Advances in Meteorology
Volume 2019 (2019), Article ID 4072497, 11 pages
https://doi.org/10.1155/2019/4072497

Research Article

Local and Regional Scale Evaluation of the Integrated Urban Land Model by Comparing with the Common Land Model

Chunlei Meng and Wenlong Zhang

Institute of Urban Meteorology, China Meteorological Administration, 100089 Beijing, China

Correspondence should be addressed to Wenlong Zhang; wlzhang@ium.cn

Received 21 September 2018; Accepted 11 December 2018; Published 20 January 2019

Academic Editor: Stefania Bonafoni

Copyright © 2019 Chunlei Meng and Wenlong Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Land surface evaporation is not only an important parameter in natural land surface modeling, but a crucial important parameter in urban hydrology modeling. A whole-layer soil evaporation scheme was developed in the integrated urban land model (IUM) to improve the soil evaporation simulation. The impervious surface evaporation (ISE) was used as a component of urban water balance equation. In this paper, the integrated urban land model was validated at one desert site and six urban road sites to emphasize the improvement in the evaporation simulations for arid and urban areas. A sensitivity analysis was implemented in seven basins to expand the utility of the whole layer soil evaporation scheme. For the urban road sites, the validation results indicate that imperious surface evaporation (ISE) plays a crucial role in road surface temperature (RST) simulations on rainy days. For the desert site, the validation results show that the inner layer evaporation is very important in arid regions. For the basins, the analysis results indicate that the relative monthly mean differences in the evapotranspiration (ET) between the simulations with (IUM) and without (Common Land Model (CoLM)) considering the inner layer evaporation range from −8% to 8%, which is proportional to the degree of dryness. In arid areas, especially deserts, the inner layer soil evaporation could not be neglected.

1. Introduction

Land surface model (LSM) is an important part in the earth system model (ESM), which is indispensable in regional and global climate change prediction. Land surface evaporation is not only an important parameter in natural land surface modeling [1], but also a crucial parameter in urban hydrology modeling [2]. For arid and semiarid areas, soil evaporation is a key factor in climate change and water cycle [3], while for urban areas, the imperious surface evaporation (ISE) [4] is an important part in urban energy and water balance.

For natural land surfaces, many parameterization schemes were developed to parameterize soil evaporation [5–11], but most of these schemes only considered the evaporation processes in soil surface. In fact, water and energy transfer are also happened in inner soil layers especially in the semiarid and arid regions. In order to solve this issue, a whole layer soil evaporation scheme was needed.

As the development of urbanization, human activities changed the land surface characteristics drastically. These altered the land surface radiation balance, energy balance, and water balance. Moreover, many parameters used in land surface models, such as the roughness length, albedo, permeability, etc., were changed. Traditional LSMs could not simulate urban land surface parameters accurately. To solve this problem, urban land surface models were developed.

Two main categories of models were developed for urban land surface modeling. One is the coupled method and the other is the integrated method. For the coupled method, the urban canopy model (UCM) e.g., [12, 13], town energy balance (TEB) model [14], and building energy model (BEM) [15] were developed and coupled with the land surface model for the natural land surfaces. For the integrated method, e.g., [16], the urban land surface model was built directly based on the traditional land surface model. In UCM [17–19], the ISE depended on the precipitation, and the drying process of the impervious surface was not considered. While in TEB [14], the drainage is not considered in the water balance equation. In BEM [15], the ISE was not included at all.

As an integrated model, the IUM integrates the land surface model for urban and natural land surfaces. It was dedicated to simulate land surface process in all kinds of land use and land cover (LULC) categories and in all kinds of spatial scales. Compared with other coupling land surface models, one of the important improvements of IUM is the mechanism study of evaporation, not only in natural land surface, but in urban impervious surface. In the integrated urban land model (IUM), the impervious water depth was used as the prognostic of urban water balance equation. The ISE depended on the impervious water depth since the inner soil evaporation is more important in arid areas. In this paper, the IUM was tested at one desert site and six urban sites to validate its improvements in the simulation of evaporation, which is a key parameter in land surface modeling and an important part of the improvements offered in the IUM. Besides, a sensitivity analysis was implemented in seven basins in order to expand the application of the whole layer soil evaporation.

2. Data and Methods

2.1. Model Description

The IUM [16] was developed based on the common land model (CoLM) [20]. IUM integrates the land surface models for urban and natural land surfaces. For natural land surfaces, a whole layer evaporation scheme was developed to improve the simulation of evaporation especially in arid areas. For urban land surfaces, both the energy and water balance model were developed.

2.1.1. Whole Layer Soil Evaporation Scheme

Soil evaporation in the CoLM is calculated as follows:

where is the soil evaporation (mm·s⁻¹) and is the soil relative humidity. Considering the water vapor transfer within the soil, the whole layer soil evaporation in the IUM could be written as follows:

where the number of soil layers, which in the CoLM, is 10; is the soil specific humidity (kg·kg⁻¹); is the depth of the soil layer interfaces (m); and is the aerodynamic resistance for evaporation at the soil layer interface (s·m⁻¹). The specific humidity of each soil layers can be estimated as follows:

where is the saturated specific humidity (kg·kg⁻¹), which is associated with the near-surface air pressure and soil temperature; is the soil matrix potential (m), which is associated with the soil texture and the soil moisture; is the gravity constant (m·s⁻²); is the gas constant for water vapor (J·Kg⁻¹·K⁻¹); and is the soil temperature (K). The method to calculate the aerodynamic resistance for evaporation at the soil layer interface could be seen in Meng [16]. Water vapor transfer cannot be neglected, especially when the soil surface is very dry, so it is more important in arid and desert regions.

2.1.2. Urban Energy Balance Model

For urban energy balance, in contrast to UCM [12], TEB [14], and BEM [15], only the anthropogenic heat (AH) was added as a radiation source term in IUM. Based on the first law of thermodynamics and the Fourier thermal conductivity law, the energy balance equation for the impervious urban surface can be described as

where is the specific heat capacity of urban surface (J·m⁻³·K⁻¹); is the surface layer depth (m); is the urban surface temperature (K); is the thermal conductivity of urban surface (W·m⁻¹·K⁻¹); is the second layer soil temperature (K); is the urban surface heat flux (W·m⁻²); is the net radiation (W·m⁻²); is the sensible heat flux (W·m⁻²); is the AH (W·m⁻²); and is the latent heat of evaporation for water (J·kg⁻¹). The diurnal cycle of the AH is from Miao et al. [21]; this methodology is based on Sailor and Lu [22]. The nighttime light data was used to expand the anthropogenic heat to regional and global scale [23].

2.1.3. Urban Water Balance Model

The ISE is an important component in urban water balance. The water depth in impervious surface is an important parameter in urban waterlogging study. In IUM, the water depth in impervious surface is considered as the prognostic variable of urban balance equation. For road surfaces, the urban water balance equation in IUM could be described as follows:

where is the water depth on impervious surface (mm); is the time (s); is the impervious surface evaporation (mm·s⁻¹); is the precipitation (mm·s⁻¹); and is the drainage of the impervious surface (mm·s⁻¹).

The ISE in the CoLM is set as 0, while in the IUM it is parameterized as follows:

where is the potential evaporation (m·s⁻¹).

Because surface runoff depends on topography and drainage, the drainage was evaluated based on the sensitivity analysis result in 18 road sites in Beijing during a whole summer season, which is approximately 10 mm·d⁻¹ [24]. can be parameterized as follows:

where is the air density (kg·m⁻³); is the water density (kg·m⁻³), which is approximately equal to 1000; is the aerodynamic resistance for evaporation (s·m⁻¹), which could be calculated as follows:

where is the von Karman constant; is the friction velocity (m·s⁻¹); is the integral of profile function for moisture, which is associated with the thermodynamic roughness [25]. is the specific humidity of the air (kg·kg⁻¹); and is the saturated specific humidity of the water surface (kg·kg⁻¹), which is associated with the aerodynamic air temperature at the surface.

The aerodynamic air temperature at the road surface is difficult to observe and simulate. In the IUM, the road water temperature is used to parameterize the potential evaporation. Road surfaces are treated as shallow lakes, and the lake model [26–28] in the CoLM is simplified to compute the road water temperature. The simplified shallow lake model can be described as follows:

where is the road water temperature (K), is the thermal conductivity of water (W·m⁻¹·K⁻¹), is the eddy diffusion coefficient (m²·s⁻¹), is the heat capacity of water (J·m⁻³·K⁻¹), and is the net heat flux from the atmosphere (W·m⁻²), which is calculated as follows:

where is the absorbed solar radiation (W·m⁻²), is the net longwave radiation (W·m⁻²), and is the sensible heat flux (W·m⁻²).

The sensible heat flux for lake surface is calculated as follows:

where is the specific heat of dry air (J·Kg⁻¹·K⁻¹); is the lake temperature (K); is the air potential temperature at reference height (K); and is the aerodynamic resistance for sensible heat flux between the atmosphere at reference height and the surface (m·s⁻¹), which could be calculated as follows:

where is the integral of profile function for heat, which is associated with the thermodynamic roughness [25].

2.2. Road Sites in Beijing

Six road weather station sites were chosen to validate the IUM. Each of these sites is located either on a major expressway that links Beijing to other cities in China or on a main road within Beijing. Figure 1 is the land cover classification of Beijing and the locations of the six road sites. The urban land use and land cover (LULC) is classified based on the 30 m resolution Landsat Thematic Mapper (TM) images. LULC is classified as seven types that is high density urban, low density urban, water body, barren, forest, cropland, and grassland. The local climate zone (LCZ) [29] of Wuyuanqiao and Muxiyuan is 1 (compact high-rise); the LCZ of Bajiao, Dayangfang, Lugouqiao, and Wenyuhe is 2 (compact midrise).

Figure 1: Land cover classification of downtown Beijing and the locations of six road sites.

2.3. Observation in HEIFE

The desert site in Heihe Basin Field Experiment (HEIFE) [30] is used for validating the whole layer evaporation scheme. HEIFE is an international land surface observational experiment which organized by World Climate Research Programme (WCRP) and International Geosphere-Biosphere Programme (IGBP). It is the first observational experiment for arid regions with complex surface in the world. Desert meteorological station is built in the south boundary of Badanjilin desert and it is 3 km from oasis. The longitude and latitude of the station are 100.15 E and 39.383 N, respectively. The land surface category of desert station is barren.

2.4. Seven River Basins

Seven river basins [31] were chosen for comparing the surface layer and whole layer soil evaporation schemes. These seven river basins were the Yellow River basin, the Nile River basin, the Niger River basin, the Indus River basin, the Tarim River basin, the Amu River basin, and the Syr River basin (Figure 2). The main characteristics of these seven basins are summarized in Table 1. Tarim basin is an extremely arid basin; the main LULC is barren land and accounts for more than 60% of the land surface. Amu and Syr basins are also arid basins, and the main LULC is grassland. The bare fraction of the Nile and Niger basins also exceeds 30%. The bare fraction of the Yellow and Indus basins is relatively low.

Figure 2: Selected river basins ((1) Yellow River. (2) Nile River. (3) Niger River. (4) Indus River. (5) Tarim River. (6) Amu River. (7) Syr River).

Table 1: Main characteristics of seven basins.

Basin name
Area (km²)
Annual mean temperature (°C)
Annual precipitation (mm)
Dominate LULC and its percentage (%)

Yellow
7.52 × 10⁵
8.62
471
Grassland (66.4)

Nile
3.25 × 10⁶
26.19
493
Savanna (35.1), Barren (35.7)

Niger
2.09 × 10⁶
28.89
513
Barren (32.4)

Indus
1.17 × 10⁶
15.41
588
Cropland (29.7), Shrubland (28.3)

Tarim
4.36 × 10⁵
5.71
218
Barren (60.2)

Amu
4.65 × 10⁵
9.24
154
Grassland (41.2)

Syr
2.19 × 10⁵
10.88
214
Grassland (85.1)

2.5. Model Configuration

2.5.1. Model Configuration for the Road Sites in Beijing

The same as that in the CoLM, soil is also divided into 10 layers. For layers 1–5, the material was set as asphalt; the road surface roughness length was set as 0.01 m; the albedo was set as the darkest soil, which in the IUM is 0.05 for visible solar radiation and 0.1 for near-infrared solar radiation; the density, heat capacity, and heat conductivity were set as 1800 kg·m⁻³, 1760000 J·m⁻³·K⁻¹, and 0.756 W·m⁻¹·K⁻¹, respectively [32, 33], and the depth of these five layers was approximately 0.212 m. For layers 6–10, the material is set as soil. Because no evaporation occurs when the impervious surface is dry, we chose a period with rainfall to perform the validation. The validation time was from the 8th to the 12th of July 2010. We utilized the 5-minute observations of the precipitation, wind speed and direction, near-surface relative air humidity, and near-surface air temperature data from ROSA™ road weather stations manufactured by Vaisala Corporation to drive the IUM. The near-surface air pressure, downward solar radiation, and downward longwave radiation data are originated from the Global Land Data Assimilation System (GLDAS) [34]. The GLDAS data were interpolated temporally using the cubic spline method from 3 hrs to 5 minutes, and for the downward solar radiation the solar zenith angle was also used to ensure that there was zero downward solar radiation at night. The spatial resolution of the GLDAS data is 0.25 degrees. To validate the accuracy of the GLDAS data, it was compared with the observed data in Chinese Academy of Sciences (CAS) 325-meter-high Meteorology and Environmental Observation Tower (hereafter called 325 m tower), which is located in downtown Beijing. The longitude and latitude are 116.3708 E and 39.9744 N, respectively. The radiation fluxes including the upward and downward shortwave and longwave radiation are measured using the radiometer at the 47-meter height. The validation time period is from 1st March to 31st October 2015.

2.5.2. Model Configuration for Bare Soil Surface

As for bare soil surface, the model settings of IUM are all the same as those in CoLM [20]. The validation time period for desert station is from 0 : 00 1 August 1991 to 17 : 00 20 August 1991, Beijing time. The time step for validation is 30 minutes. The atmospheric forcing data are all from the observation of desert site. The observed latent heat flux data were used for comparison; it was measured by using the eddy covariance (EC) technique.

2.5.3. Model Configuration for Basin Scale

We used the reanalysis data from the GLDAS to drive the land surface model. These data included the precipitation, near-surface air temperature, wind speed and direction, near-surface relative air humidity, near-surface air pressure data downward solar radiation, and downward longwave radiation.

LULC data were from MODIS (Figure 3). We used 0.05° spatial resolution LULC data (MCD12C1) from 2012. First, the LULC data were transformed from the IGBP classification to the United States Geological Survey (USGS) classification scheme using the relationship listed in Table 2. Then, the LULC data were upscaled to a 0.25° spatial resolution using the mosaic scheme [16, 35, 36]. The simulation time periods for the basin encompass the whole year of 2013. The spatial and temporal resolutions are 0.25° and 3 hrs, respectively.

Figure 3: USGS global LULC based on MODIS ((0) Ocean. (1) Urban and built-up land. (2) Dryland, cropland, and pasture. (3) Irrigated, cropland, and pasture. (4) Mixed dryland/irrigated cropland and pasture. (5) Cropland/grassland mosaic. (6) Cropland/woodland mosaic. (7) Grassland. (8) Shrubland. (9) Mixed shrubland/grassland. (10) Savanna. (11) Deciduous broadleaf forest. (12) Deciduous needleleaf forest. (13) Evergreen broadleaf forest. (14) Evergreen needleleaf forest. (15) Mixed forest. (16) Water bodies. (17) Herbaceous wetland. (18) Wooded wetland. (19) Barren or sparsely vegetated. (20) Herbaceous tundra. (21) Wooded tundra. (22) Mixed tundra. (23) Bare ground tundra. (24) Snow or ice.)

Table 2: Land use and land cover (LULC) mapping between the IGBP and the USGS classifications.

LULC type
IGBP ID
USGS ID

Water
0
0

Evergreen needleleaf forest
1
14

Evergreen broadleaf forest
2
13

Deciduous needleleaf forest
3
12

Deciduous broadleaf forest
4
11

Mixed forest
5
15

Closed shrublands
6
8

Open shrublands
7
8

Woody savannas
8
10

Savannas
9
10

Grasslands
10
7

Permanent wetlands
11
18

Croplands
12
4

Urban and built-up
13
1

Cropland/natural vegetation mosaic
14
6

Snow and ice
15
24

Barren or sparsely vegetated
16
19

3. Results and Discussion

Firstly, to emphasis the importance of ISE on urban land surface modeling, we focus on the simulation improvement of road surface temperatures (RST) in six road sites. As no observational downward solar and longwave radiation data are available for these road sites, we validate the downward solar and longwave radiation data from GLDAS through the comparison with the observation in the 325 m tower. Figure 4 shows the comparison of scattered plots of the downward solar radiation and longwave radiation from observation and GLDAS. Table 3 shows the Biases, Mean errors (MEs), root mean square errors (RMSEs), and correlation coefficient (R) between them. In general, the downward shortwave and longwave radiation from GLDAS have good correlation with those of the observation. The shortwave radiation from GLDAS is larger than that of the observation; the bias is about 20 percent of total mean observation. The longwave radiation from GLDAS is smaller than that of the observation; the bias is about 6 percent of total mean observation. The total mean bias of the downward radiation from GLDAS is about 23 W·m⁻² larger than that of the observation; it approximately accounts for 14 percent of the total downward observation. Figure 5 shows the simulated ISE, observed precipitation, and simulated water depth by the IUM for the six road sites. Precipitation occurred at noon on July 8 of 2010. Waterlogging occurred starting on the afternoon of July 8 and ended on July 12, except at the Dayangfang site, where it ended on July 11. ISE is associated with potential evaporation (road water temperature), drainage, and precipitation; it is almost continuous when the water depth is larger than zero. ISE plays a crucial role in urban land modeling on rainy days. Unfortunately, no observed ISE data are available for validation. As the observed and simulated RST for all the six sites are similar, to validate the improvement of IUM in urban areas, the mean RST simulations from the CoLM and the IUM and the observations at these six road sites are shown in Figure 6. Only days with successive ISE were considered for comparison. Figure 6 shows that ISE plays a very important role in RST simulation on rainy days. The biases of the RST between the CoLM and the observations are about 5K at night and 30K in the daytime, while the biases of RST between the IUM and the observations are lower than 2K at night and lower than 5K in the daylight. The physical mechanism of the improvement could be explained using the urban energy balance equation (equation (4)). The RST is associated with the road surface heat flux, net radiation, sensible heat flux, impervious surface evaporation, specific heat capacity, and thermal conductivity. It is inversely proportional to the impervious surface evaporation which could cool down the road surface. The deviation of downward radiation of GLDAS perhaps is another reason of the relative higher RST simulation in the daytime. But the ISE is apparently the more important reason.

(a)

(b)

Figure 4: Scattered plots of downward solar radiation (a) and longwave radiation (b) from observation and GLDAS.

Table 3: Biases, mean errors (MEs), root mean square errors (RMSEs), and correlation coefficient R of downward solar radiation (S↓) and longwave radiation (L↓) from observation and GLDAS.

Parameter
Bias (W·m⁻²)
ME (W·m⁻²)
RMSE (W·m⁻²)
R

S↓
44.72
78.43
138.68
0.871

L↓
−21.13
26.30
31.23
0.913

(a)

(b)

(c)

Figure 5: Simulated (a) impervious surface latent heat flux, (b) observed precipitation, and (c) water depth for the six road sites.

(a)

(b)

Figure 6: (a) Time series of the mean road surface temperature simulated by CoLM and IUM compared with the observation at the six road sites. (b) Time series of the mean road surface temperature differences between the estimations from the CoLM and the IUM and the observations at the six road sites.

Then, we validate the improvement of soil evaporation in an arid site by the whole layer evaporation scheme. Figure 7 shows the scattered plots of time series resulting latent heat flux value estimates from CoLM and the whole layer parameterization scheme which used in IUM compared with the observation for the desert site during the validation time period. Table 4 shows the Biases, MEs, RMSEs, and R of the latent heat fluxes simulated by CoLM and IUM compared with those of the observations. After using the whole layer soil evaporation parameterization scheme, the biases (simulation subtract observation) were reduced from −5.06 to −4.48 W·m⁻², the MEs were reduced from 23.8 to 12.6 W·m⁻², the RMSEs were reduced from 45.3 to 25 W·m⁻², and R is increased from 0.75 to 0.80. These indicate that the inner layer evaporation is very important in arid regions and could not be neglected for CoLM. From Figure 7, it could be noticed that under certain circumstances, the simulated soil evaporation by CoLM is zero. That means in these circumstances, soil evaporation only happens in the inner layers.

Figure 7: Scattered plots of time series resulting latent heat flux value estimates from CoLM and the whole layer parameterization scheme which used in IUM compared with the observation for the desert site during the validation time period (from 0 : 00 1 August 1991 to 17 : 00 20 August 1991, Beijing time).

Table 4: Biases, mean errors (MEs), root mean square errors (RMSEs), and correlation coefficient R of the latent heat fluxes simulated by CoLM and IUM compared with those of the observations.

Model
Bias (W·m⁻²)
ME (W·m⁻²)
RMSE (W·m⁻²)
R

CoLM
−5.06
23.81
45.26
0.751

IUM
−4.48
12.60
24.99
0.801

Based on the validation of the whole layer evaporation scheme in single site, a sensitivity analysis was implemented in the regional scale. We compared the simulation results of the CoLM and IUM in seven river basins. We calculated the absolute (IUM-CoLM) (Figure 8(a)) and relative (absolute differences divided by the evapotranspiration (ET) estimates from the CoLM) (Figure 8(b)) monthly mean differences of the ETs between the two models. A graduated color scheme for the different basins was used based on the annual ET (areas in red indicate the lowest ET, purple indicates the highest ET). The relative monthly mean differences in the ETs range from −8% to 8%. The absolute monthly mean differences in the ETs range from −0.025 mm·d⁻¹ to 0.015 mm·d⁻¹. The monthly mean differences in the ETs are inversely proportional to the annual ET.

(a)

(b)

Figure 8: (a) Absolute and (b) relative monthly mean differences of the ETs between the estimates from the whole layer parameterization scheme used in the IUM and the single-layer soil evaporation scheme used in the CoLM for seven basins. A graduated color scheme for the different basins was used based on the annual ET (areas in red indicate the lowest ET, purple indicates the highest ET).

To study the relationship between the inner soil layer evaporation and the other parameters, scatter plots were drawn comparing the absolute value of relative monthly mean ET differences and the monthly mean evaporation (Figure 9(a)), surface runoff (Figure 9(b)), volumetric soil moisture (Figure 9(c)), evaporative fraction (Figure 9(d)), and leaf area index (LAI) (Figure 9(e)) for these seven basins. The annual averages of the absolute value of relative monthly mean ET differences were also compared with the bare fraction (Figure 9(f)) for these seven basins. The R were also calculated, showing that the absolute value of relative monthly mean ET differences are inversely proportional to the monthly mean evaporation, runoff, volumetric soil moisture, evaporative fraction, and LAI and are proportional to the degree of the dryness. The calculated correlation coefficients reflect the significance of these parameters to the inner soil evaporation. The most important parameter to soil inner evaporation is the volumetric soil moisture. Along with the dry of the soil surface, the inner soil evaporation plays an important role in total soil evaporation. The LAI is associated with the fractional vegetation cover, and then associated with the bare fraction and the inner soil evaporation. The surface runoff is associated with the precipitation, infiltration, and soil water storage. When surface runoff is larger than zero, the soil is saturated, and the inner soil evaporation could be neglected. As the result, the surface runoff differences are also inversely proportional to the inner soil evaporation. The evaporative fraction is the latent heat flux, which is proportional to the soil evaporation, divided by the sensible heat flux plus the latent heat flux. It is also associated with the dryness of soil surface. The analysis above indicates that the inner soil layer evaporation could not be neglected in arid areas, especially in deserts.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 9: Scatter plots between the absolute value of relative monthly mean ET differences and the monthly mean evaporation (a), surface runoff (b), volumetric soil moisture (c), evaporative fraction, (d) and leaf area index (e) of seven basins. (f) Annual averages of the absolute relative monthly mean ET differences compared with the bare fraction for seven basins.

4. Conclusions

In this paper, the IUM was tested at one desert site, at six road sites, and in seven basins in order to validate the simulation of evaporation from impervious surfaces and in arid regions.

ISE is associated with the potential evaporation (road water temperature), drainage, and precipitation; it is almost continuous when the water depth is greater than zero. ISE plays a crucial important role in RST simulation on rainy days. The ISE parameterization scheme is one of the key points in urban water balance and water cycle research, which is not fully researched compared with the research of the energy balance. ISE is also extremely important in RST simulation in raining days, so it is very important in urban heat island (UHI) and urban climatic effect research, especially in rainy cities. The imperious surface water depth is the key parameter in urban waterlogging application.

Inner layer soil evaporation is important and cannot be neglected in arid and semiarid areas. At extremely arid areas, such as deserts, the inner layer soil evaporation has the same order of magnitude as the surface evaporation. The area of arid regions is very large in our world, and they are vulnerable to climate change and human activities. So the whole layer evaporation will be used as an important tool in water resources, water cycle, and climate change research in arid areas.

In the future, the whole layer evaporation and the ISE parameterization scheme will be fully validated in more sites and regions and in different time periods. A global validation was implemented to evaluate the relationship between the differences in the soil evaporation simulation with the bare fraction. The IUM will be coupled with the numerical weather model and climate model to research the land-atmosphere interaction and feedback.

Abbreviations

AH: | Anthropogenic heat
AWS: | Automatic weather station
BEM: | Building energy model
CAS: | Chinese academy of sciences
CoLM: | Common land model
EC: | Eddy covariance
ESM: | Earth system model
ET: | Evapotranspiration
GLDAS: | Global Land Data Assimilation System
HEIFE: | Heihe Basin Field Experiment
IGBP: | International Geosphere-Biosphere Programme
ISE: | Impervious surface evaporation
IUM: | Integrated urban land model
LAI: | Leaf area index
LCZ: | Local climate zone
LULC: | Land use and land cover
ME: | Mean error
R: | Correlation coefficient
RMSE: | Root mean square error
RST: | Road surface temperature
TEB: | Town energy balance model
TM: | Thematic mapper
UCM: | Urban canopy model
UHI: | Urban heat island
USGS: | United States Geological Survey
WCRP: | World Climate Research Programme.

Data Availability

Most of the data used in this article could be seen in the acknowledgments. If anyone wants the ROSA road weather stations observational data, a Email is required to the correspond author.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This work was supported by the National Natural Science Foundation of China under grants 41875125 and 41475051. We thank Beijing Meteorological Bureau (http://www.bjmb.gov.cn) for the ROSA road weather stations observational data; Environmental and Ecological Science Data Center (http://westdc.westgis.ac.cn/) for West China for desert station observational data; NASA for GLDAS data (http://disc.sci.gsfc.nasa.gov/hydrology/data-holdings); MODIS for the global LULC data (http://glcf.umd.edu/data/lc/); and Oki and Sud for the TRIP (http://journals.ametsoc.org/doi/abs/10.1175/1087-3562(1998)002%3C0001%3ADOTRIP%3E2.3.CO%3B2) river basins data.

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