Simulation of karst floods with a hydrological model improved by meteorological model coupling

2021 ◽  
Keyword(s):  
Flood Control ◽  
Underground Structure ◽  
Wrf Model ◽  
Precipitation Forecast ◽  
Karst Area ◽  
Peak Time ◽  
Flood Prediction ◽  
Meteorological Model ◽  
Flood Peak ◽  
Flood Warning

Abstract Karst basins are prone to rapid flooding because of their geomorphic complexity and exposed karst landforms with low infiltration rates. Accordingly, simulating and forecasting floods in karst regions can provide important technical support for local flood control. The study area, the Liujiang karst river basin, is the most well-developed karst area in South China, and its many mountainous areas lack rainfall gauges, limiting the availability of precipitation information. Quantitative precipitation forecast (QPF) from the Weather Research and Forecasting model (WRF) and quantitative precipitation estimation (QPE) from remote sensing information by an artificial neural network cloud classification system (PERSIANN-CCS) can offer reliable precipitation estimates. Here, the distributed Karst-Liuxihe (KL) model was successfully developed from the terrestrial Liuxihe model, as reflected in improvements to its underground structure and confluence algorithm. Compared with other karst distributed models, the KL model has a relatively simple structure and small modeling data requirements, which are advantageous for flood prediction in karst areas lacking hydrogeological data. Our flood process simulation results suggested that the KL model agrees well with observations and outperforms the Liuxihe model. The average Nash coefficient, correlation coefficient, and water balance coefficient increased by 0.24, 0.19, and 0.20, respectively, and the average flood process error, flood peak error, and peak time error decreased by 13%, 11%, and 2 hours, respectively. Coupling the WRF model and PERSIANN-CCS with the KL model yielded a good performance in karst flood simulation and prediction. Notably, coupling the WRF and KL models effectively predicted the karst flood processes and provided flood prediction results with a lead time of 96 hours, which is important for flood warning and control.

scholarly journals Effects of Increasing Horizontal Resolution in a Convection-Permitting Model on Flood Forecasting: The 2011 Dramatic Events in Liguria, Italy

2015 ◽  
Vol 16 (4) ◽  
pp. 1843-1856 ◽  
Author(s):  
Silvio Davolio ◽  
Francesco Silvestro ◽  
Piero Malguzzi
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Flood Forecasting ◽  
Horizontal Resolution ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Flood Prediction ◽  
Meteorological Model ◽  
The Impact ◽  
Atmospheric Simulations

Abstract Coupling meteorological and hydrological models is a common and standard practice in the field of flood forecasting. In this study, a numerical weather prediction (NWP) chain based on the BOLogna Limited Area Model (BOLAM) and the MOdello LOCale in Hybrid coordinates (MOLOCH) was coupled with the operational hydrological forecasting chain of the Ligurian Hydro-Meteorological Functional Centre to simulate two major floods that occurred during autumn 2011 in northern Italy. Different atmospheric simulations were performed by varying the grid spacing (between 1.0 and 3.0 km) of the high-resolution meteorological model and the set of initial/boundary conditions driving the NWP chain. The aim was to investigate the impact of these parameters not only from a meteorological perspective, but also in terms of discharge predictions for the two flood events. The operational flood forecasting system was thus used as a tool to validate in a more pragmatic sense the quantitative precipitation forecast obtained from different configurations of the NWP system. The results showed an improvement in flood prediction when a high-resolution grid was employed for atmospheric simulations. In turn, a better description of the evolution of the precipitating convective systems was beneficial for the hydrological prediction. Although the simulations underestimated the severity of both floods, the higher-resolution model chain would have provided useful information to the decision-makers in charge of protecting citizens.

scholarly journals Evaluation of ECMWF's forecasting system for probabilistic urban flood prediction: a case study in Mexico City

2021 ◽  
Author(s):  
Marco R. López ◽  
Adrián Pedrozo-Acuña ◽  
Marcela L. Severiano Covarrubias
Keyword(s):  
Mexico City ◽  
Local Level ◽  
Warning System ◽  
Precipitation Forecast ◽  
Ensemble Prediction ◽  
Forecast Horizon ◽  
Ensemble Forecasts ◽  
Flood Prediction ◽  
Urban Flood ◽  
Flood Warning

Abstract As the world continues urbanizing, including efforts to forge a new framework of urban development is necessary. Recent studies related to flood prediction and mitigation have shown that Ensemble Prediction Systems (EPSs) constitute a valuable and essential tool for an Early Warning System. However, the use of EPS for flood forecasting in urban zones has yet to be understood. This work has the objective to investigate the potential use of the Operational EPS, issued by the European Centre for Medium-Range Weather Forecasts (ECMWF), for probabilistic urban flood prediction. In this research, a precipitation forecast verification was carried out in two study zones: (1) Mexico Valley Basin and (2) Mexico City, where for the latter, forecasts were compared against real-time observed data. The results showed good forecast reliability for a rain threshold of up to 20 mm in 24-hourly accumulations, with the first 36 h of the forecast horizon being the most reliable. The EPS has sufficient resolution and precision for flood prediction in Mexico City, which represents a further step toward developing a flood warning system at the local level based on ensemble forecasts.

scholarly journals MESOSCALE METEOROLOGICAL MODEL TSUNM3 FOR THE STUDY AND FORECAST OF METEOROLOGICAL PARAMETERS OF THE ATMOSPHERIC SURFACE LAYER OVER A MAJOR POPULATION CENTER

2020 ◽  
pp. 35-55
Author(s):  
A.V. Starchenko ◽  
◽  
A.A. Bart ◽  
L.I. Kizhner ◽  
E.A. Danilkin ◽  
...  
Keyword(s):  
Meteorological Parameters ◽  
Order Approximation ◽  
Mathematical Formulation ◽  
Wrf Model ◽  
Vertical Direction ◽  
Second Order ◽  
Precipitation Forecast ◽  
Meteorological Model ◽  
Differential Formulation ◽  
Mesoscale Meteorological Model

The paper describes the mathematical formulation and numerical method of the TSUNM3 high-resolution mesoscale meteorological model being developed at Tomsk State University. The model is nonhydrostatic and includes three-dimensional nonstationary equations of hydrothermodynamics of the atmospheric boundary layer with parameterization of turbulence, moisture microphysics, long-wave and short-wave (solar) radiation, and advective and latent heat flows in the atmosphere and at the boundary of its interaction with the underlying surface. The numerical algorithm is constructed using structured grids with uniform spacing in horizontal directions and condensing to the Earth surface in the vertical direction. When approximating the differential formulation of the problem, the finite volume method with the second order approximation in the spatial variables is used. Explicit-implicit approximations in time (Adams–Bashforth and Crank–Nicolson) are used to achieve second-order accuracy in time. The paper presents results of numerical forecasting of the main meteorological parameters of the atmosphere (temperature, humidity, wind speed and direction) and precipitation in different seasons in the Siberian region. The models were tested with the help of observations obtained using the Volna-4M sodar, MTR-5 temperature profile meter, and Meteo-2 ultrasonic weather stations of the Atmosfera Collective Use Center. The improved TSUNM3 model is shown to adequately reflect the precipitation time and intensity. However, in some cases, the times of its beginning and end do not always coincide, the difference can reach several hours. The precipitation phase state is reflected reliably. Over 70% of precipitation cases are confirmed by numerical calculations. The model satisfactorily predicts temperature and humidity characteristics. The quality of the precipitation forecast model is comparable to the modern mesoscale models, such as the Weather Research and Forecasting (WRF) model.

scholarly journals The effect of karst system occurrence on flood peaks in small watersheds, southwest China

2020 ◽  
Author(s):  
Chongxun Mo ◽  
Yafang Wang ◽  
Yuli Ruan ◽  
Junkai Qin ◽  
Mingshan Zhang ◽  
...  
Keyword(s):  
Flood Control ◽  
Southwest China ◽  
Sudden Onset ◽  
Karst Area ◽  
Simulation Accuracy ◽  
Flood Warning ◽  
Study Results ◽  
Karst Areas ◽  
Before And After ◽  
Flood Simulations

Abstract Flooding at small basins is characterized by weak predictability, sudden onset, and rapid disaster formation, especially in karst areas. Therefore, an accurate flood simulation will be helpful for flood control and disaster reduction. In this study, the reservoir unit is added into the original HEC-HMS model to improve the model and analyze the stagnation of the runoff process in karst basins. Then, the HEC-HMS model before and after improvement is used to simulate floods in the Xiajia basin, a typical karst area in southwest China. Before improvement, the calibration result shows that the accuracy of 31 flood simulations is poor, and the qualified rate is only 38.71%. After improvement, the qualified rate increases to 51.61% during calibration, and the simulation accuracy is increased by 12.90%. Moreover, the qualified rate reaches 61.11% during validation, and the simulation accuracy is increased by 22.40%. The improved HEC-HMS model can be applied to flood simulations in the study area and the study results can provide useful insights for flood warning and management in karst areas.

scholarly journals Hydraulic Evaluation of the Levee System Evolution on the Kurobe Alluvial Fan in the 18th and 19th Centuries

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4406
Author(s):  
Tadaharu Ishikawa ◽  
Hiroshi Senoo
Keyword(s):  
Channel Capacity ◽  
Flood Control ◽  
River Flow ◽  
Global Climate ◽  
Flow Simulation ◽  
Japan Sea ◽  
Main Channel ◽  
Alluvial Fan ◽  
Flood Peak ◽  
Levee System

The development process and flood control effects of the open-levee system, which was constructed from the mid-18th to the mid-19th centuries, on the Kurobe Alluvial Fan—a large alluvial fan located on the Japan Sea Coast of Japan’s main island—was evaluated using numerical flow simulation. The topography for the numerical simulation was determined from an old pictorial map in the 18th century and various maps after the 19th century, and the return period of the flood hydrograph was determined to be 10 years judging from the level of civil engineering of those days. The numerical results suggested the followings: The levees at the first stage were made to block the dominant divergent streams to gather the river flows together efficiently; by the completed open-levee system, excess river flow over the main channel capacity was discharged through upstream levee openings to old stream courses which were used as temporary floodways, and after the flood peak, a part of the flooded water returned to the main channel through the downstream levee openings. It is considered that the ideas of civil engineers of those days to control the floods exceeding river channel capacity, embodied in their levee arrangement, will give us hints on how to control the extraordinary floods that we should face in the near future when the scale of storms will increase due to the global climate change.

Test on Flood Prediction-Model Using Artificial Neural Network for ShiiLiAn Hydrologic Station on MinChiang,China

2010 ◽  
Vol 39 ◽  
pp. 555-561 ◽  
Author(s):  
Qing Hua Luan ◽  
Yao Cheng ◽  
Zha Xin Ima
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flood Control ◽  
Back Propagation ◽  
Forecast Model ◽  
Flood Forecast ◽  
Back Propagation Algorithm ◽  
Nonlinear Method ◽  
Flood Prediction ◽  
Artificial Neural

The establishing of a precise simulation model for runoff prediction in river with several tributaries is the difficulty of flood forecast, which is also one of the difficulties in hydrologic research. Due to the theory of Artificial Neural Network, using Back Propagation algorithm, the flood forecast model for ShiLiAn hydrologic station in Minjiang River is constructed and validated in this study. Through test, the result shows that the forecast accuracy is satisfied for all check standards of flood forecast and then proves the feasibility of using nonlinear method for flood forecast. This study provides a new method and reference for flood control and water resources management in the local region.

Hydrological evaluation of high-resolution precipitation estimates from the WRF model in the Third Pole river basins

2021 ◽  
Author(s):  
He Sun ◽  
Fengge Su ◽  
Zhihua He ◽  
Tinghai Ou ◽  
Deliang Chen ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Hydrological Modeling ◽  
Climate Model ◽  
Regional Climate ◽  
Wrf Model ◽  
Infiltration Capacity ◽  
Flood Prediction ◽  
Daily Streamflow ◽  
Precipitation Estimates

AbstractIn this study, two sets of precipitation estimates based on the regional Weather Research and Forecasting model (WRF) –the high Asia refined analysis (HAR) and outputs with a 9 km resolution from WRF (WRF-9km) are evaluated at both basin and point scales, and their potential hydrological utilities are investigated by driving the Variable Infiltration Capacity (VIC) large-scale land surface hydrological model in seven Third Pole (TP) basins. The regional climate model (RCM) tends to overestimate the gauge-based estimates by 20–95% in annual means among the selected basins. Relative to the gauge observations, the RCM precipitation estimates can accurately detect daily precipitation events of varying intensities (with absolute bias < 3 mm). The WRF-9km exhibits a high potential for hydrological application in the monsoon-dominated basins in the southeastern TP (with NSE of 0.7–0.9 and bias of -11% to 3%), while the HAR performs well in the upper Indus (UI) and upper Brahmaputra (UB) basins (with NSE of 0.6 and bias of -15% to -9%). Both the RCM precipitation estimates can accurately capture the magnitudes of low and moderate daily streamflow, but show limited capabilities in flood prediction in most of the TP basins. This study provides a comprehensive evaluation of the strength and limitation of RCMs precipitation in hydrological modeling in the TP with complex terrains and sparse gauge observations.

Operational Precipitation Forecast Over China Using the Weather Research and Forecasting (WRF) Model at a Gray-Zone Resolution: Impact of Convection Parameterization

2021 ◽  
Author(s):  
XU ZHANG ◽  
YUHUA YANG ◽  
BAODE CHEN ◽  
WEI HUANG
Keyword(s):  
Case Studies ◽  
Southern China ◽  
Wrf Model ◽  
Warm Season ◽  
Precipitation Forecast ◽  
Weather Research And Forecasting ◽  
Operational System ◽  
Quantitative Precipitation Forecast ◽  
Geographical Regions ◽  
Convection Parameterization

AbstractThe quantitative precipitation forecast in the 9 km operational modeling system (without the use of a convection parameterization scheme) at the Shanghai Meteorological Service (SMS) usually suffers from excessive precipitation at the grid scale and less-structured precipitation patterns. Two scale-aware convection parameterizations were tested in the operational system to mitigate these deficiencies. Their impacts on the warm-season precipitation forecast over China were analyzed in case studies and two-month retrospective forecasts. The results from case studies show that the importance of convection parameterization depends on geographical regions and weather regimes. Considering a proper magnitude of parameterized convection can produce more realistic precipitation distribution and reduce excessive grid-scale precipitation in southern China. In the northeast and southwest China, however, the convection parameterization plays an insignificant role in precipitation forecast because of strong synoptic-scale forcing. A statistical evaluation of the two-month retrospective forecasts indicates that the forecast skill for precipitation in the 9-km operational system is improved by choosing proper convection parameterization. This study suggests that improvement in contemporary convection parameterizations is needed for their usage for various meteorological conditions and reasonable partitioning between parameterized and resolved convection.

scholarly journals Assessment of a Regional-Scale Weather Model for Hydrological Applications in South Korea

2016 ◽  
Vol 6 (2) ◽  
pp. 28
Author(s):  
Yong Jung ◽  
Yuh-Lang Lin
Keyword(s):  
Weather Prediction ◽  
Regional Scale ◽  
Wrf Model ◽  
Domain Size ◽  
Cumulus Parameterization ◽  
Precipitation Forecast ◽  
Mountainous Region ◽  
Parameterization Schemes ◽  
Plain Region ◽  
Cumulus Parameterization Schemes

<p class="1Body">In this study, a regional numerical weather prediction (NWP) model known as the Weather Research Forescasting (WRF) model was adopted to improve the quantitative precipitation forecasts (QPF) by optimizing combined microphysics and cumulus parameterization schemes. Four locations in two regions (plain region for Sangkeug and Imsil; mountainous region for Dongchun and Bunchun) in Korean Peninsula were examined for QPF for two heavy rainfall events 2006 and 2008. The maximum Index of Agreement (IOA) was 0.96 at Bunchun in 2006 using the combined Thompson microphysics and the Grell cumulus parameterization schemes. Sensitivity of QPF on domain size at Sangkeug indicated that the localized smaller domain had 55% (from 0.35 to 0.90) improved precipitation accuracy based on IOA of 2008. For the July 2006 Sangkeug event, the sensitivity to cumulus parameterization schemes for precipitation prediction cannot be ignored with finer resolutions. In mountainous region, the combined Thompson microphysics and Grell cumulus parameterization schemes make a better quantitative precipitation forecast, while in plain region, the combined Thompson microphysics and Kain-Frisch cumulus parameterization schemes are the best.</p>

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