Dew Yield and Its Influencing Factors at the Western Edge Of

Article Dew Yield and Its Inﬂuencing Factors at the Western Edge of Gurbantunggut Desert, China

Zhifeng Jia 1,2,3,* , Zhiqiang Zhao 2, Qianyi Zhang 2 and Weichen Wu 2

1 Institute of Water and Development, Chang’an University, Xi’an 710054, China 2 School of Environmental Science and Engineering, Chang’an University, Xi’an 710054, China; [email protected] (Z.Z.); [email protected] (Q.Z.); [email protected] (W.W.) 3 Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region, Ministry of Education, Chang’an University, Xi’an 710054, China * Correspondence: [email protected]; Tel.: +86-29-8233-9323

Received: 4 March 2019; Accepted: 8 April 2019; Published: 9 April 2019

Abstract: Dew is a significant water resource in arid desert areas. However, information regarding dew is scarce because it is difficult to measure due to the harsh environment of locations such as Gurbantunggut Desert, China. In this study, a non-destructive field experiment was conducted from 2015 to 2018 at a desert test station located in the western edge of the Gurbantunggut Desert, using a calibrated leaf wetness sensor (LWS) to measure dew yield. The results are as follows: (1) Dew formed after sunset with the atmospheric temperature gradually dropping and evaporated after sunrise with the temperature increasing in the second morning. (2) Dew was featured as ‘high frequency and low yield’. The average daily dew yield during dew days was 0.10 mm with a daily maximum of 0.62 mm, while dew days accounted for 44% of the total monitoring days, with a monthly maximum of 25 days. Compared with rainfall, dew days were two times as frequent as rainy days, while the average annual dewfall (12.21 mm) was about 1/11th of the average annual rainfall (134.6 mm), which indicates the dew contribution to regional water balance is about 9%. (3) March–April and October–November are the main periods of dew occurrence in this region because accumulated snow begins to melt slowly in March–April, providing sufficient vapor for dew formation, and the air temperature difference between day and night in October–November is the highest in the year, meaning that the temperature drops rapidly at night, making it easier to reach the dewpoint for vapor condensation. (4) Daily dew yield (D) was positively correlated to relative humidity (RH) and the difference between soil temperature at 10 cm below the ground and surface soil temperature (Tss), and negatively correlated to wind speed (V), air temperature (Ta), surface soil temperature (Ts), cloud cover (N), dewpoint temperature (Td) and the difference between air temperature and dewpoint temperature (Tad). It should be noted that the measured values of all factors above were the average value of the overnight period. The multivariate regression equation, D = −0.705 + 0.011 × RH − 0.006 × N − 0.01 × V, can estimate the daily dew yield with the thresholds of the parameters, i.e., RH > 70%, N < 7 (oktas) and V < 6 m/s..

Keywords: dew formation; dew yield; factors; desert ecosystem

1. Introduction Water availability is the most important limiting factor in arid lands. Any additional source of water may have a positive impact upon the ecosystem [1,2]. Dew is a kind of sustainable and stable water resource in arid desert areas, and it is very important in maintaining the survival of plants and small animals [3]. For example, dew often serves as a primary water resource for biological soil

Water 2019, 11, 733; doi:10.3390/w11040733 www.mdpi.com/journal/water Water 2019, 11, 733 2 of 17 crusts [4–6], lichens [7,8], small shrubs [9] and nematodes [10] in a desert ecosystem. Hence, a large number of studies have aimed to understand the hydrology and ecological significance of dew. Dew forms when the temperature of a surface is lower than or equal to the dewpoint temperature, such that water vapor in the air condenses on the colder surface [11,12]. Dew is usually attached to the leaves of plants and the soil surface or in shallow soil. Water vapor from lower atmospheres [13], soil [14,15] and transpiration of plant leaves in the lower canopy [16] can be involved in the process of dew formation in an ecosystem. Due to the different sources of water vapor, dew hydrology is very complicated and is affected by meteorological conditions and underlying surface conditions. Meteorological conditions include relative humidity, air temperature and wind speed. Previous studies have shown that low air temperature [17] and high relative humidity of air [18], as well as moderate wind speed [19], are favorable to dew formation. Underlying surface conditions include topography, vegetation and soil texture. The hilltops obtained a longer duration and higher dew yields than wadi beds in a small arid drainage basin in the Negev Highlands, Israel, which may be explained by their wind-sheltered location, impeding wind ventilation during the afternoon breeze, and by slower long wave radiational cooling at the narrow wadi [1]. The mean daily dew yield for sand (0.12 mm) was greater than that for gravel (0.071 mm) at the Gaolan Research Station of Ecology and Agriculture located in the transitional zone between arid and semi-arid regions in China, due to lower surface temperature and vapor conductivity for sand [20]. Dew yield in the planting area was significantly higher than that of the biological crust and quicksand, and the yield in the biological crust was significantly higher than that of quicksand in Southern Maowusu Sandy Land, China [21]. A similar experiment was carried out in xerophyte shrub plantations at the southeast margin of Tengger Desert, Northern China [6], and a longer daily dew production period and more dew yield were shown on moss compared with mixed crusts and dune sand, which proved that habitats with better vegetation conditions are more conducive to dew formation. Up until now, there is no unique standardized method to measure dew formation [17,22]. The difficulty of dew measurement is both in the definition of dew and in its actual measurement. However, scholars have developed various dew measurement instruments or methods in the past 70 years, including dew gauge [23], electrical resistance sensor [24], hiltner-type dew balance method [25], soil moisture sensor [1], leaf wetness sensor [26–30], artificial condensation surface method [31–34], and weighing method [6,12,35,36]. The last three items of the methods mentioned above are widely used. For example, materials (tissues, woods and plastic) were weighed before and after the dew event to calculate the dew yield [17], high precision lysimeters were weighed to obtain dew formation at night on natural ecosystems [37] and a leaf wetness sensor was used to estimate the presence or absence of wetness on its surface [26], which can also be calibrated to provide a potential dew yield [29,30,38,39]. To effectively quantify the processes of dew formation, many attempts have been carried out to predict dew yield from the parameters, such as relative humidity, air temperature, cloud coverage, wind speed and radiation [2,3]. Most of the models are usually designed from an energy balance, such as the Penman–Monteith equation, where the latent heat released by water vapor condensation is the parameter to solve for. Other than this, a purely statistical approach based on artificial networks has been successfully tested by Lekouch et al. [32]. A semi-empirical model by Beysens [40], calculated from the relationships between dew formation and its drivers: relative humidity, wind speed and cloud cover, is also reliable and gives the opportunity to study dew formation on a larger scale. Although previous studies have been resourceful in the dew formation process, many aspects of dew hydrology are site-specific due to uncertainties [30]. Dew assessment is still mainly dependent on site observation instead of hydrologic predictions, since the influence degrees of different factors to dew formation are inconsistent with different climatic regions in a manner that is still not fully and precisely modeled. Gurbantunggut Desert, located in the middle of the Junggar Basin, is the largest fixed and semi-fixed desert in China. The mean annual rainfall is no more than 200 mm [41], while vegetation coverage is up to 40% [42]. Playing an important role in fixing the sand surface, Water 2019, 11, 733 3 of 17

Water 2019, 11, x FOR PEER REVIEW 3 of 18 biological soil crusts are widely developed in the desert, especially in lowlands between hills. Mosaic distributionand more resistant of moss to crusts mechanical and lichen interference. crusts make In the biologicalanalysis of crusts the reasons thicker for and the more distribution resistant toof mechanicaldifferent types interference. of soil crusts, In the differences analysis ofin thewater reasons conditions for the have distribution also attracted of different people's types attention, of soil crusts,aside from differences the diffe in waterrences conditions in soil texture have and also soil attracted nutrients. people’s The attention, objectives aside of this from study the differences are to: (1) inassess soil texture dew yield and soil by nutrients. conducting The objectives an in-situ of observation this study are experiment to: (1) assess at dew the yield western by conducting edge of anGurbantunggut in-situ observation Desert, experiment China; (2) at thedetermine western the edge main of Gurbantunggut influencing factors Desert, in China;the dew (2) determine formation theprocess main by inﬂuencing regression factors analysis in the; (3) dew develop formation a multiple process regression by regression model analysis; for dew (3) yield develop. a multiple regression model for dew yield. 2. Materials and Methods 2. Materials and Methods 2.1. Measurement Site 2.1. Measurement Site The field experiment was carried out at Paotai Soil Improvement Test Station (44。48’ N, 85。33’ ◦ 0 ◦ 0 E), locatedThe field at experimentthe western was edge carried of the out Gurbantunggut at Paotai Soil Improvementdesert in northwestern Test Station China (44 48 (FigureN, 85 1).33 TheE), locatedclimate atin the this western region edgeis temperate of the Gurbantunggut continental, characterized desert in northwestern mainly by aridity, China (Figure long-cold1). The winter climate and inshort this-hot region summer. is temperate The mean continental, annual temperature characterized is mainly 6.6 °C by(1997 aridity,–2016), long-cold with the winter annual and cumulative short-hot ◦ summer.temperature The mean(> 10 °C annual) of 3400 temperature °C [42]. isMean 6.6 C annual (1997–2016), precipitation with the ranges annual from cumulative 70 to 180 temperature mm [41], ◦ ◦ (>10whileC) the of mean 3400 annualC[42]. Meanevaporation annual precipitationexceeds 2000 ranges mm [4 from3]. The 70 to mean 180 mm annual [41], values while theof the mean frost annual-free evaporationperiod and exceeds sunshine 2000 duration mm [43 ]. are The 160 mean days annual and values 2772 hoursof the, frost-freerespectively. period In and winter, sunshine the durationGurbantunggut are 160 Desert days and experiences 2772 h, respectively. 95–110 days In of winter, snow cover, the Gurbantunggut beginning in late Desert November experiences and 95–110ending days in mid of snow-March cover, of beginningthe following in late year November [43]. The and maximum ending in mid-Marchdepths of frozenof the following soil and yearaccumulated [43]. The snow maximum cover depths are 90 ofcm frozen and 35 soil cm and, respectively. accumulated The snow dominant cover are plants 90 cm are and Haloxylon 35 cm, respectively.ammodendron The and dominant H. persicum plants [ are43] Haloxylon. In addition ammodendron to this, there and are H. many persicum biological [43]. In soil addition crusts, tocontain this, thereing cyanobacteria are many biological-dominated, soil crusts,lichen- containingdominated, cyanobacteria-dominated, and moss-dominated crusts lichen-dominated, [44]. The soil in andthe moss-dominateddesert has a sandy crusts texture, [44]. The with soil sand in the (0.05 desert–2 mm) has a sandycontent, texture, 85%; withsilt (0.002 sand (0.05–2–0.05 mm) mm) content, content, 85%;13.7% silt; and (0.002–0.05 clay (< 0.002 mm) mm) content, content, 13.7%; 1.3%. and clay (<0.002 mm) content, 1.3%.

Figure 1. Location of the experimental site: Paotai Soil Improvement Test Station. Figure 1. Location of the experimental site: Paotai Soil Improvement Test Station. 2.2. LWS Calibration 2.2. LWSThe dielectricCalibration Leaf Wetness Sensor (LWS, Model, PHYTOS 31, by Decagon Devices, Pullman, WA, USA) was chosen to measure the duration and yield of dew. The LWS measures the dielectric constant The dielectric Leaf Wetness Sensor (LWS, Model, PHYTOS 31, by Decagon Devices, Pullman, of a zone approximately 1 cm from the upper surface of the sensor. The dielectric constant of water WA, USA) was chosen to measure the duration and yield of dew. The LWS measures the dielectric (80) and ice (5) are much higher than that of air (1), so the measured dielectric constant is strongly constant of a zone approximately 1 cm from the upper surface of the sensor. The dielectric constant dependent on the presence of moisture or frost on the sensor surface. The sensor outputs a mV signal of water (80) and ice (5) are much higher than that of air (1), so the measured dielectric constant is strongly dependent on the presence of moisture or frost on the sensor surface. The sensor outputs a mV signal proportional to the dielectric of the measurement zone, which can be converted into the

Water 2019, 11, 733 4 of 17

Water 2019, 11, x FOR PEER REVIEW 4 of 18 proportional to the dielectric of the measurement zone, which can be converted into the dew yield by calibration. Calibration was carried out by placing the LWS horizontally, with its signal output port dew yield by calibration. Calibration was carried out by placing the LWS horizontally, with its signal plugged into a data logger (Model, EM50, by Decagon Devices, USA). A small sprayer was used to output port plugged into a data logger (Model, EM50, by Decagon Devices, USA). A small sprayer spray a known amount of water on the surface of the LWS. By increasing the amount of sprayed water, was used to spray a known amount of water on the surface of the LWS. By increasing the amount of the corresponding increase of the electric signal in mini voltage is recorded in the EM50, as shown sprayed water, the corresponding increase of the electric signal in mini voltage is recorded in the in Figure2. EM50, as shown in Figure 2.

0.8 0.7 0.6 0.5 0.4 0.3

Dew yield (mm) yield Dew 0.2 0.1 0.0 400 500 600 700 800 900 1000 1100 x (mv) Figure 2. Calibration curve of leaf wetness sensor (LWS) for measuring dew yield. Figure 2. Calibration curve of leaf wetness sensor (LWS) for measuring dew yield. The calibration equation was expressed as follows: The calibration equation was expressed as follows: = b 퐷 = 푎푥푏 D ax (1)(1) where,where,x isx is the the raw raw voltage voltage counts counts (mv) (mv) recorded recorded in in EM50, EM50,D Dis is the the accumulated accumulated dew dew yield yield over over the the surfacesurface of of LWS LWS (mm), (mm),a anda andb areb are fitting fitting parameters, parameters,a =a 4= ×4 ×10 10−−1414 and b = 4.4188 4.4188 ( (rr == 0.997, 0.997, pp << 0.01). 0.01). ItIt should should be be noted noted that that LWS LWS has has been been specially specially designed designed to to closely closely approximate approximate the the thermodynamicthermodynamic properties properties of of a a leaf. leaf. IfIf thethe specificspecific heatheat ofof thethe leafleaf isis estimatedestimated at 3750 J ·kg−−11K·K−1,− the1, thedensity density is is estimated estimated to to be be 0.95 0.95 g/cm g/cm3, 3 and, and as as the the thickness thickness of of a a typical leaf leaf is is 0.4 0.4 mm, mm, the the heat heat capacitycapacity of of the the leaf leaf is 1425is 1425 J·kg J−kg1·−K1K−1−1.. ThisThis isis closely closely approximated approximated by by the the thin thin (0.65 (0.65mm) mm) fiberglass fiberglass constructionconstruction of of the the LWS, LWS, which which has has a heat a heat capacity capacity of 1480of 1480 J·kg J−kg1·K−1−K1−1[ 45[45].]. Since Since dew dew condensation condensation maymay be be affected affected by by minute minute differences differences in in the the substrate substrate temperatures, temperatures, heterogeneity heterogeneity in in the the substrate substrate properties,properties, size size and and position position may may affect affect the substratethe substrate temperatures temperatures and hence and hence dew condensation dew condensation [46]. It[46] was. hypothesizedIt was hypothesized that dew that yield dew measured yield measured by LWS by is approximatedLWS is approximated by the integrated by the integrated value on value soil crustson soil and crusts plant and leaves. plant leaves.

2.3.2.3 Experimental. Experimental Design Design AA bare bare patch patch of of ground ground was was selected selected for for the the installation installation of of instruments. instruments. The The LWS LWS sensor sensor with with 0.020.02 mm mm resolution resolution was was placed placed 5 cm5 cm above above the the ground. ground. Air Air relative relative humidity humidity (RH ()RH and) and temperature temperature at theat samethe same height height of LWS of LWS were were measured measured by a sensor by a sensor (Model, (Model, VP-3) withVP-3) 0.1% withRH 0.1%resolution RH resolution and 0.1 ◦andC temperature0.1 °C temperature resolutions, resolutions respectively., respectively. Wind speed Wind at 300 speed cm above at 300 the ground cm above was the measured ground by was a Davismeasured Cup Anemometer by a Davis with Cup a stallingAnemometer speed of with 0.5 m/s.a stalling and within speed 0.1 of m/s. 0.5 m/sresolution. and within Precipitation 0.1 m/s atresolution 160 cm above. Precipitation the ground at was160 measuredcm above the by aground high-resolution was measured rain gauge by a high (Model,-resolution ECRN-100) rain withgauge 0.2(Model, mm resolution. ECRN-100 Soil) with temperature 0.2 mm and resolution. moisture Soil at 2 cm temperature and 10 cm and below moisture the ground at 2 were cm measuredand 10 cm bybelow two sensorsthe ground (Model, were GS3) measured with 0.1 by◦ Ctwo temperature sensors (Model, resolutions GS3) with and 2%0.1 volumetric°C temperature water resolutions content, respectively.and 2% volumetric Soil temperature water content and moisture, respectively. at 2 cmSoil below temperature the ground and weremoisture approximated at 2 cm below to that the ofground the surface were soil approximated in this study. to Allthat sensors of the weresurface plugged soil in intothis thestudy data. All ports sensors of EM50 were Data plugged Logger into tothe synchronously data ports of record EM50 the Data measured Logger values to synchronously at 30 min intervals. record All the sensors measured above values were atmade 30 bymin Decagonintervals. Devices All sensors Inc., USA. above The were mounting made by positions Decagon of Devices all instruments Inc., USA. are The shown mounting in Figure positions3. Aside of all instruments are shown in Figure 3. Aside from this, the cloud cover data collected at 8:00, 14:00 and 20:00 were obtained from the local weather station within 4.8 km from the measurement site. The experiment was carried out from 1 August 2015 to 1 December 2018, excluding the period from 1 December to 1 March the following year, during which the air temperature remained below 0 °C .

Water 2019, 11, 733 5 of 17 from this, the cloud cover data collected at 8:00, 14:00 and 20:00 were obtained from the local weather station within 4.8 km from the measurement site. The experiment was carried out from 1 August 2015 to 1 December 2018, excluding the period from 1 December to 1 March the following year, during ◦ Waterwhich 2019 the, 11 air, x FOR temperature PEER REVIEW remained below 0 C. 5 of 18

FigureFigure 3. 3. ExperimentalExperimental setup. setup. Instruments Instruments include: (a) LWS;LWS; ((bb)) Davis Davis Cup Cup Anemometer; Anemometer; (c )(c Rain) Rain gaugegauge ( (ECRN-100);ECRN-100); ( (dd)) AirAir relative relative humidity humidity and and temperature temperature sensor sensor (VP-3); (VP (e-3)) Soil; (e) temperature Soil temperature and andmoisture moisture sensor sensor (GS3); (GS3) (f) Data; (f) D loggerata logger (EM50) (EM50) inside inside the security the security box. box. 2.4. Data Processing 2.4. Data Processing 2.4.1. Dew Yield, Intensity and Frequency 2.4.1. Dew Yield, Intensity and Frequency Dew yield is the vapor condensation amount within a specified period of time. For dew intensity, the timeDew period yield is is specifiedthe vapor as condensation 1 h. For the daily amount dew within yield, the a specified time period period is defined of time. to start For ondew intensity,day one andthe time endon period day twois specified at 16:00, as which 1 hour. is the For most the unlikelydaily dew time yield, for dewthe time condensation period is defined in the to24-hour start on period day[ 30 one]. Dew and frequency end on day in a day two is at defined 16:00, as which the ratio is theof the most number unlikely of dew-producing time for dew condensationtime periods toin the the total 24-hour number period of observation[30]. Dew frequency periods. In in addition, a day is it defined is assumed as the in thisratio study of the numberthat dew of does dew not-producing occur on time rainy, periods foggy, to frosty the total and snowynumber nights. of observation Here, a rainy periods. night In is determinedaddition, it is assumedby rainfall in gauge.this study A foggy that nightdew does is defined not occur as a morning on rainy, during foggy which, frosty the and visibility snowy isnight restricteds. Here, to a rainy<1 km nightfor >0.5 is determined h [36]. Nights by duringrainfall whichgauge air. A temperaturefoggy night is lessdefined than as 0 ◦aC morning are regarded during as frostywhich or the visibilitysnowy nights. is restricted to < 1 km for > 0.5 h [36]. Nights during which air temperature is less than 0 °C are regarded as frosty or snowy nights. 2.4.2. Dewpoint Temperature 2.4.2. DewpointDewpoint temperatureTemperature was calculated by using the Lawrence equation [47]: Dewpoint temperature was calculated by using the Lawrence equation [47]: B [ln( RH ) + A1T ] 1 100 B1+T Td = (2) RHRH ATA1T A1 − ln( ) − 1 B1[ln(100 ) B1+T ] 100 BT1  Td  (2) RH AT1 A1 ln( ) 100 BT1  where, RH is relative humidity (%), T and Td are air temperature and dewpoint temperature (°C ), respectively, A1 and B1 are coefficients described by Alduchov and Eskridge: A1 = 17.625, B1 = 243.04°C [48].

2.4.3. Cloud Cover

Water 2019, 11, 733 6 of 17

where, RH is relative humidity (%), T and Td are air temperature and dewpoint temperature ◦ ( C), respectively, A1 and B1 are coefﬁcients described by Alduchov and Eskridge: A1 = 17.625, ◦ B1 = 243.04 C[48].

2.4.3. Cloud Cover The cloud cover data were recorded with its value in the range 0–10, with 10 for full cloud cover, which were multiplied by 0.8 to correlate with standard measurements on a scale 0–8 (oktas) [31,49].

2.4.4. Wind Speed Wind speed was measured at 3 m above the ground. In order to correlate with standard measurements at 10 m above the ground, the measured data were converted by using the classical regression [16]: ln( z1 ) Vz1 zc = z (3) Vz ln( 2 ) 2 zc where zc (m) corresponds to the ground roughness length and taken to be 0.1 m. Vz1 (m/s) and

Vz2 (m/s) are wind speeds at different heights of z1 (m) and z2 (m), respectively. The relationship between wind speeds at heights of 10 m and 3 m was determined by Equation (3) (here, z1 is equal to 10 m and z2 is equal to 3 m).

Vz1 = 1.35Vz2 (4)

2.4.5. Statistical Analysis Linear regression was applied to analyze the relationship between dew yield and main factors. Trends, significance and fitting degree were respectively determined by correlation coefficient (R), P-value at the 0.01 level and the coefficient of determination (R2). Mean absolute error (MAE) was used to evaluate the goodness of the model:

n ∑ |Ci − Oi| = MAE = i 1 (5) n where Ci and Oi are the daily dew yield calculated and measured at the specific day (mm), respectively, with n as the measurement of days. All statistical analyses were performed using SPSS Statistics 22.0 (SPSS Inc., Chicago, IL, USA), and all figures were plotted by Office 2016 (Microsoft Corp., Redmond, WA, USA) and Origin 8.0 (Origin Lab Corp., Northampton, MA, USA).

3. Results

3.1. Daily Variation of Dew A typical daily process of dew formation (on 6–7 October 2016) was shown in Figure4. It is clear that dew existed on the LWS for 13 h from 21:00 to 10:00 the next day (UTC+8, the same as the following time involved), but really formed between 21:00 and 08:00 which corresponds with the time of dew intensity. The maximum net dew yield for the day was 0.32 mm and the maximum dew intensity was 0.044 mm/h. The red arrows mark the time of sunrise and sunset. It can be seen that dew forms after sunset and evaporates after sunrise in the second morning. Water 2019, 11, x 733 FOR PEER REVIEW 77 of 18 17

Water 2019, 11, x FOR PEER REVIEW 7 of 18 0.4

Cumulated dew yield Dew intensity ) 0.4 Sunset:Cumulated 19:41 dew yield DewSunrise: intensity 08:14 ) 0.3 Sunrise: 08:14 0.3 Sunset: 19:41 0.2

0.2 Dew yield (mm) yield Dew

Dew intensity (mm/h intensity Dew 0.1 Dew yield (mm) yield Dew

Dew intensity (mm/h intensity Dew 0.1

0.0

16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 00:00 01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00

17:00 18:00 19:00 20:00 21:00 22:00 23:00 00:00 01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 16:00 Time (UTC+8) Time (UTC+8) Figure 4. Typical daily variations of cumulative dew yield and dew intensity (the time of sunset and Figure 4. Typical daily variations of cumulative dew yield and dew intensity (the time of sunset and sunrise are marked by red arrows). sunriFigurese a re4. Typicalmarked daily by r edvariations arrows) of. cumulative dew yield and dew intensity (the time of sunset and 3.2. Annualsunrise Variation are marked of Dewby red arrows). 3.2. Annual Variation of Dew 3.2.Figure Annual5 Vshowsariation the of D variationew and distribution of dew yield in the entire monitoring period. The dailyFigure dew 5 shows yield the varied variation between and 0 anddistribution 0.62 mm of (Figure dew yield5a,b), in which the entire is extremely monitoring skewed period. toward The Figure 5 shows the variation and distribution of dew yield in the entire monitoring period. The daily dew yield varied− between 0 and 0.62 mm (Figure 5a,b), which is extremely skewed toward smallerdaily dew values yield of 0varied0.05 between mm (Figure 0 and5c). 0.62 Days mm of (Figure no dew 5a (including,b), which rainyis extremely days, foggyskewed days, toward frosty smaller values of 0−0.05 mm (Figure 5c). Days of no dew (including rainy days, foggy days, frosty dayssmaller and values snowy of days) 0−0.05 accounted mm (Figure for 5 56%c). Days of the of no total dew monitoring (including days.rainy days, Days fog of anygy days, dew frost withy an days and snowy days) accounted for 56% of the total monitoring days. Days of any dew with an amountdays and below snowy 0.05 days) mm accounted accounted for for 56% 16% of of the the total total monitoring days. Days days. of dewDays yield of any between dew with 0.05 an and amount below 0.05 mm accounted for 16% of the total days. Days of dew yield between 0.05 and 0.35amount mm accounted below 0.05 for mm 26% accounted of the total for days.16% of Thus, the total the daysdays. of Days daily of dew dew yield yield greater between than 0.05 0.35 and mm 0.35 mm accounted for 26% of the total days. Thus, the days of daily dew yield greater than 0.35 only0.35 accounted mm accounted for 2% for of 26% the totalof the days. total Thedays average. Thus, dailythe days dew of yield daily in dew the yield entire greater monitoring than 0.35 period mmwasmm 0.044only only accounted mm, accounted while for thatfor 2% 2% in of dewof thethe days totaltotal reached days.. TheThe up averageaverage to 0.10 mm.daily daily Asdew dew shown yield yield inin in Figurethe the entire entire5d, monthlymonitoring monitoring dew periodyield,period with was was its 0.044 0.044 peak mm valuemm, , whilewhile in March–April that that inin dew and days October–November, reachedreached up up to to 0.10 0.10 was mm mm consistent. As. As shown shown with in inmonthly Figure Figure 5d, dew5 d, monthlydays.monthly Thus, dew dew the yield yield optimal, , with with time its its peak forpeak collecting value value in dew March March as––April anApril alternative and and October October source–November,–November, of water was should was consistent consistent be in the withMarch–Aprilwith monthly monthly anddew dew October–November days. days. Thus, Thus, thethe optimaloptimal periods. time forfor collectingcollecting dew dew as as an an alternative alternative source source of ofwater water shouldshould be be in in th the eMarch March––AprilApril and and OctoberOctober–November periods. periods. 0.70.7 0.60.6 (a)(a) 0.50.5 0.40.4 0.30.3

0.20.2

Dew yield (mm) yield Dew Dew yield (mm) yield Dew 0.10.1

01-Jul-16

01-Jan-16

01-Jun-16

01-Oct-15 01-Oct-16

01-Feb-16

01-Sep-15 01-Sep-16

01-Dec-15 01-Dec-16

01-Apr-16

01-Mar-16

01-Jul-16

01-Aug-15 01-Aug-16

01-Nov-15 01-Nov-16

01-May-16

01-Jan-16

01-Jun-16

01-Oct-15 01-Oct-16

01-Feb-16

01-Sep-15 01-Sep-16

01-Dec-15 01-Dec-16

01-Apr-16

01-Mar-16

01-Aug-15 01-Aug-16

01-Nov-15 01-Nov-16 Date (dd-mm01-May-16 -yy) Date (dd-mm-yy)

Figure 5. Cont.

Water 2019, 11, 733 8 of 17 Water 2019, 11, x FOR PEER REVIEW 8 of 18

0.7 0.6 (b)

(mm) 0.5 0.4

yield 0.3 0.2

Dew 0.1

01-Jul-17 01-Jul-18

01-Jan-18

01-Jun-17 01-Jun-18

01-Oct-17 01-Oct-18

01-Feb-18

01-Sep-17 01-Sep-18

01-Dec-17 01-Dec-18

01-Apr-17 01-Apr-18

01-Mar-17 01-Mar-18

01-Aug-17 01-Aug-18

01-Nov-17 01-Nov-18

01-May-17 01-May-18 Date (dd-mm-yy)

18 15 (c) 12 9 6

Dew frequency (%) frequency Dew 3

0-0.05

0.05-0.1 0.1-0.15 0.15-0.2 0.2-0.25 0.25-0.3 0.3-0.35 0.35-0.4 0.4-0.45 0.45-0.5 0.5-0.55 0.55-0.6 0.6-0.65 Dew yield (mm)

5.0 25 (d) Dew yield 4.0 20 Dew days 3.0 15

2.0 10

Dew days Dew Dew yield (mm) yield Dew 1.0 5

0.0 0 Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Month

FigureFigure 5. 5.Variations Variations of of dew dew yields: yields: ( a()a daily) daily dew dew yields yields in in 1 August1 August 2015 2015 to to 1 December1 December 2016; 2016; (b ()b daily) dewdaily yields dew yields in 1 March in 1 March 2017 to2017 1December to 1 December 2018; 2018; (c) distribution(c) distribution of dailyof daily dewfall; dewfall; (d ()d monthly) monthly dew yieldsdew yields and dew and days.dew days. 3.3. Dew versus Rain 3.3. Dew versus Rain 3.3.1. Daily Rainfall 3.3.1. Daily Rainfall Distribution of daily rainfall and daily duration in the entire observation period is shown in Distribution of daily rainfall and daily duration in the entire observation period is shown in FigureFigure6. 6. Days Days of of any any rain rain withwith anan amountamount below 2 2 mm mm reached reached up up to to 133 133 days days which which accounted accounted for for 66%66% of of the the total total rain rain days days (202 (202 days), days) while, while the the days days of of daily daily rainfall rainfall greater greater than than 10 10 mm mm were were 12 12 days, whichdays, onlywhich accounted only accounted for 0.06% for of 0.06% the total of the rain total days. rain Meanwhile, days. Meanwhile, the duration the duration of daily of rainfall daily is extremely skewed toward smaller values of 0–2 h. The characteristics of daily rainfall manifested a ‘light rain and short duration’ pattern in this region.

Water 2019, 11, x FOR PEER REVIEW 9 of 18 Water 2019, 11, 733 9 of 17 rainfall is extremely skewed toward smaller values of 0–2 hours. The characteristics of daily rainfall Water 2019, 11, x FOR PEER REVIEW 9 of 18 manifested a ‘light rain and short duration’ pattern in this region. rainfall is extremely skewed toward smaller values of 0–2 hours. The characteristics of daily rainfall 140 manifested140 a ‘light rain and short duration’ pattern in this region. (a) 120 (b) 120 140100 140100 (a) 120 (b)

120 80 80 Days

100Days 60 100 60

80 40 80 40 Days

Days 60 20 60 20

40 0 40 0

2-4 4-6 6-8

20 0-2

2-4 4-6 6-8

20 0-2

8-10

10-12 12-14 14-16

12-14 14-16 16-18 0 10-12 0

Rainfall (mm) Duration (h)

0-2 2-4 4-6 6-8

2-4 4-6 6-8

0-2

8-10

10-12 12-14 14-16

12-14 14-16 16-18 Figure 6. DistributionFigure 6. Distribution of daily of10-12 rainfall daily rainfall (a) and (a) and daily daily duration duration ( (bb) )in in the the entire entire observation observation period. period. Rainfall (mm) Duration (h)

3.3.2. Dew Days3.3.2. Dew Days Figure 6. Distribution of daily rainfall (a) and daily duration (b) in the entire observation period. Figure7 showsFigure the 7 shows number the number of dew of daysdew days and and rain rain days days overover a a month monthlyly time time scale scalein the inentire the entire 3.3.2.observation Dew Days period. The number of monthly dew days was always greater than that of rain days. The observationmaximum period. number The number of monthly of monthly dew days, dew occurring days in was April always 2017, was greater 25 days, than whereas that that of rainof days. Figure 7 shows the number of dew days and rain days over a monthly time scale in the entire The maximummonthly number rain days, of monthly occurring in dew October days, 2016, occurring was 14 days. in AprilThe number 2017, of wasdew days 25 days, was 44% whereas of the that of observation period. The number of monthly dew days was always greater than that of rain days. The total observation days, which was about two times that of the rain days. monthly rainmaximum days, number occurring of monthly in October dew days, 2016, occurring was 14 in days. April The 2017, number was 25 days, of dew whereas days that was of 44% of the 30 total observationmonthly rain days, days, which occurring was in about October two 2016, times wasDew 14 that days. of The theRain number rain days. of dew days was 44% of the total observation days, 25which was about two times that of the rain days. 30 20 Dew Rain 25 15 20 10

15days of Number 5 10 0

Number of days of Number 5

Jun-16 Jun-17 Jun-18

Oct-15 Oct-16 Oct-17 Oct-18

Feb-16 Feb-17 Feb-18

Dec-16 Dec-17

0 Dec-15

Apr-16 Apr-17 Apr-18

Aug-15 Aug-16 Aug-17 Aug-18

Date (mm-yy)

Jun-16 Jun-17 Jun-18

Oct-15 Oct-16 Oct-17 Oct-18

Feb-16 Feb-17 Feb-18

Dec-15 Dec-16 Dec-17

Apr-16 Apr-17 Apr-18

Aug-16 Aug-17 Aug-18 Figure 7. ComparisonAug-15 of the number of dew days and rain days in the entire observation period. Date (mm-yy) 3.3.3. Dew Yield Figure 7.FigureComparison 7. Comparison of the of numberthe number of of dew dew days and and rain rain days days in the in entire the entireobservation observation period. period. Figure 8 shows the monthly yield of dew versus rain. The dewfall was about an order of 3.3.3. Dew3.3.3. Yieldmagnitude Dew Yield less than the rainfall. The maximum value of monthly dew yield, occurring in October 2016, was 3.9 mm, whereas that of monthly rain days, occurring in May 2016, was 56.4 mm. The Figure 8 shows the monthly yield of dew versus rain. The dewfall was about an order of average annual rainfall (134.6 mm) was 11 times that of the average annual dewfall (12.21 mm). Figuremagnitude8 shows less the than monthly the rainfall. yield The maximum of dew versusvalue of rain.monthly The dew dewfallyield, occurring was aboutin October an order of magnitude2016, less was than 3.9 the mm, rainfall. whereas Thethat maximumof monthly rain value days, of occurring monthly in dew May yield, 2016, was occurring 56.4 mm. in The October 2016, was 3.9 mm,average whereas annual thatrainfall of (1 monthly34.6 mm) was rain 11 days,times that occurring of the average in May annual 2016, dewfall was (1 56.42.21 mm). mm. The average annual rainfallWater 2019 (134.6, 11, x FOR mm) PEERwas REVIEW 11 times that of the average annual dewfall (12.21 mm).10 of 18

8 0

) 6 20 Dew Rain 4 40

2 60 (mm Rainfall Dew yield (mm) yield Dew

0 80

Jun-16 Jun-17 Jun-18

Oct-15 Oct-16 Oct-17 Oct-18

Feb-16 Feb-17 Feb-18

Dec-15 Dec-16 Dec-17

Apr-16 Apr-17 Apr-18

Aug-15 Aug-16 Aug-17 Aug-18 Date (mm-yy) FigureFigure 8. Monthly 8. Monthly amount amount of of dew dew versusversus rain rain in inthe the entire entire observation observation period. period.

3.4. Influence Factors

3.4.1 Correlation Analysis on Single Factor Data that suffered from inaccuracies caused by bad weather or instrument failure were excluded before the correlation analysis on single factor was carried out. Based on 406 sets of data, the variation of daily dew yield with the main factors, i.e., wind speed (V), relative humidity (RH), air temperature (Ta), surface soil temperature (Ts), soil moisture (Ws), cloud cover (N), dewpoint temperature (Td), the difference between air temperature and dewpoint temperature (Tad) and the difference between soil temperature at 10 cm below the ground and surface soil temperature (Tss), are shown in Figure 9. The values of all factors above were the average value of the overnight period. Daily dew yield is positively correlated with RH, Ws and Tss, and negatively correlated with V, Ta, Ts, N, Td and Tad. However, it has an insignificant linear correlation to Ws (P > 0.05), while it has extremely significant correlation to others (P < 0.01). Surface soil moisture (Figure 9e) and the peak of dew yield correspond at around 6% (m3/m3), which is also the most frequent soil moisture due to less rainfall and has little effect on dew formation. For other parameters, low wind speed (Figure 9a), high relative humidity (Figure 9b), high values of the difference between soil temperature at 10cm below the ground and surface soil temperature (Figure 9i), low temperature (Figure 9c,d,g,h) and little cloud cover (Figure 9f) are more conducive to dew formation.

Water 2019, 11, 733 10 of 17

3.4. Inﬂuence Factors

3.4.1. Correlation Analysis on Single Factor Data that suffered from inaccuracies caused by bad weather or instrument failure were excluded before the correlation analysis on single factor was carried out. Based on 406 sets of data, the variation of daily dew yield with the main factors, i.e., wind speed (V), relative humidity (RH), air temperature (Ta), surface soil temperature (Ts), soil moisture (Ws), cloud cover (N), dewpoint temperature (Td), the difference between air temperature and dewpoint temperature (Tad) and the difference between soil temperature at 10 cm below the ground and surface soil temperature (Tss), are shown in Figure9. The values of all factors above were the average value of the overnight period. Daily dew yield is positively correlated with RH, Ws and Tss, and negatively correlated with V, Ta, Ts, N, Td and Tad. However, it has an insigniﬁcant linear correlation to Ws (P > 0.05), while it has extremely signiﬁcant correlation to others (P < 0.01). Surface soil moisture (Figure9e) and the peak of dew yield correspond at around 6% (m3/m3), which is also the most frequent soil moisture due to less rainfall and has little effect on dew formation. For other parameters, low wind speed (Figure9a), high relative humidity (Figure9b), high values of the difference between soil temperature at 10 cm below the ground and Water 2019, 11, x FOR PEER REVIEW 11 of 18 surface soil temperature (Figure9i), low temperature (Figure9c,d,g,h) and little cloud cover (Figure9f) are more conducive to dew formation.

Figure 9. Relationships between the daily dew yield and main parameters: (a) wind speed at 10 m Figure 9. Relationships between the daily dew yield and main parameters: (a) wind speed at 10 m above the ground,above theV ;(groundb) relative, V; (b) relative humidity, humidityRH;(, RHc); air (c) temperature,air temperature,T Taa;;( (dd)) surface surface soil soil temperature temperature,, Ts; (e) surface soilTs; moisture,(e) surface soilWs ;(moisturef) cloud, W cover,s; (f) cloudN;( coverg) dewpoint, N; (g) dew temperature,point temperatureTd;(, Thd); ( theh) the difference difference between between air temperature and dewpoint temperature, Tad; and (i) the difference between soil air temperature and dewpoint temperature, Tad; and (i) the difference between soil temperature at temperature at 10 cm below the ground and surface soil temperature, Tss. 10 cm below the ground and surface soil temperature, Tss. 3.4.2. Multiple Regression Analysis Since dew formation is under the combined action of various factors, 308 sets of data in 1 August 2015 to 1 December 2017 were chosen to conduct a multiple regression between daily dew yield and main factors, and 98 sets of data in 1 March to 1 December 2018 were chosen for model validation. Parameters with extremely significant correlation to daily dew yield (Figure 9) were considered as independent variables. The stepwise regression method was used to choose the appropriate variable. The P-values for entry and removal criteria were 0.01 and 0.05, respectively. Models with tolerance values less than 0.1 for each variable were eliminated, owing to serious collinearity between variables. The results are shown in Table 1. All models have statistical significance in that their F-values are greater than the values with the significant level of 0.01, which means that P-values are less than 0.01. Constant and coefficients from Model 1 to 3 are extremely

Water 2019, 11, 733 11 of 17

3.4.2. Multiple Regression Analysis Since dew formation is under the combined action of various factors, 308 sets of data in 1 August 2015 to 1 December 2017 were chosen to conduct a multiple regression between daily dew yield and main factors, and 98 sets of data in 1 March to 1 December 2018 were chosen for model validation. Parameters with extremely significant correlation to daily dew yield (Figure9) were considered as independent variables. The stepwise regression method was used to choose the appropriate variable. The P-values for entry and removal criteria were 0.01 and 0.05, respectively. Models with tolerance values less than 0.1 for each variable were eliminated, owing to serious collinearity between variables. The results are shown in Table1. All models have statistical significance in that their F-values are greater than the values with the significant level of 0.01, which means that P-values are less than 0.01. Constant and coefficients from Model 1 to 3 are extremely significant (P < 0.01), and their coefficient values of determination (R Square) increase with the increase of the number of variables, which shows that the fitting degree is gradually improved.

Table 1. Multivariate regression analysis between daily dew yield and main signiﬁcance factors.

Unstandardized Model Variable Coefﬁcients Signiﬁcance Tolerance R Square F Value B Std. Error Constant −0.664 0.049 0.000 1 0.512 243.182 RH 0.0096 0.001 0.000 1.000 Constant −0.729 0.048 0.000 2 RH 0.011 0.001 0.000 0.713 0.598 158.228 N −0.007 0.003 0.001 0.713 Constant −0.705 0.051 0.000 RH 0.011 0.001 0.000 0.704 3 0.642 82.987 N −0.006 0.003 0.001 0.703 V −0.010 0.005 0.008 0.943

As for Model 3, normal P–P plot of regression standardized residual and model validation plot are shown in Figure 10. Points in Figure 10a are based around the two sides of the diagonal line, which indicates that the residual errors of the model-simulated values are of normal distribution. Although the points composed of calculated values by Model 3 and measured values are dispersed around the two sides of the diagonal line on the whole in Figure 10b (ideal for y = x), only a few events with the highest dew yields (>0.3 mm/d) are under-estimated due to fewer training samples at a higher value. The value of MAE is only 0.023 mm, which is small and approximate to the accuracy of the LWS sensor. Therefore, Model 3 is most suitable to reﬂect the relationship between daily dew yield and main factors, which is represented by Formula 6 as follows:

D = −0.705 + 0.011 × RH − 0.006 × N − 0.01 × V (6)

It should be noted that the thresholds of the parameters in Formula 6 are RH > 70%, N < 7 (oktas) and V < 6 m/s., according to the regression curve in Figure9. The positive and negative coefﬁcients of the parameters in Formula 6 reﬂect the effect of different parameters on dew yield. The water vapor condensation process is the result of overnight thermal exchanges between the substrate and the sky (radiation cooling) and the surrounding atmosphere (convective heating). The lower limit of RH corresponds to the maximum possible cooling with the available energy. Higher values of cloud cover limit the radiative cooling and lead to a decrease of the condensation, and large wind speeds are responsible for enhanced heating convective effects that hamper the condensation process on the substrate rate [2,32,33]. Water 2019, 11, 733 12 of 17 Water 2019, 11, x FOR PEER REVIEW 13 of 18

1 0.6 (a) (b) 0.8 0.5 0.4 0.6 0.3 0.4 0.2

0.2 (mm) value Calculated 0.1

Calculated cumulative probability Calculatedcumulative 0 0 0 0.2 0.4 0.6 0.8 1 0 0.1 0.2 0.3 0.4 0.5 0.6 Measured cumulative probability Measured value (mm)

Figure 10. Normal P–P plot of regression standardized residual (a) and model validation plot (b). Figure 10. Normal P–P plot of regression standardized residual (a) and model validation plot (b). 4. Discussion 4. DiscussionThe duration of dew events as well as resultant yield are dependent upon favorable atmospheric conditions,The duration the condensing of dew substrate events propertiesas well as and resultant geometry. yield Such are circumstances dependent uponare not favorable entirely restrictive,atmospheric however, conditions as, it the is generallycondensing accepted substrate that properties dew is a and recurrent geometry. phenomenon Such circumstances as evidenced are bynot droplets entirely uponrestrictive, car windshields however, as and it is grass generally observed accepted in the th earlyat dew morning is a recurrent hours [phenomenon2,3,40]. In this as study,evidenced a typical by droplets daily process upon car of windshields dew formation and shows grass observed that dew in existed the early on themorning LWS forhours 13 [2,3,40] h from. 21:00In this to study 10:00, a the typical next day,dailybut process really of formed dew formation between shows 21:00 andthat 08:00dew existed (sunset on and the sunrise LWS for are 13 at h 19:41from and21:00 08:14, to 10:00 respectively), the next day, which but demonstrated really formed that between dew forms 21:00 afterand 08:00 sunset (s withunset the and atmospheric sunrise are temperatureat 19:41 and gradually 08:14, respectively), dropping, and which evaporates demonstrated after sunrise that with dew the temperatureforms after increasingsunset with in the the secondatmospheric morning. temperature This is similar gradually with the dropping results, ofand Jacobs evaporates et al. [50 after], Moro sunrise et al. [ with26], Kabela the temperature et al. [28], Hanischincreasing et al.in the [51 ]second and Pan morning. et al. [6]. This is similar with the results of Jacobs et al. [50], Moro et al. [26], KabelaThe et daily al. [28] average, Hanisch dew et yield al. [51] in dew and days Pan waset al. 0.1 [6] mm. and the daily maximum yield was 0.62 mm, whileThe dew d daysaily average accounted dew for yield 44% in of dew the totaldays monitoring was 0.1 mm days, and andthe thedaily monthly maximum maximum yield was of dew0.62 daysmm, waswhile 25 dew days. days This accounted study re-confirmed for 44% of the that total dew monitoring manifested days a ‘high, and frequencythe monthly and maximum low yield’ of pattern.dew days Even was though 25 days daily. This dew study yield re is- veryconfirmed small, that it is beneficialdew manifested to microorganisms a ‘high frequency as long and as dew low yieldyield exceeds’ pattern. 0.03 Even mm though in desert daily areas dew [1,52 yield]. As is for very the small, desert it ecosystem, is beneficial low to frequency microorganisms of rainfall, as lightlong rain,as dew short yield duration exceeds and 0.03 intense mm in evaporation desert areas during [1,52] the. As day for inthe desert desert ecosystems ecosystem make, low itfrequency impossible of forrainfall, desert light organisms rain, short to obtain duration a water and supply intense over evaporation a long period. during Thus, the it isday very in difficult desert ecosystems for desert plantsmake toit impossible absorb water for through desert organisms their roots to due obtain to prolonged a water supply soil moisture over a deficiencieslong period. [29 Th].us, Leaf it is water very absorptiondifficult for is very desert important, plants to and absorb micro water dew can through also be their absorbed roots directly due to by prolonged plant leaves soil [ 11 moisture]. Also, frequentdeficiencies dew [ formation29]. Leaf water makes absorption the cumulated is very yield important, important forand desert micro organisms dew can and alsoimproves be absorbed the ratedirectly of seed by germinationplant leaves and[11]. the Also, survival frequent of small dew animals.formation The makes annual the dew cumulated yield pattern yield important revealed that for itdesert is higher organisms in the March–April and improves and the October–November rate of seed germination periods. and In the March–April, survival of accumulatedsmall animals snow. The beginsannual to dew melt yield slowly, pattern providing revealed sufficient that it vapor is higher for dew in the formation. March–April In October–November, and October–November the air temperatureperiods. In March difference–April, between accumulated day and snow night isbegins highest to inmelt a year, slowly, and providing the temperature sufficient drops vapor rapidly for atdew night, formation. making Init easier October to– reachNovember, the dewpoint the air fortemp vaporerature condensation. difference between March–April day and is the night main is periodhighest of in spring, a year, and and October–November the temperature drops is that rapidly of autumn. at night, Previous making studiesit easier have to reach shown the thatdewpoint some ephemeralfor vapor condensation. species in the desert,March– suchApril as is Astragalus the main period arpilobus of spring, [53], Isatis and October violascens–November [54] and Erodium is that of oxyrhynchumautumn. Previous [55] havestudies two have germination shown that seasons: some ephemeral spring and species autumn. in The the seedsdesert, of such ephemeral as Astragalus plants canarpilobus germinate [53] in, Isatis spring, violascens and complete [54] and their Erodium life cycle oxyrhynchum in summer. Then, [55] have they cantwo germinategermination in autumn,seasons: overspring winter and inautumn. the form The of seedlings,seeds of ephemeral and complete plants the can life germinate cycle in the in spring spring of, theand following completeyear their [ 56life]. Thecycle germination in summer. seasons Then, they happen canto germinate be the period in autumn, with the over most winter dew yield,in the which form furtherof seedlings, indicates and complete the life cycle in the spring of the following year [56]. The germination seasons happen to be the period with the most dew yield, which further indicates that dew may be one of the main water sources for desert organisms and plays a significant role in desert ecosystems.

Water 2019, 11, 733 13 of 17 that dew may be one of the main water sources for desert organisms and plays a significant role in desert ecosystems. Correlation analysis on single factor indicates that the occurrence of dew is not only connected to meteorological conditions, but also to soil conditions. Dew yield was positively correlated to relative humidity (RH) with its threshold greater than 70% (Figure9b) and negatively correlated to the ◦ difference between air temperature and dewpoint temperature (Tad) with its threshold less than 6 C (Figure9h). This is similar to the results of Clus et al. [ 57], Maestre-Valero et al. [58] and Beysens [2,34]. In particular, dew yield increases significantly when RH is greater than 80% and Tad is less than 3 ◦C. This indicates that dew forms before the relative humidity reaches saturation and before the air temperature drops to the dewpoint. Other factors, such as wind speed (V), air temperature (Ta), surface soil temperature (Ts), cloud cover (N) and dewpoint temperature (Td), are negatively and significantly correlated to dew yield. Wind can bring vapor, but high wind speed increases the heat exchange by convection and turbulence and hinders dew from forming [59]. Monteith thought that wind speed less than 0.5 m/s. is favorite to dew formation [60], while Lekouch et al. found that wind speed less than 3 m/s. is good for dew formation [32]. The results of this study showed that low wind speed around 0.5 m/s. (Figure9a) in this region is more conducive to dew formation. Ta and Ts are closely related to evaporation. When other factors are constant, the higher the temperature is, the greater the evaporation is [40]. Thus, dew formed is not easy to evaporate under the low air temperature and surface soil temperature. As a simple relative characterization of the radiative cooling effect between the condensing surface and the fraction of clear sky, cloud cover N (oktas) is negatively correlated 4 to cooling energy (Ri) which can be approximately expressed as Ri = (1 − εs) · σ · (285) · (1 − N/8) by Beysens [40]. εs and σ in the formula are the total sky emissivity and the Stefan–Boltzmann constant, respectively. High values of cloud cover limit radiative cooling and lead to a decrease of the condensation rate [2,32]. As for dewpoint temperature Td, lower dewpoint temperature is conducive to dew formation. The reason is that Td is negatively correlated to the sky emissivity (1 − εs), which 2 can be expressed as 1 − εs = 0.2422 × [1 + 0.204323 × H − 0.0238893 × H − (18.0132 − 1.04963 × 2 −3 H + 0.21891 × H ) × 10 × Td] by Berger and Bathiebo [61]. The variable H in the formula is the elevation of the considered site in km. Considered cooling energy (Ri), high values of Td also limit radiative cooling and lead to a decrease of the condensation rate. As for the difference between the soil temperature at 10 cm below the ground and surface soil temperature (Tss), it is positively correlated to dew yield (Figure9i). The higher temperature difference from underground to the surface created upward gradients of water vapor and heat [62], which can provide more vapors from the soil for dew condensation. As for the surface soil moisture (Ws), the higher surface soil moisture can also provide more abundant water vapor to promote the formation of dew in theory [15], but scarce rainfall in desert areas result in soil moisture at a low level for a long time and has little effect on the dew yield in this study (Figure9e). Although many models based on energy balance can estimate dew production [63–67], some parameters of the models, such as soil heat flux, heat exchange coefficient and the properties of the condensing surface, are complex and difficult to obtain. Thus, a simplified model that would demand only a few regular, simple data, available everywhere in the world, would be much more helpful. A statistical approach based on artificial networks has been successfully tested by Lekouch et al., which requires wind speed and wind direction, cloud cover, air and dewpoint temperature and/or relative humidity [32]. An analytical formula valid for planar dew collectors was built by Beysens, which only requires cloud coverage, wind speed, air and dewpoint temperature data to be collected [40]. In this study, a multivariate regression model was constructed to estimate the daily dew yield. A good agreement between calculated and measured values was found. It appears that dew yield is strongly correlated with only a few statistically independent parameters, such as wind speed (V), relative humidity (RH) and cloud cover (N). These data are collected on a regular basis in many meteorological stations and can be easily obtained. Compared with the models of Lekouch et al., wind direction was not considered as one the main factors in this study because the influence of wind direction on dew Water 2019, 11, 733 14 of 17 formation varied with geographical locations and regional prevailing wind directions. Compared with the models of Beysens, the main parameters are the same because air and dewpoint temperature data can be converted to RH by the Lawrence equation which is approximately expressed as RH = 100 − 5 × (Ta − Td) on the premise of large RH and small (Ta − Td)[2,47]. Based on meteorological data, it is easier for scientists to estimate the daily dew yield by the multivariate regression equation. However, there are some differences in dew yield at different heights from the ground [46,68,69]. The results of this study represent the dew formation characteristics on a level substrate surface at 5 cm above ground. More research focusing on dew yield at different heights should be further conducted.

5. Conclusions In a desert ecosystem, non-destructive ﬁeld measurements were carried out in the study of dew occurring on natural surfaces. It can be concluded that: (1) A typical daily process of dew formation shows that dew existed on the LWS for 13 h from 21:00 to 10:00 the next day, but really formed between 21:00 and 08:00 (sunset and sunrise are 19:41 and 08:14, respectively), which indicates dew forms after sunset with the atmospheric temperature gradually dropping and evaporates after sunrise with the temperature increasing in the second morning. (2) The daily average dew yield in dew days was 0.1 mm with a maximum of 0.62 mm, while dew days accounted for 44% of the total monitoring days, with a monthly maximum of 25 days, which can be featured as a ‘high frequency and low yield’ pattern. Compared with rainfall, the number of dew days was two times the number of rain days, while the average annual dewfall (12.21 mm) was about 1/11th of the average annual rainfall (134.6 mm). As for the annual dew yield pattern, dew mainly occurred in March–April and October–November. In March–April, accumulated snow begins to melt slowly, providing sufﬁcient vapor for dew formation. In October–November, the air temperature difference between day and night is highest in a year, and the temperature drops rapidly at night, making it easier to reach the dewpoint for vapor condensation. (3) Daily dew yield was positively correlated with relative humidity (RH) and the difference between soil temperature at 10 cm below the ground and surface soil temperature (Tss), and negatively correlated with wind speed (V), air temperature (Ta), surface soil temperature (Ts), cloud cover (N), dewpoint temperature (Td) and the difference between air temperature and dewpoint temperature (Tad). The multivariate regression equation, D = −0.705 + 0.011 × RH − 0.006 × N − 0.01 × V, can estimate daily dew yield with the thresholds of the parameters, i.e., RH > 70%, N < 7 (oktas) and V < 6 m/s..

Author Contributions: Conceptualization, Z.J.; methodology, Z.J and Z.Z.; software, Z.J., Z.Z., Q.Z. and W.W.; validation, Z.J., Z.Z., Q.Z. and W.W.; investigation, Z.J. and Z.Z.; writing—original draft preparation, Z.J.; writing—review and editing, Z.J., Z.Z., Q.Z. and W.W. Funding: This work was supported by the “111” Project (B08039), Innovation and Entrepreneurship Training Program for College Students (201810710101), National Natural Science Foundation of China (41761144059 and 41877179), special Fund for Basic Scientific Research of Central Colleges (300102299206), open fund of Key Laboratory of Subsurface Hydrology and Ecological Effect in Arid Region of Ministry of Education (300102298502) and China Postdoctoral Science Foundation (2018M633438). Acknowledgments: The authors would like to thank Zhi Wang at California State University for assisting in the dew measurements, and thank all members at Paotai Soil Improvement Test Station for providing places and facilities in the dew experiments. Conflicts of Interest: The authors declare no conflict of interest.

References

1. Kidron, G.J. Analysis of dew precipitation in three habitats within a small arid drainage basin, Negev Highlands, Israel. Atmos. Res. 2000, 55, 257–270. [CrossRef] 2. Beysens, D. Dew Water; River Publishers Series in Chemical, Environmental, and Energy Engineering; River Publishers: Gistrup, Denmark; Delft, The Netherlands, 2018. 3. Tomaszkiewicz, M.; Abou Najm, M.; Beysens, D.; Alameddine, I.; El-Fadel, M. Dew as a Sustainable Non-Conventional Water Resource: A Critical Review. Environ. Rev. 2015, 23, 425–442. [CrossRef] Water 2019, 11, 733 15 of 17

4. Kidron, G.J.; Herrnstadt, I.; Barzilay, E. The role of dew as a moisture source for sand microbiotic crusts in the Negev Desert, Israel. J. Arid Environ. 2002, 52, 517–533. [CrossRef] 5. Rao, B.Q.; Liu, Y.D.; Wang, W.B.; Hu, C.X.; Li, D.H.; Lan, S.B. Influence of dew on biomass and photosystem II activity of cyanobacterial crusts in the Hopq Desert, northwest China. Soil Biol. Biochem. 2009, 41, 2387–2393. [CrossRef] 6. Pan, Y.X.; Wang, X.P.; Zhang, Y.F.; Hu, R. Dew formation characteristics at annual and daily scale in xerophyte shrub plantations at Southeast margin of Tengger desert, Northern China. Ecohydrology 2018, 11, e1968. [CrossRef] 7. Zhang, J.; Zhang, Y.M.; Downing, A.; Cheng, J.H.; Zhou, X.B.; Zhang, B.C. The influence of biological soil crusts on dew deposition in Gurbantunggut Desert, Northwestern China. J. Hydrol. 2009, 379, 220–228. [CrossRef] 8. Kidron, G.J.; Temina, M. The effect of dew and fog on lithic lichens along an altitudinal gradient in the Negev Desert. Geomicrobiol. J. 2013, 30, 281–290. [CrossRef] 9. Pan, Y.X.; Wang, X.P. Effects of shrub species and microhabitats on dew formation in a revegetation-stabilized desert ecosystem in Shapotou, northern China. J. Arid Land 2014, 6, 389–399. [CrossRef] 10. Steinberger, Y.; Loboda, I.; Garner, W. The influence of autumn dewfall on spatial and temporal distribution of nematodes in the desert ecosystem. J. Arid Environ. 1989, 16, 177–183. [CrossRef] 11. Agam, N.; Berliner, P.R. Dew formation and water vapor adsorption in semi-arid environments—A review. J. Arid Environ. 2006, 65, 572–590. [CrossRef] 12. Groh, J.; Slawitsch, V.; Herndl, M.; Graf, A.; Vereecken, H.; Pütz, T. Determining dew and hoar frost formation for a low mountain range and alpine grassland site by weighable lysimeter. J. Hydrol. 2018, 563, 372–381. [CrossRef] 13. Wen, X.F.; Lee, X.H.; Sun, X.M.; Wang, J.L.; Hu, Z.M.; Li, S.G.; Yu, J.R. Dew water isotopic ratios and their relationships to ecosystem water pools and fluxes in a cropland and a grassland in China. Oecologia 2012, 168, 549–561. [CrossRef] 14. Richards, K. Observation and simulation of dew in rural and urban environments. Prog. Phys. Geogr. 2004, 28, 76–94. [CrossRef] 15. Saito, H.; Simunek, J.; Scanlon, B.R.; Reedy, R.C. Numerical Analysis of Coupled Water, Vapor, and Heat Transport in the Vadose Zone. Vadose Zone J. 2006, 5, 784–800. [CrossRef] 16. Monteith, J.L.; Unsworth, M. Principles of Environmental Physics, 2nd ed.; Edward Arnold: London, UK, 1990; p. 291. 17. Pan, Y.X.; Wang, X.P.; Zhang, Y.F. Dew formation characteristics in a revegetation-stabilized desert ecosystem in Shapotou area, Northern China. J. Hydrol. 2010, 387, 265–272. [CrossRef] 18. Ye, Y.H.; Zhou, K.; Song, L.Y.; Jin, J.H.; Peng, S.L. Dew amounts and its correlations with meteorological factors in urban landscapes of Guangzhou, China. Atmos. Res. 2007, 86, 21–29. [CrossRef] 19. Beysens, D. The formation of dew. Atmos. Res. 1995, 39, 215–237. [CrossRef] 20. Li, X.Y. Effects of gravel and sand mulches on dew deposition in the semiarid region of China. J. Hydrol. 2002, 260, 151–160. [CrossRef] 21. Zhang, X.; Li, X.; Wang, W. Experimental Observation Analysis on Dew Formation in Southern Mu us Sandy Land. Arid Meteorol. 2008, 26, 8–13. 22. Ritter, F.; Berkelhammer, M.; Beysens, D. Dew frequency across the US from a network of in situ radiometers. Hydrol. Earth Syst. Sci. Eur. Geosci. Union 2019, 23, 1179–1197. [CrossRef] 23. Duvdevani, S. An optical method of dew estimation. Q. J. R. Meteorol. Soc. 1947, 73, 282–296. [CrossRef] 24. Davis, D.R.; Hughes, J.E. A new approach to recording the wetting parameter by the use of electrical resistance sensors. Plant Dis. Rep. 1970, 54, 474–479. 25. Zangvil, A. Six years of dew observations in the Negev Desert, Israel. J. Arid Environ. 1996, 32, 361–371. [CrossRef] 26. Moro, M.J.; Were, A.; Villagarcía, L.; Cantoń, Y.; Domingo, F. Dew measurement by Eddy covariance and wetness sensor in a semiarid ecosystem of SE Spain. J. Hydrol. 2007, 335, 295–302. [CrossRef] 27. Sentelhas, P.C.; Marta, A.D.; Orlandini, S. Suitability of relative humidity as an estimator of leaf wetness duration. Agric. For. Meteorol. 2008, 148, 392–400. [CrossRef] 28. Kabela, E.D.; Hornbuckle, B.K.; Cosh, M.H.; Anderson, M.C.;Gleason, M.L. Dew frequency, duration, amount, and distribution in corn and soybean during SMEX05. Agric. For. Meteorol. 2009, 149, 11–24. [CrossRef] Water 2019, 11, 733 16 of 17

29. Wang, X.; Gao, Z.Y.; Wang, Y.K.; Wang, Z.; Jin, S. Dew measurement and estimation of rain-fed jujube (Zizyphus jujube Mill) in a semi-arid loess hilly region of China. J. Arid Land 2017, 9, 547–557. [CrossRef] 30. Jia, Z.F.; Wang, Z.; Wang, H. Characteristics of Dew Formation in the Semi-Arid Loess Plateau of Central Shaanxi Province, China. Water 2019, 11, 126. [CrossRef] 31. Muselli, M.; Beysens, D.; Mileta, M.; Milimouk, I. Dew and rain water collection in the Dalmatian Coast, Croatia. Atmos. Res. 2009, 92, 455–463. [CrossRef] 32. Lekouch, I.; Lekouch, K.; Muselli, M.; Mongruel, A.; Kabbachi, B.; Beysens, D. Rooftop dew, fog and rain collection in southwest Morocco and predictive dew modeling using neural networks. J. Hydrol. 2012, 448–449, 60–72. [CrossRef] 33. Beysens, D.; Milimouk, I.; Nikolayev, V.; Muselli, M.; Marcillat, J. Using radiative cooling to condense atmospheric vapour: A study to improve water yield. J. Hydrol. 2003, 276, 1–11. [CrossRef] 34. Beysens, D.; Pruvost, V.; Pruvost, B. Dew observed on cars as a proxy for quantitative measurements. J. Arid Environ. 2016, 135, 90–95. [CrossRef] 35. Zhang, Q.; Wang, S.; Yang, F.L.;Yue, P.; Yao, T.; Wang, W.Y. Characteristics of Dew Formation and Distribution, and Its Contribution to the Surface Water Budget in a Semi-arid Region in China. Bound. Layer Meteorol. 2015, 154, 317–331. [CrossRef] 36. Kidron, G.J.; Kronenfeld, R. Assessing the effect of micro-lysimeters on NRWI: Do micro-lysimeters adequately represent the water input of natural soil? J. Hydrol. 2017, 548, 382–390. [CrossRef] 37. Meissner, R.; Seeger, J.; Rupp, H.; Seyfarth, M.; Borg, H. Measurement of dew, fog, and rime with a high-precision gravitation lysimeter. J. Plant Nutr. Soil Sci. 2007, 170, 335–344. [CrossRef] 38. Ucles, O.; Villagarcia, L.; Moro, M.J.; Canton, Y.; Domingo, F. Role of dewfall in the water balance of a semiarid coastal steppe ecosystem. Hydrol. Process. 2014, 28, 2271–2280. [CrossRef] 39. Gao, Z.; Wang, Y.; Wang, X. Dew Amount of Jujube Plantation in Semi-arid Loess Hilly-gully Region. Trans. Chin. Soc. Agric. Mach. 2015, 46, 105–115. (In Chinese) 40. Beysens, D. Estimating dew yield worldwide from a few meteo data. Atmos. Res. 2016, 167, 146–155. [CrossRef] 41. Fan, L.L.; Tang, L.S.; Wu, L.F.; Ma, J.; Li, Y. The limited role of snow water in the growth and development of ephemeral plants in a cold desert. J. Veg. Sci. 2014, 25, 681–690. [CrossRef]

42. Tiemuerbieke, B.; Min, X.J.; Zang, Y.X.; Xing, P.; Ma, J.Y.; Sun, W. Water use patterns of co-occurring C3, and C4, shrubs in the Gurbantonggut desert in northwestern China. Sci. Total Environ. 2018, 634, 341–354. [CrossRef] 43. Zhao, R.; Hui, R.; Liu, L.; Xie, M.; An, L.Z. Effects of snowfall depth on soil physical–chemical properties and soil microbial biomass in moss–dominated crusts in the Gurbantunggut Desert, Northern China. Catena 2018, 169, 175–182. [CrossRef] 44. Zhou, X.B.; Zhang, Y.M.; Yin, B.F. Divergence in physiological responses between cyanobacterial and lichen crusts to a gradient of simulated nitrogen deposition. Plant Soil 2016, 399, 121–134. [CrossRef] 45. Decagon Devices, Inc. Dielectric Leaf Wetness Sensor Operator’s Manual Version 3; Decagon Devices, Inc.: Pullman, WA, USA, 2007–2010. 46. Kidron, G.J. The effect of substrate properties, size, position, sheltering and shading on dew: An experimental approach in the Negev Desert. Atmos. Res. 2010, 98, 378–386. [CrossRef] 47. Lawrence, M.G. The relationship between relative humidity and the dewpoint temperature in moist air. Am. Meteorol. Soc. 2005, 86, 225–233. [CrossRef] 48. Alduchov,O.A.; Eskridge, R.E. Improved Magnus form approximation of saturation vapor pressure. J. Appl. Meteorol. 1996, 35, 601–609. [CrossRef] 49. Beysens, D.; Muselli, M.; Nikolayev, V.; Narhe, R.; Milimouk, I. Measurement and modelling of dew in island, coastal and alpine areas. Atmos. Res. 2005, 73, 1–22. [CrossRef] 50. Jacobs, A.F.G.; Heusinkveld, B.G.; Kruit, R.J.W.; Berkowicz, S.M. Contribution of dew to the water budget of a grassland area in the Netherlands. Water Resour. Res. 2006, 42, 446–455. [CrossRef] 51. Hanisch, S.; Lohrey, C.; Buerkert, A. Dewfall and its ecological signiﬁcance in semi-arid coastal south-western Madagascar. J. Arid Environ. 2015, 121, 24–31. [CrossRef] 52. Kappen, L.; Lange, O.L.; Schulze, E.D.; Evenari, M.; Buschbom, V. Ecophysiological investigations on lichens of the Negev Desert: IV. Annual course of the photosynthetic production of Ramalina maciformis (Del.) Bory. Flora 1979, 168, 85–105. [CrossRef] Water 2019, 11, 733 17 of 17

53. Long, Y.; Tan, D.Y.; Baskin, C.C.; Baskin, J.M. Seed dormancy and germination characteristics of Astragalus arpilobus (Fabaceae, subfamily Papilionoideae), a central Asian desert annual ephemeral. S. Afr. J. Bot. 2012, 83, 68–77. [CrossRef] 54. Zhou, Y.M.; Lu, J.J.; Tan, D.Y.; Baskin, C.C.; Baskin, J.M. Seed germination ecology of the cold desert annual Isatis violascens (Brassicaceae): Two levels of physiological dormancy and role of the Pericarp. PLoS ONE 2015, 10, e0140983. [CrossRef] 55. Chen, Y.; Zhang, L.; Shi, X.; Ban, Y.; Liu,H.; Zhang, D. Life history responses of spring-and autumn-germinated ephemeral plants to increased nitrogen and precipitation in the Gurbantunggut Desert. Sci. Total Environ. 2019, 659, 756–763. [CrossRef] 56. Lu, J.J.; Tan, D.Y.; Baskin, C.C.; Baskin, J.M. Effects of germination season on life history traits and on transgenerational plasticity in seed dormancy in a cold desert annual. Sci. Rep. 2016, 6, 25076. [CrossRef] 57. Clus, O.; Ortega, P.; Muselli, M.; Milimouk, I.; Beysens, D. Study of dew water collection in humid tropical islands. J. Hydrol. 2008, 361, 159–171. [CrossRef] 58. Maestre-Valero, J.; Martinez-Alvarez, V.; Baille, A.; Martín-Górriz, B.; Gallego-Elvira, B. Comparative analysis of two polyethylene foil materials for dewharvesting in a semi-arid climate. J. Hydrol. 2011, 410, 84–91. [CrossRef] 59. Sharan, G.; Beysens, D.; Milimouk-Melnytchouk, I. A study of dew water yields on Galvanized iron roofs in Kothara (North-West India). J. Arid Environ. 2007, 69, 259–269. [CrossRef] 60. Monteith, J.L. Dew. Q. J. R. Meteorol. Soc. 1957, 83, 322–341. [CrossRef] 61. Berger, X.; Bathiebo, J. Clear sky radiation as a function of altitude. Int. J. Renew. Energy 1992, 2, 139–157. [CrossRef] 62. Hao, X.M.; Li, C.; Guo, B.; Ma, J.X.; Ayup, M.; Chen, Z.S. Dew formation and its long-term trend in a desert riparian forest ecosystem on the eastern edge of the Taklimakan Desert in China. J. Hydrol. 2012, 472–473, 90–98. [CrossRef] 63. Garratt, J.R.; Segal, M. On the contribution to dew formation. Bound.-Layer Meteorol. 1988, 45, 209–236. [CrossRef] 64. Xiao, H.; Meissner, R.; Borg, H.; Wang, R.; Cao, Q. Agreement of Four Equations for Computing Dewfall in Northern Germany. Water 2017, 9, 607. [CrossRef] 65. Jacobs, A.F.G.; Heusinkveld, B.G.; Berkowicz, S.M. Passive dew collection in a grassland area, the Netherlands. Atmos. Res. 2008, 87, 377–385. [CrossRef] 66. Maestre-Valero, J.F.; Ragab, R.; Martínez-Alvarez, V.; Baille, A. Estimation of dew yield from radiative condensers by means of an energy balance model. J. Hydrol. 2012, 460–461, 103–109. [CrossRef] 67. Wang, H.; Sanchez-Molina, A.S.; Li, M.; Díaz, F.R. Improving the Performance of Vegetable Leaf Wetness Duration Models in Greenhouses Using Decision Tree Learning. Water 2019, 11, 158. [CrossRef] 68. Subramaniam, A.R.; Kesava Rao, A.V.R. Dew fall in sand dune areas of India. Int. J. Biometeorol. 1983, 27, 271–280. [CrossRef] 69. Yan, B.X.; Deng, W. Study on the dew resource in Sanjiang Plain. J. Nat. Resour. 2004, 19, 732–737. (In Chinese)

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).