Search Results (4)

The present study, carried out in Gujarat (India) between 2005 and 2021, aims to prepare dew and rain maps of Gujarat over a long period (17 years, from 2005 to 2021) in order to evaluate the evolution of the potential for dew and rain in the state. The ratio of dew to precipitation is also determined, which is an important metric that quantifies the contribution of dew to the overall water resources. Global warming leads, in general, to a reduction in precipitation and non-rainfall water contributions such as dew. The study shows, however, a rare increase in the rainfall and dew condensation, with the latter related to an increase in relative humidity and a decrease in wind amplitudes. Rain primarily occurs during the monsoon months, while dew forms during the dry season. Although dew alone cannot resolve water scarcity, it nonetheless may provide an exigent and unignorable contribution to the water balance in time to come. According to the site, the dew–rain ratios, which are also, in general, well correlated with dew yields, can represent between 4.6% (Ahmedabad) and 37.2% (Jamnagar). The positive trend, observed since 2015–2017, is expected to continue into the future. Full article

Typically, fruit cracking in sweet cherry is associated with the occurrence of free water at the fruit surface level due to direct (rain and fog) and indirect (cold exposure and dew) mechanisms. Recent advances in close range remote sensing have enabled the monitoring of the temperature distribution with high spatial resolution based on light detection and ranging (LiDAR) and thermal imaging. The fusion of LiDAR-derived geometric 3D point clouds and merged thermal data provides spatially resolved temperature data at the fruit level as LiDAR 4D point clouds. This paper aimed to investigate the thermal behavior of sweet cherry canopies using this new method with emphasis on the surface temperature of fruit around the dew point. Sweet cherry trees were stored in a cold chamber (6 °C) and subsequently scanned at different time intervals at room temperature. A total of 62 sweet cherry LiDAR 4D point clouds were identified. The estimated temperature distribution was validated by means of manual reference readings (n = 40), where average $R^2$ values of 0.70 and 0.94 were found for ideal and real scenarios, respectively. The canopy density was estimated using the ratio of the number of LiDAR points of fruit related to the canopy. The occurrence of wetness on the surface of sweet cherry was visually assessed and compared to an estimated dew point ($Y_{dew}$) index. At mean $Y_{dew}$ of 1.17, no wetness was observed on the fruit surface. The canopy density ratio had a marginal impact on the thermal kinetics and the occurrence of wetness on the surface of sweet cherry in the slender spindle tree architecture. The modelling of fruit surface wetness based on estimated fruit temperature distribution can support ecophysiological studies on tree architectures considering resilience against climate change and in studies on physiological disorders of fruit. Full article

As a non-precipitation water source, dew is important for plant and animal survival and crop production in arid and water-scarce areas. This study assessed the amount of dew in a dry zone in a long-term (2016 to 2022) field observation experiment at the Ansai Experimental Station, a typical loess hilly area in China. Dew primarily occurred in summer and autumn, with a frequency of >50%. The average annual dew amount was 29.20 mm, with an average annual rainfall of 641.8 mm. The average annual dew-to-rain ratio was 4.58%, and the average annual number of dew days was 143.6 d/a. The surface soil moisture content increased by approximately 1.02% with increasing dew amounts. The change in the soil moisture at a 5 cm depth was 0.14% on average and lagged substantially by 1 h. Using the Beysens model, the annual estimated and measured dew amounts in 2022 were 25.27 and 29.84 mm, respectively, and the annual normalized root mean square deviation (NRMSD) was 0.17. Thus, the Beysens model evaluated the dew amount in the study area well at the monthly and annual scales. The quantification of dew resources can provide support for the development, utilization, and management of limited water resources in arid areas, promoting more accurate decision-making for the sustainable development of water resources in the future. Full article

The precipitation phase (PP) affects the hydrologic cycle which in turn affects the climate system. A lower ratio of snow to rain due to climate change affects timing and duration of the stream flow. Thus, more knowledge about the PP occurrence and drivers is necessary and especially important in cities dependent on water coming from glaciers, such as Quito, the capital of Ecuador (2.5 million inhabitants), depending in part on the Antisana glacier. The logistic models (LM) of PP rely only on air temperature and relative humidity to predict PP. However, the processes related to PP are far more complex. The aims of this study were threefold: (i) to compare the performance of random forest (RF) and artificial neural networks (ANN) to derive PP in relation to LM; (ii) to identify the main drivers of PP occurrence using RF; and (iii) to develop LM using meteorological drivers derived from RF. The results show that RF and ANN outperformed LM in predicting PP in 8 out of 10 metrics. RF indicated that temperature, dew point temperature, and specific humidity are more important than wind or radiation for PP occurrence. With these predictors, parsimonious and efficient models were developed showing that data mining may help in understanding complex processes and complements expert knowledge. Full article