Flood Frequency Analyses over Different Basin Scales in the Blue Nile River Basin, Ethiopia

Evaluation of CMIP6 model performance and extreme precipitation prediction in the Awash basin

Extreme rainfall and its accompanying hydrological extremes are happening more frequently as a result of global warming's alteration of regional and local weather patterns. This poses a serious risk to ecosystem, environment and the community livelihoods. The Awash basin in Ethiopia is especially vulnerable to these events, posing significant threats to the region. There are, however, limited information's available that could be used to characterize the condition of extreme precipitation in the basin. Therefore, this study aims to evaluate the performance of CMIP6 models in simulating extreme precipitation in the Awash basin. Additionally, the study calculated extreme precipitation using best-fit probability distribution functions (PDFs) for the period from 1985 to 2014. The Climate Hazards Group Infrared Precipitation with station data (CHIRPS) were used to evaluate the global climate models. Simulated data were interpolated using bilinear techniques. Four statistical indices (percentage of bias, root mean square error, mean absolute error, and Pearson correlation) assessed GCM performance in simulating precipitation extremes. Graphical approaches, numerical methods, and empirical distribution functions were employed to evaluate the performance of various probability distribution functions (PDFs). The study identified MIROC6, CESM2-WACCM, and Ensemble as well-performing models with PBIAS and RMSE of 6.6 %, -10.2 %, -17.2 %, and 11.5, 10, 9.7 respectively, while MPI-ESM1-2-HR and EC-Earth3 struggled with extreme rainfall simulation. The generalized extreme values distribution was found to be a good fit for extreme rainfall estimation. GFDL-ESM4 and BCC-CSM2-MR models estimated the highest extreme rainfall of 90 mm/day and 80 mm/day, respectively, however these models overestimated the return period. Conversely, MRI-ESM2-0, NorESM2-MM, ACCESS ESM1-5, and CMCC-ESM2 models underestimated the return periods. Spatially, GFDL-ESM4 and ACCESS-ESM1-5 models exhibited uniform peak rainfall values over a large area. Overall, the study suggests that employing the generalized extreme value distribution could effectively inform risk assessment and management of extreme events in the Awash basin.

Application for simulating public health problems during floods around the Loei River in Thailand: the implementation of a geographic information system and structural equation model

Background

Floods cause not only damage but also public health issues. Developing an application to simulate public health problems during floods around the Loei River by implementing geographic information system (GIS) and structural equation model (SEM) techniques could help improve preparedness and aid plans in response to such problems in general and at the subdistrict level. As a result, the effects of public health problems would be physically and mentally less severe.

Methods

This research and development study examines cross-sectional survey data. Data on demographics, flood severity, preparedness, help, and public health problems during floods were collected using a five-part questionnaire. Calculated from the population proportion living within 300 m of the Loei River, the sample size was 560 people. The participants in each subdistrict were recruited proportionally in line with the course of the Loei River. Compared to the empirical data, the data analysis examined the causal model of public health problems during floods, flood severity, preparedness, and help. The standardized factor loadings obtained from the SEM analysis were substituted as the loadings in the equations for simulating public health problems during floods.

Results

The results revealed that the causal model of public health problems during floods, flood severity, preparation, and help agreed with the empirical data. Flood severity, preparedness, and aid (χ² = 479.757, df = 160, p value <.05, CFI = 0.985, RMSEA = 0.060, χ²/df = 2.998) could explain 7.7% of public health problems. The computed values were applied in a GIS environment to simulate public health problem situations at the province, district, and subdistrict levels.

Conclusions

Flood severity and public health problems during floods were positively correlated; in contrast, preparedness and help showed an inverse relationship with public health problems. A total of 7.7% of the variance in public health problems during floods could be predicted. The analysed data were assigned in the GIS environment in the developed application to simulate public health problem situations during floods.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-14018-7.

Comparison of Three-Parameter Distributions in Controlled Catchments for a Stationary and Non-Stationary Data Series

Flood Frequency Analysis (FFA) and the non-stationary FFA approaches are used in flood study, water resource planning, and the design of hydraulic structures. However, there is still a need to develop these methods and to find new procedures that can be used in estimating simple distributions in controlled catchments. The aim of the study is a comparison of three-parameter distributions in controlled catchments for stationary and non-stationary data series and further to develop the procedure of the estimation the simple distributions. Ten rivers from the Czech Republic and Poland were selected because of their existing or planned reservoirs as well as for flood protection reasons. The annual maximum method and the three-parameter Weibull, Log-Normal, Generalized extreme value, and Pearson Type III distributions were used in this study. The analyzed time series are stationary and non-stationary. The methodology used in this study, which makes use of the Maximum Likelihood Estimation, allows one to simplify the analysis whenever there is a series of data that is both stationary and non-stationary. The novelty in our research is the standardization and development of a new procedure for a stationary and non-stationary data series, taking into account to read a specific value of the maximum flow with a given exceedance probability from the lower or upper tail. It determines the optimal choice of the theoretical distribution that can be used, for example in the design of weirs in rural areas (lower quantiles) or in the design of hydrotechnical structures in areas at risk of flooding (upper quantiles).