The impact of climatic variables on the population dynamics of the main malaria vector, Anopheles stephensi Liston(Diptera: Culicidae), in southern Iran

Objective:To determine the significance of temperature,rainfall and humidity in the seasonal abundance of Anopheles stephensi in southern Iran.Methods:Data on the monthly abundance of Anopheles stephensi larvae and adults were gathered from earlier studies conducted between 2002 and 2019 in malaria prone areas of southeastern Iran.Climatic data for the studied counties were obtained from climatology stations.Generalized estimating equations method was used for cluster correlation of data for each study site in different years.Results:A significant relationship was found between monthly density of adult and larvae of Anopheles stephensi and precipitation,max temperature and mean temperature,both with simple and multiple generalized estimating equations analysis(P<0.05).But when analysis was done with one month lag,only relationship between monthly density of adults and larvae of Anopheles stephensi and max temperature was significant(P<0.05).Conclusions:This study provides a basis for developing multivariate time series models,which can be used to develop improved appropriate epidemic prediction systems for these areas.Long-term entomological study in the studied sites by expert teams is recommended to compare the abundance of malaria vectors in the different areas and their association with climatic variables.

On the Relationship between Climatic Variables and Pressure Systems over Saudi Arabia in the Winter Season

The synoptic circulation over Saudi Arabia is complicated and frequently governed by the effect of large-scale pressure systems. In this work, we used NCEP–NCAR global data to illustrate the relationship between climatic variables and the main pressure systems that affect the weather and climate of Saudi Arabia, and also to investigate the influence of these pressure systems on surface air temperature（SAT） and rainfall over the region in the winter season. It was found that there are two primary patterns of pressure that influence the weather and climate of Saudi Arabia. The first occurs in cases of a strengthening Subtropical High（Sub H）, a weakening Siberian High（Sib H）, a deepening of the Icelandic Low（Ice L）, or a weakening of the Sudanese Low（Sud L）. During this pattern, the Sub H combines with the Sib H and an obvious increase of sea level pressure（SLP） occurs over southern European, the Mediterranean, North Africa, and the Middle East. This belt of high pressure prevents interaction between midlatitude and extratropical systems, which leads to a decrease in the SAT,relative humidity（RH） and rainfall over Saudi Arabia. The second pattern occurs in association with a weakening of the Sub H, a strengthening of the Sib H, a weakening of the Ice L, or a deepening of the Sud L. The pattern arising in this case leads to an interaction between two different air masses： the first（cold moist） air mass is associated with the Mediterranean depression travelling from west to east, while the second（warm moist） air mass is associated with the northward oscillation of the Sud L and its inverted V-shape trough. The interaction between these two air masses increases the SAT, RH and the probability of rainfall over Saudi Arabia, especially over the northwest and northeast regions.

Intertidal Biodiversity and Their Response to Climatic Variables, Temperature and pH—What We Know

As per the Essential Climate Variables (ESV) of World Meterological Organisation (WMO), the physical, chemical and biological variables critically contribute to the earth’s climate. Among them, the variables such as temperature and pH in the marine environment may affect seriously and in turn it has an impact on the biota, especially in the intertidal environment, where it has brunt force. According to United Nations Framework Convention on Climate Change (UNFCCC), the datasets should provide the empirical evidence needed to predict the climate change and evoluate the mitigation and adaptation measures. Under this context, a review was carried out to know what extent marine scientists understand this factor and what level the biodiversity was evoluated and its impact was analysed in this article. Based on the existing literature review, it was understood that only a few groups that also only few species from these groups were studied in this aspect. The remaining groups and their species and their basic trophic were not evolved in this aspect. So, the marine scientific community, environmentalist and policy makers should take stock on this aspect and give thrust on this study.

Responses of Annual Variability of Vegetation NPP to Climate Variables Using Satellite Techniques in Gadarif State, Sudan

Plants play an essential role in matter and energy transformations and are key messengers in the carbon and energy cycle. Net primary productivity (NPP) reflects the capability of plants to transform solar energy into photosynthesis. It is very sensible for factors affecting on vegetation variability such as climate, soils, plant characteristics and human activities. So, it can be used as an indicator of actual and potential trend of vegetation. In this study we used the actual NPP which was derived from MODIS to assess the response of NPP to climate variables in Gadarif State, from 2000 to 2010. The correlations between NPP and climate variables (temperature and precipitation) are calculated using Pearson’s Correlation Coefficient and ordinary least squares regression. The main results show the following 1) the correlation Coefficient between NPP and mean annual temperature is Somewhat negative for Feshaga, Rahd, Gadarif and Galabat areas and weakly negative in Faw area;2) the correlation Coefficient between NPP and annual total precipitation is weakly negative in Faw, Rahd and Galabat areas and somewhat negative in Galabat and Rahd areas. This study demonstrated that the correlation analysis between NPP and climate variables (precipitation and temperature) gives reliably result of NPP responses to climate variables that is clearly in a very large scale of study area.

Key Variables Explaining Soil Organic Carbon Content Variations in Croplands and Non-Croplands in Chinese Provinces

Soil organic carbon (SOC) plays an important role in global carbon cycles.Large spatial variations in SOC contents result in uncertain estimates of the SOC pool and its changes.In the present study,the key variables explaining the SOC contents of croplands (CPs) and non-croplands (NCPs) in Chinese provinces were investigated.Data on SOC and other soil properties (obtained from the Second National Soil Survey conducted in the late 1970s to the early 1990s),climate parameters,as well as the proportion of the CP to the total land area (Pcp) were used.SOC content variations within a province were larger than those among provinces.Soil clay and total phosphorus content,ratio of annual precipitation to mean temperature,as well as Pcp were able to explain 75% of the SOC content variations in whole soil samples.Soil pH,mean temperature during the growing season from May to October,and mean annual wind velocity were able to explain 63% of the SOC content variations in NCP soils.Compared with NCP soils,CP soils had lower SOC contents,with smaller variations within and among provinces and lower C/N ratios.Stepwise regression showed that the soil clay content was a unique factor significantly correlated with the SOC content of CP soils.However,this factor only explained 24% of the variations.This result suggested that variables related to human activities had greater effects on SOC content variations in CP soils than soil properties and climate parameters.Based on SOC contents directly averaged from soil samples and estimated by regression equations,the total SOC pool in the topsoil (0-20 cm) of China was estimated at 60.02 Pg and 57.6 Pg.Thousands of years of intensive cultivation in China resulted in CP topsoil SOC loss of 4.34-4.98 Pg.

Trends in major and minor meteorological variables and their influence on reference evapotranspiration for mid Himalayan region at east Sikkim, India

Abstract： Estimation of evapotranspiration （ET） for mountain ecosystem is of absolute importance since it serves as an important component in balancing the hydrologic cycle. The present study evaluates the performance of original and location specific calibrated Hargreaves equation （HARG） with the estimates of Food and Agricultural Organization （FAO） Penman Monteith （PM） method for higher altitudes in East Sikkim, India. The results show that the uncalibrated HARG model underestimates ET0 by 0.35 mm day^-1 whereas the results are significantly improved by regional calibration of the model. In addition, this paper also presents the variability in the trajectory associated with the climatic variables with the changing climate in the study site. Non- parametric Mann-Kendall （MK） test was used to investigate and understand the mean monthly trend of eight climatic parameters including reference evapotranspiration （ET0） for the period of 1985 - 2009. Trend of ET0 was estimated for the calculations done by FAO PM equation. The outcomes of the trend analysis show significant increasing （p ≤ 0.05） trend represented by higher Z-values, through MK test, for net radiation （Rn）, maximum temperature （Tmax） and minimum temperature （Train）, especially in the first months of the year. Whereas, significant （0.01 ≥ p ≤0.05） decreasing trend in vapor pressure deficit （VPD） and precipitation （P） is observed throughout the year. Declining trend in sunshine duration, VPD and ET0 is found in spring （March - May） and monsoon （June - November） season. The result displays significant （0.01≤ p ≤0.05） decreasing ET0 trend between （June - December） except in July, exhibiting the positive relation with VPD followed by sunshine duration at the station. Overall, the study emphasizes the importance of trend analysis of ET0 and other climatic variables for efficient planning and managing the agricultural practices, in identifying the changes in the meteorological parameters and to accurately assess the hydrologic water balance of the hilly regions.

Behavior of wood basic density according to environmental variables

The relationships between climate conditions and wood density in tropical forests are still poorly understood.To quantify spatial dependence of wood density in the state of Minas Gerais(MG,Brazil),map spatial distribution of density,and correlate density with climate variables,we extracted data from the Forest Inventory of Minas Gerais for 1988 trees scaled throughout the territory and measured wood density of discs removed from the trees.Environmental variables were extracted from the database of the Ecological-Economic Zoning of Minas Gerais.For spatial analysis,tree densities were measured at 44 georeferenced sampling points.The data were subjected to exploratory analysis,variography,cross-validation,model selection,and ordinary kriging.The relationships between wood density and environmental variables were calculated using dispersion matrices,linear correlation,and regression.Wood density proved to be highly spatially dependent,reaching a correlation of 96%,and was highly continuous over a distance of 228 km.The distribution of wood density followed a continuous gradient of 514-659 kg m^(−3),enabling corre-lation with environment variables.Density was correlated with mean annual precipitation(−0.57),temperature(0.63),and evapotranspiration(0.83).Geostatistical methods proved useful in predicting wood density in native tropical forests with different climate conditions.Our results confirmed the sensitivity of wood density to climate change,which could affect future carbon stock in forests.

Impacts of climatic and marine environmental variations on the spatial distribution of Ommastrephes bartramii in the Northwest Pacific Ocean

Ommastrephes bartramii is an ecologically dependent species and has great commercial values among the AsiaPacific countries. This squid widely inhabits the North Pacific, one of the most dynamic marine environments in the world, subjecting to multi-scale climatic events such as the Pacific Decadal Oscillation（PDO）. Commercial fishery data from the Chinese squid-jigging fleets during 1995-2011 are used to evaluate the influences of climatic and oceanic environmental variations on the spatial distribution of O. bartramii. Significant interannual and seasonal variability are observed in the longitudinal and latitudinal gravity centers（LONG and LATG） of fishing ground of O. bartramii. The LATG mainly occurred in the waters with the suitable ranges of environmental variables estimated by the generalized additive model. The apparent north-south spatial shift in the annual LATG appeares to be associated with the PDO phenomenon and is closely related to the sea surface temperature（SST）and sea surface height（SSH） on the fishing ground, whereas the mixed layer depth（MLD） might contribute limited impacts to the distribution pattern of O. bartramii. The warm PDO regimes tend to yield cold SST and low SSH, resulting in a southward shift of LATG, while the cold PDO phases provid warm SST and elevated SSH,resulting in a northward shift of LATG. A regression model is developed to help understand and predict the fishing ground distributions of O. bartramii and improve the fishery management.

Changes in climatic variability and maize yield inNortheast China

The method linking general circulation models' (GCMs') outputs with crop growthsimulation models' inputs has been the first choice in the studies of impacts of climate change.Changes in climatic variability, however were not considered in most studies due to limitedknowledge concerned Changes in climatic means derived from a general circulation model DKRZOPYC were input into a stochastic weather generator WGEN run for synthetic daily climate scenarios.Monte Carlo stochastic sampling method was adopted to generate climate change scenarios withvarious possible climatic veriabilities. A dynamic simulation model for maize growth anddevelopment of MZMOD was used to assess the potenhal implication of the changes in both climaticmeans and variability nd the boacts of crop management in changing climate on maize productionin Northeast China. The results indicated that maize yield would be reduced to various degrees inmost of the sensitivity experiments of climatic variability associating with the shortening of theduration of phenological phase of different sowing dates. The Anpacts of the diverse distributions ofclimatic factors detetmined by multiple changes in climatic variability on maire production and itsvariation, however, are not identical and have distinct regional disparities. Yield reduction caused bychanges in climatic means may be alleviated or aggravated by didributions of certain climaticvariables in line with the corresponding climatic variability according to the sensitivity analyses.Consequently, the hypothesis keeping climatic variability constant in the traditional research imposesrestriction on the overall inveshgation of the impacts of climate change on maize production.

Evaluation of CRU TS, GPCC, AgMERRA, and AgCFSR meteorological datasets for estimating climate and crop variables: A case study of maize in Qazvin Province, Iran

In the past few decades,meteorological datasets from remote sensing techniques in agricultural and water resources management have been used by various researchers and managers.Based on the literature,meteorological datasets are not more accurate than synoptic stations,but their various advantages,such as spatial coverage,time coverage,accessibility,and free use,have made these techniques superior,and sometimes we can use them instead of synoptic stations.In this study,we used four meteorological datasets,including Climatic Research Unit gridded Time Series(CRU TS),Global Precipitation Climatology Centre(GPCC),Agricultural National Aeronautics and Space Administration Modern-Era Retrospective Analysis for Research and Applications(AgMERRA),Agricultural Climate Forecast System Reanalysis(AgCFSR),to estimate climate variables,i.e.,precipitation,maximum temperature,and minimum temperature,and crop variables,i.e.,reference evapotranspiration,irrigation requirement,biomass,and yield of maize,in Qazvin Province of Iran during 1980-2009.At first,data were gathered from the four meteorological datasets and synoptic station in this province,and climate variables were calculated.Then,after using the AquaCrop model to calculate the crop variables,we compared the results of the synoptic station and meteorological datasets.All the four meteorological datasets showed strong performance for estimating climate variables.AgMERRA and AgCFSR had more accurate estimations for precipitation and maximum temperature.However,their normalized root mean square error was inferior to CRU for minimum temperature.Furthermore,they were all very efficient for estimating the biomass and yield of maize in this province.For reference evapotranspiration and irrigation requirement CRU TS and GPCC were the most efficient rather than AgMERRA and AgCFSR.But for the estimation of biomass and yield,all the four meteorological datasets were reliable.To sum up,GPCC and AgCFSR were the two best datasets in this study.This study suggests the use of meteorological datasets in water resource management and agricultural management to monitor past changes and estimate recent trends.

Development of a Low-Cost Weather Station to Measure in Situ Essential Climate Variables

A weather station is proposed especially designed for developing countries, and to meet the standards of the international scientific community making research on the earth system. The station would measure in situ several ECV （essential climate variables）. These data may enable an agricultural breakthrough in countries lacking meteorological infrastructure, help in climate change monitoring, and facilitate diffusion of wind energy. A pre-feasibility analysis is presented. It appears interesting that the station is supplied by a social enterprise. A research to establish the best shelter design using computational fluid dynamics is also reported. The criterion is the accuracy with which the surface air temperature is reproduced inside the shelter. A design following recommendations by the WMO （World Meteorological Organization）, a smaller design with identical geometry, and two alternative small designs are analyzed. All four designs are simulated in PVC, natural rubber and wood, with and without white paint coating. The smaller shelters perform better. The influence of the material, dimensions and design is smaller than that of the white paint. Shelters made of PVC or rubber, and/or in alternative designs, may be more interesting if other criteria are considered, like whether logistics, manufacturing, etc. are more sustainable, easier and/or cheaper.

Analysis of Hydro-Climatical Variability in the Mo Basin in Togo

Climate changes are affecting water resources around the world and the Mo Basin (MB) in Togo is no exception to this observation. This study aims at analyzing the influence of hydro-climatical data in the Mo Basin. To achieve this, Pettit’s stationarity break tests, Hubert’s segmentation, Nicholson’s [1] reduced centered index, Lamb [2] and flow coefficients have been applied. In addition, temperature, precipitation, evapotranspiration, relative humidity and discharge data from 1961 to 2018 have been used for this purpose. While rainfall is decreasing despite an increase of 22.8% at the Fazao station and 2.8% at Sotouboua station, the flow coefficients evolve synchronously with the precipitation data and show a strong link between both parameters. The climatic balance sheet is positive six months in the year (May to October), throughout the period of observation (1961-2018). Only 1962 and 1963 recorded an annual rainfall greater than the annual evapotranspiration. The other years undergo a climatic drought, increasingly pronounced, which strongly impacts the hydrology of rivers. This has a strong impact on water resources and food security and resources of the Fazao-Malfakassa reserve in the region.

Agro-Climatic Risks Analysis in Climate Variability Context in Ségou Region

In the Sahel region, the population depends largely on rain-fed agriculture. In West Africa in particular, climate models turn out to be unable to capture some basic features of present-day climate variability. This study proposes a contribution to the analysis of the evolution of agro-climatic risks in the context of climate variability. Some statistical tests are used on the main variables of the rainy season to determine the trends and the variabilities described by the data series. Thus, the paper provides a statistical modeling of the different agro-climatic risks while the seasonal variability of agro-climatic parameters was analyzed as well as their inter annual variability. The study identifies the probability distributions of agroclimatic risks and the characterization of the rainy season was clarified.

Seasonal Dynamic of the Fall Armyworm, Spodoptera frugiperda (J.E Smith, 1797) (Lepidoptera: Noctuidae) on Maize Crop in the Sub-Sudanese Zone of Côte d’Ivoire

In Côte d’Ivoire, maize (Zea mays L) is the second most cultivated cereal after rice. Since the first report of Spodoptera frugiperda in Côte d’Ivoire, maize production in the northern regions has been affected resulting in maize production losses. This study aims to study the seasonal dynamic of Spodoptera frugiperda in maize fields in the sub-Sudanese zone, main zone of maize cultivation in Côte d’Ivoire. The study was done using pheromone trap lures. The results revealed a variation in the moth population at various growth stages during rainy and dry seasons. Notably, the highest numbers of moths were consistently trapped during the whorl stage with counts ranging from 131 ± 35.7 during the rainy season to 70.6 ± 15.01 in the dry season. The lowest numbers of moths were observed during pod maturation, with counts ranging from 30.3 ± 13.05 during the rainy season to 11.7 ± 3.05 in the dry season. Between the 7th and 21st days after sowing, the count of moths displayed a consistent upward trajectory, reaching 188 moths during the rainy season. The damages were particularly observed at whorl stage. The relationship between the numbers of moths and some climatic variables revealed a negative correlation between moths numbers and rainfall (r= −0.44) and relative humidity (r= −0.684). In contrast, there were positive relationships with temperature (r = 0.16), highlighting the significant impact of temperature changes on moth population dynamics. The research highlights the need for integrated pest management strategies that consider climatic factors and growth stages of maize to mitigate the impact of this insect pest on maize.

Arctic Sea Ice Variations in the First Half of the 20th Century:A New Reconstruction Based on Hydrometeorological Data

The shrinking Arctic sea-ice area(SIA) in recent decades is a striking manifestation of the ongoing climate change.Variations of the Arctic sea ice have been continuously observed by satellites since 1979, relatively well monitored since the 1950s, but are highly uncertain in the earlier period due to a lack of observations. Several reconstructions of the historical gridded sea-ice concentration(SIC) data were recently presented based on synthesized regional sea-ice observations or by applying a hybrid model–empirical approach. Here, we present an SIC reconstruction for the period1901–2019 based on established co-variability between SIC and surface air temperature, sea surface temperature, and sea level pressure patterns. The reconstructed sea-ice data for March and September are compared to the frequently used Had ISST1.1 and SIBT1850 datasets. Our reconstruction shows a large decrease in SIA from the 1920 to 1940 concurrent with the Early 20th Century Warming event in the Arctic. Such a negative SIA anomaly is absent in Had ISST1.1 data. The amplitude of the SIA anomaly reaches about 0.8 mln km^(2) in March and 1.5 mln km^(2) in September. The anomaly is about three times stronger than that in the SIBT1850 dataset. The larger decrease in SIA in September is largely due to the stronger SIC reduction in the western sector of the Arctic Ocean in the 70°–80°N latitudinal zone. Our reconstruction provides gridded monthly data that can be used as boundary conditions for atmospheric reanalyses and model experiments to study the Arctic climate for the first half of the 20th century.

Responses of runoff to changes in climate and human activities in the Liuhe River Basin, China

Since the 1950s,numerous soil and water conservation measures have been implemented to control severe soil erosion in the Liuhe River Basin(LRB),China.While these measures have protected the upstream soil and water ecological environment,they have led to a sharp reduction in the downstream flow and the deterioration of the river ecological environment.Therefore,it is important to evaluate the impact of soil and water conservation measures on hydrological processes to assess long-term runoff changes.Using the Soil and Water Assessment Tool(SWAT)models and sensitivity analyses based on the Budyko hypothesis,this study quantitatively evaluated the effects of climate change,direct water withdrawal,and soil and water conservation measures on runoff in the LRB during different periods,including different responses to runoff discharge,hydrological regime,and flood processes.The runoff series were divided into a baseline period(1956-1969)and two altered periods,i.e.,period 1(1970-1999)and period 2(2000-2020).Human activities were the main cause of the decrease in runoff during the altered periods,contributing 86.03%(-29.61 mm),while the contribution of climate change was only 13.70%(-4.70 mm).The impact of climate change manifests as a decrease in flood volume caused by a reduction in precipitation during the flood season.Analysis of two flood cases indicated a 66.00%-84.00%reduction in basin runoff capacity due to soil and water conservation measures in the upstream area.Soil and water conservation measures reduced the peak flow and total flood volume in the upstream runoff area by 77.98%and 55.16%,respectively,even with nearly double the precipitation.The runoff coefficient in the reservoir area without soil and water conservation measures was 4.0 times that in the conservation area.These results contribute to the re-evaluation of soil and water conservation hydrological effects and provide important guidance for water resource planning and water conservation policy formulation in the LRB.

Improving Optimal Fingerprinting Methods Requires a Viewpoint beyond Statistical Science

While being successful in the detection and attribution of climate change,the optimal fingerprinting method(OFM)may have some limitations from a physics-and-dynamics-based viewpoint.Here,an analysis is made on the linearity,noninteraction,and stationary-variability assumptions adopted by OFM.It is suggested that furthering OFM needs a viewpoint beyond statistical science,and the method should be combined with theoretical tools in the dynamics and physics of the Earth system,so as to be applied for the detection and attribution of nonlinear climate change including tipping elements within the Earth system.

Spatial distribution characteristics of climate-induced landslides in the Eastern Himalayas

Climate warming is constantly causing hydro-meteorological perturbations,especially in high-altitude mountainous regions,which lead to the occurrences of landslides.The impact of climatic variables(i.e.,precipitation and temperature)on the distribution of landslides in the eastern regions of the Himalayas is poorly understood.To address this,the current study analyzes the relationship between the spatial distribution of landslide characteristics and climatic variables from 2013 to 2021.Google Earth Engine(GEE)was employed to make landslide inventories using satellite data.The results show that 2163,6927,and 9601 landslides were heterogeneously distributed across the study area in 2013,2017,and 2021,respectively.The maximum annual temperature was positively correlated with the distribution of landslides,whereas precipitation was found to have a non-significant impact on the landslide distribution.Spatially,most of the landslides occurred in areas with maximum annual precipitation ranging from 800 to 1600 mm and maximum annual temperature above 15℃.However,in certain regions,earthquake disruptions marginally affected the occurrence of landslides.Landslides were highly distributed in areas with elevations ranging between 3000 and 5000 m above sea level,and many landslides occurred near the lower permafrost limit and close to glaciers.The latter indicates that temperature change-induced freeze-thaw action influences landslides in the region.Temperature changes have shown a positive correlation with the number of landslides within elevations,indicating that temperature affects their spatial distribution.Various climate projections suggest that the region will experience further warming,which will increase the likelihood of landslides in the future.Thus,it is crucial to enhance ground observation capabilities and climate datasets to effectively monitor and mitigate landslide risks.

Continual Reinforcement Learning for Intelligent Agricultural Management under Climate Changes

Climate change poses significant challenges to agricultural management,particularly in adapting to extreme weather conditions that impact agricultural production.Existing works with traditional Reinforcement Learning(RL)methods often falter under such extreme conditions.To address this challenge,our study introduces a novel approach by integrating Continual Learning(CL)with RL to form Continual Reinforcement Learning(CRL),enhancing the adaptability of agricultural management strategies.Leveraging the Gym-DSSAT simulation environment,our research enables RL agents to learn optimal fertilization strategies based on variable weather conditions.By incorporating CL algorithms,such as Elastic Weight Consolidation(EWC),with established RL techniques like Deep Q-Networks(DQN),we developed a framework in which agents can learn and retain knowledge across diverse weather scenarios.The CRL approach was tested under climate variability to assess the robustness and adaptability of the induced policies,particularly under extreme weather events like severe droughts.Our results showed that continually learned policies exhibited superior adaptability and performance compared to optimal policies learned through the conventional RL methods,especially in challenging conditions of reduced rainfall and increased temperatures.This pioneering work,which combines CL with RL to generate adaptive policies for agricultural management,is expected to make significant advancements in precision agriculture in the era of climate change.

Analysing the Potential Impact of Climate Change on the Hydrological Regime of the Upper Benue River Basin (North Cameroon)

In this study, we analyse the climate variability in the Upper Benue basin and assess its potential impact on the hydrology regime under two different greenhouse gas emission scenarios. The hydrological regime of the basin is more vulnerable to climate variability, especially precipitation and temperature. Observed hydroclimatic data (1950-2015) was analysed using a statistical approach. The potential impact of future climate change on the hydrological regime is quantified using the GR2M model and two climate models: HadGEM2-ES and MIROC5 from CMIP5 under RCP 4.5 and RCP 8.5 greenhouse gas emission scenarios. The main result shows that precipitation varies significantly according to the geographical location and time in the Upper Benue basin. The trend analysis of climatic parameters shows a decrease in annual average precipitation across the study area at a rate of -0.568 mm/year which represents about 37 mm/year over the time 1950-2015 compared to the 1961-1990 reference period. An increase of 0.7°C in mean temperature and 14% of PET are also observed according to the same reference period. The two climate models predict a warming of the basin of about 2°C for both RCP 4.5 and 8.5 scenarios and an increase in precipitation between 1% and 10% between 2015 and 2100. Similarly, the average annual flow is projected to increase by about +2% to +10% in the future for both RCP 4.5 and 8.5 scenarios between 2015 and 2100. Therefore, it is primordial to develop adaptation and mitigation measures to manage efficiently the availability of water resources.