Journal of Irrigation and Drainage Engineering

The curve number (CN) method is a popular technique for estimating a rainstorm event’s direct runoff (Q from its storm depth P). It relies on the parameter CN, a lumped expression of a watershed’s potential hydrologic response. Despite its widespread use over a variety of climates worldwide, the method’s primary documentation does not consider the influence of season on CN or runoff. Accordingly, CN analysis was done on 13 Sicilian basins, using rainfall-runoff data for multiday events from 1940 to 1997, with a mean record length of 32 years. Watershed CNs were determined from these data by the asymptotic fitting method and ordered data. These watersheds showed a standard asymptotic behavior with differences in asymptotically fitted CN (${\mathrm{CN}}_{\infty }$) between dry season and wet season. The found CNs were significantly higher (about 18 CNs) in the wet season (${\mathrm{CN}}_{\mathrm{w}}$) than in the dry season (${\mathrm{CN}}_{\mathrm{d}}$). However, the wet season CNs were not significantly different than the CNs for the pooled data, ${\mathrm{CN}}_{\mathrm{ann}}$. No statistically significant relationships among seasonal CNs and watersheds’ morphoclimatic and hydrologic characteristics were found, whereas the seasonal variation of watersheds’ CNs seems linked to the seasonality of rainfall.

The traditional irrigation methods used in rice cultivation result in a huge quantity of water loss. Exploring ways to produce more rice with less water is essential for food security. A two-season field study was initiated to compare the effect of three different proposed techniques of continuous furrow irrigation (FI) with conventional flooded irrigation (CI) on the water use efficiency (WUE) of two rice cultivars. The FI system consisted of 50-cm-wide raised beds with irrigation furrows that were 25–30 cm wide and 15–20 cm in depth. Seedlings of two rice cultivars, early-maturing (Shaka 104) and late-maturing (Giza 178), were manually transplanted (five to six plants/hill) at the same plant density ($25–27\mathrm{hills}{\mathrm{m}}^{-2}$) in different FI configurations: (1) seedlings transplanted 15 cm apart in rows along on both sides of the raised bed (FI1), (2) seedlings transplanted 22 cm apart in a center row in the raised bed and in single rows on each side of the raised bed (FI2), and (3) seedlings transplanted 22 cm apart in rows on both sides of the raised bed and on the furrow (FI3). FI treatments were compared with conventional flooded irrigation (CI) with seedlings spaced 20 cm apart at the same plant density. To maintain a water depth of 10–15 cm in the furrow, irrigation was given for furrow only. The results showed that for both cultivars, FI was superior to CI in terms of saved water, rough rice yield and, thus, WUE. The results also indicated that the highest WUE was achieved using FI3, while the highest grain yield was reached using FI1. The improved WUE (146.44%) in the FI3 system is attributed to the significant water use reduction (56.8%). Among FI techniques, both FI1 and FI3 represent good options for growers to optimize yield while using significantly less water.

In this study, radial basis neural network (RBNN), self-organizing map neural network (SOMNN), and multiple linear regression (MLR) are used for the estimation of daily pan evaporation at Pantnagar, situated in the foothills of the Himalayas, Uttarakhand, India. Daily climatic data include minimum and maximum air temperatures, relative humidity measured in the morning and afternoon, wind speed, and sunshine hours. Pan evaporation data were used for model calibration and validation. The combination of significant input variables for RBNN, SOMNN, and MLR models were decided using the gamma test. The results obtained by RBNN, SOMNN, and MLR models were compared with climate-based empirical models such as Penman, Stephens-Stewart, Griffiths, Christiansen, Priestley-Taylor, and Jensen-Burman-Allen models on the basis of root-mean squared error (RMSE), coefficient of efficiency (CE), and correlation coefficient ($r$). The results indicate that the performance of RBNN model ($\mathrm{RMSE}=1.024\mathrm{mm}/\mathrm{day}$, $\mathrm{CE}=0.874$, and $r=0.934$) with six input variables was found to be superior in estimating daily pan evaporation at Pantnagar.

The Analytical Land Atmosphere Radiometer Model (ALARM) has been tested in several countries with observed evapotranspiration (ET) obtained from different ground-based methods, but none from a lysimeter, which is considered the most accurate ground-based method of measuring ET. The first objective of this work was to test ALARM against lysimeter ET data. The second objective was to compare the performance of ALARM with Surface Energy Balance Algorithm for Land (SEBAL), the most widely used model for estimating ET from remotely sensed data. An experiment was conducted on a 1.5 ha potato field in the Jordan Valley that has a weighing lysimeter located midfield. Potatoes were planted on the January 14, 2016, and harvested on the May 10, 2016. Three images from Landsat 8 were used to compute ALARM and SEBAL ET. The measured daily ET of the potatoes ranged from $5\mathrm{mm}{\mathrm{d}}^{-1}$ on March 22 to $7.5\mathrm{mm}{\mathrm{d}}^{-1}$ on April 23. ALARM underestimated the daily measured ET by 8% on March 22, 16% on April 7 and 33% on April 23 while SEBAL underestimated the daily measured ET by 4% on March 22, 15% on April 7 and 37% on April 23. Some modifications were made to improve the accuracy of ALARM and SEBAL ET estimation. The modified ALARM overestimated ET by 1% on March 22 and underestimated ET by 6 and 22% on April 7 and April 23, respectively. The modified SEBAL underestimated ET by 0.7, 10, and 35% on March 22, April 7, and April 23, respectively. These results demonstrated that the modified ALARM provides reasonable ET estimates that are superior to those obtained from SEBAL or modified SEBAL.

This paper reviews and summarizes the continued development of satellite-based estimates of insolation (incoming solar radiation), reference evapotranspiration (RET), and potential evapotranspiration (PET), computed for the State of Florida at 2-km resolution for the period from June 1, 1995, to December 31, 2015, which spans over 20 years. Insolation is a primary driver of evapotranspiration in Florida. Insolation maps generated from Geostationary Operational Environmental Satellite (GOES) imagery were used to compute PET and RET, calibrated to ground-based pyranometers. Uncalibrated model results over the 20-year period indicate a station-averaged model error of $2.1\mathrm{MJ}{\mathrm{m}}^{-2}{\mathrm{day}}^{-1}$ (12% of observed). Calibration reduced model errors to $1.6\mathrm{MJ}{\mathrm{m}}^{-2}{\mathrm{day}}^{-1}$ (9% of observed) and also removed temporal, seasonal, and satellite-sensor-related biases. As a means of demonstrating the integrity of these products, overviews of the GOES-derived insolation, PET, and RET fields are presented in terms of spatial and temporal patterns over Florida. Hydrologic features that create regional weather patterns and affect PET and RET include Lake Okeechobee and the Atlantic Gulf Stream current. Selected scientific and water-management applications are summarized, as well as planned enhancements to the insolation–ET algorithm.

Floating bridges are permanent or temporary bridges supported by pontoons. This research was initiated to experimentally study the effect of floating bridges on flow characteristics and bed morphology. Two hundred and twenty-nine experiments were carried out and categorized into two main sets; the first was static with a constant submergence ratio, and the second was a dynamic set with varied submergence ratios. The experiments were conducted under different pontoon dimensions, flow depths, submergence ratios, and constant discharge. Empirical equations were developed to predict the scour parameters and the flow characteristics. The results revealed that the Froude number and submergence of the pontoon are the dominant parameters that affect the bed morphology and flow characteristics.

The use of shallow saline groundwater may alleviate the shortage of freshwater resources in the Low Plain around the Bohai Sea in China. The soil salt levels and the responses of cotton seed emergence, growth, and yield to continuous irrigation with saline water were investigated from 2012 to 2014. Four salinity levels were tested: 1.3 (S1, control), 5.4 (S2), 8.8 (S3), and $12.4\mathrm{dS}/\mathrm{m}$ (S4). Irrigation was initiated when the soil moisture content in the 0–60 cm layer was below 60% of field capacity. Soil salinity within the cotton root zone increased with an increase in irrigation water salinity, but the effect was not cumulative over the experimental period to because of the leaching effect associated with summer rainfall. The cotton emergence percentage decreased as the irrigation water salinity increased. Compared with S1, the average seedling establishment rates over the three seasons examined decreased by 2.1, 5.1, and 20.2% with S2, S3, and S4, respectively. Large variations in seedling establishment rates were observed between the seasons, and this variability increased with increase in irrigation water salinity. Maximum plant height, leaf area index, and number of fruiting shoots decreased by S3 and S4, and some of these differences were statistically significant ($p<0.05$). Cotton growth was not negatively affected until irrigation water salinity reached $8.8\mathrm{dS}/\mathrm{m}$. Cotton yields were close to that produced by freshwater irrigation (S1) when the irrigation water salinity was below $8.8\mathrm{dS}/\mathrm{m}$, but it significantly ($p<0.05$) decreased by 7.3% in 2012, 11.1% in 2013, and 8.6% in 2014 when the irrigation water salinity was $12.4\mathrm{dS}/\mathrm{m}$, mainly due to the elevated soil osmotic potential. These results show that cotton can be irrigated successfully with water of $\le 8.8\mathrm{dS}/\mathrm{m}$. Furthermore, the mitigation of salt accumulation by heavy rainfall indicates that long-term use of saline water irrigation is feasible in this region.