Storylines: an alternative approach to representing uncertainty in physical aspects of climate change

Investigating the relationships between climate extremes and crop yield can help us understand how unfavourable climatic conditions affect crop production. In this study, two statistical models, multiple linear regression and random forest, were used to identify rainfall extremes indices affecting wheat yield in three different regions of the New South Wales wheat belt. The results show that the random forest model explained 41–67% of the year-to-year yield variation, whereas the multiple linear regression model explained 34–58%. In the two models, 3-month timescale standardized precipitation index of Jun.–Aug. (SPI_JJA), Sep.–Nov. (SPI_SON), and consecutive dry days (CDDs) were identified as the three most important indices which can explain yield variability for most of the wheat belt. Our results indicated that the inter-annual variability of rainfall in winter and spring was largely responsible for wheat yield variation, and pre-growing season rainfall played a secondary role. Frequent shortages of rainfall posed a greater threat to crop growth than excessive rainfall in eastern Australia. We concluded that the comparison between multiple linear regression and machine learning algorithm proposed in the present study would be useful to provide robust prediction of yields and new insights of the effects of various rainfall extremes, when suitable climate and yield datasets are available.     

A spatial and statistical analysis of the impact of transformation of raster cost surfaces on the variation of least-cost paths

Planners who are involved in locational decision-making often use raster-based geographic information systems to quantify the value of land in terms of suitability or cost for a certain use. From a computational point of view, this process can be seen as a transformation of one or more sets of values associated with a grid of cells into another set of such values through a function reflecting one or more criteria. While it is generally anticipated that different transformations lead to different ‘best’ locations, little has been known on how such differences arise (or do not arise). The paper attempts to answer this question in the context of path planning through a series of computational experiments using a number of random landscape grids with a variety of spatial and nonspatial structures. In the experiments, we generated least-cost paths on a number of cost grids transformed from the landscape grids using a variety of transformation parameters and analyzed the locations and (weighted) lengths of those paths. Results show that the same pair of terminal cells may well be connected by different least-cost paths on different cost grids though derived from the same landscape grid and that the variation among those paths is affected by how given values are distributed in the landscape grid as well as by how derived values are distributed in the cost grids. Most significantly, the variation tends to be smaller when the landscape grid contains more distinct patches of cells potentially attracting or distracting cost-saving passage or when the cost grid contains a smaller number of low-cost cells.     

Rapid transport of anthropogenic lead through soils in southeast Missouri

To investigate Pb transport and cycling, soils from the forest floor and cores from White Oaks (Quercus alba L.) were collected near a Pb smelter in SE Missouri at varying depths from the surface and varying distances. Lead concentrations in soil samples at the surface drop dramatically with distance from approximately 1500 mg/kg at less than 2 km from the smelter to around 100 mg/kg at localities greater than 2 km from the smelter. Lead contents in tree rings are below 0.5 mg/kg in samples dated prior to 1970, and rapidly increase in 1975–1990 samples. Isotopic compositions of soils and tree rings exhibit systematic variations of Pb isotopic compositions with depth and tree ring age. Distinguishable isotopic signatures for Pb sources allowed quantification of the contribution of smelter Pb to the soils. At depths where Pb concentrations decreased and approached constant values (10–25 cm, 10–30 mg/kg), 50–90%, 40–50% and 10–50% of the Pb could be derived from the smelter for the samples at locations less than 2, 2–4 and over 4 km from the smelter, respectively. The remaining portion was attributable to automobile emission and bedrock sources. Because the smelter operated from 1963 to 2003 and samples were collected in 1999, it is estimated that smelter Pb infiltrates at rates of 1 cm/yr (30 cm in 30 yr). At distances less than 1.5 km from the smelter, even though Pb concentrations become asymptotic at a depth of 30 cm, isotopic evidence suggests that Pb has migrated below this depth, presumably through exchange with naturally occurring Pb in the soil matrix. This implies that soils heavily polluted by Pb can exceed their Pb carrying capacity, which could have potential impacts on shallow groundwater systems and risk further exposure to human and ecological receptors.     

A hybrid genetic algorithm with local optimiser improves calibration of a vegetation change cellular automata model

Cellular automata (CA) models are commonly used to model vegetation dynamics, with the genetic algorithm (GA) being one method of calibration. This article investigates different GA settings, as well as the combination of a GA with a local optimiser to improve the calibration effort. The case study is a pattern-calibrated CA to model vegetation regrowth in central Victoria, Australia. We tested 16 GA models, varying population size, mutation rate, and level of allowable mutation. We also investigated the effect of applying a local optimiser, the Nelder?Mead Downhill Simplex (NMDS) at GA convergence. We found that using a decreasing mutation rate can reduce computational cost while avoiding premature GA convergence, while increasing population size does not make the GA more efficient. The hybrid GA-NMDS can also reduce computational cost compared to a GA alone, while also improving the calibration metric. We conclude that careful consideration of GA settings, including population size and mutation rate, and in particular the addition of a local optimiser, can positively impact the efficiency and success of the GA algorithm, which can in turn lead to improved simulations using a well-calibrated CA model.     

A Model for Understanding the Effects of Sediment Dynamics on Oyster Reef Development

Previous field studies have demonstrated that sedimentation is an important factor that can limit oyster reef growth and restoration success. High relief reefs are more productive and resilient than low relief reefs, in part, because increasing reef height reduces sedimentation and enhances oyster growth. In this study, we investigated the relationship between initial reef height and reef development using a simple model. The model contains three coupled differential equations that describe changes in oyster volume, shell volume, and sediment volume per unit area of reef with time. The model was used to investigate how parameters such as flow speed, sediment grain size, and food concentration affect reef survival and final reef height. Whether or not a reef survives depends primarily on the shape of the sediment concentration profile relative to the initial reef height. Over a long time period, three different steady-state reef heights are possible, depending on the environmental parameters and initial reef height: (1) If growth outpaces sedimentation, the reef achieves the maximum possible height, which is independent of sedimentation parameters; (2) if deposition outpaces growth and the shear stress does not exceed the critical shear stress, the reef is buried in sediment and dies; and (3) if deposition outpaces growth and the shear stress exceeds the critical shear stress, a reduced steady-state height is achieved that depends on both growth and sedimentation parameters. The model can be used to assess the ways in which measurable environmental parameters affect reef restoration success.     

User comments on climate stories: impacts of anecdotal vs. scientific evidence

Both statistical and dynamical downscaling methods are well established techniques to bridge the gap between the coarse information produced by global circulation models and the regional-to-local scales required by the climate change Impacts, Adaptation, and Vulnerability (IAV) communities. A number of studies have analyzed the relative merits of each technique by inter-comparing their performance in reproducing the observed climate, as given by a number of climatic indices (e.g. mean values, percentiles, spells). However, in this paper we stress that fair comparisons should be based on indices that are not affected by the calibration towards the observed climate used for some of the methods. We focus on precipitation (over continental Spain) and consider the output of eight Regional Climate Models (RCMs) from the EURO-CORDEX initiative at 0.44^° resolution and five Statistical Downscaling Methods (SDMs) —analog resampling, weather typing and generalized linear models— trained using the Spain044 observational gridded dataset on exactly the same RCM grid. The performance of these models is inter-compared in terms of several standard indices —mean precipitation, 90th percentile on wet days, maximum precipitation amount and maximum number of consecutive dry days— taking into account the parameters involved in the SDM training phase. It is shown, that not only the directly affected indices should be carefully analyzed, but also those indirectly influenced (e.g. percentile-based indices for precipitation) which are more difficult to identify. We also analyze how simple transformations (e.g. linear scaling) could be applied to the outputs of the uncalibrated methods in order to put SDMs and RCMs on equal footing, and thus perform a fairer comparison.     

The trophic ecology of key megafaunal species at the Pakistan Margin: Evidence from stable isotopes and lipid biomarkers

The Arabian Sea is subject to intense seasonality resulting from biannual monsoons, which lead to associated large particulate fluxes and an abundance of organic carbon, a potential food source at the seafloor for benthic detritivores. We used the stable isotopes of carbon and nitrogen alongside lipid analyses to examine potential food sources (particulate and sedimentary organic matter, POM and SOM respectively) in order to determine trophic linkages for the twelve most abundant megafaunal species (Pontocaris sp., Solenocera sp., Munidopsis aff. scobina, Actinoscyphia sp., Actinauge sp., Echinoptilum sp., Pennatula aff. grandis, Astropecten sp. Amphiura sp. Ophiura euryplax, Phormosoma placenta and Hyalinoecia sp.) at the Pakistan Margin between 140 and 1400 m water depth. This transect spans a steep gradient in oxygen concentrations and POM flux. Ranges of δ¹³C and δ¹⁵N values were narrow in POM and SOM (4‰ and 2‰ for δ¹³C and δ¹⁵N, respectively) with little evidence of temporal variability. Labile lipid compounds in SOM originating from phytoplankton did exhibit seasonal change in their concentrations at the shallowest sites, 140 and 300 m. Benthic megafauna had broad ranges in δ¹³C and δ¹⁵N (>10‰ and >8‰ for δ¹³C and δ¹⁵N, respectively) suggesting they occupy several trophic levels and utilize a variety of food sources. There is evidence for feeding niche separation between and within trophic groups. Lipid biomarkers in animal tissues indicate a mixture of food sources originating from both phytoplankton (C_20:5(n-3) and C_22:6(n-3)) and invertebrate prey (C_20:1 and C_22:1). Biomarkers originating from phytodetritus are conserved through trophic transfer to the predator/scavengers. Six species (Pontocaris sp., Solenocera sp., Actinoscyphia sp., Echinoptilum sp., Amphiura sp. and Hyalinoecia sp.) showed a significant biochemical response to the seasonal supply of food and probably adapt their trophic strategy to low food availability. Biotransformation of assimilated lipids by megafauna is evident from polyunsaturated fatty acid distributions, for example, Echinoptilum sp. converts C_20:5(n-3) to C_24:6(n-3).     

The Norwegian system and the distribution of claims to redfeed

Identifying competing interests and potential claims to a new marine resource, the plankton redfeed, which may be central as a solution to diminishing supplies of marine oils world wide, is critical prior to a commercial harvest thereof. The competing interests are found to be between those Norwegian fisheries organization taking an encompassing view of fisheries interests and those wanting more focus on the well-being of the coastal fishermen. Working within a corporatist setting, it is found, that interest organizations in Norway are included comprehensively by the national government in the decision-making process, but that the main group having the most power is Norges Fiskarlag, which will draw the quota distribution of a future redfeed harvest off shore and away from the coastal fishermen.