Journal of Irrigation and Drainage Engineering

The long-term sustainability of irrigated agriculture is contingent on application of cost-effective water conservation measures and improved management in the face of intense competition for limited freshwater resources from municipal, industrial, and environmental/ecological interests. A geographic information system (GIS)–based decision support system (GeoDSS) is presented for maximizing water savings from efficiency improvements and conservation measures through integrated management of large-scale irrigation systems. The GeoDSS incorporates the GeoMODSIM network flow optimization model as implemented within ArcGIS Desktop (ESRI, Inc.) and is applied to the large-scale, complex Imperial Irrigation District (IID) water delivery system in California in support of the IID/San Diego County Water Authority (SDCWA) Water Transfer Agreement. IID GeoDSS automatically constructs a GeoMODSIM flow network within an ESRI geodatabase composed of feature classes for IID canals, drains, reservoirs, farm gates, interceptors, and pumpback locations. Following calibration of the baseline network, GeoMODSIM simulations predict substantial water savings from mixtures of strategies of varying cost for improved water management and increased delivery system efficiency and on-farm water conservation, without the need for fallowing or retirement of IID irrigated lands.

Solar radiation ($R_s$) is a very important variable in agricultural meteorology, but it is measured at a very limited number of weather stations. In addition, $R_s$ data are usually low quality because of to sensor failure or lack of calibration. $R_s$ can be predicted from other meteorological measurements (e.g., air temperature), but the accuracy of these methods needs to be tested. In this work, the mechanistic model of Hargreaves-Samani, three modified versions of this model, a modified Bristow-Campbell equation, and the Mahmood-Hubbard model were evaluated at 56 sites in the Andalusian region in southern Spain. All of these methods use the daily temperature range of the air ($\mathrm{\Delta }T=T_{\max }-T_{\min }$) to estimate $R_s$. The estimated values of $R_s$ were compared with measurements of $R_s$ to check their suitability. The accuracy of the methods was primarily affected by the magnitude of $\mathrm{\Delta }T$, with larger $\mathrm{\Delta }T$ resulting in a greater accuracy. Finally, the parameter of the Hargreaves-Samani equation ($K_r$) was adjusted to regional conditions using $\mathrm{\Delta }T$, improving the estimations of incoming solar radiation for a wide $\mathrm{\Delta }T$ range.

For efficient irrigation water management and hydro-meteorological studies at both the field and catchment scales, a need exists to evaluate the existing evapotranspiration (ET) estimation methods under varied physiographical and data availability conditions. In this study, using the FAO-56 Penman-Monteith model as the benchmark model, a total of 16 various ET estimation methods were evaluated using both the continuous daily time series and average time series weather data of a subhumid valley cultivated rangeland in the eastern Indian Himalayas. The results revealed that some pan evaporation models have restricted performances. Consequently, for an improved performance, all of these models were standardized using a genetic algorithm–based linear corrector transformation model. Almost all of the models performed reasonably well when using the average daily time series data. Furthermore, the trend analysis of reference ET showed an increasing trend during the months of February to July, including October, and a decreasing trend during August to January, except October, with an annual decreasing rate of $0.42\mathrm{mm}/\mathrm{year}$.

Quantification of evapotranspiration (ET) is a key factor to design management strategies for improving water use efficiency of irrigated viticulture. In the study, an integrated approach of crop coefficients derived from satellite-based normalized vegetation index (NDVI), and a daily soil water balance approach was proposed to accurately estimate evapotranspiration fluxes and crop coefficient values of grapevine plants. The modeled values of $K_c$ and ET were compared with field estimates of crop ET using an energy-balance surface renewal flux tower and adjusted for calibration using the eddy covariance method. The correlation of the measured and modeled $K_c$ exhibits a linear tendency with high coefficient of determination. The study of the soil water balance suggests the importance of soil water storage in grapes. Results of the study validate the use of remote sensing as a tool for accurate estimation of evapotranspiration rates of irrigated grapevine stands.

An empirical relationship is developed for the nonlinear root water uptake parameter in the O-R moisture uptake model from easily measurable plant physiological parameters, such as maximum daily transpiration, maximum root depth, and time to attain the maximum transpiration. A nondimensional parameter, termed specific transpiration, that involves the plant physiological parameters is used in this empirical relationship. Data for determining this relationship are obtained by minimizing the deviations between the field observed moisture depletions of 28 crops reported in the literature, and the Richards equation–based numerically simulated soil moisture depletions combined with the moisture uptake model accounting for root water uptake. In addition to cross-validation, field experiments on three Indian crops (maize, Indian mustard, and wheat) are conducted to further validate the proposed empirical relationship. Comparisons of model predictions with field observations of soil moisture profiles and moisture depletions in different layers of the root zone show good agreement during different stages of crop growth. The results highlight the utility of the developed equation for modeling root water uptake over a wide range of crops.

Climate change impact reports by the Intergovernmental Panel on Climate Change (IPCC) are well established throughout the world. By now many local, regional, and central authorities are aware of these impacts and they try to take necessary precautions according to their environmental circumstances. Preliminary decisions cannot be taken unless there is a model for future climate change impacts, especially for rainfall occurrence, amount, and frequency estimations. The main purpose is to combine the general circulation (climate) model (GCM) outputs with the local meteorology station records by a suitable downscaling procedure. In this paper, a statistical quadrangle downscaling model (QDM) is developed for rainfall estimations up to 2100. This model helps to downscale the nearby four scenario rainfall amounts at GCM grids to a desired point. Two main steps in the model structure are the use of a regional dependence function (RDF) for transferring the GCM output data from the four nearest grid points to the location. The second step is to adjust the downscaled scenario monthly rainfalls with the local rainfall records. The GCM monthly rainfall outputs are downloaded from the National Center for Atmospheric Research (NCAR) with special report emission scenario A2. The application of the methodology is presented for Riyadh City, Kingdom of Saudi Arabia (KSA). The RDF is obtained from the most reliable meteorology records at 27 locations that are irregularly scattered all over the country. Then it is used for scenario data transfer from the regular grid points. The adjustment is based on the average statistical properties of concurrent monthly rainfall amounts between 1985 and 1996. The trend component in the GCM remains intact. It has been observed that there is a major increasing trend in the monthly rainfalls after 2050. In the meantime, each decade starting from 2011 gives estimates that indicate steady rainfall increases up to 2050. The QDM indicates a 40% increase in the annual total rainfall amounts, with a 15% standard deviation increase.

Orifices are often used in stormwater detention facilities for reducing runoff peak flow and achieving extended detention times to improve water quality. Orifices often flow partially full; therefore, accurate and easily implemented methods of modeling basin outflow under such conditions are needed. In this paper we describe two models of partially submerged flow in thin-walled orifices and conduct full-scale testing of these models. The best model (termed the top-width weir approach) treats the orifice as an equivalently sized rectangular weir, where the equivalent weir length equals the top width of flow in the orifice until half-full and the diameter of the orifice when greater than half-full. A single fitting coefficient $C_w$ is required, which is directly proportional to the discharge coefficient for submerged orifice flow. This method provides results that, except for low submergence conditions, are within 2 to 5% of experimental data for steady flow, a significant improvement over the existing circular weir model in which errors are $>10\%$. The model is also shown to closely match transient drainage data, demonstrating its utility for predicting drawdown times for stormwater detention facilities.

Simulation models have recently been used in many studies for simulation of water flow and solute transport in soil under different irrigation systems. The objective of this study was to compare the HYDRUS-1D and HYDRUS-2D simulation models to simulate water and nitrate transfer for three furrow irrigation technologies [conventional furrow irrigation (CFI), fixed alternate furrow irrigation (FFI), and variable alternate furrow irrigation (AFI)] under fertigation practice. Filed measured data were used to calibrate and validate the one-dimensional (1D) and two-dimensional (2D) HYDRUS models. An inverse solution technique was applied to optimize soil-hydraulic and solute transport parameters to calibrate the models. The results indicated that the HYDRUS-2D model provided better performance to predict soil water contents, nitrate concentrations, and deep percolation caused by the geometry of the infiltration domain in furrow irrigation. Standard errors for HYDRUS-1D ranged from 0.107 to 0.170 for soil water content and 0.256 to 0.295 for soil nitrate concentration; whereas these values for HYDRUS-2D varied between 0.089 and 0.096 and 0.144 and 0.205 for soil water content and soil nitrate concentration, respectively. Application of HYDRUS-1D increased the risk of overestimation of nitrate leaching. CFI had higher water and nitrate deep percolation compared to AFI and FFI. Although the HYDRUS-2D model required much more computational time than HYDRUS-1D, using this model is recommended in furrow fertigation because of its more reliable and accurate simulation results.

Daily solar radiation $R_s$ at ground level is a necessary input variable required for the evaluation of evapotranspiration and crop growth, development, and yield-simulation models. Nevertheless, it is measured in few weather stations and at many locations it is not observed; also, available $R_s$ temporal series are generally no longer than a few years. A valid surrogate of $R_s$ measurement is the diurnal air-temperature range ($\mathrm{\Delta }T$); indeed, $\mathrm{\Delta }T$ is inversely proportional to cloudiness and therefore could be a good indicator of atmospheric transmittance. As opposed to $R_s$, daily maximum and minimum air temperatures are measured at many locations and their observations in developed countries began in the 19th century. For this reason, several models that permit $R_s$ indirect evaluation from air-temperature data have been suggested in the literature. The most famous models are the simple Hargreaves-Samani (HS) formula, many times recalibrated by the authors, and the Bristow-Campbell model, which has recently been improved. In this paper, the suitability of each proposed model is tested by comparing $R_s$ real data, recorded in 33 Sicilian agrometeorological stations in the period 2003–2008, typically representing the Mid-Mediterranean area, with $R_s$ estimates obtained by the models from $\mathrm{\Delta }T$ data. In addition, a regional relationship is obtained for the scale coefficient K(T) of the Hargreaves-Samani formula: the relationship found improves $R_s$ data prediction with respect to the original HS formulation.

Side weirs are commonly installed along the sides of open channels. Computation of water surface depth over side weirs is essential to determine their discharge. Analytical solutions for water surface profiles along a rectangular side weir are available only for triangular, rectangular and trapezoidal primary channels on the basis of constant specific energy. Given that circular channels are an important application in sewer systems, this research presents an elegant semianalytical solution (which involves incomplete elliptic integrals) for establishing the water surface profile along a rectangular side weir located in a circular. The solution is a useful computational tool for the evaluation and design of rectangular side weirs in open circular channels.