| Popis: |
The topic of evaporation estimates is fundamental to land-surface hydrology. In this study, FAO-56 Penman–Monteith equation (FAO56–PM), multiple stepwise regression (MLR), and Kohonen self-organising map (K–SOM) techniques were used for the estimation of daily pan evaporation (Ep) in three treatments, where C was the standard class A pan with top water, S was a pan with sediment covered bottom, and SM was class A pan containing submerged macrophytes (Myriophyllum spicatum, Potamogeton perfoliatus, and Najas marina), at Keszthely, Hungary, in a six-season experiment, between 2015 and 2020. The modelling approach included six measured meteorological variables. Average Ep varied from 0.6 to 6.9 mm d−1 for C, 0.7 to 7.9 mm d−1 for S, and from 0.9 to 8.2 mm d−1 for SM during the growing seasons studied. Correlation analysis and K–SOM visual representation revealed that air temperature and global radiation had positive correlation, while relative humidity had a negative correlation with the Ep of C, S, and SM. The results showed that the MLR method provided close compliance (R2=0.58–0.62) with the observed pan evaporation values, but the K–SOM method (R2=0.97–0.98) yielded by far the closest match to observed evaporation estimates for all three pans. To our best knowledge, no similar work has been published previously using the three modelling methods for seeded pan evaporation estimation. The current study differs from previous evaporation estimates by using neural networks even with those pans containing sediments and submerged macrophytes. Their evaporation will be treated directly by K–SOM, in which the modelling is more than the simple Ep of a class A pan filled with clean tap water. |
