Mutsuhiro Fujita

Stochastic response of a storage function model for flood runoff estimation of higher-order moments

Abstract All real hydrologic processes are more or less stochastic, while the dynamics for the runoff process are described by differential equations. Runoff phenomena are best expressed by random differential equations. The differential equations whose solutions give the first four moments of discharge from the storage function runoff model are proposed under the condition that rainfall input from the random process is independently distributed. The validity of these equations is cross-checked by a simulation method. Furthermore, it is possible to estimate the probability density function of discharge by using the four moment obtained from the differential equations. This estimated probability density function makes it possible to evaluate a confidence limit for computed discharge.

A study on subcatchment scale for a distributed runoff model

Abstract Physically-based distributed runoff models are more advantageous than concentrated conceptual models for predicting the hydrologic response of ungauged basins and the changing response resulting from changes in land-use. In constructing distributed models, the river basin must be divided into reasonable smaller subcatchments corresponding to a characteristic scale for rainfall-runoff response. Thus, one of the still-unsolved problems in distributed models is the estimation of the appropriate subcatchment scale. This paper focuses only on the appropriate subcatchment scale considering the channel network geomorphology, although the effects of subcatchment scale on the overall river basin response are important. For this purpose, some new stochastic functions are proposed for discussing the hydrologic response of channel networks. By applying these functions into a simplified distributed runoff model, the relationship between the hydrologic response of the river basin and variation of the channel network geomorphology is shown.

Retained water in soil based on probabilistic pore structure

Abstract In this paper, a conceptual model is proposed that evaluates suction and water retention between two spheres with different radii from a microscopic view point. Assuming soil is composed of spherical particles with various grain sizes, the suction and water retention can be quantitatively analyzed as a function of both the distance between two particles and ratio of radius. In order to calculate the suction and water retention in soil, the structure of soil must be clarified. If particle size distribution and porosity are known, the combination of two particles and the distance between two particles can be described by the theory of probability. The probability distribution of pore structure is derived. The validity of the theory is checked by experiments.

Non-stationary and mutually dependent rainfall input and its stochastic response

Observed rainfall is a non-stationary and mutually dependant random variable. Based on the condition that the stochastic characteristics of rainfall are known, we derived differential equations whose solutions provide the first four moments of discharge output from a storage function runoff model. The validity of the derived differential equations was cross-checked by a simulation method. The results showed that it is possible to obtain the probability density function of discharge from the calculated first four moments of discharge.

DEVELOPMENT OF ESTIMATION METHOD OF WATER INFLOW INTO DAM RESERVOIR BASED ON FILTERING THEORY

The inflow into dam reservoir is estimated from the change of hydrostatic water level. It is difficult to obtain the estimation of inflow enough to use for practical dam control using moving average process which is adopted as smoothing method for water level data by almost dam reservoir. The aim of this study is to develop a new estimation method of hydrostatic water level and of inflow into dam reservoir. In this paper, the new smoothing method of water level, which is constituted of the combination of notch filter (NF) and low-pass filter (LPF). In the new method, NF and LPF is used for eliminating a seiche and other random noise, respectively. In the case of Kanayama Dam Reservoir, it would be able to obtain the estimated inflow, in which the power of seiche is cut off enough to use for practical dam control if it is possible to admit 20 minutes time lag of flood peak in the estimated inflow.

STOCHASTIC PROPERTY OF STORAGE FUNCTION RUNOFF MODEL(2)

Hashino had already proposed the application of Freunds bivariate exponential density function for a design storm. At present, it is impossible to use this distribution to estimate a design flood because of its nonstationarity (average and variance) and mutual dependency. As a first step to conquer these difficulties, authors assume that the average rainfall is nonstationary and the random component of rainfall is expressed by AR (1) based on the analysis of observed rainfall. Finally, authors adopt the storage function runoff model as rainfall runoff system and propose the differential equations whose solutions provide the first four moments of discharge under the impact of mutually dependent rainfall inputs. The probability density function of discharge is easily derived by the obtained first four moments of discharge.

Numerical Computation of Open Channel Flow with Shock Wave using FDS method

Numerical calculation of flow including shock wave is one of the important tools in the practical river engineering works. Flux Difference Splitting, FDS, scheme is one of the numerical method to simulate the flow with shock wave. In this method, the theory of nonlinear characteristic wave and the concept of numerical flux are taken in account and it is very easy to be applied to various condition of open channel flow. In this paper, a numerical model using FDS scheme for two dimensional open channel flow is developed and was applied to several different hydraulic conditions. The calculated results are compared with the theoretical and experimental results and good agreement is obtained.

Study on snowmelt runoff prediction using weekly weather forecast

Abstract This paper presents an attempt to apply weekly weather forecast data for snowmelt runoff prediction. The basin surveyed for this study is 1.0 km2 in area and ranges from 390 to 800 m above sea level. The snowmelt model makes use of digital terrain data with a 20-m grid spacing. Energy balance components are calculated for each grid element taking topographic variables of solar radiation into account. In order to use the snowmelt model in the forecast situation, it is necessary to predict the meteorological data for any future time period. In this study: 1) the insolation is calculated by the forecasted percentage of sunshine, which is classified into 15 weather conditions, and 2) the temperature is calculated as a function of time elapsed. A one week forecast of snowmelt runoff using weekly weather forecast data is sufficiently accurate for practical purposes.

Runoff Analysis by the Quasi Channel Network Model in the Toyohira River Basin

This paper describes runoff analysis using the quasi channel network model of the Misumai experimental basin, which is part of the Toyohira River basin. The Toyohira River flows through Sapporo which has a population of 1.7 million. Four multi-purpose dams are located in the Toyohira River basin; thus, it is very important to verify the runoff process not only analytically but also based on field observations.

Study on the Snowmelt Runoff Forecasting Using the Weekly Weather Forecast

This paper presents an attempt to apply the weekly weather forecast data for a snowmeltr unoff prediction. The basin is 1.0km2 in area and elevation ranges from390 to 800m above sea level. The snowmelt model makes use of digital terrain data with 20m grid spacing. Energy balance components are calculated for each grid element taking topographic variations of solar radiation into account. In ordar to use the snowmelt model in the forecast situation, it is necessary to predict the meteorological data for any future time period. In this study, 1) the insolation is calculated by the forecasted percentage of sunshine, which is divided into 15 weather conditions; 2) and the temperature is calculated as a function of time elapsed. One week ahead forecasting results by the snowmelt runoff model using the weather forecast data are accurate enough for practical purpose.