Frequency Analysis of Snowmelt Flood Based on GAMLSS Model in Manas River Basin, China

With the acceleration of human economic activities and dramatic changes in climate, the validity of the stationarity assumption of flood time series frequency analysis has been questioned. In this study, a framework for flood frequency analysis is developed on the basis of a tool, namely, the Generalized Additive Models for Location, Scale, and Shape (GAMLSS). We introduced this model to construct a non-stationary model with time and climate factor as covariates for the 50-year snowmelt flood time series in the Kenswat Reservoir control basin of the Manas River. The study shows that there are clear non-stationarities in the flood regime, and the characteristic series of snowmelt flood shows an increasing trend with the passing of time. The parameters of the flood distributions are modelled as functions of climate indices (temperature and rainfall). The physical mechanism was incorporated into the study, and the simulation results are similar to the actual flood conditions, which can better describe the dynamic process of snowmelt flood characteristic series. Compared with the design flood results of Kenswat Reservoir approved by the China Renewable Energy Engineering Institute in December 2008, the design value of the GAMLSS non-stationary model considers that the impact of climate factors create a design risk in dry years by underestimating the risk.

Channel narrowing by inset floodplain formation of the lower Green River in the Canyonlands region, Utah

The lower Green River episodically narrowed between the mid-1930s and present day through deposition of new floodplains within a wider channel that had been established and/or maintained during the early twentieth century pluvial period. Comparison of air photos spanning a 74-yr period (1940–2014) and covering a 61 km study area shows that the channel narrowed by 12% from 138 ± 3.4 m to 122 ± 2.1 m. Stratigraphic and sedimentologic analysis and tree ring dating of a floodplain trench corroborates the air photo analysis and suggests that the initial phase of floodplain formation began by the mid-1930s, approximately the same time that the flow regime decreased in total annual and peak annual flow. Tamarisk, a nonnative shrub, began to establish in the 1930s as well. Narrowing from the 1940s to the mid-1980s was insignificant, because floodplain formation was approximately matched by bank erosion. Air photo analysis demonstrates that the most significant episode of narrowing was underway by the late 1980s, and analysis of the trench shows that floodplain formation had begun in the mid-1980s during a multi-year period of low peak annual flow. Air photo analysis shows that mean channel width decreased by ∼7% between 1993 and 2009. A new phase of narrowing may have begun in 2003, based on evidence in the trench. Comparison of field surveys made in 1998 and 2015 in an 8.5 km reach near Fort Bottom suggests that narrowing continues and demonstrates that new floodplain formation has been a very small proportion of the total annual fine sediment flux of the Green River. Vertical accretion of new floodplains near Fort Bottom averaged 2.4 m between 1998 and 2015 but only accounted for ∼1.5% of the estimated fine sediment flux during that period. Flood control by Flaming Gorge Dam after 1962 significantly influenced flow regime, reducing the magnitude of the annual snowmelt flood and increasing the magnitude of base flows. Though narrowing was initiated by changes in flow regime, native and nonnative riparian vegetation promoted floodplain formation and channel narrowing especially through establishment on channel bars and incipient floodplains during years of small annual floods.

Automatic hydrograph separation approach provides possibility to look at less-studied characteristics of water regime

<p>The study presents an approach to automatic river hydrograph separation and analysis implemented in GrWat open source package for R programming language. In the proposed scheme of hydrograph separation, river hydrograph is separated into base and quick flow. For plain rivers quick flow is further separated into seasonal snowmelt flood quick flow; rain quick flow and thaw quick flow. For mountainous rivers seasonal snowmelt flood quick flow component is divided into “basic snowmelt flood” component and overlapping rain floods. Base and quick runoff is separated by a critical gradient. Flash-floods are separated from the seasonal snowmelt wave by critical values of air temperature and precipitation on the event for the plain rivers and using a critical gradient concept for mountainous rivers. More than 30 characteristics of river runoff regime are calculated for each water resource year: characteristics of annual and seasonal runoff, contribution of each genetic component, characteristics of maximum runoff, n-day minimum discharges and dates when they are observed. Additionally, more than 50 characteristics of each flash-flood are calculated:  characteristics of shape, volume, timing of flash-floods, the values of meteorological parameters that bring about different types of floods. The presented approach to automatic river hydrograph separation and analysis was tested on 45 plain rivers in the European part of Russia in different climatic zones and on 10 mountainous rivers in the North Caucasus. The result of application provides a possibility for analyzing previously unstudied characteristics of river runoff regime and its climate-related transformation on the European part of Russia.</p><p>The study was supported by the Russian Science Foundation grant No. 19-77-10032</p>

STUDY ON SNOWMELT FLOOD DISASTER MODEL BASED ON REMOTE SENSING AND GIS

<p><strong>Abstract.</strong> In the context of global warming, the snowmelt flood events in the mountainous area of the middle and high latitudes are increasingly frequent and create severe casualties and property damages. Carrying out the prediction and risk assessment of the snowmelt flood is of great importance in the water resources management, the flood warning and prevention. Based on the remote sensing and GIS techniques, the relationships of the variables influencing the snowmelt flood such as the snow area, the snow depth, the air temperature, the precipitation, the land topography and land covers are analyzed and a prediction and damage assessment model for snowmelt floods is developed. This model analyzes and predicts the flood submerging range, flood depth, flood grade, and the damages of different underlying surfaces in the study area in a given time period based on the estimation of snowmelt amount, the snowmelt runoff, the direction and velocity of the flood. Then it was used to predict a snowmelt flood event in the Ertis River Basin in northern Xinjiang, China, during March and June, 2017 and to assess its damages including the damages of roads, transmission lines, settlements caused by the floods and the possible landslides using the hydrological and meteorological data, snow parameter data, DEM data and land use data. A comparison was made between the prediction results from this model and flood measurement and its disaster loss data, which suggests that this model performs well in predicting the strength and impact area of snowmelt flood and its damage assessment.</p>

A stochastic event-based approach for flood estimation in catchments with mixed rainfall and snowmelt flood regimes

The estimation of extreme floods is associated with high uncertainty, in part due to the limited length of streamflow records. Traditionally, statistical flood frequency analysis and an event-based model (PQRUT) using a single design storm have been applied in Norway. We here propose a stochastic PQRUT model, as an extension of the standard application of the event-based PQRUT model, by considering different combinations of initial conditions, rainfall and snowmelt, from which a distribution of flood peaks can be constructed. The stochastic PQRUT was applied for 20 small- and medium-sized catchments in Norway and the results give good fits to observed peak-over-threshold (POT) series. A sensitivity analysis of the method indicates (a) that the soil saturation level is less important than the rainfall input and the parameters of the PQRUT model for flood peaks with return periods higher than 100 years and (b) that excluding the snow routine can change the seasonality of the flood peaks. Estimates for the 100- and 1000-year return level based on the stochastic PQRUT model are compared with results for (a) statistical frequency analysis and (b) a standard implementation of the event-based PQRUT method. The differences in flood estimates between the stochastic PQRUT and the statistical flood frequency analysis are within 50 % in most catchments. However, the differences between the stochastic PQRUT and the standard implementation of the PQRUT model are much higher, especially in catchments with a snowmelt flood regime.

Development of Aerial Space Time Volume Velocimetry for Measuring Surface Velocity Vector Distribution from UAV

Due to the remarkable development of unmanned aerial vehicle (UAV) in recent years, its application in river engineering increases widely mainly for the measurement of ground topography such as by the technique Structure from Motion (SfM) using a series of high-resolution static images. However, although UAV usually installed a high density video camera, the use of the movie is limited just for watching and observing the geometrical feature of the ground. In the light of such a present status, the authors have developed an aerial space-time image velocimetry (STIV) technique to measure streamwise river surface velocity distributions. However, as STIV is insensitive to the change of flow direction, the aerial space-time volume velocimetry (STVV) technique, which is an extension of STIV, was developed in this research. STVV examines the change of volumetric texture within a space-time volume (STV) instead of examining the change of image intensity on a line segment as in STIV. The performance of STVV was investigated during the measurement of snowmelt flood of the Shinano River by comparing it with those obtained by the other techniques such as STIV, LSPIV and ADCP. It was made clear the aerial STVV has a great advantage over the existing image-based techniques.

Key Algorithms And Its Realization About Snowmelt Flood Disaster Model Based On Remote Sensing And GIS

Based on the remote sensing and GIS techniques, the relationships of the variables influencing the snowmelt flood such as the snow area, the snow depth, the air temperature, the precipitation, the land topography and land covers are analyzed and a prediction and damage assessment model for snowmelt floods is developed. This model analyzes and predicts the flood submerging range, flood depth, flood grade, and the damages of different underlying surfaces in the study area in a given time period based on the estimation of snowmelt amount, the snowmelt runoff, the direction and velocity of the flood. Then it was used to predict a snowmelt flood event in the Ertis River Basin in northern Xinjiang, China, during March and June, 2017 and to assess its damages including the damages of roads, transmission lines, settlements caused by the floods and the possible landslides using the hydrological and meteorological data, snow parameter data, DEM data and land use data. A comparison was made between the prediction results from this model and flood measurement and its disaster loss data, which suggests that this model performs well in predicting the strength and impact area of snowmelt flood and its damage assessment.