Weather Data Series Research Articles

Performance Evaluation and Uncertainty Measurement in Irrigation Scheduling Soil Water-Balance Approach

The present work aims at approaching the study of the performance and uncertainty associated with an irrigation scheduling method based on a soil-water balance. On a daily time step, a water-balance-based irrigation scheduling model has been developed. A Monte Carlo simulation of the irrigation scheduling model is developed using a series of actual daily weather data of evapotranspiration and precipitation and bootstrapping stochastic technique to resampling them. Performance evaluation measurements and their uncertainty are studied by means of several parameters: reliability, resiliency, vulnerability, total irrigation water allocation, total water loosed by deep percolation, and actual evapotranspiration/potential evapotranspiration rate along the growing season. The behaviors of 12 different types of soils (between coarse-textured soils and fine-textured soils) are compared using pedotransfer functions. Total available water (TAW) is the most important hydraulic property of the soil as far as irrigatio...

Estimating annual GPP, NPP and stem growth in Finland using summary models

In this study we introduce and test a new simple approach for estimating annual stand-level gross primary production (GPP), net primary production (NPP) and stem biomass growth based on carbon acquisition and allocation, by combining existing summary models. The focus is on the variation of GPP and NPP across different parts of Finland caused by climate.A summary model for estimating light-use efficiency is applied to calculate the annual GPP of a fully closed canopy (P0) for different sites as a function of climatic conditions. The site-specific P0s are first determined for five sites using long-term series of daily weather data from the Finnish Meteorological Institute (FMI), and these results are further generalised to all sites using effective temperature sum (ETS) as a predictor. Actual canopy GPP is calculated as a fraction of the estimated site-specific P0 using a summary model of the effect of shading on photosynthesis in non-homogeneous forests. Leaf area is estimated from measured tree data using the pipe model. NPP:GPP ratio is estimated on the stratum level from mean height. The allocation to stem growth is estimated from stand age and site type, using a summary equation based on results from earlier studies.The method was tested against data from permanent sample plots of the Finnish National Forest Inventory (NFI) from years 1985 and 1995. The results indicate that the approach produces realistic short-term estimations for stem growth. At the stand level the model was nearly unbiased (2.1% underestimation), with RMSE of 34% and R2 of 0.52, and it provided a clearly better fit than a simple linear prediction of stem growth from the estimated GPP. More importantly, we showed in a model comparison that in the present data set our model provided results of similar accuracy as a well-established empirical tree-level growth model.

CLIMATIC EFFECTS ON THE PHENOLOGY OF GEOPHYTES

Nowadays, the scientific and social significance of the research of climatic effects has become outstanding. In order to be able to predict the ecological effects of the global climate change, it is necessary to study monitoring databases of the past and explore connections. For the case study mentioned in the title, historical weather data series from the Hungarian Meteorological Service and Szaniszlo Priszter’s monitoring data on the phenology of geophytes have been used. These data describe on which days the observed geophytes budded, were blooming and withered. In our research we have found that the classification of the observed years according to phenological events and the classification of those according to the frequency distribution of meteorological parameters show similar patterns, and the one variable group is suitable for explaining the pattern shown by the other one. Furthermore, our important result is that the dates of all three observed phenophases correlate significantly with the average of the daily temperature fluctuation in the given period. The second most often significant parameter is the number of frosty days, this also seem to be determinant for all phenophases. Usual approaches based on the temperature sum and the average temperature don’t seem to be really important in this respect. According to the results of the research, it has turned out that the phenology of geophytes can be well modelled with the linear combination of suitable meteorological parameters

Implications of precipitation patterns and antecedent soil water content for leaching of pesticides from arable land

An improved understanding of how precipitation patterns control pesticide leaching from structured soils prone to macropore flow could lead to practical mitigation strategies that would help farmers minimize losses by optimizing application timings. A sensitivity analysis of the macropore flow model MACRO was therefore carried out to examine the influence of antecedent soil water content and precipitation patterns on pesticide leaching to drainage systems and groundwater. One thousand model runs were executed (20 four-year weather data series, 50 application dates per season) for both autumn and spring applications of a hypothetical moderately sorbed and quickly degraded herbicide for one of three national scenarios for pesticide risk assessment in Sweden (Näsbygård, a loamy moraine soil in Scania, southern Sweden). Rapid and direct transport of pesticides in macropores to drainage systems and shallow groundwater was predicted to occur rather infrequently in spring (in 4 of the 20 years) and even more rarely in autumn. For autumn applications, the soil water deficit at application ( SWD tot ) and medium-term precipitation (30–90 days after application) were the two most sensitive variables controlling pesticide leaching. For spring applications, total leaching was most closely linked to rainfall the following winter, while short-term precipitation (5 days after application) and the antecedent soil water deficit were the two most important predictors for maximum pesticide concentrations in drainflow. The potential for reducing leaching by restricting applications to periods of low risk was investigated. The results showed that avoiding applications on wet soil in autumn could potentially reduce total pesticide losses by a factor of two to three. Similarly, the risk of acute toxicological effects in surface waters following pesticide applications in spring could be reduced by a factor of 2–3 by avoiding application when 5-day weather forecasts predict precipitation >10 mm.

A software component to compute agro-meteorological indicators

ClimIndices is a software component to compute agro-meteorological indicators on yearly series of daily weather data. The component is released as .NET2 dynamic link libraries (DLL), allowing the development of clients under Windows using .NET languages. The design allows extending the computing capabilities without requiring re-compilation.

Identification of key climatic factors regulating the transport of pesticides in leaching and to tile drains

Key climatic factors influencing the transport of pesticides to drains and to depth were identified. Climatic characteristics such as the timing of rainfall in relation to pesticide application may be more critical than average annual temperature and rainfall. The fate of three pesticides was simulated in nine contrasting soil types for two seasons, five application dates and six synthetic weather data series using the MACRO model, and predicted cumulative pesticide loads were analysed using statistical methods. Classification trees and Pearson correlations indicated that simulated losses in excess of 75th percentile values (0.046 mg m(-2) for leaching, 0.042 mg m(-2) for drainage) generally occurred with large rainfall events following autumn application on clay soils, for both leaching and drainage scenarios. The amount and timing of winter rainfall were important factors, whatever the application period, and these interacted strongly with soil texture and pesticide mobility and persistence. Winter rainfall primarily influenced losses of less mobile and more persistent compounds, while short-term rainfall and temperature controlled leaching of the more mobile pesticides. Numerous climatic characteristics influenced pesticide loss, including the amount of precipitation as well as the timing of rainfall and extreme events in relation to application date. Information regarding the relative influence of the climatic characteristics evaluated here can support the development of a climatic zonation for European-scale risk assessment for pesticide fate.

Assessment of the ClimGen stochastic weather generator at Cameroon sites

Simulation of agricultural risk assessment and environmental management requires long series of daily weather data for the area being modelled. Acquiring and formatting this data can be very complex and time-consuming. This has led to the development of weather generation procedures and tools. Weather generators can produce time series of synthetic weather data of any length, interpolating observed data to produce synthetic weather data at new sites. Any generator must be tested to ensure that the data that it produces is satisfactory for the purposes for which it is to be used. The aim of this paper is to test a commonly used weather generator, ClimGen (version 4.1.05) at eight sites with contrasting climates in Cameroon. Statistical test were conducted, including t-test and F-test, to compare the differences between generated weather data versus 25 years observed weather data. The results showed that the generated weather series was similar to the observed data for its distribution of monthly precipitation and its variances, monthly means and variance of minimum and maximum air temperatures. Based on the results from this study, it can be concluded that ClimGen performs well in the simulation of weather statistics in Cameroon. Key words: Weather generators, weather data, Cameroon, climate change.

Potential impacts of climate change on the grain yield of maize for the midlands of KwaZulu-Natal, South Africa

Abstract The increase in atmospheric carbon dioxide concentration and changes in associated climatic variables will likely have a major influence on regional as well as international crop production. This study describes an assessment of simulated potential maize ( Zea mays ) grain yield using (i) generated weather data and (ii) generated weather data modified by plausible future climate changes under a normal planting date and dates 15 days earlier and 15 days later using CropSyst, a cropping systems simulation model. The analysis is for maize production at Cedara, a summer rainfall location within the midlands of KwaZulu-Natal, South Africa. Baseline weather data input series were generated by a stochastic weather generator, ClimGen, using 30 years of observed weather data (1971–2000). The generated baseline weather data series was similar to the observed for its distributions of daily rainfall and wet and dry day series, monthly total rainfall and its variances, daily and monthly mean and variance of precipitation, minimum and maximum air temperatures, and solar radiant density. In addition, Penman-Monteith daily grass reference evaporation (ET o ) calculated using the observed and generated weather data series were similar except that the ET o values between 2 and 3 mm were less for the observed than for the corresponding generated values. Maize grain yields simulated using the observed and generated weather data series with different planting dates were compared. The simulated grain yields for the respective planting dates were not statistically different from each other. However, the grain yields simulated using the generated weather data had a significantly smaller variance than the grain yields simulated using the observed weather data series. The generated baseline weather data were modified by synthesized climate projections to create a number of climatic scenarios. The climate changes corresponded to a doubling of carbon dioxide concentration to 700 μl l −1 without air temperature and water regime changes, and a doubling of carbon dioxide concentration accompanied by mean daily air temperature and precipitation increases of 2 °C and 10%, 2 °C and 20%, 4 °C and 10%, and 4 °C and 20%, respectively. The increase in the daily mean minimum air temperature was taken as three times the increase in daily mean maximum air temperature. Input crop parameters of radiation use and biomass transpiration efficiencies were modified for maize in CropSyst, to account for physiological changes due to increased carbon dioxide concentration. Under increased carbon dioxide concentration regimes, maize grain yields are much more affected by changes in mean air temperature than by precipitation. The results indicate that analysis of the implications of variations in the planting date on maize production may be most useful for site-specific analyses of possible mitigation of the impacts of climate change through alteration of crop management practices.

GISAREG—A GIS based irrigation scheduling simulation model to support improved water use

To support improved irrigation scheduling in the Syr Darya basin, Uzbekistan, the ISAREG model was selected due to previous applications in similar climates. It is a conceptual non-distributed water balance model for simulating crop irrigation schedules at field level and to compute irrigation requirements under optimal and/or water stressed conditions. To provide for the use of the model at the project scale, a new version of the model, integrated with a Geographical Information System (GIS), was developed. This GIS based application is aimed at supporting the implementation of improved farm irrigation management and, in a later phase, to also help project management. The integration concerns the creation of spatial and weather GIS databases usable by ISAREG, the models operation for different water management scenarios, and the production of crop irrigation maps and time dependent irrigation depths at selected aggregation modes, including the farm scale. The resulting information on alternative irrigation schedules is, therefore, spatially distributed and shall be used both to support irrigation scheduling advising and to help in the identification of practices that may lead to water saving and provide for salinity control. The paper includes brief descriptions of the models, the databases and the integration of the models, as well as main features of the GISAREG application. Results relative to the comparative analysis of irrigation scheduling scenarios show the capabilities of the model to support the selection of water saving alternatives. The appropriateness of implementing 15–20-day time intervals between irrigations is shown. In addition, results relative to compare net irrigation requirements for wet and dry years show the usefulness of the model in handling time series of weather data.

Weather data generator to study climate change on buildings

Most computer programs that analyse the yearly performance of buildings require an input of historical series of weather data measured at the location of interest. However, analyses of climate change scenarios needs hourly weather data series that allow realistic changes in various weather parameters. This can not be done easily with the reference years based on measured weather data normally used. In this paper a weather generator is proposed in which climate changes can be implemented. Based on weather data observed over years a stochastic weather model is developed. This model is used to make a general model independent of the location and with which climate changes can be simulated. The general model can be adapted to a local climate change. Only new averaged values of the weather variables being the result of a climate change should be used as input. The model is able to generate hourly weather data that can be used as input for all kind of simulation programs to study the effect of climate change on the design and energy consumption of HVAC systems.

Improvements of stochastic weather data generators for diverse climates

Weather data generators can be used to produce long periods of synthetic weather records from a limited amount of input data. They provide a useful tool for supplementing existing climate data for risk assessment and decision support systems. They also can be adapted for use in climate change studies where only limited data are available on a course spatial grid. A number of studies have demon- strated the failure of data produced with stochastic weather data generators to mimic the statistical properties of observed daily climate data. Differences may be large enough to limit the usefulness of the output data. This study suggests that the lack-of-fit is related to simplifying assumptions incorpo- rated in these models which may be acceptable in some climates but lead to significant discrepancies when applied under very different climate conditions. Methods are proposed to address these limita- tions. The challenge related to choosing an appropriate probability distribution for each climate vari- able is addressed by approximating the probability distribution of the data using an observed fre- quency distribution. It is becoming increasingly clear that the correlation between climate variables may differ significantly on a seasonal and regional basis. To account for this, the interrelationship between climate parameters is evaluated for each site once every 2 mo. The way the relationship between the wet or dry status of the day and temperature and solar radiation is accounted for is improved in the model. Observed climate data for 3 selected Canadian climatological stations are used to assess the proposed weather data generator. The results indicate an overall improvement in the cor- respondence between the means, standard deviations, correlations, probability distributions and extreme values of the observed and estimated weather data series when compared with previously reported results. Related parameters such as the length of wet periods, the length of dry periods and the length of frost-free periods are also found to compare favourably with the observed values.

00/00357 Effect of annealing on the electrical, optical and structural properties of hydrogenated amorphous silicon films deposited in an asymmetric R.F. plasma CVD system at room temperature

It becomes popular to use computer simulation technique to evaluate the performance of energy utilizations in buildings. The hourly weather data as simulation input is a crucial factor for the successful energy system simulation, and obtaining an accurate set of weather data is necessary to represent the long-term typical weather conditions throughout a year. This paper introduces a stochastic approach, which is called the first order multivariate Markov chain model, to generate the annual weather data for better evaluating and sizing different energy systems. The process for generating the weather data time series from the multivariate Markov transition probability matrices is described using 15-years actual hourly weather data of Hong Kong. The ability of this new model for retaining the statistical properties of the generated weather data series is examined and compared with the current existing TMY and TRY data. The main statistical properties used for this purpose are mean value, standard deviation, maximum value, minimum value, frequency distribution probability and persistency probability of the weather data sequence. The comparison between the observed weather data and the synthetically generated ones shows that the statistical characteristics of the developed set of weather data are satisfactorily preserved and the developed set of weather data can predict and evaluate different energy systems more accurately.

Multiple Linear Regression for Lake Ice and Lake Temperature Characteristics

Lake ice and lake temperatures depend on climate and lake morphometry. Simulated lake ice and lake temperature characteristics of 10 large lakes in Minnesota were therefore related to climate parameters, geographic location, lake surface area, and depth by multiple linear regressions. The regression equations were developed because they are much easier to use than a deterministic, unsteady simulation model that requires time series of weather data as input and produces very detailed information as output. Some of the regression equations were then employed to hindcast the ice-in dates, ice-out dates, ice cover duration, and maximum ice thicknesses for several freshwater lakes in the United States and one in Canada. The hindcast results were compared with field data from the same lakes. The standard errors between observed and predicted ice-in dates, ice-out dates, ice cover duration, and maximum ice thicknesses for the lakes tested are 4 days, 6–7 days, 4–7 days, and 0.05–0.06 m, respectively, depending on the equations used. Ice-in date is slightly better predicted than ice-out date and ice cover duration. Because the 10 lakes used to develop the multiple linear regressions are all in Minnesota, and the lakes used to verify the regression equations are in Wisconsin, Maine, and Ontario, the regression equations are considered validated and applicable at least to lakes in the north-central United States and in southern Ontario.

Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates

Stochastic weather generators are used in a wide range of studies, such as hydrological applications, environmental management and agricultural risk assessments. Such studies often require long series of daily weather data for risk assessment and weather generators can produce time series of synthetic daily weather data of any length. Weather generators are also used to interpolate observed data to produce synthetic weather data at new sites, and they have recently been employed in the con- struction of climate change scenarios. Any generator should be tested to ensure that the data that it pro- duces is satisfactory for the purposes for which it is to be used. The accuracy required will depend on the application of the data, and the performance of the generator may vary considerably for different climates. The aim of this paper is to test and compare 2 commonly-used weather generators, namely WGEN and LARS-WG, at 18 sites in the USA, Europe and Asia, chosen to represent a range of climates. Statistical tests were selected to compare a variety of different weather characteristics of the observed and synthetic weather data such as, for example, the lengths of wet and dry series, the distribution of precipitation and the lengths of frost spells. The LARS-WG generator used more complex distributions for weather variables and tended to match the observed data more closely than WGEN, although there are certain characteristics of the data that neither generator reproduced accurately. The implications for the development and use of stochastic weather generators are discussed.

Uncertainties in crop, soil and weather inputs used in growth models: Implications for simulated outputs and their applications

Abstract Deterministic crop growth models require several inputs relating to crop/variety, soil physical properties, weather and crop management. The input values used could be significantly uncertain due to random and systematic measurement errors and spatial and temporal variation observed in many of these inputs. Often soil and weather data are approximated using GIS and/or weather generators. In this paper total uncertainty in simulated yield, evapotranspiration and crop N uptake has been quantified considering uncertainties in crop, soil and weather inputs. WTGROWS, a crop model that simulates the effect of genotypic, climatic, edaphic and management factors on productivity of spring wheat was used. The uncertainty in each input was represented by a statistical distribution of values based on literature review, actual measurement and subjective expert judgement. The Monte Carlo simulation technique was used to analyze total uncertainty. The results showed that uncertainties in crop, soil and weather inputs resulted in uncertainty in simulated grain yield, ET and N uptake, which varied depending upon the production environment. Uncertainties in outputs increased as the production system changed from a potential production level to a level where crop growth was constrained by limited availability of water and nitrogen. There was an 80% probability that the bias in the deterministic model outputs was always less than 10% in potential and irrigated production systems. In rainfed environments this bias was larger. The bias in simulated outputs was less than or equal to model error. Most of the uncertainty in outputs caused by variable soil, crop and weather inputs could be represented if the outputs were determined using fixed soil and crop data, and a large series of weather data. In potential and irrigated production systems, inputs relating to crop photosynthesis and leaf area estimation had a large ‘uncertainty importance’. Uncertainties in soil N inputs and vapor pressure were also of great importance in irrigated environments. In rainfed environments, uncertainties in soil and weather inputs were dominant and crop parameters had only limited ‘uncertainty importance’. The implications of these results in estimates of potential and rainfed productivity, database development and guiding refinement of models are discussed.

The impact of climate change on continuous corn production in the Southern U.S.A.

The Goddard Institute for Space Studies (GISS) General Circulation Model (GCM) has been used in conjunction with a field level plant process model (CERES-Maize) and a field level pesticide transport model (PRZM) to study the impacts of doubled levels of atmospheric CO2 on various aspects of corn production in the Southern U.S.A. Grid-box scale GCM output has been applied to a 38-year time series of historical weather data at 28 different locations for several typical soil profiles throughout the South. Limitations on the use of the climate scenario in conjunction with the process models are discussed. Major shortcomings include: 1) no direct impacts of atmospheric CO2 on plant growth and development in the plant process model; 2) neither macro-pore solute transport nor chemical decay rate response to temperature are included in the pesticide transport model; and 3) the climate change scenario output does not provide information concerning changes in temperature extremes and variability or precipitation frequency, intensity or duration. The latter are particularly critical parameters for the detailed simulation of hydrological processes. In spite of these omissions, the combination of the three models facilitates the study of the impacts of GCM modeled climate change on several inter-related agro-climatic issues of interest to agricultural policy makers. These issues include: changes in dryland and irrigated corn yields; changes in sowing and harvest dates; modification of crop water demand; and estimates of effects on pesticide losses from the soil surface and through leaching from the bottom of the active corn root zone. Model generated results which address these issues are presented but must be used with caution in light of the GCM and process model limitations. The results of this study suggest that substantial changes in agricultural production and management practices may be needed to respond to the climate changes expected to take place throughout the Southern U.S.A.

A simple method for generating daily rainfall data

In recent years, agricultural scientists have developed considerable interest in modeling and simulation of rainfall as new ways of analyzing rainfall data and assessing its impact on agriculture. Among the proposed methods, a combination of Markov chain and gamma distribution function is recognized as a simple approach and is demonstrated to be effective in generating daily rainfall data for many environments. Unfortunately, this method requires that many years of daily weather records be available for estimating the model parameters. Thus the availability of the weather data limits the applicability of the simulation method. When these model parameters are evaluated over time and at different places, however, certain general characteristics are revealed. First, the transitional probability of a wet day followed by a wet day tends to be greater but parallel to the transitional probability of a dry day followed by a wet day. This phenomenon leads to a linear relationship of the transitional probabilities to the fraction of wet days per month. Second, the beta parameter in a gamma distribution function, which is used to describe the amount of rainfall, is closely related to the amount of rain per wet day owing to the positive skewness of the rainfall distribution. Based on these relationships, a simple method is introduced, by which model parameters can be estimated from monthly summaries instead of from daily values. The suggested method, therefore, provides a convenient vehicle for applying weather simulation models to areas in which its use had been impossible because of the unavailability of long series of daily weather data.

Synoptic summer wind cycles and upwelling off the southern portion of the Cape Peninsula

Wind data and aerial surveys of sea surface temperature (SST) have been used to describe spatial variation in the upwelling regime off the southern portion of the Cape Peninsula (34°S, 18°E). Time series of weather data revealed a cycle controlled by synoptic weather events. Spatial and temporal variabilities in the wind fields, weather and SST are discussed on the basis of the results of case studies made in summer. Low-level equatorward winds were found to accelerate over the southern portion of the Cape Peninsula, accompanying the eastward ridging of the South Atlantic Anticyclone. Vertical profiles of wind and time series of meteorological data from land stations close to the sea are combined to describe the dynamics and mechanisms governing the mesoscale patterns of wind and upwelling phenomena around Olifantsbos, a locality on the south-west of the Cape Peninsula. The observations may be compared with similar ones made off Point Conception in California. The results highlight the dominant influence ...

Simulation of forage harvest and conservation on dairy farms

A computer model was developed to simulate forage systems on dairy farms. The model simulated alfalfa growth, corn silage and corn grain yields, harvest, storage, feeding and ration formulation for a dairy herd. A 26-year series of historical weather data from East Lansing, Michigan, was used to compare management and technological alternatives on the basis of average net return and year-to-year variations. For example, a four-cut alfalfa system was found to be more profitable than a three-cut system 90% of the time. The comparison of a hay system with a silage system was very sensitive to forage intake assumptions; the break-even point shifted from 120 ha to 40 ha when silage intake was increased by 5%. The model can be used to assess the impact of new forage conservation methods under a wide range of climatic and management conditions.

A search for associations between weather and the onset of human parturition.

Weather data were examined for association with hospital records indicating the time at which pregnant women at term first experienced labor contractions (onsets). There is a considerable advantage, compared with mortality and morbidity, to using this response to infer weather influences. Three tests were used. First, the time series of daily onsets was analyzed to determine if characteristics known to exist in time series of weather data — variability and persistence — were evident in daily onsets. Second, the frequency distributions of nine weather variables were stratified, mostly by terciles, and onset means calculated for each of these divisions. Response means much different from average were then used to specify the nature of weather at such times. Third, the weather data were organized as weather types — pre- and post-cold frontal and general cold frontal — and onsets at these times were compared with those at non-frontal times. The time series characteristics were not found, but the other analysis revealed subsets of days on which onsets were above average, and in some cases the results were statistically significant. One such subset consists of winter days with low pressure, temperature markedly lower than the day before, and high wind speeds. On such days onsets were 34% above average. They were also above average during the 48 hours before and after cold front passage, and especially so in the 12 hours prior to the front. These findings constitute a weak, but statistically significant indication that human parturition is influenced by weather. Follow-up studies are urged.