Journal of Irrigation and Drainage Engineering

Automatic inground irrigation is a common option for residential homeowners desiring high-quality landscapes in Florida. However, rapid growth is straining water supplies in some areas of the state. The first objective of this study was to document residential irrigation water use in the Central Florida ridge region on typical residential landscapes (T1). The second objective was to determine if scheduling irrigation by setting controllers based on historical evapotranspiration (ET) (T2) and reducing the percentage of turf area combined with setting the controllers based on historical ET (T3) would lead to reductions in irrigation water use. The time frame of this study was $30\text{months}$ beginning in January 2003. Irrigation accounted for 64% of the residential water use volume over all homes monitored during this project. The T1 homes had an average monthly water use of $149\mathrm{mm}∕\text{month}$ . Compared to the T1 homes, T2 resulted in a 30% reduction $(105\mathrm{mm}∕\text{month})$ , and T3 had a 50% reduction $(74\mathrm{mm}∕\text{month})$ in average monthly water use. Average monthly water use was significantly different $(p<0.001)$ across the three irrigation treatments. Setting the irrigation controllers to apply water according to seasonal demand resulted in significantly less irrigation water applied. In addition, increasing the proportion of landscape area from 23% (T1 and T2) ornamental plants irrigated with sprinklers to 62% and irrigated with micro-irrigation (T3) resulted in the largest reduction in irrigation water applied. Compared to T2 where only the irrigation controllers were adjusted, this additional decrease in irrigation water applied was a result of low volume application on only a portion of the landscaped beds where irrigation is only applied to the root zone of plants.

A $2\text{-}\text{year}$ study was conducted at a seepage-irrigated vegetable farm in south Florida to develop and evaluate an improved, soil moisture-based irrigation management practice that could potentially reduce irrigation water use, prevent water quality impairment, and maintain or improve crop yield. The improved practice reduced irrigation water use by 36% compared to the conventional irrigation management. Moreover, the improved practice also increased rainfall retention and decreased runoff events by lowering the water table $13\mathrm{cm}$ compared to the conventional practice. Total dissolved phosphorus (P) concentrations in groundwater were higher $(p<0.01)$ for the improved practice compared with the conventional practice in two of the three fields where ground water quality was monitored. Higher P concentrations for the improved practice were likely due to the dilution effect. Statistically, no differences $(p>0.05)$ were observed in groundwater nitrogen (N) ( $\mathrm{N}{\mathrm{O}}_x–\mathrm{N}$ , $\mathrm{N}{\mathrm{H}}_4–\mathrm{N}$ , and total dissolved N) concentrations between the improved and the conventional practices. Similarly, no statistical difference was observed in crop yield between the improved and the conventional practices, although the average total yield was higher for the improved practice. The improved practice also reduced the incidence of plant disease compared to the conventional practice which resulted in crop failure in some fields. Thus, use of the improved practice reduced irrigation water use without impacting crop yield.

Regression models for predicting daily pan evaporation depths from climatic data were developed using three multivariate approaches: multiple least-squares regression (MLR), principal components regression (PCR), and partial least-squares (PLS) regression. The objective was to compare the prediction accuracies of regression models developed by these three approaches using historical climatic datasets of four Indian sites that are located in distinctly different climatic regimes. In all cases (three approaches applied to four climatic datasets), regression models were developed using a part of the data and subsequently validated with the remaining data. Results indicated that although performances of the regression models varied from one climate to another, more or less similar prediction accuracies were obtained by all three approaches, and it was difficult to identify the best approach based on performance statistics. However, the final forms of the regression models developed by the three approaches differed substantially from one another. In all cases, the models derived using PLS contained the smallest number of predictor variables; between two to three out of a possible maximum of six predictor variables. The MLR approach yielded models with three to six predictor variables, and PCR models included all six predictor variables. This implies that the PLS regression models are the most parsimonious in terms of input data required for estimating $e_{\mathrm{pan}}$ from climate variables, and yet yield predictions that are almost as accurate as the more data-intensive MLR and PCR models.

Regional irrigation water-demand planning is utilized to establish appropriate cropping patterns and estimate irrigation water demand. Although optimization methods have been extensively adopted, uncertainties of meteorological conditions and the complexity of spatial contexts make developing explicit and structured decision making extremely difficult. Rather than generating a single optimal solution, decision makers prefer to generate several possible scenarios and compare results. This study proposes a novel spatial scenario-based planning framework, with a database, model base, and scenario-setting modules, to generate flexible spatial planning scenarios for improving irrigation water-demand planning. Possible demand planning scenarios for irrigation managers are discussed. A prototype of the proposed scenario-based framework is implemented on a geographic information system platform to assist in spatial decision making. Demand planning during a drought period for the Chia-Nan irrigation command area, the largest one in Taiwan, is adopted as a case study to demonstrate the proposed framework for spatial scenario analysis.

A simulation study was conducted with five soil amendments, viz., goat manure, coir pith, phosphogypsum, polyacrylamide, and a control at two rainfall intensities of 60 and $120\mathrm{mm}{\mathrm{h}}^{-1}$ under dry and wet soil conditions in a clay loam vertisol with the objective of identifying a superior soil amendment for maximum infiltration and suitable soil aggregate stability. The $F$ -test based on analysis of variance of infiltration data indicated that soil amendments, rainfall intensities, and soil conditions were significantly different from each other. Based on least significant difference test, polyacrylamide was found to be superior with a significantly higher infiltration, compared to all other amendments. An exponential model of infiltration over a time interval was calibrated for each soil amendment under dry and wet soil conditions. Based on the model, polyacrylamide and phosphogypsum were found to have a better soil aggregate stability compared to other soil amendments. The exponential model gave a significant predictability of instantaneous infiltration ranging from 0.75 to 0.99 under different situations. A grouping of treatments based on mean and coefficient of variation of infiltration in comparison with soil aggregate stability values indicated that polyacrylamide was superior under different situations in the study. Phosphogypsum was found to be the second best soil amendment with a relatively lower infiltration compared to polyacrylamide, but with a better soil aggregate stability compared to other soil amendments. Coir pith, goat manure, and control gave a significantly lower infiltration with a relatively higher variation compared to polyacrylamide and phosphogypsum, and also had a relatively lower soil aggregate stability under different situations examined in the study.

The original soil conservation service curve number (SCS-CN) technique is primarily used to transform daily rainfall into surface runoff by assuming the proportionality between retention and surface runoff based on a parameter referred to as curve number (CN). The conventional method does not take into account the temporal and spatial variability of curve number. In this paper, an attempt has been made to modify the existing SCS-CN model in two ways by varying the CN using antecedent moisture condition (designated as Model I), and by using antecedent moisture amount (designated as Model II). The daily moisture storage is updated based on varying the curve number and other hydrologic abstractions. These two different models are constructed to compute streamflow components: Direct surface runoff, base flow, and hydrological abstractions. These methodologies were successfully applied to daily data of catchments of Cauvery, Narmada, Ganga, and Ulhas Rivers, lying in different climatic regions of India, and the results were analyzed. Application of Model I to Hemavati (a tributary of River Cauvery, Karnataka State) data yielded maximum efficiency of 84% in calibration, and minimum efficiency of 54% with Ramganga (a tributary of River Ganga, Uttaranchal State) data, whereas Model II showed maximum efficiency of 85% in Hemavati catchment and minimum efficiency of 64% in Kalu catchment (a tributary of River Ulhas, Maharashtra State). Model II performed better than Model I on all four catchments. It is found that the proposed models reasonably simulate the catchment response and these SCS-CN-based models are applicable to complex natured watersheds.

The development of a chance-constrained optimization-based model for Muskingum model parameter estimation is presented. The desired Muskingum model parameters are to be useful to give the flood forecast in terms of expected flood for given limits of tolerance and probability of occurrence. When errors of observation occur, an error term is added to a mean flow to give the actual flow. The developed model minimizes the sum of squares of difference between the actual observed and computed outflows in order to determine the Muskingum model parameters. The constraints are the chance-constrained Muskingum flow routing equations. The first-order second moment method of chance-constrained optimization is used to develop the optimization model. The developed model is demonstrated for four scenarios of Muskingum model parameter estimation. The results show that, given the allowable limits of error in Muskingum model parameters, the developed model has a capability to give expected values of Muskingum model parameters when the historic data that are used for the parameter estimation process contain a specified amount of observation errors and obey a specified probability distribution. The chance-constrained optimization-based model for Muskingum model parameter estimation results into Muskingum model parameters that can give flood forecasts such that the forecasted flood allows the provision of better flood damage mitigation facilities.

A cutthroat flume is commonly used as flow measuring device for open-channel flow due to ease of fabrication and installation. In most of the cases it is difficult to calibrate the flume in the field. Therefore, accurate relation between discharge and upstream head applicable for all sizes of cutthroat flume is needed. Seven different sizes of cutthroat flumes, having different length to throat width ratios, are fabricated and tested in the laboratory under free-flow condition. Selecting groups of different variables describing flow through a cutthroat flume number of dimensionless parameters are formed. Regression analysis of experimental data is carried out between all possible combinations of pairs of dimensionless parameters and the pair giving the best correlation is selected. Using the selected pair, relation between dimensionless parameters of discharge and head is developed. The relation is simple and convenient to use and, at the same time, more accurate compared to methods available in literature for prediction of discharge.

The controlled ponding of water over level terrain in basin irrigation or wetland flooding is described quantitatively as a three-phase process. During the first phase, water is applied at a known rate until ponding emerges at the time of ponding initiation. In the second phase, water continues to be applied at the same rate until a desired ponded depth is attained. In the third phase, water is applied to maintain the desired ponded depth during an arbitrarily long period. The desired ponded depth is maintained by adjusting the water-application rate to equal the infiltration rate plus the evaporation rate. The time of ponding, the ordinary differential equations (ODEs) governing cumulative infiltration during the second and third phases, and the water-application rate during the third phase are derived in this work using an extended Green-and-Ampt formulation of infiltration. Computational examples illustrate the solutions of the derived ODEs and their application in the control of basin irrigation and wetland flooding.

A new accurate approximation for the well function for instantaneous charge of water for large diameter wells is proposed. Using this approximation with an optimization method, storage coefficient and transmissivity are estimated from slug test data on large diameter wells. The proposed method is void of the subjectivity of curve matching procedure.