Modelling crop yields and climate risk under limited climate data conditions

Agricultural management needs relevant climate information to reduce the climate uncertainty and support crucial management decisions. Risk profiles of modelled crop yields (cumulative probability curves) are effective tools for summarising long-term yield variability, exploring the benefit and limitations of agricultural management decisions and serve to quantify the impact of future climate conditions. However, modelling reliable crop yield and risk profiles requires continuous, accurate, and long-term (>100 years) local weather records for rainfall, temperature, and solar radiation, which are not always available. This study aimed to systematically assess spatial and temporal factors that limit the accuracy of risk profile of modelled crop yields. The specific objectives were (1) to analyse if and to what degree short time series of weather data can be used to provide reliable risk profiles, (2) to test how simple adjustments of high-quality local data can be used to extrapolate risk profiles across broad climatic regions, and (3) to address a combination of sparse spatial coverage of climate data and short daily weather observations. Here we focused on the Australian grain-belt selected on the basis of the availability to high-quality, long-term climate data, widely used and calibrated process-based crop model (APSIM, Agricultural Production Systems sIMulator). To examine the sensitivity of risk profiles of modelled crop yields to the temporal coverage of the climate data, 15 wheat-growing sites were selected based on their proximity to weather stations with high-quality daily weather records for the last 100 years (baseline period). Risk profiles were constructed using variable temporal coverages and compared with risk profiles obtained for the baseline period. Results indicated a decline of modelled wheat grain yields, particularly for the last three decades. They also highlight the interactions between model complexity and data demand. The sensitivity of the risk profiles to record length was increased in models accounting for severe frost and heat events. The second research objective of this study addresses spatial extrapolation and explores to what extent a simple method for adjusting daily weather data using seasonal and monthly factors could produce robust estimates of risk profiles at a continental scale. Adjustment factors were calculated as the difference in long-term average of a given climate variable between 49 test sites and the reference site. Risk profiles modelled with observed weather data were compared with those modelled with adjusted data. Simple adjustments of both precipitation and temperatures produced reliable risk profiles in 80% of the sites. This study implies that for regions with limited availability of high-quality climate data, simple scaling of climate inputs can provide basic climate data for modelling and generating robust spatial patterns of risk profiles of crop yield. The third objective addresses the realistic scenario of using modern, process-based crop models, which are data hungry, in data sparse environments. Models that can capture combinations of potential climate and management impacts on food production require complex climate data that are either not available or difficult to access at high spatial detail and/or temporal extent for many parts of the world. Here, we assess the sensitivity of the risk profile accuracy to the temporal coverage of the climate data combined with spatial adjustments of daily weather data for risk profile modelling purposes. In this case, adjustment factors were determined using a variable temporal coverage at every study site. Risk profiles were modelled using observed and adjusted weather data covering different periods. Results indicated that although adjustment factors are very sensitive to the record length of the climate data, it was possible to produced reliable risk profiles with only 10-30 years of climate data. This research has increased our understanding of the sensitivity of risk profiles to the temporal and spatial aspects of climate data availability. It highlights the usefulness of risk profiles to characterise spatial and temporal patterns of yield and will help to improve agricultural management under climate uncertainty.Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Biological Sciences, 201

Climate change refugia for terrestrial biodiversity

AbstractWe are currently facing the likelihood of severe climate change before the close of the century. In the face of such a global driver of species loss, we urgently need to identify refugia that will shelter species from the worst impacts of climate change. This  will be  a critical component of successful conservation and management of our biodiversity. Despite this, little is known about how best to identify refugia in the landscape, and the practical strategies needed to identify, protect and expand refugia are just beginning to be developed. Identifying refugia that will protect most species, or large numbers of species, remains a complex and daunting endeavour due to the large variations in climatic and biotic requirements of species. A first step to identifying refugia for biodiversity across Australia is to locate the areas which show the least change into the future (i.e. the most environmentally stable), particularly along axes of temperature and precipitation. The second and crucial step is to identify the areas that will retain most of their biodiversity and provide opportunities for additional species to relocate to into the future. Using these approaches in this project, we take the first steps to identify refugial areas across the Australian continent under contemporary climate change scenarios. We find that the southern and eastern parts of the continent contain refugia that many species will retreat to over the next 75 years, but that the current reserve system may be inadequate to allow species to shift to and persist in these areas. Disturbingly, we also find that there is a large portion of the Australian vertebrate community for which adequate natural refugia do not appear to exist. Fine-scaled regional analyses will be required to clarify these broad findings, and we examine a number of case studies demonstrating how these regional analyses might best proceed. Lessons learnt across the multiple techniques employed in this study include:1. High elevation areas are important refugia.2. Tasmania and the east coast of mainland Australia contain most of the key areas for refugia into the future.3. Results are dependent on which objectives, techniques, taxonomic groups and climate scenarios are used.Please cite this report as:Reside, AE, VanDerWal, J, Phillips, B, Shoo, LP, Rosauer, DF, Anderson, BA, Welbergen, J, Moritz, C, Ferrier, S, Harwood, TD, Williams, KJ, Mackey, B, Hugh, S, Williams, SE 2013 Climate change refugia for terrestrial biodiversity: Defining areas that promote species persistence and ecosystem resilience in the face of global climate change, National Climate Change Adaptation Research Facility, Gold Coast, pp. 216We are currently facing the likelihood of severe climate change before the close of the century. In the face of such a global driver of species loss, we urgently need to identify refugia that will shelter species from the worst impacts of climate change. This  will be  a critical component of successful conservation and management of our biodiversity. Despite this, little is known about how best to identify refugia in the landscape, and the practical strategies needed to identify, protect and expand refugia are just beginning to be developed. Identifying refugia that will protect most species, or large numbers of species, remains a complex and daunting endeavour due to the large variations in climatic and biotic requirements of species. A first step to identifying refugia for biodiversity across Australia is to locate the areas which show the least change into the future (i.e. the most environmentally stable), particularly along axes of temperature and precipitation. The second and crucial step is to identify the areas that will retain most of their biodiversity and provide opportunities for additional species to relocate to into the future. Using these approaches in this project, we take the first steps to identify refugial areas across the Australian continent under contemporary climate change scenarios. We find that the southern and eastern parts of the continent contain refugia that many species will retreat to over the next 75 years, but that the current reserve system may be inadequate to allow species to shift to and persist in these areas. Disturbingly, we also find that there is a large portion of the Australian vertebrate community for which adequate natural refugia do not appear to exist. Fine-scaled regional analyses will be required to clarify these broad findings, and we examine a number of case studies demonstrating how these regional analyses might best proceed. Lessons learnt across the multiple techniques employed in this study include:High elevation areas are important refugia.Tasmania and the east coast of mainland Australia contain most of the key areas for refugia into the future.Results are dependent on which objectives, techniques, taxonomic groups and climate scenarios are used

High-resolution global grids of revised Priestley–Taylor and Hargreaves–Samani coefficients for assessing ASCE-standardized reference crop evapotranspiration and solar radiation

Abstract. The objective of the study is to provide global grids (0.5°) of revised annual coefficients for the Priestley–Taylor (P-T) and Hargreaves–Samani (H-S) evapotranspiration methods after calibration based on the ASCE (American Society of Civil Engineers)-standardized Penman–Monteith method (the ASCE method includes two reference crops: short-clipped grass and tall alfalfa). The analysis also includes the development of a global grid of revised annual coefficients for solar radiation (Rs) estimations using the respective Rs formula of H-S. The analysis was based on global gridded climatic data of the period 1950–2000. The method for deriving annual coefficients of the P-T and H-S methods was based on partial weighted averages (PWAs) of their mean monthly values. This method estimates the annual values considering the amplitude of the parameter under investigation (ETo and Rs) giving more weight to the monthly coefficients of the months with higher ETo values (or Rs values for the case of the H-S radiation formula). The method also eliminates the effect of unreasonably high or low monthly coefficients that may occur during periods where ETo and Rs fall below a specific threshold. The new coefficients were validated based on data from 140 stations located in various climatic zones of the USA and Australia with expanded observations up to 2016. The validation procedure for ETo estimations of the short reference crop showed that the P-T and H-S methods with the new revised coefficients outperformed the standard methods reducing the estimated root mean square error (RMSE) in ETo values by 40 and 25 %, respectively. The estimations of Rs using the H-S formula with revised coefficients reduced the RMSE by 28 % in comparison to the standard H-S formula. Finally, a raster database was built consisting of (a) global maps for the mean monthly ETo values estimated by ASCE-standardized method for both reference crops, (b) global maps for the revised annual coefficients of the P-T and H-S evapotranspiration methods for both reference crops and a global map for the revised annual coefficient of the H-S radiation formula and (c) global maps that indicate the optimum locations for using the standard P-T and H-S methods and their possible annual errors based on reference values. The database can support estimations of ETo and solar radiation for locations where climatic data are limited and it can support studies which require such estimations on larger scales (e.g. country, continent, world). The datasets produced in this study are archived in the PANGAEA database (https://doi.org/10.1594/PANGAEA.868808) and in the ESRN database (http://www.esrn-database.org or http://esrn-database.weebly.com)

A climatically-derived global soil moisture data set for use in the GLAS atmospheric circulation model seasonal cycle experiment

Algorithms for point interpolation and contouring on the surface of the sphere and in Cartesian two-space are developed from Shepard's (1968) well-known, local search method. These mapping procedures then are used to investigate the errors which appear on small-scale climate maps as a result of the all-too-common practice of of interpolating, from irregularly spaced data points to the nodes of a regular lattice, and contouring Cartesian two-space. Using mean annual air temperatures field over the western half of the northern hemisphere is estimated both on the sphere, assumed to be correct, and in Cartesian two-space. When the spherically- and Cartesian-approximted air temperature fields are mapped and compared, the magnitudes (as large as 5 C to 10 C) and distribution of the errors associated with the latter approach become apparent

Remote sensing of peanut cropping areas and modelling of their future geographic distribution and disease risks

Peanut or groundnut (Arachis hypogaea L), one of the most important oil seed crops, faces several challenges due to climate change. The unfavourable climate in Australia, as a result of high climate variability, could easily affect peanut production. For example, the incidence of drought stress will increase the likelihood of one of the major problems in the peanut industry, i.e. aflatoxin. In addition, if the climate changes as projected, shifts in geographic distribution of peanut crops and the associated diseases are inevitable. In view of these concerns, this study set the following objectives: 1) to assess the effectiveness of PROBA-V imagery in mapping peanut crops; 2) to study the effects of climate change on the future geographic distribution of peanut crops in Australia; and 3) to examine the effects of climate change on the future distribution of aflatoxin in peanut crops, and to locate high risk areas of aflatoxin in the future areas of peanut crop production. In this study, the area of peanut crop mapping was the South Burnett region in Queensland, while the area of future geographic distribution of peanut crops and aflatoxin covered the entire continent of Australia. To address the first objective, the peanut crop areas were mapped using time-series PROBA-V NDVI by stacking time-series imagery and generating the phenological parameter imagery. Three classification algorithms were used: maximum likelihood classification (MLC), spectral angle mapper (SAM), and minimum distance classification (Min). The results reveal that the overall accuracy of mapping using time-series imagery outweighed phenological parameter imagery, although both datasets performed very well in mapping peanut crops. MLC application in the time-series imagery dataset produced the best result, i.e. overall accuracy of 92.75%, with producer and user accuracy of each class ≥ 78.79%. Specifically for peanut crops, all the algorithms tested produced satisfactory results (≥75.95% of producer and user accuracy), except for the producer accuracy of Min algorithm. Overall, PROBA-V imagery can provide satisfactory results in mapping peanut crops in the study area. For the second objective, the effects of climate change in the potential future geographic distribution of peanut crops in Australia for 2030, 2050, 2070, and 2100 were studied using the CLIMEX program (a Species Distribution Model) under Global Climate Models (GCMs) of CSIRO-Mk3.0 and MIROC-H. The results show an increase in unsuitable areas for peanut cultivation in Australia throughout the projection years for the two GCMs. However, the CSIRO-Mk3 projection of unsuitable areas for 2100 was higher (76% of Australian land) than MIROC-H projection (48% of Australian land). Both GCMs agreed that some current peanut cultivation areas will become unsuitable in the future, while only limited areas will still remain suitable for peanut cultivation. The present study confirms the effects of climate change on the suitability of peanut growing areas in the future. In the third objective, the impacts of climate change on future aflatoxin distribution in Australia and the high risk areas of aflatoxin incidence in the projected future distribution of peanut crops were examined. The projected future distribution of aflatoxin for 2030, 2050, 2070, and 2100 was also modelled using CLIMEX under CSIRO-Mk3.0 and MIROC-H GCMs. The results demonstrated that only a small portion of the Australian continent will be optimal/suitable for aflatoxin persistence, due to the incidence of heat and dry stresses. The map overlay results between the future projections of aflatoxin and peanut crops resulted in small areas of low aflatoxin risk in the future projected areas of peanut crops. It is projected that most of the current peanut cultivation areas will have a high aflatoxin risk, while others will no longer be favourable for peanut cultivation in the future. This study has clearly demonstrated the ability of PROBA-V satellite imagery in mapping peanut crops. It has also demonstrated that climate change incidence will affect the suitability areas of future geographical distribution of peanut crops and the associated aflatoxin disease. This study provides strategic information on current peanut growing areas, future suitable areas for peanut crops in Australia, and future high risk areas of aflatoxin incidence. This information will provide valuable contributions to the long-term planning of peanut cultivation in the country

Satellite remote sensing for ice sheet research

Potential research applications of satellite data over the terrestrial ice sheets of Greenland and Antarctica are assessed and actions required to ensure acquisition of relevant data and appropriate processing to a form suitable for research purposes are recommended. Relevant data include high-resolution visible and SAR imagery, infrared, passive-microwave and scatterometer measurements, and surface topography information from laser and radar altimeters