Journal of Irrigation and Drainage Engineering

Reliable estimates of evapotranspiration (ET) from vegetation are needed for many types of water-resource investigations. How well models can estimate ET from vegetation varies, depending on the capabilities of the model as well as the nature of the targeted vegetation. Model accuracy also depends heavily on the quality and quantity of the data used. Several ET models have been developed that use an energy balance approach in which the data used by the models are derived from satellite imagery. This research introduces an enhanced surface energy balance-based model, the remote sensing of evapotranspiration or ReSET model, for estimating ET. ReSET is an ET estimation model that takes into consideration the spatial variability in weather parameters, which makes it particularly applicable for calculating regional scale ET. ReSET also has the capability of interpolating between the available weather stations in time and space. The model’s accuracy at daily and seasonal time scales is evaluated in several case studies.

In Nebraska, historically, there have been differences among the water regulatory agencies in terms of the methods used to compute reference evapotranspiration $\left({\mathrm{ET}}_{\mathrm{ref}}\right)$ to determine actual crop water requirements and hydrologic balances of watersheds. Because simplified and/or empirical temperature or radiation-based methods lack some of the major weather parameters that can significantly affect grass and alfalfa-reference ET ( ${\mathrm{ET}}_o$ and ${\mathrm{ET}}_r$ ) the performance of these methods needs to be investigated to help decision makers to determine the potential differences associated with using various ${\mathrm{ET}}_{\mathrm{ref}}$ equations relative to the standardized ASCE Penman–Monteith (ASCE-PM) equations. The performance of 12 ${\mathrm{ET}}_o$ and five ${\mathrm{ET}}_r$ equations were analyzed on a daily basis for south central Nebraska from 1983 to 2004. The standardized ASCE-PM ${\mathrm{ET}}_o$ and ${\mathrm{ET}}_r$ values were used as the basis for comparisons. The maximum ASCE-PM ${\mathrm{ET}}_o$ value was estimated as $12.6\mathrm{mm}{\mathrm{d}}^{-1}$ , and the highest ${\mathrm{ET}}_r$ value was estimated as $19\mathrm{mm}{\mathrm{d}}^{-1}$ on June 21, 1988. On this day, the atmospheric demand for evaporation was extremely high and the vapor pressure deficit (VPD) reached a remarkably high value of $4.05\mathrm{kPa}$ . The combination-based equations exhibited significant differences in performance. The 1963 Penman method resulted in the lowest RMSD of $0.30\mathrm{mm}{\mathrm{d}}^{-1}$ $(r^2=0.98)$ and its estimates were within 2% of the ASCE-PM ${\mathrm{ET}}_o$ estimates. The 1948 Penman estimates were similar to the 1963 Penman ( $r^2=0.98$ , $\mathrm{RMSD}=0.39\mathrm{mm}{\mathrm{d}}^{-1}$ ). Kimberly forms of alfalfa-reference Penman equations performed well with RMSD of $0.48\mathrm{mm}{\mathrm{d}}^{-1}$ for the 1972 Kimberly–Penman and $0.67\mathrm{mm}{\mathrm{d}}^{-1}$ for the 1982 Kimberly–Penman. The locally-calibrated High Plains Regional Climate Center (HPRCC) Penman method, ranked 6th, performed well and underestimated the ASCE-PM ET by 5% ( $\mathrm{RMSD}=0.56\mathrm{mm}{\mathrm{d}}^{-1}$ ). Most of the underestimations occurred at the high ET range $(>11\mathrm{mm})$ and this was attributed to the upper limits applied by the HPRCC on VPD, $\left(2.3\mathrm{kPa}\right)$ and wind speed $\left(5.1\mathrm{m}{\mathrm{s}}^{-1}\right)$ . The lack of ability of the radiation methods in accounting for the wind speed and relative humidity hindered the performance of these methods in the windy and rapidly changing VPD conditions of south central Nebraska. The 1977 FAO24 Blaney–Criddle method was the highest ranked (seventh) noncombination method ( $\mathrm{RMSD}=0.64\mathrm{mm}{\mathrm{d}}^{-1}$ , $r^2=0.94$ ). The FAO24 Penman estimates were within 4% of the ASCE-PM ${\mathrm{ET}}_o$ . Overall, there were large differences between the ASCE-PM ${\mathrm{ET}}_o$ and ${\mathrm{ET}}_r$ versus other ${\mathrm{ET}}_{\mathrm{ref}}$ equations that need to be considered when other forms of the combination or radiation and temperature-based equations are used to compute ${\mathrm{ET}}_{\mathrm{ref}}$ . We recommend that the ASCE-PM ${\mathrm{ET}}_o$ or ${\mathrm{ET}}_r$ equations be used for estimating ${\mathrm{ET}}_{\mathrm{ref}}$ when necessary weather variables are available and have good quality. The results of this study can be used as a reference tool to provide practical information, for Nebraska and similar climates, on the potential differences between the ASCE-PM ${\mathrm{ET}}_o$ and ${\mathrm{ET}}_r$ and other ${\mathrm{ET}}_{\mathrm{ref}}$ equations. Results can aid in selection of the alternative method(s) for reasonable ${\mathrm{ET}}_{\mathrm{ref}}$ estimations when all the necessary weather inputs are not available to solve the ASCE-PM equation.

In planning, designing, and managing of surface and groundwater supply, it is essential to accurately quantify actual evapotranspiration $\left({\mathrm{ET}}_c\right)$ from various vegetation surfaces within the water supply areas to allow water management agencies to manipulate the land use pattern alternatives and scenarios to achieve a desired balance between water supply and demand. However, significant differences among water regulatory agencies and water users exist in terms of methods used to quantify ${\mathrm{ET}}_c$ . It is essential to know the potential differences associated with using various empirical equations in quantifying ${\mathrm{ET}}_c$ as compared with the measurements of this critical variable. We quantified and analyzed the differences associated with using 15 grass $\left({\mathrm{ET}}_o\right)$ and alfalfa-reference $\left({\mathrm{ET}}_r\right)$ combination, temperature and radiation-based reference ET $\left({\mathrm{ET}}_{\mathrm{ref}}\right)$ equations in quantifying grass-reference actual ET $\left({\mathrm{ET}}_{\mathrm{co}}\right)$ and alfalfa-reference actual ET $\left({\mathrm{ET}}_{\mathrm{cr}}\right)$ as compared with the Bowen ratio energy balance system (BREBS)-measured ${\mathrm{ET}}_c$ $\left({\mathrm{ET}}_{c\text{-}\mathrm{BREBS}}\right)$ for field corn (Zea mays L.). We analyzed the performance of the equations for their full season, irrigation season, peak ET month, and seasonal cumulative ${\mathrm{ET}}_c$ estimates on a daily time step for 2005 and 2006. The step-wise $K_c$ values instead of smoothed curves were used in the ${\mathrm{ET}}_c$ calculations. The seasonal ${\mathrm{ET}}_{c\text{-}\mathrm{BREBS}}$ was measured as 572 and $561\mathrm{mm}$ in 2005 and 2006, respectively. The root-means-quare difference (RMSD) was higher for the full season than the irrigation season and peak ET month estimates for all equations. The standardized ASCE Penman-Monteith (PM) ${\mathrm{ET}}_{\mathrm{co}}$ had a RMSD of $1.37\mathrm{mm}{\mathrm{d}}^{-1}$ for the full growing season, $1.05\mathrm{mm}{\mathrm{d}}^{-1}$ for the irrigation season, and $0.76\mathrm{mm}{\mathrm{d}}^{-1}$ for the peak month ET. The ASCE-PM, 1963 and 1948 Penman ${\mathrm{ET}}_c$ estimates were closest to the ${\mathrm{ET}}_{c\text{-}\mathrm{BREBS}}$ . The FAO-24 radiation and the HPRCC Penman ${\mathrm{ET}}_c$ estimates also agreed well with the ${\mathrm{ET}}_{c\text{-}\mathrm{BREBS}}$ . Most combination equations performed best during the peak ET month except the temperature and radiation-based equations. There was an excellent correlation between the ASCE-PM ${\mathrm{ET}}_{\mathrm{co}}$ and ${\mathrm{ET}}_{\mathrm{cr}}$ with a high $r^2$ of 0.99 and a low RMSD of $0.34\mathrm{mm}{\mathrm{d}}^{-1}$ . The difference between the ${\mathrm{ET}}_{\mathrm{cr}}$ and ${\mathrm{ET}}_{\mathrm{co}}$ was found to be larger at the high ${\mathrm{ET}}_c$ range (i.e., $>8\mathrm{mm}$ ), but overall, the ${\mathrm{ET}}_{\mathrm{cr}}$ and ${\mathrm{ET}}_{\mathrm{co}}$ values were within 3%. Significant differences were found between the cumulative ${\mathrm{ET}}_{\mathrm{co}\text{-}\text{METHOD}}$ and ${\mathrm{ET}}_{\mathrm{cr}\text{-}\text{METHOD}}$ versus ${\mathrm{ET}}_{c\text{-}\mathrm{BREBS}}$ . Most combination equations, including the standardized ASCE-PM ${\mathrm{ET}}_{\mathrm{co}}$ and ${\mathrm{ET}}_{\mathrm{cr}}$ underestimated ${\mathrm{ET}}_{c\text{-}\mathrm{BREBS}}$ during the early periods of the growing season where the soil evaporation was the dominant energy flux of the energy balance and in the late season near and after physiological maturity when the transpiration rates were less than the midseason. The underestimations early in the season can be attributed to the lack of ability of the physical structure of the ${\mathrm{ET}}_{\mathrm{ref}}\times \text{crop}$ coefficient approach to “fully” account for the soil surface conditions when complete canopy cover is not present. The results of this study can be used as a reference tool by the water resources regulatory agencies and water users and can provide practical information on which method to select based on the data availability for reliable estimates of daily ${\mathrm{ET}}_c$ for corn.

Experiments to measure the evapotranspiration of an improved, irrigated pasture were conducted at the University of California, Davis, CA field station and over a commercial irrigated pasture on Twitchell Island in the Sacramento-San Joaquin River Delta using the surface renewal (SR) method. In Davis, the SR method was used to determine well-watered crop evapotranspiration $\left({\mathrm{ET}}_c\right)$ over short grass, and the results were compared with the ASCE-EWRI standardized reference evapotranspiration $\left({\mathrm{ET}}_0\right)$ for a short canopy to establish that a crop coefficient $K_c=1.00$ is appropriate for estimating well-watered pasture ${\mathrm{ET}}_c$ . In the Twitchell Island study, surface renewal was used to determine the actual evapotranspiration $\left({\mathrm{ET}}_a\right)$ from a commercial pasture. A stress coefficient of $K_s={\mathrm{ET}}_a∕{\mathrm{ET}}_0\approx 0.90$ was observed during the high ET period $({\mathrm{ET}}_0>7\mathrm{mm}{\text{day}}^{-1})$ from about mid-June through mid-July for the Twitchell Island pasture. Otherwise, the pasture was mainly unstressed, so the $K_s=1.0$ . Thus, assuming no future changes in irrigation management, using ${\mathrm{ET}}_0$ from Twitchell Island, a $K_c=1.00$ , and $K_s=1.00$ will provide good estimates of ${\mathrm{ET}}_a$ during low to moderate ET periods and $K_s\approx 0.90$ should be used when ${\mathrm{ET}}_0>7.0\mathrm{mm}{\text{day}}^{-1}$ . In general, a thermocouple for SR measurements costs about $100, whereas the price for a sonic anemometer varies between $3,000 and $20,000, so the SR method provides a low-cost method to measure ${\mathrm{ET}}_a$ .

Hydrologic modeling and agricultural water consumption analyses typically require some estimation of expected evapotranspiration (ET) from any given land cover or crop type. The main parameter used to make such estimations is the “crop coefficient” $\left(K_c\right)$ , also known as “reference ET fraction” (ETrF). An efficient remote-sensing methodology to obtain ETrF is mapping evapotranspiration at high resolution and with internalized calibration (METRIC), which requires thermal data, as obtained from the Landsat satellite. Unfortunately, the possibility of future Landsat satellites containing thermal sensors is uncertain, and other satellites’ data have access or spatial resolution concerns. Even when thermal-band data are available, energy-balance approaches can be costly. In this paper we continue ongoing efforts by the University of Idaho and others to estimate the evapotranspiration parameter $K_c$ (or $K_{cb}$ ) using the more readily available normalized difference vegetation index (NDVI) satellite-derived data product. Results from a case study in Idaho for irrigated agriculture indicate that the $\mathrm{NDVI}∕K_c$ method has significant potential to estimate $K_c$ because it is a fully objective and repeatable process, is comparably fast, easy, and less costly to apply, and does not require images from the thermal band. Preliminary work suggests that NDVI-based estimates produce results comparable to METRIC estimates over large spatial areas and full-season sample periods, even when the estimation equations were derived from different locations or crop type and management settings. While it is unclear whether empirical methods to derive the $K_c$ product are more robust than either remote-sensing estimation method, this ongoing $\mathrm{NDVI}∕K_c$ method is studied as an alternative, especially when empirical data are sparse or too costly to obtain, and when the METRIC approach cannot be taken. We find that NDVI-based estimation equations may be practically applied to areas other than the area of development.

In this study, we have attempted to enhance the utility of soil–plant–atmosphere–water (SPAW) model that has been used successfully by various workers in different countries for soil moisture prediction under different cropping conditions. One of the major climatic inputs for SPAW model is pan evaporation, which in many places is not readily available. To address the above, and to get the benefit of this model in regions characterized by limited weather data availability, this study was undertaken using computed ${\mathrm{ET}}_0$ from air temperature by the 1985 Hargreaves equation, as one of the inputs in place of pan evaporation. For the purpose, actual air temperature collected from experimental farm area, as well as forecast air temperature collected from National Centre for Medium Range Weather Forecasting, Government of India, were used. First, the SPAW model was calibrated and its performance was evaluated under wheat, taking layerwise and profile soil moisture as the variables for comparison between the predicted and observed values. The results showed that the root-mean-square error (RMSE) varied from $0.30\text{to}0.58\mathrm{cm}$ for measured values ranging between 2.24 and $4.25\mathrm{cm}$ . The index of agreement $\left(d\right)$ varied from 0.81 to 0.92 and coefficient of determination $\left(r^2\right)$ from 0.46 to 0.73 for 0–15, 15–30, 30–45, and $45–60\mathrm{cm}$ soil depths. For the whole $60\mathrm{cm}$ profile, the RMSE was $1.07\mathrm{cm}$ with $d$ and $r^2$ values of 0.94 and 0.85 respectively. The RMSE and $d$ varied from $0.36\text{to}0.63\mathrm{cm}$ and 0.77 to 0.89 respectively when ${\mathrm{ET}}_0$ computed from actual air temperature was used in place of pan evaporation, where as when ${\mathrm{ET}}_0$ computed from forecast air temperature data was used, the corresponding values were $0.35–0.64\mathrm{cm}$ and 0.68–0.85 respectively for the four soil layers. There was a tendency of the models to underestimate when the computed ${\mathrm{ET}}_0$ was used as input in place of pan evaporation. In general, performance of the models were better at lower depths.

A typical irrigation scheduling problem is one of preparing a schedule to service a group of outlets which may be serviced simultaneously. This problem has an analogy with the classical earliness/tardiness problem in operations research. In previously published work an integer program was used to solve this problem, however such scheduling problems belong to a class of combinatorial problems known to be computationally demanding ( $N\text{-}P$ hard). This is widely reported in operations research. Hence integer programs can only be used to solve relatively small problems usually in a research environment where considerable computational resources and time can be allocated to solve a single schedule. For practical applications metaheuristics such as genetic algorithms, simulated annealing, or tabu search methods need to be used. However as reported in the literature, these need to be formulated carefully and tested thoroughly. This paper demonstrates the importance of robust testing of one such genetic algorithm formulated to solve the irrigation scheduling problem with simultaneous outlets serviced against an integer program formulated to solve the same problem.

The effect of reduced corn evapotranspiration (ET) during solid-set sprinkler irrigation on application efficiency was analyzed on two subplots. During each irrigation event, one subplot was irrigated (moist treatment) while the other was not (dry treatment). ET (weighing lysimeter) and transpiration (heat balance method) rates were determined at each subplot before, during, and after the irrigations. During daytime irrigations, there was a significant decrease in ET (32–55%) and transpiration (58%) for the moist treatment. After the irrigations $(1–2\mathrm{h})$ , ET significantly increased (34%) and transpiration decreased (20%). Gross wind drift and evaporation losses $\left({\mathrm{WDEL}}_g\right)$ were found to be 19.3% of the applied water. Taking into account the ET changes during and after the irrigations, net sprinkler evaporation losses $\left({\mathrm{SEL}}_n\right)$ were 14.4–17.5% of the applied water. During nighttime irrigations, changes in ET and transpiration were almost negligible, and ${\mathrm{SEL}}_n$ were slightly greater than ${\mathrm{WDEL}}_g$ (9.5 and 8.1%, respectively, of applied water). ${\mathrm{SEL}}_n$ was mainly a function of wind speed. Reduced ET and transpiration during daytime irrigations moderately increased solid-set sprinkler application efficiency.

A model to calculate the quantity and quality of river flows by simulating hydro-chemical processes in soil and the spatial/temporal distribution of irrigation return flows is introduced. By simulating the hydro-chemical processes, the quantity and quality of the deep percolating water can be predicted. The spatial and temporal distribution of the deep percolating water is simulated by constructing a groundwater flow path and calculating the groundwater travel time using response functions. A probabilistic approach was developed to calculate the groundwater travel time taking into account the fact that some irrigated fields have subsurface drainage which shortens travel times. All related hydrological components are integrated into the computation of river flow quantity and quality including groundwater return flow, irrigation tail water, tributary inflow, river diversion, phreatophyte consumption, river channel losses, and river depletion due to pumping. An illustrative example is included to demonstrate the capabilities of the model. The results of this example show that river salinity is lower during the irrigation season and higher during the off season. Due to salts carried by return flows, downstream reaches have higher salinity levels than upstream reaches.

The aim of this study was to develop a methodology for collecting soil salinity samples. The objectives of this paper are to: (1) estimate the number of soil salinity samples needed to capture the variability in the soil salinity data with high accuracy; and (2) compare two types of satellite images with different resolutions: Ikonos with $4\mathrm{m}$ resolution and Landsat 5 with $30\mathrm{m}$ resolution. To achieve these objectives, two satellite images were acquired (one for Ikonos and one for Landsat 5) to evaluate the correlation between the measured soil salinity and remote sensing data. From the observed data, three subsets were randomly extracted with each subset containing: 75, 50, and 25% of the data for each field. These three subsets were then used in the modeling process. Ordinary least squares (OLS) (i.e., multiple regression) was used to explore the coarse-scale variability in soil salinity as a function of the Ikonos and Landsat 5 bands. A stepwise procedure was used to identify the best subset of satellite bands to include in the regression models that minimized the Akakie information criteria (AIC). Then, the spatial structure of the residuals from the OLS models were described using sample variogram models. The variogram model with the smallest AIC was selected to describe the spatial dependencies in the soil salinity data. If the residuals were spatially correlated, ordinary kriging was used to model the spatial distribution of soil salinity in the fields. A tenfold cross validation was used to estimate the prediction error for soil salinity. To evaluate the effectiveness of the models, various measures of the prediction error were computed. This study provides an accurate methodology that can be used by researchers in reducing the number of soil samples that need to be collected. This is especially valuable in projects that last several years. The results of this study suggest that the number of soil samples that need to be collected and therefore their cost can be significantly reduced and soil salinity estimation can be significantly improved by using kriging. The results also show that the Ikonos image performed better than the Landsat 5 image.

This paper deals with the problem of fault detection and isolation in irrigation canals. We have developed a method which combines static and dynamic data reconciliation for the validation of measurements, detection, and isolation of sensors and actuator faults and reconstruction of missing data. Static data reconciliation uses static models at a regulation gate to validate measurements and detect sensor and actuator faults. It also enabled us to detect a drift in the stage discharge rating curve. The dynamic data reconciliation uses additional measurements and a dynamic model of the canal in order to validate measurements and detect faults and withdrawals. The combination of the two methods allowed us to distinguish between withdrawals and faults. Both methods are evaluated on measurements from a real irrigation canal located in the South of France.

This paper presents a simple and quick method for estimating furrow infiltration using a single advance point based on the volume balance equation. The furrow infiltration and water front advance along the furrow are assumed to follow the modified Kostiakov infiltration and power advance equations, respectively. The volume balance equation, including these equations, is simplified to a function containing two parameters, i.e., the exponents of power advance and Kostiakov infiltration equation (with a prior-known basic infiltration rate). These parameters are estimated by minimizing the function to zero using a quasi-Newton search algorithm, provided with Excel Solver. The estimated exponents are used to determine the Kostiakov infiltration parameters. The proposed one-point method is tested with seven independent furrow irrigation evaluation data sets and the estimated cumulative infiltration is compared with the observed counterparts. Performance of the proposed method was evaluated using the root-mean-square error and index of agreement $\left(I_a\right)$ . The results show that the proposed one-point method estimated cumulative infiltration is closer to the observed; the method performed as good as Valiantzas’ method. Shepard’s method did not perform well for the tested data sets. The algorithm and the results of the proposed method reveal that the proposed method can be used as a tool for quick estimation of furrow infiltration using a single advance point.

The major water quality impairment in the midwest United States is sediment eroded from agricultural lands. Yet, few understand the spatial and temporal variability of erosion, or soil erosion dynamics, in relation to precipitation, topography, land management, and severe events. The objectives of this paper are to (1) develop a methodology for estimating long-term spatial soil erosion and water runoff losses and (2) explore issues in applying an established physical-based process model, Water Erosion Prediction Project (WEPP), to a large area by establishing a prototype system for the state of Iowa. This study for the first time provides a comparison of the model predictions against long-term measurements of the sediment delivery ratio (SDR) in the South Amana Catchment of the Clear Creek Watershed (CCW), a heavily instrumented watershed that is roughly 10 times the maximum WEPP fold size. To further examine the performance of WEPP in a semihumid environment, such as the CCW, where runoff and raindrop impact to erosion may be significant, the SDR was plotted as a function of the runoff coefficient, defined as the runoff/rainfall ratio. In addition, the WEPP predictions are compared against the statistical relation of SDR vs. runoff coefficient developed by Piest et al. in 1975) for watersheds in Iowa. It is shown that WEPP follows the trend shown by Piest et al. quite closely and performs well for continuous simulations extended up to $300\text{years}$ .

Soil erosion from agricultural areas continues to be problematic in terms of both financial and environmental measures. Ephemeral gullies contribute to the soil loss both by the volume of sediment eroded from the gullies and because they act as delivery channels for surface erosion. High resolution aerial imagery was used to quantify the amounts and locations of ephemeral gullies in the subbasins of the Potlatch River system. Areal ephemeral gully erosion rates varied from $33.6\mathrm{mt}∕{\mathrm{km}}^2$ (0.15 U.S. t/acre) in the Big Bear Creek Subbasin to $88.4\mathrm{mt}∕{\mathrm{km}}^2$ (0.39 U.S. t/acre) in the Middle Potlatch Creek Subbasin representing 2.3–7.7% of the total surface sediment load. An erosion potential index was proposed to assist resource managers predict gully locations at the watershed scale using readily available remote sensing and geographic information system layers.

A comprehensive analytical solution for the quantity of seepage into an array of fully penetrating ditches from a ponded surface has been obtained using hodograph and Schwarz–Christoffel transformation. The solution includes equations for the quantity of seepage from the seepage face part as well as the water depth part of the ditch. The solution also comprises expressions for the velocity potential at the stagnation point and the variation in seepage velocity. The variation in seepage quantity is like the shape of a curved channel whose boundary maps along a circle onto the hodograph plane. This shape is average of a semiellipse and a parabola. The seepage contribution from the nonseepage face is maximum for half full condition and it is half of the total seepage in an empty ditch (full seepage face). Irrespective of the spacing between ditches the quantities of seepage from the seepage face part and the nonseepage part are equal for one third full ditch. The solution also deals with special cases like single ditch, unequal spacing between ditches, and unequal depth of water in adjacent ditches. The expressions the quantity of seepage have been simplified in explicit algebraic equations through minimization of errors. The simplified expressions, which are near exact, result in answers in single step computations. Also, an example and graphs have been included to demonstrate the sensitivity of the parameters.

To reduce required detention volume, a detention basin outlet structure (DBOS) was tested that can control the maximum allowable outlet discharge rate independent of the upstream driving head. Unlike traditional outlet control structures, such as culverts, orifices, and weirs, whose maximum discharge represents a single point in time on the outflow hydrograph, the DBOS can discharge at the maximum allowable discharge for an extended period of time, thus reducing the required detention volume. Data are presented and methods discussed regarding techniques for altering the head–discharge characteristics of the DBOS in order to meet various head–discharge requirements.

In current practice, entrance loss coefficients and inlet control head–discharge relationships for buried-invert culverts designed for fish passage applications are either ignored or approximated using traditional culvert design data due to a lack of data specific to these alternative culvert geometries. This study experimentally determined entrance loss coefficients and inlet control head–discharge relationships for circular culverts with invert burial depths of 20, 40, and 50% and an elliptical culvert with 50% invert burial depth. In general, the inlet loss coefficients for buried-invert culverts were higher than for traditional culverts of the same cross-sectional shape without invert burial. The influence of approach flow conditions (ponded or channelized) on inlet loss coefficients and inlet control head–discharge relationships was also investigated. This paper outlines the experimental methods used to determine entrance loss coefficients and inlet control head–discharge regression constants relative to these alternative culvert geometries and presents the data relevant to the hydraulic design and evaluation of these culverts.

An iterative step method for solving the nonlinear ordinary differential equation, governing spatially varied flows with decreasing discharge, like the flow over side weirs, is developed. In the procedure, starting at a known flow depth and discharge in the control section, the analytical integration of the dynamic equation with bed and friction slope is carried out. The specific energy, the weir coefficient and the velocity distribution coefficient are considered as local variables, then for the explicit integration, the respective average values along the short side weir elements are assumed. The water surface profiles and the discharges for flow over side weirs, obtained with the proposed relation and valid for rectangular channels, are compared with experimental data for subcritical and supercritical flow conditions. The validation of the method is accomplished by the comparison with the solution obtained by De Marchi’s classical hypothesis, about the specific energy, which is constant along a side weir. In addition, the influence of the coefficient velocity distribution is considered.

The determination of critical depth and the position of control sections is important in open-channel hydraulics. Calculation of critical depth in open channels is useful not only for determining the condition of a flow but also for hydraulic design and analysis of experimental and analytical results. In this study, unlike the conventional approaches, an alternative method, based on a genetic algorithm (GA), for the calculation of critical depth in conduits is presented. In our model, the governing equations are transferred into an objective function that is then minimized using a GA in order to calculate critical depth. This method does not have the limitations of existing empirical and semiempirical methods and can be used for any prismatic or nonprismatic open-channel cross section. The concepts presented in this paper can be generalized for solving other tortuous hydraulic engineering equations and problems.

The modified advection dispersion equation (MADE) model is compared to the transient storage model (TSM) and fractional advection dispersion equation (FRADE) model for modeling riverine transport. The comparisons are illustrated using field examples. The parameter $F$ of the FRADE model is found to be inconsistent to its physical meaning and is unrelated to dead zone fraction. Also, the estimated parameters of TSM are found to be inconsistent to their physical meaning. On the contrary, the parameters of MADE are consistent and related to the dead zone fraction. It is observed that the MADE model is easy to use and yields reliable and consistent results.

A numerical simulation is presented to obtain the flow characteristics of cut-throat flumes in rectangular open channels. Cut-throat flumes with a horizontal floor are used as simple devices for flow measurement in open channels. Since the flow in the throat section is highly three dimensional and curvilinear, the three-dimensional turbulence Reynolds stress model was applied in the present study to obtain the flow parameters such as the water surface profiles, the pressure distributions, and the mean velocity distributions. The volume of fluid scheme was used to determine the shape of the free surface by computing the fraction of each near-interface cell of a fixed grid that is partially filled with water. The previously published experimental data as well as data based on a new test related to cut-throat flumes were used to validate the simulation results.

A computationally simple approximation of the Hantush $M$ function is proposed. Using this approximation, an optimization method is proposed for identifying the aquifer parameters from early drawdowns around partially penetrating wells. The aquifer parameters are hydraulic conductivity and specific storage. The application of the method is illustrated using a field example. The proposed method results in a 120-fold saving in time when compared to the prior method. It is hoped that the proposed approximation and method will be of help to the field engineers and practitioners.

The existing equation for leaky aquifers is transformed into a nondimensional form using new parameters and a scaled well function for leaky aquifers is proposed. A computationally simple function is developed for accurately approximating the scaled well function for the practical range of the parameters. Utilizing this function (approximation), an optimization method is proposed for identifying the leaky-aquifer parameters from observed drawdowns. The new function has an enhanced utility when a repetitive numerical evaluation of the well function for leaky aquifers is needed, e.g., while estimating the aquifer parameters using optimization or Kalman filter or artificial neural network methods. The application of the proposed method is illustrated using a few sets of published data. The proposed method outperforms the extended Kalman filter method, based on the reported results in the literature.

The concept of low impact development (LID) applies decentralized on-site runoff source control to storm water management. LID is an integration of bioretention and vegetated landscapes to maintain a catchment’s hydrologic and ecological functions. In current practice, the LID implementation is quantified for the specified watershed development. During the dynamic development process, the existing LID facilities have to be improved according to the incremental changes in the watershed. This technical note presents an on-site hydrologic approach to relate the required incremental storm water retention volume to the alteration of surface imperviousness in the tributary area. This approach allows the storm water retention volume to be tailored according to the stage of the watershed development. Cumulatively, the total storage volume can be achieved though multiple stages of the watershed development. The incremental retention volume is found to be related to the local average event rainfall depth. Design charts were produced and normalized by the local average rainfall event depth for generalized applicability.