Comparative study on the reservoir operation planning with the climate change adaptation

The management planning of Pedu–Muda reservoir, Kedah, was investigated in the context of the climate change evolution. The aim of this study was to evaluate the impact of the climate change to the reservoir operating management system and its sustainability. The study was divided into two sections; Analysis 1 refers to the reservoir optimization adapted with the climate assessment. The statistical downscaling model reacted as the climate model to generate the long-term pattern of the local climates affected by the greenhouse gases. Analysis 2 refers to the reservoir optimization but excluded the climate changes assessment in the analyses. The non-dominated sorting genetic algorithm version II (NSGA-II) was applied in both analyses to optimize the water use due to the multi-objectives demand, maximizing water release, minimizing water shortage and maximizing reservoir storage. The formation of Pareto optimal solutions from both analyses was measured and compared. The results showed the Analysis 1 potential to produce consistence monthly flow with lesser error and higher correlation values. It also produced better Pareto optimal solution set and considered all the objectives demands. The NSGA-II also successfully improves and re-manages the reservoir storage efficiently and reduce the dependency of these reservoirs

Assessment of the Potential Occurrence of Dry Period in the Long Term for Pahang State, Malaysia

The interference of climate circulation and continuous rising of surface temperature every year has caused the atmosphere composition change which gives serious impact to water resource management. Pahang is among of the affected states by El Nino that hit Malaysia in recent years which led to water depletion at several water plants. Based on the current situation, this study focuses on 1) simulate the average rain pattern using statistical downscaling; 2) identify the severity index and dry duration occurrence in the catchment area. Predicting potential changes in the climate events is important to evaluate the level of climate change in the critical region. Therefore, the integration of Statistical Downscaling Model (SDSM) and Standard Precipitation Index (SPI) have been conducted to study the potential occurrence of the dry period due to climate change for year 2020s and year 2050s. The results reveal that the dry condition is high during the mid-year. The lowest SPI value is estimated to reach -2.2 which can be classified as extreme. The potential dry period is expected to increase 2.5% and 3.3% in 2020 and 2050, respectively

Evaluation the performances of stochastic streamflow models for the multi reservoirs

Pedu-Muda reservoirs responsible to supply sufficient water capacity during paddy cultivation period twice a year. Thus, improper management and operation of the reservoirs creating the scarcity issue of water availability especially during dry season. Synthetic streamflow being as a main role in predicting the capability and sustainability of these reservoirs to cope the demand. This study evaluated the performances of stochastic streamflow model to produce the synthetic streamflow generation. There were two comparable models; Valencia Schaake (VS) and Thomas Fiering (TF) which represented in disaggregation and aggregation models, respectively. The basis of the statistical characteristics consist of lag one correlation, mean, mean absolute error (MAE), and standard deviation (St.D) of annual and monthly levels for both models were compared to identify the model performances. The results revealed the generated streamflow series produced by VS models had better performances to the historical streamflow record than the TF model in term of annual and monthly. However, both models can preserve a good agreement to the mean even the range of monthly streamflow were overestimated/underestimated by VS and TF models respectively

Comparison of different methods in estimating potential évapotranspiration at muda irrigation scheme of Malaysia

Evapotranspiration (ET) is a complex process in the hydrological cycle that influences the quantity of runoff and thus the irrigation water requirements. Numerous methods have been developed to estimate potential evapotranspiration (PET). Unfortunately, most of the reliable PET methods are parameter rich models and therefore, not feasible for application in data scarce regions. On the other hand, accuracy and reliability of simple PET models vary widely according to regional climate conditions. The objective of the present study was to evaluate the performance of three temperature-based and three radiation-based simple ET methods in estimating historical ET and projecting future ET at Muda Irrigation Scheme at Kedah, Malaysia. The performance was measured by comparing those methods with the parameter intensive Penman-Monteith Method. It was found that radiation based methods gave better performance compared to temperature-based methods in estimation of ET in the study area. Future ET simulated from projected climate data obtained through statistical downscaling technique also showed that radiation-based methods can project closer ET values to that projected by Penman-Monteith Method. It is expected that the study will guide in selecting suitable methods for estimating and projecting ET in accordance to availability of meteorological dat

Assessment on the climate change impact using CMIP6

The impact of global warming is resulting in flood, land slide, soil erosion and drought that are anticipated to become more intense and frequent. The history record shows that the flood has been occur repeatedly in this study area. The study of future climate needs to be done to a greater extent as a planning for the infrastructure and as mitigation actions on flood based on the future climate. The aims of this study, to ascertain the climate projection by CMIP6 are compatible to be subjected for the future climate and to identify the trend of future climate projection in Kemaman, Terengganu. The comprehensive study of future climate that using CMIP6 that consist of SSPs help in providing a clear explanation of future society's socio-economic appraisal in assessment modelling. The Sen's Slope Test were used to analyze the trend of future climate projection in this study area. In this study, we found out that the trend of future rainfall is increasing with the positive values of Sen's Slope Test. Study shows that the Sen's Slope Test values for eight (8) stations in Kemaman, which is Ban Ho, Hulu Jabor, Rumah Pam Paya Kempian, JPS Kemaman, Klinik Bidan Kg Ibok, SK Kemasek, Jambatan Tebak, and Jambatan Air Putih are 2.381, 1.158, 1.333, 1.252, 2.293, 1.06, 3.113, and 1.961 respectively. The frequency and intensity of forecasting rainfall also increasing compared to the historical data. In conclusion, the proper planning and mitigation action need to be done to minimize the losses that happen due to climate changes impacts

Forecasting of Hydropower Production Using Box-Jenkins Model at Tasik Kenyir, Terengganu

Hydropower is one of the most essential mainstays in the long list of renewable energy resources that implements the use of potential energy of water to generate power by transforming the energy in the form of electricity. Forecasting the future energy production benefits in maintaining the effectiveness of the hydropower plant in the long term. This study aims to forecast the hydropower energy produced as electricity in Sultan Mahmud Hydropower Plant, Lake Kenyir, Terengganu using Box-Jenkins model in the short term from October 2020 until December 2022. Analysis and forecast is based on the historical data from a total of four unit of electricity generator from January 1997 to September 2020. Evaluation is made on the forecasted result using Mean Absolute Percentage Error (MAPE) to validate the accuracy of the model. The results demonstrated that by using the proposed model and numerical calculation, Box-Jenkins model is effective in forecasting the monthly electricity energy produced by the hydropower plant. The best model obtained with the smallest MAPE value of 26.4% is ARIMA (2,0,0)

Comparative assessment on climate prediction from CMIP5 and CMIP6 models over Hulu Terengganu, Malaysia

The uncertainties of climate change in the future year cause the contribution factors and greenhouse gasses (GHGs) effects on the local climates need to be revised. The development of new climate scenarios in the 6th Coupled Model Intercomparison Project (CMIP6) is consistent with the technological exploration and increment of GHGs dispersion compared to the consideration factors in CMIP5. The purpose of this study was to compare the performance of CMIP5 (based on Representative Concentration Pathways, RCPs) and CMIP6 (based on Shared Socioeconomic Pathways, SSPs) in simulating seasonal rainfall and estimating trends in Hulu Terengganu, Malaysia. The linear scaling (LS) method was used in this study to treat the gaps between observed and simulated results, and the climate trend was examined using the Mann-Kendall (MK) and Sen's Slope tests. The results show that the SSPs scenario outperforms the RCPs in simulating historical rainfall (2015-2020) by producing a higher r value and a smaller percentage difference. According to the MK test, there was no significant trend in projected rainfall across all scenarios (2020-2099). Based on Sen's Slope test, RCP 4.5 and RCP 8.5 show an increasing trend for all rainfall stations. However, all SSP scenarios show a declining trend in projected rainfall, with SSP1-2.6 producing the largest declining trend magnitude. In contrast, when compared to observed rainfall from the baseline period (1988-2017), the SSPs scenario indicates the potential for a greater increase in future annual rainfall projections than the RCPs scenario. All SSP scenarios show an increasing annual rainfall magnitude in 2040-2069 (Δ2050). However, the annual rainfall for SSP2-4.5 and SSP5-8.5 began to decrease in 2070-2099 (Δ2080). Meanwhile, RCP 2.6 has the greatest reduction in annual rainfall projections for both projected time periods when compared to other scenarios. It can be concluded that although all SSPs scenarios show a declining trend in projected rainfall from 2020 to 2099, the total annual rainfall projected for SSPs remains higher than RCPs in Δ2050 and Δ2080 periods

Comparative Analyses on Disaggregation Methods for the Rainfall Projection

Climate modeling data are typically available in the daily climate time series for a particular year of observation. However, studies for urban drainage and stormwater management require rainfall data on sub-daily time scales for design such as the development of IDF curves. Most hydrological studies dealing with the impacts of climate change are particularly challenging due to this explicit requirement. Therefore, this study aims to establish more accurate disaggregation methods for constructing hourly rainfall under the projected climate scenarios. Three disaggregation methods with different theoretical underpinnings have been evaluated: Scaling Properties (SP), Indian Reduction Formula (IRF), and Stochastic Method (SM). The results show that the SP method generally outperforms the other methods based on statistical analyses and comparisons of statistical properties with historical data. The SP method performs well by having the lowest RMSE and percentage difference values across all rainfall stations. Moreover, the hourly mean and standard deviation of disaggregated rainfall from the SP method correspond well to the historical data. The projected rainfall data from 2025 to 2100 were obtained from the MRI-ESM2-0 model and disaggregated from daily-time to hourly-time series using the SP method. In general, the SSP5-8.5 scenario showed the highest projected rainfall compared with the other scenarios

Analysis of Linear Scaling Method in Downscaling Precipitation and Temperature

Climate change is one of the greatest challenges in the 21st century that may influence the long haul and the momentary changeability of water resources. The vacillations of precipitation and temperature will influence the runoff and water accessibility where it tends to be a major issue when the interest for consumable water will increase. Statistical downscaling model (SDSM) was utilized in the weather parameters forecasting process in every 30 years range (2011-2040, 2041-2070, and 2071-2100) by considering Representative Concentration Pathways (RCP2.6, RCP4.5, and RCP8.5). The Linear Scaling (LS) method was carried out to treat the gaps between ground/ observed data and raw/ simulated results after SDSM. After the LS method was executed to raw/ simulated data after SDSM, the error decrease reaches over 13% for rainfall data. The Concordance Correlation Coefficient (CCC) value clarifies the correlation of rainfall amount among observed and corrected data for all three (3) RCPs categories. There are very enormous contrasts in rainfall amount during the wet season where CCC-values recorded are 0.22 and beneath (low correlation). The findings demonstrated that the rainfall amount during the dry season will contrast for all RCPs with the CCC-values are between 0.44-0.53 (moderate correlation). RCP8.5 is the pathway with the the most elevated ozone-depleting substance emanations and demonstrated that the climate change impact is going on and turn out to be more awful step by step

Impacts of Climate Change on Rainfall Trends Under RCP Scenarios in Johor, Malaysia

Changes in the spatial and temporal rainfall pattern affected by the climate change need to be investigated as its significant characteristics are often used for managing water resources. In this study, the impacts of climate change on rainfall variability in Johor was investigated by using General Circulation Model (GCM) on the availability of daily simulation for three representative concentration pathways (RCP) scenarios, RCP 2.6, RCP 4.5 and RCP 8.5 for interval year of Δ2030, Δ2050 and Δ2080. In addition, the annual future rainfall trend for the first interval year of Δ2030 was also made. Daily rainfall series from eight (8) stations in Johor, Malaysia capturing 30 years period (1988-2017) with less than 10% missing data were chosen. The annual mean rainfall for RCP 2.6, 4.5 and 8.5 was predicted increase by 17.5%, 18.1% and 18.3%, respectively as compared to historical data. Moreover, the Mann-Kendall (MK) test was used to detect the trend and resulted in no trend for RCP 2.6. Even so, RCP 4.5 showed a significant upward trend in Muar and Kota Tinggi, and for RCP 8.5, all regions were detected to have an upwards trend except for Pontian and Kluang. In general, the concentration of greenhouse gases from RCP 8.5 gave the highest rainfall in future