Impact Of Future Climate Change Research Articles

Predicting current and future potential distributions of the greater bandicoot rat (Bandicota indica) under climate change conditions.

Rodent infestation is a global problem. Rodents cause huge harm to agriculture, forestry, and animal husbandry around the world and spread various zoonoses. In this study, we simulated the potentially suitable habitats of Bandicota indica and predicted the impact of future climate change on its distribution under different socio-economic pathway scenarios of CMIP6 using a parameter-optimized maximum entropy (MaxEnt) model. The average area under the receiver operating characteristic curve (AUC) value (0.958 ± 0.006) after ten repetitions proved the high accuracy of the MaxEnt model. Model results show that the annual mean temperature (≥ 15.93 °C), isothermality (28.52-80.49%), annual precipitation (780.13-3863.13 mm), precipitation of the warmest quarter (≥ 204.37 mm), and nighttime light (≥ 3.38) were important limiting environmental variables for the distribution of B. indica. Under current climate conditions, the projected potential suitable habitats for B. indica were mainly in India, China, Myanmar, Thailand, and Vietnam, which cover a total area of 301.70 × 104 km2 . The potentially suitable areas of B. indica in the world will expand under different future climate change scenarios by 1.61-17.65%. These results validate the potential influence of climate change on the distribution of B. indica and aid in understanding the linkages between B. indica niches and the relevant environment, thereby identifying urgent management areas where interventions may be necessary to develop feasible early warning and prevention strategies to protect against this rodent's spread. © 2023 Society of Chemical Industry.

Simulating carbon accumulation and loss in the central Congo peatlands.

Peatlands of the central Congo Basin have accumulated carbon over millennia. They currently store some 29 billion tonnes of carbon in peat. However, our understanding of the controls on peat carbon accumulation and loss and the vulnerability of this stored carbon to climate change is in its infancy. Here we present a new model of tropical peatland development, DigiBog_Congo, that we use to simulate peat carbon accumulation and loss in a rain-fed interfluvial peatland that began forming ~20,000 calendar years Before Present (cal. yr BP, where 'present' is 1950 CE). Overall, the simulated age-depth curve is in good agreement with palaeoenvironmental reconstructions derived from a peat core at the same location as our model simulation. We find two key controls on long-term peat accumulation: water at the peat surface (surface wetness) and the very slow anoxic decay of recalcitrant material. Our main simulation shows that between the Late Glacial and early Holocene there were several multidecadal periods where net peat and carbon gain alternated with net loss. Later, a climatic dry phase beginning ~5200 cal. yr BP caused the peatland to become a long-term carbon source from ~3975 to 900 cal. yr BP. Peat as old as ~7000 cal. yr BP was decomposed before the peatland's surface became wetter again, suggesting that changes in rainfall alone were sufficient to cause a catastrophic loss of peat carbon lasting thousands of years. During this time, 6.4 m of the column of peat was lost, resulting in 57% of the simulated carbon stock being released. Our study provides an approach to understanding the future impact of climate change and potential land-use change on this vulnerable store of carbon.

Statistical-dynamical modeling of the maize yield response to future climate change in West, East and Central Africa using the regional climate model REMO

Africa is vulnerable to the impacts of climate change, particularly in terms of its agriculture and crop production. The majority of climate models project a negative impact of future climate change on crop production, with maize being particularly vulnerable. However, the magnitude of this change remains uncertain. Therefore, it is important to reduce the uncertainties related to the anticipated changes to guide adaptation options. This study uses a combination of local and large-scale empirical orthogonal function (EOF) predictors as a novel approach to model the impacts of future climate change on crop yields in West, East and Central Africa. Here a cross-validated Bayesian model was developed using predictors derived from the regional climate model REMO for the period 1982–2100. On average, the combined local and large-scale EOF predictors explained around 28 % of maize yield variability from 1982 to 2016 of the entire study regions. Notably, climate predictors played a significant role in West Africa, explaining up to 51 % of the maize yield variability. Large-scale climate EOF predictors contributed most to the explained variance, reflecting the role of regional climate in future maize yield variability. Under a high-emissions scenario (RCP8.5), maize yield is projected to decrease over the entire study region by 20 % by the end of the century. However, a minor increase is projected in eastern Africa. This study highlights the importance of incorporating climate predictors at various scales into crop yield modeling. Furthermore, the findings will offer valuable guidance to decision-makers in shaping adaptation options.

The acoustic activity of Physalaemus cuvieri (Anura Leptodactylidae) is strongly influenced by climatic conditions, but not by latitude or landscape metrics

Understanding the factors that influence acoustic activity of anuran species is essential for proper species management and herein, we investigated the influence of landscape metrics (% native vegetation cover and number of patches), latitude and climatic variables (temperature and relative humidity) on the acoustic activity of Physalaemus cuvieri. We used Passive Acoustic Monitoring (PAM) to record the acoustic activity in males of the target species in 29 water bodies distributed throughout the Cerrado, the Brazilian savanna. We investigated whether the rate of call emission by P. cuvieri males is influenced by predictor variables based on generalized linear mixed models. The call rate of P. cuvieri males was influenced only by air temperature variation throughout night. We found a peak of acoustic activity in the warmer hours in the beginning of the night followed by a decrease as the air temperature declines throughout the dawn. Contrary to our expectations, we did not find any significant influence of latitude, relative humidity or landscape metrics (number of fragments or percentage of native vegetation cover) on the acoustic activity of P. cuvieri. The impacts of landscape metrics on the acoustic activity of P. cuvieri are not significant, because it is a species with more generalist ecological requirements, which to allow the individuals to move across the landscape to be successful in colonizing water bodies situated in altered environments. Previous studies have suggested that changes in temperature may affect species phenology, since individuals alter their vocal activity, or their time spent in chorus in response to climate change. In this sense, our results are important because they allow us to predict the impact of future climate change on the acoustic activity of the target species, as well as demographic implications.

A review of ENSO teleconnections at present and under future global warming

AbstractThe El Niño‐Southern Oscillation (ENSO) is a major component of the Earth's climate that largely influences global climate variability through long‐distance teleconnections. Rossby wave trains emerging from the tropical convection and their propagation into extratropical regions are the key mechanism for tropical and extratropical teleconnections. Despite significant progress in the understanding of ENSO teleconnections over the recent past decades, several important issues have remained to be addressed. The global atmospheric teleconnections of ENSO vary substantially with the seasonal cycle, on the decadal timescale, and under the influence of global warming. It is essential to separate the internal decadal variability of ENSO teleconnections from changes caused by the external forcing of global warming. However, the post‐satellite observations are not long enough to compose a large number of ENSO events to distinguish the decadal variability of ENSO teleconnections from changes related to increasing greenhouse concentrations. The current climate models also suffer from common biases, such that they are unable to properly reproduce both the tropical mean state and some features of ENSO. Nevertheless, observational records can be extended back in time via reconstruction methods. Efforts have also already been made to remove some main common biases of climate models and to improve the representation of ENSO characteristics. The reliable reconstructed data along with a large number of ensemble members of the improved climate model simulations can be applied to advance our understanding of ENSO global teleconnections and their responses to internal decadal variability and externally forced global warming.This article is categorized under: Paleoclimates and Current Trends > Modern Climate Change Climate Models and Modeling > Earth System Models Assessing Impacts of Climate Change > Evaluating Future Impacts of Climate Change

Multibranch Modelling of Flow and Water Quality in the Dhaka River System, Bangladesh: Impacts of Future Development Plans and Climate Change

Long-term development and pollution clean-up plans are a continuing feature of megacities such as Dhaka, Bangladesh. Bangladesh needs to deal with a legacy of past pollution and manage current pollution from a rapidly expanding economy. Surveys in the rivers around Dhaka show extremely high pollution and very low dissolved oxygen levels, with subsequent ecological impacts. Millions of people are not on public treatment of effluents and thousands of factories discharge into the rivers. The Bangladesh Government is planning to install over 12 large Sewage Treatment Plants (STPs) over the next 20 years. To assess the efficacy of these, a water quality model has been applied to the Dhaka River System. Results show that the proposed plan has beneficial effects in the short term for the most densely populated areas of Dhaka, along the Turag and Buriganga Rivers, and in the medium term in other parts of the city (Tongi Khal). However, in several reaches dissolved oxygen levels will remain low or very low due to the lack of STP capacity, remaining misconnections of untreated sewage and large effluent loads. The proposed STPs, while certainly beneficial, will need to be upgraded in the future if the predicted rates of population growth are confirmed and industrial pollution is not significantly reduced alongside. Climate change is expected to have an impact on the Dhaka River System water quality, with increased monsoon flows and lower summer flows, but these changes will not greatly affect the extremes of water quality to any great extent due to the overwhelming impact of pollutant discharges into the system.

Integrating machine learning and environmental variables to constrain uncertainty in crop yield change projections under climate change

Robust crop yield projections under future climates are fundamental prerequisites for reliable policy formation. Both process-based crop models and statistical models are commonly used for this purpose. Process-based models tend to simplify processes, minimize the effects of extreme events, and ignore biotic pressures, while statistical models cannot deterministically capture intricate biological and physiological processes underpinning crop growth. We attempted to integrate and overcome shortcomings in both modelling frameworks by integrating the dynamic linear model (DLM) and random forest machine learning model (RF) with nine global gridded crop models (GGCM), respectively, in order to improve projections and reduce uncertainties of maize (Zea mays L.) and soybean (Glycine max [L.] Merrill) yield projections. Our results demonstrated substantial improvements in model performance accuracy by using RF in concert with GGCM across China’s maize and soybean belt. This improvement surpasses that achieved using DLM. For maize, the GGCM+RF models increased the r values from 0.15 to 0.61–0.64–0.77 and decreased nRMSE from approximately 0.20 to 0.50–0.13–0.17 compared with using GGCM alone. For soybean, the models increased r from 0.37 to 0.70–0.54–0.70 and decreased nRMSE from 0.17 to 0.35–0.17–0.20 compared with using GGCM alone. The main factors influencing maize yield changes included chilling days (CD), crop pests and diseases (CPDs), and drought, while for soybean the primary influencing factors included CPD, tropical days (based on exceeding a maximum temperature), and drought. Our approach decreased uncertainties by 33–78% for maize and by 56–68% for soybean. The main source of uncertainty for GGCM was the crop model. For GGCM+RF, the main source of uncertainty for the 2040–2069 period was the global climate model, while the main source of uncertainty for the 2070–2099 period was the climate scenario. Our results provide a novel, robust, and pragmatic framework to constrain uncertainties in order to accurately assess the impact of future climate change on crop yields. These results could be used to interpret future ensemble studies by accounting for uncertainty in crop and climate models, as well as to assess future emissions scenarios.

Integrating a choice experiment into an agent-based model to simulate climate-change induced migration: The case of the Mekong River Delta, Vietnam

Forecasting the future impact of climate change on migration is difficult, for many reasons, including the interactive and dynamic nature of many decisions and the heterogeneity of behavior. One popular solution, agent-based models (ABM) cope well with dynamics and heterogeneity, but often lack rigorous foundations in terms of individual behavior. Moreover, given limited exposure to actual climate change, it can be a challenge to build adequate behavioral models of migration choice based on historical data. To tackle this issue, we build an ABM of future migration using a bespoke choice experiment (CE) designed to examine intention to migrate among farmers living in the Vietnamese Mekong Delta (VMD). In the CE, respondents are asked to make migration choices for scenarios constructed using six attributes: drought intensity, flood frequency, income gain from migration, migration networks, neighbors' choice, and crop choice restriction. The simulation runs to 2050 and is based on two scenarios of future global emissions of greenhouse gases—Representative Concentration Pathway (RCP) 4.5 and RCP8.5. The results suggest potentially high levels of migration as a result of climate change and the particular importance of positive feedback from pre-existing migration and neighbor's choices. The results also suggest that crop-restriction regulations have a significant impact on migration for coastal provinces of VMD. Finally, we find that migration drivers vary significantly across provinces, which suggests the policymakers point to targeted action for each province. In summary, the study demonstrates how integrating CE into ABM can foster the predictive modeling of climate-induced migration.

Regional inequalities of future climate change impact on rice (Oryza sativa L.) yield in China

The implications of climate change for rice yield have significant repercussions for food security, particularly in China, where rice cultivation is diverse, involving various cropping intensities, management practices, and climate conditions across numerous regions. The regional discrepancies in the impact of climate change on rice yield in China, however, are yet to be fully understood. Using the ORYZA(v3) model and future climate data from 2025 to 2084, gathered from ten climate models and three climate change scenarios (RCP2.6, RCP4.5, and RCP8.5), we conducted an investigation into these regional discrepancies. Our findings suggest a projected average decline in rice yield ranging from 3.7 % to 16.4 % under both rainfed and fully irrigated conditions across different scenarios. Central, eastern, and northwestern China could face the most significant climate change impacts on both rainfed and irrigated rice, with yield reductions reaching 41.5 %. In contrast, low levels of climate change under the RCP2.6 scenario may benefit northeastern (2.4 %) and southern (1.0 %) regions for rainfed and irrigated rice, respectively. Fertilization effects from elevated CO2 could counterbalance climate change's negative impact, resulting in yield increases in all Chinese rice-growing regions, excluding the northwest. The primary factor influencing rice yield changes in all regions under the RCP4.5 and RCP8.5 scenarios was temperature. However, precipitation, solar radiation, and relative humidity had notable and sometimes dominant effects, especially under the RCP2.6 scenario. These results highlight the divergent, even contradictory, rice yield responses to climate change across China, underlining the need to account for regional differences in large-scale impact studies. The study's findings can inform future policy decisions regarding ensuring regional and national food security in China.

Assessment of groundwater salinization impact in coastal aquifers based on the shared socioeconomic pathways: An integrated modeling approach

Seawater intrusion (SWI) has become a significant threat to human health and sustainable economic development in coastal areas with the rapid pace of climate change. Therefore, it is crucial to determine the response of SWI to climate change. However, most studies cannot reflect the direct impact of future climate change on groundwater salinity. This study first established the SWAT-MODFLOW coupled model after unifying both spatiotemporal computational units. Streamflow, groundwater level observation data, etc., were used to calibrate and validate the coupled model. And then SEAWAT model was loaded into the coupled model to form a new integrated model. Finally, precipitation of six Global Climate Models (GCMs) under two shared socioeconomic pathways (1–2.6 and 5–8.5 scenarios) was imported into the above calibrated integrated model separately to make SWI prediction from December 30, 2020, to December 30, 2030. The results show that this integrated model accurately reflected the study area's current flow and concentration field distribution. Precipitation under different ssps had little effect on future SWI, while the uncertainty of SWI prediction was mainly derived from different GCMs. This study provides important implications for exploring the occurrence and the prediction of SWI in the coastal aquifer. It has specific reference significance for the optimal management of water resources in coastal areas and the effective mitigation of SWI.

Projection of hydrological responses to changing future climate of Upper Awash Basin using QSWAT model

BackgroundProjecting future streamflow variation or the hydrological impact of climate change plays a pivotal role in the sustainable implication of planning water resources management. Therefore, this study predicts the potential of climate change’s impact on hydrological components in the Upper Awash Basin (UAB). The study applied a statistical downscaling model (SDSM) to generate future high-resolution climate data from the climate model output of the Canadian Second Generation Earth System Model (CanESM2) and the National Centers for Environmental Prediction (NCEP) under the representative concentration pathways (RCP4.5 and RCP8.5) scenarios. To analyze the trend of future rainfall and temperature, non-parametric Mann-Kendall, Modified Mann-Kendall tests, Sen’s slope estimator, and changing point (Pettit) tests were used. The output of downscaled climate data is used as input to a calibrated and validated Soil and Water Assessment Tool (QSWAT) model to assess the impact of future climate change on UAB hydrology.ResultsThe results show that annual rainfall and temperature are significantly increased (p < 0.05) in the UAB under RCP4.5 and 8.5 for the model ensemble mean for both short- and long-term scenarios. The change in the rainfall, the maximum and minimum temperature is mostly visible in the second period (the 2060s). Climate change is likely to cause persistent decreases in surface runoff (SUR_Q) and increases in actual evapotranspiration (ET) under all climate scenarios in the three periods. Reduction in SUR_Q despite an increase in rainfall could be due to an increment in both temperature and ET. The study also identified inconsistent seasonal changes in projected future precipitation that considerably impact overall climatic conditions.ConclusionsThis research is essential to develop an interdisciplinary approach that integrates environmental policies for the coherent use and management of water resources for future climate change and ecological protection in the basin, including other similar basins.

Paleohydrogeology of the Horonobe area, Northern Hokkaido, Japan: Groundwater flow conditions during glacial and postglacial periods estimated from chemical and isotopic data for fracture and pore water

Understanding the difference in groundwater flow between glacial and interglacial periods is crucial for predicting the impact of future climate changes on groundwater movement. This study assesses the difference in groundwater flow between the last glacial period (LGP) and the postglacial period (PGP) in fractured mudstones of the Horonobe area, Japan, by combining the data for stable isotopes (δD and δ18O) and Cl− concentration of fracture and pore waters with radiocarbon (14C) age. The isotopic compositions of fractures and pore waters indicate that groundwater at 28–250 m deep in a borehole closest to the recharge area comprises meteoric water, recharged under the same climates as the present. The fracture water has isotopic compositions more similar to meteoric water than the matrix pore water near the fracture. The 14C age of fracture water suggests meteoric water recharge during the PGP. At greater depths in the borehole and sampling points in other boreholes, the isotopic compositions indicate the mixing of glacial meteoric and altered connate water, with the fracture water having comparable isotopic compositions with the matrix pore water. The recharge timing of meteoric water is inferred to be the LGP or before based on 14C dating. These results suggest that the meteoric water recharged during the PGP flows at a shallow depth, whereas the meteoric water recharged during the LGP intruded to greater depths. This result is consistent with previous inferences from surface geophysical and geological surveys that the depths of local valleys during the LGP were greater by < 50 m than the present ones and enhanced the downward hydraulic gradient. Combining the chemical and isotopic compositions of groundwater with 14C age helps assess the groundwater flow during the LGP and PGP in fractured rocks.

When will a changing climate outpace adaptive evolution?

AbstractDecades of research have illuminated the underlying ingredients that determine the scope of evolutionary responses to climate change. The field of evolutionary biology therefore stands ready to take what it has learned about influences upon the rate of adaptive evolution—such as population demography, generation time, and standing genetic variation—and apply it to assess if and how populations can evolve fast enough to “keep pace” with climate change. Here, our review highlights what the field of evolutionary biology can contribute and what it still needs to learn to provide more mechanistic predictions of the winners and losers of climate change. We begin by developing broad predictions for contemporary evolution to climate change based on theory. We then discuss methods for assessing climate‐driven contemporary evolution, including quantitative genetic studies, experimental evolution, and space‐for‐time substitutions. After providing this mechanism‐focused overview of both the evidence for evolutionary responses to climate change and more specifically, evolving to keep pace with climate change, we next consider the factors that limit actual evolutionary responses. In this context, we consider the dual role of phenotypic plasticity in facilitating but also impeding evolutionary change. Finally, we detail how a deeper consideration of evolutionary constraints can improve forecasts of responses to climate change and therefore also inform conservation and management decisions.This article is categorized under: Climate, Ecology, and Conservation &gt; Observed Ecological Changes Climate, Ecology, and Conservation &gt; Extinction Risk Assessing Impacts of Climate Change &gt; Evaluating Future Impacts of Climate Change

Ecosystem Resistance and Resilience after Dry and Wet Events across Central Asia Based on Remote Sensing Data

Climate change forecasts indicate that the frequency and intensity of extreme climate events will increase in the future; these changes will have important effects on ecosystem stability and function. An important arid region of the world, Central Asia has ecosystems that are extremely vulnerable to extreme weather events. However, few studies have investigated the resistance and resilience of this region’s ecosystems to extreme weather events. In this study, first, the extreme drought/wet threshold was calculated based on the 113-year (1901–2013) standardized precipitation–evapotranspiration index (SPEI); second, moderate resolution imaging spectroradiometer (MODIS) remote sensing data were applied to calculate ecosystem water use efficiency (WUE) and quantify ecosystem resistance and resilience after different extreme climate events; and finally, differences in the changes of various ecosystem types before and after climate events were assessed. The results showed the following: (1) The average SPEI was 0.073, and the thresholds of extreme wetness and drought were 0.91 and −0.67, respectively. Central Asia experienced extreme wet periods in 2002 and 2003 and a drought period in 2008. (2) Suitable wetness levels can increase the resistance of an ecosystem; however, continuous wetness reduces ecosystem resistance, as does drought. Wet areas had strong resilience after wet events, and arid areas had strong resilience after drought events. (3) During both wet and drought years, the transition between shrubland and grassland caused changes in ecosystem resistance and resilience. These findings are important for understanding the impact of future climate change on ecosystem stability.

Impact of Future Climate and Land Use Changes on Runoff in a Typical Karst Basin, Southwest China

Climate change and land use change are the two main factors affecting the regional water cycle and water resources management. However, runoff studies in the karst basin based on future scenario projections are still lacking. To fill this gap, this study proposes a framework consisting of a future land use simulation model (FLUS), an automated statistical downscaling model (ASD), a soil and water assessment tool (SWAT) and a multi-point calibration strategy. This frameword was used to investigate runoff changes under future climate and land use changes in karst watersheds. The Chengbi River basin, a typical karst region in southwest China, was selected as the study area. The ASD method was developed for climate change projections based on the CanESM5 climate model. Future land use scenarios were projected using the FLUS model and historical land use data. Finally, the SWAT model was calibrated using a multi-site calibration strategy and was used to predict future runoff from 2025–2100. The results show that: (1) the developed SWAT model obtained a Nash efficiency coefficient of 0.83, which can adequately capture the spatial heterogeneity characteristics of karst hydro-climate; (2) land use changes significantly in all three future scenarios, with the main phenomena being the interconversion of farmland and grassland in SSPs1-2.6, the interconversion of grassland, farmland and artificial surfaces in SSPs2-4.5 and the interconversion of woodland, grassland and artificial surfaces in SSPs5-8.5; (3) the average annual temperature will show an upward trend in the future, and the average annual precipitation will increase by 11.53–14.43% and (4) the future annual runoff will show a significant upward trend, with monthly runoff mainly concentrated in July–September. The variability and uncertainty of future runoff during the main-flood period may increase compared to the historical situation. The findings will benefit future water resources management and water security in the karst basin.

Bias-Corrected CMIP5 Projections for Climate Change and Assessments of Impact on Malaria in Senegal under the VECTRI Model.

On the climate-health issue, studies have already attempted to understand the influence of climate change on the transmission of malaria. Extreme weather events such as floods, droughts, or heat waves can alter the course and distribution of malaria. This study aims to understand the impact of future climate change on malaria transmission using, for the first time in Senegal, the ICTP's community-based vector-borne disease model, TRIeste (VECTRI). This biological model is a dynamic mathematical model for the study of malaria transmission that considers the impact of climate and population variability. A new approach for VECTRI input parameters was also used. A bias correction technique, the cumulative distribution function transform (CDF-t) method, was applied to climate simulations to remove systematic biases in the Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) that could alter impact predictions. Beforehand, we use reference data for validation such as CPC global unified gauge-based analysis of daily precipitation (CPC for Climate Prediction Center), ERA5-land reanalysis, Climate Hazards InfraRed Precipitation with Station data (CHIRPS), and African Rainfall Climatology 2.0 (ARC2). The results were analyzed for two CMIP5 scenarios for the different time periods: assessment: 1983-2005; near future: 2006-2028; medium term: 2030-2052; and far future: 2077-2099). The validation results show that the models reproduce the annual cycle well. Except for the IPSL-CM5B model, which gives a peak in August, all the other models (ACCESS1-3, CanESM2, CSIRO, CMCC-CM, CMCC-CMS, CNRM-CM5, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, inmcm4, and IPSL-CM5B) agree with the validation data on a maximum peak in September with a period of strong transmission in August-October. With spatial variation, the CMIP5 model simulations show more of a difference in the number of malaria cases between the south and the north. Malaria transmission is much higher in the south than in the north. However, the results predicted by the models on the occurrence of malaria by 2100 show differences between the RCP8.5 scenario, considered a high emission scenario, and the RCP4.5 scenario, considered an intermediate mitigation scenario. The CanESM2, CMCC-CM, CMCC-CMS, inmcm4, and IPSL-CM5B models predict decreases with the RCP4.5 scenario. However, ACCESS1-3, CSIRO, NRCM-CM5, GFDL-CM3, GFDL-ESM2G, and GFDL-ESM2M predict increases in malaria under all scenarios (RCP4.5 and RCP8.5). The projected decrease in malaria in the future with these models is much more visible in the RCP8.5 scenario. The results of this study are of paramount importance in the climate-health field. These results will assist in decision-making and will allow for the establishment of preventive surveillance systems for local climate-sensitive diseases, including malaria, in the targeted regions of Senegal.

Impacts of climate change on crop growing season characteristics in Northern Ethiopia

Abstract Understanding the likely impact of climate change on crop growth is very important to identify possible areas of intervention and consider climate-related impacts. This study aimed to investigate the future impact of climate change on the crop growing season in the Tigray region. Five global climate models under two representative concentration paths were projected for future periods using a delta downscaling approach. Results indicate that projections of rainfall showed an increase in annual and summer (Kiremt) rainfall at most stations. However, the Belg rainfall season had a declining trend except under RCP4.5 in a mid-term period that showed positive changes at most stations. On the contrary, projections of maximum and minimum temperatures indicated a continuous increase. In line with the increase in temperatures, the reference evapotranspiration consistently increased at all stations. Cumulatively, late onset and early cessation of rainfall are observed, accompanied by a 5.5–19% reduction in the length of the growing period (LGP), exacerbating the current short LGP in the study area and affecting the proper growth and maturity of major crops. The findings of this study have global implications in that similar areas may be alarmed to get prepared ahead and develop adaptive and sustainable crop production strategies.

Meteorological and hydrological drought monitoring and trend analysis for selected gauged watersheds in the Lake Tana basin, Ethiopia: Under future climate change impact scenario

Since most of the countries in Africa rely on Agriculture supported by rainfall, they are especially vulnerable to dangers brought on by climate change, such as floods and droughts. Droughts in the Horn of Africa are immediately in danger of starvation, particularly in Ethiopia. This study uses ensemble GCM data at the Coupled Model Intercomparison Project 5 (CMIP5) to examine the future metrological and hydrological drafts of the chosen gauged watershed, specifically Gilgel Abay, Gumara, Megech, and Ribb. For the analysis, the Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), and Streamflow Drought Index (SDI) were used, along with data from the RCP4.5 and RCP8.5 scenarios. The SPI and RDI were used to analyze metrological drought, and the SDI was used to identify hydrological drought in the mid (2040–2079) to long-term (2050–2079) period. Overall meteorological drought results show that annual time scale drought occurrence is higher than biannual time scale in both indices. Under the biannual time scale (SPI6) and annual time scale (RDI12) for RCP8.5 scenarios of all time horizons, 20.69% of the maximum drought record was observed. While the (SPI12) indices of RCP4.5 and RCP8.5 for all time domains (mid and far) display a drought frequency of 17.24%. And according to the RCP8.5 scenario, RDI6 also predicts that droughts would occur 18.10% more frequently in the far future. Additionally, the RCP8.5 scenario has the greatest frequency record for hydrological droughts across all time horizons at 18.10%. Gumara and Ribb gauged watersheds are typically the most susceptible to drought in the mid periods. The study revealed that drought analysis is important to take planning activities for those who are working as researchers, water resource managers and other professions in the basin. As a result, the study will assist the government, policymakers, and decision-makers in putting drought adoptions and early warning actions in place.

A Model of the Sea–Land Transition of the Mean Wind Profile in the Tropical Cyclone Boundary Layer Considering Climate Changes

The tropical cyclone boundary layer (TCBL) connecting the underlying terrain and the upper atmosphere plays a crucial role in the overall dynamics of a tropical cyclone system. When tropical cyclones approach the coastline, the wind field inside the TCBL makes a sea–land transition to impact both onshore and offshore structures. So better understanding of the wind field inside the TCBL in the sea–land transition zone is of great importance. To this end, a semiempirical model that integrates the sea–land transition model from the Engineering Sciences Data Unit (ESDU), Huang’s refined TCBL wind field model, and the climate change scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6) is used to investigate the influence of climate changes on the sea–land transition of the TCBL wind flow in Hong Kong. More specifically, such a semiempirical method is employed in a series of Monte-Carlo simulations to predict the wind profiles inside the TCBL across the coastline of Hong Kong under the impact of future climate changes. The wind profiles calculated based on the Monte-Carlo simulation results reveal that, under the influences of the most severe climate change scenario, slightly higher and significantly lower wind speeds are found at altitudes above and below 400 m, respectively, compared to the wind speeds recommended in the Hong Kong Wind Code of Practice. Such findings imply that the wind profile model currently adopted by the Hong Kong authorities in assessing the safety of low- to high-rise buildings may be unnecessarily over-conservative under the influence of climate change. On the other hand, the coded wind loads on super-tall buildings slightly underestimate the typhoon impacts under the severe climate change conditions anticipated for coastal southern China.

The impact of future hydrology stresses and climate change on submarine groundwater discharge in arid regions: A case study of the Nile Delta aquifer, Egypt

Study regionClimate change is expected to severely impact Egypt's Nile Delta Aquifer (NDA). Despite its large freshwater reservoir, estimated at 400 Billion Cubic metres (BCM), climate-change-induced drivers (drought and sea-level rise) coupled with increasing groundwater over-abstraction will cause a gradual reduction of the available freshwater volume. This study used the numerical model SEAWAT to study the impact of the main hydrogeological and anthropogenic factors on the response of the submarine groundwater discharge (SGD) (i.e., the net fresh seaward groundwater flux) and seawater intrusion (SWI) in the NDA. Study focusFive scenarios were examined, including (i) a probable Sea-level rise (SLR), (ii) expected reduction in Nile hydrograph and its branches, (iii) freshwater overpumping, (iv) the combination of reduction in Nile hydrograph and overpumping, and (v) the combination of these scenarios in the years 2030, 2050 and 2070. New hydrological insights for the regionThe results show that the increasing saltwater head due to SLR coupled with a reduction in Nile flow and overpumping ultimately results in the landward shifting of the saltwater within the aquifer. In addition, the resulting salinity increase in the aquifer caused a significant increase in the deterioration of a large quantity of freshwater volume with a subsequent reduction of the SGD. Also, the salt mass variation (SMV) in scenario 5 increased to 7.09%, 10.69%, and 12.99%, while the groundwater discharges variation (SGDV) to the sea declined by 21.90%, 42.38%, and 61.95% in the years 2030, 2050 and 2070, respectively. Moreover, the coastal aquifers required the management of the SGD to keep the balance between the freshwater and saltwater interface. This study is useful for the future planning and water resources management in coastal regions for integrated management of SGD, SWI, and aquifer freshwater storage. Also, the applications of smart measurements of SGD and groundwater salinity are required for coastal aquifers management.