https://swat.tamu.edu/media/90111/sdarticle.pdf

Optimal use of the SCE-UA global optimization method for calibrating watershed models

Publisher Summary Accurate knowledge of the overall convective heat transfer from circular cylinders is of importance in a number of fields, such as boiler design, hotwire anemometry, and the rating of electrical conductors. The wide dispersion in the published experimental data for the heat transfer from smooth circular cylinders by natural and forced convection is attributed to various factors associated with the experiments. The error due to heat conduction to the supports is particularly important with natural convection, especially where the heat loss and the temperature rise of the cylinder are calculated from the voltage drop across it. A common cause of error is the use of too small a space ratio, so that the temperature and velocity fields are distorted. To reduce this error to less than l%, the space ratio D c /D for natural convection or D T /D for forced convection should exceed 100. The error caused by blockage with wind tunnel measurements can be calculated depending on the type of tunnel. One of the greatest sources of error with forced convection is the failure to allow for the effect of stream turbulence.

The Overall Convective Heat Transfer from Smooth Circular Cylinders

Understanding, management and modelling of urban hydrology and its consequences for receiving waters: A state of the art

TOPMODEL (a TOPography based hydrological MODEL) is now 20 years old and has been the subject of numerous applications to a wide variety of catchments. This paper represents a critical review of some of the issues involved in application of the TOPMODEL concepts, including the basic assumptions involved; the derivation of topographic index distributions from digital terrain data; additional model components; meaning and calibration of the model parameters; and issues involved in model validation and predictive uncertainty. The aim is to provoke a thoughtful approach to hydrological modelling and the interaction of modelling and field work. Some recommendations are made for future modelling practice.

TOPMODEL : a critique.

Karst hydrology: recent developments and open questions

From an engineering perspective, this book provides a practical treatment of groundwater flow; substance transport, well construction, groundwater production, site characterization, and remediation of groundwater pollution.

The handbook of groundwater engineering

The Evolution of Urban Hydrology: Past, Present, and Future

The Integrated Autoregressive Moving Average (ARIMA) model was applied to the average monthly rainfall time series over 15 basins located in Indiana, Illinois and Kentucky, with areas varying between 240 and 4000 mi2 approximately. The record length varied from 492 to 684 months. The first-order, mixed, autoregressive, moving average model emerged as the most suitable one for forecasting and generation of cyclicly standardized monthly rainfall square roots series. The model passed the goodness-of-fit test in all cases studied. The seasonally differenced, multiplicative model applied to monthly rainfall square roots also passed the goodness-of-fit test in all cases. This model has the advantage of requiring fewer parameters than the previous one. However, the use of the differenced models is limited to forecasting of monthly rainfall series and cannot be used for the generation of synthetic rainfall time series, as it does not preserve the monthly standard deviations. Seasonal differencing is effe...

https://journals.ametsoc.org/downloadpdf/journals/apme/17/10/1520-0450_1978_017_1528_smfmrf_2_0_co_2.pdf

Stochastic Models for Monthly Rainfall Forecasting and Synthetic Generation

The ARMA (autoregressive moving average) processes may be fitted to stationarized series obtained by subtraction of the seasonal means and division by the seasonal standard deviations. This standardization is effective in removing the cyclicities in the means and in the variances, but the ensemble cross-correlations are virtually unchanged. A modified family of ARMA models is presented in which the seasonality of the means, the variances, and the cross-variances are preserved. The model has seasonally varying coefficients based upon seasonally varying serial correlation coefficients. To further preserve the characteristics of the historical series, the probability distribution of the random numbers used for data generation should be similar to those of the residuals of the stochastic model. The ARMA models have been applied to 20 daily, weekly, and monthly runoff series for watersheds in the lower Ohio Basin.

Seasonal and Nonseasonal ARMA Models in Hydrology

EPA'S Storm Water Management Model (SWMM) simulates all aspects of the hydrologic and quality cycles. Using expert system technology, an interactive user‐support framework has been developed to automate the calibration of the runoff block. It acts as a front end to assist the user in the initial estimation of the parameter values and in building the SWMM input files. It interprets the simulation results and suggests some useful adjustments in the value of significant parameters thus reducing the user's time and effort. For the interpretation of simulation results, production rules are employed to help the user decide what parameters need to be adjusted. Some heuristics have been developed to evaluate the new parameter values. The combination of simulation techniques and expert system methodologies facilitates the use of sophisticated models such as SWMM.

Jacques W. Delleur

Papers

The handbook of groundwater engineering

The Evolution of Urban Hydrology: Past, Present, and Future

Stochastic Models for Monthly Rainfall Forecasting and Synthetic Generation

Seasonal and Nonseasonal ARMA Models in Hydrology

Expert System for Calibrating Swmm