Journal of Irrigation and Drainage Engineering

In this paper, new algebraic expressions are derived for estimating reference evapotranspiration ($E{T}_0$) from limited meteorological data. Reference evapotranspiration is commonly computed from readily available measured data at weather stations; e.g., solar radiation, $R_S$ ($\mathrm{MJ}/{\mathrm{m}}^2/\mathrm{d}$); relative humidity, $RH$ (%); wind speed, $u$ ($\mathrm{m}/\mathrm{s}$); and average temperature, $T$ (°C). Recently, a simple algebraic formula, equivalent in accuracy to the Penman equation, was derived by Valiantzas for computing $E{T}_0$ from readily available measured data. The derivation of the formula is based on simplifications made to the standardized form of the Penman equation. In a first place, an improved and more simplified form of Penman’s equation is proposed, requiring a full set of data. The new full set of data formula is obtained by calibration using meteorological data obtained from the CLIMWAT global database. For many places where reliable wind speed data are not available, an expression is also suggested that does not require wind data. Further simplifications were applied in the proposed formula to derive the following new formula based on $R_S$ and $T$ measurements alone: $E{T}_0\approx 0.0393R_S\sqrt{T+9.5}-0.19R_S^{0.6}{\phi }^{0.15}+0.0061(T+20){(1.12T-T_{\min }-2)}^{0.7}$ where $T_{\min }$ = minimum temperature; and $\phi$ (rad) = latitude of the station. The proposed formula, which requires limited data, can be also applied when taking the local long-term average wind speed value into account. Evaluation of the formulas and comparisons with other methods using daily meteorological data are given in the companion paper.

In the companion paper, a set of simplified formulas was derived to approximate the Penman equation for estimating reference evapotranspiration (${ET}_0$) from limited data. The first suggested formula is explicit for routinely measured data: air temperature, solar radiation, relative humidity, and wind velocity. Simplifications of this formula were made for places where wind speed and/or relative humidity data are not available. The performance of the new derived formulas was tested under various climatic conditions using high-quality daily weather data from 17 agricultural weather stations across California obtained from the California Irrigation Management Information System (CIMIS) database. The daily CIMIS-Penman estimations, obtained by summing hourly computations over 24 h periods and by using the standardized FAO-56 Penman Monteith scheme applied on a daily basis, were used as the basis for comparisons at the 17 stations in California. The performance of the formula with a full set of data is excellent. The formula without wind data was compared with other common empirical methods (reduced-set FAO procedure not requiring wind data, Turk, and radiation-based Hargreaves), whereas the formula without wind and humidity data was compared with the radiation-based Hargreaves and the reduced-set FAO procedure that does not require wind and humidity. Comparisons of the methods indicated that the proposed formulas is a better option than the other empirical methods for estimating ${ET}_0$ when wind and/or relative humidity data are missing.

This paper describes the crop water stress index (CWSI) related to the soil moisture content (SMC) in a chernozem of the Dobrogea region, Romania. The lower baselines (LBLs) for both sunny and shaded leaf conditions were drawn as regression equations between the difference of peach canopy temperature ($T_c$) and air temperature ($T_a$) versus the vapor pressure deficit (VPD). The lower baseline determined in the case of sunny leaves showed the highest coefficient of determination. The relationship between CWSI and the soil moisture content is inverse and highly significant, but with a low coefficient of determination. Soil variability, wind gusts, and agronomic factors could be responsible for this correlation. Irrigation should usually be applied at CWSI values of 0.18–0.20 in the case of sunny leaves. CWSI determined with the help of shaded leaves is of limited use in irrigation scheduling. The authors’ conclusion is that the sunny CWSI values of 0.18–0.20 are appropriate for irrigation application in the case of such medium-textured and relatively homogeneous soils in irrigation scheduling for peach orchards grown under conditions similar to Constanţa, Romania, and neighboring countries.

In this paper, the authors have modified a traditional transpiration formula using a soil-gas stress response function of the soil water content to appropriately depict the leaf water head. This allowed identification of the soil waterlogging (gas deficit) stress status. The authors then analyzed the status of soil moisture in an irrigated pecan field in El Paso, Texas, using a drainage equation and a modified transpiration formula. In the formula, four soil moisture statuses were identified: waterlogging stress (gas deficit), favorable water uptake, tolerable moisture deficit, and intolerable moisture deficit. Among these, the water uptake favorable and tolerable moisture deficit statuses are the two favorable for healthy pecan growth and good production. Waterlogging (gas deficit) and intolerable moisture deficit statuses are detrimental for pecan growth. The results also showed that soil moisture status is significantly different during the four growing stages of irrigated pecans: budbreak, early shooting-leafing, primary shooting-leafing-blooming, and fruiting. The results provide guidelines for precision irrigation for achieving water conservation and improved production of pecans and other similar crops.

The feedforward control canal problem is stated as a nonlinear optimization problem with constraints on the gate movements. It is numerically solved with the use of the sequential quadratic problem (SQP) method and the active set. The main objective of the resulting GoRoSo algorithm is the scheduling of the gate openings over a programmed operation scenario, and their calculation relies on the use of the Saint-Venant’s complete model over a prediction horizon. A numerical and analytical procedure is developed to quantify the influence of any gate movement over the canal flow conditions for a prediction time horizon. All these influences are lumped together in a global matrix, which is referred to as the hydraulic influence matrix. The GoRoSo algorithm is validated by means of a series of test developed by the ASCE task committee on canal automation algorithms.

Storm-water harvesting from a pond for irrigation of adjacent lands is promoted as one way that may reduce pond discharge while supplementing valuable potable water used for irrigation. Reduction of pond discharge reduces the mass of pollutants in the discharge. In this study, a storm water harvesting and assessment for reduction of pollution (SHARP) model was developed to predict operation of a wet pond used for storm-water harvesting at Miramar Lakes, Miramar, FL. The model integrates the interaction of surface water and groundwater in a catchment area. The SHARP model was calibrated and validated for pond-water elevation. Model evaluation showed adequate prediction of pond-water elevation with root mean square error (RMSE) between 0.07 and 0.12 m, mean absolute error (MAE) between 0.018 and 0.07 m, and relative index of agreement ($d_{\mathrm{rel}}$) between 0.74 and 0.98 for both calibration and validation periods. The SHARP model is capable of assessing harvest safe yield and discharge from a pond, including the prediction of the percentage of runoff into a harvesting pond that is not discharged.

Urban rainfall-runoff modeling is important to analyze the health of urban water systems. The Storm Water Management Model (SWMM) developed by the U.S. Environmental Protection Agency (USEPA) is one of the most widely used rainfall-runoff models. In this paper, SWMM was integrated with a sampling based on a combined objective, aiming at (1) reducing the uncertainty of model parameters distribution in the sampling-based method, and (2) reducing uncertainty bounds of model outputs to obtain more accurate predictions. The Latin hypercube sampling (LHS) method was selected as the sampling strategy, and the Nash-Sutcliffe (NS) coefficient, correlation coefficient ($R$), percent bias (%BIAS), root mean square error (RMSE), and a combination with the previous four objectives were used to get behavioral parameter sets, respectively. A case study was made in an experimental catchment of Macau, China, to test the efficiency of different objectives on reducing uncertainty of the rainfall-runoff model. Results indicated that the combined objective method could reduce the uncertainties of both the model parameters and the predictions.

This paper considers flow measurement in open channels with transverse weirs of finite crest length with sloping crests and upstream or downstream ramps—or both—for free flows. Using the broad-crested weir equation with a discharge coefficient as the basis and following the earlier studies of broad-crested weirs, robust correlations have been developed for the discharge coefficient by using the experimental results available in the literature. Effects of upstream and downstream ramps were studied by introducing the upstream and the downstream length scales, respectively. The proposed correlations of embankment weirs were compared with the head-discharge formulations of embankment weirs in the literature. Triangular (hump) weirs with either upstream or downstream ramps (or both) have also been considered. It was found that in triangular (hump) weirs, the discharge coefficient increases when the downstream slope increases, but it decreases when the upstream slope increases.