How does humidity data impact land surface modeling of hydrothermal regimes at a permafrost site in Utqiaġvik, Alaska?

Humidity is a basic and crucial meteorological indicator commonly measured in several forms, including specific humidity, relative humidity, and absolute humidity. These different forms can be inter-derived based on the saturation vapor pressure (SVP). In past decades, dozens of formulae have been developed to calculate the SVP with respect to, and in equilibrium with, liquid water and solid ice surfaces, but many prior studies use a single function for all temperature ranges, without considering the distinction between over the liquid water and ice surfaces. These different approaches can result in humidity estimates that may impact our understanding of surface-subsurface thermal-hydrological dynamics in cold regions. In this study, we compared the relative humidity (RH) downloaded and calculated from four data sources in Alaska based on five commonly used SVP formulas. These RHs, along with other meteorological indicators, were then used to drive physics-rich land surface models at a permafrost-affected site. We found that higher values of RH (up to 40 %) were obtained if the SVP was calculated with the over-ice formulation when air temperatures were below freezing, which could lead to a 30 % maximum difference in snow depths. The choice of whether to separately calculate the SVP over an ice surface in winter also produced a significant range (up to 0.2 m) in simulated annual maximum thaw depths. The sensitivity of seasonal thaw depth to the formulation of SVP increases with the rainfall rate and the height of above-ground ponded water, while it diminishes with warmer air temperatures. These results show that RH variations based on the calculation of SVP with or without over-ice calculation meaningfully impact physically-based predictions of snow depth, sublimation, soil temperature, and active layer thickness. Under particular conditions, when severe flooding (inundation) and cool air temperatures are present, care should be taken to evaluate how humidity data is estimated for land surface and earth system modeling.

Integrating climate change adaptation policies in spatial development planning in hyperarid regions of Kerman province, Iran

In recent years, lifestyle changes and urbanization of societies, as well as macro-environmental changes, i.e. climate changes (CCs), have caused changes in the land spatial structure and the transfer of resources between different economic sectors of the land. The development of long-term spatial development plans (SDPs) needs to be compatible with CCs, especially in hyperarid areas with low supplies and high demands. In this research, machine learning methods; including Cellular Automata (CA), Random Forest (RF) and regression models through PLUS model were used to simulate the amount of supplies and demands based on land cover (LC) maps during the years 2000, 2010 and 2020 in the hyperarid areas of Kerman, Iran. Then, the best predicted model (Kappa = 0.94, overall accuracy = 0.98) was used to simulate changes in LC classes under climate change scenarios (CCSs) for 2050. The results showed the efficiency of machine learning in simulating land cover changes (LCCs) under CCSs. Findings revealed that SDPs of these areas are not compatible under any possible consideration of CCSs. The modeling results showed that spatial development plans under CCSs is not environmentally efficient and there is no compatibility between supplies, based on agricultural lands, and demands, based on increased population, by 2050. Overall, under the scenario of RCP 8.5, man-made, agriculture and natural LC classes with 106.9, 2.9, and 18.6% changes, respectively, showed the greatest changes compared to 2020. Population control, adjustment of infrastructures, and changes in LC plans can reduce socio-economical and socio-environmental problems in the future of hyperarid areas to some extent.

Bacteriological and eutrophication risk assessment of an Argentinian temperate shallow urban lagoon

The urban lagoons receive strong anthropic pressures and the tensions often coexist between the "urban" and the "natural," and this consequently generates pollution and risks to the environment and human health. Our main objective was to study the water quality and to assess the bacteriological and eutrophication risks in the temperate shallow urban lagoon of the Parque Unzué (Gualeguaychú, Argentina), and to predict these risks in climate change scenarios considering the temperature and the rains as indicators. This urban shallow lagoon is in a recreative multiuse park (Gualeguaychú city), in the floodplain of the Gualeguaychú river in the Center-East of Argentina (Neotropical region). Twenty-seven sampling in 3 sampling points (n = 81) were carried out during 2015-2019, and physicochemical and bacteriological parameters were measured. Phosphorus, organic matter, chlorophyll-a (Chl-a), and total coliforms (TC) frequently had a moderate and very high contamination factor (CF), and the pollution load index (PLI) indicated contamination with a frequency of 74.1 %. Moreover, the index (WQI) indicated poor (66.7 %) and good (33.3 %) water quality. Bacteriological and eutrophication predictive risk models showed an increase of the TC and the Chl-a concentration generating a current and future high risk of contamination of the lagoon under climate change scenarios that could generate ecosystemic function losses in the short-term.

An improved deep learning procedure for statistical downscaling of climate data

Recent climate change (CC) scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6) have just been released in coarse resolution. Deep learning (DL) based on statistical downscaling has recently been used, but more research is needed, particularly in arid regions, because little is known about their suitability for extrapolating future CC scenarios. Here we analyzed this issue by downscaling maximum, and minimum temperature over the Egyptian domain based on one General Circulation Model (GCM) as CanESM5 and two shared socioeconomic pathways (SSPs) as SSP4.5 and SSP8.5 from CMIP6 using Convolutional Neural Network (CNN) herein after called CNNSD. The downscaled maximum and minimum temperatures based CNNSD was able to reproduce the observed climate over historical and future periods at a finer resolution (0.1°), reducing the biases exhibited by the original scenario. To the best of our knowledge, this is the first time CNN has been used to downscale CMIP6 scenarios, particularly in arid regions. The downscaled analysis showed that maximum and minimum temperatures are expected to rise by 4.8 °C and 4.0 °C, respectively, in the future (2015-2100), compared to the historical period, under the moderate scenario (SSP4.5). Meanwhile, under the Fossil-fueled Development scenario (SSP8.5), these values will rise by 6.3 °C and 4.2 °C, respectively as analyzed by the CNNSD. The developed approach could be used not only in Egypt but also in other developing countries, which are especially vulnerable to climate change and has a scarcity of related research. The established downscaled approach's supply can be used to provide climate services, as a driver for impact studies and adaptation decisions, and as information for policy development. More research is needed, however, to include multi-GCMs to quantify the uncertainties between GCMs and SSPs, improving the outputs for use in climate change impacts and adaptations for food and nutrition security.

Hydrology and hydrological extremes under climate change scenarios in the Bosque watershed, North-Central Texas, USA

This study evaluates hydrology and hydrological extremes under future climate change scenarios. The climate change scenarios were developed from multiple Global Circulation Models (GCMs), Representative Concentration Pathway (RCP) scenarios, and statistical downscaling techniques. To ensure hydrological model robustness, the Soil Water Assessment Tool (SWAT) was calibrated and validated using the Differential Split Sample Test (DSST) approach. The model was also calibrated and validated at the multi-gauges of the watershed. Future climate change scenarios revealed a reduction in precipitation (in the order of -9.1% to 4.9%) and a consistent increase in maximum temperature (0.34°C to 4.10°C) and minimum temperature (-0.15 °C to 3.7°C) in different climate model simulations. The climate change scenarios triggered a reduction of surface runoff and streamflow and a moderate increase in evapotranspiration. Future climate change scenarios projected a decrease in high flow (Q5) and low flow (Q95). A higher reduction of Q5 and annual minimum flow is also simulated in future climate scenarios, whereas an increase in annual maximum flow is simulated in climate change scenarios developed from the RCP8.5 emission scenario. The study suggests optimal water management structures which can reduce the effect of change in high and low flows.

Climate change simulation and trend analysis of extreme precipitation and floods in the mesoscale Rur catchment in western Germany until 2099 using Statistical Downscaling Model (SDSM) and the Soil & Water Assessment Tool (SWAT model)

Due to climate change and global warming, speed and intensity of the hydrological cycle will accelerate. In order to carry out regional risk assessment, integrated water resources management and flood protection, far reaching predictions and future scenarios of climate change effects on extreme precipitation and flooding are of particular relevance. In this study, trends in frequencies of extreme precipitation and floods until 2099 are analysed for the German Rur catchment, which is half located in highlands and half in lowlands and therefore has a high topographical and climatological contrast. To predict future trends, coupled modeling is performed based on NCEP reanalysis data and a General Circulation Model (GCM). Assuming HadCM3 future emission scenarios A2a and B2a, an empirical Statistical Downscaling Model (SDSM) is developed and daily precipitation amounts are projected until 2099 by a stochastic weather generator. The generated precipitation data are used as an input for the ecohydrological Soil & Water Assessment Tool (SWAT model) to simulate daily water discharge until 2099. Statistical trend analyses are implemented based on three annual extreme precipitation indices (EPIs) and the magnitudes of ten flood return periods derived with GEV and Gumbel extreme value distributions for 109 30-year moving periods using regression analyses and Mann-Kendall tendency tests to check for significant trends in the frequencies until 2099. As a result, it could be demonstrated for all EPIs that the frequency of extreme precipitation in the upper Rur catchment will significantly increase by +33% to +51% until 2099 compared to the base period 1961-1990, whereas mostly non-significant negative trends of extreme precipitation can be projected in the lowlands. For runoff, it was found that the magnitudes of the ten flood return periods will significantly increase by +31% for B2a to +36% for A2a until 2099 compared to the base period.

Predicting soil erosion susceptibility associated with climate change scenarios in the Central Highlands of Sri Lanka

Soil erosion hazard is one of the prominent climate hazards that negatively impact countries' economies and livelihood. According to the global climate index, Sri Lanka is ranked among the first ten countries most threatened by climate change during the last three years (2018-2020). However, limited studies were conducted to simulate the impact of the soil erosion vulnerability based on climate scenarios. This study aims to assess and predict soil erosion susceptibility using climate change projected scenarios: Representative Concentration Pathways (RCP) in the Central Highlands of Sri Lanka. The potential of soil erosion susceptibility was predicted to 2040, depending on climate change scenarios, RCP 2.6 and RCP 8.5. Five models: revised universal soil loss (RUSLE), frequency ratio (FR), artificial neural networks (ANN), support vector machine (SVM) and adaptive network-based fuzzy inference system (ANFIS) were selected as widely applied for hazards assessments. Eight geo-environmental factors were selected as inputs to model the soil erosion susceptibility. Results of the five models demonstrate that soil erosion vulnerability (soil erosion rates) will increase 4%-22% compared to the current soil erosion rate (2020). The predictions indicate average soil erosion will increase to 10.50 t/ha/yr and 12.4 t/ha/yr under the RCP 2.6 and RCP 8.5 climate scenario in 2040, respectively. The ANFIS and SVM model predictions showed the highest accuracy (89%) on soil erosion susceptibility for this study area. The soil erosion susceptibility maps provide a good understanding of future soil erosion vulnerability (spatial distribution) and can be utilized to develop climate resilience.

Current distribution and voltinism of the brown marmorated stink bug, Halyomorpha halys, in Switzerland and its response to climate change using a high-resolution CLIMEX model

Climate change can alter the habitat suitability of invasive species and promote their establishment. The highly polyphagous brown marmorated stinkbug, Halyomorpha halys Stål (Hemiptera: Pentatomidae), is native to East Asia and invasive in Europe and North America, damaging a wide variety of fruit and vegetable crops. In Switzerland, crop damage and increasing populations have been observed since 2017 and related to increasing temperatures. We studied the climatic suitability, population growth, and the number of generations under present and future climate conditions for H. halys in Switzerland, using a modified version of the bioclimatic model package CLIMEX. To address the high topographic variability in Switzerland, model simulations were based on climate data of high spatial resolution (approx. 2 km), which significantly increased their explanatory power, and identified many more climatically suitable areas in comparison to previous models. The validation of the CLIMEX model using observational records collected in a citizen science initiative between 2004 and 2019 revealed that more than 15 years after its accidental introduction, H. halys has colonised nearly all bioclimatic suitable areas in Switzerland and there is limited potential for range expansion into new areas under present climate conditions. Simulations with climate change scenarios suggest an extensive range expansion into higher altitudes, an increase in generations per year, an earlier start of H. halys activity in spring and a prolonged period for nymphs to complete development in autumn. A permanent shift from one to two generations per year and the associated population growth of H. halys may result in increasing crop damages in Switzerland. These results highlight the need for monitoring the spread and population development in the north-western part of Switzerland and higher altitudes of the valleys of the south.

Dynamics of solute/matric stress interactions with climate change abiotic factors on growth, gene expression and ochratoxin A production by Penicillium verrucosum on a wheat-based matrix

Penicillium verrucosum contaminates temperate cereals with ochratoxin A (OTA) during harvesting and storage. We examined the effect of temperature (25 vs 30 ^oC), CO₂ (400 vs 1000 ppm) and matric/solute stress (-2.8 vs -7.0 MPa) on (i) growth, (ii) key OTA biosynthetic genes and (iii) OTA production on a milled wheat substrate. Growth was generally faster under matric than solute stress at 25 ^oC, regardless of CO₂ concentrations. At 30 ^oC, growth of P. verrucosum was significantly reduced under solute stress in both CO₂ treatments, with no growth observed at -2.8 MPa (=0.98 water activity, a_w) and 1000 ppm CO₂. Overall, growth patterns under solute stress was slower in elevated CO₂ than under matric stress when compared with existing conditions. The otapksPV gene expression was increased under elevated CO₂ levels in matric stress treatments. There was fewer effects on the otanrpsPV biosynthetic gene. This pattern was paralleled with the production of OTA under these conditions. This suggest that P. verrucosum is able to actively grow and survive in both soil and on crop debris under three way interacting climate-related abiotic factors. This resilience suggests that they would still be able to pose an OTA contamination risk in temperate cereals post-harvest.

Spatial and temporal evaluation of soil erosion in Turkey under climate change scenarios using the Pan-European Soil Erosion Risk Assessment (PESERA) model

The impacts of climate change on soil erosion are mainly caused by the changes in the amount and intensity of rainfall and rising temperature. The combination of rainfall and temperature change is likely to be accompanied by negative or positive variations in agricultural and forest management. Turkey contains vast fertile plains, high mountain chains and semi-arid lands, with a climate that ranges from marine to continental and therefore is susceptible to soil erosion under climate change, particularly on high gradients and in semi-arid areas. This study aims to model the soil erosion risk under climate change scenarios in Turkey using the Pan-European Soil Erosion Assessment (PESERA) model, predicting the likely effects of land use/cover and climate change on sediment transport and soil erosion in the country. For this purpose, PESERA was applied to estimate the monthly and annual soil loss for 12 land use/cover types in Turkey. The model inputs included 128 variables derived from soil, climate, land use/cover and topography data. The total soil loss from the land surface is speculated to be approximately 285.5 million tonnes per year. According to the IPCC 5th Assessment Report of four climate change scenarios, the total soil losses were predicted as 308.9, 323.5, 320.3 and 355.3 million tonnes for RCP2.6, RCP4.5, RCP6.0 and RCP8.5 scenarios respectively from 2060 to 2080. The predicted amounts of fertile soil loss from agricultural land in a year were predicted to be 55.5 million tonnes at present, and 62.7, 59.9, 61.7 and 58.1 under RCP2.6, RCP4.5, RCP6.0 and RCP8.5 respectively. This confirms that approximately 30% of the total erosion occurs over the agricultural lands. In this respect, degraded forests, scrub and arable lands were subjected to the highest erosion rate (68%) of the total, whereas, fruit trees and berry plantations reflected the lowest erosion rates. Low soil organic carbon, sparse vegetation cover and variable climatic conditions significantly enhanced the erosion of the cultivated lands by primarily removing the potential food for organisms. Finally, process-based models offer a valuable resource for decision-makers when improving environmental management schemes and also decrease uncertainty when considering risks.

Modeling the impact of climate change on energy consumption and carbon dioxide emissions of buildings in Iran

In this study, it has been attempted to quantify model climate change effects of the coming decades on energy demand and carbon dioxide emissions of a dominant building brigade under hot and humid climates on the southern coast of Iran, based on three stations of Bushehr, Bandar Abbas and Chabahar. In this research, the Meteonorm and DesignBuilder software have been used for climate and thermal simulation of building. One of the results of this study is the increase in temperature and relative humidity for the coming decades for all three study stations. The findings of this study showed that the average annual temperature for the 2060s compared to the present decade, will increase by 2.82 °C for Bandar Abbas, by 2.79 °C for Bushehr and for Chabahar it will reach 2.14 °C. This increase in temperature has led to an increase in discomfort warmer days and a decrease in discomfort cold days. But given the climatic type of the area, a decrease in the heating energy demand for the coming decades will not have a significant effect on the pattern of energy consumption inside buildings. Because for two stations of Bandar Abbas and Chabahar, more than 95% of the energy demand for the 2060s is for cooling energy demand, which is about 80% of energy for Bushehr. In total, due to the increased demand for cooling energy in the coming decades, this will further increase carbon dioxide emissions, which is higher in Chabahar than in other study stations.

Cumulative Impact Index for the Adriatic Sea: Accounting for interactions among climate and anthropogenic pressures

Assessing and managing cumulative impacts produced by interactive anthropogenic and natural drivers is a major challenge to achieve the sustainable use of marine spaces in line with the objectives of relevant EU acquis. However, the complexity of the marine environment and the uncertainty linked to future climate and socio-economic scenarios, represent major obstacles for understanding the multiplicity of impacts on the marine ecosystems and to identify appropriate management strategies to be implemented. Going beyond the traditional additive approach for cumulative impact appraisal, the Cumulative Impact Index (CI-Index) proposed in this paper applies advanced Multi-Criteria Decision Analysis techniques to spatially model relationships between interactive climate and anthropogenic pressures, the environmental exposure and vulnerability patterns and the potential cumulative impacts for the marine ecosystems at risk. The assessment was performed based on spatial data characterizing location and vulnerability of 5 relevant marine targets (e.g. seagrasses and coral beds), and the distribution of 17 human activities (e.g. trawling, maritime traffic) during a reference scenario 2000-2015. Moreover, projections for selected physical and biogeochemical parameters (temperature and chlorophyll 'a') for the 2035-2050 timeframe under RCP8.5 scenario, were integrated in the assessment to evaluate index variations due to changing climate conditions. The application of the CI-Index in the Adriatic Sea, showed higher cumulative impacts in the Northern part of the basin and along the Italian continental shelf, where the high concentration of human activities, the seawater temperature conditions and the presence of vulnerable benthic habitats, contribute to increase the overall impact estimate. Moreover, the CI-Index allowed understanding which are the phenomena contributing to synergic pressures creating potential pathways of environmental disturbance for marine ecosystems. Finally, the application in the Adriatic case showed how the output of the CI-Index can provide support to evaluate multi-risk scenarios and to drive sustainable maritime spatial planning and management.

Projecting the impact of climate change on phenology of winter wheat in northern Lithuania

Climate warming and a shift in the timing of phenological phases, which lead to changes in the duration of the vegetation period may have an essential impact on the productivity of winter crops. The main purpose of this study is to examine climate change-related long-term (1961-2015) changes in the duration of both initial (pre-winter) and main (post-winter) winter wheat vegetation seasons and to present the projection of future phenological changes until the end of this century. Delay and shortening of pre-winter vegetation period, as well as the advancement and slight extension of the post-winter vegetation period, resulted in the reduction of whole winter wheat vegetation period by more than 1 week over the investigated 55 years. Projected changes in the timing of phenological phases which define limits of a main vegetation period differ essentially from the observed period. According to pessimistic (Representative Concentration Pathways 8.5) scenario, the advancement of winter wheat maturity phase by almost 30 days and the shortening of post-winter vegetation season by 15 days are foreseen for a far (2071-2100) projection. An increase in the available chilling amount is specific not only to the investigated historical period (1960-2015) but also to the projected period according to the climate change scenarios of climate warming for all three projection periods. Consequently, the projected climate warming does not pose a threat of plant vernalization shortage in the investigated geographical latitudes.

Soil organic carbon distribution in Mediterranean areas under a climate change scenario via multiple linear regression analysis

Over time, the interest on soil studies has increased due to its role in carbon sequestration in terrestrial ecosystems, which could contribute to decreasing atmospheric CO₂ rates. In many studies, independent variables were related to soil organic carbon (SOC) alone, however, the contribution degree of each variable with the experimentally determined SOC content were not considered. In this study, samples from 612 soil profiles were obtained in a natural protected (Red Natura 2000) of Sierra Morena (Mediterranean area, South Spain), considering only the topsoil 0-25cm, for better comparison between results. 24 independent variables were used to define it relationship with SOC content. Subsequently, using a multiple linear regression analysis, the effects of these variables on the SOC correlation was considered. Finally, the best parameters determined with the regression analysis were used in a climatic change scenario. The model indicated that SOC in a future scenario of climate change depends on average temperature of coldest quarter (41.9%), average temperature of warmest quarter (34.5%), annual precipitation (22.2%) and annual average temperature (1.3%). When the current and future situations were compared, the SOC content in the study area was reduced a 35.4%, and a trend towards migration to higher latitude and altitude was observed.

Future climate and land uses effects on flow and nutrient loads of a Mediterranean catchment in South Australia

Mediterranean catchments experience already high seasonal variability alternating between dry and wet periods, and are more vulnerable to future climate and land use changes. Quantification of catchment response under future changes is particularly crucial for better water resources management. This study assessed the combined effects of future climate and land use changes on water yield, total nitrogen (TN) and total phosphorus (TP) loads of the Mediterranean Onkaparinga catchment in South Australia by means of the eco-hydrological model SWAT. Six different global climate models (GCMs) under two representative concentration pathways (RCPs) and a hypothetical land use change were used for future simulations. The climate models suggested a high degree of uncertainty, varying seasonally, in both flow and nutrient loads; however, a decreasing trend was observed. Average monthly TN and TP load decreased up to -55% and -56% respectively and were found to be dependent on flow magnitude. The annual and seasonal water yield and nutrient loads may only slightly be affected by envisaged land uses, but significantly altered by intermediate and high emission scenarios, predominantly during the spring season. The combined scenarios indicated the possibility of declining flow in future but nutrient enrichment in summer months, originating mainly from the land use scenario, that may elevate the risk of algal blooms in downstream drinking water reservoir. Hence, careful planning of future water resources in a Mediterranean catchment requires the assessment of combined effects of multiple climate models and land use scenarios on both water quantity and quality.

Climate and land use changes effects on soil organic carbon stocks in a Mediterranean semi-natural area

A thorough knowledge of the effects of climate and land use changes on the soil carbon pool is critical to planning effective strategies for adaptation and mitigation in future scenarios of global climate and land use change. In this study, we used CarboSOIL model to predict changes in soil organic carbon stocks in a semi-natural area of Southern Spain in three different time horizons (2040, 2070, 2100), considering two general circulation models (BCM2 and ECHAM5) and three IPCC scenarios (A1b, A2, B2). The effects of potential land use changes from natural vegetation (Mediterranean evergreen oak woodland) to agricultural land (olive grove and cereal) on soil organic carbon stocks were also evaluated. Predicted values of SOC contents correlated well those measured (R2 ranging from 0.71 at 0-25cm to 0.97 at 50-75cm) showing the efficiency of the model. Results showed substantial differences among time horizons, climate and land use scenarios and soil depth with larger decreases of soil organic carbon stocks in the long term (2100 time horizon) and particularly in olive groves. The combination of climate and land use scenarios (in particular conversion from current 'dehesa' to olive groves) resulted in yet higher losses of soil organic carbon stocks, e.g. -30, -15 and -33% in the 0-25, 25-50 and 50-75cm sections respectively. This study shows the importance of soil organic carbon stocks assessment under both climate and land use scenarios at different soil sections and point towards possible directions for appropriate land use management in Mediterranean semi natural areas.

Spatial distribution estimation of malaria in northern China and its scenarios in 2020, 2030, 2040 and 2050

BACKGROUND

Malaria is one of the most severe parasitic diseases in the world. Spatial distribution estimation of malaria and its future scenarios are important issues for malaria control and elimination. Furthermore, sophisticated nonlinear relationships for prediction between malaria incidence and potential variables have not been well constructed in previous research. This study aims to estimate these nonlinear relationships and predict future malaria scenarios in northern China.

METHODS

Nonlinear relationships between malaria incidence and predictor variables were constructed using a genetic programming (GP) method, to predict the spatial distributions of malaria under climate change scenarios. For this, the examples of monthly average malaria incidence were used in each county of northern China from 2004 to 2010. Among the five variables at county level, precipitation rate and temperature are used for projections, while elevation, water density index, and gross domestic product are held at their present-day values.

RESULTS

Average malaria incidence was 0.107 ‰ per annum in northern China, with incidence characteristics in significant spatial clustering. A GP-based model fit the relationships with average relative error (ARE) = 8.127 % for training data (R(2) = 0.825) and 17.102 % for test data (R(2) = 0.532). The fitness of GP results are significantly improved compared with those by generalized additive models (GAM) and linear regressions. With the future precipitation rate and temperature conditions in Special Report on Emission Scenarios (SRES) family B1, A1B and A2 scenarios, spatial distributions and changes in malaria incidences in 2020, 2030, 2040 and 2050 were predicted and mapped.

CONCLUSIONS

The GP method increases the precision of predicting the spatial distribution of malaria incidence. With the assumption of varied precipitation rate and temperature, and other variables controlled, the relationships between incidence and the varied variables appear sophisticated nonlinearity and spatially differentiation. Using the future fluctuated precipitation and the increased temperature, median malaria incidence in 2020, 2030, 2040 and 2050 would significantly increase that it might increase 19 to 29 % in 2020, but currently China is in the malaria elimination phase, indicating that the effective strategies and actions had been taken. While the mean incidences will not increase even reduce due to the incidence reduction in high-risk regions but the simultaneous expansion of the high-risk areas.

Simulating social-ecological systems: the Island Digital Ecosystem Avatars (IDEA) consortium

Systems biology promises to revolutionize medicine, yet human wellbeing is also inherently linked to healthy societies and environments (sustainability). The IDEA Consortium is a systems ecology open science initiative to conduct the basic scientific research needed to build use-oriented simulations (avatars) of entire social-ecological systems. Islands are the most scientifically tractable places for these studies and we begin with one of the best known: Moorea, French Polynesia. The Moorea IDEA will be a sustainability simulator modeling links and feedbacks between climate, environment, biodiversity, and human activities across a coupled marine–terrestrial landscape. As a model system, the resulting knowledge and tools will improve our ability to predict human and natural change on Moorea and elsewhere at scales relevant to management/conservation actions.

Disentangling the effects of feedback structure and climate on Poaceae annual airborne pollen fluctuations and the possible consequences of climate change

Pollen allergies are the most common form of respiratory allergic disease in Europe. Most studies have emphasized the role of environmental processes, as the drivers of airborne pollen fluctuations, implicitly considering pollen production as a random walk. This work shows that internal self-regulating processes of the plants (negative feedback) should be included in pollen dynamic systems in order to give a better explanation of the observed pollen temporal patterns. This article proposes a novel methodological approach based on dynamic systems to investigate the interaction between feedback structure of plant populations and climate in shaping long-term airborne Poaceae pollen fluctuations and to quantify the effects of climate change on future airborne pollen concentrations. Long-term historical airborne Poaceae pollen data (30 years) from Cordoba city (Southern Spain) were analyzed. A set of models, combining feedback structure, temperature and actual evapotranspiration effects on airborne Poaceae pollen were built and compared, using a model selection approach. Our results highlight the importance of first-order negative feedback and mean annual maximum temperature in driving airborne Poaceae pollen dynamics. The best model was used to predict the effects of climate change under two standardized scenarios representing contrasting temporal patterns of economic development and CO2 emissions. Our results predict an increase in pollen levels in southern Spain by 2070 ranging from 28.5% to 44.3%. The findings from this study provide a greater understanding of airborne pollen dynamics and how climate change might impact the future evolution of airborne Poaceae pollen concentrations and thus the future evolution of related pollen allergies.

The impact of climate change on water provision under a low flow regime: a case study of the ecosystems services in the Francoli river basin

Mediterranean basin is considered one of the most vulnerable regions of the world to climate change and with high probability to face acute water scarcity problem in the coming years. Francolí River basin (NE Spain), located in this vulnerable region is selected as a case study to evaluate the impact of climate change on the delivery of water considering the IPCC scenarios A2 and B1 for the time spans 2011-2040, 2041-2070 and 2071-2100. InVEST model is applied in a low flow river as a new case study, which reported successful results after its model validation. The studied hydrological ecosystem services will be highly impacted by climate change at Francolí River basin. Water yield is expected to be reduced between 11.5 and 44% while total drinking water provisioning will decrease between 13 and 50% having adverse consequences on the water quality of the river. Focusing at regional scale, Prades Mountains and Brugent Tributary provide most of the provision of water and also considered highly vulnerable areas to climate change. However, the most vulnerable part is the northern area which has the lowest provision of water. Francolí River basin is likely to experience desertification at this area drying Anguera and Vallverd tributaries.

Climate change scenarios for temperature and precipitation in Aragón (Spain)

By applying a two-step statistical downscaling technique to four climate models under different future emission scenarios, we produced future projections of the daily precipitation and the maximum and minimum temperatures over the Spanish region of Aragón. The reliability of the downscaling technique was assessed by a verification process involving the comparison of the downscaled reanalysis data with the observed data--the results were very good for the temperature and acceptable for the precipitation. To determine the ability of the climate models to simulate the real climate, their simulations of the past (the 20C3M output) were downscaled and then compared with the observed climate. The results are quite robust for temperature and less conclusive for the precipitation. The downscaled future projections exhibit a significant increase during the entire 21st century of the maximum and minimum temperatures for all the considered IPCC future emission scenarios (A2, A1B, B1), both for mid-century (increases relative to the 1971-2000 averages between 1.5°C and 2.5°C, depending on the scenario) and for the end of the century (for the maximum temperature of approximately 3.75°C, 3.3°C, and 2.1°C for A2, A1B, and B1 scenarios respectively, and for the minimum temperature of 3.1°C, 2.75°C, and 1.75°C). The precipitation does not follow such a clear tendency (and exhibits greater uncertainties), but all the scenarios suggest a moderate decrease in rainfall for the mid-century (2-4%) and for the end of the century (4.5-5.5%). Due to the clear spatial differences in climate characteristics, we divided the studied area into five sub-regions to analyse the different changes on these areas; we determined that the high mountains (Pyrenees, Mediterranean-Oceanic transitional climate) and the lands of the Ebro River Basin (Continental sub-Mediterranean climate) will probably be the most affected.