Estimating Evaporation from Bare or Nearly Bare Soil

A model is presented that uses a daily mean evapotranspiration ETo rate to estimate energy-limited (potential or Stage 1) soil evaporation, and it also uses daily mean ETo and a soil hydraulic β factor to estimate soil hydraulic property-limited (Stage 2) evaporation. The model provides good estimates of cumulative soil evaporation on both hourly and daily bases when compared to observed soil evaporation in three field trials. Crop coefficient Kc values from cumulative hourly and cumulative daily soil evaporation estimates and ETo data were comparable. Using a soil hydraulic factor (β = 2.6) in the model gave a fair approximation for the widely used Kc curves for initial growth of crops presented in the United Nations Food and Agricultural Organization's Irrigation and Drainage Paper 24. However, using a site-specific β factor should improve soil evaporation and Kc estimates for site-specific applications.     

Estimating Evaporation Using ANFIS

Water resources engineering assessment requires simple but effective evaporation estimation procedures, especially from readily measurable meteorological factors. Unfortunately, such approaches are rather scarce in the literature. In this paper, an adaptive neural-based fuzzy inference system (ANFIS) was applied to daily meteorology data from the Lake E?irdir region in the southwestern part of Turkey. Daily evaporation, solar radiation, air and water temperatures, and relative humidity measurements were used to develop the ANFIS method, which helps to assess possible contributions that each input variable has on the evaporation estimates. Such an assessment is not possible by any conventional procedure including the Penman method. However, the Penman method daily evaporation estimations were used as output data for the verification of the ANFIS approach. Classical evaporation estimation models treat the data individually. However, ANFIS models process past data collectively and then adaptively provide estimates as new sets of data become available. In the ANFIS architecture as developed in this paper, there are four measured input variables and one output variable to estimate evaporation. The estimation results from the ANFIS model had a high coefficient of determination of 0.98 when compared with the Penman method results and a low average performance error of 4.6% among other alternatives. The average performance error is less than the practically acceptable limit of 10%.     

Estimating Evaporation from Bare Soil Using Soil Moisture Data

A method is presented that uses continuous soil moisture measurements and hourly reference evapotranspiration data to estimate a soil hydraulic factor (β) for modeling soil evaporation. The β factor is used to assess the end of the energy limited soil evaporation phase (Stage 1) and the evaporation rate during the soil hydraulic limited phase (Stage 2) of a two-stage soil evaporation model. A previously developed and tested method to determine β uses an energy balance approach with sensible heat flux density estimated using the surface renewal method to obtain the continuous soil evaporation. A new method is presented, which uses a hydroprobe soil moisture measuring device to estimate the continuous soil evaporation. The estimation of evaporation with soil moisture sensors was simpler and less expensive when compared to the energy balance technique. The methods, evaluated in two field experiments, showed good agreement with evaporation data. Using the evaporation model and β derived from either method provided a good estimate of measured soil evaporation. Modeled daily soil evaporation, using either energy balance or soil measurements to obtain β, gave a root-mean-square error of 0.6 mm?day?1 when compared with soil evaporation measured using the energy balance method. When daily soil evaporation from soil moisture measurements was compared with soil evaporation estimated from energy balance measurements, the root-mean-square error was 1.3 mm?day?1. Direct soil monitoring method had bigger error, but the method is less costly.     

FAO-56 Dual Crop Coefficient Method for Estimating Evaporation from Soil and Application Extensions

Crop coefficient curves provide simple, reproducible means to estimate crop evapotranspiration (ET) from weather-based reference ET values. The dual crop coefficient (Kc) method of the Food and Agricultural Organization of the United States (FAO) Irrigation and Drainage Paper No. 56 (FAO-56) is intended to improve daily simulation of crop ET by considering separately the contribution of evaporation from soil. The dual method utilizes “basal” crop coefficients representing ET from crops having a dry soil surface and separately predicts evaporation from bare soil based on a water balance of the soil surface layer. Three extensions to the evaporation calculation procedure are described here that are intended to improve accuracy when applications warrant the extra complexity. The first extension uses parallel water balances representing the portion of the soil surface wetted by irrigation and precipitation together and the portion wetted by precipitation alone. The second extension uses three “stages” for surface drying and provides for application to deep cracking soils. The third extension predicts the extraction of the transpiration component from the soil surface layer. Sensitivity and analyses and illustrations indicate moderate sensitivity of daily calculated ET to application of the extensions. The dual Kc procedure, although relatively simple computationally and structurally, estimates daily ET as measured by lysimeter relatively well for periods of bare soil and partial and full vegetation cover.     

Estimating Evaporation from Bare Soil and the Crop Coefficient for the Initial Period Using Common Soils Information

The crop coefficient during the initial period (Kc?ini) varies with wetting frequency, evaporative demand, and water-holding capacity of the upper soil layer. It is possible to develop a semitheoretical integrated function to predict the average Kc?ini representing the initial period of a growing season when the soil is mostly bare and that incorporates these three factors. The function is based on a two-stage evaporation function as used in the Food and Agriculture Organization Irrigation and Drainage Paper No. 56 (FAO-56) dual crop coefficient method. Parameters in the integrated equation are soil based and can be calculated a priori without field measurements. The procedure can be used to produce graphical figures similar to that introduced in FAO-24 for Kc?ini. Similar to FAO-24, the function utilizes the mean time between wetting events and reference evapotranspiration. In this paper, the development of the procedure and figures for Kc?ini are described. Comparisons with measured evaporation and Kc?ini in southern California indicate relatively good performance by the function without calibration.     

Data Mining Process for Integrated Evaporation Model

The purpose of this study is to develop an integrating evaporation estimation model using a data mining process for the Lakes District in the southern part of Turkey. Lakes E?irdir, Kovada, and Karaca?ren Dam are located in the Lakes District. The proposed data mining process is applied on these lakes for evaporation estimation. The daily pan evaporation data used in the data mining process are taken from State Hydraulic Works in southern Turkey. These data cover an 8-year period between 1998 and 2005 inclusively for daily pan evaporation of Lakes E?irdir, Kovada, and Karaca?ren Dam. It is known that a developed integrated daily pan evaporation model is necessary for these lakes, which are so important to the Lakes District. Therefore, a data mining model having two inputs and one output is developed. Input parameters used in the developed models for Lakes E?irdir, Kovada, and Karaca?ren Dam were daily pan evaporation values of Lakes Kovada + Karaca?ren Dam, Lakes E?irdir + Karaca?ren Dam, and Lakes E?irdir Kovada, respectively. As a result, in comparing the developed models with measured daily pan evaporation values, the REP tree model has better agreement with measured daily pan evaporation than other models. The results show the developed model was more accurate.     

Measurement of Evaporation on Bare Soil and Estimating Surface Resistance

A new evaporation measuring device incorporating an evaporation chamber has been developed and checked for its accuracy. This device is unique that it uses a chamber that is completely open at one end and thereby minimizes the effect of the chamber on the natural profiles of temperature, humidity, and turbulence. It was used in estimating a newly formulated surface resistance to bare soil evaporation under dry topsoil conditions. A simple energy balance model incorporating the depth of evaporating surface, blended with a new approach for describing surface resistance was developed and successfully tested with a limited set of data obtained from a simple experiment, assuming ideal conditions. In addition to the newly formulated surface resistance, the depth of the dry soil layer was also estimated and was relatively comparable with measured value. The newly formulated surface resistance was found to be insignificant compared to the resistance imposed by the dry soil layer. The total surface resistance was modeled as a power function of soil moisture in the top 0–1 cm of soil, while the newly formulated resistance did not show any relation with the soil moisture.     

Neural Network Model for Estimating Construction Productivity

The paper describes an approach developed to estimate construction productivity for concrete formwork tasks. The system utilizes artificial neural networks, historical information, and input from experienced superintendents employed by a leading construction general contractor. It also summarizes a study undertaken to determine factors that affect labor productivity, the survey conducted to collect relevant data, and the design, training, and implementation of artificial neural networks at the participating company. A number of alternative neural network structures were investigated, the adopted one was a three-layered network with a fuzzy output structure. It was found that this structure provided the most suitable model since much of the input was subjective. A brief overview of the computer implementations and the overall experience with the system development is also provided. The method was compared to an existing statistical model developed by the same contractor and was found to improve the quality of the estimates attained. A case study conducted in the context of a workshop with senior estimators is also presented.     

New Structure-Based Model for Estimating Undrained Shear Strength

This paper presents an experimental study of the strength in anisotropic clays by means of centrifuge model, cone penetration, and vane shear tests. To understand the effects of void ratio, overconsolidation ratio, and testing rate on the undrained shear strength (Su) of anisotropic Speswhite clay, a new centrifugal testing technique is designed to obtain constant overconsolidation ratio (OCR) profiles with varying void ratios (e), called the “descending gravity test.” The parameters controlling the generation of peak shear strength are quantified. As a result of this function, a new material and rate-dependent surface is defined in the e-OCR-Su space, which is identified as a “structural state capacity surface” since it relates the anisotropic structure to structure inherent capacity and properties. A new function for the estimation of excess pore pressure (uex) generated by cone penetration is found. By combining the strength and pore pressure functions a new model is proposed, called the “CU model.” The CU model is a structure-based model that provides reliable estimates of shear strength for in situ saturated clays using the knowledge of void and overconsolidation ratios. Finally, by combining Su-e-OCR and uex-e-OCR relationships, it estimates the void ratio and OCR profiles of anisotropic clays from piezocone penetration test results.     

SERC—A Model for Estimating Construction Inputs

One of the main problems in the process of design and management of construction projects is obtaining accurate information for preliminary estimates. This information is crucial for the development of integrated systems for construction management because of the relationship between construction input data and subjects such as estimating, cost control, scheduling, resource management, etc. Existing methods for estimating input that originated in industrial engineering are inadequate for the unique conditions of the construction industry. The model described in this paper applies statistical analysis of data from past projects, and enables the user to estimate the data needed for the construction of a new project. The model is based on the following components: Project items and their quantities; inputs needed to produce those items; and factors that affect inputs of a specific project. The model equation was calculated using multiple regression techniques. The paper concludes with a case study of a construction input configuration for a concrete structure.     

Index for Estimating Physical and Mechanical Parameters of Model Ice

A series of experiments on the physical and mechanical properties of ethanol model ice were conducted to determine the basis for quality control of fine grain model ice. The measured physical parameters of the model ice included the ethanol solution freezing point, air temperature profiles in an ice basin, ice temperature profiles, solution temperature profiles, unfrozen liquid in ice, and ice density. The measured mechanical parameters of the model ice were compressive strength, flexural strength, and characteristic length and strain modulus. Based on these measurements, the dominating factor influencing the physical and mechanical parameters of model ice is found to be the amount of unfrozen liquid in ice. Therefore, a new index of quality control for the physical and mechanical parameters of ice is developed. The statistical relationships of these measured physical and mechanical parameters with the new index are shown.     

A New Method for Estimating Model Parameters for Multinomial Data

A new procedure for estimating the parameters of a scientific model is described, and the method is applied and illustrated for the class of experiments with multinominal data structure. The procedure is referred to as the method of population-parameter mapping, and it has a number of novel and advantageous features. The method is a variation of a standard Bayesian analysis. However, instead of directly developing a posterior distribution for the model parameters, this procedure first characterizes the population proportions for the multinomial cells. Random samples are then drawn from the posterior distribution for these proportions, and these samples are mapped to the parameters of the scientific model. This method leads naturally to a definition of model identifiability, and leads to a direct probability estimate of the coherence of the scientific model. Moreover, the new procedure can circumvent the problem of dealing with computationally difficult integrals that frequently occur with Bayesian analyses of complex multinomial models. The method is illustrated by means of several memory measurement models as well as a signal-detection model. Copyright 1998 Academic Press.     

Fuzzy Clustering Model for Estimating Haulers’ Travel Time

This paper presents a two-step fuzzy clustering method for estimating haulers’ travel time. The proposed method provides a generic tool that can be incorporated in models dedicated for estimating earthmoving production. The estimated travel time takes into account the acceleration and deceleration in the transition zones. The developed method utilizes linear regression and fuzzy subtractive clustering. Seven factors influencing haulers’ travel time were first identified and their significance was then quantified using linear regression. The regression analysis was performed utilizing 180 training cases, generated using commercially available software for different models of haulers. The data were generated randomly to represent a wide range of possible combinations of factors affecting travel time of haulers across different types of road segments. The training data were subsequently used in the development of the proposed method. Unoptimized subtractive clustering, optimized Takagi–Sugeno zeroth-order subtractive clustering, and optimized Takagi–Sugeno first-order subtractive clustering were used in estimating haulers’ travel time. Their performance was evaluated using 36 test cases, also generated randomly in a similar manner to those utilized for training. The optimized Takagi–Sugeno first-order subtractive clustering model was found to outperform the other two, and was accordingly used in the proposed method. A numerical example is presented to demonstrate the use of the developed method and illustrate its accuracy.     

Gray System Model for Estimating the Pavement International Roughness Index

The international roughness index (IRI) is a measurement of pavement roughness that is widely accepted for evaluating pavement serviceability, especially its riding quality. Generally, as the age of pavement increases, its condition deteriorates and its IRI value increases. However, the IRI data collected from the Indiana highway system indicate that the IRI values vary considerably for similar pavements and traffic conditions at any given pavement age. This makes it difficult to establish the relationship between IRI and pavement age. In this study, the gray system theory was used to estimate the maximum, mean, and minimum IRI values at different pavement ages. It is believed that the three IRI values are essential for evaluating pavement serviceability. This paper presents the process of the gray system modeling for IRI estimation and discusses the effects of traffic volume on pavement roughness and the estimation accuracy of the gray system models.     

Orthotropic Plate Model for Estimating Deflections in Filled Grid Decks

Concrete filled grid bridge decks exhibit orthogonal elastic properties and significant two-way bending action enabling orthotropic plate theory to determine structural response for these elements. Current American Association of State Highway and Transportation Officials load and resistance factor design (LRFD) specifications employ an orthotropic plate model to predict live load moment in concrete filled grid bridge decks but provide no guidance for computing displacement, a potentially important serviceability consideration. This paper presents equations to approximate the maximum deflection in concrete filled grid bridge decks based on orthotropic plate theory, multiple patch loads, LRFD design truck and tandem load cases, the influence of multiple spans, and the two most common deck orientations.     

基于相似理论的蒸发冷却器实验模型设计

对高温烟气降温,使其出口温度与设计要求达到良好的一致,一直是实际工程中需要解决的重要问题.宝钢LT系统蒸发冷却器内的流动可以归结为具有蒸发、辐射效应的液滴一空气一蒸汽一灰分的四相流动,文章利用相似理论,导出了原型与模型相似的准则数及边界条件,根据关键准则数相等来确定模型的几何比例、速度比例等,并由此来设计实验模型装置.     

Estimating Winter Streamflow Using Conceptual Streamflow Model

Ice-affected periods represent a significant portion of the annual hydrograph for most Canadian hydrometric stations. Because the stage-discharge relation is not reliable under ice-cover conditions, Water Survey of Canada subjectively interpolates winter streamflow from as few as two observations of discharge during the ice-covered season, which may last 6 months or longer. An alternative method of producing discharge estimates is proposed that uses a combination of conceptual and statistical hydrological modeling to overcome limitations in both the availability of data and our understanding of relevant processes. A conceptual hydrological model is tested to evaluate the utility of this approach for data-sparse regions. When model predictions were adjusted to fit two winter measurements, 79% of all verification measurements were within 20% of predicted estimates. There was a seasonal bias to the error distribution, with most measurements within the first 30 days after freeze-up being less than predicted and most measurements after April 1 being greater than predicted. These deviations probably result from hydraulic and hydrologic processes not represented within the model.     

Capillarity and Evaporation Exacerbated Seepage Losses from Unlined Channels

Steady, essentially two-dimensional, tension-saturated seepage from a flat-bottomed soil channel in an arid environment is studied analytically. Physically, the action of capillary spreading, evaporative drive to the atmosphere both from the near-channel banks and capillary fringe boundary, Darcian resistance of the matrix and gravity are juxtaposed and result in trifurcation of infiltrated water into deep percolation and semi-infinite evaporation “wings” with two hinge points on the soil surface and two dividing streamlines. Mathematically, free boundary problem is solved by conformal mappings and the Polubarinova-Kochina boundary-value problem method. The dependence of deep percolation losses and evaporative return flow on the channel width, conductivity, static capillary rise height, and intensity of evaporation is found.     

Evaporation Theory for Deformable Soils

Desiccation of a deformable soil is basically the removal of water by evaporation, which is controlled by evaporativity and evaporability. Surface evaporation improves the trafficability, which is essential for the access of construction equipment in areas reclaimed with soft clay. The existing traditional methods for evaluating evaporation cannot account for the deformation of soils during evaporation. Therefore, a theoretical model for predicting the rate of evaporation from the surface of a deformable material is proposed. The model is based on a system of equations for coupled heat and mass transfer in unsaturated soils. The modified pressure plate extractor test and glass desiccator test were carried out to obtain the soil-water characteristic curve for a deformable soil. A column-drying test was conducted to investigate one-dimensional water flow, heat flow, and evaporation in the surface. A finite difference program was developed to solve the coupled nonlinear partial differential equations, which permits the study of liquid, diffusive vapor, and heat flows in the deformable soil. Comparison between measured and simulated values shows good agreement.