Flood Estimation and Prediction Using Particle Filters

Session: 2-2-6

Flood Estimation and Prediction Using Particle Filters
Yasuto Tachikawa, Seong Jin Noh, Yeon Su Kim, and Michiharu Shiiba
Department of Civil and Earth Resources Engineering,
Graduate School of Engineering, Kyoto University, Japan
E-mail: tachikawa@hywr.kuciv.kyoto-u.ac.jp

Data assimilation methods have received increased attention to accomplish uncertainty assessment and enhancement of forecasting capability in various areas.
Despite their potential, applicable software frameworks for probabilistic approaches and data assimilation are still limited because most hydrologic modeling software are based on a deterministic approach. In
this study, we developed a hydrologic modeling framework for data assimilation, namely MPI-OHyMoS.
While adapting object-oriented features of the original OHyMoS, MPI-OHyMoS allows user to build aprobabilistic hydrologic model with data assimilation.
In this software framework, sequential data assimilation based on particle filtering is available for any hydrologic models considering various sources of uncertainty originating from input forcing, parameters, andobservations. Ensemble simulations are parallelized
by a message passing interface (MPI), which can takeadvantage of high-performance computing (HPC) systems. Structure and implementation processes of data
assimilation via MPI-OHyMoS are illustrated using asimple lumped model. We apply this software framework for uncertainty assessment of a distributed hydrologic model in synthetic and real experiment cases.
In the synthetic experiment, dual state-parameter updating results in a reasonable estimation of parameters to cover synthetic true within their posterior distributions. In the real experiment, dual updating withidentifiable parameters results in a reasonable agreement to the observed hydrograph with reduced uncertainty of parameters.
Keywords: data assimilation, MPI-OHyMoS, hydrologicmodeling framework, particle filtering, uncertainty assessment, dual state-parameter updating

1. Particle Filtering with MPI-OHyMoSAs MPI-OHyMoS is a stochastic and interactive version of OHyMoS, OHyMoS is constructed as a set ofdynamic elements communicating with each other basedon object-oriented programming. It provides an operation module, including the common functions required inhydrological simulations, such as initialization of parameters and state variables and setting the computational time
steps and data exchange among element modules throughinput/output (I/O) ports. Through OHyMoS, users canJournal of Disaster Research Vol.8 No.1, 2013

Fig. 1. Concept of particle filtering via MPI-OHyMoS.

easily develop their own hydrologic modules by connecting them to other modules and transferring data using predefined ports in the system library. In MPI-OHyMoS, hydrologic modeling is implemented in the stochastic way.
Fig. 1 shows how model ensembles are interactively assimilated in MPI-OHyMoS. Each ensemble member, representing a probable projection based on different parameters and state variables, is implemented independently.
When a new observation arrives, the likelihood of ensemble members is estimated. In the resampling step, thewhole information of each ensemble is renewed, depending on its weight. In this way, ensembles can move tothe regions with high conditional probability in each timestep. Fig. 2 illustrates the processes of particle filtering
via MPI-OHyMoS using a linear reservoir model.

2. Uncertainty Assessment Using a DistributedHydrologic Model
Synthetic and real experiments are implemented for
uncertainty assessment of a fully distributed hydrologic
model to illustrate the applicability of the MPI-OHyMoS.
The study area is the Maruyama River catchment in Japan
with an area of about 909 km2 . We construct a probabilistic distributed hydro-logic model for the Maruyama
River catchment based on three element modules: a hillslope runoff generation module, a river routing module,
and a likelihood function. The hillslope and river routing
modules were developed as elements of a deterministic
distributed hydrological model using the kinematic wave
theory. In this model, it is considered that the catchment
consists of a number of rectangular slope elements which
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Fig. 2. Data assimilation processes of a linear reservoir
model via MPI-OHyMoS.
Table 1. Summary of model performance for real experiment.
Fig. 3. Parallel simulations of the distributed hydrologic
model with PF in the synthetic experiment’s preliminary
stage (Event 3). Red dots represent synthetic observation.
Grey lines represent stream flow ensembles. Grey dots represent parameter ensembles. Blue lines represent means ofensembles. Black lines represent 60% confidence intervals.

drain to the deepest gradient of its surrounding.
A synthetic experiment is implemented using a distributed hydrologic model to demonstrate the applicabilityof MPI-OHyMoS for missing data problems in complex
cases and assess the identifiability of parameters. Fig. 3
shows results of the preliminary stage of synthetic experiment. Simulated stream flow, which is one-step-ahead
prediction, shows good conformity with synthetic observation in terms of ensemble mean and distributions. Uncertainty of parameters lasts before the flood event as in
the initial distributions and reduces sharply around theflood peak. Dual state-parameter updating via PF results
in a reasonable estimation of parameters to cover synthetic true within their posterior distributions. However,identifiability of parameters is different and the roughness
coefficient of slope shows diffusive distribution.
Following the synthetic experiment, a real experimentis conducted for different three events. Model performance is summarized in Table 1 using two indices: NashSutcliffe efficiency (NSE) and root mean square error168(RMSE). Statistics show the improvement of the model
performance via PF in all events compared to deterministic modeling cases. Parameter distributions estimatedby PF at Event 1 result in good performance in Event 3,
whose peak flood is about six times higher than Event 1.
In Event 3, deterministic modeling presents improved performance, demonstrating transferability of the parameters
for an unexperienced high flood. However, application
into a smaller flood (Event 2) shows limited performance.
Due to uncertainties coming from hydrologic models
and observations, optimal parameters may change according to the magnitude of flood events and initial conditions.
The results of deterministic modeling show that parameters estimated at large events (Event 1) may not be appropriate for small events (Event 2) or vice versa. This situation is found frequently in numerous hydrologic modeling cases. However, probabilistic approach and dual
stateparameter updating could compensate the uncertainty
of model structures.
Journal of Disaster Research Vol.8 No.1, 2013