A field experiment was conducted with soybean to observe evapotranspiration (ET) and crop water stress index (CWSI) with three watering levels at Keszthely, Hungary, during the growing seasons 2017–2020. The three different watering levels were rainfed, unlimited, and water stress in flowering. Traditional and converted evapotranspirometers documented water stress levels in two soybean varieties (Sinara, Sigalia), with differing water demands. ET totals with no significant differences between varieties varied from 291.9 to 694.9 mm in dry, and from 205.5 to 615.6 mm in wet seasons. Theoretical CWSI, CWSIt was computed using the method of Jackson. One of the seasons, the wet 2020 had to be excluded from the CWSIt analysis because of uncertain canopy temperature, Tc data. Seasonal mean CWSIt and Tc were inversely related to water use efficiency. An unsupervised Kohonen self-organizing map (K-SOM) was developed to predict the CWSI, CWSIp based on easily accessible meteorological variables and Tc. In the prediction, the CWSIp of three watering levels and two varieties covered a wide range of index values. The results suggest that CWSIp modelling with the minimum amount of input data provided opportunity for reliable CWSIp predictions in every water treatment (R2 = 0.935–0.953; RMSE = 0.033–0.068 mm, MAE = 0.026–0.158, NSE = 0.336–0.901, SI = 0.095–0.182) that could be useful in water stress management of soybean. However, highly variable weather conditions in the mild continental climate of Hungary might limit the potential of CWSI application. The results in the study suggest that a less than 450 mm seasonal precipitation caused yield reduction. Therefore, a 100–160 mm additional water use could be recommended during the dry growing seasons of the country. The 150 year-long local meteorological data indicated that 6 growing seasons out of 10 are short of precipitation in rainfed soybean.

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A field experiment was carried out in plant litter decomposition at three sites of the Balaton System (Balaton — Kis Balaton wetland — Zala Mouth) differing in their environment type during winter 2019/2020. The largest freshwater shallow lake in Central Europe (Carpathian Basin) is the Balaton, with a surface area of about 600 km2 and an average depth of 3.25 m. Right around the lake, a nutrient filtering system, the Kis-Balaton wetland, is functioning to avoid water deterioration and eutrophication. The aim of the study was to investigate crop-weather relations in two sample species, the widely distributed native P. australis and the allied S. canadensis incubated beneath the water using leaf-bag technique to characterise plant organ decomposition. Based on our results, the most consistent meteorological variable regarding decomposition process was global radiation (r =  − 0.62* to − 0.91**; r: correlation coefficient; * and ** mean that correlations are significant at the 0.05 and 0.01 levels), in each treatment. In modelling the decomposition process, out of eight meteorological variables, only the daily mean air temperatures and humidity were excluded from regression equations. On dominatingly windy days, with the increase in water temperature of the Zala Mouth, the sensitivity of the decomposition of S. canadensis litter tended to decrease as compared to P. australis. The remaining litter masses were in a Kis-Balaton > Balaton > Zala order, contrasting the water temperature gradient that decreased from the Zala to the Kis-Balaton wetland under wind-dominated conditions. Considering all sampling places in three aquatic ecosystems, there was a 2.2 and a 2.7% daily mean detritus mass loss in P. australis and S. canadensis, respectively. We concluded that the invasive S. canadensis litter decomposed more quickly than those of native P. australis, irrespective to sampling site. Increase in winter water temperature significantly promoted the litter decomposition of both plant species. The originality of the study is that it quantifies the litter decomposition for an Eastern European wetland, during wintertime.

Abstract. 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.

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