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It is now common knowledge that many water resources stresses relate to access to water within a basin. Yi River Basin, a typical river basin characterized by intensive agricultural processes, significant population growth, and water management, has been undergoing grave water problems. In this paper, the long-term trend of precipitation and streamflow in Yi River Basin, from 1964 to 2010, was investigated via Mann-Kendall test. The change point occurred in the year 1965 dividing the long-term series into two periods. Climate elasticity method and linear regression method were implemented to quantify the impact of precipitation and human activities on runoff and presented basically consistent results of the percentage change in an annual runoff for the postchange period. The results reveal that the decline of annual runoff in postchange period is mainly attributed to precipitation variability of 53.66–58.25% and human activities of 46.34–41.74%, as estimated by climate elasticity method and linear regression method, respectively. This study detected the changes in the precipitation-streamflow relationship and investigated the possible causes in the Yi River, which will be helpful for providing a reference for the management of regional water resources. [ABSTRACT FROM AUTHOR]

Climate change has become a challenging and emerging research problem in many research related areas. One of the key parameters in analyzing climate change is to analyze temperature variations in different regions. The temperature variation in a region is periodic within the interval. Temperature variations, though periodic in nature, may vary from one region to another and such variations are mainly dependent on the location and altitude of the region and also on other factors like the nearness of sea and vegetation. In this paper, we analyze such periodic variations using recurrence plot (RP), cross recurrence plot (CRP), recurrence rate (RR), and correlation of probability of recurrence (CPR) methods to find similarities of periodic variations between and within climatic regions and to identify their connectivity trend. First, we test the correctness of our method by applying it on voice and heart rate data and then experimentation is performed on synthetic climate data of nine regions in the United States and eight regions in China. Finally, the accuracy of our approach is validated on both real and synthetic datasets and demonstrated using ANOVA, Kruskal–Wallis, and z-statistics significance tests. [ABSTRACT FROM AUTHOR]

It is not the first time in human history, nor will it be the last for that matter, that a collective problem calls for a collective response. Climate change fueled by greenhouse emissions affects humankind alike. Despite the disagreement among policymakers and scientists on the severity of the issue, the truth is that the problem remains. A broad look at different technologies being used today in different fields has led to the idea of bringing them together in an attempt to offer a viable solution to reducing anthropogenic CO2. The following paper describes how the nanotechnologies, available or soon to be available, would make CO2 capture, cache, and conversion (coined the three Cs) a valid way for achieving a more sustainable energy society. Authors also set out to highlight with this work how knowledge transfer is instrumental in the development of technology and how methodical assessment of crossovers can expedite research when time plays against us. [ABSTRACT FROM AUTHOR]

This paper reported a comprehensive analysis on the diurnal variation of the Atmospheric Boundary Layer (ABL) in summer of Badain Jaran Desert and discussed deeply the effect of surface thermal to ABL, including the Difference in Surface-Air Temperature (DSAT), net radiation, and sensible heat, based on limited GPS radiosonde and surface observation data during two intense observation periods of experiments. The results showed that (1) affected by topography of the Tibetan Plateau, the climate provided favorable external conditions for the development of Convective Boundary Layer (CBL), (2) deep CBL showed a diurnal variation of three- to five-layer structure in clear days and five-layer ABL structure often occurred about sunset or sunrise, (3) the diurnal variation of DSAT influenced thickness of ABL through changes of turbulent heat flux, (4) integral value of sensible heat which rapidly converted by surface net radiation had a significant influence on the growth of CBL throughout daytime. The cumulative effect of thick RML dominated the role after CBL got through SBL in the development stage, especially in late summer, and (5) the development of CBL was promoted and accelerated by the variation of wind field and distribution of warm advection in high and low altitude. [ABSTRACT FROM AUTHOR]

In the context of global climate change, the extent of snow cover in Siberia has significantly decreased since the 1970s, especially in spring. The changes of snow cover at middle and high latitudes have significant impacts on the meteorological and hydrological processes because the snow cover can affect the surface energy, water balance, and the development of the atmospheric boundary layer. In this paper, the temporal and spatial changes in snow cover were firstly estimated based on a long time series of remote sensing snow cover data, both showing a decreased trend. Based on this, we estimated the radiative forcing caused by the snow cover changes from the 1970s to the 2010s and compared it with the radiative forcing caused by the vegetation cover changes over the same time period in Siberia, indicating that the snow cover changes in Siberia can accelerate climate warming and the vegetation cover changes here have the opposite effect. Furthermore, the snow cover changes may play a more important role than the vegetation cover changes in regulating the surface radiation balance in Siberia on the regional scale. [ABSTRACT FROM AUTHOR]

Precipitation data from 30 stations in the Huaihe River basin (HRB), a climatic transitional zone in east China, were used to investigate the spatiotemporal variability and trends of extreme precipitation on multitimescales for the period 1961–2010. Results indicated that (1) the spatial pattern of the annual precipitation, rainy days, extreme precipitation, and maximum daily precipitations shows a clear transitional change from the south (high) to the north (low) in the HR; it confirmed the conclusion that the HRB is located in the transitional zone of the 800 mm precipitation contour in China, where the 800 mm precipitation contour is considered as the geographical boundary of the south and the north. (2) Higher value of the extreme precipitation intensity mainly occurs in the middle of the east and the central part of the basin; it reveals a relatively distinct west-east spatial disparity, and this is not in line with the spatial pattern of the extreme precipitation total, the sum of the precipitation in 95th precipitation days. (3) Annual precipitation of 22 stations exhibits increasing trend, and these 22 stations are located from the central to the northern part. There is no significant trend detected for the seasonal precipitation. The summer precipitation exhibits a larger change range; this might cause the variation of the flood and drought in the HBR. However, the increasing trend in winter precipitation may be beneficial to the relief of winter agricultural drought. Rainy days in 12 stations, mostly located in and around the central northeastern part, experienced significant decreasing trend. Extreme precipitation days and precipitation intensity have increasing trends, but no station with significant change trend is detected for the maximum precipitation of the basin. (4) The spatiotemporal variability in the HRB is mainly caused by the geographic differences and is largely influenced by the interdecadal variations of East Asian Summer Monsoon in eastern China. The output of this paper could provide references for the practical decision making and integrated basin management of Huaihe River basin. [ABSTRACT FROM AUTHOR]

Floods and droughts have been two of the most devastating consequences of the climate crisis affecting billions of people in the world. However, unlike the other natural hazards, flooding is manageable through appropriate flood management mechanisms. This study emphasizes on developing a flood hazard zone for the Upper Awash River Basin (UARB), Ethiopia. Six relevant climate, physiographic, and biophysical factors were considered. Then, a flood hazard map was developed employing the analytic hierarchy process (AHP) method and further validated using sensitivity analysis and collected flood marks. The results revealed that drainage density, rainfall, and elevation have higher significance, while land use and soil permeability have a low impact in the process of flood generation. The map showed vulnerable areas at different levels and can serve as a valuable input for the decision makers to consider in the process of implementing emergency plans as well as long-term flood mitigation options. [ABSTRACT FROM AUTHOR]

Global warming increases global average precipitation and evaporation, causing extreme climate and hydrological events to occur frequently. Future changes in temperature, precipitation, and runoff from 2021 to 2050 in the upper reaches of the Minjiang River were analyzed using a distributed hydrological model, the SWAT (Soil and Water Assessment Tool), under a future climate scenario. Simultaneously, future variation characteristics of extreme climate hydrological elements in the upper reaches of the Minjiang River were analyzed using extreme climate and runoff indicators. The research shows that the frequency and intensity of the extreme temperature warming index will increase, while those of the extreme temperature cooling index will increase and then weaken in the upper reaches of the Minjiang River under a future climate scenario. The duration of precipitation, the intensity of continuous heavy precipitation, and the frequency of heavy precipitation will increase, whereas the intensity of short-term heavy precipitation and the frequency of heavy precipitation will decrease. However, spatial distribution of flood in the upper reaches is different, and thus flood risk in the upstream source area will still tend to increase. Particular attention should be given to the increase in autumn flood risk in the upper reaches of the Minjiang River. [ABSTRACT FROM AUTHOR]

Climate change is a terrible global concern and one of the greatest future threats to societal development as a whole. The accelerating pace of climate change is becoming a major challenge for agricultural production and food security everywhere. The present study uses the midcentury climate derived from the ensemble of 29 general circulation models (GCMs) on a spatial grid to quantify the anticipated climate change impacts on rice, maize, black gram, and red gram productivity over Tamil Nadu state in India under RCP 4.5 and RCP 8.5 scenarios. The future climate projections show an unequivocal increase of annual maximum temperature varying from 0.9 to 2.2°C for RCP 4.5 and 1.4 to 2.7°C in RCP 8.5 scenario by midcentury, centered around 2055 compared to baseline (1981–2020). The projected rise in minimum temperature ranges from 1.0 to 2.2°C with RCP 4.5 and 1.8 to 2.7°C under RCP 8.5 scenario. Among the monsoons, the southwest monsoon (SWM) is expected to be warmer than the northeast monsoon (NEM). Annual rainfall is predicted to increase up to 20% under RCP 4.5 scenario in two-third of the area over Tamil Nadu. Similarly, RCP 8.5 scenario indicates the possibility of an increase in rainfall in the midcentury with higher magnitude than RCP 4.5. Both SWM and NEM seasons are expected to receive higher rainfall during midcentury under RCP 4.5 and RCP 8.5 than the baseline. In the midcentury, climate change is likely to pose a negative impact on the productivity of rice, maize, black gram, and red gram with both RCP 4.5 and RCP 8.5 scenarios in most places of Tamil Nadu. The magnitude of the decline in yield of all four crops would be more with RCP 8.5 over RCP 4.5 scenario in Tamil Nadu. Future climate projections made through multi-climate model ensemble could increase the plausibility of future climate change impact assessment on crop productivity. The adverse effects of climate change on cereal and legume crop productivity entail the potential adaptation options to ensure food security. [ABSTRACT FROM AUTHOR]

As one of the most productive plantation producers in the world, Indonesia also faces rapid change in both social and environmental systems. These conditions are predicted to become more disruptive to the agricultural sector in the future. Therefore, understanding the impact of social and environmental disruption on smallholder plantations' resilience is vital to formulate a strategy for the sustainability of farmers' livelihoods in this country. Using survey data from 360 smallholding farmers in six villages from three districts in Bengkulu Province, Indonesia, the study deployed a multidimensional approach to assess smallholders' resilience to social and environmental disruption as well as towards economic dynamics. There are four dimensions of smallholder resilience, namely, the ability of adaptation, recoverability, anticipation, and farmers' innovation level. Social disruption was indicated by farmers' demography, epidemic/family health, social conflict, culture clash, and intention on land conversion. Meanwhile, environmental disruption was shown by natural catastrophe incidents, climate variations, environmentally unfriendly cultivation activities, and land fires. Since the resilience level was classified as binary, bivariate probit model was used in the analysis. The result shows that smallholder plantations in Bengkulu Indonesia are categorized as innovative, and recoverable, but less adaptive, and less anticipatory farmers. Overall, more than 50% of smallholder plantations are classified as less resilient smallholders. The statistical result empirically uncovers that the intentions of land conversion, climate change, and environmentally unfriendly farming activities statistically have a significant contribution to the reduction of smallholder plantations' resilience. Furthermore, the economic dynamisms such as lack of input availability, price volatility, demand uncertainty, and capital limitation have a significant negative impact on smallholder plantation resilience. [ABSTRACT FROM AUTHOR]

The purpose of this work is to present phenology as a valid indicator and methodology for monitoring and assessing the impact of climate change on plant-based tourist activities. Fruit-picking has become a popular rural tourism activity worldwide. However, fruit maturity dates (FMD) have been affected by climate change (CC), which has in turn profoundly affected fruit-picking tourism activities (FPTA). In this paper, phenological data on the FMD for 45 types of plants in 1980–2012, dates for more than 200 fruit-picking festivals, and data on monthly average air temperature in 1980–2013 were used to assess the impact of CC on FPTA by wavelet and correlation analyses. The findings indicated that the study area had been significantly affected by CC. Prevailing temperatures at one or three months prior have a decisive influence on FMD. Among the 11 plants directly related to FPTA, the FMD of four were significantly advanced, while 6-7 were significantly delayed owning to increased temperature. Of the 11 FPTA, only two had realized the impact of CC and had adjusted festival opening dates based on dynamic changes. However, a considerable number of festival activities remained fixed or scheduled on the weekends. [ABSTRACT FROM AUTHOR]

This paper aims to detect climate change points and compare the extreme temperature changes with the average-value changes in the Amur River basin. The daily air temperatures of 44 stations in the Amur River basin were collected from April 1, 1954, to March 31, 2013. The change points for annual mean and extreme temperature in 44 individual stations and their average were detected by the Mann-Kendall test, respectively. The annual mean temperature changed during 1980s in terms of increased mean value and relative stable standard deviation. The annual maximum temperature from 31 stations mostly located in the central and northwest basin changed significantly, and their change points occurred mainly in 1990s. For the annual minimum temperature, 32 stations mainly located in the central basin had significant changes. The generalized extreme value distribution was fitted to the postchange point subseries of annual extreme temperature and the parameters were estimated by the maximum likelihood method. The 10/50/100-year return levels were estimated by the method of profile likelihood. For the areas in the central and Northwestern basin, the probability of occurrence of hot extremes increased, while the occurrence probability of cold extremes was decreased in the central basin under climate change. [ABSTRACT FROM AUTHOR]

Power curve of a wind turbine depicts the relationship between output power and hub height wind speed and is an important characteristic of the turbine. Power curve aids in energy assessment, warranty formulations, and performance monitoring of the turbines. With the growth of wind industry, turbines are being installed in diverse climatic conditions, onshore and offshore, and in complex terrains causing significant departure of these curves from the warranted values. Accurate models of power curves can play an important role in improving the performance of wind energy based systems. This paper presents a detailed review of different approaches for modelling of the wind turbine power curve. The methodology of modelling depends upon the purpose of modelling, availability of data, and the desired accuracy. The objectives of modelling, various issues involved therein, and the standard procedure for power performance measurement with its limitations have therefore been discussed here. Modelling methods described here use data from manufacturers’ specifications and actual data from the wind farms. Classification of modelling methods, various modelling techniques available in the literature, model evaluation criteria, and application of soft computing methods for modelling are then reviewed in detail. The drawbacks of the existing methods and future scope of research are also identified. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) generators suffer from fluctuating output power due to the highly fluctuating primary energy source. With significant PV penetration, these fluctuations can lead to power system instability and power quality problems. The use of energy storage systems as fluctuation compensators has been proposed as means to mitigate these problems. In this paper, the behavior of PV power fluctuations in Northern European climatic conditions and requirements for sizing the energy storage systems to compensate them have been investigated and compared to similar studies done in Southern European climate. These investigations have been performed through simulations that utilize measurements from the Tampere University of Technology solar PV power station research plant in Finland. An enhanced energy storage charging control strategy has been developed and tested. Energy storage capacity, power, and cycling requirements have been derived for different PV generator sizes and power ramp rate requirements. The developed control strategy leads to lesser performance requirements for the energy storage systems compared to the methods presented earlier. Further, some differences on the operation of PV generators in Northern and Southern European climates have been detected. [ABSTRACT FROM AUTHOR]

The growing number of reported avian influenza cases has prompted awareness of the importance of research methods to control the spread of the disease. Seasonal variation is one of the important factors that affect the spread of avian influenza. This paper presents a “nonautonomous” model to analyze the transmission dynamics of avian influenza with the effects of climate change. We obtain and discuss the global stability conditions of the disease-free equilibrium; the threshold conditions for persistence, permanence, and extinction of the disease; and the parameters with periodicity for controlling and eliminating the avian influenza. [ABSTRACT FROM AUTHOR]

Change-Point (CP) detection has attracted considerable attention in the fields of data mining and statistics; it is very meaningful to discuss how to quickly and efficiently detect abrupt change from large-scale bioelectric signals. Currently, most of the existing methods, like Kolmogorov-Smirnov (KS) statistic and so forth, are time-consuming, especially for large-scale datasets. In this paper, we propose a fast framework for abrupt change detection based on binary search trees (BSTs) and a modified KS statistic, named BSTKS (binary search trees and Kolmogorov statistic). In this method, first, two binary search trees, termed as BSTcA and BSTcD, are constructed by multilevel Haar Wavelet Transform (HWT); second, three search criteria are introduced in terms of the statistic and variance fluctuations in the diagnosed time series; last, an optimal search path is detected from the root to leaf nodes of two BSTs. The studies on both the synthetic time series samples and the real electroencephalograph (EEG) recordings indicate that the proposed BSTKS can detect abrupt change more quickly and efficiently than KS, t-statistic (t), and Singular-Spectrum Analyses (SSA) methods, with the shortest computation time, the highest hit rate, the smallest error, and the highest accuracy out of four methods. This study suggests that the proposed BSTKS is very helpful for useful information inspection on all kinds of bioelectric time series signals. [ABSTRACT FROM AUTHOR]

The frequent occurrence of geophysical disasters under climate change has drawn Chinese scholars to pay their attention to disaster relations. If the occurrence sequence of disasters could be identified, long-term disaster forecast could be realized. Based on the Earth Degassing Effect (EDE) which is valid, this paper took the magnitude, epicenter, and occurrence time of the earthquake, as well as the epicenter and occurrence time of the rainstorm floods as basic factors to establish an integrated model to study the correlation between rainstorm floods and earthquakes. 2461 severe earthquakes occurred in China or within 3000 km from China and the 169 heavy rainstorm floods occurred in China over the past 200+ years as the input data of the model. The computational results showed that although most of the rainstorm floods have nothing to do with the severe earthquakes from a statistical perspective, some floods might relate to earthquakes. This is especially true when the earthquakes happen in the vapor transmission zone where rainstorms lead to abundant water vapors. In this regard, earthquakes are more likely to cause big rainstorm floods. However, many cases of rainstorm floods could be found after severe earthquakes with a large extent of uncertainty. [ABSTRACT FROM AUTHOR]

Based on the analysis of the feature of Asian continent becoming cold in the early 21st century winter under the background of global warming, the Asian Warming Hole (AWH) index is proposed in this paper to measure the intensity during the cold events process. Result shows that the Asian continent is indeed experiencing a cold stage in the early 21st century winter, but it is just in a cold phase and will become warmer in the future. In recent years the activity of the winter cold events has an obvious quasi-four-year cycle, which can be reflected by atmospheric circulation, and AO (Arctic Oscillation) may play a very important role. It is credible that Asian surface temperature will be higher in the coming 3-4 years compared with 2011 winter, signifying that the precipitation may increase correspondingly. [ABSTRACT FROM AUTHOR]

By using detrended fluctuation analysis (DFA), the present paper analyzed the nonlinearity and fractal properties of tree-ring records from two types of trees in northwestern China, and then we disclosed climate change characteristics during the past 500 years in this area. The results indicate that climate change in northwestern China displayed a long-range correlation (LRC), which can exist over time span of 100 years or longer. This conclusion provides a theoretical basis for long-term climate predictions. Combining the DFA results obtained from daily temperatures records at the Xi’an meteorological observation station, which is near the southern peak of the Huashan Mountains, self-similarities widely existed in climate change on monthly, seasonal, annual, and decadal timescales during the past 500 years in northwestern China, and this change was a typical nonlinear process. [ABSTRACT FROM AUTHOR]

Climate change is mainly caused by excessive emissions of carbon dioxide and other greenhouse gases. In order to reduce carbon emissions, cap and trade policy is implemented by governments in many countries, which has significant impacts on the decisions of companies at all levels of the low carbon supply chain. This paper investigates the decision-making and coordination of a low carbon supply chain consisting of a low carbon manufacturer who produces one product and is allowed to invest in green technology to reduce carbon emissions in production and a retailer who faces stochastic demands formed by homogeneous strategic customers. We investigate the optimal production, pricing, carbon trading, and green technology investment strategies of the low carbon supply chain in centralized (including Rational Expected Equilibrium scenario and quantity commitment scenario) and decentralized settings. It is demonstrated that quantity commitment strategy can improve the profit of the low carbon supply chain with strategic customer behavior. We also show that the performance of decentralized supply chain is lower than that of quantity commitment scenario. We prove that the low carbon supply chain cannot be coordinated by revenue sharing contract but by revenue sharing-cost sharing contract. [ABSTRACT FROM AUTHOR]

Coastal flood regimes have been irreversibly altered by both climate change and human activities. This paper aims to quantify the impacts of multiple factors on delta flood. The Pearl River Delta (PRD), with dense river network and population, is one of the most developed coastal areas in China. The recorded extreme water level (m.s.l.) in flood season has been heavily interfered with by varied income flood flow, sea-level rise, and dredged riverbeds. A methodology, composed of a numerical model and the index R, has been developed to quantify the impacts of these driving factors in the the PRD. Results show that the flood level varied 4.29%–53.49% from the change of fluvial discharge, 3.35%–38.73% from riverbed dredging, and 0.12%–16.81% from sea-level rise. The variation of flood flow apparently takes the most effect and sea-level rise the least. In particular, dense river network intensifies the impact of income flood change and sea-level rise. Findings from this study help understand the causes of the the PRD flood regimes and provide theoretical support for flood protection in the delta region. [ABSTRACT FROM AUTHOR]

As the water source area for the middle route of China’s South-to-North Water Diversion Project, the upper Hanjiang basin is of central concern for future management of the country’s water resources. The upper Hanjiang is also one of the most flood-prone rivers in China. This paper explores the process of extreme floods by using multivariate analysis to characterize flood and precipitation event data in combination, for historical data and simulated data from global climate models. The results suggested that the generalized extreme value and Gamma models better simulated the extreme precipitation and flood volume sequence than the generalized Pareto model for the annual maximum series, while the generalized Pareto distribution model was the best-fit model for peaks over threshold series. For the two-dimensional joint distributions of precipitation and flood volume, the Frank Copula was preferred in simulation of the annual maximum flood series whereas the Gumbel Copula was the most appropriate function to simulate the points over threshold flood series. We concluded that, compared with the traditional univariate approach, multivariate statistical analysis produced flood estimates that were more physically based and statistically sound and carried lower risk for flood design purposes. [ABSTRACT FROM AUTHOR]

Meteorological observation plays a critical role in climatic study, and in situ measurements are the foundation of meteorological observation, especially in the Tibetan Plateau, the surface of which is fairly complex. Several field stations in the Northern Tibetan Plateau, which features relatively homogeneous surface, were selected as the study area. A detailed description on the significance of site observation for climate prediction was given in this paper. Data from weather stations can be used to verify satellite data and provide parameters for initial mode field in the study of weather and climate changes. The field observation data in the Northern Tibetan Plateau from 2001 to 2013 is analyzed. The results show that in El Nino year, values of land surface temperature (Ts), air temperature (Ta) and wind speed are all greater than their mean values and that soil moisture values are lower than the averaged, while the opposite is the case in La Nina year. The warming rate in the Northern Tibetan Plateau is greater than that in global areas. The diurnal variations of Ts and Ta are various in different seasons and underlying surfaces, with the diurnal variations greater in spring, and less in summer and autumn. Furthermore, the diurnal variation in the area with drier underlying surface is more obvious than that in area with moist surface. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an efficient field‐programmable gate array (FPGA) implementation method for a photovoltaic (PV) simulator based on two‐diode model applied to control a PV emulator. The hardware set up allows to repeat the real PV system behavior in the laboratory regardless of external climatic conditions change. The proposed approach has been designed through Xilinx System Generator (XSG) tools and MATLAB/Simulink environment and verified across cosimulation method. A real‐time test bench is built based on the implemented PV simulator with a closed loop control using proportional integral (PI) regulator, a direct current/direct current (DC/DC) buck converter and a resistive load. Experimental results are in agreement compared with simulation ones and prove the high accuracy and efficiency of the proposed implementation method for allowing a good emulation of PV characteristics. From some experiments, the development of PV emulator constitutes an important challenge task in the PV energy field. [ABSTRACT FROM AUTHOR]

Identification of spatial gradient in the vulnerability of white leg shrimp production to climate change is imperative in the formulation and implementation of suitable adaptive measures. A composite vulnerability index was computed by employing 36 variables pertaining to exposure (11), sensitivity (11) and adaptive capacity (14) dimensions to map the extent of vulnerability in white leg shrimp production across Indian states. Based on its magnitude, the vulnerability index was categorized into three groups, namely low, moderate and high. Results showed that the mean composite vulnerability index was 0.65 and ranged from 0.34 to 0.99 indicating that there was a strong spatial pattern. Among the nine states, Goa (0.99), Kerala (0.84) and Odisha (0.77) were highly vulnerable; Gujarat (0.75), Karnataka (0.57) and West Bengal (0.56) were moderately vulnerable; and Tamil Nadu (0.54), Andhra Pradesh (0.46) and Maharashtra (0.34) were less vulnerable to shrimp production. About one‐fourth of the production and culture area of white leg shrimp were in moderate and highly vulnerable regions. The impact of climate change on shrimp production is diverse but can be reduced by implementing adaptive measures—suitable policies and investment plans—which should be region‐specific. [ABSTRACT FROM AUTHOR]

Background and Aims: Cross‐sectional models are useful for estimating the impact that climate and climate change have on grape prices due to changes in grape composition. The aim of this study is to estimate the impact of growing season temperature (GST) on grape prices in Australia using cross‐sectional data. Methods and Results: We use data on average price by cultivar and region for a 10 year period. We estimate a model using (area‐) weighted least squares and variables from a principal component analysis to control for 103 characteristics that relate to the production system used in each of 26 regions. Results suggest that a GST increase of 1°C leads to a decrease of 9% in the average price of grapes. A LASSO model that we use as a robustness check suggests similar results: a GST increase of 1°C leads to a decrease of 7.3% in the average price of grapes. Conclusions: Failing to control for characteristics that relate to the production system overestimates the impact of GST on grape prices, suggesting that changes to variables in a production system may mitigate deleterious changes to grape composition due to climate change. Significance of the Study: This study contributes to the understanding of the issue of omitted variable bias in cross‐sectional models, and how to deal with this issue when analysing the impact of climate and climate change in grape and wine research. [ABSTRACT FROM AUTHOR]

The period 2020/2021 was an unprecedented and historic time for industrial, economic, and societal activities all over the world with great challenges to human health, the ecosystems, and other aspects of human endeavors owing to the COVID-19 or SARS-CoV-2 (CV-19) pandemic which is now a topical aspect of research interest. Despite the negative impacts of the CV-19 pandemic, there are also positive reports during the CV-19 pandemic such as the reduction of gas flare, reduction in the burning of fossil fuels from automobile exhaust and a reduction in the other ensuing factors of greenhouse gases emissions (which is one of the major drives for global warming and climate change as well as other environmental effluences). Hence, this brief perspective review study is centered on greenhouse gas (GHG) emission. The study employs a methodical approach to analyze some already available research studies from existing publications and databases on GHG emission using the perception during the CV-19 pandemic. The specific findings from this review show that, from the meteorological perspective, the global response to the catastrophe ensuing from the CV-19 pandemic has a great influence on the reduction of GHGs, the reduction in the burning of fossil fuels from automobiles and industrial devices, and the reduction in the other ensuing factors of GHG emission. Hence, it will not be far from the truth to conclude that there is a possible positive connection between the CV-19 pandemic and GHG emissions. The study has a direct impact on the environment owing to the negative and positive environmental consequences of the CV-19 pandemic. Suggestions and recommendations in the form of future prospects of GHG emission vis-à-vis global warming and climate change are also discussed. Furthermore, suggestions on how to improve food security and agriculture during a pandemic such as the CV-19 outbreak period are highlighted. [ABSTRACT FROM AUTHOR]

This study was conducted on Manoka Island (Littoral Region of Cameroon) with the aim of analyzing climate change vulnerability and local adaptation strategies based on the local community's perceptions and biophysical evidence. We used household surveys, focus group discussions, field observation, GIS, and remote sensing to collect data on variables of exposure, sensitivity, and adaptive capacity. Historical changes in rainfall and temperature, mangrove cover, and the occurrence of extreme climatic events were used as indicators of exposure. Property losses and income structure were used as indicators of sensitivity, while human, natural, social, financial, and physical assets represented adaptive capacity. 89 households were interviewed in the nine settlements of the island. Results show that Manoka Island is experiencing irregular rainfall patterns (with average annual values deviating from the mean by -1.9 to +1.8 mm) and increasing temperature (with annual values deviating from the mean by -1.2 to +3.12). The dynamics of the coastline between 1975 and 2017 using EPR show average setbacks of more than ±3 m/year, with erosion levels varying depending on the period and location. The number of households perceiving extreme climatic events like seasonal variability, flood, and rain storm was higher. From respondents' perception, housing and health are the sectors most affected by climate change. The reported high dependence of households on fishing for income, their overall low livelihood diversification, and their poor access to climate information reported by 65% of respondents portray their poor adaptive capacity. Local response initiatives are ineffective and include among others constructing buildings on stilts and using car wheels to counter the advancement of seawater inland. The study concludes that households on Manoka Island are vulnerable to the impacts of climate change. Income diversification, mangrove reforestation, the development of sustainable supply chains for wood fuel, and sustainable fish smoking devices are the main pathways for adaptation planning in this area. [ABSTRACT FROM AUTHOR]

Climate change adversely affected agricultural productivity in developing countries. This study aimed to explore the effects of this climate change, particularly on cereal crops production in Ethiopia. The study employed Autoregressive Distributed Lag (ARDL) model approach to the co-integration with an error correction term. ARDL technique was selected due to its stationarity assumption and unbiased estimates of its long-run coefficients. The estimated model justifies the existence of a long-run relationship between cereal crops production, climate change variables (temperature and precipitation), and other explanatory variables. Precipitation has a positive and significant effect on cereal crops production both in the long and short runs, while temperature change has a significant negative effect. In the long run, cereal crops production was positively and significantly affected by arable land, fertilizer consumption, and carbon dioxide emissions, while in the short run, labor force participation has a positive and significant effect on cereal crops production. The study results confirmed that there is a long-run relationship between cereal crops production and climate change variables. In agriculture, research and development should focus on varieties of cereal crops that can tolerate high temperatures. Climate Resilient Green Economy should have to strengthen in the country. All countries should have to work hand-in-hand to mitigate the effect of climate change. [ABSTRACT FROM AUTHOR]

Climate change will expose the food‐producing sector to a range of challenges. Inland aquaculture farms are particularly vulnerable, due to the difficulty in changing their location, and therefore require specific tools to predict the influence of direct and indirect effects on production, environment and economic feasibility. The objective of our study was to apply a simple set of models to produce a set of growth, risk and suitability maps for stakeholders within the common carp sector in Poland, to assist decision‐making under two different scenarios of climate change: a moderate situation (RCP 4.5) and an extreme situation (RCP 8.5). We used present (2000–2019) and future projections (2080–2099) for water surface temperature based on land surface temperature data from regionally downscaled climate models to draw maps to: (i) show optimal temperature conditions for carp growth, (ii) assess risk of disease outbreak caused by three important common carp pathogens: Cyprinid herpesvirus 3 (CyHV‐3), carp oedema virus (CEV) and spring viremia of carp (SVCV) and (iii) predict potential suitability changes of carp farming in Poland. The study identified areas with the most and least favourable temperature conditions for carp growth, as well as those areas with the highest/lowest number of days with suitable temperatures for virus infection. These suitability maps showed the combined effect of direct and indirect effects of climate change projections under RCP 8.5 and RCP 4.5 scenarios. The approach applied herein will be of use worldwide for analysing the risks of temperature increase to land‐based aquaculture, and the results presented are important for carp farmers in Poland and elsewhere, industry in general, and government stakeholders, to understand the direct and indirect effects of climate change on the triple bottom line of people, planet, and profit. [ABSTRACT FROM AUTHOR]

Risk assessment of drought disaster is necessary for the sustained agriculture development under the background of global climate change, and, meanwhile, it is an urgent scientific issue needed to be solved in agricultural risk assessment discipline. In this paper, collecting the areas covered by drought disasters, areas affected by drought disasters, areas with no harvest by drought disasters, areas planted, and yield per unit area data, agricultural drought disasters losses of the southern five provinces in China were calculated. The best probability density function was obtained according to the goodness of fit test results. Then, the value at risk (VaR) from financial market risk research method was used to the effective measure of agricultural drought risk. The results show that, in southern five provinces of our research region, risk of agricultural drought disaster in Yunnan and Guizhou is greater than other three provinces. In the scenario of 50 years and 100 years for a return, the crop loss caused by drought will reach 13.6% and 17.4% in Yunnan province, and the crop loss will be more serious in Guizhou province and be up to 15.3% and 18.1%. The above results can provide multifaceted information about drought risk that can help to guide management of drought disasters. [ABSTRACT FROM AUTHOR]

Prediction of aerosol optical thickness (AOT) is important to study worldwide climate changes. Researchers have built multiple AOT prediction models. However, few researches were focused on the validation of input attributes for AOT regression. In this paper, we proposed a support vector regression (SVR) model-based sensitivity analysis approach to order 35 MODIS input attributes according to their sensitivity to prediction outputs. Next, the attribute sensitivity orders are used for feature selection in the context of regression by removing insensitive attribute one at a time or by removing attributes whose sensitive orders are larger than number k. The experimental results based on the collocated data between MODIS and AERONET from 2009 to 2011 showed that the top 10 insensitive attributes can be screened to speed up prediction model computation with very little loss of accuracy. The results also suggested that the top sensitive attributes are the most informative attributes, requiring the highest precision for accurate AOT prediction. Thereby, our approach will be valuable for remote sensing scientists or atmospheric scientists to optimize the design precision of top sensitive attributes in scanning equipment like MODIS and therefore improve AOT retrieval accuracy. [ABSTRACT FROM AUTHOR]

Indian monsoon is an important climatic phenomenon and a global climatic marker. Both statistical and numerical prediction schemes for Indian monsoon have been widely studied in literature. Statistical schemes are mainly based on regression or neural networks. However, the variability of monsoon is significant over the years and a single model is often inadequate. Meteorologists revise their models on different years based on prevailing global climatic incidents like El-Niño. These indices often have degree of severity associated with them. In this paper, we cluster the monsoon years based on their fuzzy degree of associativity to these climatic event patterns. Next, we develop individual prediction models for the year clusters. A weighted ensemble of these individual models is used to obtain the final forecast. The proposed method performs competitively with existing forecast models. [ABSTRACT FROM AUTHOR]

Ecosystem services are the benefit human populations derive directly and indirectly from the natural environment. They suffer from both the human intervention, like land use zoning change, and natural intervention, like the climate change. Under the background of climate change, regulation services of ecosystem could be strengthened under proper land use zoning policy to mitigate the climate change. In this paper, a case study was conducted in the middle reaches of the Heihe River Basin to assess the ecosystem services conservation zoning under the change of land use associated with climate variations. The research results show the spatial impact of land use zoning on ecosystem services in the study area which are significant reference for the spatial optimization of land use zoning in preserving the key ecosystem services to mitigate the climate change. The research contributes to the growing literature in finely characterizing the ecosystem services zones altered by land use change to alleviate the impact of climate change, as there is no such systematic ecosystem zoning method before. [ABSTRACT FROM AUTHOR]

Decadal prediction experiments are conducted by using the coupled global climate model FGOALS-s2, following the CMIP 5 protocol. The paper documents the initialization procedures for the decadal prediction experiments and summarizes the predictive skills of the experiments, which are assessed through indicators adopted by the IPCC AR5. The observational anomalies of surface and subsurface ocean temperature and salinity are assimilated through a modified incremental analysis update (IAU) scheme. Three sets of 10-year-long hindcast and forecast runs were started every five years in the period of 1960–2005, with the initial conditions taken from the assimilation runs. The decadal prediction experiment by FGOALS-s2 shows significant high predictive skills in the Indian Ocean, tropical western Pacific, and Atlantic, similar to the results of the CMIP5 multimodel ensemble. The predictive skills in the Indian Ocean and tropical western Pacific are primarily attributed to the model response to the external radiative forcing associated with the change of atmospheric compositions. In contrast, the high skills in the Atlantic are attributed, at least partly, to the improvements in the prediction of the Atlantic multidecadal variability coming from the initialization. [ABSTRACT FROM AUTHOR]

Land degradation is a complex process which involves both the natural ecosystem and the socioeconomic system, among which climate and land use changes are the two predominant driving factors. To comprehensively and quantitatively analyze the land degradation process, this paper employed the Normalized Difference Vegetation Index (NDVI) as a proxy to assess land degradation and further applied the binary panel logit regression model to analyze the impacts of the driving factors on land degradation in the North China Plain. The results revealed that an increase in rainfall and temperature would significantly and positively contribute to the land improvement, and conversion from cultivated land to grassland and forest land showed positive relationship with land improvement, while conversion to built-up area will lead to land degradation. Besides, human agricultural intensification represented by fertilizer utilization will help to improve the land quality. The economic development may exert positive impacts on land quality to alleviate land degradation, although the rural economic development and agricultural production will exert negative impacts on the land and lead to land degradation. Infrastructure construction would modify the land surface and further resulted in land degradation. The findings of the research will provide scientific information for sustainable land management. [ABSTRACT FROM AUTHOR]

Both the “urban minus rural” (UMR) and the “observation minus reanalysis” (OMR) methods were used to quantify the potential impacts of urbanization on regional temperature changes in the Beijing-Tianjin-Hebei metropolitan area in China. DMSP/OLS nighttime light imagery and population census data were used to classify stations into four classes: rural, small city, medium city, and large city groups. The regional average annual mean temperature was estimated to increase by 0.12°C decade−1 derived from urban warming, accounting for 40% of total climate warming from 1960 to 2009 using the UMR method. The OMR method also indicates that rapid urbanization has a significant influence on surface warming trend although the urban warming intensity is dependent on reanalysis dataset. The seasonal cycle patterns from all three datasets are consistent with each other, which is contrary to the UMR result owing to the cooling effect of agriculture activity in the rural stations confusing the UMR result. So in this paper we found a deficiency of the UMR method where it would underestimate the effects of urbanization in summer. In this regard, the results from the OMR method are relatively more convincing although we admit it still has many other problems. [ABSTRACT FROM AUTHOR]

In ecologically fragile areas with arid climate, such as the Heihe River Basin in northwestern China, sustainable social and economic development depends largely on the availability and sustainable uses of water resource. However, there is more and more serious water resource shortage and decrease of water productivity in Heihe River Basin under the influence of climate change and human activities. This paper attempts to identify the severe water scarcity under climate change and presents possible solutions for sustainable development in Heihe River Basin. Three problems that intervened land use changes, water resource, the relevant policies and institutions in Heihe River basin were identified, including (1) water scarcity along with serious contradiction between water supply and demand, (2) irrational water consumption structure along with low efficiency, and (3) deficient systems and institutions of water resource management along with unreasonable water allocation scheme. In this sense, we focused on reviewing the state of knowledge, institutions, and successful practices to cope with water scarcity at a regional extent. Possible solutions for dealing with water scarcity are explored and presented from three perspectives: (1) scientific researches needed by scientists, (2) management and institution formulation needed by governments, and (3) water resource optimal allocation by the manager at all administrative levels. [ABSTRACT FROM AUTHOR]

The article offers insights on issues and developments relating to indoor air science. Included is an illustration highlighting the interconnectedness of indoor air sciences. Topics discussed include building energy use, link between indoor chemistry and microbiology, and impact of solar radiation on indoor air quality. The relationship between climate change and indoor air quality and rapid urbanization are also discussed.

This study focuses on extreme precipitation changes in Xinjiang Province of Northwest China, which has experienced an increase in climate disasters in recent years. This paper investigates extreme precipitation events in Xinjiang, using 54 stations with daily precipitation records from the period 1961–2008. Different statistical tests and approaches were used to check the significance of trends of single and Xinjiang regionally aggregated precipitation series for intensity and in frequency. There were predominantly positive trends in annual maximum precipitation and a remarkable increment in the frequency of extreme precipitation over certain thresholds (from 10 to 40 mm). Although the series of frequencies exceeding thresholds had positive trends, only a minority were statistically significant. This lack of significance is because of the high variability of extreme precipitation in space and time. Thus, significant trends were evident when we assessed the extreme precipitation indicators of intensity and frequency at the regional level, both in intensity and frequency over thresholds, with a clearer signal in Xinjiang. [ABSTRACT FROM AUTHOR]

When compared to the wide range of atmospheric sensing techniques, global navigation satellite system (GNSS) offers the advantage of operating under all weather conditions, is continuous, with high temporal and spatial resolution and high accuracy, and has long-term stability. The utilisation of GNSS ground networks of continuous stations for operational weather and climate services is already in place in many nations in Europe, Asia, and America under different initiatives and organisations. In Africa, the situation appears to be different. The focus of this paper is to assess the conditions of the existing and anticipated GNSS reference network in the African region for meteorological applications. The technical issues related to the implementation of near-real-time (NRT) GNSS meteorology are also discussed, including the data and network requirements for meteorological and climate applications. We conclude from this study that the African GNSS network is sparse in the north and central regions of the continent, with a dense network in the south and fairly dense network in the west and east regions of the continent. Most stations lack collocated meteorological sensors and other geodetic observing systems as called for by the GCOS Reference Upper Air Network (GRUAN) GNSS Precipitable Water Task Team and the World Meteorological Organization (WMO). Preliminary results of calculated zenith tropospheric delay (ZTD) from the African GNSS indicate spatial variability and diurnal dependence of ZTD. To improve the density and geometry of the existing network, countries are urged to contribute more stations to the African Geodetic Reference Frame (AFREF) program and a collaborative scheme between different organisations maintaining different GNSS stations on the continent is recommended. The benefit of using spaced based GNSS radio occultation (RO) data for atmospheric sounding is highlighted and filling of geographical gaps from the station-based observation network with GNSS RO is also proposed. [ABSTRACT FROM AUTHOR]

Climate change is a serious threat to humankind. As broad public participation is required in climate change mitigation efforts, it is critical to understand how the public talk about climate change on social media. This study sets out to increase the understanding of Chinese public awareness of climate change, as well as explore the potential and limitations of social media for public engagement on climate change issues. It examines the Chinese public's discussion about climate change on social media Weibo during the last six years through data mining and text analysis. The analyses include volume analysis, keyword extraction, topic modeling, and sentiment analysis. The results indicate three main aspects of public awareness and concern regarding climate change. First, public awareness of climate change is growing in China. Second, the sentiment analysis shows that the general sentiment toward climate change is becoming more positive over time. Third, based on keyword extraction and topic modeling, the discussion on climate change shows a top-down perspective, an optimistic economic perspective, and a preference for celebrity content. The study provides a comprehensive picture of Chinese social media users' views on climate change issues, based on large-scale research data. It contributes to a better understanding of what Chinese people think about climate change on social media generally. These findings may provide government and environmental organizations with valuable insights for better climate change campaigns on social media. [ABSTRACT FROM AUTHOR]

Climate change is amongst the most serious issues nowadays. Climate change has become a concern for the scientific community as it could affect human health. Researchers have found that climate change potentially impacts human mental health, especially among depressive patients. However, the relationship is still unclear and needs further investigation. The purpose of this systematic review is to systematically evaluate the evidence of the association between climate change effects on depressive patients, investigate the effects of environmental exposure related to climate change on mental health outcomes for depressive patients, analyze the current technique used to determine the relationship, and provide the guidance for future research. Articles were identified by searching specified keywords in six electronic databases (Google Scholar, PubMed, Scopus, Springer, ScienceDirect, and IEEE Digital Library) from 2012 until 2021. Initially, 1823 articles were assessed based on inclusion criteria. After being analyzed, only 15 studies fit the eligibility criteria. The result from included studies showed that there appears to be strong evidence of the association of environmental exposure related to climate change in depressive patients. Temperature and air pollution are consistently associated with increased hospital admission of depressive patients; age and gender became the most frequently considered vulnerability factors. However, the current evidence is limited, and the output finding between each study is still varied and does not achieve a reasonable and mature conclusion regarding the relationship between the variables. Therefore, more evidence is needed in this domain study. Some variables might have complex patterns, and hard to identify the relationship. Thus, technique used to analyze the relationship should be strengthened to identify the relevant relationship. [ABSTRACT FROM AUTHOR]

Livestock feed mix or feed choice decision-making is encountered by farmers in their daily operations. Livestock feed choice and mixing is emerging as a key research area considering the impact of climate change and emergence of new technology. Smallholder dairy farmers are usually capital constrained and hence a need to investigate cost-effective feed choice that maximises profit. A study to investigate the cost-effective feed among forage and browse legumes was conducted among smallholder dairy farmers in Zimbabwe. An optimisation problem was formulated with the objective of maximising profit by selecting the most cost-effective feed among forage and browse legumes. Secondary data are used to solve the optimisation problem by implementing the Jaya optimisation algorithm. Results show that grain crop silage is the best feed choice resulting in a maximum profit of $ 66.00 per day per farmer. Further research can be directed towards investigating the effect of combining the next best feed, quality hay, and grain crop silage for profit contribution. [ABSTRACT FROM AUTHOR]

The production of cement results in the depletion of natural resources and consumption of huge energy and CO2 emissions to the environment that cause global warming and climate change. To alleviate the problem, there is a growing interest of researchers to find an alternative material to partially cement by industrial and agricultural wastes. This research was therefore aimed to examine the potential of using coffee husk ash (CHA) as partial replacement for ordinary Portland cement (OPC) in C-25 concrete production. Concrete mixtures were prepared by partially replacing cement with CHA in different proportions (0%, 5%, 10%, and 20%) by weight with a constant w/c of 0.5. The consistency, setting time, workability, compressive strength, water absorption, sulphate attack, Fourier transform infrared (FTIR), and thermo gravimetric (TGA) tests were conducted on concrete samples. The test results revealed that the workability of mixtures showed a decreasing trend with an increase in the share of the CHA content in which the measured slump flow values ranged between 15 and 35 mm. Contrarily, the setting time of concrete mixtures showed an increasing trend with an increase in CHA content. The initial and final setting times were in the range of 67–126 minutes and 310–524 minutes, respectively, which is in the range of the standards. The compressive strength of concrete decreased with an increase in the share of the CHA, in which the results were measured in the range of 35.1–22.7 MPa for the 28th day sample with 5% and 20% of CHA, respectively. However, it increases as the curing days increase, whereas the water absorption of the concrete increases as the CHA increases but decreases as the curing days increase due to the porous nature of the CHA. From the study microstructure of concrete using Fourier transform infrared spectroscopy and thermogravimetric analysis, differential thermal analysis results show that the C-S-H gel would be from 950–1100 cm−1 wavenumber and at 500oC, and the CHA sample was decomposed and mass loss was observed at 500oC. In general, it was also observed that, from the compressive strength, the concrete satisfies its design strength up to 10% replacement level without compromising the performance of concrete. [ABSTRACT FROM AUTHOR]

The importance of agricultural crops in India has been understated in terms of production during the last two decades, owing to global warming and other factors. Policymakers and farmers would benefit from imagining crop yields well before harvest to help them make appropriate marketing and storage decisions. Crop yields will benefit from such estimates as well. Several systems for predicting and modelling agricultural yields have been developed in the past, with varying degrees of effectiveness, due to the fact that they do not objectively account for meteorological aspects and seasonal climate variations. The importance of agricultural crops in India has been understated in terms of production during the last two decades, owing to global warming and other factors. Policymakers and farmers would benefit from imagining crop yields well before harvest to help them make appropriate marketing and storage decisions. Crop yields will benefit from such estimates as well. Several systems for predicting and modelling agricultural yields have been developed in the past, with varying degrees of effectiveness, due to the fact that they do not objectively account for meteorological aspects and seasonal climate variations. The method makes use of climate data from Thanjavur, India's soils. To choose the optimal crop for a particular set of input and climate conditions, the system leverages real-time input of location-specific soil attributes. The model is been tested with various machine learning techniques such as NB, KNN, SVM, and decision tree. Among all these methods, the SVM gives the good results. The accuracy of the model was determined using LSTM, SVM, and Tabu search optimization. TSO with SVM has an 89% accuracy value. [ABSTRACT FROM AUTHOR]

Background and Aims: Global warming will threaten vineyard sustainability in warm and arid regions. Therefore, increasing water use efficiency (WUE) is essential for long‐term viticulture sustainability. Nevertheless, studies concerning the response of stomata and WUE to temperature are scarce. We evaluated the effect of sustained warming on WUE and its components in cultivars with contrasting stomatal behaviour. Methods and Results: We applied a 1.1°C increase in field‐grown, well‐watered plants, during the growing season and compared them to Control plants. We measured net assimilation (An), transpiration rate (E), stomatal conductance (gs), stomata density and length, intrinsic and yield WUE (WUEint and WUEyield). Gas exchange and WUEint in response to heating were cultivar‐dependent. Heated Grenache plants increased An, gs, E and stomatal length but decreased WUEint. In contrast, gas exchange and WUEint were similar between heated Syrah and Control plants. Both cultivars decreased WUEyield in response to heating. Conclusions: An increase in air temperature decreased WUEint in Grenache, whereas Syrah could tolerate heating without modifying leaf functioning. We proposed these strategies to cope with heating as 'avoiding' for Grenache and 'tolerant' for Syrah in well‐watered conditions. Significance of the Study: This research contributes to an understanding of the strategies to cope with high temperature of different cultivars, which should be considered to select better‐adapted genotypes for the expected warmer conditions. [ABSTRACT FROM AUTHOR]

Climate changes significantly cause the precipitation deficiency and in turn reduce the inflow amount in reservoir affecting hydroelectric power generation. The primary objective of this study was to evaluate hydropower generation and reservoir operation under climate change from Kesem reservoir. Recent Representative Pathway (RCP) scenarios were used to evaluate the impact of climate change on power generation. Power transformation equation and variance scaling approach were amalgamated to adjust the bias correction of precipitation and temperature, respectively. Bias, root mean square error, and coefficient of variation were used to check the accuracy of projected rainfall. The base and future precipitation, temperature, and evaporation trend was analysed using the Mann–Kendall test. The flow calibration and validation were carried out by the Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS), and hydropower generation was evaluated with reservoir simulation model (MODSIM 8.1) under climate scenarios. The performance of the model was found good with Nash–Sutcliffe coefficient (NSE) of 0.72 and coefficient of determination (R2) of 0.73 for calibration and NSE of 0.74 and R2 of 0.75 for validation. Projected future climate scenarios predicted increasing and decreasing trend of temperature and precipitation, respectively. For RCP4.5 climate scenario, the average energy generation is likely to decrease by 0.64% and 0.82% in both short-term (2021–2050) and long-term (2051–2080), respectively. In case of RCP8.5 climate scenario, the average energy generation will be decreased by 1.06% and 1.35% for short-term and long-term, respectively. Remarkable reduction of energy generation was revealed in RCP8.5 with relation to RCP4.5 scenario. This indicates that there will be high energy fluctuation and decreasing trend in the future energy generation. The research finding is crucial for decision-makers, power authorities, governmental and nongovernmental organizations, and watershed management agencies to take care for sustainability in the future hydropower generation in the Kesem reservoir. [ABSTRACT FROM AUTHOR]

This research was done in Arsi-Bale Massif remnant vegetation of southeast Ethiopia to investigate vascular plant diversity and endemism in forest patch, riverine, and grazing land-use types. A total of 126 quadrats, each with 900 m2 (30 m × 30 m), were selected systematically along the altitudinal gradient. Shannon Weiner Diversity index and R Package 3.2 were applied to analyze species composition and diversity, whereas the similarity ratio among land use and other related vegetation was determined using Sorensen's index. From the study, 382 vascular plant species under 223 genera and 92 families were identified. The growth forms revealed that there were 118 woody (26 trees, 10 lianas, and 82 shrubs) and 239 herbaceous species of plant. Of these, 216, 165, and 154 species are shared by riverine, forest patch, and grazing land-use types, respectively. Asteraceae with 68 species was the most dominant family in the present study, followed by Poaceae, Lamiaceae, and Cyperaceae with 30, 21, and 13 species, respectively. Forty-four endemic species were documented in this study. According to the IUCN Red List, 22 species were not evaluated; 10 species were least concerned; 1 species was endangered; and 1 species was vulnerable, while 4 species have been categorized as not threatened. The highest similarity was observed between grazing and riverine land-use types; this may be due to the presence of many adjacent plots in both land use, associated mechanisms of adaptation, and requirements for species occurrence. Moreover, the floristic composition similarity of the study area in comparison with other vegetation types of Ethiopia shows variation (Ss = 0.223–0.526), maybe due to climatic variation, ecological distance, and disturbances. Generally, the total diversity and evenness of the studied remnant vegetation were 2.917 and 0.948, respectively. This lower diversity value clearly indicates that there are many human-induced factors deteriorating the plant species of the studied vegetation. Therefore, the concerned body of the government should work with local farmers to conserve the remaining plant species including many endemic species by developing appropriate conservation and management plans. [ABSTRACT FROM AUTHOR]