Assessment of first-order-moment-based sample reconstruction method for design flood estimation in changing environment

Estimating the design flood under nonstationary conditions is challenging. In this study, a sample reconstruction approach was developed to transform a nonstationary series into a stationary one in a future time window (FTW). In this approach, the first-order moment (EFTW) of an extreme flood series in the FTW was used, and two possible methods of estimating EFTW values in terms of point values and confidence intervals were developed. Three schemes were proposed to analyze the uncertainty of design flood estimation in terms of sample representativeness, uncertainty from EFTW estimation, and both factors, respectively. To investigate the performance of the sample reconstruction approach, synthesis experiments were designed based on the annual peak series of the Little Sugar Creek in the United States. The results showed that the sample reconstruction approach performed well when the high-order moment of the series did not change significantly in the specified FTW. Otherwise, its performance deteriorated. In addition, the uncertainty of design flood estimation caused by sample representativeness was greater than that caused by EFTW estimation.

A Vector-based Method for the Extraction of Catchment from Grid DEMs

The methodology of catchment extraction especially from regular grid digital elevation models (DEMs) is briefly reviewed. Then an efficient algorithm, which combines vector process and traditional neighbourhood raster process, is designed for extracting the catchments and subcatchments from depressionless DEMs. The catchment area of each river in the grid DEM data is identified and delineated, then is divided into subcatchments as required. Compared to traditional processes, this method for identifying catchments focuses on the boundaries instead of the area inside the catchments and avoids the boundary intersection phenomena. Last, the algorithm is tested with a set of DEMs of different sizes, and the result proves that the computation efficiency and accuracy are better than existent methods.

Application of Bayesian approach to hydrological frequency analysis

An existing Bayesian flood frequency analysis method is applied to quantile estimation for Pearson type three (P-III) probability distribution. The method couples prior and sample information under the framework of Bayesian formula, and the Markov Chain Monte Carlo (MCMC) sampling approach is used to estimate posterior distributions of parameters. Different from the original sampling algorithm (i.e. the important sampling) used in the existing approach, we use the adaptive metropolis (AM) sampling technique to generate a large number of parameter sets from Bayesian parameter posterior distributions in this paper. Consequently, the sampling distributions for quantiles or the hydrological design values are constructed. The sampling distributions of quantiles are estimated as the Bayesian method can provide not only various kinds of point estimators for quantiles, e.g. the expectation estimator, but also quantitative evaluation on uncertainties of these point estimators. Therefore, the Bayesian method brings more useful information to hydrological frequency analysis. As an example, the flood extreme sample series at a gauge are used to demonstrate the procedure of application.

Long-term variations in runoff of the Syr Darya River Basin under climate change and human activities

In this study,we analyzed the hydrological and meteorological data from the Syr Darya River Basin during the period of 1930–2015 to investigate variations in river runoff and the impacts of climate change and human activities on river runoff.The Syr Darya River,which is supplied by snow and glacier meltwater upstream,is an important freshwater source for Central Asia,as nearly half of the population is concentrated in this area.River runoff in this arid region is sensitive to climate change and human activities.Therefore,estimation of the climatic and hydrological changes and the quantification of the impacts of climate change and human activities on river runoff are of great concern and important for regional water resources management.The long-term trends of hydrological time series from the selected 11 hydrological stations in the Syr Darya River Basin were examined by non-parametric methods,including the Pettitt change point test and Mann-Kendall trend tests.It was found that 8 out of 11 hydrological stations showed significant downward trends in river runof f.Change of river runoff variations occurred in the year around 1960.Moreover,during the study period(1930–2015),annual mean temperature,annual precipitation,and annual potential evapotranspiration in the river basin increased substantially.We employed hydrological sensitivity method to evaluate the impacts of climate change and human activities on river runoff based on precipitation and potential evapotranspiration.It was estimated that human activities accounted for over 82.6%–98.7%of the reduction in river runoff,mainly owing to water withdrawal for irrigation purpose.The observed variations in river runoff can subsequently lead to adverse ecological consequences from an ecological and regional water resources management perspective.

Extraction and Application of Small and Middle River Basins in Jiangxi Province

[Objective] The study aimed to develop the forecasting and early waming system of floods in small and middle river basins. [Methed] Taking small and middle rivers in Jiangxi Province as the research object, using the Hydro ArcTools module of arcgisl0, the digital drainage network and basin boundaries of Jiangxi Province were extracted from the 1：50 000 digital elevation model （ DEM） of Jiangxi Province. Afterwards, the geo- graphic information database of small and middle rivers were established, and areal precipitation in Chongyi region was calculated using Thiessen polygon method based on precipitation data during June 20 -22, 2012. [Resttlt] When the threshold was 78 65,5, the extracted river systems and basin boundaries could meet the demand of actual application and could be used as the basic parameters of areal precipitation prediction model. The areal precipitation in Chongyi region was higher than 7 mm, and it was higher in the east compared with the west. [ Conclusion] The research could provide scientific references for the control and prevention of flood in future.

Implementation of Remote Sensing and GIS Techniques to Study the Flash Flood Risk at NEOM Mega-City, Saudi Arabia

Southern Red Sea flooding is common. Assessing flood-prone development risks helps decrease life and property threats. It tries to improve flood awareness and advocate property owner steps to lessen risk. DEMs and topography data were analyzed by RS and GIS. Fifth-through seventh-order rivers were studied. Morphometric analysis assessed the area’s flash flood danger. NEOM has 14 catchments. We determined each catchment’s area, perimeter, maximum length, total stream length, minimum and maximum elevations. It also uses remote sensing. It classifies Landsat 8 photos for land use and cover maps. Image categorization involves high-quality Landsat satellite images and secondary data, plus user experience and knowledge. This study used the wetness index, elevation, slope, stream power index, topographic roughness index, normalized difference vegetation index, sediment transport index, stream order, flow accumulation, and geological formation. Analytic hierarchy considered all earlier criteria (AHP). The geometric consistency index GCI (0.15) and the consistency ratio CR (4.3%) are calculated. The study showed five degrees of flooding risk for Wadi Zawhi and four for Wadi Surr, from very high to very low. 9.16% of Wadi Surr is vulnerable to very high flooding, 50% to high flooding, 40% to low flooding, and 0.3% to very low flooding. Wadi Zawhi’s flood risk is 0.23% high, moderate, low, or extremely low. They’re in Wadi Surr and Wadi Zawhi. Flood mapping helps prepare for emergencies. Flood-prone areas should prioritize resilience.