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A major challenge in flood mapping using multi‐criteria decision analysis (MCDA) is the selection of the flood risk factors and the estimation of their relative importance. A novel MCDA method through the integration of two state‐of‐the‐art MCDA methods based on catastrophe and entropy theory is proposed for mapping flood risk in the Peninsular Malaysia, an area very susceptible to flooding events, is presented. A literature review was undertaken which identified the various socioeconomic, physical and environmental factors which can influence flood vulnerability and risk. A set of variables was selected using an importance index which was developed based on a questionnaire survey. Population density, percentage of vulnerable people, household income, local economy, percentage of foreign nationals, elevation and forest cover were all deemed highly relevant in mapping flood risk and determining the zones of maximum vulnerability. Spatial integration of factors using the proposed MCDA revealed that coastal regions are highly vulnerable to floods when compared to inland locations. Flood risk maps indicate that the northeastern coastal region of Malaysia is at greatest risk of flooding. The prediction capability of the integrated method was found to be 0.93, which suggests good accuracy of the proposed method in flood risk mapping.

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Expansion of arid lands due to climate change, particularly in water stressed regions of the world can have severe implications on the economy and people’s livelihoods. The spatiotemporal trends in aridity, the shift of land from lower to higher arid classes and the effect of this shift on different land uses in Syria have been evaluated in this study for the period 1951–2010 using high-resolution monthly climate data of the Terrestrial Hydrology Research Group of Princeton University. The trends in rainfall, temperature and potential evapotranspiration were also evaluated to understand the causes of aridity shifts. The results revealed an expansion of aridity in Syria during 1951–1980 compared to 1981–2010. About 6.21% of semi-arid land was observed to shift to arid class and 5.91% dry-subhumid land to semi-arid land between the two periods. Analysis of results revealed that the decrease in rainfall is the major cause of increasing aridity in Syria. About 28.3% of agriculture land located in the north and the northwest was found to shift from humid to dry-subhumid or dry-subhumid to semi-arid. Analysis of results revealed that the shifting of drylands mostly occurred in the northern agricultural areas of Syria. The land productivity and irrigation needs can be severely affected by increasing aridity which may affect food security and the economy of the country.

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Changes in precipitation and temperature have crucial implications in the arid region due to their fragile environment. This study was an attempt to estimate possible spatiotemporal alteration of annual and seasonal precipitation and temperature in Iraq. Statistical downscaling of Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate model (GCM) simulations for different shared socioeconomic pathways (SSP) was utilized. The GCMs were ranked according to their skills in simulating climate research unit (CRU) precipitation and temperature climatology along with their seasonality. Nonlocal model output statistics (MOS) models were implemented using a support vector machine (SVM) for downscaling and projection of selected GCM precipitation and temperature. Results revealed ACCESS-CM2, BCC-CSM2-MR, GISS-E2-1-G and MRI-ESM2-0 GCMs are most suitable for Iraq. The spatiotemporal changes in precipitation indicated a substantial decrease to north (up to −7.8%Ámm −1 ) while an increase (around 3.0%) to south for different SSPs. Far future (2060-2099) showed both increase and decrease in precipitation than near future (2020-2059). The precipitation was projected to reduce in winter and increase in summer for all climate zones during both periods. The maximum temperature was projected to increase by 4.5 C to the north and 0.9-2 C to the south. In contrast, the minimum temperature was projected to rise by 1.0-3.5 C to both north and south. Both maximum and minimum temperatures may increase; however, more increases might be in winter and less in summer. The minimum temperature increase will be higher than the maximum temperature in the cold northern region and vice versa. Uncertainty in precipitation and temperature projections was higher for the far-future period with higher SSPs than for the near-future period with lower SSPs. The results of this study can guide the development of strategic policies for climate resiliency development in Iraq.

A study has been conducted for projection of monthly rainfall in Baghdad of Iraq using a General Circulation Models (GCM) of Coupled Model Intercomparison Project Phase 5 (CMIP5) under four representative concentration pathways (RCP) scenarios namely RCP2.6, RCP4.5, RCP6.0 and RCP8.5. For this purpose, monthly gridded precipitation datasets produced by the centre for climatic research, University of Delaware (UDel) and GCM BCC-CSM1-1 simulated precipitation data at 46 grid points surrounding Baghdad were used. The statistical downscaling models were developed using Support Vector Machine (SVM) and Random Forest (RF). The performance of downscaling model assessed using different statistical measures showed that SVM could simulate historical rainfall in the region very well. Projection of rainfall using SVM revealed that rainfall at Baghdad will change in the range of 3.5% to -6.2% in the end of this century.

Reliable projection of climate is essential for climate change impact assessment and mitigation planning. General Circulation Models (GCMs) simulations are generally downscaled into much finer spatial resolution for climate change impact studies at local and regional scales. The objective of the present study is to use a two-stage bias correction approach for downscale and project future changes of daily average temperature. The approach was applied for downscaling and projection of daily average temperature of Senai meteorological station located in Johor Bahru, Malaysia using a GCM of Coupled Model Intercomparison Project Phase 5 (CMIP5) under four representative concentration pathways (RCP) scenarios. The two-stage bias correction method was based on correction in mean factor and variability inflation factor. The model performances were assessed using different statistical measures including mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), index of agreement (MD), Nash–Sutcliffe model efficiency (NSE) and coefficient of determination (R2). Results showed that the downscaling method could simulate historical daily average temperature at the station very well. The GCM projected an increase in daily average temperature by 1.4ºC, 2.2ºC, 2.5ºC, and 3.4ºC under RCP2.6, RCP4.5, RCP6.0 and RCP8.5 scenarios, respectively in the end of this century. It is expected that the finding of the study would help in climate change impact assessment and adopting necessary adaptation measures.