B1 Scenarios Research Articles

China's carbon budget inventory from 1997 to 2017 and its challenges to achieving carbon neutral strategies

The global climate change situation partly depends on the climate change policies of countries around the world, including China. Therefore, it is necessary to reasonably reduce carbon emissions (CEs) and increase carbon sink in order to progressively achieve the carbon neutral (CN) goal. However, the capacity and the potential to achieve carbon neutral with the support of its ecosystem, that is to say the carbon neutral capacity (CNC) and potential, are still unclear. To this end, based on China's energy emissions data, meteorological and hydrological data, lithology data, and vegetation data, we used the GEM-CO2 model, soil respiration model, spatial autocorrelation analysis method and other methods to establish the spatial information map of China city-scale CNC from 1997 to 2017. Furthermore, based on the future climate, vegetation data, CEs data and their influencing factors in 2025–2060, the Back Propagation neural network model was used to predict the CN potential of China's provinces. This study found during the study period, annual CEs of 5.63 Pg CO2 were not absorbed, which is about 90% of the average annual CEs. And the carbon surplus regions were mainly concentrated in the less developed northeastern and southwestern border regions. Moreover, the change in the CNC in China from 1997 to 2017 was −13.37 Tg/yr, indicating that the CNC of China's terrestrial ecosystems overall reduced. It should be noted that most provinces are not highly polarized, that is, there is no significant differences in CNC between cities in the provinces. Moreover, the scenario simulation's method of IPCC provides a reference for this manuscript, so we set up two future scenarios (A2 and B1 scenarios). In the future, China is expected to achieve a carbon emission peak before 2030 under the B1 scenario while it will continue to grow under the A2 scenario. From 2017 to 2060, the CNC under the two scenarios (A2/B1) will decrease by 44.58% and increase by 15.54%. It follows then that the road to CN in China will be difficult without corresponding policy intervention. In short, this study has clarified China's CNC from the past to the future, as well as CNC's spatial distribution and changing trends. This provided theoretical and data support for China to introduce corresponding zero-carbon solutions based on its understanding of CNC.

Assessment of impacts of climate change on rice and wheat in the Indo-Gangetic plains

In this paper, the climate change scenarios of A2 and B2 for 2070-2100 time scale (denoted as 2080) for several key locations of India and its impact on rice and wheat crops based on regional climate model (PRECIS) were described. The PRECIS projects an increase in temperature over most parts of India especially in the IGP (Indo-Gangetic Plains), the region that presently experiences relatively low temperatures. Extreme high temperature episodes and rainfall intensity days are projected to become more frequent and the monsoon rainfall is also projected to increase. Rabi (mid Nov-March) season is likely to experience higher increase in temperature which could impact and hence become threat to the crops which really require low temperature for their growth. Climatic variability is also projected to increase in both A2 and B2 scenarios. All these projected changes are likely to reduce the wheat and rice yields in Indo-Gangetic plains of India. It is likely that there will be more number of years with low yields occurs towards the end of the century. Such yield reductions in rice and wheat crops due to climate change are mediated through reduction in crop duration, grain number and grain filling duration. The yield loss will be more in A2 scenario compared to B2. These quantitative estimates still have uncertainties associated with them, largely due to uncertainties in climate change projections, future technology growth, availability of inputs such as water for irrigation, changes in crop management and genotype. These projections nevertheless provide a direction of likely change in crop productivity in future climate change scenarios.

Simulated impacts of climate change on Lake Simcoe water quality

ABSTRACT Lake Simcoe has undergone eutrophication and hypoxia since the 1960s. Climate change, leading to enhanced summer thermal stratification, has been identified as a key stressor. In this study, we modeled the impacts of climate change on hydrodynamics and biogeochemistry in Lake Simcoe by applying a 1-dimensional (vertical) model forced with A2 and B1 scenario outputs from a global climate model over 2000–2100. The model was calibrated in 2008 and validated in 2009, with maximum root mean square error (RMSE) of modelled temperature between 1.5 and 3.0 °C and dissolved oxygen RMSE between 0.5 and 2.5 mg L−1. Phytoplankton chlorophyll a was simulated with RMSE between 1.25 µg L−1 (large diatoms) and ∼0.5 µg L−1 (other groups). Interannual variability in spring water temperature and length of stratification were related to changes in the North Atlantic and Artic Oscillation indices, respectively. Under A2 and B1 forcing, the duration of stratification will increase by 45 and 38 days in summer between spring and fall turnover, respectively. The extended stratified period leads to a reduction in hypolimnetic dissolved oxygen from 3–7 to <3 mg L−1, thereby reducing the quality of cold-water fish habitat and increasing internal phosphorus loading from the benthos. These internal loads, combined with increased water temperatures, lead to increased cyanobacteria concentrations, beginning around 2070.

Maize yield projections under different climate change scenarios in different districts of Punjab

The study aimed to find out possible changes in climatic data (temperature and rainfall) from the regional climate model viz. PRECIS(Providing Regional Climates for Impact Studies)under different SRES scenarios (A1B, A2 and B2 scenario) by the mid (2021-2050) and end (2071-2100) century at six locations of Punjab representing different agroclimatic zones and to study their impact on maize yield using the crop growth simulation model.The results revealed that the different zones of the state are expected to bewarmer during the mid century and this trend has been projected to continue by the end of century because of increase in maximum and minimum temperature at all the locations.The CERES-Maize simulated significant decrease in duration and grain yield of maize crop under projected climate scenarios. The reduction in the crop duration and grain yield was found to be more under the A1B and A2 scenario (high emission scenario) followed by B2 scenario (low emission scenario)due to adverse effects on crop physiology.

Global projections of future wilderness decline under multiple IPCC Special Report on Emissions Scenarios

Globally, wilderness areas are being lost at a rate that outpaces their protection, which has adverse effects on the global environment. Rapid action is needed to understand the trends and consequences of global wilderness change. We present projections of global wilderness decline in 2100 under the influence of land-use change within the framework of the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (IPCC SRES). The projections revealed that the decline of wilderness was deeply affected by different global socioeconomic development pathways. The total wilderness loss (4.74%) in scenario A2 (with slow technological innovation and traditional demand for biofuels) was much higher than in the other scenarios. Around 76.51% of the loss of global wilderness globally occurs occurred in South America, which will occur in Tropical and Subtropical Moist Broadleaf Forests. The smallest loss (0.08%) occurred in scenario B1 (with a high level of environmental consciousness). We found that wilderness losses in 2100 will be concentrated in some important biomes, which have relatively high-density carbon storage. These findings stress the importance of targeted wilderness protection to ensure the long-term integrity of ecosystems and the balance of the carbon cycle.

Statistical downscaling of global circulation models to assess future climate changes in the Black Volta basin of Ghana

Statistical Downscaling Model (SDSM) is a powerful model for climate change assessment. However, its usage remains very gray with limited studies on climate change (CC) assessment in Ghana. This study explored the applicability and suitability of SDSM for CC assessment in the Black Volta section of Ghana. The hydro-climatic parameters of Hadley center Coupled Model, version 3 (HadCM3) under the A2 and B2 Emissions Scenarios and the second-generation Canadian Earth System Model (CanESM2) under the Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5 of the Coupled Model Intercomparison Project Phase 5 were downscaled with SDSM over the Black Volta section in Ghana using 40-year ground station data. The R2, NSE, Pbias, RMSE, and KGE of the calibrated and validated results ranged from 64% to 99%, 50–99%, -0.30–21.1, 0.01 °C–1.48 °C and 49%–99%, respectively for both models indicating a good agreement between the historical and the simulated data. The future climate change showed an increase in average minimum temperature of 0.05 °C (2020s), 0.11 °C (2050s), 0.21 °C (2080s) under the A2 scenario, 0.05 °C (2020s), 0.13 °C (2050s), 0.19 °C (2080s) under the B2 scenario, 0.01 °C (2020s), 0.02 °C (2050s), 0.02 °C (2080s) under the RCP 2.6, 0.06 °C (2020s), 0.13 °C (2050s), 0.19 °C (2080s) under the RCP 4.5, and 0.06 °C (2020s), 0.15 °C (2050s), 0.32 °C (2080s) under the RCP 8.5. For Maximum temperature, the average changes showed an increase of 0.17 °C (2020s), 0.36 °C (2050s), 1.14 °C (2080s) under the A2 scenario, 0.18 °C (2020s), 0.39 °C (2050s), 1.01 °C (2080s) under the B2 scenario, 0.03 °C (2020s), 0.16 °C (2050s), 0.17 °C (2080s) under the RCP 2.6, 0.02 °C (2020s), 0.26 °C (2050s), 0.45 °C (2080s) under the RCP 4.5, and 0.03 °C (2020s), 0.29 °C (2050s), 0.61 °C (2080s) under the RCP 8.5. The change in precipitation is not uniform with increase and decrease depending on the months and the scenarios. Overall, A2, B2 scenarios showed higher decrease in precipitation compared to RCPs scenarios. The SDSM is suitable for CC assessment and impact studies. The results from this study are to support the Climate Action, goal 13 of the SDGs.

Irrigation Scheme Selection Based on Water Footprint Analysis of Winter Wheat Production in Piedmont Plains of Hebei Province under Future Climate Scenarios

The statistical downscaling tool of a statistical downscaling model (SDSM) to generate the future climate of the piedmont plain area in Hebei Province for a 30-year period. The Xinji city was selected as a typical example of this area. The crop growth model of the decision support system for agrotechnology transfer (DSSAT) was adopted to estimate the changing trends of the water footprint of winter wheat production in this area under future climate conditions, and to obtain the optimal irrigation scheme of winter wheat for an ‘acceptable yield’. According to the test results, all the temperature indices of the piedmont plain area increased in the two selected future climate scenarios. In addition, the effective precipitation exhibited a slight decrease in scenario A2 and a remarkable increase in scenario B2. Both the total water footprint and green water footprint increased. A yield of 500 kg per mu was taken as the acceptable yield. In scenario A2, to achieve this acceptable yield, it was required to irrigate once in the jointing period with an irrigation rate of 105 mm. In scenario B2, one-time irrigation with an amount of 85 mm was sufficient to reach the acceptable yield.

SIMULATING OF FUTURE CLIMATE CONDITIONS ON THE EFFECT OF IRRIGATION ON SUGARCANE YIELD IN SOUTHERN BRAZIL

The objective of this study was to evaluate the technical and economic feasibility of installing central pivot irrigation systems in current days (Scenario A) and in two future scenarios, one with a 10% increase in rainfall (Scenario B1) and the other with 10% reduction in rainfall (Scenario B2). For both future scenarios, an increase of 2ºC in the global temperature and the concentration of atmospheric carbon dioxide (CO2) increasing to 528 ppm (parts per million) were considered. For the study, the recommendation for an area of clayey soil and with the sugarcane cycle was evaluated on three different planting dates: May 15, August 15 and November 15. The DSSAT/CANEGRO model was used, and under the current conditions, the simulations indicated greater yield for planting in May, both for rainfed and irrigated. For scenario B1, there was an increase in yield of 22% for rainfed and 33% for irrigated. In addition, irrigation provided yield gains in the two future scenarios of 13% and 14% for B1 and B2, respectively. Based on the results obtained, central pivot irrigation proved economically viable in the simulated climatic conditions. Therefore, the increase in sugarcane production provided by irrigation in agronomic management was sufficient to make investment in the region in question feasible. It is suggested to repeat the study in other regions, since the existing synergy between the factors that define agricultural yield can change the decision making about the implantation of irrigation systems in the production environment.

Development of Decision-Making Support Tools for Future Reservoir Management Under Climate and Land Cover Variability: a Case Study

The effects of climate change have been already experienced globally. The uncertainties of climate and land cover changes in the watershed above the reservoir have direct impacts on the inflow into the reservoir and the water resource management that uses reservoir rule curves. This study aims to assess the impacts of climate and land cover changes on the inflow into the Huai Luang Reservoir, Udonthani province during the period of 2018-2067 by using the simulation of two climate scenarios with the data from the PRECIS model under the emission conditions of B2. The prediction of future inflow has used the InVEST model. The improvement of the reservoir rule curves has used the Cultural Algorithm (CA) technique connected with the reservoir system simulation model. Monthly inflow data flowing into the reservoir from the past 33 years has been used to synthesize 1,000 events in order to evaluate the efficiency of the new obtained reservoir rule curves. Therefore, the data results of water shortage and excess water discharge situation have been illustrated in form of frequency, magnitude, duration of events, average amount of water, and maximum amount of water. The results have showed that average rainfall and temperature have tended to increase. The optimal reservoir rule curves improved by the CA technique can effectively reduce the frequency of water shortage situations and the one of the excess water discharge occurring during inflow changes in normal situation, and future situation under emission scenario B2.

Use of surface water and groundwater under climate change: Khorramabad basin, Iran

The impacts of climate change on the conjunctive use of surface water and groundwater resources of the Khorramabad basin were evaluated. Monthly temperature and rainfall modelled using HadCM3 and CGCM2 under greenhouse gas emissions scenarios (GHGESs) A2 and B2 were downscaled to baseline (1971–2000) and future periods (2040–2069 and 2070–2099). Simulations were performed for four climate change scenarios (CCSs) (A2-2040–2069, A2-2070–2099, B2-2040–2069 and B2-2070–2099). The projections indicated an increase in temperature and a decrease in rainfall. Future surface water resources were simulated using IHACRES. The results indicated that average annual runoff under GHGESs A2 and B2 would decrease by, respectively, 2.03% and 4.17% in 2040–2069 and by 6.64% and 8.94% in 2070–2099. Groundwater simulation was carried out using ModFlow. The results showed that under, the four CCSs, groundwater level would decline by 2.3, 3.0, 2.5 and 3.4%, respectively, relative to the baseline. Aquifer recharge under the four scenarios would decline by 1.43, 5.71, 2.86 and 7.14%, respectively. The results from IHACRES and ModFlow were employed to develop a conjunctive operation model with the Weap model. Several CCSs with various levels of future water demand were assessed using Weap. A 20% increase in water demand relative to the baseline, for instance, was projected to produce annual deficits in future agricultural water supply of 3.3, 5.08, 3.05 and 5.18 (× 106 m3), respectively, under the four CCSs.

What Will Remain? Predicting the Representation in Protected Areas of Suitable Habitat for Endangered Tropical Avifauna in Borneo under a Combined Climate- and Land-Use Change Scenario

The responses of threatened tropical avian species to projected climate change and land-use change are important for evaluating the ability of the existing protected areas to provide habitat to these species under future scenarios in biodiversity hotspots. This study uses Maxent, a species distribution model that employs a maximum entropy machine learning approach to map the spatial distributions of habitats suitable for the International Union for Conservation of Nature threatened birds under present and future climate and land-use change in Borneo. We find that the existing protected areas provide very low coverage of the threatened bird species’ suitable habitat areas (95%CI = 9.3–15.4%). Analysis of habitat suitability projections for 18 species of threatened birds suggests that in 2050, under Special Report on Emissions Scenarios A1B and B1, avian species with currently little suitable habitat may gain area but lose in the proportion of this that is protected. Large-ranged species are likely to lose habitat area and this will inflate the proportion of this remaining in protected areas. The present availability of suitable habitat was the most important determinant of future habitat availability under both the scenarios. Threat level, as measured by the International Union for Conservation of Nature and the habitat preferences considered here, Lowland or Lowland–Montane, are poor predictors of the amount of habitat contraction or expansion undergone by the species.

System dynamics approaches to assess the impacts of climate change on surface water quality and quantity: case study of Karoun River, Iran.

The aim of this research is to gain a better understanding of the effects of climate change with a comprehensive and dynamic perspective. Therefore, by using the System Dynamics (SD) approach to simulate the effects of climate change on the quality and quantity of the Karoun River and regarding the water supply and demand systems in the region and their feedback relations, a model was developed in Vensim. CGCM3 outputs under A2, B1, and A1B emission scenarios have been used to investigate the effects of climate change on both the quality/quantity of the water resources system. Also, to determine the effects of climate change on agricultural demand, the water requirement of selected crops for the next period (2015-2050) has been calculated via CROPWAT model. The results show that the maximum and minimum temperature and evaporation will increase. The results of the developed SD model show that if the current development process continues under all three climate change scenarios, the system will be able to meet the domestic, industrial, and environmental demand. However, the supply of agricultural demand will be deficient. Also, the average EC value in Ahvaz station under three emission scenarios has increased more than 21%, compared to the 15-year average. The average pH value did not change much. Then, several proposed management scenarios were evaluated to improve system performance. The results show that the scenario of optimal operation of upstream dams has the best performance. However, due to the unrealistic growing trend, despite applying this scenario, the development of the agricultural sector will fail down after a few years. Therefore, to reach a long-term solution to the problem of water shortage, the growth trend of this sector for the next period should be reviewed in light of the effects of climate change.

Techno-economics of lignocellulose biorefineries at South African sugar mills using the biofine process to co-produce levulinic acid, furfural and electricity along with gamma valeractone

Lignocellulose biorefineries process biomass into ‘green’ chemicals and bioenergy and play a key role in sustainable bio-economies. Using sugarcane bagasse and trash feedstocks, biorefineries were simulated in Aspen Plus ® v 8.6 to generate mass and energy balances. Four scenarios with electricity and furfural as common products were generated. Scenario A in addition produced high volume, low value levulinic acid whilst scenarios B1 and B2 generated low volume, high value levulinic acid at varied feedstock capacities. Scenario C produced low volume, high value levulinic acid and bulk gamma valerolactone. The four biorefineries were also compared to a combined heat and power plant that only produced steam and electricity. Aspen Plus ® Economic Analyser and a discounted cash flow rate of return methodology were used to determine the economic feasibility of the bioenergy self-sufficient biorefineries annexed to a conventional South African sugar mill. Levulinic acid production via the Biofine process makes economic sense when produced for niche markets, due to the anticipated decrease in market prices when production volumes increase. Scenario (C) co-producing a low volume of levulinic acid (0.15 t h−1) together with 3.3 t h−1 furfural, electricity (14 MWh) and 6.8 t h−1 gamma valerolactone, was the most profitable. It attained an internal rate of return of 23% and 253 US$ million net present value at a 9.7% hurdle rate by matching chemicals' production volumes to market demands, to maintain high market prices for multiple revenue streams.

Spatio-Temporal Changes in Wildlife Habitat Quality in the Middle and Lower Reaches of the Yangtze River from 1980 to 2100 Based on the InVEST Model

The Yangtze River (YZR) regions have experienced rapid changes after opening up to economic reforms, and human activities have changed the land cover, ecology, and wildlife habitat quality. However, the specific ways in which those influencing factors changed the habitat quality during different periods remain unknown. This study assessed the wildlife habitat quality of the middle and lower YZR in the past (1980–2018) and in future scenarios (2050, 2100). We analyzed the relationships between habitat quality and various topological social-economic factors, and then mapped and evaluated the changes in habitat quality by using the Integrated Valuation of Environmental Services and Tradeoffs (InVEST) model. The results show that the slope (R = 0.502, P < 0.01, in 2015), elevation (R = 0.003, P < 0.05, in 2015), population density (R = –0.299, P < 0.01, in 2015), and NDVI (R = 0.366, P < 0.01, in 2015) in the study area were significantly correlated with habitat quality from 2000 to 2015. During the period of 1980–2018, 61.93% of the study area experienced habitat degradation and 38.07% of the study area had improved habitat quality. In the future, the habitat quality of the study area will decline under either the A2 scenario (high level of population density, low environmental technology input, and high traditional energy cost) or the B2 scenario (medium level of population density, medium green technology and lack of cooperation of regional governments). The results also showed that habitat in the lower reaches or north of the YZR had degraded more than in the middle reaches or the south of YZR. Therefore, regional development should put more effort into environmental protection, curb population growth, and encourage green technology innovation. Inter-province cooperation is necessary when dealing with ecological problems. This study can serve as a scientific reference for regional wildlife protection and similar investigations in different areas.

Rainfall prediction to evaluate the flood risk using remote sensing and geographical information system

Abstract: In this study, the runoff curve number map for Navrud watershed in north of Iran was determined based on the soil hydrological group, land-use and land-cover using remote sensing and geographical information system. For this objective, land-cover and Land-use situation maps were prepared using NDVI index and Landsat satellite data, respectively. Runoff curve number maps were determined using the overlay prepared maps in GIS and SCS table. For evaluating the accuracy of estimated curve numbers, runoff maximum discharge was calculated using HEC-HMS model and compared to the observed values. Furthermore, the climate change trend and probabilistic distribution functions were considered to predict the flood risk. The effects of climate change were defined by atmospheric general circulation models for A1B, A2 and B1 scenarios. Error analysis between calculated and observed discharge showed that watershed curve number was determined with acceptable accuracy.

Evaluation of the impact of climate change on reference crop evapotranspiration in Hamedan-Bahar plain

Climate change was simulated using the HADCM3 general circulation model and was downscaled using the LARS-WG downscaling model. After downscaling and uncertainty analysis, climatic parameters were generated until year 2090, assuming three emission scenarios A1, B1 and A1B. After fitting an equation for ETo, its future values were calculated for the simulation period. Results indicated an increase in the mean annual ETo under all three scenarios. The highest increase occurred under the A2 scenario and the lowest increase occurred under the B1 scenario. Comparison of monthly ETo values showed that although greenhouse gas emissions in the next 30-year period did not lead to a perceptible increase in ETo but severe long-term increase was predictable. According to predictions, the mean monthly ETo in the [cold months] in different scenarios did not change significantly and much of the increase occurring in the mean annual ETo was due to a significant increase in the monthly ETo in [hot months] of the year.

Studying the impact of climate change on the average temperature using CanESM2 and HadCM3 modelling in Iraq

The main objective of the current study was thus to investigate the expected changes in average annual temperature through to the end of the 21st century in the south-west of Iraq, using a base period of 1979 to 2013. Changes in the average temperature were predicted for the southwest of Iraq during the period 2020 to 2099 using two general circulation models (GCMs), CanESM2 and HadCM3, based on the Representative Concentration Pathway (RCP2.6, RCP4.5 and RCP8.5) scenarios and emission scenarios A2 and B2. The statistical downscaling model (SDSM) used to downscale the climatic data referred to a baseline (historical) period of 1979 to 2013, and the daily average (maximum and minimum) temperatures projected for the future study period were given for seven meteorological stations. The results show rises in average annual temperatures ranging from 0.3°C to 1.2°C over the study area by the end of the 21st century.

Studying the impact of climate change on the average temperature using CanESM2 and HadCM3 modelling in Iraq

The main objective of the current study was thus to investigate the expected changes in average annual temperature through to the end of the 21st century in the south-west of Iraq, using a base period of 1979 to 2013. Changes in the average temperature were predicted for the southwest of Iraq during the period 2020 to 2099 using two general circulation models (GCMs), CanESM2 and HadCM3, based on the Representative Concentration Pathway (RCP2.6, RCP4.5 and RCP8.5) scenarios and emission scenarios A2 and B2. The statistical downscaling model (SDSM) used to downscale the climatic data referred to a baseline (historical) period of 1979 to 2013, and the daily average (maximum and minimum) temperatures projected for the future study period were given for seven meteorological stations. The results show rises in average annual temperatures ranging from 0.3°C to 1.2°C over the study area by the end of the 21st century.

Downscaling diurnal temperature over west and southwest Iran : A comparison of statistical downscaling approaches

This study aimed to forecast temperature variations in the western and southwestern part of Iran using a general circulation model and artificial neural networks (ANN). The data included mean diurnal temperatures from synoptic stations, National Centers for Environmental Prediction/ National Center for Atmospheric Research (NCEP/NCAR) reanalysis data, and outputs of a third-generation global climate model, the Hadley Centre Coupled Model version 3 (HadCM3), under A2 and B2 scenarios for the baseline period (1961–1990). The data of the first (1961–1975) and second 15 years (1976–1990) of the baseline period were used for model calibration and validation, respectively. Both models, however, produced reliable estimates at the plain stations with neither outperforming the other due to their negligible errors. However, the neural network results of mountain synoptic stations showed a lower error rate than the statistical downscaling model (SDSM) outputs. All in all, we can say that there was a larger amount of error in the outputs of the atmospheric general circulation models (AGCMs) in the mountainous regions. According to the outputs of the neural network and the AGCMs, temperatures at the studied stations were on the rise. In fact, this increase was more noticeable at the plain stations. This can be attributed to their proximity to the sea, to their latitude, and to the more intensive industrial activities (especially, extraction of petroleum and production of petroleum products) taking place near the plain stations.

Assessment of Climate Hazards Using PRECIS Regional Climate Model (RCM): A Case Study in Cameron Highlands, Pahang, Malaysia

This study aimed to assess the differences in modelling disaster risks results when using historical precipitation and when using simulated precipitation associated with future Intergovernmental Panel on Climate Change (IPCC) climate scenarios. Subsequently, the relationship between climate change and climate hazards was analyzed in this study. The secondary data analyzed included historical precipitation (1983-2017), flood and landslide events records, and Providing Regional Climates for Impacts Studies (PRECIS) regional climate model (RCM):A1B, A2 and B2 scenarios. By comparing the historical precipitation data with the RCM scenarios, the results showed that the precipitation was correlated with A1B scenario (r= 0.695). The relationship between climate change and hazards was identified to be a positive correlation. The historical daily precipitation (1983-2017) showed a positive correlation with flood and landslide events (r= 0.530, r = 0.797, respectively). As for prediction of climate hazards, the RCM A1B, A2 and B2 scenarios showed correlations with flood event: r= 0.648, 0.384 and 0.417, respectively. Similar results were obtained for landslide and the RCM A1B, A2 and B2 scenario: r = 0.498, 0.751 and 0.654, respectively. Precipitation simulation by PRECIS RCM indicated increased levels of precipitation in the Cameron Highlands for the 2018 - 2069. Commensurate with this, great possibility of increasingly serious consequential hazards such as flood and landslide events are expected.