data are first compared for the evaluation process. Then we use the GiST model combined with the change factor method （CFM） to investigate future changes of precipitation （2071 2100） in the study area using one global climate model, Hadgem2 ES, and an extreme emission scenario RCP 8.5, The final results show that the simulated precipitation amount and occurrence by GiST matched their historical counterparts reasonably. The correlation coefficients between simulated and his- torical precipitations show good consistence as well. Compared with the baseline period （1961 1990）, precipitation in the future time period （2071-2100） at high elevation stations will probably increase while at other stations decreases will occur. This study implies potential application of the GiST stochastic model in investigating the impact of climate change on hydrology and water resources.展开更多
This paper describes a new weather generator e the 10-state empirical model e that combines a 10-state, first-order Markov chain with a non-parametric precipitation amounts model. Using a doubly-stochastic transition-...This paper describes a new weather generator e the 10-state empirical model e that combines a 10-state, first-order Markov chain with a non-parametric precipitation amounts model. Using a doubly-stochastic transition-matrix results in a weather generator for which the overall precipitation distribution(including both wet and dry days) and the temporal-correlation can be modified independently for climate change studies. This paper assesses the ability of the 10-state empirical model to simulate daily area-average precipitation in the Torne River catchment in northern Sweden/western Finland in the context of 3 other models: a 10-state model with a parametric(Gamma) amounts model; a wet/dry chain with the empirical amounts model; and a wet/dry chain with the parametric amounts model. The ability to accurately simulate the distribution of multi-day precipitation in the catchment is the primary consideration.Results showed that the 10-state empirical model represented accumulated 2- to 14-day precipitation most realistically. Further, the distribution of precipitation on wet days in the catchment is related to the placement of a wet day within a wet-spell, and the 10-state models represented this realistically, while the wet/dry models did not. Although all four models accurately reproduced the annual and monthly averages in the training data, all models underestimated inter-annual and inter-seasonal variance. Even so, the 10-state empirical model performed best.We conclude that the multi-state model is a promising candidate for hydrological applications, as it simulates multi-day precipitation well, but that further development is required to improve the simulation of interannual variation.展开更多
Based on former studies on weather simulator modules in IPMist laboratory, study on visual programming of stochastic weather generator(VS-WGEN)was continued. In this study, Markov Chain, Monte Carlo, Fourier Series, a...Based on former studies on weather simulator modules in IPMist laboratory, study on visual programming of stochastic weather generator(VS-WGEN)was continued. In this study, Markov Chain, Monte Carlo, Fourier Series, and weak stationary process were used to generate the daily weather data in software Matlab 6. 0, with the data input from 40 years' weather data recorded by Beijing Weather Station. The generated data includes daily maximum temperature, minimum temperature, precipitation and solar radiation. It has been verified that the weather data generated by the VS-WGEN were more accurate than that by the old WGEN, when twenty new model parameters were included. VS-WGEN has wide software applications, such as pest risk analysis, pest forecast and management. It can be implemented in hardware development as well, such as weather control in weather chamber and greenhouse for researches on ecological adaptation of crop varieties to a given location over time and space. Overall, VS-WGEN is a very useful tool for studies on theoretical and applied ecology.展开更多
Long series of daily weather data are frequently needed to evaluate diachronic climatic impact on water resources,the effects of watershed changes on hydrology and to use in a variety of weather and general circulatio...Long series of daily weather data are frequently needed to evaluate diachronic climatic impact on water resources,the effects of watershed changes on hydrology and to use in a variety of weather and general circulation models.A computer generation model called WGEN that was developed in the United States of America,was modified in this study and applied to Nigerian weather data spanning the period between 1969 to 1988 and covering 17 sites located in the different climatic zones in Nigeria.The model generated the monthly mean maximum and minimum temperatures,solar radiation,total rainfall,and number of wet and dry days with high accuracy, 95% of the times.The Mann-Whitney U-test revealed that the number of months per year for which observed and generated weather variables were significant,was less than 4 in majority of the sites.展开更多
The formation of urban climates constitutes a distinctive system intrinsically linked to the urban environment. This study aims to delve into the impact of the urban environment on climatic variables. The Urban Weathe...The formation of urban climates constitutes a distinctive system intrinsically linked to the urban environment. This study aims to delve into the impact of the urban environment on climatic variables. The Urban Weather Generator (UWG) algorithm was employed to generate climatic data, facilitating the creation of an epw climate file that corresponds to the urban characteristics surrounding the Centro Politécnico campus at the Federal University of Paraná (UFPR). Comprehensive analyses encompassing land use, occupancy patterns, albedo, surface absorption, anthropogenic heat, and architectural attributes were conducted. A comparative assessment between the UWG-derived air temperature values and meteorological station data revealed that the UWG effectively characterizes the air temperature patterns around the UFPR campus. The anticipated air temperature values consistently surpass the original dataset (SWERA), which was utilized as input, primarily during the hours from 3 p.m. to 7 a.m., showcasing the unmistakable urban heat island phenomenon.展开更多
A stochastic model for daily precipitation simulation in China was developedbased on the framework of a 'Richardson-type' weather generator that is an important tool instudying impacts of weather/climate on a ...A stochastic model for daily precipitation simulation in China was developedbased on the framework of a 'Richardson-type' weather generator that is an important tool instudying impacts of weather/climate on a variety of systems including ecosystem and risk assessment.The purpose of this work is to develop a weather generator for applications in China. The focus ison precipitation simulation since determination of other weather variables such as temperature isdependent on precipitation simulation. A framework of first order Markov Chain with GammaDistribution for daily precipitation is adopted in this work. Based on this framework, fourparameters of precipitation simulation for each month at 672 stations all over China were determinedusing daily precipitation data from 1961 to 2000. Compared with previous works, our estimation forthe parameters was made for more stations and longer observations, which makes the weather generatormore applicable and reliable. Spatial distributions of the four parameters are analyzed in aregional climate context. The seasonal variations of these parameters at five stations representingregional differences are discussed. Based on the estimated monthly parameters at 672 stations, dailyprecipitations for any period can be simulated. A 30-year simulation was made and compared withobservations during 1971-2000 in terms of annual and monthly statistics. The results aresatisfactory, which demonstrates the usefulness of the weather generator.展开更多
The University of California, Davis and the California Department of Water Resources have developed a weather generator application program “SIMETAW” to simulate weather data from climatic records and to estimate re...The University of California, Davis and the California Department of Water Resources have developed a weather generator application program “SIMETAW” to simulate weather data from climatic records and to estimate reference evapotranspiration （ETo） and crop evapotranspiration （ETc） with the generated simulation data or with observed data. A database of default soil depth and water holding characteristics, effective crop rooting depths, and crop coefficient （Kc） values to convert ETo to ETc are input into the program. After calculating daily ETc, the input and derived data are used to determine effective rainfall and to generate hypothetical irrigation schedules to estimate the seasonal and annual evapotranspiration of applied water （ETaw）, where ETaw is the net amount of irrigation water needed to produce a crop. in this paper, we will discuss the simulation model and how it determines ETaw for use in water resources planning.展开更多
The California Simulation of Evapotranspiration of Applied Water （CaI-SIMETAW） model is a new tool developed by the California Department of Water Resources and the University of California, Davis to perform daily s...The California Simulation of Evapotranspiration of Applied Water （CaI-SIMETAW） model is a new tool developed by the California Department of Water Resources and the University of California, Davis to perform daily soil water balance and determine crop evapotranspiration （ETo）, evapotranspiration of applied water （ETaw）, and applied water （AW） for use in California water resources planning. ETaw is a seasonal estimate of the water needed to irrigate a crop assuming 100% irrigation efficiency. The model accounts for soils, crop coefficients, rooting depths, seepage, etc. that influence crop water balance. It provides spatial soil and climate information and it uses historical crop and land-use category information to provide seasonal water balance estimates by combinations of detailed analysis unit and county （DAU/County） over Califomia. The result is a large data base of ETc and ETaw that will be used to update information in the new California Water Plan （CWP）. The application uses the daily climate data, i.e., maximum （Tx） and minimum （Tn） temperature and precipitation （Pcp）, which were derived from monthly USDA-NRCS PRISM data （PRISM Group 2011） and daily US National Climate Data Center （NCDC） climate station data to cover California on a 4 kmx4 km change grid spacing. The application uses daily weather data to determine reference evapotranspiration （ETo）, using the Hargreaves-Samani （HS） equation （Hargreaves and Samani 1982, 1985）. Because the HS equation is based on temperature only, ETo from the HS equation were compared with CIMIS ETo at the same locations using available CIMIS data to determine correction factors to estimate CIMIS ETo from the HS ETo to account for spatial climate differences. CaI-SIMETAW also employs near real-time reference evapotranspiration （ETo） information from Spatial CIMIS, which is a model that combines weather station data and remote sensing to provide a grid of ETo information. A second database containing the available soil water holding capacity and soil depth information for all of California was also developed from the USDA-NRCS SSURGO database. The Cal-SIMETAW program also has the ability to generate daily weather data from monthly mean values for use in studying climate change scenarios and their possible impacts on water demand in the state. The key objective of this project is to improve the accuracy of water use estimates for the California Water Plan （CWP）, which provides a comprehensive report on water supply, demand, and management in California. In this paper, we will discuss the model and how it determines ETaw for use in water resources planning.展开更多
The method linking general circulation models' (GCMs') outputs with crop growthsimulation models' inputs has been the first choice in the studies of impacts of climate change.Changes in climatic variabilit...The method linking general circulation models' (GCMs') outputs with crop growthsimulation models' inputs has been the first choice in the studies of impacts of climate change.Changes in climatic variability, however were not considered in most studies due to limitedknowledge concerned Changes in climatic means derived from a general circulation model DKRZOPYC were input into a stochastic weather generator WGEN run for synthetic daily climate scenarios.Monte Carlo stochastic sampling method was adopted to generate climate change scenarios withvarious possible climatic veriabilities. A dynamic simulation model for maize growth anddevelopment of MZMOD was used to assess the potenhal implication of the changes in both climaticmeans and variability nd the boacts of crop management in changing climate on maize productionin Northeast China. The results indicated that maize yield would be reduced to various degrees inmost of the sensitivity experiments of climatic variability associating with the shortening of theduration of phenological phase of different sowing dates. The Anpacts of the diverse distributions ofclimatic factors detetmined by multiple changes in climatic variability on maire production and itsvariation, however, are not identical and have distinct regional disparities. Yield reduction caused bychanges in climatic means may be alleviated or aggravated by didributions of certain climaticvariables in line with the corresponding climatic variability according to the sensitivity analyses.Consequently, the hypothesis keeping climatic variability constant in the traditional research imposesrestriction on the overall inveshgation of the impacts of climate change on maize production.展开更多
Stochastic weather generators are statistical models that produce random numbers that resemble the observed weather data on which they have been fitted; they are widely used in meteorological and hydrologi- cal simula...Stochastic weather generators are statistical models that produce random numbers that resemble the observed weather data on which they have been fitted; they are widely used in meteorological and hydrologi- cal simulations. For modeling daily precipitation in weather generators, first-order Markov chain–dependent exponential, gamma, mixed-exponential, and lognormal distributions can be used. To examine the perfor- mance of these four distributions for precipitation simulation, they were fitted to observed data collected at 10 stations in the watershed of Yishu River. The parameters of these models were estimated using a maximum-likelihood technique performed using genetic algorithms. Parameters for each calendar month and the Fourier series describing parameters for the whole year were estimated separately. Bayesian infor- mation criterion, simulated monthly mean, maximum daily value, and variance were tested and compared to evaluate the fitness and performance of these models. The results indicate that the lognormal and mixed-exponential distributions give smaller BICs, but their stochastic simulations have overestimation and underestimation respectively, while the gamma and exponential distributions give larger BICs, but their stochastic simulations produced monthly mean precipitation very well. When these distributions were fitted using Fourier series, they all underestimated the above statistics for the months of June, July and August.展开更多
Parameters of weather generator BCC/RCG-WG for daily non-precipitation variables including maximum temperature, minimum temperature and sunshine hours at 669 stations in China are estimated using history daily records...Parameters of weather generator BCC/RCG-WG for daily non-precipitation variables including maximum temperature, minimum temperature and sunshine hours at 669 stations in China are estimated using history daily records from 1951 to 1978 and from 1979 to 2007 respectively. The changes in the parameters for the two periods are revealed to explore the impact of climate change on these parameters. The results show that the parameters of the non-precipitation variables have experienced different changes. While the annual means and the amplitudes of the seasonal cycle show a clear change, the interannual variability, the timings of the seasonal cycles, and the temporal correlations for each variable remain practically unchanged. This indicates that climate changes in China over the last 57 years are mainly reflected in variations in the means and in the strength of the seasonal cycles. The changed parameters have implications for the stationary assumption implied in the parameter estimation and use of the weather generator for climate change studies.展开更多
Based on climate change scenarios projected from GCMs (GFDL, UKMO and MPI), this study evaluates possible impacts of climate warming on rice production in China using numerical simulation experiments. A stochastic wea...Based on climate change scenarios projected from GCMs (GFDL, UKMO and MPI), this study evaluates possible impacts of climate warming on rice production in China using numerical simulation experiments. A stochastic weather generator is used to make the projected climatic change scenarios suitable to the input of crop model, ORYZA1. The results show that the duration of rice growing season will be lengthened by 6-11 days and the accumulated temperature will increase by 200℃.d-330℃.d when CO2 concentration in the atmosphere doubles. The probability of cool injury in reproductive and grain filling period will decrease while that of heat stress will increase. Rice yield will decrease if cultivars and fanning practices are unchanged. If the dates of rice development stages can be maintained unchanged through cultivar adjustment although rice yield in most parts of the areas will decrease, the decrements will be much less than that when cultivars and farming practices are unchanged.展开更多
Stochastic weather generators create time series that reproduce key weather dynamics present in long-term observations.The dataset detailed herein is a large-scale gridded parameterization for CLImate GENerator(CLIGEN...Stochastic weather generators create time series that reproduce key weather dynamics present in long-term observations.The dataset detailed herein is a large-scale gridded parameterization for CLImate GENerator(CLIGEN)that fills spatial gaps in the coverage of existing regional CLIGEN parameterizations,thereby obtaining near-global availability of combined coverages.This dataset primarily covers countries north of 40°latitude with 0.25°spatial resolution.Various CLIGEN parameters were estimated based on 20-year records from four popular global climate products.Precipitation parameters were statistically downscaled to estimate point-scale values,while point-scale temperature and solar radiation parameters were approximated by direct calculation from high-resolution datasets.Surrogate parameter values were used in some cases,such as with wind parameters.Cross-validation was done to assess the downscaling approach for six precipitation parameters using known point-scale values from ground-based CLIGEN parameterizations.These parameter values were derived from daily accumulation records at 7,281 stations and high temporal resolution records at 609 stations.Two sensitive parameters,monthly average storm accumulation and maximum 30-minute intensity,were shown have RMSE values of 1.48 mm and 4.67 mm hr^(−1),respectively.Cumulative precipitation and the annual number of days with precipitation occurrence were both within 5%of ground-based parameterizations,effectively improving climate data availability.展开更多
This study presents an extended version of a single site daily weather generator after Richardson. The model is driven by daily precipitation series derived by a first-order two-state Markov chain and considers the an...This study presents an extended version of a single site daily weather generator after Richardson. The model is driven by daily precipitation series derived by a first-order two-state Markov chain and considers the annual cycle of each meteorological variable. The evaluation of its performance was done by deploying its synthetic time series into the physical based hydrological model BROOK90. The weather generator was applied and tested for data from the Anchor Station at the Tharandt Forest, Germany. Additionally its results were compared to the output of another weather generator with spell-length approach for the precipitation series (LARS-WG). The comparison was distinguished into a meteoro-logical and a hydrological part in terms of extremes, monthly and annual sums and averages. Extreme events could be preserved adequately by both models. Nevertheless a general underestimation of rare events was observed. Natural correlations between vapour pressure and minimum temperature could be conserved as well as annual cycles of the hydro-logical and meteorological regime. But the simulated spectrums of extremes, especially, of precipitation and temperature, are more limited than the observed spectrums. While LARS-WG already finds application in practice, the results show that the data derived from the presented weather generator is as useful and reliable as those from the established model for the simulation of the water balance.展开更多
Rainfall is the main cause of erosion of Brazilian soils,which makes assessing the rainfall erosivity factor(RE)and the erosivity density(ED)fundamental for soil and water conservation.Therefore,the objectives of this...Rainfall is the main cause of erosion of Brazilian soils,which makes assessing the rainfall erosivity factor(RE)and the erosivity density(ED)fundamental for soil and water conservation.Therefore,the objectives of this study were:i)to estimate the RE and ED for Sao Paulo State,Brazil,using synthetic series of pluviographic data;ii)to define homogeneous regions regarding rainfall erosivity;and iii)to generate regression models for rainfall erosivity estimates in each of the homogeneous regions.Synthetic series of pluviographic data were initially obtained on a sub-daily scale from the daily rainfall records of 696 rainfall gauges.The RE values were then estimated from the synthetic rainfall data,and ED was calculated from the relationship between erosivity and rainfall amounts.Monthly and annual maps for RE and ED were obtained.Hierarchical clustering analysis was used to define homogeneous regions in terms of rainfall erosivity,and regionalized regression models for estimating RE were generated.The results demonstrate high spatial variability of RE in Sao Paulo,where the highest annual values were observed in the coastal region.December to March concentrate approximately 60%of the intra-annual erosivity.The highest values of annual ED were observed in regions with intense agricultural activity.The definition of five homogeneous regions concerning the rainfall erosive potential evidenced distinct seasonal patterns of the spatial distribution of erosivity.Finally,the high predictive accuracy of the regionalized models obtained characterizes them as essential tools for reliable estimates of rainfall erosivity,and contribute to better soil conservation planning.展开更多
基金supported by Korea Institute of Civil Engineering and Building Technology (Project name: 2015 Development of a micro raingauge using electromagnetic wave)
文摘Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied to seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society （IRI）. In conjunction with the GLM （generalized linear modeling） weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts （the IRI format only specifies probabilities for three categories： below normal, near normal, and above normal） into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and minimum and maximum temperature, as well as more meaningful daily weather statistics for crop yields, such as the number of dry days and the amount of precipitation on wet days. The approach is extended to the case of climate change scenarios, treating a hypothetical return to a previously observed drier regime in the Pampas.
基金This study was supported by the third sub-project of the national key research project in the 9thFive-Year Plan: Study on the
文摘By adopting various stochastic weather generators, different research groups in their recent studies have realized the importance of the effects of climatic variability on crop growth and development. The conventional assessments derived climate change scenarios from General Circulation Models (GCMs) experiments, however, are incapable of helping to understand this importance. The particular interest here is to review the general methodological scheme to incorporate stochastic weather generator into climate impact studies and the specific approaches in our studies, and put forward uncertainties that still exist. A variety of approaches have been taken to develop the parameterization program and stochastic experiment, and adjust the parameters of atypical stochastic weather generator called WGEN. Usually, the changes in monthly means and variances of weather variables between controlled and changed climate are used to perturb the parameters to generate the intended daily climate scenarios. We establish a parameterization program and methods for stochastic experiment of WGEN in the light of outputs of short-term climate prediction models, and evaluate its simulations on both temporal and spatial scales. Also, we manipulated parameters in relation to the changes in precipitation to produce the intended types and qualitative magnitudes of climatic variability. These adjustments yield various changes in climatic variability for sensitivity analyses. The impacts of changes in climatic variability on maize growth, final yield, and agro-climatic resources in Northeast China are assessed and presented as the case studies through the above methods. However, this corporation is still equivocal due to deficiencies of the generator and unsophisticated manipulation of parameters. To detect and simulate the changes in climatic variability is one of the indispensable ways to reduce the uncertainties in this aspect.
基金Projcct supportcd by the International Scicncc ＆ Technology Co- operation Program of China （No. 2010DFA24320）, and the National Natural Science Foundation of China （Nos. 51379183 and 50809058） ~ Zhcjiang Univcrsity and Springcr-Vcrlag Bcrlin Hcidclberg 2014
文摘Recent years have seen a surge in assessment of potential impacts of climate change. As one of the most important tools for generating synthetic hydrological model inputs, weather generators have played an important role in climate change impact analysis of water management. However, most weather generators like statistical downscaling model （SDSM） and long Ashton research station weather generator （LARS-WG） are designed for single site data generation. Considering the significance of spatial correlations of hydro-meteorological data, multi-site weather data generation becomes a necessity. In this study we aim to evaluate the performance of a new multi-site stochastic model, geo-spatial temporal weather generator （GIST）, in simulating precipitation in the Qiantang River Basin, East China. The correlation matrix, precipitation amount and occurrence of observed and GiST-generated data are first compared for the evaluation process. Then we use the GiST model combined with the change factor method （CFM） to investigate future changes of precipitation （2071 2100） in the study area using one global climate model, Hadgem2 ES, and an extreme emission scenario RCP 8.5, The final results show that the simulated precipitation amount and occurrence by GiST matched their historical counterparts reasonably. The correlation coefficients between simulated and his- torical precipitations show good consistence as well. Compared with the baseline period （1961 1990）, precipitation in the future time period （2071-2100） at high elevation stations will probably increase while at other stations decreases will occur. This study implies potential application of the GiST stochastic model in investigating the impact of climate change on hydrology and water resources.
基金Financial support for this study by the Swedish Civil Contingencies Agency (2011-3778), though the project "Future rainfall and flooding in Sweden:a framework to support climate adaptation actions"
文摘This paper describes a new weather generator e the 10-state empirical model e that combines a 10-state, first-order Markov chain with a non-parametric precipitation amounts model. Using a doubly-stochastic transition-matrix results in a weather generator for which the overall precipitation distribution(including both wet and dry days) and the temporal-correlation can be modified independently for climate change studies. This paper assesses the ability of the 10-state empirical model to simulate daily area-average precipitation in the Torne River catchment in northern Sweden/western Finland in the context of 3 other models: a 10-state model with a parametric(Gamma) amounts model; a wet/dry chain with the empirical amounts model; and a wet/dry chain with the parametric amounts model. The ability to accurately simulate the distribution of multi-day precipitation in the catchment is the primary consideration.Results showed that the 10-state empirical model represented accumulated 2- to 14-day precipitation most realistically. Further, the distribution of precipitation on wet days in the catchment is related to the placement of a wet day within a wet-spell, and the 10-state models represented this realistically, while the wet/dry models did not. Although all four models accurately reproduced the annual and monthly averages in the training data, all models underestimated inter-annual and inter-seasonal variance. Even so, the 10-state empirical model performed best.We conclude that the multi-state model is a promising candidate for hydrological applications, as it simulates multi-day precipitation well, but that further development is required to improve the simulation of interannual variation.
基金supported jointly by the grant of Project“973”:Fundamental Studies on Invasion and Control of Extra Pest(2002CB111400)the grant of Key Project of Ministry of Science and Technology of China:Development of New Technologies for Pest Forecasting(2001BA50PB01).
文摘Based on former studies on weather simulator modules in IPMist laboratory, study on visual programming of stochastic weather generator(VS-WGEN)was continued. In this study, Markov Chain, Monte Carlo, Fourier Series, and weak stationary process were used to generate the daily weather data in software Matlab 6. 0, with the data input from 40 years' weather data recorded by Beijing Weather Station. The generated data includes daily maximum temperature, minimum temperature, precipitation and solar radiation. It has been verified that the weather data generated by the VS-WGEN were more accurate than that by the old WGEN, when twenty new model parameters were included. VS-WGEN has wide software applications, such as pest risk analysis, pest forecast and management. It can be implemented in hardware development as well, such as weather control in weather chamber and greenhouse for researches on ecological adaptation of crop varieties to a given location over time and space. Overall, VS-WGEN is a very useful tool for studies on theoretical and applied ecology.
文摘Long series of daily weather data are frequently needed to evaluate diachronic climatic impact on water resources,the effects of watershed changes on hydrology and to use in a variety of weather and general circulation models.A computer generation model called WGEN that was developed in the United States of America,was modified in this study and applied to Nigerian weather data spanning the period between 1969 to 1988 and covering 17 sites located in the different climatic zones in Nigeria.The model generated the monthly mean maximum and minimum temperatures,solar radiation,total rainfall,and number of wet and dry days with high accuracy, 95% of the times.The Mann-Whitney U-test revealed that the number of months per year for which observed and generated weather variables were significant,was less than 4 in majority of the sites.
文摘The formation of urban climates constitutes a distinctive system intrinsically linked to the urban environment. This study aims to delve into the impact of the urban environment on climatic variables. The Urban Weather Generator (UWG) algorithm was employed to generate climatic data, facilitating the creation of an epw climate file that corresponds to the urban characteristics surrounding the Centro Politécnico campus at the Federal University of Paraná (UFPR). Comprehensive analyses encompassing land use, occupancy patterns, albedo, surface absorption, anthropogenic heat, and architectural attributes were conducted. A comparative assessment between the UWG-derived air temperature values and meteorological station data revealed that the UWG effectively characterizes the air temperature patterns around the UFPR campus. The anticipated air temperature values consistently surpass the original dataset (SWERA), which was utilized as input, primarily during the hours from 3 p.m. to 7 a.m., showcasing the unmistakable urban heat island phenomenon.
基金National Meteorological Center Project，Distinguished Overseas Scholar Foundation of CAS，科技部资助项目，Chinese Ministry of Water Resources, The Swedish Research Council fund
文摘A stochastic model for daily precipitation simulation in China was developedbased on the framework of a 'Richardson-type' weather generator that is an important tool instudying impacts of weather/climate on a variety of systems including ecosystem and risk assessment.The purpose of this work is to develop a weather generator for applications in China. The focus ison precipitation simulation since determination of other weather variables such as temperature isdependent on precipitation simulation. A framework of first order Markov Chain with GammaDistribution for daily precipitation is adopted in this work. Based on this framework, fourparameters of precipitation simulation for each month at 672 stations all over China were determinedusing daily precipitation data from 1961 to 2000. Compared with previous works, our estimation forthe parameters was made for more stations and longer observations, which makes the weather generatormore applicable and reliable. Spatial distributions of the four parameters are analyzed in aregional climate context. The seasonal variations of these parameters at five stations representingregional differences are discussed. Based on the estimated monthly parameters at 672 stations, dailyprecipitations for any period can be simulated. A 30-year simulation was made and compared withobservations during 1971-2000 in terms of annual and monthly statistics. The results aresatisfactory, which demonstrates the usefulness of the weather generator.
文摘The University of California, Davis and the California Department of Water Resources have developed a weather generator application program “SIMETAW” to simulate weather data from climatic records and to estimate reference evapotranspiration （ETo） and crop evapotranspiration （ETc） with the generated simulation data or with observed data. A database of default soil depth and water holding characteristics, effective crop rooting depths, and crop coefficient （Kc） values to convert ETo to ETc are input into the program. After calculating daily ETc, the input and derived data are used to determine effective rainfall and to generate hypothetical irrigation schedules to estimate the seasonal and annual evapotranspiration of applied water （ETaw）, where ETaw is the net amount of irrigation water needed to produce a crop. in this paper, we will discuss the simulation model and how it determines ETaw for use in water resources planning.
基金supported and funded by the California Department of Water Resources(DWR)
文摘The California Simulation of Evapotranspiration of Applied Water （CaI-SIMETAW） model is a new tool developed by the California Department of Water Resources and the University of California, Davis to perform daily soil water balance and determine crop evapotranspiration （ETo）, evapotranspiration of applied water （ETaw）, and applied water （AW） for use in California water resources planning. ETaw is a seasonal estimate of the water needed to irrigate a crop assuming 100% irrigation efficiency. The model accounts for soils, crop coefficients, rooting depths, seepage, etc. that influence crop water balance. It provides spatial soil and climate information and it uses historical crop and land-use category information to provide seasonal water balance estimates by combinations of detailed analysis unit and county （DAU/County） over Califomia. The result is a large data base of ETc and ETaw that will be used to update information in the new California Water Plan （CWP）. The application uses the daily climate data, i.e., maximum （Tx） and minimum （Tn） temperature and precipitation （Pcp）, which were derived from monthly USDA-NRCS PRISM data （PRISM Group 2011） and daily US National Climate Data Center （NCDC） climate station data to cover California on a 4 kmx4 km change grid spacing. The application uses daily weather data to determine reference evapotranspiration （ETo）, using the Hargreaves-Samani （HS） equation （Hargreaves and Samani 1982, 1985）. Because the HS equation is based on temperature only, ETo from the HS equation were compared with CIMIS ETo at the same locations using available CIMIS data to determine correction factors to estimate CIMIS ETo from the HS ETo to account for spatial climate differences. CaI-SIMETAW also employs near real-time reference evapotranspiration （ETo） information from Spatial CIMIS, which is a model that combines weather station data and remote sensing to provide a grid of ETo information. A second database containing the available soil water holding capacity and soil depth information for all of California was also developed from the USDA-NRCS SSURGO database. The Cal-SIMETAW program also has the ability to generate daily weather data from monthly mean values for use in studying climate change scenarios and their possible impacts on water demand in the state. The key objective of this project is to improve the accuracy of water use estimates for the California Water Plan （CWP）, which provides a comprehensive report on water supply, demand, and management in California. In this paper, we will discuss the model and how it determines ETaw for use in water resources planning.
文摘The method linking general circulation models' (GCMs') outputs with crop growthsimulation models' inputs has been the first choice in the studies of impacts of climate change.Changes in climatic variability, however were not considered in most studies due to limitedknowledge concerned Changes in climatic means derived from a general circulation model DKRZOPYC were input into a stochastic weather generator WGEN run for synthetic daily climate scenarios.Monte Carlo stochastic sampling method was adopted to generate climate change scenarios withvarious possible climatic veriabilities. A dynamic simulation model for maize growth anddevelopment of MZMOD was used to assess the potenhal implication of the changes in both climaticmeans and variability nd the boacts of crop management in changing climate on maize productionin Northeast China. The results indicated that maize yield would be reduced to various degrees inmost of the sensitivity experiments of climatic variability associating with the shortening of theduration of phenological phase of different sowing dates. The Anpacts of the diverse distributions ofclimatic factors detetmined by multiple changes in climatic variability on maire production and itsvariation, however, are not identical and have distinct regional disparities. Yield reduction caused bychanges in climatic means may be alleviated or aggravated by didributions of certain climaticvariables in line with the corresponding climatic variability according to the sensitivity analyses.Consequently, the hypothesis keeping climatic variability constant in the traditional research imposesrestriction on the overall inveshgation of the impacts of climate change on maize production.
基金supported by the National Key Developing Program for Basic Sciences of China (GrantNo. 2010CB951404)Chinese Nature Science Foundation(Grant No. 40971024)the Special Meteorology Project[GYHY(QX)2007-6-1]
文摘Stochastic weather generators are statistical models that produce random numbers that resemble the observed weather data on which they have been fitted; they are widely used in meteorological and hydrologi- cal simulations. For modeling daily precipitation in weather generators, first-order Markov chain–dependent exponential, gamma, mixed-exponential, and lognormal distributions can be used. To examine the perfor- mance of these four distributions for precipitation simulation, they were fitted to observed data collected at 10 stations in the watershed of Yishu River. The parameters of these models were estimated using a maximum-likelihood technique performed using genetic algorithms. Parameters for each calendar month and the Fourier series describing parameters for the whole year were estimated separately. Bayesian infor- mation criterion, simulated monthly mean, maximum daily value, and variance were tested and compared to evaluate the fitness and performance of these models. The results indicate that the lognormal and mixed-exponential distributions give smaller BICs, but their stochastic simulations have overestimation and underestimation respectively, while the gamma and exponential distributions give larger BICs, but their stochastic simulations produced monthly mean precipitation very well. When these distributions were fitted using Fourier series, they all underestimated the above statistics for the months of June, July and August.
基金The Special Scientific Research Fund of Meteorological Public Welfare Profession of China,No.GYHY201106018The Swedish Foundation for International Cooperation in Research and High Education
文摘Parameters of weather generator BCC/RCG-WG for daily non-precipitation variables including maximum temperature, minimum temperature and sunshine hours at 669 stations in China are estimated using history daily records from 1951 to 1978 and from 1979 to 2007 respectively. The changes in the parameters for the two periods are revealed to explore the impact of climate change on these parameters. The results show that the parameters of the non-precipitation variables have experienced different changes. While the annual means and the amplitudes of the seasonal cycle show a clear change, the interannual variability, the timings of the seasonal cycles, and the temporal correlations for each variable remain practically unchanged. This indicates that climate changes in China over the last 57 years are mainly reflected in variations in the means and in the strength of the seasonal cycles. The changed parameters have implications for the stationary assumption implied in the parameter estimation and use of the weather generator for climate change studies.
文摘Based on climate change scenarios projected from GCMs (GFDL, UKMO and MPI), this study evaluates possible impacts of climate warming on rice production in China using numerical simulation experiments. A stochastic weather generator is used to make the projected climatic change scenarios suitable to the input of crop model, ORYZA1. The results show that the duration of rice growing season will be lengthened by 6-11 days and the accumulated temperature will increase by 200℃.d-330℃.d when CO2 concentration in the atmosphere doubles. The probability of cool injury in reproductive and grain filling period will decrease while that of heat stress will increase. Rice yield will decrease if cultivars and fanning practices are unchanged. If the dates of rice development stages can be maintained unchanged through cultivar adjustment although rice yield in most parts of the areas will decrease, the decrements will be much less than that when cultivars and farming practices are unchanged.
文摘Stochastic weather generators create time series that reproduce key weather dynamics present in long-term observations.The dataset detailed herein is a large-scale gridded parameterization for CLImate GENerator(CLIGEN)that fills spatial gaps in the coverage of existing regional CLIGEN parameterizations,thereby obtaining near-global availability of combined coverages.This dataset primarily covers countries north of 40°latitude with 0.25°spatial resolution.Various CLIGEN parameters were estimated based on 20-year records from four popular global climate products.Precipitation parameters were statistically downscaled to estimate point-scale values,while point-scale temperature and solar radiation parameters were approximated by direct calculation from high-resolution datasets.Surrogate parameter values were used in some cases,such as with wind parameters.Cross-validation was done to assess the downscaling approach for six precipitation parameters using known point-scale values from ground-based CLIGEN parameterizations.These parameter values were derived from daily accumulation records at 7,281 stations and high temporal resolution records at 609 stations.Two sensitive parameters,monthly average storm accumulation and maximum 30-minute intensity,were shown have RMSE values of 1.48 mm and 4.67 mm hr^(−1),respectively.Cumulative precipitation and the annual number of days with precipitation occurrence were both within 5%of ground-based parameterizations,effectively improving climate data availability.
基金supported by the German Academic Exchange Service(DAAD).
文摘This study presents an extended version of a single site daily weather generator after Richardson. The model is driven by daily precipitation series derived by a first-order two-state Markov chain and considers the annual cycle of each meteorological variable. The evaluation of its performance was done by deploying its synthetic time series into the physical based hydrological model BROOK90. The weather generator was applied and tested for data from the Anchor Station at the Tharandt Forest, Germany. Additionally its results were compared to the output of another weather generator with spell-length approach for the precipitation series (LARS-WG). The comparison was distinguished into a meteoro-logical and a hydrological part in terms of extremes, monthly and annual sums and averages. Extreme events could be preserved adequately by both models. Nevertheless a general underestimation of rare events was observed. Natural correlations between vapour pressure and minimum temperature could be conserved as well as annual cycles of the hydro-logical and meteorological regime. But the simulated spectrums of extremes, especially, of precipitation and temperature, are more limited than the observed spectrums. While LARS-WG already finds application in practice, the results show that the data derived from the presented weather generator is as useful and reliable as those from the established model for the simulation of the water balance.
基金This study was supported by the Brazilian Council of Technological and Scientific Development(Conselho Nacional de Desenvolvimento Científico e Tecnoloogico-CNPq)and the Coordination for the Improvement of Higher Education Personnel(Coordenaç~ao de Aperfeiçoamento de Pessoal de Nível Superior-CAPES,grant number 001).
文摘Rainfall is the main cause of erosion of Brazilian soils,which makes assessing the rainfall erosivity factor(RE)and the erosivity density(ED)fundamental for soil and water conservation.Therefore,the objectives of this study were:i)to estimate the RE and ED for Sao Paulo State,Brazil,using synthetic series of pluviographic data;ii)to define homogeneous regions regarding rainfall erosivity;and iii)to generate regression models for rainfall erosivity estimates in each of the homogeneous regions.Synthetic series of pluviographic data were initially obtained on a sub-daily scale from the daily rainfall records of 696 rainfall gauges.The RE values were then estimated from the synthetic rainfall data,and ED was calculated from the relationship between erosivity and rainfall amounts.Monthly and annual maps for RE and ED were obtained.Hierarchical clustering analysis was used to define homogeneous regions in terms of rainfall erosivity,and regionalized regression models for estimating RE were generated.The results demonstrate high spatial variability of RE in Sao Paulo,where the highest annual values were observed in the coastal region.December to March concentrate approximately 60%of the intra-annual erosivity.The highest values of annual ED were observed in regions with intense agricultural activity.The definition of five homogeneous regions concerning the rainfall erosive potential evidenced distinct seasonal patterns of the spatial distribution of erosivity.Finally,the high predictive accuracy of the regionalized models obtained characterizes them as essential tools for reliable estimates of rainfall erosivity,and contribute to better soil conservation planning.
