Mean Annual Minimum Temperature Research Articles

Representative concentration pathways (RCPs) used as a tool to evaluate climate change impact on maize crop production in the Woybo catchment of Ethiopia

ABSTRACT The research was conducted to assess the climate change impact on crop water requirements (CWRs) in the Woybo catchment of southern Ethiopia. The impact of climate change was assessed through climate models under RCP4.5 and RCP8.5 emission scenarios for the 2050s compared to 1971–2005. During the 2050s, the annual mean minimum temperature increased by 1.9–2.3 °C, while the maximum temperature over the catchment increased by 1.4–2.0 °C. The projected rainfall is expected to increase in autumn, summer, and annually by 3.7–16.8 and 8.7–12.2% in the 2050s under RCP4.5 and 8.5, respectively. But it is likely to decrease in winter and spring by 1.5–10.2% and 8.7–2.8%, respectively. The average annual potential evapotranspiration will rise by 0.1–6.8%. Under both RCPs, CWRs may increase from 6.2 to 17.2% during the growing season, while irrigation water requirements (IWRs) may rise from 4.6 to 16.4%. Future period projection results show that potential evapotranspiration and IWRs will increase for maize crops compared to the baseline period. The study will help water managers, users, and agricultural developers in preparing new water-saving strategies and achieving agricultural sustainability.

Introduction to the special issue of The Natural History of Two Peoples Bay Nature Reserve, Western Australia

Context This paper introduces the special issue of Pacific Conservation Biology devoted to the natural history of Two Peoples Bay Nature Reserve on the south coast of Western Australia. Methods This paper provides the background to the special issue. Key results Two Peoples Bay Nature Reserve was gazetted in 1967 for the conservation of the native biota, including two species; the Djimaalap/noisy scrub-bird (Atrichornis clamosus) and Ngilgaitch/Gilbert’s potoroo (Potorous gilbertii), both believed extinct for over 100 years before being rediscovered on the Reserve. The Reserve is 4774.7 ha in area, with wetlands, heathlands, granite outcrops, sand dunes, beaches, cliffs, and islands. Since it was established, mean annual rainfall has decreased by 16.8%, mean annual maximum temperature has increased by 0.2°C, and mean annual minimum temperature has increased by 0.7°C. Conclusions The paper poses the question: what do the changes of drier winters, hotter summers, and more extreme weather events mean for managers of conservation areas such as Two Peoples Bay? Implications Changing climate will pose problems for the managers of Two Peoples Bay Nature Reserve in ensuring the conservation of the Djimaalap/noisy scrub-bird and Ngilgaitch/Gilbert’s potoroo.

Variability and Changes in Climate in Northern Uganda

Variability and changes in climate are generally expected to occur. However, there remain gaps on dynamics of expected regional variations in climatic changes. This study assessed historic and projected climatic conditions up to the year 2033. The study hypothesized that temperature rather than rainfall significantly increased for the period 1980-2010 and rainfall rather than temperature is likely to decrease significantly by 2033 for Gulu District in northern Uganda. To determine historic climatic trends, rainfall and temperature data were obtained from Uganda National Meteorological Authority (UNMA) while for future climate, the PRECIS (Providing Regional Climates for Impact Studies) modelled data based on projected conditions at a 50 km spatial resolution was used. These data sets were subjected to trend analysis and the differences in means were detected at a 95% confidence level. Contrary to the evidences from other empirical studies, results generally indicated decreasing rainfall for the period 1980-2010. However, the decrease was not significant (P > 0.05) while both historic mean annual maximum and minimum temperature trends showed a statistically significant increase (P<0.05). Projections for 2033 reveal a significant decrease in rainfall (P < 0.05) while both maximum and minimum temperature will remain quasi uniform

Grid-based climate variability analysis of Addis Ababa, Ethiopia

Climate change is an intricate global environmental concern. However, its impact is more pervasive in developing nations such as Ethiopia. Hence, this manuscript examines temperature variability and the magnitude of change over 38 years in the specific case of Addis Ababa, Ethiopia. Gridded meteorological data consisting of minimum and maximum temperatures on a monthly time scale ranging from 1981 to 2018 was obtained from the National Meteorological Agency of Ethiopia. The coefficient of variation (CV) and standardized anomaly index (SAI) were used to examine the rate and extent of temperature anomalies. Geostatistical models, particularly ordinary kriging, are presented as a means of spatially interpolating temperature data. Modified Mann-Kendall test (MMK), Sen's Slope (SS) estimator, principal component analysis (PCA), and T-test were employed to determine the monthly, annual, and seasonal trends using Geospatial technologies, “R” programming, and statistical software. The findings revealed substantial spatial and temporal variation in Addis Ababa’s annual and seasonal maximum and minimum temperatures. The long-term mean annual maximum and minimum temperatures were 25.8 °C and 12.6 °C, respectively. The monthly, annual, and seasonal temperatures accrued significantly except in the months of January and September. It is noteworthy that the decadal maximum temperature has risen by 2.7 °C, while minimum temperatures have displayed comparatively minor fluctuations. Moreover, the findings also exhibited that the average maximum and minimum temperatures increased by 1.88 °C and 1.72 °C, correspondingly and the highest temperature occurred during the spring (Belg) season. The first two PCAs (Annual and Kiremt Tmax) account for 90% of the temperature variation. In conclusion, the findings underscore the pressing need for the implementation of climate adaptation strategies and policy measures, which can strengthen the city’s resilience to imminent climate change-induced hazards. The mounting temperature presents substantial challenges across various sectors within the city, emphasizing the urgency of preemptive actions to mitigate potential repercussions.

Analysis of projected climate change in sorghum growing semi-arid rift valley of Ethiopia

Global warming projected to have a significant impact on agricultural water availability for crops in Africa in particularly in Ethiopia. Therefore, the study was conducted to analyze the projected change of temperature and rainfall in sorghum growing semi-arid rift valley of Ethiopia. The weather data for 11 stations were generated using AgiMP5 technique for RCP 4.5 and 8.5 and for the period of 2050s and 2080s. MAKESENS employed for the detection of trend of extreme indices. InStat v3.37 were used for the analysis of start, end, and length of growth season. Under RCP 4.5, the projected mean annual minimum air temperatures in CRV, ERVE and NRVE could increase respectively by 1.9 °C (2050s) and 2.6 °C (2080s), 1.8 °C (2050s) and 2.5 °C (2080s) and 1.88 °C (2050s) and 2.69 °C (2080s). Under RCP 8.5, however, with same location it will projected to increase in both time frames (2050s and 2080s) in all studied sites. The mean annual maximum air temperature, projected under RCP 4.5 in the CRV, ERV and NRVE will increase by 1.59 °C (2050s) and 2.18°C (2080s),1.42 °C (2050s) and 2.08 °C (2080s) and 1.46 °C (2050s) and 2.09 °C (2080s) respectively. However, under the RCP 8.5, at same regions it projected to rise in both periods (2050s and 2080s). This will be convoy with increase of the hot and cold extremes’ indices in regions. The mean percentage change of annual rainfall is projected to decrease insignificantly in (0.6-5.5% and 2.6%) and increase (0.85-12.3% and 22.3%) in half of the stations located in CRV and ERVE, whereas, in NRVE it will projected to increase (6.1-14.6%) under RCP 4.5 in all stations in 2050s. Though the annual projected rainfall under RCP 4.5 in 2080s will decline in range of 2.1-10.1%, 3.12-4.5% and 0.9-4.6% at CRV, ERVE and NRVE respectively. In most of the location in CRV and ERVE stations growing season rainfall will projected to decline from 1.45% to 53.8% and 0.8 to 8.8%, whereas, in NRVE it will projected to increase in 9.2 to 19% under RCP 4.5 in period of 2050. The projected SOS will be changed in mixed pattern, whereas and LGP will prolonged in most locations. The findings indicated that there has been a change of projected climate change in semi-arid RV, which will be alter the agricultural practices in depletion of soil moisture. Therefore, in adjusting planting timing and using early/medium maturation sorghum varieties, may be necessary for farmers in the region. Soil and water management needs attention.

Evaluation of CORDEX Africa regional climate models performance in simulating climatology of Zarima sub-basin northwestern Ethiopia

Climate models are basic tools to obtain reliable estimates of future climate change and its effects on the water resources and agriculture in given basin. However, all climate models are not equally valuable for all areas. Therefore, determining the most appropriate climate model for a specific study area is essential. This study examines the performance of 10 CORDEX-AFRICA-220 Regional Climate Models (RCMs), three downscaling institutional based ensembles mean (Reg ensemble, CCLM ensemble and REMOO ensemble) and the multi-model ensemble mean. The models were evaluated based on their ability in replicating the seasonal and annual rainfall, minimum and maximum temperature and inter-annual variability for the period of 1986–2005 using statistical metrics such as BIAS, Root Mean Square Error (RMSE), Pearson correlation coefficient (r), coefficient of variation (CV), Kling Gupta Efficiency (KGE) and Taylor diagram. The findings indicated that HadREMOO, MPI-Reg4-7, HadReg4-7, Reg ensemble, and multi-model ensemble mean performed relatively better in representing the mean annual observed rainfall at the Adiramets, Debarik Ketema, Niguse Maystebri, and Zarima stations, respectively. Whereas, NorESM-CCLM, MPI-CCLM, NorESM-Reg4-7, and NorESM-REMOO exhibited a weak performance in reproducing the observed mean annual rainfall at the Adiramets, Debarik Ketema Niguse, Maystebri, and Zarima stations, respectively. Similarly, RCMs generally capture the mean annual maximum temperature of climatic stationsof Zarima subbasin well. Specifically, the MPI-Reg4-7 simulation performs well in representing the mean annual observed maximum temperature at Adiramets and Maytsebri stations, while the Debarik and Ketema Niguse stations exhibit superior performance in the HadReg4-7 simulation and the Zarima station shows better representation in the CCLM ensemble simulations. The majority of the model simulations exhibit good representation of mean annual minimum temperature at Adiramets, Debarik, and Zarima stations. Specifically, CanESM-RCM, HadReg4-7, REMOOensemble, multi-model ensemble, and Regensemble simulations perform better at Adiramets, Debarik, Ketema niguse, Maystebri and Zarima stations respectively. This suggests that these models may have biases or shortcomings in capturing the temperature values in the subbasin. Furthermore, NorESM-CCLM at Adiramets, Ketema niguse, and Zarima stations, NorESM-REMOO at Debarik station, and HadReg4-7 at Maystebri station demonstrate poor performance in representing the observed mean minimum temprature. Majority of the RCMs, all institutional based ensemble means and the multi-model ensemble mean simulations overestimate the observed mean annual rainfall of the Zarima subbasin with minimum bias of 0.02 mm at Ketema niguse HadReg4-7and maximum bias of 2.81 mm at Maytsebri MPI-CCLM simulation. Similarly, HadReg4-7 simulation of Ketama Niguse MPI-CCLM showed a minimum 0.02 mm and Maytsebri simulation kiremit season mean rainfall showed a maximum bias of and 2.99 mm. Regarding mean annual and kiremit season maximum and minimum temperature of the Zarima subbasin were overestimated by majority of the simulation and the ensemble means. The correlation (r) of observed and model simulated mean annual and kiremit season rainfall was strong (0.60–0.79) and very strong (0.80–0.99) in the majority of the simulations except Ketema niguse station mean annual and kiremit season rainfall simulations of MPI-REMOO, NorESM-Reg4-7; Debarik station kiremit season rainfall of NorESM-CCLM and NorESM-REMOO, MPI-Reg4-7 and MPI-REMOO, which showed moderate correlation. The performance of the RCMs, institutional based ensemble means and multi-model ensemble mean were different in statistical metrics (BIAS, RMSE, r, CV and KGE) and Taylor diagram. Among the simulations and ensemble means, the multi-model ensemble mean was superiors in two or more of statistical metrics at each station of the Zarima subbasin except Maytsebri station kiremit season rainfall, where the CCLM ensemble was better. Consistently, the Taylor diagram showed that the multi-model ensemble was better in the replication of the areal annual and kiremit season rainfall, maximum and minimum temperature of the subbasin. This finding evidenced that selecting the best RCMs and ensemble mean is necessary for climate projection and climate change impact assessment study.

Vulnerability of communities' livelihoods to the impacts of climate change in north-western highlands of Tanzania

Climate change has emerged as one of the critical challenges to human well-being. Changing climate places direct and indirect impacts on human activities and is mainly triggered by non-climatic stressors, which are diverse and inherent in many communities. However, there has been diversity in how climate and non-climatic stress factors affect human well-being and thus requiring locally specific contextualisation of how climate change is driving change in many ecosystems. This study analysed climate and non-climate factors for communities' vulnerability and impacts using a landscape approach in selected villages in the western highlands of Tanzania, specifically in Kasulu and Buhigwe districts in Kigoma Region. The aim was to understand communities' vulnerability to the impacts of climate, and non-climate processes, particularly with regard to livelihoods and impacts, particularly in forest resources. Study sites and respondents were selected through simple random and purposive sampling methods. Data were collected through household surveys, key informants interviews, and focus group discussions. Meteorological data were used for the analysis of climatic changes. Quantitative data analysis tools included meteorological statistics software such as CLIDATA, INSTAT, XLSTAT and SPSS. Theme-content analysis was used to analyse qualitative data. Findings indicate inter-annual and seasonal variabilities of rainfall and temperature levels from the normal across the study sites. The study revealed significant increasing trends for mean annual Maximum and Minimum temperature. Significant changes in rainfall patterns were decreasing seasonal and annual rainfall trends, unreliable onset and cessation, and erratic seasonal rainfall. The maximum decline of seasonal rainfall had slope values of −14.51, while the maximum decline of annual rainfall trends had a slope value of −12.76. The minimum decline for seasonal rainfall had slope values of −2.17, and annual rainfall trends had slope values of −1.36. Villages in the highland zone were found to be highly vulnerable to the impacts of climate change and non-climate stress factors, with a 0.61 vulnerability index value. Major climate stress factors for communities' vulnerability included seasonal variability of rainfall and temperature increase, which influenced the increase of pests and diseases and other unfavourable climatic conditions that affect crop production and livestock keeping. The effects, directly and indirectly, influenced the low agricultural yields and decreased income. Non-climate stress factors such as landscape characteristics, mobility, inadequate extension services, and low household asset endowment might have been accelerating communities' vulnerability to the effects of the changing climate. Moreover, the study shows the climate change on a community's resilience. Generally, changes in climate conditions result in many adverse consequences and have been influencing communities' vulnerability.

Modeling the impact of climate change on streamflow responses in the Kessem watershed, Middle Awash sub-basin, Ethiopia

Abstract In this study, we examined how future climate change will affect streamflow responses in the Kessem watershed. Climate variables from SSP2-4.5 and SSP5-8.5 emission scenarios were extracted from GCMs for the 2040s (2031–2060) and 2070s (2061–2090). The bias-corrected precipitation and temperature were converted into streamflow using a calibrated SWAT model. The simulated output of the future streamflow for the periods 2040s and 2070s was compared with the base period (1992–2020) and presented as percentage changes. During calibration and validation, the SWAT model showed Nash–Sutcliffe efficiency (NSE) values of 0.79 and 0.77, as well as coefficient of determination (R2) values of 0.8 and 0.79, demonstrating its capability of simulating streamflow. The annual mean maximum and minimum temperatures are predicted to increase, with a pronounced increase in the minimum temperature for the mid-term and long-term futures under both emission scenarios. As we approach the end of the century, we see an increase in annual mean rainfall and streamflow under the SSP5-8.5 emission scenario. The increment in annual mean rainfall (streamflow) is expected to be 3% (12.5%) and 23% (48.8%) for the 2040s and 2070s, respectively, under the SSP5-8.5 emission scenario.

Effects of climate factors on wheat and maize under different crop rotations and irrigation strategies in the North China Plain

The North China Plain (NCP) is a crucial agricultural region for grain production in China, primarily focusing on wheat and maize cultivation. However, these crop yields are highly vulnerable to fluctuations in temperature and precipitation, with climate change being a significant factor. This study investigates the impact of climate factors on wheat and maize yields in the NCP under various crop rotations and water supply strategies. Using the Agricultural Production Systems sIMulator crop mechanism model, we evaluated the effects of different crop rotations and water supply strategies on wheat and maize yields. A comprehensive analysis of the simulated results determined the yield variation trends and their correlation and sensitivity to different climate factors. The findings revealed that precipitation levels over the past 40 years showed no significant trend, while radiation levels showed a significant decreasing trend, and annual mean maximum and minimum temperatures displayed significant increasing trends. Furthermore, the study found that irrigation practices and crop rotations substantially impact grain yield in the study area, with average yields ranging from 8105.5 kg ha−1 under rainfed conditions to 13 088.8 kg ha−1 under fully irrigated conditions. There was a statistically significant trend of increasing yields for fully irrigated Monocrop-Wheat and decreasing yields for fully irrigated Monocrop-Maize over the past 40 years. Sensitivity analysis also showed that rational rotation and irrigation can reduce grain yield sensitivity to climate change. In conclusion, the prudent use of rotation and irrigation can enhance food production resilience to climate change. These findings have significant implications for developing strategies to optimize crop yields and adapt to climate change in the NCP while considering trade-offs.

Influence of climate factors on population density and damage of the leopard moth, Zeuzera pyrina L., in walnut orchards, Iran.

The effect of climate factors (temperature, humidity, precipitation, and frost days) on the population changes, damage, and infestation area of the leopard moth, Zeuzera pyrina L., was studied during 2006-2018 in four parts of Iran including Saman, Arak, Najaf-abad, and Baft. For trend analysis, the Mann-Kendall test was run on time series data of both climate and pest population. According to the results, the annual mean (Kendall's statistics, T = 0.64 and 0.48), annual minimum (T = 0.60 and 0.42), and January mean (T = 0.64 and 0.61, respectively) temperatures showed increasing trends in Saman and Najaf-abad. Moreover, the annual mean minimum and January temperatures (T = 0.41 and 0.45, respectively) in Arak and the annual mean maximum temperature (T = 0.79) in Baft showed increasing trends. The number of frost days/year (Kendall's statistics, T = -0.63, -0.53, -0.32 and -0.37) and annual mean relative humidity (T = -0.43, -0.63, -0.64 and -0.42, respectively) showed decreasing trends in Saman, Arak, Baft, and Najaf-abad stations. Trend analysis indicated significant increases in the mean number of moths caught (T = 0.59, 0.76 and 0.90), the percentage of infested branches/tree (T = 0.66, 0.58, and 0.90), the number of active holes/tree (T = 0.79, 0.55, and 0.68) and the infested areas (T = 0.99, 0.73, and 0.98, respectively) in Saman, Arak and Najaf-abad stations. According to stepwise regression, the mean temperatures of January, autumn, and winter were the most effective variables for increasing Z. pyrina damage and population, while relative humidity and the number of frost days played the major role in reducing it.

Exploring temperature dynamics in Madhya Pradesh: a spatial-temporal analysis.

Understanding the dynamics of temperature trends is vital for assessing the impacts of climate change on a regional scale. In this context, the present study focuses on Madhya Pradesh state in Central Indian region to explore the spatial-temporal distribution patterns of temperature changes from 1951 to 2021. Gridded temperature data obtained from the Indian Meteorological Department (IMD) in 1° × 1° across the state are utilised to analyse long-term trends and variations in temperature. The Mann-Kendall (MK) test and Sen's slope (SS) estimator were used to detect the trends, and Pettitt's test was utilised for change point detection. The analysis reveals significant warming trends in Madhya Pradesh during the study period during specific time frames. The temperature variables, such as the annual mean temperature (Tmean), maximum temperature (Tmax), and minimum temperature (Tmin), consistently increase, with the most pronounced warming observed during winter. The trend analysis reveals that the rate of warming has increased in the past few years, particularly since the 1990s. However, Pettitt's test points out significant changes in the temperature, with Tmean rising from 25.46 °C in 1951-2004 to 25.78 °C in 2005-2021 (+0.33 °C), Tmax shifting from 45.77 °C in 1951-2010 to 46.24 °C in 2011-2021 (+0.47°C), and Tmin increasing from 2.65 °C in 1951-1999 to 3.19 °C in 2000-2021 (+0.46 °C). These results, along with spatial-temporal distribution maps, shed important light on the alterations and variations in monthly Tmean, Tmax, and Tmin across the area, underlining the dynamic character of climate change and highlighting the demand for methods for adaptation and mitigation.

Analysis of Trend in Meteorological and Hydrological Time-series using Mann-Kendall and Sen’s Slope Estimator Statistical Test in Akwa Ibom State, Nigeria

The monthly, seasonal, annual, and decadal trends of seven hydro-meteorological variables were analysed for stations in Akwa Ibom State, Nigeria, controlled by the Nigerian Meteorological Agency (NiMet) and the Cross River Basin Development Authority (CRBDA) from 1972 to 2021. At the 5% statistical significance level, the non-parametric Mann-Kendall and Sen's slope estimator techniques were used to detect if there was a positive or negative trend and the magnitude of the trend in hydro-meteorological data. In the present study, there was a significant statistically increasing (positive) trend in mean seasonal and annual rainfall, maximum temperature, minimum temperature, and runoff. However, there was a significant statistically decreasing (negative) trend in average annual relative humidity, solar radiation, and potential evapotranspiration. The magnitudes of the trends were 19.39mm/year, 0.0314oC/year, 0.013oC/year, -0.104%/year, -8.78MJ/m2/year, -1.440mm/year, and 0.028m3/s/year for annual rainfall, maximum temperature, minimum temperature, relative humidity, solar radiation, PET, and runoff, respectively. The rising trends in precipitation, temperature, and runoff in this research area show that this region is subject to climatic variability. The results of the Mann-Kendall and Sen's slope estimator statistical tests revealed the consistency of performance in the detection of the trend for the hydro-meteorological variables.

Evaluation of historical CMIP6 model simulations and future climate change projections in the Baro River Basin

Abstract This study evaluated the performance of five Global Climate Model (GCM) outputs from the Coupled Model Intercomparison Project phase 6 (CMIP6) in reproducing the historical precipitation and temperature. Observational data from the National Meteorological Agency are used for model evaluation and bias correction. Then, the projections from representative GCMs are used to understand the future climate (2031–2060) of the Baro River Basin under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) with respect to the historical datasets (1985–2014). Statistical metrics (percent of bias, root mean square error, and coefficient of determination) are used to assess the model's performance in reproducing precipitation and temperature and Compromise Programming (CP) was used in ranking GCMs. GFDL-CM4, INM-CM5-0, and INM-CM4-8 models for precipitation; CMCC-ESM2, MRI-ESM2-0, and INM-CM4-8 for maximum temperature; and GFDL-CM4, INM-CM4-8, and INM-CM5-0 for minimum temperature were selected based on their better simulation. The projected annual precipitation shows increases of 6% under SSP2-4.5 and 16.46% under SSP5-8.5. The mean annual maximum and minimum temperature show increases of 1.43 and 1.96 °C under SSP2-4.5, and 1.81 and 3.11 °C under SSP5-8.5, respectively. Overall, the ensemble of three models outperforms the ensemble of all models for the Baro River Basin when utilising the representative GCMs.

THE IMPACT OF CLIMATIC FACTORS AND FOOD SECURITY ON DEMOGRAPHIC FERTILITY IN PUNJAB, PAKISTAN THROUGH GEOSPATIAL STRUCTURAL MODELLING (GSSM)

: Punjab is not only the most fertile province of Pakistan but also the most fertile demographically with 3.4 total fertility rate. The fertility rate is an important demographic phenomenon that has significant implications for sustainable development, particularly in agricultural regions such as Punjab, Pakistan. In recent years, scholars and policymakers have increasingly focused on the link between fertility rate and soil fertility. As soil fertility is an important determinant of agricultural productivity and food security. This research article has explored the relationship and impact of the main climatic factors like maximum and minimum temperature, precipitation and Gross Primary Productivity on total fertility rate of districts of Punjab, for the year 2021. Geospatial Structural modeling (GSSM) has been designed to estimate the influence of climatic factors directly on fertility and its indirect influence on fertility through gross primary productivity and child mortality rate on total fertility rate of districts of Punjab. All the said geospatial primary and secondary datasets are analyzed in ArcGIS environment and resultant map are generated. The results shows that there is the direct and significant impact of gross primary productivity and child mortality rate on the total fertility rate. While there is indirect impact of mean annual precipitation, mean annual maximum and minimum temperature on the total fertility rate. Hence, there is negative direct impact of said factors on total fertility rate of the districts of the Punjab

Modelling the Impacts of the Changing Climate on Streamflow in Didesa Catchment, Abay Basin, Ethiopia

The Didesa catchment, which is the second largest subbasin of the Abay basin, significantly contributes to the Blue Nile’s outflow. Understanding the dynamics of water availability under the changing climate in such a basin assists in the proper planning of land use and other development activities. This study presents changes in climatic elements such as rainfall, temperature, and evapotranspiration using observation data and regional climate models (RCMs) under two representative concentration pathways (RCPs) for three future periods. We use a calibrated hydrological model to further assess climate change’s effects on streamflow. We select three RCMs and their ensemble’s mean by evaluating their performance with respect to observations. We apply the modified Mann–Kendall test to detect trends in each dataset. The result shows that annual mean maximum and minimum temperatures increase in the catchment for the 2021–2040, 2041–2070, and 2071–2100 periods as compared to baseline (1989–2018) under both RCP4.5 and RCP8.5 scenarios. Annual mean maximum temperature and potential evapotranspiration experienced a significant decreasing trend during the year from 1989 to 2018. Furthermore, there was an increasing trend in annual rainfall from 1989 to 2018, which could be related to the cooling of sea surface temperature over the equatorial Pacific. We detect an increasing trend in temperature in both scenarios and all periods; however, no clear trend pattern is found in rainfall. The result from hydrological model simulations reveals that the mean monthly streamflow slightly increases in the winter season while it decreases during the main rainy season. Further study of detailed weather systems, which affect the subbasin’s climate, is recommended.

Geo-climatic risk factors for chronic rhinosinusitis in southwest Iran.

Chronic rhinosinusitis (CRS) is a prevalent and disabling paranasal sinus disease associated with some environmental factors. In this study, we evaluated the effect of geo-climatic factors on CRS in a region of southwest Iran. The study mapped the residency addresses of 232 patients with CRS who lived in Kohgiluyeh and Boyer-Ahmad province and had undergone sinus surgery from 2014 to 2019. The effects of Mean Annual Humidity (MAH), Mean Annual Rainfall (MAR), Mean Annual Temperature (MAT), maximum MAT (maxMAT), minimum MAT (minMAT), Mean Annual Evaporation (MAE), wind, elevation, slope, and land cover were assessed on the occurrence of CRS using Geographical Information System (GIS). Statistical analysis was performed using univariate and multivariate binary logistic regression. Patients came from 55 points including villages, towns, and cities. In univariate analysis, climatic factors including MAT (OR = 0.537), minMAT (OR = 0.764), maxMAT (OR = 0.63), MAR (OR = 0.994), and MAH (OR = 0.626) were significantly related to CRS occurrence. Elevation (OR = 0.999), slope (OR = 0.9), and urban setting (OR = 24.667) were the significant determinants among geographical factors when analyzed independently. The multivariate analysis found maxMAT (OR = 0.5), MAR (OR = 0.994), elevation (OR = 0.998), and urban (OR = 16.8) as significant factors affecting CRS occurrence. The urban setting is the most critical factor affecting CRS disease. Cold and dry areas and low attitude are the other risk factors for CRS in Kohgiluyeh and Boyer-Ahmad province, southwest Iran.

Evaluating climate change impact on the hydrology of Kessie Watershed, Upper Blue Nile Basin, Ethiopia

Climate change affects ecosystems, agriculture, human health, forestry, and water resource availability. This study is mainly aimed at assessing the climate change effect on the water resources of the Kessie Watershed in the Upper Blue Nile Basin, Ethiopia. The updated Coupled Model Intercomparison Project Phase 6 (CMIP-6) data outputs were used. The three climate model outputs: ACESS_ESM1-5, FGOALS_g3, and GFDL_ESM4 with two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5) scenarios, were used. The climate model output rainfall and temperature data were downscaled to the station level through bias correction. The catchment hydrology was represented by the SWAT—Soil and Water Assessment Tool—through calibration and validation. Future temperatures and rainfall change were evaluated by the Mann–Kendall trend test and Sen’s slope estimator. Future climate change trend analysis and streamflow simulation were done on two time horizons: the 2050s (2041–2070) and the 2080s (2071–2100). The baseline streamflow data (1985–2014) were used as a reference. The global climate model projection data indicated mean annual precipitation and temperatures show a slight increase for the future in both scenarios for all climate model outputs. According to the SSP2-4.5 and SSP5-8.5 scenarios, respectively, mean annual precipitation is expected to increase by 5% and 4.89% in the 2050s and 10.13% and 6.8% in the 2080s based on ACCESS_ESM1-5; 4.7% and 3.8% in the 2050s and 4.3% and 4.84% in the 2080s based on FGOALS_g3; and 4.67% and 3.81% in the 2050s and 4.67% and 3.81% based on GFDL_ESM4 models data. Yearly average maximum temperature may increase by 3.62 °C and 1.87 °C in the 2050s and 3.31 °C and 2.99 °C in the 2080s based on ACCESS ESM1-5, 1.76 °C and 1.25 °C in the 2050s and 3.44 °C and 2.61 °C in the 2080s based on FGOALS-g3, and 2.15 °C and 3.83 °C in the 2050s and 1.37 °C and 2.66 °C in the 2080s based on GFDL-ESM4 model data. Similarly, the mean annual minimum temperature is also expected to increase by 2.73 °C and 1.90 °C in the 2050s and 5.63 °C and 4.52 °C in the 2080s based on ACCESS ESM1-5, 3.04 °C and 2.43 °C in the 2050s and 3.55 °C and 4.36 °C in the 2080s based on FGOALS-g3, and 2.31 °C and 3.29 °C in the 2050s, and 3.16 °C and 3.87 °C in the 2080s based on GFDL-ESM4 model data. The streamflow is also expected to increase. In the 2050s, simulated annual streamflow is expected to increase from 12.1 to 21.8% and 9.8 to 15.4% in SSP2-4.5 and SSP5-8.5, respectively, whereas in the 2080s, the change is expected to increase from 15.14 to 24.08% and 13.08 to 41% in SSP2-4.5 and SSP5-8.5, respectively. Future water resource potential of the case study watershed seems able to support irrigation and other projects.

Littleseed canarygrass (Phalaris minor Retz.) a major weed of rice-wheat system in India is predicted to experience range contraction under future climate

Modelling was carried out using maximum entropy model (MaxEnt) to explore and predict the invasion potential of littleseed canarygrass (Phalaris minor Retz.) in India under current as well as future climatic conditions under Representative Concentration Pathways (RCPs) 4.5 and 8.5 for the years 2050 and 2070. Mutually least correlated 8 bioclimatic variables along with soil and elevation data were used for the modelling over 223 occurrence locations of the species. Jackknife test revealed the significance of temperature derived variables viz. temperature seasonality, annual mean temperature and minimum temperature of the coldest month in governing the potential distribution of P. minor. Currently, 21% of India’s area is either highly (9%) or moderately (12%) suitable as habitat for P. minor. Our model predicts approximately 90% contraction in the area considered to be highly or moderately suitable climatically between 2050 and 2070 under both moderate and high emissions scenarios. Thus, under future climate, a significant niche shift by the species and decreased suitability was observed compared to the current distribution. The present study is first of its kind in exploring the invasion potential of alien invasive weed P. minor under climate change scenarios which is a current threat to rice-wheat system in Indo-Gangetic plains of India.

Declines of bees and butterflies over 15 years in a forested landscape

Despite growing concerns about pollinator declines,1,2,3,4 evidence that this is a widespread problem affecting entire communities remains limited.5 There is a particular shortage of pollinator time series from relatively undisturbed natural habitats, such as forests, which are generally thought to provide refuge to biodiversity from anthropogenic stressors.6 Here, we present the results from standardized pollinator sampling over 15 years (2007-2022) at three relatively undisturbed forested locations in the southeastern United States. We observed significant declines in the richness (39%) and abundance (62.5%) of bees as well as the abundance of butterflies (57.6%) over this time period. Unexpectedly, we detected much stronger declines in the richness and abundance of above-ground-nesting bees (81.1% and 85.3%, respectively) compared with below-ground-nesting bees. Even after dropping the first or last year of sampling, which happened to yield the greatest and lowest numbers of pollinators, respectively, we still detected many of the same negative trends. Our results suggest that sharp declines in pollinators may not be limited to areas experiencing direct anthropogenic disturbances. Possible drivers in our system include increasing mean annual minimum temperatures near our study sites as well as an invasive wood-nesting ant that has become increasingly widespread and abundant in the region over the course of this study.

Geospatial mapping and biophysical analysis of factors that affect oil palm (Elaeis guineensis) yields in Peninsular Malaysia

The objective of this study is to model oil palm yield distributions and investigate the factors that influence oil palm yields in Peninsular Malaysia using remote sensing and geographical information system (GIS) techniques. Herein, we investigate six factors that influence oil palm yield in Peninsular Malaysia, including mean annual minimum and maximum temperatures, mean annual rainfall, average number of rainy days per year, average annual relative humidity, and elevation. In order to model oil palm yield in Peninsular Malaysia, a large yield dataset covering Peninsular Malaysia for 37 years (1983 to 2020), as well as related explanatory variables, were collected. Areal interpolation was used to model the average yield distribution across the study area. The findings of this study show that oil palm yields vary across Peninsular Malaysia. Due to favourable climate and elevation, southern and southwestern Peninsular Malaysia, including Johor, Pahang, Melaka, and Selangor, recorded the highest amount of yield.