Search Results (4)

The variability of natural parameters in border fields is one of the main factors leading to poor irrigation performance. To explore the impact of natural parameter variation on border irrigation performance, a four-year field irrigation experiment was conducted in this study, and the obtained parameters were combined with the WinSRFR V4.1 model to simulate the advance–recession process under different discharge scenarios. Based on the observed and simulated data, the influence of natural parameter variation on the flow process of constant discharge irrigation was analyzed, and thus the optimal observation points for advancing and discharge regulation strategy were further established. The results indicated that in a constant discharge border irrigation system, the irrigation performance index showed a trend of first increasing and then decreasing with the increase in discharge, and the variation range of the reduced section was smaller than that of the increased section. Therefore, the risk of inefficient irrigation caused by flow fluctuations could be reduced to a certain extent by increasing the discharge. Additionally, variability in natural parameters also caused the water advance time to deviate gradually from expectations, and the deviation would be obvious when the flow advanced to the point of 40 m. The adjustment range of the optimal regulation discharge q_M was greater than the corresponding optimal constant discharge q_D2 under natural parameter variations. In the ideal situation of uniform natural parameters within the border, the optimal discharge regulation scheme could improve the application efficiency, irrigation uniformity, and water storage efficiency to 97.3%, 95.5%, and 96.9%, respectively. The results of this study can provide a theoretical basis for the development of automatic regulation of border irrigation systems. Full article

Quantitatively analyzing models’ uncertainty is essential for agricultural models due to the effect of inputs parameters and processes on increasing models’ uncertainties. The main aim of the current study was to explore the influence of input parameter uncertainty on the output of the well-known surface irrigation software model of WinSRFR. The generalized likelihood uncertainty estimation (GLUE) framework was used to explicitly evaluate the uncertainty model of WinSRFR. The epistemic uncertainties of WinSRFR furrow irrigation simulations, including the advance front curve, flow depth hydrograph, and runoff hydrograph, were assessed in response to change key input parameters related to the Kostiakov–Lewis infiltration function, Manning’s roughness coefficient, and the geometry cross section. Three likelihood measures of Nash–Sutcliffe efficiency (NSE), percentage bias (PBIAS), and the coefficient of determination (R²) were used in GLUE analysis for selecting behavioral estimations of the model outputs. The uncertainty of the WinSRFR model was investigated under two furrow irrigation system conditions, closed end and open end. The results showed the likelihood measures considerably influence the width of uncertainty bounds. WinSRFR outputs have high uncertainty for cross section parameters relative to soil infiltration and roughness parameters. Parameters of soil infiltration and roughness coefficient play an important role in reducing the uncertainty bound width and number of observations, especially by filtering non-behavioral data using likelihood measures. The simulation errors of advance front curve and runoff hydrograph outputs with a PBIAS function were relatively lower and stable compared with other those of the likelihood functions. The 95% prediction uncertainties (95PPU) of the advance front curve were calculated to be 87.5% in both close-ended and open-ended conditions whereas, it was 91.18% for the runoff hydrograph in the open-ended condition. Additionally, the NSE likelihood function more explicitly determined the uncertainty related to flow depth hydrograph estimations. The outputs of the model showed more uncertainty and instability in response to variability in soil infiltration parameters than the roughness coefficient did. Therefore, applying accurate field methods and equipment and proper measurements of soil infiltration is recommended. The results highlight the importance of accurately monitoring and determining model input parameters to access a suitable level of WinSRFR uncertainty. In conclusion, considering and analyzing the uncertainty of input parameters of WinSRFR models is critical and could provide a reference to obtain realistic and stable furrow irrigation simulations. Full article

Soil moisture sensors and hydraulic modeling play a vital role in managing surface irrigation systems. Crop water productivity can be improved by managing the inflow cut-off time and optimizing the other field scale measurements. As such, hydraulic modelling and field experiments were carried out at the University of Agriculture Faisalabad-Pakistan. The soil moisture sensor (SEN-13322) and the WinSRFR model were used for this purpose. In total, nineteen treatments including eighteen simulated treatments and one conventional treatment were designed at two levels of discharge (Q₁:0.0025 and Q₂:0.0035 m³s⁻¹), at three sensor positions (S₁:55%, S₂:65%, and S₃:75%) across the field length, as well as with three different border widths (B₁:6.4m, B₂:8.5m, and B₃:10.7m) after successful sensor and model calibration during the two growing seasons of 2016–2017 and 2017–2018. The results revealed a significant difference between the means and the treatment T₁₀ i.e., Q₂S₁B₁ that were found to be highly efficient and uniform. Full article

Improving traditional surface irrigation technology and vigorously promoting water-saving surface irrigation are important ways to improve the efficiency of water resource utilization. In our study, we propose a new technology of surface irrigation, micro-furrow irrigation, which combines the advantages of furrow irrigation and border irrigation. The objective of this experiment was to evaluate the effects of micro-furrow depth and bottom width on surface water flow and irrigation performance. Field experiments were conducted from 2019 to 2020 in Zhengzhou City, northern China. This work designed three bottom widths, BW1 (18 cm), BW2 (12 cm), and BW3 (6 cm), respectively, and three depths, D1 (15 cm), D2 (10 cm), and D3 (5 cm), respectively. Moreover, border irrigation was set as control treatment (CK). Additionally, field experiments were validated and simulated using the WinSRFR 5.1 model (Arid-Land Agricultural Research Center, USA). The results showed a significant negative correlation between depth and advance time and between depth and recession time. However, no significant correlation was found between bottom width and advance time, nor between bottom width and recession time. The advance times of micro-furrow irrigation were 1.23–4.77 min less than those of border irrigation. Concerning irrigation performance, compared to that of border irrigation, the application efficiency and distribution uniformity increased by 8–30% and −5–18%, respectively. However, the requirement efficiency decreased by 0–40%. Compared to that of border irrigation, the irrigation quota increased 21.61% under BW3D3 but decreased by 10.46–57.94% under other treatments. Therefore, micro-furrow irrigation can meet irrigation requirements despite low irrigation quota. Comprehensively considering the advance time, recession time, irrigation performance, and irrigation quota, we recommend a micro-furrow shape with a depth of 10 cm or 15 cm and bottom width of 6 cm. Full article