scholarly journals Application of a High‐Resolution Distributed Hydrological Model on a U.S.‐Canada Transboundary Basin: Simulation of the Multiyear Mean AnnualHydrograph and 2011 Flood of theRichelieu River Basin

2020 ◽  
Vol 12 (4) ◽  
Author(s):  
Philippe Lucas‐Picher ◽  
Richard Arsenault ◽  
Annie Poulin ◽  
Simon Ricard ◽  
Simon Lachance‐Cloutier ◽  
...  
Keyword(s):  
High Resolution ◽  
River Basin ◽  
Hydrological Model ◽  
Distributed Hydrological Model ◽  
Transboundary Basin

scholarly journals Large-watershed flood forecasting with high-resolution distributed hydrological model

2017 ◽  
Vol 21 (2) ◽  
pp. 735-749 ◽  
Author(s):  
Yangbo Chen ◽  
Ji Li ◽  
Huanyu Wang ◽  
Jianming Qin ◽  
Liming Dong
Keyword(s):  
High Resolution ◽  
River Basin ◽  
Spatial Resolution ◽  
Hydrological Model ◽  
Threshold Model ◽  
Grid Cell ◽  
Flood Forecasting ◽  
Model Parameters ◽  
Distributed Hydrological Model ◽  
Computation Efficiency

Abstract. A distributed hydrological model has been successfully used in small-watershed flood forecasting, but there are still challenges for the application in a large watershed, one of them being the model's spatial resolution effect. To cope with this challenge, two efforts could be made; one is to improve the model's computation efficiency in a large watershed, the other is implementing the model on a high-performance supercomputer. This study sets up a physically based distributed hydrological model for flood forecasting of the Liujiang River basin in south China. Terrain data digital elevation model (DEM), soil and land use are downloaded from the website freely, and the model structure with a high resolution of 200 m  ×  200 m grid cell is set up. The initial model parameters are derived from the terrain property data, and then optimized by using the Particle Swarm Optimization (PSO) algorithm; the model is used to simulate 29 observed flood events. It has been found that by dividing the river channels into virtual channel sections and assuming the cross section shapes as trapezoid, the Liuxihe model largely increases computation efficiency while keeping good model performance, thus making it applicable in larger watersheds. This study also finds that parameter uncertainty exists for physically deriving model parameters, and parameter optimization could reduce this uncertainty, and is highly recommended. Computation time needed for running a distributed hydrological model increases exponentially at a power of 2, not linearly with the increasing of model spatial resolution, and the 200 m  ×  200 m model resolution is proposed for modeling the Liujiang River basin flood with the Liuxihe model in this study. To keep the model with an acceptable performance, minimum model spatial resolution is needed. The suggested threshold model spatial resolution for modeling the Liujiang River basin flood is a 500 m  ×  500 m grid cell, but the model spatial resolution with a 200 m  ×  200 m grid cell is recommended in this study to keep the model at a better performance.

scholarly journals Large watershed flood forecasting with high resolution distributed hydrological model

2016 ◽  
Author(s):  
Yangbo Chen ◽  
Ji Li ◽  
Huanyu Wang ◽  
Jianming Qin ◽  
Liming Dong
Keyword(s):  
High Resolution ◽  
River Basin ◽  
Spatial Resolution ◽  
Hydrological Model ◽  
Threshold Model ◽  
Grid Cell ◽  
Flood Forecasting ◽  
Model Parameters ◽  
Distributed Hydrological Model ◽  
Computation Efficiency

Abstract. Flooding is one of the most devastating natural disasters in the world with huge damages, and flood forecasting is one of the flood mitigation measurements. Watershed hydrological model is the major tool for flood forecasting, although the lumped watershed hydrological model is still the most widely used model, the distributed hydrological model has the potential to improve watershed flood forecasting capability. Distributed hydrological model has been successfully used in small watershed flood forecasting, but there are still challenges for the application in large watershed, one of them is the model’s spatial resolution effect. To cope with this challenge, two efforts could be made, one is to improve the model's computation efficiency in large watershed, another is implementing the model on high performance supercomputer. By employing Liuxihe Model, a physically based distributed hydrological model, this study sets up a distributed hydrological model for the flood forecasting of Liujiang River Basin in southern China that is a large watershed. Terrain data including DEM, soil type and land use type are downloaded from the website freely, and the model structure with a high resolution of 200 m * 200 m grid cell is set up. The initial model parameters are derived from the terrain property data, and then optimized by using the PSO algorithm, the model is used to simulate 29 observed flood events. It has been found that by dividing the river channels into virtual channel sections and assuming the cross section shapes as trapezoid, the Liuxihe Model largely increases computation efficiency while keeping good model performance, thus making it applicable in larger watersheds. This study also finds that parameter uncertainty exists for physically deriving model parameters, and parameter optimization could reduce this uncertainty, and is highly recommended. Computation time needed for running a distributed hydrological model increases exponentially at a power of 2, not linearly with the increasing of model spatial resolution, and the 200 m * 200 m model resolution is proposed for modeling Liujiang River Basin flood with Liuxihe Model in this study. To keep the model with an acceptable performance, minimum model spatial resolution is needed. The suggested threshold model spatial resolution for modeling Liujiang River Basin flood is 500 m * 500 m grid cell, but the model spatial resolution at 200 m * 200 m grid cell is recommended in this study to keep the model a better performance.

scholarly journals Uncertainty analysis of hydrological ensemble forecasts in a distributed model utilising short-range rainfall prediction

2009 ◽  
Vol 13 (3) ◽  
pp. 293-303 ◽  
Author(s):  
Y. Xuan ◽  
I. D. Cluckie ◽  
Y. Wang
Keyword(s):  
High Resolution ◽  
Short Range ◽  
River Flow ◽  
Hydrological Model ◽  
Uncertainty Propagation ◽  
Weather Forecast ◽  
Distributed Model ◽  
Distributed Hydrological Model ◽  
Weather System ◽  
Considerable Uncertainty

Abstract. Advances in mesoscale numerical weather predication make it possible to provide rainfall forecasts along with many other data fields at increasingly higher spatial resolutions. It is currently possible to incorporate high-resolution NWPs directly into flood forecasting systems in order to obtain an extended lead time. It is recognised, however, that direct application of rainfall outputs from the NWP model can contribute considerable uncertainty to the final river flow forecasts as the uncertainties inherent in the NWP are propagated into hydrological domains and can also be magnified by the scaling process. As the ensemble weather forecast has become operationally available, it is of particular interest to the hydrologist to investigate both the potential and implication of ensemble rainfall inputs to the hydrological modelling systems in terms of uncertainty propagation. In this paper, we employ a distributed hydrological model to analyse the performance of the ensemble flow forecasts based on the ensemble rainfall inputs from a short-range high-resolution mesoscale weather model. The results show that: (1) The hydrological model driven by QPF can produce forecasts comparable with those from a raingauge-driven one; (2) The ensemble hydrological forecast is able to disseminate abundant information with regard to the nature of the weather system and the confidence of the forecast itself; and (3) the uncertainties as well as systematic biases are sometimes significant and, as such, extra effort needs to be made to improve the quality of such a system.

scholarly journals A hindcast study of the Piedmont 1994 flood: the CIMA Research Foundation hydro-meteorological forecasting chain

2020 ◽  
Author(s):  
Antonio Parodi ◽  
Martina Lagasio ◽  
Agostino N. Meroni ◽  
Flavio Pignone ◽  
Francesco Silvestro ◽  
...  
Keyword(s):  
High Resolution ◽  
Hydrological Model ◽  
Weather Prediction ◽  
Warning System ◽  
Atmospheric Model ◽  
Added Value ◽  
Distributed Hydrological Model ◽  
Nwp Model ◽  
North Western ◽  
Discharge Curves

AbstractBetween the 4th and the 6th of November 1994, Piedmont and the western part of Liguria (two regions in north-western Italy) were hit by heavy rainfalls that caused the flooding of the Po, the Tanaro rivers and several of their tributaries, causing 70 victims and the displacement of over 2000 people. At the time of the event, no early warning system was in place and the concept of hydro-meteorological forecasting chain was in its infancy, since it was still limited to a reduced number of research applications, strongly constrained by coarse-resolution modelling capabilities both on the meteorological and the hydrological sides. In this study, the skills of the high-resolution CIMA Research Foundation operational hydro-meteorological forecasting chain are tested in the Piedmont 1994 event. The chain includes a cloud-resolving numerical weather prediction (NWP) model, a stochastic rainfall downscaling model, and a continuous distributed hydrological model. This hydro-meteorological chain is tested in a set of operational configurations, meaning that forecast products are used to initialise and force the atmospheric model at the boundaries. The set consists of four experiments with different options of the microphysical scheme, which is known to be a critical parameterisation in this kind of phenomena. Results show that all the configurations produce an adequate and timely forecast (about 2 days ahead) with realistic rainfall fields and, consequently, very good peak flow discharge curves. The added value of the high resolution of the NWP model emerges, in particular, when looking at the location of the convective part of the event, which hit the Liguria region.

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