Runoff modelling under noise-corrupted rainfall conditions

1989 ◽  
Vol 16 (5) ◽  
pp. 669-677
Author(s):  
G. G. Patry ◽  
A. Kennedy ◽  
S. Potter
Keyword(s):  
Monte Carlo ◽  
Surface Runoff ◽  
Valuable Insight ◽  
Prediction Errors ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Unit Hydrograph ◽  
Order Analysis ◽  
First Order ◽  
Trade Offs

Hydrological models are now routinely used in planning, design, operation, and control of water resources systems. However, all models, no matter how complex, are approximations of the real world and consequently are subject to various levels of errors. The analysis of uncertainty in hydrological models can provide valuable insight into the limitations and advantages of various surface runoff models. The benefits derived from such an analysis are many: first, it provides the modeller with a direct estimate of runoff prediction errors under specific rainfall conditions; second, it enables the user to analyze the trade-offs between different rainfall-runoff models; and finally, it can provide useful information for the design of data collection systems designed to achieve a given level of performance. This paper describes the application of uncertainty analysis to rainfall-runoff modelling under noise-corrupted rainfall conditions. The statistical properties of surface runoff subject to noise-corrupted rainfall conditions are examined. Methods of analysis described in this paper include (a) derived probability distribution, (b) first-order analysis, and (c) Monte Carlo simulations. The techniques are applied to linear and nonlinear runoff models, including the unit hydrograph, and the Soil Conservation Service model. Key words: runoff, uncertainty, error analysis, statistics, stochastic modelling, first-order analysis, Monte Carlo simulation, SCS model, unit hydrograph.

Modular Structural Component Design Using the First Order Analysis and Decomposition-Based Assembly Synthesis

2001 ◽  
Author(s):  
Onur L. Cetin ◽  
Kazuhiro Saitou ◽  
Hidekazu Nishigaki ◽  
Shinji Nishiwaki ◽  
Tatsuyuki Amago ◽  
...  
Keyword(s):  
Optimization Method ◽  
Two Dimensional ◽  
Order Analysis ◽  
First Order ◽  
Automotive Body ◽  
Automated Method ◽  
Trade Offs ◽  
Component Design ◽  
Module Geometry ◽  
Topology Optimization Method

Abstract This paper discusses an automated method for designing modular components that can be shared within multiple structural products, such as automotive bodies for sibling vehicles. The method is an extension of the concept of decomposition-based assembly synthesis. A beam-based topology optimization method, originally developed for First Order Analysis (FOA) of the automotive body structures, is utilized in order to obtain the “base” structures subject to decomposition. It is expected that the method will facilitate the early decisions on module geometry in automotive body structures, by enhancing the capability of the FOA system. Several case studies with two-dimensional structures are reported to demonstrate the effectiveness of the proposed method. The results indicate that two structures optimized for a similar, but slightly different boundary loading conditions are successfully decomposed to contain a component that can be shared by the structures. Several Pareto optimal decompositions are presented to illustrate the trade-offs among multiple decomposition criteria, with different weights for each objective function.

Parameter Uncertainty Propagation Analysis for Urban Rainfall Runoff Modelling

1994 ◽  
Vol 29 (1-2) ◽  
pp. 145-154 ◽  
Author(s):  
Jianhua Lei ◽  
Wolfgang Schilling
Keyword(s):  
Parameter Uncertainty ◽  
Uncertainty Propagation ◽  
Parameter Sensitivity ◽  
Model Output ◽  
Rainfall Runoff ◽  
Order Analysis ◽  
First Order ◽  
Output Uncertainty ◽  
Uncertainty Propagation Analysis ◽  
Propagation Analysis

The paper proposes a strategy for model uncertainty propagation analysis. As an example, parameter uncertainty propagation analysis in the runoff block of the HYSTEM-EXTRAN model is carried out. The model is a modification of the well-known SWMM (Storm Water Management Model). Uncertainty propagation methods such as first-order analysis, sensitivity analysis, statistical linearization and Monte-Carlo analysis are discussed and applied. A pathway of parameter uncertainty propagation analysis is given based on validity, simplicity, and computational requirements. The pathway starts with sensitivity analysis which may help to reduce the dimensions of a multidimensional model by discarding insensitive parameters. This is to obtain a mathematically tractable uncertainty propagation problem for a complicated model. Then, the nonlinearity of the model must be quantified to check the validity of first-order analysis. If first-order analysis is not valid, and if components of model output uncertainty need to be known, the application of statistical linearization is the only analytical alternative. Monte-carlo analysis can always be applied and taken as a reference as long as the components of the model output uncertainty are not of interest. The parameter sensitivity is characterized by its sensitivity coefficient which is defined as the ratio of the coefficient of variance of a model output to the coefficient of variance of the model parameter itself. A nonlinear rainfall runoff model usually results in a variable parameter sensitivity. Hence, recommendations about parameter sensitivity cannot be generalized for a given rainfall-runoff model, but depend on the type and the range of the model output variable. It is shown that the type of probability density function describing the parameter uncertainty with known mean and variance has only a small effect on the results of the model output uncertainty.

scholarly journals Predicting Surface Runoff from Catchment to Large Region

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Hongxia Li ◽  
Yongqiang Zhang ◽  
Xinyao Zhou
Keyword(s):  
Surface Runoff ◽  
Land Surface ◽  
Hydrological Model ◽  
Large Region ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Ungauged Catchments ◽  
Research Fields ◽  
Surface Models ◽  
Distributed Hydrological Models

Predicting surface runoff from catchment to large region is a fundamental and challenging task in hydrology. This paper presents a comprehensive review for various studies conducted for improving runoff predictions from catchment to large region in the last several decades. This review summarizes the well-established methods and discusses some promising approaches from the following four research fields: (1) modeling catchment, regional and global runoff using lumped conceptual rainfall-runoff models, distributed hydrological models, and land surface models, (2) parameterizing hydrological models in ungauged catchments, (3) improving hydrological model structure, and (4) using new remote sensing precipitation data.

Nappes d'huile: prediction de leurs deplacements comme agent efficace d'un plan de mesure d'urgence

1979 ◽  
Vol 14 (1) ◽  
pp. 89-109
Author(s):  
B. Coupal ◽  
M. de Broissia
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Finite Elements ◽  
Mass Balance ◽  
Stochastic Method ◽  
Stochastic Methods ◽  
Deterministic Approach ◽  
First Order ◽  
The Third ◽  
Comme Agent

Abstract The movement of oil slicks on open waters has been predicted, using both deterministic and stochastic methods. The first method, named slick rose, consists in locating an area specifying the position of the slick during the first hours after the spill. The second method combines a deterministic approach for the simulation of current parameters to a stochastic method simulating the wind parameters. A Markov chain of the first order followed by a Monte Carlo approach enables the simulation of both phenomena. The third method presented in this paper describes a mass balance on the spilt oil, solved by the method of finite elements. The three methods are complementary to each other and constitute an important point for a contingency plan.

scholarly journals Testing for Negative Spillovers: Is Promoting Human Rights Really Part of the “Problem”?

2021 ◽  
Vol 75 (1) ◽  
pp. 71-102
Author(s):  
Anton Strezhnev ◽  
Judith G. Kelley ◽  
Beth A. Simmons
Keyword(s):  
Human Rights ◽  
International Law ◽  
Instrumental Variable ◽  
Burden Of Proof ◽  
Negative Consequences ◽  
Human Rights Law ◽  
International Human Rights ◽  
First Order ◽  
Trade Offs ◽  
International Human

AbstractThe international community often seeks to promote political reforms in recalcitrant states. Recently, some scholars have argued that, rather than helping, international law and advocacy create new problems because they have negative spillovers that increase rights violations. We review three mechanisms for such spillovers: backlash, trade-offs, and counteraction and concentrate on the last of these. Some researchers assert that governments sometimes “counteract” international human rights pressures by strategically substituting violations in adjacent areas that are either not targeted or are harder to monitor. However, most such research shows only that both outcomes correlate with an intervention—the targeted positively and the spillover negatively. The burden of proof, however, should be as rigorous as those for studies of first-order policy consequences. We show that these correlations by themselves are insufficient to demonstrate counteraction outside of the narrow case where the intervention is assumed to have no direct effect on the spillover, a situation akin to having a valid instrumental variable design. We revisit two prominent findings and show that the evidence for the counteraction claim is weak in both cases. The article contributes methodologically to the study of negative spillovers in general by proposing mediation and sensitivity analysis within an instrumental variables framework for assessing such arguments. It revisits important prior findings that claim negative consequences to human rights law and/or advocacy, and raises critical normative questions regarding how we empirically evaluate hypotheses about causal mechanisms.

First Order Analysis for Automotive Body Structure Design-Part 2: Joint Analysis Considering Nonlinear Behavior

2004 ◽  
Author(s):  
Yasuaki Tsurumi ◽  
Hidekazu Nishigaki ◽  
Toshiaki Nakagawa ◽  
Tatsuyuki Amago ◽  
Katsuya Furusu ◽  
...  
Keyword(s):  
Nonlinear Behavior ◽  
Structure Design ◽  
Joint Analysis ◽  
Body Structure ◽  
Order Analysis ◽  
First Order ◽  
Automotive Body

scholarly journals Derivation of S-Curve from Oscillatory Hydrograph Using Digital Filter

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1456
Author(s):  
Kee-Won Seong ◽  
Jang Hyun Sung
Keyword(s):  
Digital Filter ◽  
Model Performance ◽  
Performance Criteria ◽  
Rainfall Runoff ◽  
Small Watershed ◽  
Unit Hydrograph ◽  
Time To Peak ◽  
S Curve ◽  
And Performance ◽  
Smooth Data

An oscillatory S-curve causes unexpected fluctuations in a unit hydrograph (UH) of desired duration or an instantaneous UH (IUH) that may affect the constraints for hydrologic stability. On the other hand, the Savitzky–Golay smoothing and differentiation filter (SG filter) is a digital filter known to smooth data without distorting the signal tendency. The present study proposes a method based on the SG filter to cope with oscillatory S-curves. Compared to previous conventional methods, the application of the SG filter to an S-curve was shown to drastically reduce the oscillation problems on the UH and IUH. In this method, the SG filter parameters are selected to give the minimum influence on smoothing and differentiation. Based on runoff reproduction results and performance criteria, it appears that the SG filter performed both smoothing and differentiation without the remarkable variation of hydrograph properties such as peak or time-to peak. The IUH, UH, and S-curve were estimated using storm data from two watersheds. The reproduced runoffs showed high levels of model performance criteria. In addition, the analyses of two other watersheds revealed that small watershed areas may experience scale problems. The proposed method is believed to be valuable when error-prone data are involved in analyzing the linear rainfall–runoff relationship.

An Equilibrium Prediction Method for Control and Fault Detection of Energy Systems

2021 ◽  
Author(s):  
Austin Rogers ◽  
Fangzhou Guo ◽  
Bryan Rasmussen
Keyword(s):  
Monte Carlo ◽  
Fault Detection ◽  
Monte Carlo Simulations ◽  
Case Studies ◽  
Air Conditioning ◽  
Prediction Method ◽  
Worker Safety ◽  
Static System ◽  
Control Logic ◽  
First Order

Abstract Many fault detection, optimization, and control logic methods rely on sensor feedback that assumes the system is operating at steady state conditions, despite persistent transient disturbances. While filtering and signal processing techniques can eliminate some transient effects, this paper proposes an equilibrium prediction method for first order dynamic systems using an exponential regression. This method is particularly valuable for many commercial and industrial energy system, whose dynamics are dominated by first order thermo-fluid effects. To illustrate the basic advantages of the proposed approach, Monte Carlo simulations are used. This is followed by three distinct experimental case studies to demonstrate the practical efficacy of the proposed method. First, the ability to predict the carbon dioxide level in classrooms allows for energy efficient control of the ventilation system and ensures occupant comfort. Second, predicting the optimal time to end the cool-down of an industrial sintering furnace allows for maximum part throughput and worker safety. Finally, fault detection and diagnosis methods for air conditioning systems typically use static system models; however, the transient response of many air conditioning signals may be approximated as first order, and therefore, the prediction model enables the use of static fault detection methods with transient data. In this paper, the equilibrium prediction method's performance will be quantified using both Monte Carlo simulations and case studies.

scholarly journals Modelling monthly runoff generation processes following land use changes: groundwater–surface runoff interactions

2004 ◽  
Vol 8 (5) ◽  
pp. 903-922 ◽  
Author(s):  
M. Bari ◽  
K. R. J. Smettem
Keyword(s):  
Land Use ◽  
Water Balance ◽  
Surface Runoff ◽  
Land Use Changes ◽  
Annual Rainfall ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Mean Annual Rainfall ◽  
Monthly Runoff ◽  
Stream Bed

Abstract. A conceptual water balance model is presented to represent changes in monthly water balance following land use changes. Monthly rainfall–runoff, groundwater and soil moisture data from four experimental catchments in Western Australia have been analysed. Two of these catchments, "Ernies" (control, fully forested) and "Lemon" (54% cleared) are in a zone of mean annual rainfall of 725 mm, while "Salmon" (control, fully forested) and "Wights" (100% cleared) are in a zone with mean annual rainfall of 1125 mm. At the Salmon forested control catchment, streamflow comprises surface runoff, base flow and interflow components. In the Wights catchment, cleared of native forest for pasture development, all three components increased, groundwater levels rose significantly and stream zone saturated area increased from 1% to 15% of the catchment area. It took seven years after clearing for the rainfall–runoff generation process to stabilise in 1984. At the Ernies forested control catchment, the permanent groundwater system is 20 m below the stream bed and so does not contribute to streamflow. Following partial clearing of forest in the Lemon catchment, groundwater rose steadily and reached the stream bed by 1987. The streamflow increased in two phases: (i) immediately after clearing due to reduced evapotranspiration, and (ii) through an increase in the groundwater-induced stream zone saturated area after 1987. After analysing all the data available, a conceptual monthly model was created, comprising four inter-connecting stores: (i) an upper zone unsaturated store, (ii) a transient stream zone store, (ii) a lower zone unsaturated store and (iv) a saturated groundwater store. Data such as rooting depth, Leaf Area Index, soil porosity, profile thickness, depth to groundwater, stream length and surface slope were incorporated into the model as a priori defined attributes. The catchment average values for different stores were determined through matching observed and predicted monthly hydrographs. The observed and predicted monthly runoff for all catchments matched well with coefficients of determination (R2) ranging from 0.68 to 0.87. Predictions were relatively poor for: (i) the Ernies catchment (lowest rainfall, forested), and (ii) months with very high flows. Overall, the predicted mean annual streamflow was within ±8% of the observed values. Keywords: monthly streamflow, land use change, conceptual model, data-based approach, groundwater

Sign in / Sign up

Export Citation Format

Share Document