CLIMA: a weather generator framework

Abstract: Weather generators (WG) can be defined as collections of models to estimate site specific weather data and derived variables. Their use spans from providing inputs to a variety of biophysical models to deriving weather indices. Also, using either global circulation models or local area models inputs, sets of parameters calculated from long term weather series specific to a site can be modified to reproduce via WG synthetic series representing climate change scenarios. Finally, models implemented in WG are used for estimating missing data and to perform quality control on data collected from sensors in weather stations. The models implemented in WG vary from purely empirical to physically based. There are several models to either estimate or to generate each weather variable, with different input requirements. New models are continuously being proposed, and, whether some models to estimate specific variables are commonly accepted as reference methods, the lack of some inputs requires at times using alternate approaches. Currently available WG are applications which implement a predefined set of modelling options, in software implementations which do not allow for independent extensions by third parties. The CLIMA weather generator is a component based application which consist of a set of reusable graphical user interface (GUI) components, and of a set of extensible model components. The latter are subdivided into six namespaces to estimate variables related to air temperature, rainfall, solar radiation, evapotranspiration, wind, and leaf wetness. The time characteristic of the variables estimated varies from a day to ten minutes. Another library allows estimating climatic indices from one year of daily data at the time. The current implementation consists of a total of more than 300 models. Components are usable either via the CLIMA GUI, or via custom developed applications in a client-server architecture. The architecture of components is based on the composite and strategy as keystone design patterns. Models are implemented as single approaches (simple strategies), and as composite models (composite strategies) which are associated to models of finer granularity. Another type of model unit is represented by context strategies, which implement logic to select within associated models. Finally, the GUI allows building composite models which can be saved as libraries, to be reused both within CLIMA for weather series generation, or independently by other applications. The components are implemented as .NET libraries. They implement the test of pre- and post-conditions, and a scalable tracing via .NET listeners. All variables and parameters are documented via a description, units, default, maximum, and minimum values. Components are extensible: new models can be added independently by third parties and detected by the CLIMA application, which can also use them for data generation via building new composite libraries. Each component is made available via a software development kit which includes the code of two sample projects, either to extend or to reuse the component. CLIMA and its model components are freely available for reuse in no-profit applications.JRC.DG.G.3-Monitoring agricultural resource

identifying the most promising agronomic adaptation strategies for the tomato growing systems in southern italy via simulation modeling

Abstract The main cultivation area of the Italian processing tomato is the Southern Capitanata plain. Here, the hardest agronomic challenge is the optimization of the irrigation water use, which is often inefficiently performed by farmers, who tend to over-irrigate. This could become unsustainable in the next years, given the negative impacts of climatic changes on groundwater availability and heat stress intensification. The aim of the study was to identify the most promising agronomic strategies to optimize tomato yield and water use in Capitanata, through a modeling study relying on an extensive dataset for model calibration and evaluation (22 data sets in 2005–2018). The TOMGRO simulation model was adapted to open-field growing conditions and was coupled with a soil model to reproduce the impact of water stress on yield and fruit quality. The new model, TomGro_field, was applied on the tomato cultivation area in Capitanata at 5 × 5 km spatial resolution using an ensemble of future climatic scenarios, resulting from the combination of four General Circulation Models, two extreme Representative Concentration Pathways and five 10-years time frames (2030–2070). Our results showed an overall negative impact of climate change on tomato yields (average decrease = 5–10%), which could be reversed by i) the implementation of deficit irrigation strategies based on the restitution of 60–70% of the crop evapotranspiration, ii) the adoption of varieties with longer cycle and iii) the anticipation of 1–2 weeks in transplanting dates. The corresponding irrigation amounts applied are around 360 mm, thus reinforcing that a rational water management could be realized. Our study provides agronomic indications to tomato growers and lays the basis for a bio-economic analysis to support policy makers in charge of promoting the sustainability of the tomato growing systems

Comparison of modelling approaches to simulate the phenology of agricultural insect pests under future climate scenarios

The phenological development of insects is simulated predominantly via models based on the response of the organisms to air temperature. Despite of a large body of literature supporting the evidence that the organism physiological response to temperature is nonlinear, including a declining phase, most of these models calculate the rate of development using a linear approach, assuming that air temperatures mostly does not fall outside of the linear region of response to temperature of the organism. Another simplification is represented by the calculation of the rate of development using daily mean air temperature, which has been demonstrated being a reliable method only in limited number of conditions. It can be hypothesized that the use of models based on linear developmental rates, which can be successfully applied under climate conditions to which organisms are well adapted, could be inadequate under either future climatic scenarios or when extreme events occur. In such contexts, linear responses might lead to interpretations of climate effects not consistent with the real organism physiological response to temperature. In this work the case of Ostrinia nubilalis Hübner (European Corn Borer – ECB) development was taken as an example to compare i) a non-linear approach with hourly air temperature as input (HNL), ii) a linear based approach with hourly air temperature as input (HL), iii) a linear based approach with daily air temperature as input (DL), and iv) a linear based approach using an horizontal cutoff temperature (development continues at a constant rate at temperatures in excess of an upper temperature threshold) with daily air temperature as input (DLcutoff). The comparison was performed on a European scale for the IPCC (Intergovernmental Panel for Climate Change) emission scenario A1B, at three time frames: Baseline - 2000s, 2020s, 2050s. The SRES A1B was selected as one of those for which the projected raise of temperature is estimated to be one of the highest. Using degree-days (DD) as a proxy for the rate of development, results (Figure 1) showed that the DL approach predicts a higher accumulation of DD than the HNL in all the time frames in almost all Europe with the exception of Southern Italy and the Mediterranean coasts of France and Spain where the differences were negligible. These effects were due i) to the linear relationship used by the DL approach which do not take into account the stressful effects of temperature higher than the optimum, and partially ii) to the averaging operation that decrease the effects of high temperatures in regions with high (but not extreme) warm temperatures. The HNL and HL approach predicted the same pattern of degree-days accumulation in all Europe with the exception of the regions of Southern Iberian Peninsula (across all the timeframes), Balkans, and Turkey (under the 2050 scenario). This effect was due to the different HNL and HL accumulation of degree-days at temperatures higher than the ECB optimum temperature. The comparison between the DLcutoff and the HNL approaches showed similar results as the DL vs HNL approach in central and Northern Europe, while in Southern Europe negative differences (more DD accumulated for the HNL approach) were observed: in regions characterized by high temperatures, the cutoff temperature, setting a limit to the maximum temperatures diminished the calculated average temperature and as a consequence the calculated degree-days. The results of this work showed that according to the method chosen for simulations, different results can be obtained, hence leading to different conclusions about the effect of a warming climate on pest development. These results stress the need of reconsidering the appropriateness of models to be used, which cannot be assumed as correct on the basis of their effectiveness under current conditions.JRC.H.4-Monitoring Agricultural Resource

Evaluating the suitability of a generic fungal infection model for pest risk assessment studies

Pest risk assessment studies are aimed at evaluating if weather conditions are suitable for the potential entry and establishment of an organism in a new environment. For fungal plant pathogens, the crucial aspect to be explored is the fulfillment of the infection process, that constitutes the first phase of the development of an epidemic as mainly driven by temperature and leaf wetness duration. This is of particular interest for climate change studies, because the modified pattern of temperature and moisture regimes could completely alter the known distribution and severity of plant disease epidemics. Biophysical process-based models could effectively be used in such studies, because they allow, within their applicability range, estimating organisms responses to climatic drivers in environmental conditions not yet experienced. One of the prerequisite of their adoption in operational contexts is a sensitivity analysis assessment aimed at understanding their ability (i) to differentiate the responses according to different parameterizations and (ii) to be sensitive to the variability of the input data. In this study, a generic potential fungal infection model simulating four pathogens chosen to provide a wide range in temperature and moisture requirements was analyzed. The model was run under diverse climatic conditions. The sensitivity of the model significantly changed according to the pathogen tested, and the relevance of its parameters in explaining model output resulted strongly linked to the environmental conditions tested, indicating its to be used in pest risk assessment studies.JRC.H.4-Monitoring Agricultural Resource

Fungal infections of rice, wheat, and grape in Europe in 2030–2050

Although models to predict climate impact on crop production have been used since the 1980s, spatial and temporal diffusion of plant diseases are poorly known. This lack of knowledge is due to few models of plant epidemics, high biophysical complexity, and difficulty to couple disease models to crop simulators. The first step is the evaluation of disease potential growth in response to climate drivers only. Here, we estimated the evolution of potential infection events of fungal pathogens of wheat, rice, and grape in Europe. A generic process-based infection model driven by air temperature and leaf wetness data was parameterized with the thermal and moisture requirements of the pathogens. The model was run on current climate as baseline, and on two time frames centered on 2030 and 2050. Our results show an overall increase in the number of infection events, with differences among the pathogens, and showing complex geographical patterns. For wheat, Puccinia recondita, or brown rust, is forecasted to increase +20–100 % its pressure on the crop. Puccinia striiformis, or yellow rust, will increase 5–20 % in the cold areas. Rice pathogens Pyricularia oryzae, or blast disease, and Bipolaris oryzae, or brown spot, will be favored all European rice districts, with the most critical situation in Northern Italy (+100 %). For grape, Plasmopara viticola, or downy mildew, will increase +5–20 % throughout Europe. Whereas Botrytis cinerea, or bunch rot, will have heterogeneous impacts ranging from −20 to +100 % infection events. Our findings represents the first attempt to provide extensive estimates on disease pressure on crops under climate change, providing information on possible future challenges European farmers will face with in the coming years.JRC.H.4-Monitoring Agricultural Resource

An Integrated Evaluation of Thirteen Modelling Solutions for the Generation of Hourly Values of Air Relative Humidity

The availability of hourly air relative humidity (HARH) data is a key requirement for the estimation of epidemic dynamics of plant fungal pathogens, in particular for the simulation of both the germination of the spores and the infection process. Most of the existing epidemic forecasting models require these data as input directly or indirectly, in the latter case for the estimation of leaf wetness duration. In many cases, HARH must be generated because it is not available in historical series, and when there is the need to simulate epidemics either on a wide scale or with different climate scenarios. Thirteen modelling solutions (MS) for the generation of this variable were evaluated, with different inputs requirement and alternative approaches, on a large dataset including several sites and years. A composite index was developed using fuzzy logic to compare and to evaluate the performances of the models. The indicator consists of four modules: Accuracy, Correlation, Pattern, and Robustness. Results showed that, when available, daily maximum and minimum air relative humidity data substantially improved the estimation of HARH. When such data are not available, the choice of the MS is crucial. given the difference in predicting skills obtained during the analysis, which allowed a clear detection of the best performing MS. This study represents the first step of the creation of a robust modelling chain coupling the MS for the generation of HARH and disease forecasting models, aiming at an improvement and an enhancement of their use through the systematic validation of each step of the simulation.JRC.DDG.H.4-Monitoring agricultural resource

Multi metric evaluation of leaf wetness models for large-area application of plant disease models

Leaf wetness (LW) is one of the most important input variables of disease simulation models 3 because of its fundamental role in the development of the infection process of many fungal 4 pathogens. The low reliability of LW sensors and/or their rare use in standard weather stations has 5 led to an increasing demand for reliable models that are able to estimate LW from other 6 meteorological variables. When working on large databases in which data are interpolated in grids 7 starting from weather stations, LW estimation is often penalized by the lack of hourly inputs (e.g., 8 air relative humidity and air temperature), leading researchers to generate such variables from the 9 daily values of the available weather data. 10 Although it is possible to find several papers about models for the estimation of LW, the behavior 11 and reliability of these models were never assessed by running them with inputs at different time 12 resolutions aiming at large-area applications. Furthermore, only a limited number of papers have 13 assessed the suitability of different LW models when used to provide inputs to simulate the 14 development of the infection process of fungal pathogens. In this paper, six LW models were 15 compared using data collected at 12 sites across the U.S. and Italy between 2002 and 2008 using an 16 integrated, multi metric and fuzzy-based expert system developed ad hoc. The models were 17 evaluated for their capability to estimate LW and for their impact on the simulation of the infection 18 process for three pathogens through the use of a potential infection model. This study indicated that 19 some empirical LW models performed better than physically based LW models. The classification 20 and regression tree (CART) model performed better than the other models in most of the conditions 21 tested. Finally, the estimate of LW using hourly inputs from daily data led to a decline of the LW 22 models performances, which should still be considered acceptable. However, this estimate may 23 require further work in data collection and model evaluation for applications at finer spatial 24 resolutions aimed at decision support systems.JRC.H.4-Monitoring agricultural resource

Comparing Modelling Solutions At Submodel Level: A Case On Soil Temperature Simulation

What is commonly referred as either crop or cropping system simulation model is a set of interlinked mathematical representations of approaches which are abstractions of a single biological or physical process. The methodology for their evaluation has evolved in time, but it has always targeted models as unique and immutable, except for versioning, discrete units. This paper has explored aspects both of model composition in the perspective of evaluating alternate modelling approaches, and of modelling solutions evaluation. Soil temperature was chosen as case study, evaluating nine modelling solutions against a multi-year, multi-location database of field recorded time series of data. Multi-metric indices were also developed to quantify different aspects of model performance and to get a better insight on the impact of sub-model replacement. Results showed that the hybrid solution implementing the cascading model (soil water redistribution), Parton’s approach (surface temperature), and SWAT (temperature along the soil profile) led to the best compromise between agreement and robustness under the explored conditions. The model libraries used to run the analysis, in form of extensible model components, are freely available for download, and they allow for further extension of the composition exercise.JRC.H.4-Monitoring Agricultural Resource