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Article

Prediction of Biome-Specific Potential Evapotranspiration in Mongolia under a Scarcity of Weather Data

1
Department of Earth and Atmospheric Sciences, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
2
School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
3
Department of Agricultural Sciences, University of Naples Federico II, 80055 Naples, Italy
*
Author to whom correspondence should be addressed.
Water 2021, 13(18), 2470; https://doi.org/10.3390/w13182470
Received: 6 July 2021 / Revised: 2 September 2021 / Accepted: 4 September 2021 / Published: 8 September 2021
(This article belongs to the Section Hydrology)

Abstract

:
We propose practical guidelines to predict biome-specific potential evapotranspiration (ETp) from the knowledge of grass-reference evapotranspiration (ET0) and a crop coefficient (Kc) in Mongolia. A paucity of land-based weather data hampers use of the Penman–Monteith equation (FAO-56 PM) based on the Food and Agriculture Organization (FAO) guidelines to predict daily ET0. We found that the application of the Hargreaves equation provides ET0 estimates very similar to those from the FAO-56 PM approach. The Kc value is tabulated only for crops in the FAO-56 guidelines but is unavailable for steppe grasslands. Therefore, we proposed a new crop coefficient, Kc adj defined by (a) net solar radiation in the Gobi Desert (Kc adjD) or (b) leaf area index in the steppe region (Kc adjS) in Mongolia. The mean annual ETp obtained using our approach was compared to that obtained by FAO-56 guidelines for forages (not steppe) based on tabulated Kc values in 41 locations in Mongolia. We found the differences are acceptable (RMSE of 0.40 mm d−1) in northern Mongolia under high vegetation cover but rather high (RMSE of 1.69 and 2.65 mm d−1) in central and southern Mongolia. The FAO aridity index (AI) is empirically related to the ETp/ET0 ratio. Approximately 80% and 54% reduction of ET0 was reported in the Gobi Desert and in the steppe locations, respectively. Our proposed Kc adj can be further improved by considering local weather data and plant phenological characteristics.

Graphical Abstract

1. Introduction

Groundwater represents a vital water resource for ecosystems within an arid continental climate. Management of this resource relies on the knowledge of groundwater recharge (GR). However, in vast territories such as Mongolia, direct measurements of GR are unrealistic because they involve excessive costs from time-consuming and labor-intensive efforts. The use of hydrological models for simulating the water balance (and GR) within the groundwater–soil–plant–atmosphere continuum represents a valid alternative to direct measurements if the soil hydraulic properties and vegetation characteristics are properly assessed and initial and boundary conditions are well-known [1]. The main advantage of modeling is that the user needs only crop-specific potential evapotranspiration (ETp) and precipitation (P) for obtaining simulations of GR. Precipitation measurements are commonly available and reasonably accurate. In contrast, obtaining a reliable prediction of ETp represents a challenge in countries with limited availability of land-based, full suite weather data. This ETp may be estimated by multiplying a grass-reference crop evapotranspiration (ET0) by a time-variant crop coefficient, Kc [2]. The reference grass is defined as a hypothetical crop with a height of 0.12 m, a surface resistance of 70 s m−1, and an albedo of 0.23 in a well-watered field [2]. Subsequently, ETp is partitioned into potential evaporation (Ep) and potential transpiration (Tp) depending on the knowledge of leaf area index (LAI) or vegetation cover fraction.
The concept of potential evapotranspiration is broadly used in hydrology and supported by established methods and software packages that account for non “well-watered” soil moisture conditions (e.g., HYDRUS [3] and MODFLOW [4]). The Ep and P represent the system-dependent boundary conditions of the hydrologic model over the soil profile, while Tp refers to potential root water uptake. Therefore, a reliable prediction of ETp is fundamental in numerical models for obtaining the GR and actual evapotranspiration (ETa) that represents a reduction of ETp induced by water stress.
The available models to estimate ET0 can be classified as: (i) full physically based models describing mass and energy conservation principles; (ii) semi-physically based models that deal with either mass or energy conservation; and (iii) black-box models based on empirical relationships and machine learning algorithms [5,6,7]. The Penman–Monteith equation, based on the Food and Agriculture Organization (FAO) guidelines (FAO-56 PM), is internationally recognized as the standard approach for computing ET0 [2,8]. This equation is considered the most reliable method as it is based on the energy balance incorporating physiological and aerodynamic parameters without any local calibration under all types of climatic conditions [2,9]. However, the FAO-56 PM method entails the availability of a complete, continuous suite of weather data including solar radiation, wind speed, air temperature, and relative humidity. However, these variables are often unavailable in data-limited countries where meteorological parameters are hard to obtain or are available only in the form of useless short-time series that differ among stations by periods of data collection. Some countries do not have a uniform distribution of full-suite weather sites or lack public access to these data, hindering large-scale and long-term agro-hydrological studies. However, long-term air temperature data are primarily available in spatially dense weather networks across Mongolia.
Daily ET0 is converted into biome-specific potential evapotranspiration (ETp), which refers to the evapotranspiration demand from a grassland biome (e.g., steppe grassland in Mongolia) under optimum soil water conditions [10]. This conversion entails the knowledge of the time-variant crop coefficient, Kc, which is well-documented for vegetables, cereals, forages, and fruit trees [2]. However, to our knowledge, a Kc representing vegetation properties under natural vegetation conditions in Mongolia is currently unavailable in the body of literature [11,12,13]. Obtaining this coefficient will allow one to estimate ETp at daily time steps. This approach is more efficient than inferring values from images of remote sensing data only as noted by [14], who studied ET0 from the north China regions bordering with Mongolia.
According to [15], the normalized difference vegetation index (NDVI) shows strong positive correlations with evapotranspiration over the majority of grassland areas except for the region near the Gobi Desert [16]. Significant difference occurs between ET0 and ETa in this biome. Hence, the regular Kc taken from FAO guidelines is not appropriate in the arid, sparsely vegetated Gobi Desert areas. Research from Inner Mongolia (China) supports this assumption [13]. Therefore, one may adapt a time-variant crop coefficient, Kc adj to steppe grasslands in Mongolia depending on satellite-derived leaf area index (LAI) in the steppe region (Kc adjS)and solar net radiation in the Gobi Desert (Kc adjD). Net solar radiation can be estimated from easily accessible weather data, while monthly maps of leaf area index can be retrieved from remote sensing products.
To address this ETp knowledge gap in Mongolia, we propose a two-fold approach. The first objective is to select a suitable temperature method to estimate daily ET0 values under data paucity constraints. A limited number of sites with full-suite weather data may be used to evaluate estimates of ET0. The second objective is to develop a new time-variant crop coefficient (Kc adj) to convert ET0 into ETp in Mongolian biomes. The mean annual ETp obtained using this approach could be compared to that obtained by FAO-56 guidelines for forages (not steppe) based on tabulated Kc values in locations across Mongolia.
Ultimately, these estimates of ETp will be used as crucial input data for ground water recharge models which will be presented in future work.

2. Materials and Methods

2.1. Environmental Settings and Data Availability

Mongolia is a landlocked territory covering about 1.6 million km2, located in the heart of the Asian continent. Mongolia has borders with the Russian Federation in the north (3543 km long) and China in the south (4709 km) [17]. Mongolia lies on a high plateau surrounded by mountain ridges in the transition zone between the Siberian taiga and the dry steppes and semi-deserts of central Asia. The country has a dry subarctic continental climate with long cold winters and short hot summers.
The average number of rainy days per year is 105–115. Annual average precipitation (P) is generally low decreasing from the north (350–500 mm) to the south (less than 50 mm) [17]. Maximum (almost 85%) seasonal P occurs in summer [8]. The average annual temperature ranges from −8 °C to +2 °C and is negative from October to March in most parts of the country [17].
In this territory, 80% is pasture-land, 10% forest, 1% farmland, and 9% other types of land. Steppe vegetation is the most common in Mongolia and occupies about 83% of the territory [18]. It lies mainly in the central part of the country, the transitional zone bordering the Gobi Desert to the south, and mountain taiga to the north. The steppe ecosystems are associated with the semi-arid and arid continental temperate climates of the region and are ecologically fragile and sensitive to climate change and anthropogenic disturbances [19]. Perennial plants (50–90%) dominate the Mongolian steppe. The highest percentage of perennial plants occurs in the high-cold steppe. In contrast, the percentage of shrub, dwarf shrub, biennials, and annuals is minimum in the high-cold steppe and gradually increases in the desert steppe [18]. About half of the Mongolian territory is mountainous with an average elevation of 1580 m a.s.l.; about 81% of its territory is above 1000 m and 19% below 1000 m [17]. These mountains are divided into cool and dry types according to their formation of vertical vegetation range. Khentii, Khuvsgul, northwestern Mongolian Altai, Northern Khangai, and Khyangan are referred to as cool type mountains and comprise steppe vegetation. Southern Altai, Gobi Altai, Gobi, and Zuungar mountains are referred to as dry type with desert vegetation and high-cold steppe [18].
We identified a total of 41 locations relatively uniformly distributed across Mongolia with available weather data (Figure 1). We specified that ten weather stations (blue triangles in Figure 1) had a complete set of weather data, while the remaining 31 stations (black triangles) provided only T and P data. The study locations were chosen considering the density, physical geography, latitude and altitude, land use, climate class, and data availability.
The complete data set belonging to the ten weather stations will be exploited for estimating daily ET0 with the FAO-56 PM equation and temperature-based equations (Section 2.2.). Table 1 shows information on data sources used in this study for all 41 study locations.
Daily P data were retrieved from the National Agency of Meteorology and Environmental Monitoring (NAMEM) [21]. T data at Khatgal weather station were validated with those obtained from the National Oceanic and Atmospheric Administration (NOAA) website [22]. This comparison shows high correlation between remote-sensing and ground-truthing data (available in Figure S1 in Supplemental Materials). Since the ground-based leaf area index (LAI) measurements are highly variable in space and time, the estimates from the remotely sensed products were assumed valid and reliable and were not validated with ground-truth measurements [24]. The LAI monthly mean values were obtained from the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) website [23] providing a global 0.25° × 0.25° gridded monthly mean LAI over the period from August 1981 to August 2015. The data were derived from the Advanced Very High-Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS), and the bi-weekly LAI values were averaged for every month (Figure S2 in Supplemental Materials). Due to the low vegetation cover, the LAI in some parts of the Gobi Desert is unavailable. The inverse distance weighted interpolation tool was used in ArcGIS software (Esri, West Redlands, CA, USA) to estimate the weighted average of the LAI values in the neighborhood of each processing cell. As it follows from the name, this method uses the inverse distance to each point when assigning weights. The monthly average values of LAI extracted from the map at study locations are available (Figure S3 in Supplemental Materials).

2.2. Prediction of Reference Evapotranspiration, ET0, and Biome-Specific Potential Evapotranspiration, ETp

We considered two well-known limited-data-requirement equations to predict ET0, namely, Hargreaves (Har) [25] and Thornthwaite (Tho) [26,27]. We evaluated the prediction performance of Har and Tho equations by comparing these two methods with the FAO-56 PM equation by using a complete meteorological dataset at 10 weather stations [28,29,30] and selected the best performing temperature method to estimate ET0 (Figure 2a).
The equations are reported in Table 2, and we briefly report necessary input data for each equation. In the FAO-56 PM equation, Rn is net radiation at plant surface (MJ m−2 d−1), G is the soil heat flux density (MJ m−2 d−1), T is air temperature (°C), u2 is the wind speed at 2 m height above ground (m s−1), es and ea are saturated and actual vapor pressures (kPa), Δ is the slope of the vapor pressure curve (kPa °C−1), and γ is the psychometric constant (kPa °C−1). Net radiation is usually indirectly measured by a pyranometer. If a weather station lacks pyranometer data, Rn can be estimated from the actual daily duration of bright sunshine (hours per day) [2]. The term G is computed as a fraction of Rn, as suggested by [2] for the reference crop. In the Har equation, Ra is the extraterrestrial radiation expressed in mm d−1 (obtained by multiplying MJ m−2 d−1 by 0.408), Tm (°C), Tmin (°C), and Tmax (°C) represent mean, minimum, and maximum temperature, respectively. In the Tho equation, the value I represents the annual heat index, Tm represents i-th month mean air temperature (°C), and h depicts hours of sunlight (hours).
The daily maximum, minimum, mean temperature, relative humidity, and wind speed data were obtained from NAMEM [21]. The hours of sunlight data were downloaded at the nearest available station [31]. The terms Ra and Rn were indirectly estimated from the guidelines on missing climatic data from the FAO-56 report [2].
The FAO-based aridity index (AI) is computed as the ratio between mean annual P and ET0 [32] and will be used in our study. This index can be considered a proper indicator for climate classification in Mongolia.
After selecting the best temperature model, daily ET0 was calculated in all 41 study locations with easily accessible data. The crop coefficient considers plant characteristics, such as height, leaf area index, and leaf and stomata properties in order to convert the reference grass ET0 into ETp. These plant characteristics indeed influence the aerodynamic resistance, the albedo of crop-soil surface, and canopy resistance. The FAO-56 guidelines report [2] tabulated Kc values for crops, vegetables, forages, and fruit trees referring to a sub-humid climate with an average daytime minimum relative humidity of about 45% and with wind speeds averaging 2 m s−1. These tabulated Kc values under the abovementioned climatic conditions are not present in Mongolia. A study carried out in neighboring parts of Inner Mongolia [12] across the border demonstrates that a developed Kc for steppe in the arid and semi-arid zone is lower than the available Kc value (grazing pasture) taken from the FAO-56 guidelines [2]. The natural vegetation conditions in those climate zones have not been studied in-depth, unlike agricultural crops. Permanent monitoring stations are absent, and the exact vegetation condition in those arid, semi-arid areas is mostly unknown. Therefore, the use of guidelines to develop Kc from FAO-56 report [2] is fraught with uncertainties.
Nevertheless, the natural zones can be grouped into land cover zones of the Gobi Desert and the steppe (Table A1 in Appendix A). Then, different methods to develop Kc based on different climate and phenological characteristics for those two zones were explored and implemented in our study (Figure 2b). Previous studies have indicated that the Kc values vary significantly during the growing season; therefore, it is impossible to assume Kc as constant over time. This study attempts to propose a time-variant crop coefficient, Kc adj, using easily-retrievable data, such as solar radiation or LAI, and therefore, ETp values can be obtained (ETp = ET0 × Kc adj) in study locations.
In the Gobi Desert ecosystem, the crop coefficient taken from the FAO-56 guidelines [2] is not suitable due to scarce and sparse vegetation. The following simple relation is, therefore, used to estimate daily variations of the proposed crop coefficient [12], Kc adjD, based on the measurement of solar radiation:
K c   a d j D = 0.02 R n
where Rn is net solar radiation.
In the steppe zone in Mongolia, similar studies are scarce, and there are no readily available approaches providing crop coefficients in the FAO-56 report [2]. Some guidelines for (non-crop) grassland in arid climates require parameters such as vegetation height, air relative humidity, and wind speed, and their use is not straightforward. For example, the crop coefficient, Kc,p, in a non-irrigated pasture site in Florida, USA [33] ranged from 0.47 to 0.92 and could be presented by a linear function of leaf area index (LAI):
K c , p = a L A I + b
where the empirical parameters are assumed as a = 0.330 and b = 0.451. However, the natural vegetation growth is subject to various constraints. Therefore, we propose to adapt the crop coefficient to steppe grassland (Kc adjS) proposed by [2] for sparse vegetation under local conditions:
K c   a d j S = K c , p   A c m
where Acm is another empirical parameter given by the following equation:
A c m = 1 [ L A I L A I d e n s e ] 0.5
where LAIdense is the LAI expected for the same crop under normal, standard crop management practices. The LAIdense can be predicted from the ground cover ratio.
The same linear function could not be applied in all study locations throughout the steppe due to contrasting vegetation characteristics. After multiple attempts, the LAIdense values in the steppe zone in study locations with higher-than-average LAI values (LAI > 0.6) and locations with lower-than-average values (LAI < 0.6) were calculated using the following equations:
L A I d e n s e = { 0.95 0.2 0.6 0 L A I + 0.2 , L A I < 0.6 3.03 0.95 2.53 0.6 ( L A I 0.6 ) + 0.95 , L A I > 0.6
Figure 3 shows Acm and LAIdense as a function of LAI.
By estimating LAIdense from Equation (5), daily Kc adjS and Acm can be calculated in Equations (3) and (4), respectively, and therefore, ETp values can be obtained (ET0 × Kc adjS) in study locations belonging to the steppe zone in Mongolia. Grass pasture is assumed to be predominant in the steppe. The growing season of grass pasture is assumed to start seven days before recording 4 °C in spring for the last time until seven days after recording −4 °C in fall for the first time in all study locations. The crop coefficient in the dormant season is considered as 0.1 in all study locations by the guidance of [2].
As indicated above, leaf area index LAI plays a critical role in generating input data for modeling the water balance of the vadose zone. This parameter is used in Beer’s law, which partitions ETp into potential evaporation, Ep, and potential transpiration, Tp [34]:
E p = E T p e k L A I = E T p ( 1 S C F ) = E T p e 0.463 L A I
T p = E T p ( 1 e k L A I ) = E T p S C F = E T p E p
where SCF is the soil cover fraction (–), and k is the radiation extinction constant (–), usually assumed to be equal to 0.463 as indicated by various studies, e.g., [34,35,36].

2.3. Evaluation Criteria

To measure the predictive capability of all prediction methods mentioned above, we selected two statistical performance indicators: the root mean square error (RMSE), which combines both bias and lack of precision, and the coefficient of determination (R2), which measures how well the data pairs fit to a line:
R M S E = 1 n i = 1 n ( o i e i ) 2
R 2 = i = 1 n ( o i e i ) 2 i = 1 n ( o i o ¯ ) 2
where oi is the reference value (for example, FAO-56 PM ET0), o ¯ is the mean of reference values, and ei indicates estimated values (for example, ET0-Har and ET0-Tho). Subscript i is the index of data in series (or day number), and n is the total number of days. Daily values of ET0 and ETp will also be aggregated at monthly and annual sums; therefore, RMSE units are also expressed as mm month−1 and mm year−1, respectively.

3. Results

3.1. Prediction of Grass-Reference Evapotranspiration, ET0

The relationship between meteorological variables and FAO-56 PM daily ET0 for 10 weather stations is expressed in terms of Pearson correlation coefficients presented in Table 3.
High positive correlation coefficients are observed between ET0 and air temperature and net radiation, while negative correlation coefficients are reported when relating ET0 to the relative humidity. The results agree with the conclusion of [37] that the net radiation and air temperature are the most important controlling factors on ET0. This proves the potential of temperature-based ET0 models in Mongolia.
When considering estimates of daily ET0 values, the comparison between Har and FAO-56 PM equations (blue line in Figure 4) leads to a minimum RMSE of 0.56 mm d−1 (R2 = 0.93) in Khatgal and a maximum RMSE of 1.44 mm d−1 (R2 = 0.87) in Tsogtovoo. The comparison between Tho and FAO-56 PM equations (red line in Figure 4) spans between RMSE of 0.76 mm d−1 (R2 = 0.88) in Bulgan and RMSE of 1.74 mm d−1 (R2 = 0.85) in Tsogtovoo. The Har ET0 method shows lower RMSE and higher R2 than those obtained from the Tho equation over the 10 test locations. The comparison between FAO-56 PM-based and temperature-based equations for predicting daily values of ET0 at Khatgal and Tsogtovoo weather stations are presented in Figure S4 in Supplemental Materials.
A temperature-based model ignores the importance of meteorological variables such as relative humidity, solar radiation, and vapor pressure deficit, as diagnosed by high correlation coefficients listed in Table 3. The FAO-56 PM-based monthly cumulative ET0 also better matches with the Har method, as can be seen in Figure S5 in Supplemental Materials. One of the drawbacks of the Tho method is that ET0 can not be calculated in the winter months when the temperature drops below 0 °C. Therefore, according to the cumulative results, the Tho method systematically underestimates the reference ET0 estimated by the FAO-56 PM equation.
The mean annual ET0 sums predicted by the three methods are visualized in Figure 5. The FAO-56 PM-based predictions of mean annual ET0 sums are in accordance with the estimates in Inner Mongolia (China) reported by [38]. ET0, predicted by the Har and Tho equations, consistently suffers from underestimation. RMSE is 159.3 mm y−1 and 248.3 mm y−1 for Har and Tho equation, respectively.
As seen from the statistical results and cumulative comparisons, the Har outperforms the Tho equation for estimating ET0. We concur with [39] that lack of local calibration might be unacceptable for practical applications. We are also aware that [40] observed that the Har equation generates a significant bias in northeastern China. However, we are forced to use the uncalibrated Har ET0 method due to the chronic data paucity in Mongolia. Therefore, we selected the Har equation over the 41 study locations to predict ET0, which will be converted into ETp in Section 3.2. Annual sums of Har-ET0 are presented in Table A2 in Appendix A. The ET0 annual sums broadly varied in space with low temporal variability in each station through 5 years (2007–2011). The coefficient of variation (CV) was lower than 10% in 40 out of 41 stations. If we consider the spatial-average ET0 in each year, 2007 and 2011 had the highest and lowest values, respectively.
Finally, we carried out a second validation of the Har ET0 predictions. A global map of monthly ET0 estimated with FAO-56 PM referring to the period 1961–1990 [41] was used to aggregate the mean annual ET0 (Figure 6) and extract corresponding values over the 41 weather stations.
ET0 values tend to increase towards the south in general. The lowest mean annual ET0 value is 634 mm, while the highest is 1129 mm. The Har ET0 values are compared to the corresponding FAO-56 PM ET0 by obtaining RMSE = 93 mm y−1 and R2 = 0.69. Nevertheless, the Har-based ET0 results look consistent with [41] and previous studies carried out in Mongolia [8,25,42] and Inner Mongolia [14] and appear to form a reliable basis for further processing and modeling.

3.2. Prediction of Biome-Specific Potential Evapotranspiration, ETp

The ETp is calculated by multiplying ET0 with the time-variant Kc adj in the Gobi Desert (Kc adjD) and in the steppe grasslands (Kc adjS), as described beforehand. By assuming the reliability of Har ET0 predictions, our attention is now focused on the assessment of crop coefficient, Kc adj in Mongolia, and its impact on the prediction of ETp. To this end, we considered six weather stations in the steppe region (Sukhbaatar, Tosontsengel, Baruunkharaa, Undurkhaan, Erdenesant, Arvaikheer) and one representative weather station in the Gobi Desert (Saikhanovoo) (Figure S6 in Supplemental Materials) where we predicted ETp by using the developed Kc adj for natural vegetation (Figure 7b) or the tabulated Kc (Figure S7 in Supplemental Material) based on grazing pasture in the FAO-56 guidelines (see Table 17 in [2]). Our developed crop coefficient, Kc adj, depends on LAI (Figure 7a) retrieved from remote-sensing monthly maps in the grassland steppe (Kc adjS) or on solar radiation in the Gobi Desert (Kc adjD). We observed a decrease in LAI (Figure S2 in Supplemental Materials) and Kc adjS towards the south, where conditions become arid. The use of time-variant LAI has the advantage of generating time-variant Kc adjS according to our methodology. In contrast, tabulated values in the FAO-56 report provide constant Kc values in three growing season phases.
The developed (Kc adj) and tabulated Kc values are used to convert ET0 into biome-specific ETp. Figure 8 shows an illustrative example by comparing predictions of ETp at three stations (Sukhbaatar, Arvaikheer and Saikhanovoo) with contrasting vegetation cover. Kc adjS is used at Sukhbaatar and Arvaikheer (steppe region) while Kc adjD is used at Saikhanovoo (Gobi Desert). The prediction of ETp with both Kc adj and tabulated Kc values was similar over the first weather station (Sukhbaatar), with vegetation cover characterized by LAI > 1 and Kc adjS > 0.6 during the growing season in summer (green circles in Figure 7). Nevertheless, vegetation cover halved at the second weather station (Arvaikheer) or reduced to almost zero at the third weather station in the Gobi Desert (Saikhanovoo) in terms of LAI and Kc adj (Figure 7). The impact of the developed Kc adj value on the ETp prediction can be visualized in Figure 8. The tabulated Kc values (red circles in Figure 8a–c) generated similar predictions of ETp over the three stations despite the contrast in vegetation cover. On the other hand, the impact of the scarce vegetation cover on the developed Kc adj value over Arvaikheer and especially Saikhanovoo in the Gobi Desert induced a drastic reduction in ETp (blue circles in Figure 8a–c). The comparison between ETp predictions based on tabulated Kc and ETp predictions based on developed Kc adj is shown in Figure 8d–f. Despite high R2 values diagnosing similar temporal trends in response to seasonal temperature change, we report consistent bias over the weather station at Arvaikheer (RMSE = 1.69 mm d−1, Figure 8e) and Saikhanovoo (RMSE = 2.65 mm d−1, Figure 8f) in the Gobi Desert.
In contrast, both tabulated and developed Kc adjS at the weather station of Sukhbaatar led to a similar prediction of ETp (Figure 8d) as diagnosed by a very low RMSE (RMSE = 0.40 mm d−1).
The mean annual sums of ETp based on tabulated (orange bars) and developed (blue bars) Kc over the seven weather stations are displayed in Figure 9 with increasing discrepancy under scarce vegetation cover conditions, characterized by a gradient from north to south. Very low differences of 52.6 mm and 140.5 mm are shown at Sukhbaatar and Tosontsengel, respectively. The highest differences were reported in the following four stations with 298.8 mm, 385.2 mm, 365.7 mm, and 407.4 mm at Baruunkharaa, Undurkhaan, Erdenesant, and Arvaikheer, respectively. The highest difference of 674.3 mm was reported at Saikhanovoo in the Gobi Desert.
Mean annual sums of P, ET0, ETp (based on our developed Kc), Tp, and Ep and corresponding aridity index, AI, over the 41 study locations in Mongolia are presented in Table A3 in Appendix A.
We observe the consistent reduction of ET0 into ETp in accordance with the study presented by [13] in arid and semi-arid classes. The difference between ET0 and ETp increases over the Gobi Desert region, where the Kc adj values decrease consistently.
The aridity increases to the south with mean annual temperature increase, and precipitation decrease (Figure S8 in Supplemental Materials). Therefore, the effect from mountain ranges also can be perceived, especially in the south part of Mongol-Altai and Gobi-Altai mountain ranges. The ratio of ET0 to P varies from 3 to 12 times in study locations, while the ratio ETp to P is about 2.5 to 3 times. Ep constitutes a large portion of ETp with low LAI in the southern Gobi Desert locations (Figure S9 in Supplemental Materials). In contrast, in northern Mongolia, Tp importance increases, as seen from Table A3. Especially in the Gobi Desert region, Ep constitutes more than 86% of ETp over the study locations. The variations in Tp closely follow the variation in LAI. All study locations in Mongolia are categorized into the arid macro-class, and AI ranges from 0.05 to 0.40. According to Table A3, climate classes belong to arid to semi-arid classes in study locations. ET0 values are high in the Gobi Desert region due to the high mean temperatures. On the other hand, the ETp values consistently decrease in the Gobi Desert Region due to very low LAI values characterizing scarce vegetation cover as depicted by very low daily Kc adjD values in the Gobi Desert in Figure 10a. The difference between mean annual ET0 and ETp gets higher in the Gobi Desert and closer in the steppe (Figure S10 in Supplemental Materials).
The relation between climate aridity (in terms of AI) and the ratio of mean annual ETp to mean annual ET0 (or ETp/ET0) is shown in Figure 10b. Data are grouped according to the land cover class (Gobi Desert and steppe) and climate class (vertical dashed line delimits arid and semi-arid climate classes, respectively). The locations belonging to the Gobi Desert (yellow circles) cluster very closely in the arid class, while the steppe points are scattered mostly in the semi-arid climate. The fit of the linear regression shows an acceptable R2 value and describes more than 80% reduction of ET0 in the Gobi Desert and about 54% reduction in the steppe locations under semi-arid conditions.

4. Discussion

The first goal of this study was to find a suitable temperature-based method to reliably estimate ET0 in Mongolia [6]. We compared the Hargreaves (Har) and Thornthwaite (Tho) equations with the Penman–Monteith (FAO-56 PM) equation. The latter is highly recommended by the International Commission for Irrigation Drainage, Food and Agriculture Organization of the United Nations and ASCE-Evapotranspiration in Irrigation and Hydrology Committee [43,44]. Both Har-based and Tho-based models mostly underestimate FAO-56 PM-based average annual ET0 in most weather stations in arid and semi-arid conditions, as reported by previous studies [38,45]. The underestimation is observed in other studies evaluating the Har model in arid and semi-arid conditions in several parts of the world [46,47,48,49]. In this study, we propose to select the Har method, which outperforms the Tho equation to predict daily ET0. A comparison between FAO-56 PM and Har equations for predicting ET0 has been carried out all over the world. Similar RMSE values between FAO-56 PM and the uncalibrated Har equation reported in Mongolia have also been observed in southern Italy [50], in south-east Spain [51], in the U.S. High Plains [39], and northwest China [52], while lower RMSE values are reported in arid and semi-arid areas in China [53]. The Har ET0 estimates can be considered acceptable as reported in some studies carried out in Mongolia [8,19,42].
The second goal of this study is the assessment of the biome-specific potential evapotranspiration, ETp, in Mongolia. The key step is to find a suitable method to describe the crop coefficient, Kc, to convert ET0 into ETp. The FAO-56 guidelines recommend the calculation of Kc in each crop growing phase. Larger-than-normal crop coefficient values should be attributed to a well-watered crop under arid and semi-arid climate conditions. It is expected that Kc should be larger under arid conditions when the agricultural crop has leaf area and roughness height greater than that of the reference grass. However, this is not the case in Mongolia. Kc values in Mongolia are currently unavailable in any type of natural vegetation. Very few studies have been conducted in semi-arid natural environments [13].
Even though two different methods were used to develop Kc adj-values depending on the biome (LAI-dependent Kc adjS in steppe, Rn-dependent Kc adjD in the Gobi Desert), the results have a smooth transition in Kc adj-values in the study locations. The Kc adjD estimates in the Gobi Desert region are similar to the results presented by [12,13] in Inner Mongolia. In well-managed cropland, the standard conditions are generally the actual field conditions. The ETp is partitioned in potential evaporation and potential transpiration using available maps of LAI in Mongolia. The Ep constitutes a major part of ETp with low LAI in the southern Gobi Desert locations, while Tp increases toward the northern region. According to [12,13], daily Kc values in the growing season ranged from 0.02 to 0.50 with an average value of 0.17 and 0.15 to 0.17 in a temperate desert in Inner Mongolia. Both studies were performed in the Gobi Desert (Inner Mongolia).
We observe that using tabulated Kc values in northern Mongolia is acceptable, while under sparse vegetation cover conditions (in central and southern Mongolia), we highly recommend to relate crop coefficient to easily-retrievable LAI (remote sensing maps) in the steppe region or to solar radiation in the Gobi Desert region. A proper prediction of ETp is key to obtain successful model simulations. The impact of ETp prediction on GR simulations will be treated in a follow-up study.

5. Conclusions

This study evaluates a protocol for estimating the ETp in Mongolia under limited data availability. The majority of the auxiliary data required for this study have been collected from remote-sensing products and local ground-based measurements of meteorological data. This practice can be helpful under data paucity constraints in many areas with an arid/semi-arid climate.
It is necessary to reliably estimate ET0 based on temperature data in Mongolia. Despite some underestimation bias, we recommend the temperature-based Hargreaves equation that reliably predicts ET0, as verified by comparison with FAO-56 PM results.
It is also necessary to assess the crop coefficient, Kc, to convert ET0 into ETp. In this study, we introduce a modified time-variant crop coefficient, Kc adj, specific for the two main biomes in Mongolia, namely, the Gobi Desert (Kc adjD) and the steppe grasslands (Kc adjS). The Kc values tabulated in the FAO guidelines generate spatially uniform predictions of ETp over the 41 experimental locations characterized by contrasting vegetation cover. In contrast, the developed Kc adj value induces a drastic reduction in ETp towards the arid zone. We, therefore, recommend using LAI-dependent Kc adjS-values in the steppe and radiation-dependent Kc adjD-values in the Gobi Desert zone. Approximately 80% and 54% reduction of ET0 into ETp is reported in the Gobi Desert and in the steppe locations, respectively.
Model performance can be improved by increasing data availability and quality. On the one hand, the Hargreaves equation would benefit from long time series (in the order of decades) of air temperature to estimate ET0. On the other hand, the empirical equations describing the adjusted crop coefficient values would be enhanced if based on direct measurements over representative hotspots in Gobi Desert and steppe biomes. Results can be used to develop Kc adj under natural vegetation conditions in similar areas. This methodology for predicting ETp can be used in modeling tools of the vadose zone, climate models, and water balance studies with only a few parameters and without labor-demanding field measurements.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/w13182470/s1, Figure S1: Daily temperature (T) data retrieved from NAMEM (blue dots) and NOAA (red dots) at Khatgal weather station; Figure S2: Monthly mean LAI values in the growing season, extracted from the dataset by Mao and Yan (2019); Figure S3: Monthly mean LAI retrieved from ORNL DAAC over 41 study locations, Mongolia; Figure S4: Comparison between FAO-56 PM-based and temperature-based equations (Har ET0 values are represented by blue circles, and Tho ET0 values are depicted by red circles) for predicting daily values of ET0 at (a) Khatgal weather station, and (b) Tsogtovoo weather station; Figure S5: Comparison among three different methods (FAO-56 PM, Har, Tho) to predict monthly ET0 in 10 test locations, Mongolia; Figure S6: Geographical locations of six weather stations in the steppe region (Sukhbaatar, Tosontsengel, Baruunkharaa, Undurkhaan, Erdenesant, Arvaikheer) and one representative weather station in the Gobi Desert (Saikhanovoo); Figure S7: Mean monthly values of tabulated crop coefficient, Kc over six weather stations (Sukhbaatar, Tosontsengel, Baruunkharaa, Undurkhaan, Erdenesant, Arvaikheer) in the steppe zone (colored circles) and one weather station (Saikhanovoo) in the Gobi Desert (black square). Figure S8: Values of (a) mean annual temperature and (b) mean annual precipitation over the 41 weather stations (circles of varying size indicate variations in magnitude of the attribute) on digital elevation model (DEM) map from Earth Resources Observation And Science center, 2017 (SRTM); Figure S9: Mean annual values of ET0, ETp and LAI over the 41 weather stations grouped in the Gobi Desert and steppe zones Figure S10: Mean annual values of ET0, ETp and AI over the 41 weather stations grouped in the Gobi Desert and steppe zones.

Author Contributions

Conceptualization, K.B., V.A.Z., A.S. and P.N.; methodology, K.B., A.S., P.N. and V.A.Z.; software, K.B.; validation, P.N. and K.B.; formal analysis, K.B.; investigation, K.B.; resources, V.A.Z.; data curation, K.B. and P.N.; writing—original draft preparation, K.B., P.N. and V.A.Z.; writing—review and editing, K.B. and A.S.; visualization, P.N. and A.S.; supervision, V.A.Z., A.S. and P.N.; project administration, V.A.Z.; funding acquisition, V.A.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Fulbright scholarship from 2019 to 2021 to Khulan Batsukh and grant to Vitaly Zlotnik from Daugherty Water for Food Institute, University of Nebraska.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

We are grateful to the National Agency for Meteorology and Environmental Monitoring (NAMEM), Mongolia, and Jadambaa Namjil (Institute of Geography and Geoecology), Gomboluudev Purevjav (Institute of Meteorology and Hydrology), and Ariunaa Chinbat (Mongolian Association of Hydrogeologists) for sharing data.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Categorization of natural zones.
Table A1. Categorization of natural zones.
ID.StationsNatural ZoneID.StationsNatural Zone
1SukhbaatarSteppe22Baynuulsteppe
2TseterlegSteppe23Galuutsteppe
3Bulgan MgSteppe24Ulaangomsteppe
4KhatgalSteppe25Arvaikheersteppe
5TosontsengelSteppe26Choirsteppe
6BinderSteppe27Mandalgobisteppe
7RinchinlhumbeSteppe28Altaisteppe
8Khalkh golSteppe29Khoriultsteppe
9ErdenemandalSteppe30HovdGobi Desert
10BaruunkharaaSteppe31UlgiiGobi Desert
11BaruunturuunSteppe32EkhiingolGobi Desert
12ErdenetsagaanSteppe33GurvantesGobi Desert
13Chingis khaan (UB)Steppe34TooroiGobi Desert
14ChoibalsanSteppe35SainshandGobi Desert
15UndurkhaanSteppe36KhanbogdGobi Desert
16MatadSteppe37Zamiin UudGobi Desert
17MurunSteppe38BaitagGobi Desert
18UliastaiSteppe39DalanzadgadGobi Desert
19Baruun-UrtSteppe40Saikhan-OvooGobi Desert
20ErdenesantSteppe41Tsogt-OvooGobi Desert
21DarigangaSteppe
Table A2. Annual sums of ET0 over the 41 weather stations in Mongolia.
Table A2. Annual sums of ET0 over the 41 weather stations in Mongolia.
ID.StationsAnnual Har ET0 (mm)Mean Har ET0 (mm)CV (%)
20072008200920102011
1Sukhbaatar8618479298128128525.6
2Tseterleg 8458198017637697994.3
3Bulgan Mg8918548548357828434.7
4Khatgal 7427156946916957073
5Tosontsengel 7917627536967297464.8
6Binder9078188237938408365.1
7Rinchinlhumbe 7236866636666876853.5
8Khalkh gol 9108728348638398633.5
9Erdenemandal 8718368107937898204.2
10Baruunkharaa 9218898848468498783.5
11Baruunturuun7927697277137497504.2
12Erdenetsagaan 9268488478498208584.7
13Chingis khaan (UB)58456157175769463413.8
14Choibalsan9528818768608808904
15Undurkhaan9879148838748949105
16Matad 9768828828778548945.3
17Murun8658348147777988174.1
18Uliastai 8428187907557727954.4
19Baruun-Urt 9728969008808629024.6
20Erdenesant8818438047727508106.5
21Dariganga8358418198648668452.4
22Baynuul8197707417147357565.4
23Galuut7697977637397087554.4
24Ulaangom 8638467987908198233.8
25Arvaikheer 8308088257807768043.1
26Choir 9548938988578398885
27MandalGobi 9549159228618719054.2
28Altai 7787537406917067334.8
29Khoriult98798210019559529752.2
30Hovd 9028988678248468673.9
31Ulgii8468387817778088103.9
32Ekhiingol1121113911591106111811291.8
33Gurvantes8868919218698718882.4
34Tooroi1104113011211039103810864.1
35Sainshand 1029100010129849529962.9
36Khanbogd1034995102997198310022.8
37Zamiin Uud 1033101010411002101010191.7
38Baitag 100010069578869649625
39Dalanzadgad 98598510129649609812.1
40Saikhan-Ovoo 98195710039469269633.1
41Tsogt-Ovoo 101598510129559369813.5
Spatial-average ET0902873867840843
Table A3. Mean annual sums of water fluxes over 41 weather stations in Mongolia.
Table A3. Mean annual sums of water fluxes over 41 weather stations in Mongolia.
ID.StationsP (mm)ET0 (mm)ETp (mm)Ep (mm)Tp (mm)AIClass
1Sukhbaatar2778526953293660.32Semi-arid
2Tseterleg3237996152703440.4Semi-arid
3Bulgan Mg2878434482841640.34Semi-arid
4Khatgal2777074492452040.39Semi-arid
5Tosontsengel1937465012712300.26Semi-arid
6Binder3018365212972240.36Semi-arid
7Rinchinlhumbe1936854282431850.28Semi-arid
8Khalkh gol2918634892991900.34Semi-arid
9Erdenemandal2548204232691540.31Semi-arid
10Baruunkharaa3168784602981620.36Semi-arid
11Baruunturuun2107504002661350.28Semi-arid
12Erdenetsagaan2078583532331200.24Semi-arid
13Chingis khaan (UB)244634293207860.39Semi-arid
14Choibalsan2058903051971080.23Semi-arid
15Undurkhaan238910389291980.26Semi-arid
16Matad211849362267950.25Semi-arid
17Murun227755323232900.3Semi-arid
18Uliastai188795338247910.24Semi-arid
19Baruun-Urt172902359270880.19Arid
20Erdenesant246810308237710.3Semi-arid
21Dariganga145845305239660.17Arid
22Baynuul190756290225650.25Semi-arid
23Galuut193755244196470.26Semi-arid
24Ulaangom109823271223480.13Arid
25Arvaikheer219804228187410.27Semi-arid
26Choir111888237200380.12Arid
27MandalGobi93731188161260.13Arid
28Altai156733173150230.21Semi-arid
29Khoriult95975247212350.1Arid
30Khovd119867242210320.14Arid
31Ulgii95810126108180.12Arid
32Ekhiingol571129159139200.05Arid
33Gurvantes100888162141210.11Arid
34Tooroi601086157137210.05Arid
35Sainshand109996148133160.11Arid
36Khanbogd1151002174156180.11Arid
37Zamiin Uud931019154138160.09Arid
38Baitag93962145131140.1Arid
39Dalanzadgad128981176161150.13Arid
40Saikhan-Ovoo113963147134130.12Arid
41Tsogt-Ovoo79981239218210.08Arid

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Figure 1. Locations of 41 weather stations (represented by the triangle symbol) on the digital elevation model (DEM) retrieved from the Shuttle Radar Topography Mission (SRTM) in Mongolia [20]. The ten blue triangles indicate the weather stations with a complete set of meteorological data used to compare FAO-56 PM with temperature-based equations.
Figure 1. Locations of 41 weather stations (represented by the triangle symbol) on the digital elevation model (DEM) retrieved from the Shuttle Radar Topography Mission (SRTM) in Mongolia [20]. The ten blue triangles indicate the weather stations with a complete set of meteorological data used to compare FAO-56 PM with temperature-based equations.
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Figure 2. Schematic overview for estimating (a) grass-reference potential evapotranspiration, ET0 derived from FAO-56 PM (gray boxes) and temperature-based equations, Har and Tho (blue boxes), using data from 10 weather stations (garnet boxes); and (b) biome-specific potential evapotranspiration, ETp, derived from Kc tabulated in the FAO-56 guidelines (green boxes) and from a new Kc adj (blue boxes) using data from 41 weather stations (garnet boxes). The new crop coefficient, Kc adj depends on net solar radiation in the Gobi Desert (Kc adjD) or (b) leaf area index in the steppe region (Kc adjS).
Figure 2. Schematic overview for estimating (a) grass-reference potential evapotranspiration, ET0 derived from FAO-56 PM (gray boxes) and temperature-based equations, Har and Tho (blue boxes), using data from 10 weather stations (garnet boxes); and (b) biome-specific potential evapotranspiration, ETp, derived from Kc tabulated in the FAO-56 guidelines (green boxes) and from a new Kc adj (blue boxes) using data from 41 weather stations (garnet boxes). The new crop coefficient, Kc adj depends on net solar radiation in the Gobi Desert (Kc adjD) or (b) leaf area index in the steppe region (Kc adjS).
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Figure 3. LAIdense and Acm values depending on LAI.
Figure 3. LAIdense and Acm values depending on LAI.
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Figure 4. Model performance indicators comparing the FAO-56 PM with Har (blue line) and Tho (red line) to estimate daily values of ET0: (a) RMSE (mm d−1) values and (b) values of R2 (-) for 10 weather stations.
Figure 4. Model performance indicators comparing the FAO-56 PM with Har (blue line) and Tho (red line) to estimate daily values of ET0: (a) RMSE (mm d−1) values and (b) values of R2 (-) for 10 weather stations.
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Figure 5. Mean annual grass-reference evapotranspiration, ET0 calculated with FAO-56 PM (blue bars), Har (orange bars), and Tho (yellow bars) for 10 weather stations in Mongolia.
Figure 5. Mean annual grass-reference evapotranspiration, ET0 calculated with FAO-56 PM (blue bars), Har (orange bars), and Tho (yellow bars) for 10 weather stations in Mongolia.
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Figure 6. Map of mean annual FAO-56 PM ET0 estimated by FAO (2009) with corresponding Har ET0 values (blue circles). Plot on the right shows the comparison between FAO-56 PM ET0 and Har ET0. Diagonal dashed line depicts the identity line (1:1 line).
Figure 6. Map of mean annual FAO-56 PM ET0 estimated by FAO (2009) with corresponding Har ET0 values (blue circles). Plot on the right shows the comparison between FAO-56 PM ET0 and Har ET0. Diagonal dashed line depicts the identity line (1:1 line).
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Figure 7. Mean monthly values of (a) leaf area index (LAI) and (b) crop coefficient, Kc adjS at six weather stations (Sukhbaatar, Tosontsengel, Baruunkharaa, Undurkhaan, Erdenesant, Arvaikheer) in the steppe zone (colored circles) and crop coefficient, Kc adjD at one weather station (Saikhanovoo) in the Gobi Desert (black square).
Figure 7. Mean monthly values of (a) leaf area index (LAI) and (b) crop coefficient, Kc adjS at six weather stations (Sukhbaatar, Tosontsengel, Baruunkharaa, Undurkhaan, Erdenesant, Arvaikheer) in the steppe zone (colored circles) and crop coefficient, Kc adjD at one weather station (Saikhanovoo) in the Gobi Desert (black square).
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Figure 8. Comparison between daily ETp values based on Kc adj (blue circles) and ETp values based on tabulated Kc (red circles) over (a) Sukhbaatar, (b) Arvaikheer, and (c) Saikhanovoo. The data scatter (green and yellow circles distinguish steppe and Gobi Desert) around the identity line (1:1 line represented by the diagonal dashed line) is illustrated in (d) Sukhbaatar, (e) Arvaikheer, and (f) Saikhanovoo.
Figure 8. Comparison between daily ETp values based on Kc adj (blue circles) and ETp values based on tabulated Kc (red circles) over (a) Sukhbaatar, (b) Arvaikheer, and (c) Saikhanovoo. The data scatter (green and yellow circles distinguish steppe and Gobi Desert) around the identity line (1:1 line represented by the diagonal dashed line) is illustrated in (d) Sukhbaatar, (e) Arvaikheer, and (f) Saikhanovoo.
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Figure 9. Mean annual sums of ETp based on Kc adj (blue bars) and on tabulated Kc (orange bars) over six weather stations in Mongolia.
Figure 9. Mean annual sums of ETp based on Kc adj (blue bars) and on tabulated Kc (orange bars) over six weather stations in Mongolia.
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Figure 10. (a) daily Kc adj values (DOY depicts day of the year) and (b) relationship between aridity index, AI, and mean annual ETp to ET0 ratio over 41 weather stations in Mongolia. Solid black line represents linear regression equation reported with associated R2. Data are grouped according to natural vegetation zones, namely, steppe (green circles) and Gobi desert (yellow circles). Vertical dashed lines delimit climate classes (arid and semi-arid classes).
Figure 10. (a) daily Kc adj values (DOY depicts day of the year) and (b) relationship between aridity index, AI, and mean annual ETp to ET0 ratio over 41 weather stations in Mongolia. Solid black line represents linear regression equation reported with associated R2. Data are grouped according to natural vegetation zones, namely, steppe (green circles) and Gobi desert (yellow circles). Vertical dashed lines delimit climate classes (arid and semi-arid classes).
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Table 1. Information (time-period, location, and reference) of data sources.
Table 1. Information (time-period, location, and reference) of data sources.
Collected Data/ParametersUnitPeriod of Data AvailabilitySourceReferences
Daily Pmm2007–2011NAMEM[21]
Daily max T, min T, mean T (°C)2001–2020NOAA National Centers for Environmental Information [22]
LAI(-)1981–2015ORNL DAAC [23]
Table 2. FAO-56 PM, Har and Tho equations with required input data to calculate daily ET0.
Table 2. FAO-56 PM, Har and Tho equations with required input data to calculate daily ET0.
The MethodsMinimum Meteorological Data RequirementsEquations
Mean TMax TMin TRelative HumidityWind SpeedRa or Rn
FAO-56 PM++++++ E T 0 = 0.408 ( R n G ) + γ 900 T + 273 u 2 ( e s e a ) + γ ( 1 + 0.34 u 2 )
Hargreaves+++ + E T 0 = 0.0023 ( T m + 17.8 ) ( T m a x T m i n ) 0.5 ( 0.408 R a )
Thornthwaite modified + E T 0 = { 0 ,   T < 0 ° 0.553 ( 10 T I ) a ,   0 T 26.5 ° ( 13.86 + 1.075 T 0.0144 T 2 ) h 12 , T 26.5 °
I = 1 12 ( T m 5 ) 1.514
α = ( 6.75 × 10 7 I 3 ) ( 7.71 × 10 5 I 2 ) + ( 1.79 × 10 2 I ) + 0.492
Table 3. Pearson correlation coefficients between FAO-56 PM daily ET0 and meteorological variables over the 10 weather stations in Mongolia.
Table 3. Pearson correlation coefficients between FAO-56 PM daily ET0 and meteorological variables over the 10 weather stations in Mongolia.
Wind SpeedRelative HumidityAir TemperatureNet RadiationSunshine Hours
1. Galuut0.51−0.570.910.920.74
2. Tsetserleg0.16−0.230.910.910.84
3. Bulgan0.30−0.470.900.920.75
4. Khovd0.58−0.760.900.920.84
5. Erdenetsagaan−0.02−0.630.910.880.69
6. Choibalsan0.04−0.690.910.910.83
7. Khatgal0.01−0.280.900.920.85
8. Baruunurt0.20−0.720.910.900.71
9. Undurkhaan0.20−0.730.900.900.72
10. Tsogtovoo0.21−0.680.910.910.84
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Batsukh, K.; Zlotnik, V.A.; Suyker, A.; Nasta, P. Prediction of Biome-Specific Potential Evapotranspiration in Mongolia under a Scarcity of Weather Data. Water 2021, 13, 2470. https://doi.org/10.3390/w13182470

AMA Style

Batsukh K, Zlotnik VA, Suyker A, Nasta P. Prediction of Biome-Specific Potential Evapotranspiration in Mongolia under a Scarcity of Weather Data. Water. 2021; 13(18):2470. https://doi.org/10.3390/w13182470

Chicago/Turabian Style

Batsukh, Khulan, Vitaly A. Zlotnik, Andrew Suyker, and Paolo Nasta. 2021. "Prediction of Biome-Specific Potential Evapotranspiration in Mongolia under a Scarcity of Weather Data" Water 13, no. 18: 2470. https://doi.org/10.3390/w13182470

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