Journal of Irrigation and Drainage Engineering

The crop water stress index (CWSI) is a reliable indicator of water status in plants and has been utilized for stress monitoring, yield prediction, and irrigation scheduling. Despite this, however, its use is limited because its estimation requires baseline temperatures under similar environmental conditions, which can be problematic. In this study, field crop experiments were performed to monitor the canopy temperature of Indian mustard (Brassica juncea) from crop development through harvest under different irrigation treatment levels during the 2017 and 2018 growing seasons. Kohonen self-organizing map (KSOM), feed-forward neural network (FFNN), and multiple linear regression (MLR) models were developed for estimating the well-watered canopy temperature ($T_{c\text{-}ww}$) using air temperature and relative humidity as input predictor variables. Comparisons were performed between model-estimated and measured $T_{c\text{-}ww}$ values. The findings indicate that the KSOM-modeled values presented a better agreement with the measured values in comparison to MLR- and FFNN-based estimates, with $R^2$ values of 0.978, 0.924, and 0.923 for KSOM, MLR, and FFNN, respectively, during model validation. The dry canopy temperature was estimated to be air temperature plus 2°C. The CWSI computed using KSOM-based estimates of $T_{c\text{-}ww}$ was compared with the CWSI obtained from measured values of $T_{c\text{-}ww}$. The results suggest a significant potential of KSOM for reliable estimation of the $T_{c\text{-}ww}$ for calculating the CWSI that can be automated for developing precision irrigation systems.

Tomatoes are popular worldwide and represent a high water-dependent horticultural crop cultivated both in open fields and greenhouses. Several irrigation management strategies are currently practiced in greenhouse tomato production. Among them, deficit irrigation has been generally applied in areas where access to fresh water is difficult or expensive. The overall objective of this review is to synthesize studies related to deficit irrigation for greenhouse tomato cultivation and to explore its strengths, limitations, and potential and future outlook. The effects of deficit irrigation on water productivity, yields, and crop quality parameters when planted in different soil types using different cultivars were reviewed. We infer that deficit irrigation can result in decreased tomato yields and improved water productivity and crop quality parameters at defined levels of irrigation deficiency. The effects of deficit irrigation on tomato yield, water productivity, and crop quality were found to be cultivar specific. Furthermore, the same cultivar, when subjected to different deficit irrigation techniques, demonstrates different yield and quality responses.

Water control operations in South Florida often involve the estimation of transition flow at low-head gated spillways for flood control, water supply, and water quality compliance purposes. Approaches to nontransitional flow computation have been extensively investigated, and their accuracy and applicability have been rigorously verified. However, computational algorithms for transitional flows have not generally been accepted by engineering practitioners; arguments even exist with regard to the criteria used to delineate different transitional flow regions. In this study, a generalized discharge rating equation for spillways is proposed that, in contrast to the classical flow-type-separation approaches, can estimate both transitional and nontransitional flow rates. Several verification tests were performed, including a consistency analysis of the proposed equation compared to classical flow equations for different flow conditions and its verification with field and laboratory data. The performance of the proposed equation in transitional flow regions was further evaluated using computational fluid dynamics (CFD) simulations. The discharge estimates agree well with measurements and classical rating equations, with overall computation absolute relative errors of less than 8%. Finally, the inherent limitations of traditional spillway flow-type-separation approaches were analyzed, and the analysis suggested that the generalized equation is a better alternative to overcome the hard-to-define transition thresholds and resolve the discharge discontinuity issue.

Settling basins are generally used as sediment removal structures in which flow velocity is reduced, resulting in surplus settlement of sediment particles. The accuracy of the available empirical equations for sediment removal efficiency is checked using data available in the literature. The existing relationships of removal efficiency were not found to yield satisfactory results. Therefore, the data were reanalyzed and new models were developed using nonlinear regression and the group method of data handling (GMDH). On the basis of various performance parameters, it was observed that the proposed nonlinear regression model had the highest accuracy compared with other available relationships. It was also observed that the efficiency estimated using the GMDH model ($R=0.960$, $AAD=14.698$, and $RMSE=0.094$) was more accurate than that estimated by the regression model ($R=0.863$, $AAD=30.675$, and $RMSE=0.171$). Sensitivity analysis indicated that the ratio of particle fall velocity to average flow velocity in the basin was the most effective parameter for sediment removal efficiency. This work will help in quantifying and subsequently improving the management of surplus sediment in canals.

Clément’s first formula was initially intended to determine discharge in on-demand pressurized irrigation networks; nevertheless, it can also be applied to canal systems because the irrigation process is similar. Discharges at a secondary branch of the Canal of Aragon and Catalonia (Spain) with an arranged schedule of 24-h duration and 1-day delay were analyzed. Since the canal was high capacity and, as such, no request restrictions were necessary, Clément’s formula could potentially simulate discharges in this case. The canal’s intakes were modeled assuming normally distributed variables represented by their average and standard deviation. Clément’s first formula accurately fit the observed cumulative probability curve of canal discharge at different sections. The relationship between the average intake’s discharge and the product of the specific continuous discharge by the service area was also assessed. Clemmens’ arranged delivery schedule equation does not apply to this case because the canal delivers pressurized irrigation and high turnout opening probabilities occur.