Range Of Climate Change Scenarios Research Articles

Amphibians at risk: Effects of climate change in the southwestern North American drylands

Climate change can affect species' geographical range in several ways, leading to species' geographical range expansion and contraction. Amphibians are the most threatened vertebrate class, and deterioration is due, in part, to the loss of suitable habitat. This taxonomic group is especially sensitive because of their biphasic life cycles (aquatic and terrestrial) and microhabitat requirements. Herein, we propose a combined approach, which uses biological and life history information to understand species' sensitivity to climate change. The goals of our study were to (1) identify the contribution of environmental variables to the broad-scale distribution of amphibians in drylands of the southwestern United States and Mexico, (2) describe how the habitat suitability of amphibians can potentially respond to a range of climate change scenarios across drylands, and (3) identify amphibian traits related to increased vulnerability and adaptation to the specific pressures and mechanisms of climate change projected for the dryland areas. Results show that climate strongly influences the geographic distribution of amphibian diversity in drylands and that most amphibians are expected to lose habitat in the upcoming decades. This study also shows that areas with the greatest loss of high habitat suitability are related to temperature increases and precipitation reductions. Our results build a framework that will significantly improve the current state of knowledge on the drylands' biogeography while providing comprehensive information and analyses for decision-makers to conserve and protect endangered and threatened amphibian species and their habitats.

Heterogeneity, climate change, and crop yield distributions: Solvency implications for publicly subsidized crop insurance programs

AbstractClimate change continues to fuel concern about the future cost of publicly subsidized crop insurance programs in developed nations. These changes in climate are expected to alter the upper and lower tails of crop yield distributions differently. This may best be captured by modeling the climate–yield relationship heterogeneously across different parts of the yield distribution. To this end, we consider a mixture model with the parameters expressed as nonparametric functions (to capture any nonlinearities) of weather variables estimated by machine learning methods (neural net). By doing so, we are able to identify possibly heterogeneous effects of climate change on each component, the mixing probabilities, and thus all moments of the yield distribution. We find changing climate alters, quite significantly, the entire shape of the yield distribution. The overall probability of the lower tail tends to increase as temperatures rise, to the point where some yield distributions become positively skewed. Across a range of climate change scenarios, premium rates for fixed guarantees are expected to rise 20–66% relative to no climate change by 2040. However, if we allow the yield guarantees to also fall because of additional losses from climate change, premium rates (albeit not comparable given yield guarantees are different) increase notably less (6–14%), suggesting less solvency issues than first thought.

Multivariate logarithmic modeling of grain production in the Yangtze River Basin: incorporating extreme weather factors.

Abstract Whilst essential to the nutrition of societies, grain crops are demonstrated to be largely susceptible to the influence of anthropological climate change and extreme weather. However, few previous attempts at modeling grain yield took thorough consideration about the potential impact of extreme temperature events (ETEs) on average (or per-hectare) grain yield. From historical data in a Chinese agriculture hub, namely the Middle-Lower Yangtze Plains (MLYP) region, through a 2-step, nested OLS-FGLS multivariate log-log regression model, this study underscored the strong, sustained and significant negative influence ETEs had on grain production in the last 32 years in MLYP provinces of Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei, and Hunan; supported the literature with further evidence of global warming reducing crop productivity; and corroborated previous studies highlighting a reduction in crop productivity sourced from inefficient distribution and management of labor in the context of technological advancements. Climate-model-based provincial predictions through Shared Socioeconomic Pathways (SSPs) indicate a strong need for agricultural workers and scientists to address the increasing threat of future heat and cold stress through both micro-level (such as genomics-assisted breeding) and macro-level (such as AI-mediated farm management tools), in order for them to be prepared for a wide range of climate change scenarios.

Disentangling the effects of management and climate change on habitat suitability for saproxylic species in boreal forests

Forest degradation induced by intensive forest management and temperature increase by climate change are resulting in biodiversity decline in boreal forests. Intensive forest management and high-end climate emission scenarios can further reduce the amount and diversity of deadwood, the limiting factor for habitats for saproxylic species in European boreal forests. The magnitude of their combined effects and how changes in forest management can affect deadwood diversity under a range of climate change scenarios are poorly understood. We used forest growth simulations to evaluate how forest management and climate change will individually and jointly affect habitats of red-listed saproxylic species in Finland. We simulated seven forest management regimes and three climate scenarios (reference, RCP4.5 and RCP8.5) over 100 years. Management regimes included set aside, continuous cover forestry, business-as-usual (BAU) and four modifications of BAU. Habitat suitability was assessed using a species-specific habitat suitability index, including 21 fungal and invertebrate species groups. “Winner” and “loser” species were identified based on the modelled impacts of forest management and climate change on their habitat suitability. We found that forest management had a major impact on habitat suitability of saproxylic species compared to climate change. Habitat suitability index varied by over 250% among management regimes, while overall change in habitat suitability index caused by climate change was on average only 2%. More species groups were identified as winners than losers from impacts of climate change (52%–95% were winners, depending on the climate change scenario and management regime). The largest increase in habitat suitability index was achieved under set aside (254%) and the climate scenario RCP8.5 (> 2%), while continuous cover forestry was the most suitable regime to increase habitat suitability of saproxylic species (up to + 11%) across all climate change scenarios. Our results show that close-to-nature management regimes (e.g., continuous cover forestry and set aside) can increase the habitat suitability of many saproxylic boreal species more than the basic business-as-usual regime. This suggests that biodiversity loss of many saproxylic species in boreal forests can be mitigated through improved forest management practices, even as climate change progresses.

Predicting the distribution of Ixodes ricinus and Dermacentor reticulatus in Europe: a comparison of climate niche modelling approaches

BackgroundThe ticks Ixodes ricinus and Dermacentor reticulatus are two of the most important vectors in Europe. Climate niche modelling has been used in many studies to attempt to explain their distribution and to predict changes under a range of climate change scenarios. The aim of this study was to assess the ability of different climate niche modelling approaches to explain the known distribution of I. ricinus and D. reticulatus in Europe.MethodsA series of climate niche models, using different combinations of input data, were constructed and assessed. Species occurrence records obtained from systematic literature searches and Global Biodiversity Information Facility data were thinned to different degrees to remove sampling spatial bias. Four sources of climate data were used: bioclimatic variables, WorldClim, TerraClimate and MODIS satellite-derived data. Eight different model training extents were examined and three modelling frameworks were used: maximum entropy, generalised additive models and random forest models. The results were validated through internal cross-validation, comparison with an external independent dataset and expert opinion.ResultsThe performance metrics and predictive ability of the different modelling approaches varied significantly within and between each species. Different combinations were better able to define the distribution of each of the two species. However, no single approach was considered fully able to capture the known distribution of the species. When considering the mean of the performance metrics of internal and external validation, 24 models for I. ricinus and 11 models for D. reticulatus of the 96 constructed were considered adequate according to the following criteria: area under the receiver-operating characteristic curve > 0.7; true skill statistic > 0.4; Miller’s calibration slope 0.25 above or below 1; Boyce index > 0.9; omission rate < 0.15.ConclusionsThis comprehensive analysis suggests that there is no single ‘best practice’ climate modelling approach to account for the distribution of these tick species. This has important implications for attempts to predict climate-mediated impacts on future tick distribution. It is suggested here that climate variables alone are not sufficient; habitat type, host availability and anthropogenic impacts, not included in current modelling approaches, could contribute to determining tick presence or absence at the local or regional scale.Graphical abstract

The Social Cost of Carbon When We Wish for Full-Path Robustness

We compute the social cost of carbon (SCC) when decision makers want robust estimates in the face of deep (or “Knightian”) uncertainty. We introduce the notion of full-path accumulated robust preferences from stochastic control theory to an integrated assessment model. Robust preferences are appropriate for analyzing climate-related problems because, given the large uncertainty in climate science, they enable decision makers to attain solutions that are robust to a wide range of climate change scenarios. We solve the resulting model, which includes uncertainty about climate change and the ensuing economic damage and show the existence of optimal solutions and time-consistent optimal deterministic Markov policies. Additionally, we also prove that the standard Hansen–Sargent recursive utility provides an upper bound of this full-path utility. In our baseline model specification, we find that the year 2020’s optimal SCC is US$162 per tCO2 with an average annual growth rate of 2.5%, setting the world on a 1.37°C path, which requires full decarbonization by 2068. We introduce the notion of the SCC robustness premium, which we define as the additional SCC price tag for robustness. For a plausible range of preference parameters, the SCC robustness premium in 2020 is between US$1.41 and US$25.89 per tCO2 with US$2.20 per tCO2 in our baseline calibration. Over time, this premium grows significantly. The forecasts of our model facilitate managerial decision making during the world’s transition from a carbon- and emission-intensive economy to a regenerative economy. The high estimates for the SCC predict drastic rises in emission costs for high-emission industries. This paper was accepted by Elke Weber, Special Section of Management Science on Business and Climate Change. Funding: Y. Zhao and T. S. Lontzek acknowledge funding by the German Ministry of Research and Education (BMBF), Project CDR: “DAC-Tales”, under [Grant 01LS2196A]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4736 .

Improving statistical projections of ocean dynamic sea-level change using pattern recognition techniques

Abstract. Regional emulation tools based on statistical relationships, such as pattern scaling, provide a computationally inexpensive way of projecting ocean dynamic sea-level change for a broad range of climate change scenarios. Such approaches usually require a careful selection of one or more predictor variables of climate change so that the statistical model is properly optimized. Even when appropriate predictors have been selected, spatiotemporal oscillations driven by internal climate variability can be a large source of statistical model error. Using pattern recognition techniques that exploit spatial covariance information can effectively reduce internal variability in simulations of ocean dynamic sea level, significantly reducing random errors in regional emulation tools. Here, we test two pattern recognition methods based on empirical orthogonal functions (EOFs), namely signal-to-noise maximizing EOF pattern filtering and low-frequency component analysis, for their ability to reduce errors in pattern scaling of ocean dynamic sea-level change. We use the Max Planck Institute Grand Ensemble (MPI-GE) as a test bed for both methods, as it is a type of initial-condition large ensemble designed for an optimal characterization of the externally forced response. We show that the two methods tested here more efficiently reduce errors than conventional approaches such as a simple ensemble average. For instance, filtering only two realizations by characterizing their common response to external forcing reduces the random error by almost 60 %, a reduction that is only achieved by averaging at least 12 realizations. We further investigate the applicability of both methods to single-realization modeling experiments, including four CMIP5 simulations for comparison with previous regional emulation analyses. Pattern filtering leads to a varying degree of error reduction depending on the model and scenario, ranging from more than 20 % to about 70 % reduction in global-mean root mean squared error compared with unfiltered simulations. Our results highlight the relevance of pattern recognition methods as a tool to reduce errors in regional emulation tools of ocean dynamic sea-level change, especially when one or only a few realizations are available. Removing internal variability prior to tuning regional emulation tools can optimize the performance of the statistical model, leading to substantial differences in emulated dynamic sea level compared to unfiltered simulations.

Potential Utility of a Climate-Sensitive Structural Stand Density Management Model for Red Pine Crop Planning

The objectives of this study were to evaluate and exemplify the potential utility of a climate-sensitive modular-based structural stand density management model (SSDMM) developed for red pine (Pinus resinosa Aiton) in crop planning decision making. Firstly, the model’s predictive ability was assessed using a retrospective validation approach without consideration of climate change effects. Although limited in scope and applicability, the preliminary results revealed that the magnitude of the mean prediction error for the principal determinates governing stand development did not exceed ±15%. Secondly, the potential utility of the model was illustrated within a spatial-based forest management planning context for a range of climate change scenarios. These exemplifications included three conventional crop plan simulations (initial spacing (IS), IS plus one commercial thinning (CT) treatment, and IS plus two CTs) developing under three climate change scenarios (1971–2000 climate norms, and 4.5 and 8.5 representative concentration pathways) over 75-year rotations (2022–2097) at three geographically diverse locales (north-eastern (Kirkland Lake), north-central (Thessalon), and north-western (Thunder Bay) Ontario, Canada). Resultant developmental indices and (or) productivity metrics were contrasted in terms of (1) regional-specific differences in temporal stand dynamical patterns and rotational yields with increasing climatic change severity, and (2) silvicultural effectiveness of the crop plans within and across locales for each climate change scenario. Climate-wise, although the results revealed marginal regional differences across a multitude of rotational outcome metrics, declines in mean tree size and merchantable volume productivity, and most importantly utility pole production within unthinned plantations, were among the most consequential and consistent negative outcomes associated with climate-induced site productivity declines. Silviculturally, crop plans that included thinning treatments relative to their counterparts that did not, yielded trees of greater mean size and were able to maintain utility pole production status while not achieving similar levels of site occupancy or volumetric productivity. Management-wise, maintenance of pole production status along with concurrent increases in fiscal worth even in light of climate change outweighed the marginal decline in volumetric productivity that was associated with the thinning regimes. In summary, the validation results provided a measure of predictive performance relative to the underlying calibration data set whereas the exemplifications illustrated the model’s potential operational utility in spatial-based forest management planning. For managers aspiring to maintain the historical productivity legacy of red pine through optimal density management decision making while acknowledging prediction uncertainty when forecasting stand development trajectories under climate change, the SSDMM provides an optional decision-support tool for designing climate-smart crop plans during the Anthropocene.

Feasibility of enhancing carbon sequestration and stock capacity in temperate and boreal European forests via changes to management regimes

Forest management practices might act as nature-based methods to remove CO2 from the atmosphere and slow anthropogenic climate change and thus support an EU forest-based climate change mitigation strategy. However, the extent to which diversified management actions could lead to quantitatively important changes in carbon sequestration and stocking capacity at the tree level remains to be thoroughly assessed. To that end, we used a state-of-the-science bio-geochemically based forest growth model to simulate effects of multiple forest management scenarios on net primary productivity (NPP) and potential carbon woody stocks (pCWS) under twenty scenarios of climate change in a suite of observed and virtual forest stands in temperate and boreal European forests. Previous modelling experiments indicated that the capacity of forests to assimilate and store atmospheric CO2 in woody biomass is already being attained under business-as-usual forest management practices across a range of climate change scenarios. Nevertheless, we find that on the long-term, with increasing atmospheric CO2 concentration and warming, managed forests show both higher productivity capacity and a larger potential pool size of stored carbon than unmanaged forests as long as thinning and tree harvesting are of moderate intensity.

Can NDVI identify drought refugia for mammals and birds in mesic landscapes?

Refugia within landscapes are increasingly important as climate change intensifies, yet identifying refugia, and how they respond to climatic perturbations remains understudied. We use Normalized Difference Vegetation Index (NDVI) developed during extreme drought to identify drought refugia. We then utilise camera trapping to understand the ecological role and importance of these refugia under fluctuating rainfall conditions. Ground foraging mammals and birds were surveyed annually from 2016 to 2019 whereby 171 remote-sensing cameras were deployed in the southern section of the Grampians, Australia. NDVI values were calculated during Australia's millennium drought, allowing the assessment of how NDVI calculated during extreme drought predicts drought refugia and the response of biodiversity to NDVI under rainfall fluctuations. Site occupancy of bird and mammal assemblages were dependent on NDVI, with areas of high NDVI during drought exhibiting characteristics consistent with refugia. Rainfall pulses increased site occupancy at all sites with colonisation probability initially associated with higher NDVI sites. Extinction probabilities were greatest at low NDVI sites when rainfall declined. Within mesic systems, remotely sensed NDVI can identify areas of the landscape that act as drought refugia enabling landscape management to prioritise species conservation within these areas. The protection and persistence of refugia is crucial in ensuring landscapes and their species communities therein are resilient to a range of climate change scenarios.

Genomic data reveal local endemism in Southern California Rubber Boas (Serpentes: Boidae, Charina) and the critical need for enhanced conservation actions

The mountains of southern California represent unique, isolated ecosystems that support distinct high-elevation habitats found nowhere else in the area. Analyses of several moisture-dependent species across these sky-islands indicate they exist as locally endemic lineages that occur across these fragmented mountains ranges. The Rubber Boa is a semi-fossorial snake species that is widely distributed in the cooler and more moist ecoregions regions of western North America, including isolated populations across southern California mountain ranges. We developed a genomic and ecological dataset to examine genetic diversity within Rubber Boas and to determine if the endemic Southern Rubber Boa represents a distinct lineage. We quantified current and future habitat suitability under a range of climate change scenarios, and discuss the possible environmental threats facing these unique montane isolates. Our results support four major lineages within Rubber Boas, with genetic breaks that are consistent with biogeographic boundaries observed in other co-distributed, cool-temperature, moisture adapted species. Our data support previous studies that the Southern Rubber Boa is an independent evolutionary unit and now includes multiple locally endemic sky-island populations, restricted to isolated mountain tops and ranges across southern California. Analyses of future habitat suitability indicate that many of these sky-island populations will lose most of their suitable habitat over the next 70 years given predicted increases in drought, rising temperatures, and wildfires. Collectively these data emphasize the critical conservation needs of these montane ecosystems in southern California under current and projected climate change conditions.

Effects of Climate Change on Natural-Caused Fire Activity in Western U.S. National Forests

Climate change, with warming temperatures and shifting precipitation patterns, may increase natural-caused forest fire activity. Increasing natural-caused fires throughout western United States national forests could place people, property, and infrastructure at risk in the future. We used the fine K nearest neighbor (KNN) method coupled with the downscaled Multivariate Adaptive Constructed Analogs (MACA) climate dataset to estimate changes in the rate of natural-caused fires in western United States national forests. We projected changes in the rate of minor and major forest fires from historical (1986–2015) to future (2070–2099) conditions to characterize fire-prone national forests under a range of climate change scenarios. The results indicate that climate change can add to the occurrence of forest fires in western United States national forests, particularly in Rocky Mountain, Pacific Southwest, and Southwestern United States Forest Service regions. Although summer months are projected to have the highest rate of natural-caused forest fire activity in the future, the rate of natural-caused forest fires is likely to increase from August to December in the future compared to the historical conditions. Improved understanding of altered forest fire regimes can help forest managers to better understand the potential effects of climate change on future fire activity and implement actions to attenuate possible negative consequences.

Habitat use as indicator of adaptive capacity to climate change

AbstractAimPopulations of cold‐adapted species at the trailing edges of geographic ranges are particularly vulnerable to the negative effects of climate change from the combination of exposure to warm temperatures and high sensitivity to heat. Many of these species are predicted to decline under future climate scenarios, but they could persist if they can adapt to warming climates either physiologically or behaviourally. We aim to understand local variation in contemporary habitat use and use this information to identify signs of adaptive capacity. We focus on moose (Alces alces), a charismatic species of conservation and public interest.LocationThe northeastern United States, along the trailing edge of the moose geographic range in North America.MethodsWe compiled data on occurrences and habitat use of moose from remote cameras and GPS collars across the northeastern United States. We use these data to build habitat suitability models at local and regional spatial scales and then to predict future habitat suitability under climate change. We also use fine‐scale GPS data to model relationships between habitat use and temperature on a daily temporal scale and to predict future habitat use.ResultsWe find that habitat suitability for moose will decline under a range of climate change scenarios. However, moose across the region differ in their use of climatic and habitat space, indicating that they could exhibit adaptive capacity. We also find evidence for behavioural responses to weather, where moose increase their use of forested wetland habitats in warmer places and/or times.Main conclusionsOur results suggest that there will be significant shifts in moose distribution due to climate change. However, if there is spatial variation in thermal tolerance, trailing‐edge populations could adapt to climate change. We highlight that prioritizing certain habitats for conservation (i.e., thermal refuges) could be crucial for this adaptation.

Simulating future trends in stormwater runoff at a university campus related to climate and land use changes

Stormwater management is essential for the management of sewerage and stormwater systems as insufficient drainage can result in flood damage and impact public health. During the planning of new infrastructure at Campus I of the Royal University of Phnom Penh, it was observed that a hydrological assessment had not yet been completed. Without such an assessment, climate change and poor land-use planning may result in more frequent flood events in the future. Thus, this study aims to quantify the potential impacts of changes to stormwater runoff related to climate change and an increase in the area of impervious surfaces on campus. PCSWMM modeling software was employed to simulate surface runoff volumes flowing to the catchment of the Building D pond. Three different scenarios for rainfall intensity (2, 5, and 10-year return period) and five scenarios for changes to impervious area (10%, 30%, 50%, 70% and 100%) were used in the simulation. A baseline was also established by analysing rainfall intensity, land use characteristics, and topography in the catchment area. Under present conditions the volume of the Building D pond is about 18,933 m3 with a surface area of 6,173 m2. The average permanent water volume of the pond is about 9,916 m3, with a remaining storage capacity of 9,016 m3. It is estimated that the pond will flood within one hour of intense rainfall (63.70 mm/h) under these conditions. The surface runoff volume for the Building D catchment was estimated for a range of scenarios. It was found that even under intense rainfall (63.70 mm/h); the pond would not flood if measures were implemented to reduce the impervious area on campus by 10%. This shows that preserving permeable surfaces in the catchment is likely to reduce the frequency of flood events on campus over a range of climate change scenarios.

Distributional responses to climate change for alpine species of Cyananthus and Primula endemic to the Himalaya-Hengduan Mountains

Global warming increases the vulnerability of plants, especially alpine herbaceous species, to local extinction. In this study, we collected species distribution information from herbarium specimens for ten selected Cyananthus and Primula alpine species endemic to the Himalaya-Hengduan Mountains (HHM). Combined with climate data from WorldClim, we used Maximum Entropy Modeling (MaxEnt) to project distributional changes from the current time period to 2070. Our predictions indicate that, under a wide range of climate change scenarios, the distributions of all species will shift upward in elevation and northward in latitude; furthermore, under these scenarios, species will expand the size of their range. For the majority of the species in this study, habitats are available to mitigate upward and northward shifts that are projected to be induced by changing climate. If current climate projections, however, increase in magnitude or continue to increase past our projection dates, suitable habitat for future occupation by alpine species will be limited as we predict range contraction or less range expansion for some of the species under more intensified climate scenarios. Our study not only underscores the value of herbarium source information for future climate model projections but also suggests that future studies on the effects of climate change on alpine species should include additional biotic and abiotic factors to provide greater resolution of the local dynamics associated with species persistence under a warming climate.

Climate Change, Crop Selection and Agricultural Revenue in Ghana: A Structural Ricardian Analysis

AbstractWe apply a Structural Ricardian Model (SRM) to farm‐level data from Ghana for seven principal food crops in order to model the factors which influence farmers' decisions about which food crops to grow and the revenue conditional on these choices. Our application of the SRM incorporates a flexible functional form to allow for the possibility that the effects of temperature and rainfall may not be linearly separable. We use this model as a basis for simulations of the effects of climate change. These simulations suggest that extreme climate change will lead to a significant reduction in the average net revenue per hectare from maize, which accounts for over half of current food production. Across a range of climate change scenarios, there is also substantial substitution of maize for heat‐tolerant millet, and a reduction in the cultivation of other crops. Under most of the climate change scenarios that we consider, these results imply a substantial reduction in the aggregate value of agricultural production, since millet is the lowest‐value crop.

Forecasting oak decline caused by Phytophthora cinnamomi in Andalusia: Identification of priority areas for intervention

Abstract Since the mid-20th century, trees in the Andalusian oak dehesa and forests have exhibited stress that often ends in the death of the tree. These events have been associated with Phytophthora cinnamomi, a soil-borne root pathogen, which causes root rot, bark cankers, decay and mortality – known as oak decline. Phytophthora cinnamomi is most virulent under high ambient temperatures combined with moist soils, i.e., in Mediterranean areas. We used presence/absence point locations of the Andalusian Network for Damage Monitoring in Forest Ecosystems (RED SEDA) pathogen survey and four categories of environmental variables – meteorological, edaphic, topographic and tree cover – to accurately predict Phytophthora cinnamomi current and future potential distribution within Andalusia, for a range of climate change scenarios, using ensemble species distribution models (SDMs). We assessed which categories of environmental variables explained the distribution of the pathogen, obtained accurate predictions for the current potential distribution of Phytophthora cinnamomi (AUC > 0.95, TSS > 0.70, Kappa > 0.65) and forecasted its future potential distribution. Subsequently, we classified the sites of the pathogen survey within the RED SEDA network in three zones according to the already-recorded presence of the pathogen and the current and future predicted probability of occurrence. Finally, we suggested phytosanitary management strategies for each zone.

Identifying trends and associated uncertainties in potential rice production under climate change in Mediterranean areas

The future of global rice productions in top producing countries is undermined by the impact of climate change threatening food security in the near future. In those European Mediterranean areas where rice is cultivated, this peculiar cropping system plays a crucial role in terms of sociocultural and ecological issues, and the climate change impact is still scarcely investigated. In this study, we explored the future trends of potential rice yields in the region considering the multiple sources of uncertainty associated with climate and yield predictions. Two rice crop models (STICS and WARM) were calibrated using 20 field experiments carried out in two main European rice producing areas − i.e., the Italian Lomellina and the French Camargue. These models were then applied under a range of climate change scenarios in 2030 and 2070 time frames, considering projections from the combination of four General Circulation Models and two extreme Representative CO2 Concentration Pathways (RCP 2.6 and 8.5). We compared the simulated yield levels with no adaptation, and designed adaptation strategies based on the anticipation of sowing date and the adoption of varieties with longer crop cycle. Our results showed that with no adaptation yields would decrease on average by 8% in 2030 and 12% in 2070 in Camargue and Lomellina. Future simulated yields in the two areas were lower than in the baseline in 67% (Camargue) and 84% (Lomellina) of the cases. The implementation of both adaptation strategies proved to be effective in reversing the situation, leading to an average yield increase of 28% and 25% in 2030 and 2070, respectively. The associated probability of lower yields than in current conditions was 24% in the two sites. Despite the uncertainty in predictions, mainly related to site, GCM and RCP, our findings indicate that the European rice sector has the potential to enhance current production levels in a changing climate, if longer cycle varieties will be grown in Mediterranean rice areas.

An Earth's Future Special Collection: Impacts of the coastal dynamics of sea level rise on low‐gradient coastal landscapes

AbstractRising sea level represents a significant threat to coastal communities and ecosystems, including altered habitats and increased vulnerability to coastal storms and recurrent inundation. This threat is exemplified in the northern Gulf of Mexico, where low topography, marshes, and a prevalence of tropical storms have resulted in extensive coastal impacts. The ability to facilitate adaptation and mitigation measures relies, in part, on the development of robust predictive capabilities that incorporate complex biological processes with physical dynamics. Initiated in 2010, the 6‐year Ecological Effects of Sea Level Rise—Northern Gulf of Mexico project applied a transdisciplinary science approach to develop a suite of integrated modeling platforms informed by empirical data that are capable of evaluating a range of climate change scenarios. This special issue highlights resultant integrated models focused on tidal hydrodynamics, shoreline morphology, oyster ecology, coastal wetland vulnerability, and storm surges that demonstrate the need for dynamic models to incorporate feedbacks among physical and biological processes in assessments of sea level rise effects on coastal systems. Effects are projected to be significant, spatially variable and nonlinear relative to sea level rise rates. Scenarios of higher sea level rise rates are projected to exceed thresholds of wetland sustainability, and many regions will experience enhanced storm surges. Influenced by an extensive collaborative stakeholder engagement process, these assessments on the coastal dynamics of sea level rise provide a strong foundation for resilience measures in the northern Gulf of Mexico and a transferable approach for application to other coastal regions throughout the world.

Modeling the Agricultural Water–Energy–Food Nexus in the Indus River Basin, Pakistan

AbstractNexus thinking is critical to jointly address growing water, energy, and food security challenges. This paper evaluates the water, energy, and food nexus (WEFN) in the Indus River of Pakistan using the Indus Basin Model Revised—Multi Year, a hydro-agro-economic model extended with an agricultural energy use module. Impacts of a range of climate change scenarios on the WEFN in the Indus Basin were modeled and then the potential of different alternative water allocation mechanisms and water infrastructure developments to address growing water, energy and food security concerns in the country were assessed. Results show growing water and energy use under hotter and wetter climate conditions. While more flexible surface water allocation policies can mitigate negative climate change impacts on agricultural water and energy use allowing for larger crop and hydropower production, such policies might also increase the inter-annual variability of resource use. Moreover, a more flexible surface water alloca...