Long-term Daily Weather Data Research Articles

Potential of Bayesian additive regression trees for predicting daily global and diffuse solar radiation in arid and humid areas

This study aims to evaluate the potential of Bayesian additive regression trees (BART) for predicting global and diffuse solar radiation. Long-term daily weather data were collected at four stations in arid and humid areas. Models with different input combinations were created. The default parameters within R language package of BART were used. Model accuracy was assessed with Pearson correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), relative root mean square error (RRMSE), and Nash-Sutcliffe efficiency coefficient (NSE). Taylor diagram was applied to illustrate model performance. On average, the model with sunshine duration, theoretical sunshine duration, mean temperature, maximum temperature, minimum temperature, relative humidity, and rainfall performed best for predicting global solar radiation, with mean R of 0.973, RMSE of 1.685 MJ/m2d, NSE of 0.944, RRMSE of 0.124, and MAE of 1.265 MJ/m2d. The model with sunshine duration, theoretical sunshine duration, global solar radiation, extraterrestrial solar radiation, and day of year outperformed others for predicting diffuse solar radiation, with mean R of 0.912, RMSE of 1.291 MJ/m2d, NSE of 0.827, RRMSE of 0.214, and MAE of 0.933 MJ/m2d. The results showed that BART was a suitable method for predicting global and diffuse solar radiation using climatic variables.

Climate Change Effects on Temperate Grassland and Its Implication for Forage Production: A Case Study from Northern Germany

The effects of climate change on agricultural ecosystems are increasing, and droughts affect many regions. Drought has substantial ecological, social, and economic consequences for the sustainability of agricultural land. Many regions of the northern hemisphere have not experienced a high frequency of meteorological droughts in the past. For understanding the implications of climate change on grassland, analysis of the long-term climate data provides key information relevant for improved grassland management strategies. Using weather data and grassland production data from a long-term permanent grassland site, our aims were (i) to detect the most important drought periods that affected the region and (ii) to assess whether climate changes and variability significantly affected forage production in the last decade. For this purpose, long-term daily weather data (1961–2019) and the standardized precipitation index (SPI), De Martonne index (IDM), water deficit (WD), dryness index (DI), yield anomaly index (YAI), and annual yield loss index (YL) were used to provide a scientific estimation. The results show that, despite a positive trend in DI and a negative trend in WD and precipitation, the time-series trends of precipitation, WD, and DI indices for 1961–2019 were not significant. Extreme dry conditions were also identified with SPI values less than −2. The measured annual forage yield (2007–2018) harvested in a four-cut silage system (with and without organic N-fertilization) showed a strong correlation with WD (R = 0.64; p ˂ 0. 05). The main yield losses were indicated for the years 2008 and 2018. The results of this study could provide a perspective for drought monitoring, as well as drought warning, in grassland in northwest Europe.

Performance of predicted evapotranspiration and yield of rainfed wheat in the northeast Iran using gridded AgMERRA weather data.

High quality of long-term daily weather data is essential for simulating crop production and its variability. However, daily weather data with adequate duration and required quality are not available in many regions. This study has evaluated the suitability of AgMERRA (The Modern-Era Retrospective Analysis for Research and Applications) weather data for simulating rainfed wheat evapotranspiration (ETc) and yield. Daily AgMERRA were compared with corresponding observed weather data of 11 land stations across the northeast Iran, considering the different periods from 1980 to 2010. Cropwat and CSM-CERES-Wheat models were used to simulate ETc and yield of rainfed wheat, respectively. The comparison of daily AgMERRA with observations resulted in the highest correlation (r2 > 70%) and good agreement (d > 0.77 and NRMSE < 30%) between climate variables, except for daily wind speed and precipitation at all locations. However, when daily precipitation data were aggregated into 15-day periods, agreement and correlation improved. According to the monthly comparison, the largest bias between AgMERRA temperature and radiation with land observations was obtained from June to August (summer season). Results also indicated that the distribution of simulated ETc and yield using AgMERRA was within 10% of the simulated yield using observations at 73% and 100% of locations, respectively. The degree of variation of AgMERRA-simulated ETc and yield was very similar to the calculated coefficient of variation in simulated ETc and yield based on observations at 73% of locations. However, simulation of ETc and yield using AgMERRA for single years was more uncertain when compared with simulated ETc and yield based on observations for the same year. It is concluded that AgMERRA can provide a robust estimate of long-term average ETc and yield of wheat than the ETc and yield of a single year in regions that there is no long-term weather data available.

Can the dormant seeding of rainfed lentil improve productivity and water use efficiency in arid and semi-arid conditions?

Dormant seeding (DS) has become popular in dry areas because it provides a longer growing season and optimum temperature during the early spring to grow crop. Long-term daily weather data for the period of 1984–2014, were collected for ten locations in northwestern Iran with a cold semi-arid and arid climate. The SSM-legumes model was used to investigate the effect of dormant seeding versus different fixed spring sowing dates as well as cultivars on the yield and water use efficiency (WUE) of lentil. The results showed that, the highest average grain yield across all the study locations was obtained for DS management and decreased around 49% when sowing date was delayed to 4 April. Furthermore, with a delay in sowing date, the decline in grain yield of short- cycle and long- cycle cultivars was 42% and 58%, respectively. On average, WUE for short- cycle cultivar was much higher than long-season cultivar (6.2 and 4.0 kg ha−1 mm−1, respectively). The combination of short-cycle cultivar × DS management resulted in 30% and 29% increase in grain yield and WUE, respectively when compared with a long-cycle cultivar × DS management. This conclusion was reached because DS sowing and short-cycle cultivar increased yield and its stability due to the proper establishment and appropriate growth conditions of lentil in late winter and early spring. DS also resulted in better use of precipitation and escape from the heat and drought stresses in late spring and early summer, provided that there is no frost risk in the target regions. Overall, the results of the simulations for all the study locations suggested that sowing lentil using DS is indeed superior over the other fixed spring sowing practices because it not only shortens the frost risk to seedlings but also provides optimal conditions for the growth and use of rainfall and soil moisture as well as lentil growth.

Irrigation water requirements for seed corn and coffee under potential climate change scenarios

The impact of potential future climate change scenarios on the irrigation water requirements (IRRs) of two major agricultural crops (coffee and seed corn) in Hawai'i was studied using the Irrigation Management System (IManSys) model. In addition to IRRs calculations, IManSys calculates runoff, deep percolation, canopy interception, and effective rainfall based on plant growth parameters, site specific soil hydrological properties, irrigation system efficiency, and long-term daily weather data. Irrigation water requirements of two crops were simulated using historical climate data and different levels of atmospheric CO2 (330, 550, 710 and 970 ppm), temperature (+1.1 and +6.4 °C) and precipitation (±5, ±10 and ±20%) chosen based on the Intergovernmental Panel on Climate Change (IPCC) AR4 projections under reference, B1, A1B1 and A1F1 emission scenarios. IRRs decreased as CO2 emission increased. The average percentage decrease in IRRs for seed corn is higher than that of coffee. However, runoff, rain canopy interception, and deep percolation below the root zone increased as precipitation increased. Canopy interception and drainage increased with increased CO2 emission. Evapotranspiration responded positively to air temperature rise, and as a result, IRRs increased as well. Further studies using crop models will predict crop yield responses to these different irrigation scenarios.

Advancing the climate data driven crop-modeling studies in the dry areas of Northern Syria and Lebanon: An important first step for assessing impact of future climate

Inter-annual and seasonal variability in climatic parameters, most importantly rainfall, have potential to cause climate-induced risk in long-term crop production. Short-term field studies do not capture the full nature of such risk and the extent to which modifications to crop, soil and water management recommendations may be made to mitigate the extent of such risk. Crop modeling studies driven by long-term daily weather data can predict the impact of climate-induced risk on crop growth and yield however, the availability of long-term daily weather data can present serious constraints to the use of crop models. To tackle this constraint, two weather generators namely, LARS-WG and MarkSim, were evaluated in order to assess their capabilities of reproducing frequency distributions, means, variances, dry spell and wet chains of observed daily precipitation, maximum and minimum temperature, and solar radiation for the eight locations across cropping areas of Northern Syria and Lebanon. Further, the application of generated long-term daily weather data, with both weather generators, in simulating barley growth and yield was also evaluated. We found that overall LARS-WG performed better than MarkSim in generating daily weather parameters and in 50years continuous simulation of barley growth and yield. Our findings suggest that LARS-WG does not necessarily require long-term e.g., >30years observed weather data for calibration as generated results proved to be satisfactory with >10years of observed data except in area with higher altitude. Evaluating these weather generators and the ability of generated weather data to perform long-term simulation of crop growth and yield is an important first step to assess the impact of future climate on yields, and to identify promising technologies to make agricultural systems more resilient in the given region.

ADDING VALUE TO FIELD-BASED AGRONOMIC RESEARCH THROUGH CLIMATE RISK ASSESSMENT: A CASE STUDY OF MAIZE PRODUCTION IN KITALE, KENYA

SUMMARYIn sub-Saharan Africa (SSA), rainfed agriculture is the dominant source of food production. Over the past 50 years much agronomic crop research has been undertaken, and the results of such work are used in formulating recommendations for farmers. However, since rainfall is highly variable across seasons the outcomes of such research will depend upon the rainfall characteristics of the seasons during which the work was undertaken. A major constraint that is faced by such research is the length of time for which studies could be continued, typically ranging between three and five years. This begs the question as to what extent the research was able to ‘sample’ the natural longer-term season-to-season rainfall variability. Without knowledge of the full implications of weather variability on the performance of innovations being recommended, farmers cannot be properly advised about the possible weather-induced risks that they may face over time. To overcome this constraint, crop growth simulation models such as the Agricultural Production Systems Simulator (APSIM) can be used as an integral part of field-based agronomic studies. When driven by long-term daily weather data (30+ years), such models can provide weather-induced risk estimates for a wide range of crop, soil and water management innovations for the major rainfed crops of SSA. Where access to long-term weather data is not possible, weather generators such as MarkSim can be used. This study demonstrates the value of such tools in climate risk analyses and assesses the value of the outputs in the context of a high potential maize production area in Kenya. MarkSim generated weather data is shown to provide a satisfactory approximation of recorded weather data at hand, and the output of 50 years of APSIM simulations demonstrate maize yield responses to plant population, weed control and nitrogen (N) fertilizer use that correspond well with results reported in the literature. Weather-induced risk is shown to have important effects on the rates of return ($ per $ invested) to N-fertilizer use which, across seasons and rates of N-application, ranged from 1.1 to 6.2. Similarly, rates of return to weed control and to planting at contrasting populations were also affected by seasonal variations in weather, but were always so high as to not constitute a risk for small-scale farmers. An analysis investigating the relative importance of temperature, radiation and water availability in contributing to weather-induced risk at different maize growth stages corresponded well with crop physiological studies reported in the literature.

Distribution and Potential Spread of Japanese Knotweed (Polygonum cuspidatum) in Canada Relative to Climatic Thresholds

AbstractJapanese knotweed (JK) is one of the most aggressive invasive plants known in the U.K., where its biology has been well-studied. It was introduced into Canada around 1900, but only recently has it become a serious concern in the province of British Columbia (BC). Climatic conditions, including annual degree days and mean-annual minimum temperatures at knotweed sites in British Columbia were modeled in BioSIM, using weather normals and long-term daily weather data, and compared to published thresholds (degree day = 2,505 DD, minimum temperature = −30.2 C, base temperature 0 C). The degree-day threshold was more limiting to JK in British Columbia than mean-minimum temperature (12.3% of province habitat was suitable for JK based on degree days compared with 26% for mean-minimum temperature). A new annual-precipitation threshold of 735 mm/year based on 95% of known knotweed sites in BC was identified. The best-fit logistic regression model included degree days and annual precipitation and predicted knotweed presence/absence with over 97% efficiency. Existing knotweed sites occupy just over half of the suitable habitat in BC, indicating there are still significant areas to be invaded. The limiting threshold for knotweed was reversed in Southern Ontario with between 35 to 53% of the habitat suitable based on minimum temperatures, whereas degree-day accumulations and annual precipitation were not limiting. Warmer temperatures from 2000 to 2008 resulted in an increase to 53% of the habitat in Southern Ontario being suitable for knotweed, compared to 35% when 1971 to 2000 weather normals were used. Different climatic thresholds among provinces might result in selection for different invasive knotweed genotypes. This could influence the success of biological control agents because of differential host suitability of knotweed genotypes. Habitat suitability maps generated will enable better targeting of knotweed surveys based on the risk of knotweed establishment.

Minimum data requirements for parameter estimation of stochastic weather generators

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 25:109-119 (2003) - doi:10.3354/cr025109 Minimum data requirements for parameter estimation of stochastic weather generators Afshin Soltani1,*, Gerrit Hoogenboom2 1Department of Agronomy and Plant Breeding, Gorgan University of Agricultural Sciences, Gorgan, Iran 2Department of Biological and Agricultural Engineering, The University of Georgia, Griffin, Georgia 30223, USA *Email: asoltani_99@yahoo.com ABSTRACT: Long-term daily weather data are commonly required for the application of simulation models in agricultural systems. The objective of this study was to evaluate the impact of the length of input weather data on the quality of generated weather data and to determine the minimum data required for an accurate estimation of the climate coefficients for a weather generator. Five locations in Iran with contrasting climates were selected for this study. The WGEN and SIMMETEO weather generators were parameterized using 5, 10, 15, 20 and 30 yr of weather data as the base period (BP), and then 30 yr daily weather data sets were generated corresponding to each BP. While WGEN requires daily weather data for parameter estimation, SIMMETEO uses monthly summaries. The quality of the generated weather was measured as percentage significant differences between observed and generated data. Statistical tests were conducted, including t- and F-tests, to compare differences between generated weather data versus 30 yr historical weather data. The results showed that the number of years required to obtain generated data that were similar to observed data was a function of the weather generator and the weather variable. WGEN was more sensitive to the amount of input data than SIMMETEO. In 8 of the 14 cases WGEN required a specific minimum amount of data to be efficient in weather generation versus 6 of the 14 cases for SIMMETEO. There was a correlation between generator performance and its minimum data requirement. For the cases where the generators showed a good performance, i.e. low-percentage significant differences between observed and generated data, a specific minimum number of years was required. For generating precipitation, a smaller number of years was required than for generating temperature. Based on the results from this study, it can be concluded that at least 15 yr of input data are needed for WGEN and SIMMETEO to obtain generated sequences with statistical characteristics similar to the observed data. However, a greater length of input data may lead to an improved set of generated data as shown through more stable generated data. KEY WORDS: Weather generation · Weather data · Climate · Simulation models · Decision support systems Full text in pdf format PreviousNextExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 25, No. 2. Online publication date: December 05, 2003 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2003 Inter-Research.

Estimation of solar radiation for use in crop modelling

The aim of this study was to determine the accuracy and applicability of a number of existing and newly developed formulae for calculating solar radiation from other weather variables. Data were taken from eight sites in Ontario, Canada, that had long-term daily weather data sets which included solar radiation. Daily recorded values of minimum and maximum air temperature and precipitation, together with calculated values for clear sky radiation, were used in the formulae. Clear sky radiation was calculated as a function of latitude, day of year, solar angle, and solar constant. Coefficients for five formulae were fitted using data from Elora, Canada. Correlation coefficients between values calculated for individual days and measured data, and errors, were then computed. A newly developed formula that included the maximum temperature, the difference between maximum and minimum temperature, precipitation, and precipitation squared, provided estimates with less error than other formulae. Coefficients for this model and for one other formula were then fitted to data from seven sites. The root mean square error (RMSE) between values calculated for individual days with the newly developed model and measured data for Ontario (Elora) was 4.1 MJ m −2 day −1 with a standard deviation of 0.29 MJ m −2 day −1. When coefficients from one site were used for other sites, the RMSE increased as a linear function of distance between sites; in contrast, when measurements from one site were used for other sites, the RMSE increased curvilinearly with distance. Comparison of errors involved when using coefficients from one site as contrasted to using measured solar radiation indicated that it would be preferable to use measured radiation values if the distance between sites was less than 390 km. Because the RMSE at this separation may not be acceptable for some applications, it was suggested that the curve relating RMSE to separation of sites could be used to establish a `critical' distance within which measurements should be made.

The value of skill in seasonal climate forecasting to wheat crop management in a region with high climatic variability

In Australia, and particularly in the northern part of the grain belt, wheat is grown in an extremely variable climate. The wheat crop manager in this region is faced with complex decisions on choice of planting time, varietal development pattern, and fertiliser strategy. A skilful seasonal forecast would provide an opportunity for the manager to tailor crop management decisions more appropriately to the season. Recent developments in climate research have led to the development of a number of seasonal climate forecasting systems. The objectives of this study were to determine the value of the capability in seasonal forecasting to wheat crop management, to compare the value of the existing forecast methodologies, and to consider the potential value of improved forecast quality. We examined decisions on nitrogen (N) fertiliser and cultivar maturity using simulation analyses of specific production scenarios at a representative location (Goondiwindi) using long-term daily weather data (1894-1989). The average profit and risk of making a loss were calculated for the possible range of fixed (i.e. the same every year) and tactical (i.e. varying depending on seasonal forecast) strategies. Significant increase in profit (up to 20%) and/or reduction in risk (up to 35%) were associated with tactical adjustment of crop management of N fertiliser or cultivar maturity. The forecasting system giving greatest value was the Southern Oscillation Index (SOI) phase system of Stone and Auliciems (1992), which classifies seasons into 5 phases depending on the value and rate of change in the SOI. The significant skill in this system for forecasting both seasonal rainfall and frost timing generated the value found in tactical management of N fertiliser and cultivar maturity. Possible impediments to adoption of tactical management, associated with uncertainties in forecasting individual years, are discussed. The scope for improving forecast quality and the means to achieve it are considered by comparing the value of tactical management based on SO1 phases with the outcome given perfect prior knowledge of the season. While the analyses presented considered only one decision at a time, used specific scenarios, and made a number of simplifying assumptions, they have demonstrated that the current skill in seasonal forecasting is sufficient to justify use in tactical management of crops. More comprehensive studies to examine sensitivities to location, antecedent conditions, and price structure, and to assumptions made in this analysis, are now warranted. We have examined decisions related only to management of wheat. It would be appropriate to pursue similar analyses in relation to management decisions for other crops, cropping sequences, and the whole farm enterprise mix.

Development of a neural network model to predict daily solar radiation

Many computer simulation models which predict growth, development, and yield of agronomic and horticultural crops require daily weather data as input. One of these inputs is daily total solar radiation, which in many cases is not available owing to the high cost and complexity of the instrumentation needed to record it. The aim of this study was to develop a neural network model which can predict solar radiation as a function of readily available weather data and other environmental variables. Four sites in the southeastern USA, i.e. Tifton, GA, Clayton, NC, Gainesville, FL, and Quincy, FL, were selected because of the existence of longterm daily weather data sets which included solar radiation. A combined total of 23 complete years of weather data sets were available, and these data sets were separated into 11 years for the training data set and 12 years for the testing data set. Daily observed values of minimum and maximum air temperature and precipitation, together with daily calculated values for daylength and clear sky radiation, were used as inputs for the neural network model. Daylength and clear sky radiation were calculated as a function of latitude, day of year, solar angle, and solar constant. An optimum momentum, learning rate, and number of hidden nodes were determined for further use in the development of the neural network model. After model development, the neural network model was tested against the independent data set. Root mean square error varied from 2.92 to 3.64 MJ m −2 and the coefficient of determination varied from 0.52 to 0.74 for the individual years used to test the accuracy of the model. Although this neural network model was developed and tested for a limited number of sites, the results suggest that it can be used to estimate daily solar radiation when measurements of only daily maximum and minimum air temperature and precipitation are available.