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Dew is one of the most important supplementary water sources in arid and semi-arid regions. The influence of dew amount on the water balance can be evaluated for water management, ecosystem protection, and the optimization of agricultural practices. Moreover, the influencing factors of dew formation can provide some insights into how climate change may impact water availability and ecosystems. However, it is still unclear in the dew formation, due to the difficulty of observation without considering the dew in the water balance. This study aims to quantify the dew amount for a better understanding of dew formation. A series of lysimeter experiments were carried out to continuously observe the dew amount in the Mu Us desert from October 2020 to September 2021. Among them, the dew amount included the fog, dew, and hoar frost formation. In addition, the meteorological factors were monitored at the experimental site, including air temperature, precipitation, wind speed, relative humidity, net radiation, and surface temperature. All data were recorded every ten minutes. The dew amount was evaluated for the water balance during formation. Boosted regression tree (BRT) model was used to investigate the influence of meteorological factors on dew formation. The results were as follows. 1) The dew amount was 47.1 mm in the experimental period, corresponding to 19.6% of the annual rainfall. The dew started to form at night when the air temperature was significantly higher than the soil surface temperature. The dew was mainly formed from October 2020 to January 2021 (cold months), with a maximum amount of 9.00 mm in December 2020, and a minimum of 0.55 mm in June 2021. The dew amount was larger in the colder months than that in the warmer ones. 2) The BRT model showed that the largest contribution (43.4%) was found in the relative humidity to the dew formation. It infers that relative humidity was a key factor in the formation of dew. The contribution rates of the net radiation at night and the wind speed to the dew formation were 21.7% and 15.2%, respectively. The much fewer contributions to the dew formation were the air temperature and vapor pressure deficit. The optimal combination of parameters was achieved in the dew formation, such as the relative humidity greater than 53%, net radiation at night less than -37 W/m², and wind speeds ranging from 0.5 to 1.8 m/s. 3) The surrounding air temperature and relative humidity of Artemisia ordosica posed the more significant influence on the dew formation, compared with bare ground. The errors of the water balance were reduced from 25.3% to 5.7%, considering the amount of dew in the semi-arid regions. The dew amount was quantified to improve the accuracy of the water balance. The finding can provide a strong reference to accurately evaluate the water resources and then better understand the formation process of dew amounts under the coverage of Artemisia ordosica. In addition, the Artemisia ordosica in the water cycle can be expected to protect the vegetation ecosystems in the arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET0) is an important variable to estimate all the forms of evaporation and transpiration from crops and vegetation. An accurate estimation of ET0 plays a critical role in crop water management. The Food and Agriculture Organization of the United Nations (FAO) has recommended the Penman Monteith (PM) model as the standard to estimate the ET0. However, the PM model requires a large number of input parameters, including the solar radiation (Rs) or sunshine hours (n), the maximum and minimum air temperatures (Tmax and Tmin), Relative Humidity (RH), and wind speed at 2m high (u2). In addition to temperature data, most datasets are often unavailable, incomplete, or of uncertain quality in practice. Thus, the ET0 estimation using temperature data has drawn much attention in recent years. In this study, the recent estimation models were compared to improve the high accuracy of ET0 using the temperature data. The traditional Hargreves-Samani (HS) model (HS1), the improved HS model (HS2), and the PM temperature approach (PMTPP) were recently proposed by Paredes and Pereira. Some latest models were also introduced, including the ea estimation model proposed by Qiu et al., and the temperature-based Rs model (Rs-K) proposed by Korachagaon to improve the PM temperature approach (PMTNew). All models were calibrated and tested using the dataset collected from 51 radiation stations in China during the period 1967-2016. The results showed that the Rs-K model performed a better accuracy for the ET0 estimation, compared with the Rs model proposed by Paredes and Pereira (Rs-HS). For instance, the values of modelling efficiency (EF) were much closer to 1.00, the correlation coefficient (r) increased from 0.92-0.99 to 0.94-0.99, the values of Root Mean Square Error (RMSE), and the Relative Root Mean Square Error (RRMSE) were reduced by 0.08-0.25 mm/d and 0.03-0.09, respectively, at different climate zones. Except for the arid and hyper-arid climate zones, the average values of RMSE and RRMSE in the HS2 model decreased by 0.05-0.09 mm/d and 0.02-0.03, respectively, and the average values of EF increased by 0.03-0.10, compared with the HS1 model. Although the PMTPP model produced a similar r, the mean values of RMSE and RRMSE from the dry sub-humid and humid climate zones were reduced by 0.01-0.15 mm/d and 0-0.05, respectively, while the EF values increased by 0.01-0.06, compared with the HS2 model. Therefore, the PMTPP model presented a better accuracy than the HS model in estimating the ET0 at various climate zones. Except for the arid and semi-arid climate zones, the PMTNew model further reduced the RMSE and RRMSE by 0.04-0.12 mm/d and 0-0.04, respectively, indicating the higher EF and r values, compared with the PMTPP. In addition, the HS2 model presented a higher Global Performance Index (GPI) value than the HS1 model, indicating a better GPI value in the humid zone. Except for the humid climate zones, the PMTPP model presented a higher GPI value at various climate zones, the mean value of GPI range -0.06-0.42, compared with the HS2 model. The PMTNew model also presented a better GPI of each station, 90% of which ranked the first. Therefore, it is strongly recommended to use the PMTNew model to estimate the ET0 of each station, when the limited data is available. [ABSTRACT FROM AUTHOR]

Water resources in arid region of Northwest China depend heavily on mountain precipitation and snowmelt runoff. The precipitation falling as snow or rain has different hydrological effects. As a meteorological indicator reflecting snowfall/precipitation conversion, the ratio of snowfall to precipitation (S/P) is quite important, because even small changes in S/P may influence the runoff processes and available water resources. However, the long-term trend of S/P in Tianshan Mountains, China, which is critical to the future water management in the context of climate change, has been unclear. Based on the daily meteorological data from 49 stations during 1961—2018, this study conducted a parameterization scheme to desociate the snowfall from precipitation and analyze historical spatial-temporal variations of snowfall, precipitation and S/P during cold season (from October to April) within the above timeframe. Meanwhile, considering of the impacts of climate change on S/P, five GCM models of the fifth phase of the Coupled Model Intercomparision Project (CMIP5) at RCP4.5 emission scenarios were selected to project the future change of snowfall, precipitation and S/P by bias-correction with Quantile Delta Mapping (QDM). The results demonstrated that the S/P in Tianshan Mountains was affected by the topography. The S/P was larger in the mountain area than that in the basin, greater on the northern slopes than that on the southern slopes, which was significantly and positively correlated with the altitude. During the past 58 years, the snowfall and precipitation increased significantly, and the distribution of regions with the obvious changes were concentrated in the central region of the northern Tianshan Mountains and Yili River valley. Due to the synchronous trend of snowfall and rainfall, the S/P in Tianshan Mountains was found to fluctuate between 0.35 and 0.67, revealing a slight decrease trend at a rate of 0.016%/10a. A negative correlation was also noticeable between the average wet day air temperature and S/P, and air temperature change is an important factor for S/P change. Under the RCP4.5 climate scenario, it is estimated that the snowfall in cold season will increase slowly before 2040 s±5, and then gradually decrease, showing a slow declining trend overall. In contrast, precipitation will increase significantly in the future, reaching its peak around 2080s ± 5. In this case, S/P presents a continuous declining trend during the 21st century, which is expected to decline from 0.52 in the 2010s to 0.43 in the 2090s. In comparison with the base period (1986—2005), snowfall in 70% sites in the Tianshan Mountains will reduced by the 2050s, with the average regional snowfall in cold season decrease by 8.9%; Whereas the precipitation of 80% stations increased, with the average regional precipitation increased by 10.1%. Affected by the amount of snowfall and precipitation, S/P of 47 out of 49 stations will decrease, and the average S/P in the 2050s will decrease by 14.7% comparing to the base period. Despite of the uncertainty in the projection results of the model, the quantitative analysis of future S/P in this study is helpful in understanding the changing process of different forms of precipitation and is of great significance to the regulation of regional water resources in relation to global warming. [ABSTRACT FROM AUTHOR]