Muhammad Adib bin Mohd Nasir received a Certificate and Diploma in Mechanical Engineering (Agriculture) from Politeknik Kota Bharu, Kelantan, Malaysia. He obtained a Polytechnic Director Award in 2009 for being excellent in academics and co-curriculum. Upon obtaining Public Service Department (Jabatan Perkhidmatan Awam - JPA) scholarship in 2011, he furthered his study in Bachelor of Engineering (Agricultural and Biosystem) and successfully graduated with first-class honors in 2015 from Universiti Putra Malaysia (UPM). He also obtained an award from the Malaysian Society of Agricultural Engineers (MSAE) for being the Best Student in the program. Starting earlier in 2017, Adib has become one of the irrigation and drainage engineering researchers at UPM. He is now pursuing his Ph.D. in irrigation and water resource systems operation and management at Universiti Teknologi Malaysia (UTM) after completing his Master's study at UPM. His research interests include irrigation and drainage management, climate change impact on water resources, reservoir operation and management, decision support systems, and machine learning.
Future climate prediction at a local scale is one of the pressing challenges affecting water mana... more Future climate prediction at a local scale is one of the pressing challenges affecting water management-related mitigation plans. The rice irrigation demands are always related to the climate of the area. This study presents possible changes in the monthly rice irrigation demand patterns under future climate scenarios in the Kerian Irrigation Scheme, Malaysia. An ensemble of five Global Climate Models under three Shared Socioeconomic Pathways (SSPs) (SSP1-2.6, SSP2-4.5, and SSP5-8.5) was employed to help project irrigation demand from 2021 to 2080. The study compared the future projections with the baseline period (1985-2014) and revealed that future irrigation demand changes for two planting periods range between − 1.0 to 0.1% and − 5.3 to − 2.6% during the dry season (February-July) and wet season (August-January), respectively. A significant decrease in irrigation water demand was predicted in September and October for each SSP scenario due to increased rainfall during the wet season, with SSP5-8.5 being the most prominent. Although the temperature and reference evapotranspiratopn (ET o) were predicted to increase, mainly during the near future (2021-2050) rather than the far future (2051-2080), the increase in predicted monthly rainfall successfully copes with the risk of the possible high demand for irrigation supply. Climate change potentially alters the future monthly irrigation water demand pattern, resulting in challenges to water resource management. Predicting the impacts of rice irrigation water demand under the potential future climate change is crucial for Bukit Merah Reservoir to help establish appropriate operational policies for irrigation release for its sustainability.
Monthly streamflow forecasting is crucial in water resources management to assess the possible fu... more Monthly streamflow forecasting is crucial in water resources management to assess the possible future streamflow patterns. It becomes vital where streamflow of Kurau River is the primary water source to irrigate the large-scale rice scheme of Kerian, Perak, coupled with future climate change uncertainty. In this context, machine learning algorithms have received outstanding attention due to their high accuracy in forecasting through high-speed input-output data processing of selflearning from physical processes. In this study, two machine learning algorithms, support vector regression (SVR) and random forest (RF), were considered to forecast the streamflow of Kurau River in Malaysia using gauged hydro-meteorological dataset for the period from 1976 to 2005. The predictions of monthly streamflows were based on hydro-meteorological data such as rainfall, minimum and maximum temperature, relative humidity, and wind speed. A comparative study is executed to evaluate the efficiency of SVR and RF in performing the streamflow predictions of Kurau River. The results show that RF outperformed the SVR in both the training and testing phases. The results have proven that machine learning algorithms, especially the RF model, can be implemented for forecasting streamflow by using only hydrometeorological data with high accuracy, which will improve future water resources management.
Spatial and temporal variability of streamflow due to climate change affects hydrological process... more Spatial and temporal variability of streamflow due to climate change affects hydrological processes and irrigation demands at a basin scale. This study investigated the impacts of climate change on the Kurau River in Malaysia using metalearning, an ensemble machine learning technique using support vector regression (SVR) and random forest (RF) coupled with the Coupled Model Intercom- parison Project CMIP6 multi-Global Climate Model (GCM). Five global climate models and three shared socioeconomic pathways (SSP1- 2.6, SSP2-4.5, and SSP5-8.5) were used. The climate sequences generated by the delta change factor method were applied as input to the metalearning model to predict the streamflow changes in the Kurau River from 2021 to 2080. The model fitted reasonably well, with Kling–Gupta efficiency (KGE), Nash–Sutcliffe efficiency (NSE), percent bias (PBias), and RMS Error (RMSE) of 0.79, 0.83, 2.52, and 4.51, respectively, for the training period (1976–1995) and 0.72, 0.72, 5.85, and 6.90, respectively, for the testing period (1995–2005). Future projections of multi-GCM over the 2021–2080 period under three SSPs predicted an increase in rainfall for all months except April–June during the dry period (off-season), with a higher increase occurring during the wet period (main season). Temperature pro- jections indicated an increase in maximum and minimum temperatures under all SSP scenarios, with a higher increase of approximately 2.0°C under SSP5-8.5 predicted during the 2051–2080 period relative to the baseline period of 1976–2005. The model predicted that the seasonal changes in streamflow of two planting periods range between −7.5% and 7.1% and between 1.2% and 5.9% during the off-season and the main season, respectively. A significant streamflow decrease was predicted in April and May for all SSP scenarios due to high temperatures during the off-season, with SSP5-8.5 being the worst. The impact assessment of climate variabilities on the availability of water resources is vital to identify appropriate adaptation strategies to deal with an expected increase in irrigation demand due to global warming in the future. The predicted future streamflow under the potential climate change impacts is crucial for the Bukit Merah Reservoir to establish suitable operational policies for irrigation release.
Rainfall is a vital component in the rice water demand model for estimating irrigation requiremen... more Rainfall is a vital component in the rice water demand model for estimating irrigation requirements. Information on how the future patterns are likely to evolve is essential for rice-growing management. This study presents possible changes in the future monthly rainfall patterns by perturbing model parameters of a stochastic rainfall using the change factor method for the Kerian rice irrigation scheme in Malaysia. An ensemble of five Global Climate Models under three Shared Socioeconomic Pathways (SSPs) (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were employed to project rainfall from 2021 to 2080. The results show that the stochastic rainfall generator performed well at preserving the statistical properties between simulated and observed rainfall. Most scenarios predict the increasing trend of the mean monthly rainfall with only a few months decreasing in April and May occurring in off (dry) season. The future patterns 2051-2080 show a significant increasing trend during main (wet) season compared to the near future period (2021-2050). The projected future rainfall under SSP1-2.6 and SSP2-4.5 are higher than SSP5-8.5 from January to July, and December but lower from August to November. The projected annual rainfall will significantly increase toward 2080 during the main-season but uniform during the off-season except under SSP5-8.5, which is significantly decreasing. The output results are essential for rice farmers and water managers to manage and secure future rice irrigation water under the impact of future climate change. The projected changes in rainfall on the river basin demand further study before concluding the impact consequences for the rice sector.
Climate change-induced spatial and temporal variability of stremflow has significant implications... more Climate change-induced spatial and temporal variability of stremflow has significant implications for hydrological processes and water supplies at basin scale. This study investigated the impacts of climate change on streamflow of the Kurau River Basin in Malaysia using a Climate-Smart Decision Support System (CSDSS) to predict future climate sequences. For this, we used 25 reliazations consisting from 10 Global Climate Models (GCMs) and three IPCC Representative Concentration Pathways (RCP4.5, RCP6.0 and RCP8.5). The generated climate sequences were used as input to Soil and Water Assessment Tool (SWAT) to simulate projected changes in hydrological processes in the basin over the period 2021–2080. The model performed fairly well for the Kurau River Basin, with coefficient of determination (R2), Nash-Sutcliffe Efficiency (NSE) and Percent Bias (PBIAS) of 0.65, 0.65 and –3.0, respectively for calibration period (1981–1998) and 0.60, 0.59 and −4.6, respectively for validation period (1996–2005). Future projections over 2021–2080 period show an increase in rainfall during August to January (relatively wet season, called the main irrigation season) but a decrease in rainfall during February to July (relatively dry season, called the off season). Temperature projections show increase in both the maximum and minimum temperatures under the three RCP scenarios, with a maximum increase of 2.5 °C by 2021–2080 relative to baseline period of 1976–2005 under RCP8.5 scenario. The model predicted reduced streamflow under all RCP scenarios compared to the baseline period. Compared to 2021–2050 period, the projected streamflow will be higher during 2051–2080 period by 1.5 m3/s except in February for RCP8.5. The highest streamflow is predicted during August to December for both future periods under RCP8.5. The seasonal changes in streamflow range between –2.8% and –4.3% during the off season, and between 0% (nil) and –3.8% during the main season. The assessment of the impacts of climatic variabilities on the available water resources is necessary to identify adaptation strategies. It is supposed that such assessment on the Kurau River Basin under changing climate would improve operation policy for the Bukit Merah reservoir located at downstream of the basin. Thus, the predicted streamflow of the basin would be of importance to quantify potential impacts of climate change on the Bukit Merah reservoir and to determine the best possible operational strategies for irrigation release.
Climate projection at local scale is one of the crucial challenges that affects the development o... more Climate projection at local scale is one of the crucial challenges that affects the development of water management related mitigation plans. Moreover, the currently available climate models do not directly simulate some of the hydro-climatic parameters (e.g., effective rainfall, reference evapotranspiration, irrigation requirements), which are of interest in irrigation sector. Modeling crop-water demands under changing climate involves several step-by-step approaches that are tedious and time-consuming for many water users. This study developed a water management tool, hereafter called Climate-Smart Decision-Support System (CSDSS), for modeling water demand of rice irrigation schemes under climate change impacts. The CSDSS is a user-friendly interactive program consisting of three main modules integrated in MATLAB and a graphical user interface development environment (GUIDE). The model runs with ten Global Climate Models (GCMs) and three emission scenarios (RCP 4.5, 6.0 and 8.5). It can generate several hydro-climatic parameters based on a daily water balance model, with input data from GCMs projections, crop, soil and field conditions. The model allows water managers to make fast decision for paddy water management. The generated outputs can be obtained through individual GCMs as well as through multi-models (ensemble) projection and can be converted into excel format for further analysis. The model was applied to evaluate the impacts of climate change on irrigation water demand and other key hydro-climatic parameters in Tanjung Karang Rice Irrigation Scheme in Malaysia for the period 2010-2099 with reference to the baseline period of 1976-2005. The results show that irrigation water demand will increase during the off-season (January-June) but decrease during the main season (July-December) due to significant contribution from effective rainfall in the latter season. The CSDSS tool can be used for managing water resources under changing climate and would, therefore, be helpful in promoting appropriate adaptation and mitigation strategies that can lead to more sustainable water use at farm level. Some future improvements of the tool, due to methodological limitations of the study, will however improve its performance.
Future climate prediction at a local scale is one of the pressing challenges affecting water mana... more Future climate prediction at a local scale is one of the pressing challenges affecting water management-related mitigation plans. The rice irrigation demands are always related to the climate of the area. This study presents possible changes in the monthly rice irrigation demand patterns under future climate scenarios in the Kerian Irrigation Scheme, Malaysia. An ensemble of five Global Climate Models under three Shared Socioeconomic Pathways (SSPs) (SSP1-2.6, SSP2-4.5, and SSP5-8.5) was employed to help project irrigation demand from 2021 to 2080. The study compared the future projections with the baseline period (1985-2014) and revealed that future irrigation demand changes for two planting periods range between − 1.0 to 0.1% and − 5.3 to − 2.6% during the dry season (February-July) and wet season (August-January), respectively. A significant decrease in irrigation water demand was predicted in September and October for each SSP scenario due to increased rainfall during the wet season, with SSP5-8.5 being the most prominent. Although the temperature and reference evapotranspiratopn (ET o) were predicted to increase, mainly during the near future (2021-2050) rather than the far future (2051-2080), the increase in predicted monthly rainfall successfully copes with the risk of the possible high demand for irrigation supply. Climate change potentially alters the future monthly irrigation water demand pattern, resulting in challenges to water resource management. Predicting the impacts of rice irrigation water demand under the potential future climate change is crucial for Bukit Merah Reservoir to help establish appropriate operational policies for irrigation release for its sustainability.
Monthly streamflow forecasting is crucial in water resources management to assess the possible fu... more Monthly streamflow forecasting is crucial in water resources management to assess the possible future streamflow patterns. It becomes vital where streamflow of Kurau River is the primary water source to irrigate the large-scale rice scheme of Kerian, Perak, coupled with future climate change uncertainty. In this context, machine learning algorithms have received outstanding attention due to their high accuracy in forecasting through high-speed input-output data processing of selflearning from physical processes. In this study, two machine learning algorithms, support vector regression (SVR) and random forest (RF), were considered to forecast the streamflow of Kurau River in Malaysia using gauged hydro-meteorological dataset for the period from 1976 to 2005. The predictions of monthly streamflows were based on hydro-meteorological data such as rainfall, minimum and maximum temperature, relative humidity, and wind speed. A comparative study is executed to evaluate the efficiency of SVR and RF in performing the streamflow predictions of Kurau River. The results show that RF outperformed the SVR in both the training and testing phases. The results have proven that machine learning algorithms, especially the RF model, can be implemented for forecasting streamflow by using only hydrometeorological data with high accuracy, which will improve future water resources management.
Spatial and temporal variability of streamflow due to climate change affects hydrological process... more Spatial and temporal variability of streamflow due to climate change affects hydrological processes and irrigation demands at a basin scale. This study investigated the impacts of climate change on the Kurau River in Malaysia using metalearning, an ensemble machine learning technique using support vector regression (SVR) and random forest (RF) coupled with the Coupled Model Intercom- parison Project CMIP6 multi-Global Climate Model (GCM). Five global climate models and three shared socioeconomic pathways (SSP1- 2.6, SSP2-4.5, and SSP5-8.5) were used. The climate sequences generated by the delta change factor method were applied as input to the metalearning model to predict the streamflow changes in the Kurau River from 2021 to 2080. The model fitted reasonably well, with Kling–Gupta efficiency (KGE), Nash–Sutcliffe efficiency (NSE), percent bias (PBias), and RMS Error (RMSE) of 0.79, 0.83, 2.52, and 4.51, respectively, for the training period (1976–1995) and 0.72, 0.72, 5.85, and 6.90, respectively, for the testing period (1995–2005). Future projections of multi-GCM over the 2021–2080 period under three SSPs predicted an increase in rainfall for all months except April–June during the dry period (off-season), with a higher increase occurring during the wet period (main season). Temperature pro- jections indicated an increase in maximum and minimum temperatures under all SSP scenarios, with a higher increase of approximately 2.0°C under SSP5-8.5 predicted during the 2051–2080 period relative to the baseline period of 1976–2005. The model predicted that the seasonal changes in streamflow of two planting periods range between −7.5% and 7.1% and between 1.2% and 5.9% during the off-season and the main season, respectively. A significant streamflow decrease was predicted in April and May for all SSP scenarios due to high temperatures during the off-season, with SSP5-8.5 being the worst. The impact assessment of climate variabilities on the availability of water resources is vital to identify appropriate adaptation strategies to deal with an expected increase in irrigation demand due to global warming in the future. The predicted future streamflow under the potential climate change impacts is crucial for the Bukit Merah Reservoir to establish suitable operational policies for irrigation release.
Rainfall is a vital component in the rice water demand model for estimating irrigation requiremen... more Rainfall is a vital component in the rice water demand model for estimating irrigation requirements. Information on how the future patterns are likely to evolve is essential for rice-growing management. This study presents possible changes in the future monthly rainfall patterns by perturbing model parameters of a stochastic rainfall using the change factor method for the Kerian rice irrigation scheme in Malaysia. An ensemble of five Global Climate Models under three Shared Socioeconomic Pathways (SSPs) (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were employed to project rainfall from 2021 to 2080. The results show that the stochastic rainfall generator performed well at preserving the statistical properties between simulated and observed rainfall. Most scenarios predict the increasing trend of the mean monthly rainfall with only a few months decreasing in April and May occurring in off (dry) season. The future patterns 2051-2080 show a significant increasing trend during main (wet) season compared to the near future period (2021-2050). The projected future rainfall under SSP1-2.6 and SSP2-4.5 are higher than SSP5-8.5 from January to July, and December but lower from August to November. The projected annual rainfall will significantly increase toward 2080 during the main-season but uniform during the off-season except under SSP5-8.5, which is significantly decreasing. The output results are essential for rice farmers and water managers to manage and secure future rice irrigation water under the impact of future climate change. The projected changes in rainfall on the river basin demand further study before concluding the impact consequences for the rice sector.
Climate change-induced spatial and temporal variability of stremflow has significant implications... more Climate change-induced spatial and temporal variability of stremflow has significant implications for hydrological processes and water supplies at basin scale. This study investigated the impacts of climate change on streamflow of the Kurau River Basin in Malaysia using a Climate-Smart Decision Support System (CSDSS) to predict future climate sequences. For this, we used 25 reliazations consisting from 10 Global Climate Models (GCMs) and three IPCC Representative Concentration Pathways (RCP4.5, RCP6.0 and RCP8.5). The generated climate sequences were used as input to Soil and Water Assessment Tool (SWAT) to simulate projected changes in hydrological processes in the basin over the period 2021–2080. The model performed fairly well for the Kurau River Basin, with coefficient of determination (R2), Nash-Sutcliffe Efficiency (NSE) and Percent Bias (PBIAS) of 0.65, 0.65 and –3.0, respectively for calibration period (1981–1998) and 0.60, 0.59 and −4.6, respectively for validation period (1996–2005). Future projections over 2021–2080 period show an increase in rainfall during August to January (relatively wet season, called the main irrigation season) but a decrease in rainfall during February to July (relatively dry season, called the off season). Temperature projections show increase in both the maximum and minimum temperatures under the three RCP scenarios, with a maximum increase of 2.5 °C by 2021–2080 relative to baseline period of 1976–2005 under RCP8.5 scenario. The model predicted reduced streamflow under all RCP scenarios compared to the baseline period. Compared to 2021–2050 period, the projected streamflow will be higher during 2051–2080 period by 1.5 m3/s except in February for RCP8.5. The highest streamflow is predicted during August to December for both future periods under RCP8.5. The seasonal changes in streamflow range between –2.8% and –4.3% during the off season, and between 0% (nil) and –3.8% during the main season. The assessment of the impacts of climatic variabilities on the available water resources is necessary to identify adaptation strategies. It is supposed that such assessment on the Kurau River Basin under changing climate would improve operation policy for the Bukit Merah reservoir located at downstream of the basin. Thus, the predicted streamflow of the basin would be of importance to quantify potential impacts of climate change on the Bukit Merah reservoir and to determine the best possible operational strategies for irrigation release.
Climate projection at local scale is one of the crucial challenges that affects the development o... more Climate projection at local scale is one of the crucial challenges that affects the development of water management related mitigation plans. Moreover, the currently available climate models do not directly simulate some of the hydro-climatic parameters (e.g., effective rainfall, reference evapotranspiration, irrigation requirements), which are of interest in irrigation sector. Modeling crop-water demands under changing climate involves several step-by-step approaches that are tedious and time-consuming for many water users. This study developed a water management tool, hereafter called Climate-Smart Decision-Support System (CSDSS), for modeling water demand of rice irrigation schemes under climate change impacts. The CSDSS is a user-friendly interactive program consisting of three main modules integrated in MATLAB and a graphical user interface development environment (GUIDE). The model runs with ten Global Climate Models (GCMs) and three emission scenarios (RCP 4.5, 6.0 and 8.5). It can generate several hydro-climatic parameters based on a daily water balance model, with input data from GCMs projections, crop, soil and field conditions. The model allows water managers to make fast decision for paddy water management. The generated outputs can be obtained through individual GCMs as well as through multi-models (ensemble) projection and can be converted into excel format for further analysis. The model was applied to evaluate the impacts of climate change on irrigation water demand and other key hydro-climatic parameters in Tanjung Karang Rice Irrigation Scheme in Malaysia for the period 2010-2099 with reference to the baseline period of 1976-2005. The results show that irrigation water demand will increase during the off-season (January-June) but decrease during the main season (July-December) due to significant contribution from effective rainfall in the latter season. The CSDSS tool can be used for managing water resources under changing climate and would, therefore, be helpful in promoting appropriate adaptation and mitigation strategies that can lead to more sustainable water use at farm level. Some future improvements of the tool, due to methodological limitations of the study, will however improve its performance.
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