Model-Based Forecasting of Agricultural Crop Disease Risk at the Regional Scale, Integrating Airborne Inoculum, Environmental, and Satellite-Based Monitoring Data

Crop diseases have the potential to cause devastating epidemics that threaten the world's food supply and vary widely in their dispersal pattern, prevalence, and severity. It remains unclear what the impact disease will have on sustainable crop yields in the future. Agricultural stakeholders are increasingly under pressure to adapt their decision-making to make more informed and efficient use of irrigation water, fertilizers, and pesticides. They also face increasing uncertainty in how best to respond to competing health, environment, and (sustainable) development impacts and risks. Disease dynamics involves a complex interaction between a host, a pathogen, and their environment, representing one of the largest risks facing the long-term sustainability of agriculture. New airborne inoculum, weather, and satellite-based technology provide new opportunities for combining disease monitoring data and predictive models—but this requires a robust analytical framework. Integrated model-based forecasting frameworks have the potential to improve the timeliness, effectiveness, and foresight for controlling crop diseases, while minimizing economic costs and environmental impacts, and yield losses. The feasibility of this approach is investigated involving model and data selection. It is tested against available disease data collected for wheat stripe (yellow) rust (Puccinia striiformis f.sp. tritici) (Pst) fungal disease within southern Alberta, Canada. Two candidate, stochastic models are evaluated; a simpler, site-specific model, and a more complex, spatially-explicit transmission model. The ability of these models to reproduce an observed infection pattern is tested using two climate datasets with different spatial resolution—a reanalysis dataset (~55 km) and weather station network township-aggregated data (~10 km). The complex spatially-explicit model using weather station network data had the highest forecast accuracy. A multi-scale airborne surveillance design that provides data would further improve disease risk forecast accuracy under heterogeneous modeling assumptions. In the future, a model-based forecasting approach, if supported with an airborne surveillance monitoring plan, could be made operational to provide agricultural stakeholders with reliable, cost-effective, and near-real-time information for protecting and sustaining crop production against multiple disease threats

Toward designing a sustainable watershed reclamation strategy

Oil sands mining results in significant disturbances to natural ecosystems when soil and overburden materials are removed and stockpiled to provide access to mined materials. The mining process must be followed by land reclamation, whereby disturbed landscapes are recovered with the intent to replicate the performance of natural watersheds. Modeling hydrological processes in reclaimed landscapes is essential to assess the hydrological performance of the reclamation strategies as well as their evolution over time, and requires a reliable and continuous source of input data. In pursuit of simulating the various hydrological processes, such as soil moisture and actual evapotranspiration, a lumped generic system dynamics watershed (GSDW) model has been developed. The validity of the proposed model has been assessed in terms of its capacity to reproduce the hydrological behaviour of both reconstructed and natural watersheds. Data availability is a major challenge that constrains not only the type of models used but also their predictive ability and accuracy. This study evaluates the utility of precipitation and temperature data from the North American Regional Reanalysis (NARR) versus conventional platform data (e.g., meteorological station) for the hydrological modeling. Results indicate NARR data is a suitable alternative to local weather station data for simulating soil moisture patterns and evapotranspiration fluxes despite the high complexity involved in simulating such processes. Initially, the calibrated GSDW model was used along with available historical meteorological records, from both Environment Canada and NARR, to estimate the maximum soil moisture deficit and annual evapotranspiration fluxes. A probabilistic framework was adopted, and frequency curves of the maximum annual moisture deficit values were consequently constructed and used to assess the probability that various reconstructed and natural watersheds would provide the desired moisture demands. The study shows a tendency for the reconstructed watersheds to provide less moisture for evapotranspiration than natural systems. The probabilistic framework could be implemented to integrate information gained from mature natural watersheds (e.g., the natural system canopy) and transfer the results to newly reconstructed systems. Finally, this study provided some insight into the sensitivity of soil moisture patterns and evapotranspiration to possible changes in the projected precipitation and air temperature in the 21st century. Climate scenarios were generated using daily, statistically downscaled precipitation and air temperature outputs from global climate models (CGCM3), under A2 and B1 emission scenarios, to simulate the corresponding soil moisture and evapotranspiration using the GSDW model. Study results suggest a decrease in the maximum annual moisture deficit will occur due to the expected increase in annual precipitation and air temperature patterns, whereas actual evapotranspiration and runoff are more likely to increase

Modeling and analysis of actual evapotranspiration using data driven and wavelet techniques

Large-scale mining practices have disturbed many natural watersheds in northern Alberta, Canada. To restore disturbed landscapes and ecosystems’ functions, reconstruction strategies have been adopted with the aim of establishing sustainable reclaimed lands. The success of the reconstruction process depends on the design of reconstruction strategies, which can be optimized by improving the understanding of the controlling hydrological processes in the reconstructed watersheds. Evapotranspiration is one of the important components of the hydrological cycle; its estimation and analysis are crucial for better assessment of the reconstructed landscape hydrology, and for more efficient design. The complexity of the evapotranspiration process and its variability in time and space has imposed some limitations on previously developed evapotranspiration estimation models. The vast majority of the available models estimate the rate of potential evapotranspiration, which occurs under unlimited water supply condition. However, the rate of actual evapotranspiration (AET) depends on the available soil moisture, which makes its physical modeling more complicated than the potential evapotranspiration. The main objective of this study is to estimate and analyze the AET process in a reconstructed landscape. Data driven techniques can model the process without having a complete understanding of its physics. In this study, three data driven models; genetic programming (GP), artificial neural networks (ANNs), and multilinear regression (MLR), were developed and compared for estimating the hourly eddy covariance (EC)-measured AET using meteorological variables. The AET was modeled as a function of five meteorological variables: net radiation (Rn), ground temperature (Tg), air temperature (Ta), relative humidity (RH), and wind speed (Ws) in a reconstructed landscape located in northern Alberta, Canada. Several ANN models were evaluated using two training algorithms of Levenberg-Marquardt and Bayesian regularization. The GP technique was employed to generate mathematical equations correlating AET to the five meteorological variables. Furthermore, the available data were statistically analyzed to obtain MLR models and to identify the meteorological variables that have significant effect on the evapotranspiration process. The utility of the investigated data driven models was also compared with that of HYDRUS-1D model, which is a physically based model that makes use of conventional Penman-Monteith (PM) method for the prediction of AET. HYDRUS-1D model was examined for estimating AET using meteorological variables, leaf area index, and soil moisture information. Furthermore, Wavelet analysis (WA), as a multiresolution signal processing tool, was examined to improve the understanding of the available time series temporal variations, through identifying the significant cyclic features, and to explore the possible correlation between AET and the meteorological signals. WA was used with the purpose of input determination of AET models, a priori. The results of this study indicated that all three proposed data driven models were able to approximate the AET reasonably well; however, GP and MLR models had better generalization ability than the ANN model. GP models demonstrated that the complex process of hourly AET can be efficiently modeled as simple semi-linear functions of few meteorological variables. The results of HYDRUS-1D model exhibited that a physically based model, such as HYDRUS-1D, might perform on par or even inferior to the data driven models in terms of the overall prediction accuracy. The developed equation-based models; GP and MLR, revealed the larger contribution of net radiation and ground temperature, compared to other variables, to the estimation of AET. It was also found that the interaction effects of meteorological variables are important for the AET modeling. The results of wavelet analysis demonstrated the presence of both small-scale (2 to 8 hours) and larger-scale (e.g. diurnal) cyclic features in most of the investigated time series. Larger-scale cyclic features were found to be the dominant source of temporal variations in the AET and most of the meteorological variables. The results of cross wavelet analysis indicated that the cause and effect relationship between AET and the meteorological variables might vary based on the time-scale of variation under consideration. At small time-scales, significant linear correlations were observed between AET and Rn, RH, and Ws time series, while at larger time-scales significant linear correlations were observed between AET and Rn, RH, Tg, and Ta time series

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

A comparative analysis of the hydrological performance of reconstructed and natural watersheds

An example of watershed disturbance activity undertaken to gain access to the oil sands is large scale mining in the Athabasca basin, Alberta, Canada. One of the remedial activities of this disturbance is the reclamation of the disturbed lands. In the process of reclamation, the overburden soil is placed back into the mined pits and reformed with soil covers (alternatively called reconstructed watersheds). In the design process of reclamation, a major concern is hydrological sustainability, which includes the soil’s ability to store enough moisture for the water requirements of vegetation growth and land-atmospheric moisture fluxes. Typically, the goal of the reclamation is to restore the disturbed watersheds, so that they mimic the natural watersheds in terms of the ecological sustainability. Therefore, a comparative evaluation of the hydrological sustainability of the reconstructed watersheds with natural watersheds is required.The considered reconstructed watershed in this study (the flat top of the South Bison Hill, Fort McMurray, Alberta, which is about 6 years old) constitutes a thin layer of a peat-mineral mix (20 cm thick) overlying an 80 cm thick secondary (glacial till) layer on the shale formation, mimicking the natural soil horizons of undisturbed watersheds. As the reconstructed watershed is located in the boreal forest region, a mature boreal forest (Old Aspen site, about 88 years old) located in the Southern Study Area (SSA), BOREAS, Saskatchewan, Canada, is considered as a representative of natural watershed. The A-horizon with 25 cm of sandy loam texture, the B-horizon with 45 cm-thick sandy clay loam, and the C-horizon with 40 cm of a mixture of sandy clay loam and loam are considered in this study.An existing System Dynamics Watershed (SDW) model (lumped and site-specific) is modified and adapted to model the hydrological processes of the reconstructed and natural watersheds, such as soil moisture, evapotranspiration, and runoff. The models are calibrated and validated on daily time scale using two years data (growing season) in each case. The hydrological processes are simulated reasonably well despite the high complexity involved in the processes of soil moisture dynamics and the evapotranspiration, for both study areas. Using the modified and calibrated models, long term simulations (48 years) are carried out on both the reconstructed and natural watersheds. Vegetation properties are switched between the reconstructed and natural watersheds and two scenarios are generated. Consequently, long term simulations are performed. With the help of a probabilistic approach, the daily soil moisture results are used to address the comparative soil moisture storage capability of the watersheds.The results indicate that the selected reconstructed watershed is able to provide its designed store-and-release moisture of 160 mm (a requirement of the land capability classification for forest ecosystems in the oil sands) for the vegetation and meteorological moisture demands at a non-exceedance probability of 93%. The comparative study shows that the reconstructed watershed provides less moisture for evapotranspiration requirements than the natural watershed. The reconstructed watershed is able to provide less moisture than the natural watershed for both small and also mature vegetation scenarios. A possible reason for this may be that the reconstructed site is still in the process of restoration and that it may take a few more years to get closer to natural watersheds in terms of the hydrological sustainability. The study also demonstrates the utility of the system dynamics approach of modeling the case study under consideration. The future addition of a vegetation growth model to the hydrological model, and the development of a generic watershed modeling technique would be helpful in decision making and management practices of watershed reclamation