Journal of Irrigation and Drainage Engineering

Two-dimensional shallow water and advection-dispersion equations were applied to describe the surface water and solute transport processes in basin fertigation. Then the fully implicit temporal solution was constructed for all terms of the governing equations in triangular spatial grid and an iterative coupled model for basin fertigation was developed. Furthermore, the lower-upper matrix decomposition and symmetric Gauss-Seidel iteration solution methods were applied to construct a noniterative coupled model for basin fertigation. The convergence of the simulated results with decreasing time step was exhibited by the dam-break test. Furthermore, the performance comparison was analyzed on the basis of three basin fertigation experiments. The results show that the noniterative coupled model presents higher efficiency than and accuracy similar to the iterative coupled model. Thus, the noniterative coupled model was proposed for the management and design of basin fertigation. Finally, the noniterative coupled model was applied to obtain the diagram of the relationship between fertilizer application time and uniformity.

The aim of this study was to improve streamflow predictability using the soil and water assessment tool (SWAT) model for baseflow estimation in a steeply sloped watershed. The two objectives were (1) to appraise streamflow predictions using SWAT for different slope magnitude and slope lengths in hydrologic response units (HRUs); and (2) to analyze baseflow contribution to streamflow through a baseflow separation method. To achieve these objectives, the SWAT model and the web-based hydrograph analysis tool (WHAT) system were implemented for five scenarios, comprising various slope magnitudes and slope length calibrations in the geologically distinct Haean-myeon watershed in South Korea. The results showed that SWAT streamflow predictions differed significantly when SWAT’s default slope magnitude and slope length was used versus the observed slope magnitude and slope length for baseflow estimation. Moreover, better prediction occurred when a representative slope length based on the observation was used rather than SWAT’s default slope magnitude and slope length. In this context, the effect of slope magnitude and slope length on baseflow and streamflow using SWAT was illustrated. Therefore, proper slope magnitude and slope length characterization will significantly improve the prediction ability of the SWAT model.

The adoption of the Penman-Monteith reference evapotranspiration (ETo) method necessitates the use of climatic data and parameters measured above a reference surface (e.g., reference grass), which is rarely available across agro-ecosystems in all countries. In such cases, alternatively, climate data collected above other vegetation surfaces can be potential sources of data for ETo estimations. The objective of this study was to determine whether microclimatic data measured above irrigated lowland rice can be used in estimating ETo using the Penman-Monteith model in conditions where climatic data measured in reference weather stations do not exist. Maximum and minimum air temperatures (Tmax and Tmin, respectively) and maximum and minimum relative humidity (RHmax and RHmin, respectively) were measured above a rice canopy in 2014, 2015, and 2016, when a full data set was also collected above well-maintained grass canopy, which was 100 m from the rice research plots. Daily average Tmin and Tmax for rice and Tmin and Tmax for grass were linearly and strongly correlated with regression slopes of 1.0085 and 0.9829, $R^2$ greater than 0.83, and root-mean square error (RMSE) of 1.30 and 1.53°C for Tmin and Tmax, respectively. Dew point temperature (Td) varied from $-3.73$ to 33.44°C over grass and from $-0.96$ to 34.26°C above rice canopy, with an average of 19.32 and 21.10°C above grass and rice, respectively. The observed RMSE and mean bias error (MBE) for RHmax and RHmin were 4.1% and 4.8% and 2.2% and 4.1%, respectively. The relative humidity difference of 3.2% was observed above the rice canopy. RHmin, RHmax, and RHmean measured over the rice canopy were linearly correlated with those measured over grass with regression slopes of 1.13, 1.03, and 1.06, respectively, and $R^2$ values of 0.98, 0.96, and 0.98, respectively. The vapor pressure deficit (VPD) difference was 9.4% greater over the grass canopy and averaged 2.15 kPa, while it averaged 1.97 kPa over rice with a very low RMSE of 0.27 kPa between VPD values measured above the two surfaces. The estimated net radiation (Rn) values above grass and rice canopies were highly correlated, with a regression slope of 1.017, $R^2$ of 0.9976 and a RMSE as low as $0.25\mathrm{MJ}/{\mathrm{m}}^2$, which would result in only $0.1\mathrm{mm}/\mathrm{d}$ of water. Daily ETo computed using the weather data measured above grass and flooded rice canopies showed that daily ETo-grass varied from 1.66 to 10.78 mm and averaged 5.38 mm. Similarly, daily ETo-rice varied from 1.53 to 10.16 mm, averaging 5.21 mm. The MBE between ETo-grass and ETo-rice was $0.17\mathrm{mm}/\mathrm{day}$ and the RMSE was as low as $0.24\mathrm{mm}/\mathrm{day}$, representing a relative error of only 4.5%. There was strong agreement between ETo-rice and ETo-grass and the simple linear regression slope between ETo-rice and ETo-grass was 0.943, 0.966, and 0.977 in 2014, 2015, and 2016, respectively, with an $R^2\ge 0.98$. Overall, it was determined that $\mathrm{ETo}\text{-}\mathrm{rice}=0.9687$ ETo-grass with an $R^2$ of 0.99. The good fit between ETo-rice and ETo-grass demonstrated the possibility of using weather data measured above a rice canopy to compute ETo as a viable alternative for data sources in conditions where grass-reference weather stations are not available.