Climate change impact on rainfall and temperature in Muda irrigation area using multicorrelation matrix and downscaling method

Tukimat, Nurul Nadrah Aqilah; Harun, Sobri

doi:10.2166/wcc.2015.015

Cited by 8 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the statistical downscaling has several limitations (Wangsoh et al., 2017), however the SDSM model does not require high computational demand to view the simulation results but has ability to produce high quality of projection results. These advantages, as a whole, had made SDSM a reliable tool for climate downscaling (Samadi et al., 2013, Tukimat and Harun, 2015) and was selected as a downscaling tool to generate the future climate trend at the study site.…”

Section: Methodsmentioning

confidence: 99%

Projection the long-term ungauged rainfall using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model

Tukimat

Syukri

Malek

2019

Heliyon

View full text Add to dashboard Cite

An accuracy in the hydrological modelling will be affected when having limited data sources especially at ungauged areas. Due to this matter, it will not receiving any significant attention especially on the potential hydrologic extremes. Thus, the objective was to analyse the accuracy of the long-term projected rainfall at ungauged rainfall station using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model. The SDSM was used as a climate agent to predict the changes of the climate trend in Δ2030s by gauged and ungauged stations. There were five predictors set have been selected to form the local climate at the region which provided by NCEP (validated) and CanESM2-RCP4.5 (projected). According to the statistical analyses, the SDSM was controlled to produce reliable validated results with lesser %MAE (<23%) and higher R. The projected rainfall was suspected to decrease 14% in Δ2030s. All the RCPs agreed the long term rainfall pattern was consistent to the historical with lower annual rainfall intensity. The RCP8.5 shows the least rainfall changes. These findings then used to compare the accuracy of monthly rainfall at control station (Stn 2). The GIS-Kriging method being as an interpolation agent was successfully to produce similar rainfall trend with the control station. The accuracy was estimated to reach 84%. Comparing between ungauged and gauged stations, the small %MAE in the projected monthly results between gauged and ungauged stations as a proved the integrated SDSM-GIS model can producing a reliable long-term rainfall generation at ungauged station.

show abstract

Section: Methodsmentioning

confidence: 99%

Projection the long-term ungauged rainfall using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model

Tukimat

Syukri

Malek

2019

Heliyon

View full text Add to dashboard Cite

show abstract

“…those with more than Regional monthly precipitation of the MRB as measured by the mean of the eight stations shown in Figure 1c. Similar to other Malaysian river basins, the climate system of the MRB can be divided into the northeast monsoon (NEM, November to March), southwest monsoon (SWM, May to September) and two inter-monsoon seasons [19]. The MRB is less affected by the NEM because of the Titiwangsa range blocks the heavy precipitation [13].…”

Section: Data and Quality Controlmentioning

confidence: 99%

Analysis of Precipitation and Temperature Extremes over the Muda River Basin, Malaysia

Tan

Samat

Chan

et al. 2019

Water

View full text Add to dashboard Cite

Trends in precipitation and temperature extremes of the Muda River Basin (MRB) in north-western Peninsular Malaysia were analyzed from 1985 to 2015. Daily climate data from eight stations that passed high quality data control and four homogeneity tests (standard normal homogeneity test, Pettitt test, Buishand range test, and von Neumann ratio test) were used to calculate 22 Expert Team on Climate Change Detection and Indices (ETCCDI) extreme indices. Non-parametric Mann–Kendall, modified Mann–Kendall and Sens’ slope tests were applied to detect the trend and magnitude changes of the climate extremes. Overall, the results indicate that monthly precipitation tended to increase significantly in January (17.01 mm/decade) and December (23.23 mm/decade), but decrease significantly in May (26.21 mm/decade), at a 95% significance level. Monthly precipitation tended to increase in the northeast monsoon, but decrease in the southwest monsoon. Mann–Kendall test detected insignificant trends in most of the annual climate extremes, except the extremely wet days (R99p), mean of maximum temperature (TXmean), mean of minimum temperature (TNmean), cool days (TX10p), cool nights (TN10p), warm days (TX90p) and warm nights (TN90p) indices. The number of heavy (R10mm), very heavy (R20mm), and violent (R50mm) precipitation days changed at magnitudes of 0~2.73, −2.14~3.33, and −1.67~1.29 days/decade, respectively. Meanwhile, the maximum 1-day (Rx1d) and 5-day (Rx5d) precipitation amount indices changed from −10.18 to 3.88 mm/decade and −21.09 to 24.69 mm/decade, respectively. At the Ampangan Muda station, TNmean (0.32 °C/decade) increased at a higher rate compared to TXmean (0.22 °C/decade). The number of the cold days and nights tended to decrease, while an opposite trend was found in the warmer days and nights.

show abstract

“…Climate change substantially impacts two crucial aspects of rice production: availability of seasonal water resources (Tukimat and Harun, 2015) and rice productivity (Firdaus et al, 2020). To mitigate the effects of climate change on rice production in MIS, it is necessary to identify high-risk areas and optimize cultivation timing.…”

Section: Benefits Of Eo Datasets For Adherence Monitoringmentioning

confidence: 99%

Efficient agricultural monitoring: a methodology for assessing individual farmer adherence to rice-planting schedule for tertiary irrigation system under the Muda Irrigation Scheme using Earth observation datasets

Zahir,

Somura,

Moroizumi

2024

Hydrological Research Letters

View full text Add to dashboard Cite

The tertiary irrigation system (TIS) was designed for the Muda Irrigation Scheme (MIS) to distribute irrigation water to farmers' fields to ensure the reliability of water supply for cultivating rice paddies twice a year. Variability in farming practices, influenced by farmer autonomy along the tertiary canal adds complexity and uncertainty to adherence monitoring. Traditional on-site data collection methods are limited in scope and efficiency, whereas Earth observation (EO) enables continuous monitoring. In this study, we introduced a methodology that uses EO datasets to monitor individual field adherence to rice-planting schedules under TIS. These tools improve the monitoring of rice-planting schedule adherence by identifying non-adherent fields for further countermeasures. This study highlights the potential use of EO datasets and advanced data processing techniques for efficient agricultural monitoring.

show abstract

Climate change impact on rainfall and temperature in Muda irrigation area using multicorrelation matrix and downscaling method

Cited by 8 publications

References 12 publications

Projection the long-term ungauged rainfall using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model

Projection the long-term ungauged rainfall using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model

Analysis of Precipitation and Temperature Extremes over the Muda River Basin, Malaysia

Efficient agricultural monitoring: a methodology for assessing individual farmer adherence to rice-planting schedule for tertiary irrigation system under the Muda Irrigation Scheme using Earth observation datasets

Contact Info

Product

Resources

About