Surface water pattern changes in terms of quantity and directions is caused by many hydrological hazards such as flooding. Rapid urbanization in Kuala Lumpur has destroyed urban forest trees as the most valuable urban green infrastructure (UGI). Then surface, vegetation changes and land transformation, concrete roads, covered roofs, and climate changes impacts changed the water pattern and hydrological system in Kuala Lumpur. Satellite data used to collect data from 1990 and 2021 to show how significant has been these changes. To demonstrate the spatial and temporal changes of hydrological changes in Kuala Lumpur The Normalized Difference Water Index (NDWI) applied for a 30 years assessment. Remote sensing (RS) and Geographical Information Systems (GIS) have helped us detect dynamic changes on the Earth's surface. The mean NDWI of one year was calculated in this study using Kuala Lumpur NDWI from 1990/01/01 to 1990/12/30. The extracted image is larger than the study area and extends beyond Kuala Lumpur's city limits. This image only detected water bodies on the surface of Kuala Lumpur, which is represented in grey or white, such as lakes, rivers, ponds, or any degree of wetness. Because urban forest covered more than 15% of Kuala Lumpur in 1990, surface water was naturally controlled and saved by forest tree and forest soil. Image data for 2021 was taken from Landsat 8, which uses different bands and the same algorithm to visualize NDWI based on the availability of satellite data. The data was averaged over a one-year period between 2021/01/01 to 2021/12/30; as a result, the NDWI in Kuala Lumpur was depicted using the same algorithm from Landsat 8 with different bands. The researchers discovered that urban surface water bodies have increased over the last 30 years, rising from 0.383635 and -.0593184 in 1990 to 0.593288 and -0.44379 in 2021. This indicates that the NDWI index has increased the most in Kuala Lumpur. As previously stated, rapid changes in land use and land cover in the last 30 years have had a negative impact on Kuala Lumpur's NDWI.

Debris flows are among the fatal geological hazards in Malaysia, with 23 incidents recorded in the last two decades. To date, very few studies have been carried out to understand the debris flow processes, causes, and runouts nationwide. This study simulated the debris flow at the Mesilau watershed of Kundasang Sabah caused by the prolonged rainfall after the 2015 Ranau earthquake. Several interrelated processing platforms, such as ArcGIS, HEC-HMS, and HyperKANAKO, were used to extract the parameters, model the debris flow, and perform a sensitivity analysis to achieve the best-fit debris flow runout. The debris flow travelled at least 18.6 km to the Liwagu Dam. The best-fit runout suggested that the average velocity was 12.5 m/s and the lead time to arrive at the Mesilau village was 4.5 min. This high debris flow velocity was probably due to the high-water content from the watershed baseflow with a discharge rate of 563.8 m³/s. The flow depth and depositional thickness were both lower than 5.0 m. This study could provide crucial inputs for designing an early warning system, improving risk communication, and strengthening the local disaster risk reduction and resilience strategy in a tectonically active area in Malaysia.

Using rainfall gauge on its own as input carries great uncertainties regarding runoff estimation, especially when the area is large and the rainfall is measured and recorded at irregular spaced gauging stations. Hence spatial interpolation is the key to obtain continuous and orderly rainfall distribution at unknown points to be the input to the rainfall runoff processes for distributed and semi-distributed numerical modelling. It is crucial to study and predict the behaviour of rainfall and river runoff to reduce flood damages of the affected area along the Kelantan river. Thus, a good knowledge on rainfall distribution is essential in early flood prediction studies. Forty six rainfall stations and their daily time-series were used to interpolate gridded rainfall surfaces using inverse-distance weighting (IDW), inverse-distance and elevation weighting (IDEW) methods and average rainfall distribution. Sensitivity analysis for distance and elevation parameters were conducted to see the variation produced. The accuracy of these interpolated datasets was examined using cross-validation assessment.

Flood vulnerability is a complex concept involving the interactions between environment, social and economic dimensions. Indicator‐based vulnerability assessment is widely used in vulnerability studies to summarise complexity and multidimensionality issues to gauge the level of vulnerability. A set of 21 environmental and socio‐economic indicators is used to quantitatively assess the three factors of vulnerability, namely exposure, susceptibility and resilience to flood at the subnational level. The construction of the vulnerability index involved the selection of indicators, their normalisation, weightage and aggregation to a final index. In addition to the Flood Vulnerability Index, three sub‐indices namely Exposure Index, Susceptibility Index and Resilience Index were generated. Based on composite indicator approach, the vulnerability of the states in Malaysia was categorised from very low to very high. The source of vulnerability is due not only to the environmental exposure to flood hazard but also contributed by the internal status of the socio‐economic factors within the vulnerable systems.

The idea of this study came to address potential challenges in water management in the Gaza Strip in light of the Israeli invasions. This research is closely re The idea of this study came to address potential challenges in water management in the Gaza Strip in light of the Israeli invasions. This research is closely re