Flood Forecasting Module of the Distributed Hydrological Model EasyDHM

2012 ◽  
Vol 7 (5) ◽  
pp. 534-539
Author(s):  
Xiaohui Lei ◽  
◽  
Weihong Liao ◽  
Yunzhong Jiang ◽  
Hao Wang
Keyword(s):  
Hydrological Model ◽  
Flood Forecasting ◽  
Flood Forecast ◽  
Distributed Hydrological Model ◽  
Hydrological Simulation ◽  
New Techniques ◽  
Weather Information ◽  
Adaptive Interpolation ◽  
Operational Management ◽  
Simulation Results

A flood forecasting module of the independentlydeveloped distributed hydrological model EasyDHM is developed mainly aiming to support the flood operational management. In this flood forecasting module, the accuracy of hydrological simulation is the most important task. In order to increase accuracy, some new techniques such as the management of initial parameters, management and adaptive interpolation of realtime weather information data, autocalibration of parameters, real-time flood correction, and multimodel combination techniques are introduced to this module. The module is then applied to the Nen River basin in China for confirmation of results. It is revealed that the accuracy of simulation results from the flood forecasting module is obviously higher than that from regular simulation in EasyDHM, and this independent flood module is of great importance for flood forecast and management projects.

scholarly journals Ensemble flood forecasting based on ensemble NWP and the GMKHM distributed hydrological model

2018 ◽  
Vol 246 ◽  
pp. 01108
Author(s):  
Lili Wang ◽  
Hongjun Bao
Keyword(s):  
Overland Flow ◽  
Hydrological Model ◽  
Weather Prediction ◽  
Flood Forecasting ◽  
False Alarms ◽  
Generation Model ◽  
Flood Forecast ◽  
Distributed Hydrological Model ◽  
Runoff Generation ◽  
Numerical Weather

The incorporation of numerical weather predictions (NWP) into a flood forecasting system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and lead to a high number of false alarms. The availability of global ensemble numerical weather prediction systems through the THORPEX Interactive Grand Global Ensemble’ (TIGGE) offers a new opportunity for flood forecast. The GMKHM distributed hydrological model, which is based on a mixed runoff generation model and overland flow routing model based on kinematic wave theory, and the topographical information of each grid cell extracted from the Digital Elevation Model (DEM), is coupled with ensemble weather predictions based on the TIGGE database (CMC, CMA, ECWMF, UKMO, NCEP) for flood forecast. This paper presents a case study using the coupled flood forecasting model on the Xixian catchment (a drainage area of 8826 km2) located in Henan province, China. A probabilistic discharge is provided as the end product of flood forecast. Results show that the association of the GMKHM model and the TIGGE database gives a promising tool for the anticipation of flood events several days ahead,, comparable with that driven by raingauge observation.

scholarly journals Coupling ensemble weather predictions based on TIGGE database with Grid-Xinanjiang model for flood forecast

2011 ◽  
Vol 29 ◽  
pp. 61-67 ◽  
Author(s):  
H.-J. Bao ◽  
L.-N. Zhao ◽  
Y. He ◽  
Z.-J. Li ◽  
F. Wetterhall ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Grid Cell ◽  
Flood Forecasting ◽  
False Alarms ◽  
Flood Forecast ◽  
Distributed Hydrological Model ◽  
Xinanjiang Model ◽  
Numerical Weather ◽  
Number Of False Alarms ◽  
Promising Tool

Abstract. The incorporation of numerical weather predictions (NWP) into a flood forecasting system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and lead to a high number of false alarms. The availability of global ensemble numerical weather prediction systems through the THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a new opportunity for flood forecast. The Grid-Xinanjiang distributed hydrological model, which is based on the Xinanjiang model theory and the topographical information of each grid cell extracted from the Digital Elevation Model (DEM), is coupled with ensemble weather predictions based on the TIGGE database (CMC, CMA, ECWMF, UKMO, NCEP) for flood forecast. This paper presents a case study using the coupled flood forecasting model on the Xixian catchment (a drainage area of 8826 km2) located in Henan province, China. A probabilistic discharge is provided as the end product of flood forecast. Results show that the association of the Grid-Xinanjiang model and the TIGGE database gives a promising tool for an early warning of flood events several days ahead.

scholarly journals Operational flood-forecasting in the Piemonte region – development and verification of a fully distributed physically-oriented hydrological model

2009 ◽  
Vol 17 ◽  
pp. 111-117 ◽  
Author(s):  
D. Rabuffetti ◽  
G. Ravazzani ◽  
S. Barbero ◽  
M. Mancini
Keyword(s):  
Cross Sections ◽  
System Reliability ◽  
Hydrological Model ◽  
Warning System ◽  
Flood Forecasting ◽  
Large River ◽  
Distributed Hydrological Model ◽  
Flood Events ◽  
Discharge Time ◽  
Po River

Abstract. A hydrological model for real time flood forecasting to Civil Protection services requires reliability and rapidity. At present, computational capabilities overcome the rapidity needs even when a fully distributed hydrological model is adopted for a large river catchment as the Upper Po river basin closed at Ponte Becca (nearly 40 000 km2). This approach allows simulating the whole domain and obtaining the responses of large as well as of medium and little sized sub-catchments. The FEST-WB hydrological model (Mancini, 1990; Montaldo et al., 2007; Rabuffetti et al., 2008) is implemented. The calibration and verification activities are based on more than 100 flood events, occurred along the main tributaries of the Po river in the period 2000–2003. More than 300 meteorological stations are used to obtain the forcing fields, 10 cross sections with continuous and reliable discharge time series are used for calibration while verification is performed on about 40 monitored cross sections. Furthermore meteorological forecasting models are used to force the hydrological model with Quantitative Precipitation Forecasts (QPFs) for 36 h horizon in "operational setting" experiments. Particular care is devoted to understanding how QPF affects the accuracy of the Quantitative Discharge Forecasts (QDFs) and to assessing the QDF uncertainty impact on the warning system reliability. Results are presented either in terms of QDF and of warning issues highlighting the importance of an "operational based" verification approach.

A new numerical model for simulating top surface soil moisture and runoff

2018 ◽  
Vol 35 (3) ◽  
pp. 1344-1363 ◽  
Author(s):  
Jiongfeng Chen ◽  
Wan-chang Zhang
Keyword(s):  
Soil Moisture ◽  
Stream Flow ◽  
Hydrological Model ◽  
Surface Soil ◽  
Flow Simulation ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Surface Soil Moisture ◽  
Content Type ◽  
Simulation Results

PurposeThis paper aims to construct a simplified distributed hydrological model based on the surveyed watershed soil properties database.Design/methodology/approachThe new established model requires fewer parameters to be adjusted than needed by former hydrological models. However, the achieved stream-flow simulation results are similar and comparable to the classic hydrological models, such as the Xinanjiang model and the TOPMODEL.FindingsGood results show that the discharge and the top surface soil moisture can be simultaneously simulated, and that is the exclusive character of this new model. The stream-flow simulation results from two moderate hydrological watershed models show that the daily stream-flow simulation achieved the classic hydrological results shown in the TOPMODEL and Xinanjiang model. The soil moisture validation results show that the modeled watershed scale surface soil moisture has general agreement with the obtained measurements, with a root-mean-square error (RMSE) value of 0.04 (m3/m3) for one of the one-measurement sites and an averaged RMSE of 0.08 (m3/m3) over all measurements.Originality/valueIn this paper, a new simplified distributed hydrological model was constructed.

scholarly journals Extending flood forecasting lead time in large watershed by coupling WRF QPF with distributed hydrological model

2016 ◽  
Author(s):  
Ji Li ◽  
Yangbo Chen ◽  
Huanyu Wang ◽  
Jianming Qin ◽  
Jie Li
Keyword(s):  
Lead Time ◽  
Hydrological Model ◽  
Southern China ◽  
Weather Forecast ◽  
Forecast Model ◽  
Flood Forecasting ◽  
Rain Gauge ◽  
Model Parameters ◽  
Distributed Hydrological Model ◽  
Flood Warning

Abstract. Long lead time flood forecasting is very important for large watershed flood mitigation as it provides more time for flood warning and emergency responses. Latest numerical weather forecast model could provide 1–15 days quantitative precipitation forecasting products at grid format, by coupling this product with distributed hydrological model could produce long lead time watershed flood forecasting products. This paper studied the feasibility of coupling the Liuxihe Model with the WRF QPF for a large watershed flood forecasting in southern China. The QPF of WRF products has three lead time, including 24 hour, 48 hour and 72 hour, the grid resolution is 20 km × 20 km. The Liuxihe Model is set up with freely downloaded terrain property, the model parameters were previously optimized with rain gauge observed precipitation, and re-optimized with WRF QPF. Results show that the WRF QPF has bias with the rain gauge precipitation, and a post-processing method is proposed to post process the WRF QPF products, which improves the flood forecasting capability. With model parameter re-optimization, the model's performance improves also, it suggests that the model parameters be optimized with QPF, not the rain gauge precipitation. With the increasing of lead time, the accuracy of WRF QPF decreases, so does the flood forecasting capability. Flood forecasting products produced by coupling Liuxihe Model with WRF QPF provides good reference for large watershed flood warning due to its long lead time and rational results.

Sign in / Sign up

Export Citation Format

Share Document