A Numerical Method Combining the IADI Method with the Modified Picard Iteration Method for Solving Multi-Dimensional Saturated-Unsaturated Flow Equation

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Flood Estimation and Prediction Using Particle Filters

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 a probabilistic 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, and observations. Ensemble simulations are parallelized by a message passing interface (MPI), which can take advantage of high-performance computing (HPC) systems. Structure and implementation processes of data assimilation via MPI-OHyMoS are illustrated using a simple 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 with identifiable parameters results in a reasonable agreement to the observed hydrograph with reduced uncertainty of parameters

A Simple Water Balance Model for a Mesoscale Catchment Based on Heterogeneous Soil Water Storage Capacity

The work presented in this paper has been motivated by the need to develop a simple parameterization method for rainfall-runoff modelling considering spatial heterogeneity. A concept of tension water storage capacity distribution has been incorporated into a rainfall-runoff model to explain the runoff generation phenomenon more realistically. Originally, the concept was initiated by Zhao et al. (1980) in the Xinanjiang model, and later adopted by many researchers, e.g., in the VIC model by Wood et al. (1992). However, the expression for the tension water storage distribution in the Xinanjiang model is unable to describe all the situations in natural catchments. We therefore propose a general expression for the tension water storage capacity distribution. Applying this general expression, we have compared the performances of four kinds of rainfall-runoff simulation models, which are, the VIC model (Model 1), the unconfined version of VIC model (Model 2), and two models with a different concept of runoff generation mechanism with confined (Model 3) and unconfined (Model 4) aquifer cases, for the mesoscale catchments of Japan and Thailand. Sensitivity analysis of the model parameters has been conducted as well. The effect of time and spatial scale is also brought out. As a result, we can say that Model 3 and Model 4 indicate better runoff estimation and realistic soil-water storage time series, whereas, Model 1 and Model 2 are not able to represent a realistic time series of soil-water storage

Applying Sequential Monte Carlo Methods into a Distributed Hydrologic Model: Lagged Particle Filtering Approach with Regularization

Data assimilation techniques have received growing attention due to their capability to improve prediction. Among various data assimilation techniques, sequential Monte Carlo (SMC) methods, known as "particle filters", are a Bayesian learning process that has the capability to handle non-linear and non-Gaussian state-space models. In this paper, we propose an improved particle filtering approach to consider different response times of internal state variables in a hydrologic model. The proposed method adopts a lagged filtering approach to aggregate model response until the uncertainty of each hydrologic process is propagated. The regularization with an additional move step based on the Markov chain Monte Carlo (MCMC) methods is also implemented to preserve sample diversity under the lagged filtering approach. A distributed hydrologic model, water and energy transfer processes (WEP), is implemented for the sequential data assimilation through the updating of state variables. The lagged regularized particle filter (LRPF) and the sequential importance resampling (SIR) particle filter are implemented for hindcasting of streamflow at the Katsura catchment, Japan. Control state variables for filtering are soil moisture content and overland flow. Streamflow measurements are used for data assimilation. LRPF shows consistent forecasts regardless of the process noise assumption, while SIR has different values of optimal process noise and shows sensitive variation of confidential intervals, depending on the process noise. Improvement of LRPF forecasts compared to SIR is particularly found for rapidly varied high flows due to preservation of sample diversity from the kernel, even if particle impoverishment takes place

Simultaneous Estimation of Inflow and Channel Roughness Using 2D Hydraulic Models and Particle Filters

The Sequential Importance Resampling (SIR) method is introduced to a 2D hydraulic model to estimate inflow and Manning roughness coefficient (Manning’s n) simultaneously. The equifinality problem between theManning’s n and the inflow is considered using the proposed method. To solve the problem, we introduce the variance reduction factor and the correction factor in the perturbation step of the proposed method. The perturbed inflow and Manning’s n are updated according to the observed water stage with state variables. The result of the proposed method shows good agreement with the observed discharge, which enable us to estimate theManning’s n and inflow discharge at the same time considering the uncertainties of the existing rating curve. Finally, it showed that the methodology is not only to estimate the appropriate Manning’s n, but also to improve the existing rating curve

Estimation of field irrigation water demand based on lumped kinematic wave model considering soil moisture balance

An estimation model of farm field irrigation water demand is developed. The model is based on the lumped kinematic wave model considering soil water balance. The lumped model approach reduces the computational load in rainfall–runoff analysis and allows application to large river basins. Evapotranspiration is estimated on hourly basis by the improvement of FAO’s method. Not only water volume necessary for farm field irrigation but also the number of the water charge and its interval can be estimated by the combined use of the lumped runoff model and the hourly evapotranspiration model