The Role of Global Data Sets for Riverine Flood Risk Management at National Scales

Over the last two decades, several data sets have been developed to assess flood risk at the global scale. In recent years, some of these data sets have become detailed enough to be informative at national scales. The use of these data sets nationally could have enormous benefits in areas lacking existing flood risk information and allow better flood management decisions and disaster response. In this study, we evaluate the usefulness of global data for assessing flood risk in five countries: Colombia, England, Ethiopia, India, and Malaysia. National flood risk assessments are carried out for each of the five countries using six data sets of global flood hazard, seven data sets of global population, and three different methods for calculating vulnerability. We also conduct interviews with key water experts in each country to explore what capacity there is to use these global data sets nationally. We find that the data sets differ substantially at the national level, and this is reflected in the national flood risk estimates. While some global data sets could be of significant value for national flood risk management, others are either not detailed enough, or too outdated to be relevant at this scale. For the relevant global data sets to be used most effectively for national flood risk management, a country needs a functioning, institutional framework with capability to support their use and implementation

Projection of spatial and temporal changes of rainfall in Sarawak of Borneo Island using statistical downscaling of CMIP5 models

This study assesses the possible changes in rainfall patterns of Sarawak in Borneo Island due to climate change through statistical downscaling of General Circulation Models (GCM) projections. Available in-situ observed rainfall data were used to downscale the future rainfall from ensembles of 20 GCMs of Coupled Model Intercomparison Project phase 5 (CMIP5) for four Representative Concentration Pathways (RCP) scenarios, namely, RCP2.6, RCP4.5, RCP6.0 and RCP8.5. Model Output Statistics (MOS) based downscaling models were developed using two data mining approaches known as Random Forest (RF) and Support Vector Machine (SVM). The SVM was found to downscale all GCMs with normalized mean square error (NMSE) of 48.2–75.2 and skill score (SS) of 0.94–0.98 during validation. The results show that the future projection of the annual rainfalls is increasing and decreasing on the region-based and catchment-based basis due to the influence of the monsoon season affecting the coast of Sarawak. The ensemble mean of GCMs projections reveals the increased and decreased mean of annual precipitations at 33 stations with the rate of 0.1% to 19.6% and one station with the rate of − 7.9% to − 3.1%, respectively under all RCP scenarios. The remaining 15 stations showed inconsistency neither increasing nor decreasing at the rate of − 5.6% to 5.2%, but mainly showing a trend of decreasing rainfall during the first period (2010–2039) followed by increasing rainfall for the period of 2070–2099

Trends analysis of rainfall and rainfall extremes in Sarawak, Malaysia using modified Mann–Kendal test

This study assesses the spatial pattern of changes in rainfall extremes of Sarawak in recent years (1980–2014). The Mann–Kendall (MK) test along with modified Mann–Kendall (m-MK) test, which can discriminate multi-scale variability of unidirectional trend, was used to analyze the changes at 31 stations. Taking account of the scaling effect through eliminating the effect of autocorrelation, m-MK was employed to discriminate multi-scale variability of the unidirectional trends of the annual rainfall in Sarawak. It can confirm the significance of the MK test. The annual rainfall trend from MK test showed significant changes at 95% confidence level at five stations. The seasonal trends from MK test indicate an increasing rate of rainfall during the Northeast monsoon and a decreasing trend during the Southwest monsoon in some region of Sarawak. However, the m-MK test detected an increasing trend in annual rainfall only at one station and no significant trend in seasonal rainfall at any stations. The significant increasing trends of the 1-h maximum rainfall from the MK test are detected mainly at the stations located in the urban area giving concern to the occurrence of the flash flood. On the other hand, the m-MK test detected no significant trend in 1- and 3-h maximum rainfalls at any location. On the contrary, it detected significant trends in 6- and 72-h maximum rainfalls at a station located in the Lower Rajang basin area which is an extensive low-lying agricultural area and prone to stagnant flood. These results indicate that the trends in rainfall and rainfall extremes reported in Malaysia and surrounding region should be verified with m-MK test as most of the trends may result from scaling effect

A novel framework for selecting general circulation models based on the spatial patterns of climate

General circulation models (GCMs), used for climate change projections, should be able to simulate both the temporal variability and spatial patterns of the observed climate. However, the selection of GCMs in most previous studies was either based on temporal variability or mean spatial pattern of past climate. In this study, a framework is proposed for the selection of GCMs based on their ability to reproduce the spatial patterns for different climate variables. The Kling-Gupta efficiency (KGE) was used to assess GCMs ability to simulate the annual spatial patterns of maximum and minimum temperatures (Tmx and Tmn, respectively) and rainfall depth. The mean and standard deviation of KGEs were used as performance indicators to present the GCMs' overall skill. Finally, the global performance indicator was used as a multi-criteria decision-making approach to integrate the results of different climate variables and seasons in order to rank the GCMs. Egypt was considered as a case study. The results revealed the better performance, in order, of the MRI-CGCM3, followed by FGOALS-g2, GFDL-ESM2G, GFDL-CM3 and lastly MPI-ESM-MR over Egypt. The final set of GCMs showed a similar spatial pattern for the projected change in temperature over Egypt. For different scenarios, Tmx was projected to increase in the range of 1.63–4.2°C while the increase in Tmn ranged between 1.28 and 4.43°C. A projected increase in temperature in winter is likely greater than in summer. The selected models also projected a 62% decrease in rainfall depth over the northern coastline where rain is currently most abundant while an increase in the dry southern zones. The rise in temperature and decrease in rainfall depth could have severe implications for a country with dwindling water resources