scholarly journals Uncertainty assessment of extreme flood estimation in the Dongting Lake basin, China

2019 ◽  
Vol 50 (4) ◽  
pp. 1162-1176 ◽  
Author(s):  
Yunbiao Wu ◽  
Lianqing Xue ◽  
Yuanhong Liu ◽  
Lei Ren
Keyword(s):  
Confidence Intervals ◽  
Dongting Lake ◽  
Mcmc Methods ◽  
Lake Basin ◽  
Return Periods ◽  
Bayesian Mcmc ◽  
Extreme Floods ◽  
Class Distribution ◽  
Extreme Flood ◽  
Design Floods

Abstract In this paper, we study uncertainty in estimating extreme floods of the Dongting Lake basin, China. We used three methods, including the Delta, profile likelihood function (PLF), and the Bayesian Markov chain Monte Carlo (MCMC) methods, to calculate confidence intervals of parameters of the generalized extreme value (GEV) distribution and quantiles of extreme floods. The annual maximum flow (AMF) data from four hydrologic stations were selected. Our results show that AMF data from Taoyuan and Xiangtan stations followed the Weibull class distribution, while the data from Shimen and Taojiang stations followed the Fréchet class distribution. The three methods show similar confidence intervals of design floods for short return periods. However, there are large differences between results of the Delta and the other two methods for long return periods. Both PLF and Bayesian MCMC methods have similar confidence intervals to reflect the uncertainty of design floods. However, because the PLF method is quite burdensome in computation, the Bayesian MCMC method is more suitable for practical use.

Navigating through extreme flood simulations with intelligently chosen parameter sets

2020 ◽  
Author(s):  
Anna E. Sikorska-Senoner ◽  
Bettina Schaefli ◽  
Jan Seibert
Keyword(s):  
Climate Variability ◽  
Hydrological Model ◽  
Weather Generator ◽  
Model Parameters ◽  
Large Set ◽  
Return Periods ◽  
Simulation Framework ◽  
Model Parameter ◽  
Extreme Floods ◽  
Extreme Flood

<p>The quantification of extreme floods and associated return periods remains to be a challenge for flood hazard management and is particularly important for applications where the full hydrograph shape is required (e.g., for reservoir management). One way of deriving such estimates is by employing a comprehensive hydrological simulation framework, including a weather generator, to simulate a large set of flood hydrographs. In such a setting, the estimation uncertainties originate from the hydrological model, but also from the climate variability. While the uncertainty from the hydrological model can be described with common methods of uncertainty estimation in hydrology (in particular related to model parameters), the uncertainties from climate variability can only be represented with repeated realizations of meteorological scenarios. These scenarios can be generated with the help of the selected weather generator(s), which are capable of providing numerous and continuous long time series. Such generated meteorological scenarios are then used as input for a hydrological model to simulate a large sample of extreme floods, from which return periods can be computed based on ranking.</p><p>In such a simulation framework, many thousands of possible combinations of meteorological scenarios and of hydrological model parameter sets may be generated. However, these simulations are required at a high temporal resolution (hourly), needed for the simulation of extreme floods and for determining infrequent floods of a return period equal to or lower than 1000 years. Accordingly, due to computational constraints related to any hydrological model, one often needs to preselect meteorological scenarios and representative model parameter sets to be used within the simulation framework. Thus, some kind of an intelligent parameter selection for deriving the uncertainty ranges of extreme model simulations for such rare events would be very beneficial but is currently missing.</p><p>Here we present results from an experimental study where we tested three different methods of selecting a small number of representative parameter sets for a Swiss catchment. We used 100 realizations of 100 years of synthetic precipitation-streamflow data. We particularly explored the reliability of the extreme flood uncertainty intervals derived from the reduced parameter set ensemble (consisting of only three representative parameter sets) compared to the full range of 100 parameter sets available. Our results demonstrated that the proposed methods are efficient for deriving uncertainty intervals for extreme floods. These findings are promising for the simulation of extreme floods in comparable simulation frameworks for hydrological risk assessment.</p>

scholarly journals Projection of Hydro-Climatic Extreme Events under Climate Change in Yom and Nan River Basins, Thailand

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 665
Author(s):  
Chanchai Petpongpan ◽  
Chaiwat Ekkawatpanit ◽  
Supattra Visessri ◽  
Duangrudee Kositgittiwong
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Extreme Events ◽  
River Basins ◽  
Annual Rainfall ◽  
Extreme Floods ◽  
Extreme Flood ◽  
Rcp 8.5 ◽  
Near Future ◽  
Far Future

Due to a continuous increase in global temperature, the climate has been changing without sign of alleviation. An increase in the air temperature has caused changes in the hydrologic cycle, which have been followed by several emergencies of natural extreme events around the world. Thailand is one of the countries that has incurred a huge loss in assets and lives from the extreme flood and drought events, especially in the northern part. Therefore, the purpose of this study was to assess the hydrological regime in the Yom and Nan River basins, affected by climate change as well as the possibility of extreme floods and droughts. The hydrological processes of the study areas were generated via the physically-based hydrological model, namely the Soil and Water Assessment Tool (SWAT) model. The projected climate conditions were dependent on the outputs of the Global Climate Models (GCMs) as the Representative Concentration Pathways (RCPs) 2.6 and 8.5 between 2021 and 2095. Results show that the average air temperature, annual rainfall, and annual runoff will be significantly increased in the intermediate future (2046–2070) onwards, especially under RCP 8.5. According to the Flow Duration Curve and return period of peak discharge, there are fluctuating trends in the occurrence of extreme floods and drought events under RCP 2.6 from the future (2021–2045) to the far future (2071–2095). However, under RCP 8.5, the extreme flood and drought events seem to be more severe. The probability of extreme flood remains constant from the reference period to the near future, then rises dramatically in the intermediate and the far future. The intensity of extreme droughts will be increased in the near future and decreased in the intermediate future due to high annual rainfall, then tending to have an upward trend in the far future.

scholarly journals Distribution of Triarrhena lutarioriparia and its reserve characteristics of nitrogen and phosphorus in Dongting Lake

2021 ◽  
Vol 237 ◽  
pp. 01004
Author(s):  
Youze Xu ◽  
Guangyi Fu ◽  
Nan Tang ◽  
Zhonghao He ◽  
Lincheng Jian ◽  
...  
Keyword(s):  
Scientific Basis ◽  
Dongting Lake ◽  
Lake Basin ◽  
Nitrogen And Phosphorus ◽  
Total N ◽  
Remote Sensing Interpretation ◽  
Storage Characteristics ◽  
Total P ◽  
Lake Wetlands

Triarrhena lutarioriparia, a typical and most abundant macrophyte in Dongting lake wetland, was in the state of abandonment following the papermaking industry revocation in the lake basin. In order to provide scientific basis for precise management of T. lutarioriparia, the T. lutarioriparia distribution charateristics in Dongting Lake and its storage characteristics of nutrients were investigated in this study. Remote sensing interpretation results showed that the total area of T. lutarioriparia in Dongting Lake wetland was 58, 450 ha, 48.31% of which distributed in South Doting Lake wetlands. The nutrients contents were significantly different in T. lutarioriparia tissues, ranking in the descending order of spikes (TN 27.90 mg/g, TP 3.46 mg/g)>leaves (TN 16.38 mg/g, TP 2.11 mg/g)>stems (TN 5.38 mg/g, TP 0.85 mg/g). The total P quantities in each T. lutarioriparia tissue were ranked in the order: stems (560.26 t)>leaves (396.52 t)>spikes (284.67 t), while the total N quantities were within the range of 2170.02-2801.3 t. It was estimated that about 7712.99 t of TN and 1241.45 t of TP were annually removed from Dongting Lake by reaping T. lutarioriparia. The nutrients stored in the dead tissues of T. lutarioriparia might possess non-negligible impact on the water quality of Doting Lake.

scholarly journals Modelling Spatiotemporal Dynamics of Large Wood Recruitment, Transport, and Deposition at the River Reach Scale during Extreme Floods

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1134 ◽  
Author(s):  
Andreas Zischg ◽  
Niccolo Galatioto ◽  
Silvana Deplazes ◽  
Rolf Weingartner ◽  
Bruno Mazzorana
Keyword(s):  
Simulation Model ◽  
Transport Model ◽  
Large Wood ◽  
Flow Paths ◽  
Extreme Floods ◽  
Reliable Assessment ◽  
Extreme Flood ◽  
Floodplain Inundation ◽  
Irregular Meshes ◽  
River Reach

Large wood (LW) can lead to clogging at bridges and thus cause obstruction, followed by floodplain inundation. Moreover, colliding logs can cause severe damage to bridges, defense structures, and other infrastructure elements. The factors influencing spatiotemporal LW dynamics (LWD) during extreme floods vary remarkably across river basins and flood scenarios. However, there is a lack of methods to estimate the amount of LW in rivers during extreme floods. Modelling approaches allow for a reliable assessment of LW dynamics during extreme flood events by determining LW recruitment, transport, and deposition patterns. Here, we present a method for simulating LWD on a river reach scale implemented in R (LWDsimR). We extended a previously developed LW transport model with a tree recognition model on the basis of Light Detection and Ranging (LiDAR) data for LW recruitment simulation. In addition, we coupled the LWD simulation model with the hydrodynamic simulation model Basic Simulation Environment for Computation of Environmental Flow and Natural Hazard Simulation (BASEMENT-ETH) by adapting the existing LW transport model to be used on irregular meshes. The model has been applied in the Aare River basin (Switzerland) to quantify mobilized LW volumes and the associated flow paths in a probable maximum flood scenario.

Holocene extreme paleofloods and their climatological context, Upper Colorado River Basin, USA

2020 ◽  
Vol 44 (5) ◽  
pp. 727-745
Author(s):  
Tao Liu ◽  
Lin Ji ◽  
Victor R Baker ◽  
Tessa M Harden ◽  
Michael L Cline
Keyword(s):  
River Basin ◽  
Colorado River ◽  
Meta Analysis ◽  
Flood Frequency ◽  
Colorado River Basin ◽  
Extreme Floods ◽  
Upper Colorado River Basin ◽  
Extreme Flood ◽  
Southwestern Usa ◽  
Extreme Flooding

Given its singular importance for water resources in the southwestern USA, the Upper Colorado River Basin (UCRB) is remarkable for the paucity of its conventional hydrological record of extreme flooding. Short-term record-based flood frequency analyses lead to very great aleatory uncertainties about infrequent extreme flood events and their climate-driven causal associations. This study uses paleoflood hydrology to examine a small portion of the underutilized, but very extensive natural record of Holocene extreme floods in the UCRB. We perform a meta-analysis of 82 extreme paleofloods from 18 slack water deposit sites in the UCRB to show linkages between Holocene climate patterns and extreme floods. The analysis demonstrates several clusters of extreme flood activity: 8040–7960, 4400–4300, 3600–3460, 2900–2740, 2390–1980, 1810–720, and 600–0 years BP. The extreme paleofloods were found to occur during both dry and wet periods in the paleoclimate record. When compared with independent paleoclimatic records across the Rocky Mountains and the southwestern USA, the observed temporal clustering pattern of UCRB extreme paleofloods shows associations with periods of abruptly intensified North Pacific-derived storms connected with enhanced variability of El Niño. This approach demonstrates the value of creating paleohydrological databases and comparing them with hydro-climatic proxies in order to identify natural patterns and to discover possible linkages to fundamental processes such as changes in climate.

scholarly journals Dependence Between Extreme Rainfall Events and the Seasonality and Bivariate Properties of Floods. A Continuous Distributed Physically-Based Approach

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1896 ◽  
Author(s):  
Gabriel-Martin ◽  
Sordo-Ward ◽  
Garrote ◽  
García
Keyword(s):  
Peak Flow ◽  
Total Duration ◽  
Weather Generator ◽  
Return Periods ◽  
Storm Events ◽  
Stochastic Weather Generator ◽  
Rainfall Events ◽  
Annual Peak ◽  
Extreme Flood ◽  
Physically Based

This paper focuses on proposing the minimum number of storms necessary to derive the extreme flood hydrographs accurately through event-based modelling. To do so, we analyzed the results obtained by coupling a continuous stochastic weather generator (the Advanced WEather GENerator) with a continuous distributed physically-based hydrological model (the TIN-based real-time integrated basin simulator), and by simulating 5000 years of hourly flow at the basin outlet. We modelled the outflows in a basin named Peacheater Creek located in Oklahoma, USA. Afterwards, we separated the independent rainfall events within the 5000 years of hourly weather forcing, and obtained the flood event associated to each storm from the continuous hourly flow. We ranked all the rainfall events within each year according to three criteria: Total depth, maximum intensity, and total duration. Finally, we compared the flood events obtained from the continuous simulation to those considering the N highest storm events per year according to the three criteria and by focusing on four different aspects: Magnitude and recurrence of the maximum annual peak-flow and volume, seasonality of floods, dependence among maximum peak-flows and volumes, and bivariate return periods. The main results are: (a) Considering the five largest total depth storms per year generates the maximum annual peak-flow and volume, with a probability of 94% and 99%, respectively and, for return periods higher than 50 years, the probability increases to 99% in both cases; (b) considering the five largest total depth storms per year the seasonality of flood is reproduced with an error of less than 4% and (c) bivariate properties between the peak-flow and volume are preserved, with an error on the estimation of the copula fitted of less than 2%.

scholarly journals Analyzing Water Dynamics Based on Sentinel-1 Time Series—a Study for Dongting Lake Wetlands in China

2020 ◽  
Vol 12 (11) ◽  
pp. 1761 ◽  
Author(s):  
Juliane Huth ◽  
Ursula Gessner ◽  
Igor Klein ◽  
Hervé Yesou ◽  
Xijun Lai ◽  
...  
Keyword(s):  
Time Series ◽  
Wetland Management ◽  
Dongting Lake ◽  
Water Dynamics ◽  
Minimum Size ◽  
Optical Data ◽  
Small Scale ◽  
Lake Area ◽  
Ramsar Site ◽  
Extreme Flood

In China, freshwater is an increasingly scarce resource and wetlands are under great pressure. This study focuses on China’s second largest freshwater lake in the middle reaches of the Yangtze River—the Dongting Lake—and its surrounding wetlands, which are declared a protected Ramsar site. The Dongting Lake area is also a research region of focus within the Sino-European Dragon Programme, aiming for the international collaboration of Earth Observation researchers. ESA’s Copernicus Programme enables comprehensive monitoring with area-wide coverage, which is especially advantageous for large wetlands that are difficult to access during floods. The first year completely covered by Sentinel-1 SAR satellite data was 2016, which is used here to focus on Dongting Lake’s wetland dynamics. The well-established, threshold-based approach and the high spatio-temporal resolution of Sentinel-1 imagery enabled the generation of monthly surface water maps and the analysis of the inundation frequency at a 10 m resolution. The maximum extent of the Dongting Lake derived from Sentinel-1 occurred in July 2016, at 2465 km2, indicating an extreme flood year. The minimum size of the lake was detected in October, at 1331 km2. Time series analysis reveals detailed inundation patterns and small-scale structures within the lake that were not known from previous studies. Sentinel-1 also proves to be capable of mapping the wetland management practices for Dongting Lake polders and dykes. For validation, the lake extent and inundation duration derived from the Sentinel-1 data were compared with excerpts from the Global WaterPack (frequently derived by the German Aerospace Center, DLR), high-resolution optical data, and in situ water level data, which showed very good agreement for the period studied. The mean monthly extent of the lake in 2016 from Sentinel-1 was 1798 km2, which is consistent with the Global WaterPack, deviating by only 4%. In summary, the presented analysis of the complete annual time series of the Sentinel-1 data provides information on the monthly behavior of water expansion, which is of interest and relevance to local authorities involved in water resource management tasks in the region, as well as to wetland conservationists concerned with the Ramsar site wetlands of Dongting Lake and to local researchers.

scholarly journals Evaluation of bias-adjusted satellite precipitation estimations for extreme flood events in Langat river basin, Malaysia

2019 ◽  
Vol 51 (1) ◽  
pp. 105-126 ◽  
Author(s):  
Eugene Zhen Xiang Soo ◽  
Wan Zurina Wan Jaafar ◽  
Sai Hin Lai ◽  
Faridah Othman ◽  
Ahmed Elshafie ◽  
...  
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Monsoon Season ◽  
Rainfall Threshold ◽  
Careful Consideration ◽  
Northeast Monsoon ◽  
Satellite Precipitation ◽  
Extreme Floods ◽  
Extreme Flood ◽  
Langat River

Abstract Even though satellite precipitation products have received an increasing amount of attention in hydrology and meteorology, their estimations are prone to bias. This study investigates the three approaches of bias correction, i.e., linear scaling (LS), local intensity scaling (LOCI) and power transformation (PT), on the three advanced satellite precipitation products (SPPs), i.e., CMORPH, TRMM and PERSIANN over the Langat river basin, Malaysia by focusing on five selected extreme floods due to northeast monsoon season. Results found the LS scheme was able to match the mean precipitation of every SPP but does not correct standard deviation (SD) or coefficient of variation (CV) of the estimations regardless of extreme floods selected. For LOCI scheme, only TRMM and CMORPH estimations in certain floods have showed some improvement in their results. This might be due to the rainfall threshold set in correcting process. PT scheme was found to be the best method as it improved most of the statistical performances as well as the rainfall distribution of the floods. Sensitivity of the parameters used in the bias correction is also investigated. PT scheme is found to be least sensitive in correcting the daily SPPs compared to the other two schemes. However, careful consideration should be given for correcting the CMORPH and PERSIANN estimations.

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