. College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China . Key Laboratory of Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest A&F University, Yangling 712100, China . Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede 7522 NH, the Netherlands . College of Soil and Water Conservation Science and Engineering, Northwest A&F University, Yangling 712100, China
Different canopy resistance (rc) parameterization has been used in land surface models to simulate actual evapotranspiration (ETc) and soil hydraulic variable for crop fields. However, the influence of rc parameterization on evapotranspiration (ET) partitioning and soil water dynamics has not been fully investigated with consideration of the coupled soil water and vapor physics. This study investigated the influential mechanisms of five rc methods (viz., Jarvis, Katerji-Perrier, Massman, Kelliher-Leuning, and Farias) on ET partitioning and soil water contents in an irrigated maize field under a semiarid climate through a soil water and vapor transfer model. The Jarvis method presented the best ET results (R2 = 0.86 and RMSE = 0.71 mm·d–1). Different rc parameterization mainly altered the simulated amount of soil water contents, while not changed the response of soil water dynamics to irrigation events. By the integrated analysis of the ET partitioning and root-zone water budget, different rc methods varied in the choice of the optimum irrigation water use strategies. This study identified the direct and indirect impacts of rc on the ET partitioning and emphasizes the necessity of both the ET partitioning and water supply sources in the decision-making for irrigation water management in semiarid regions.
Just Accepted Date: 30 August 2024Online First Date: 14 October 2024Issue Date: 12 November 2024
Cite this article:
Lianyu YU,Huanjie CAI,Delan ZHU, et al. Effects of canopy resistance parameterization on evapotranspiration partitioning and soil water contents in a maize field under a semiarid climate[J]. Front. Agr. Sci. Eng. ,
2024, 11(4): 544-560.
Fig.1 The experimental site and lysimeter: (a) summer maize growing in the filed with a rainproof shelter, (b) summer maize growing in the lysimeter, and (c) the structure of the lysimeter.
Crop growth stage
Date
Crop height (m)
LAI (m2·m–2)
Irrigation amount (mm)
Initial
Start
23 Jun.
0
0
Irrigation
26 Jun.
12.3
2 Jul.
8.97
Crop development
Start
6 Jul.
0.22
0.47
Irrigation
13 Jul.
51.2
22 Jul.
4.13
2 Aug.
58.8
Middle season
Start
14 Aug.
1.65
5.24
Irrigation
16 Aug.
67.5
9 Sep.
53.1
Late season
Start
14 Sep.
2.17
4.94
Harvest
2 Oct.
2.17
2.30
Tab.1 Crop growth stages and crop height for maize in 2013
Fig.2 Schematic of the STEMMUS-ET model with different canopy resistance parameterization. Blue module, i.e., root water uptake module, which used the potential transpiration as the input, and the actual transpiration and root water uptake sink term was calculated as the output. Gray module, i.e., soil water/vapor transport module, in which, soil water and vapor transfer among the topsoil, root-zone soil and bottom soil layers followed the Richards equation. Topsoil water is crucial in estimating actual soil evaporation. Bottom soil water was important in the calculation of the deep drainage water flux. Root-zone soil water was utilized to calculated the actual transpiration, which connects with the root water uptake module. Rns, net solar radiation; LAI, leaf area index; G, soil ground heat flux; VPD, vapor pressure deficit; ra, aerodynamic resisitance.
Models
Statistics
Soil moisture (m3·m–3)
20 cm
40 cm
60 cm
80 cm
100 cm
JA
BIAS
–0.0077
–0.0110
–0.0186
–0.0083
–0.0031
d
0.799
0.541
0.478
0.527
0.444
RMSE
0.0151
0.0207
0.0206
0.0115
0.0116
KP
BIAS
–0.0055
–0.0083
–0.0160
–0.0059
–0.0020
d
0.827
0.566
0.549
0.666
0.482
RMSE
0.0139
0.0195
0.0179
0.0093
0.0111
MA
BIAS
–0.0001
–0.0015
–0.0091
0.0011
0.0022
d
0.691
0.216
0.394
0.552
0.580
RMSE
0.0174
0.0235
0.0158
0.0077
0.0069
KL
BIAS
0.0076
0.0079
0.0001
0.0099
0.0055
d
0.567
0.0667
0.148
0.356
0.519
RMSE
0.0219
0.0288
0.0169
0.0148
0.0071
FA
BIAS
–0.0076
–0.0109
–0.0184
–0.0081
–0.0028
d
0.787
0.516
0.474
0.539
0.463
RMSE
0.0155
0.0212
0.0206
0.0111
0.0110
Tab.2 Comparative statistics values of models with different canopy resistance parameterization for soil moisture in 2013
Fig.3 Time series of measured and model simulated hourly soil water contents at different depths using the Jarvis (JA), Katerji-Perrier (KP), Massman (MA), Kelliher-Leuning (KL), and Farias (FA) canopy resistance methods.
Fig.4 Daily variation in and the correlation relationship between observed evapotranspiration (ET) and simulated ET, based on the (a) and (b) Jarvis (JA), (c) and (d) Katerji-Perrier (KP), (e) and (f) Massman (MA), (g) and (h) Kelliher-Leuning (KL), (i) and (j) Farias (FA) methods. I, irrigation.
Fig.5 Time series of (a) the observed and simulated daily soil evaporation (E), and (b) the cumulative errors (), using the Jarvis (JA), Katerji-Perrier (KP), Massman (MA), Kelliher-Leuning (KL), and Farias (FA) canopy resistance methods. I, irrigation.
Crop stage
Initial
Crop development
Middle season
Late season
All
Observed
ETc (mm)
37.5
141
125
31.2
335
JA
E (mm)
29.0
28.6
22.6
8.71
89.0
Tr (mm)
5.21
94.5
108
27.0
235
ET (mm)
34.2
123
131
35.7
324
EF (%)
84.8
23.3
17.3
24.4
27.5
KP
E (mm)
28.9
30.3
23.1
8.61
90.9
Tr (mm)
4.84
71.2
105
28.4
210
ET (mm)
33.8
102
124
37.0
296
EF (%)
85.7
29.8
18.6
23.3
30.7
MA
E (mm)
28.4
29.8
26.0
10.3
94.6
Tr (mm)
7.58
71.4
69.6
25.2
174
ET (mm)
36.0
101
95.6
35.5
268
EF (%)
78.9
29.5
27.2
29.1
35.3
KL
E (mm)
29.4
31.9
28.1
11.8
101
Tr (mm)
2.30
57.7
53.5
20.3
134
ET (mm)
31.7
89.6
81.6
32.0
235
EF (%)
92.8
35.6
34.4
36.7
43.1
FA
E (mm)
28.3
28.3
23.1
8.63
88.3
Tr (mm)
8.28
92.5
100
29.8
231
ET (mm)
36.6
121
123
38.4
319
EF (%)
77.4
23.4
18.8
22.5
27.7
Tab.3 The actual evapotranspiration (ETc), and model simulated soil evaporation (E), transpiration (Tr), evapotranspiration (ET), and evaporation fraction (EF) for each development stage of maize using the Jarvis (JA), Katerji-Perrier (KP), Massman (MA), Kelliher-Leuning (KL), and Farias (FA) canopy resistance methods
Fig.6 Model estimated water budget components for the root zone, using the Jarvis (JA), Katerji-Perrier (KP), Massman (MA), Kelliher-Leuning (KL), and Farias (FA) canopy resistance methods. qbot, bottom water flux; ΔV, change of soil water storage; I, irrigation; E, soil evaporation; and Tr, crop transpiration.
Fig.7 Integrated irrigation water use efficiency (IUE) index estimated using the model with the Jarvis (JA), Katerji-Perrier (KP), Massman (MA), Kelliher-Leuning (KL), and Farias (FA) canopy resistance methods.
1
J, Bu G, Gan J, Chen Y, Su M, García Y Gao . Biophysical constraints on evapotranspiration partitioning for a conductance-based two source energy balance model. Journal of Hydrology, 2021, 603: 127179 https://doi.org/10.1016/j.jhydrol.2021.127179
2
X, Jia T S, Zha J N, Gong B, Wu Y Q, Zhang S G, Qin G P, Chen W, Feng S, Kellomäki H Peltola . Energy partitioning over a semi-arid shrubland in northern China. Hydrological Processes, 2016, 30(6): 972–985 https://doi.org/10.1002/hyp.10685
3
P, Wang X Y, Li L, Wang X, Wu X, Hu Y, Fan Y Tong . Divergent evapotranspiration partition dynamics between shrubs and grasses in a shrub-encroached steppe ecosystem. New Phytologist, 2018, 219(4): 1325–1337 https://doi.org/10.1111/nph.15237
4
M A, Forster T D H, Kim S, Kunz M, Abuseif V R, Chulliparambil J, Srichandra R N Michael . Phenology and canopy conductance limit the accuracy of 20 evapotranspiration models in predicting transpiration. Agricultural and Forest Meteorology, 2022, 315: 108824 https://doi.org/10.1016/j.agrformet.2022.108824
5
J, Bu G, Gan J, Chen Y, Su M, Yuan Y, Gao F, Domingo A, López-Ballesteros M, Migliavacca T S, El-Madany P, Gentine J, Xiao M Garcia . Dryland evapotranspiration from remote sensing solar-induced chlorophyll fluorescence: constraining an optimal stomatal model within a two-source energy balance model. Remote Sensing of Environment, 2024, 303: 113999 https://doi.org/10.1016/j.rse.2024.113999
6
R E, Dickinson A, Henderson-Sellers C, Rosenzweig P J Sellers . Evapotranspiration models with canopy resistance for use in climate models, a review. Agricultural and Forest Meteorology, 1991, 54(2-4): 373–388 https://doi.org/10.1016/0168-1923(91)90014-H
7
A, Ershadi M F, Mccabe J P, Evans E F Wood . Impact of model structure and parameterization on Penman-Monteith type evaporation models. Journal of Hydrology, 2015, 525: 521–535 https://doi.org/10.1016/j.jhydrol.2015.04.008
8
C A, Federer C, Vörösmarty B Fekete . Intercomparison of methods for calculating potential evaporation in regional and global water balance models. Water Resources Research, 1996, 32(7): 2315–2321 https://doi.org/10.1029/96WR00801
9
G, Rana N, Katerji P, Lazzara R M Ferrara . Operational determination of daily actual evapotranspiration of irrigated tomato crops under Mediterranean conditions by one-step and two-step models: multiannual and local evaluations. Agricultural Water Management, 2012, 115: 285–296 https://doi.org/10.1016/j.agwat.2012.09.015
10
D I Stannard . Comparison of Penman-Monteith, Shuttleworth-Wallace, and Modified Priestley-Taylor Evapotranspiration Models for wildland vegetation in semiarid rangeland. Water Resources Research, 1993, 29(5): 1379–1392 https://doi.org/10.1029/93WR00333
11
W J, Shuttleworth J S, Wallace , 0 0. Calculating the water requirements of irrigated crops in Australia using the matt-shuttleworth approach. Transactions of the ASABE, 2009, 52(6): 1895–1906 https://doi.org/10.13031/2013.29217
12
M C, Zhou H, Ishidaira K Takeuchi . Estimation of potential evapotranspiration over the Yellow River basin: reference crop evaporation or Shuttleworth-Wallace. Hydrological Processes, 2007, 21(14): 1860–1874 https://doi.org/10.1002/hyp.6339
13
W Z, Zhao X B, Ji E S, Kang Z H, Zhang B W Jin . Evaluation of Penman-Monteith model applied to a maize field in the arid area of Northwest China. Hydrology and Earth System Sciences, 2010, 14(7): 1353–1364 https://doi.org/10.5194/hess-14-1353-2010
14
J, Cui L, Tian Z, Wei C, Huntingford P, Wang Z, Cai N, Ma L Wang . Quantifying the controls on evapotranspiration partitioning in the highest alpine meadow ecosystem. Water Resources Research, 2020, 56(4): e2019WR024815
15
R L, Scott J F, Knowles J A, Nelson P, Gentine X, Li G, Barron-Gafford R, Bryant J A Biederman . Water availability impacts on evapotranspiration partitioning. Agricultural and Forest Meteorology, 2021, 297: 108251 https://doi.org/10.1016/j.agrformet.2020.108251
16
C, Liu X, Zhang Y Zhang . Determination of daily evaporation and evapotranspiration of winter wheat and maize by large-scale weighing lysimeter and micro-lysimeter. Agricultural and Forest Meteorology, 2002, 111(2): 109–120 https://doi.org/10.1016/S0168-1923(02)00015-1
17
J, Xu B, Wu D, Ryu N, Yan W, Zhu Z Ma . Quantifying the contribution of biophysical and environmental factors in uncertainty of modeling canopy conductance. Journal of Hydrology, 2021, 592: 125612 https://doi.org/10.1016/j.jhydrol.2020.125612
18
H, Wang H, Guan Z, Deng C T Simmons . Optimization of canopy conductance models from concurrent measurements of sap flow and stem water potential on Drooping Sheoak in South Australia. Water Resources Research, 2014, 50(7): 6154–6167 https://doi.org/10.1002/2013WR014818
19
X, Li P, Gentine C, Lin S, Zhou Z, Sun Y, Zheng J, Liu C Zheng . A simple and objective method to partition evapotranspiration into transpiration and evaporation at eddy-covariance sites. Agricultural and Forest Meteorology, 2019, 265: 171–182 https://doi.org/10.1016/j.agrformet.2018.11.017
20
H, Komatsu Y, Kang T, Kume N, Yoshifuji N Hotta . Transpiration from a Cryptomeria japonica plantation, part 2: responses of canopy conductance to meteorological factors. Hydrological Processes, 2006, 20(6): 1321–1334 https://doi.org/10.1002/hyp.6094
21
L, Yu H, Cai Z, Zheng Z, Li J Wang . Towards a more flexible representation of water stress effects in the nonlinear Jarvis model. Journal of Integrative Agriculture, 2017, 16(1): 210–220 https://doi.org/10.1016/S2095-3119(15)61307-7
22
V, Flo J, Martínez-Vilalta V, Granda M, Mencuccini R Poyatos . Vapour pressure deficit is the main driver of tree canopy conductance across biomes. Agricultural and Forest Meteorology, 2022, 322: 109029 https://doi.org/10.1016/j.agrformet.2022.109029
23
X, Niu Z, Chen Y, Pang X, Liu S Liu . Soil moisture shapes the environmental control mechanism on canopy conductance in a natural oak forest. Science of the Total Environment, 2023, 857: 159363 https://doi.org/10.1016/j.scitotenv.2022.159363
24
G, Cai M, König A, Carminati M, Abdalla M, Javaux F, Wankmüller M A Ahmed . Transpiration response to soil drying and vapor pressure deficit is soil texture specific. Plant and Soil, 2022, 500(1−2): 129–145
25
S, Chen Z, Zhang Z, Chen H, Xu J Li . Responses of canopy transpiration and conductance to different drought levels in Mongolian pine plantations in a semiarid urban environment of China. Agricultural and Forest Meteorology, 2024, 347: 109897 https://doi.org/10.1016/j.agrformet.2024.109897
26
P G Jarvis . Interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 1976, 273(927): 593–610
27
B E, Medlyn R A, Duursma D, Eamus D S, Ellsworth Prentice I, Colin C V M, Barton K Y, Crous Angelis P, De M, Freeman L Wingate . Reconciling the optimal and empirical approaches to modelling stomatal conductance. Global Change Biology, 2011, 17(6): 2134–2144 https://doi.org/10.1111/j.1365-2486.2010.02375.x
28
R, Leuning Y Q, Zhang A, Rajaud H, Cleugh K Tu . A simple surface conductance model to estimate regional evaporation using MODIS leaf area index and the Penman-Monteith equation. Water Resources Research, 2008, 44(10): W10419 https://doi.org/10.1029/2007WR006562
29
X, Chen L, Yu N, Cui H, Cai X, Jiang C, Liu Z, Shu Z Wu . Modeling maize evapotranspiration using three types of canopy resistance models coupled with single-source and dual-source hypotheses—A comparative study in a semi-humid and drought-prone region. Journal of Hydrology, 2022, 614: 128638 https://doi.org/10.1016/j.jhydrol.2022.128638
30
S, Li L, Zhang S, Kang L, Tong T, Du X, Hao P Zhao . Comparison of several surface resistance models for estimating crop evapotranspiration over the entire growing season in arid regions. Agricultural and Forest Meteorology, 2015, 208: 1–15 https://doi.org/10.1016/j.agrformet.2015.04.002
31
Q, Yu Y, Zhang Y, Liu P Shi . Simulation of the stomatal conductance of winter wheat in response to light, temperature and CO2 changes. Annals of Botany, 2004, 93(4): 435–441 https://doi.org/10.1093/aob/mch023
32
H, Yan Y, Zhou J, Zhang G, Wang C, Zhang J, Yu M, Li S, Zhao S, Deng S, Liang J, Jiang Y Ni . Parametrization of canopy resistance and simulation of latent heat fluxes for typical crops in southern Jiangsu Province. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(9): 101−107 (in Chinese)
33
W, Shao M, Li Y, Su H, Gao L Vlček . A modified Jarvis model to improve the expressing of stomatal response in a beech forest. Hydrological Processes, 2023, 37(8): e14955 https://doi.org/10.1002/hyp.14955
34
R Leuning . Modelling stomatal behaviour and photosynthesis of Eucalyptus grandis. Australian Journal of Plant Physiology, 1990, 17(2): 159–175
35
J T, Ball I E, Woodrow J A Berry . A Model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions. In: Biggins J, ed. Progress in Photosynthesis Research: Volume 4, Proceedings of the VIIth International Congress on Photosynthesis Providence, Rhode Island, USA, August 10–15, 1986. Dordrecht: Springer Netherlands, 1987
36
G, Damour T, Simonneau H, Cochard L Urban . An overview of models of stomatal conductance at the leaf level. Plant, Cell & Environment, 2010, 33(9): 1419–1438 https://doi.org/10.1111/j.1365-3040.2010.02181.x
37
Z, Ye Q Yu . Mechanism model of stomatal conductance. Acta Phytoecologica Sinica, 2009, 33(4): 772−782 (in Chinese)
38
M R, Blatt M, Jezek V L, Lew A Hills . What can mechanistic models tell us about guard cells, photosynthesis, and water use efficiency. Trends in Plant Science, 2022, 27(2): 166–179 https://doi.org/10.1016/j.tplants.2021.08.010
39
G B Bonan . Land-atmosphere CO2 exchange simulated by a land surface process model coupled to an atmospheric general circulation model. Journal of Geophysical Research, 1995, 100(D2): 2817–2831 https://doi.org/10.1029/94JD02961
40
P J, Sellers R E, Dickinson D A, Randall A K, Betts F G, Hall J A, Berry G J, Collatz A S, Denning H A, Mooney C A, Nobre N, Sato C B, Field A Henderson-Sellers . Modeling the exchanges of energy, water, and carbon between continents and the atmosphere. Science, 1997, 275(5299): 502–509 https://doi.org/10.1126/science.275.5299.502
41
D, Wang D, Lebauer G, Kling T, Voigt M C Dietze . Ecophysiological screening of tree species for biomass production: trade-off between production and water use. Ecosphere, 2013, 4(11): 138 https://doi.org/10.1890/ES13-00156.1
42
R K, Srivastava R K, Panda A, Chakraborty D Halder . Comparison of actual evapotranspiration of irrigated maize in a sub-humid region using four different canopy resistance based approaches. Agricultural Water Management, 2018, 202(1): 156–165 https://doi.org/10.1016/j.agwat.2018.02.021
43
H, Yan J, Yu C, Zhang G, Wang S, Huang J Ma . Comparison of two canopy resistance models to estimate evapotranspiration for tea and wheat in southeast China. Agricultural Water Management, 2021, 245: 106581 https://doi.org/10.1016/j.agwat.2020.106581
44
M, Bittelli F, Ventura G S, Campbell R L, Snyder F, Gallegati P R Pisa . Coupling of heat, water vapor, and liquid water fluxes to compute evaporation in bare soils. Journal of Hydrology, 2008, 362(3−4): 191–205 https://doi.org/10.1016/j.jhydrol.2008.08.014
45
L, Yu Y, Zeng Z, Su H, Cai Z Zheng . The effect of different evapotranspiration methods on portraying soil water dynamics and et partitioning in a semi-arid environment in Northwest China. Hydrology and Earth System Sciences, 2016, 20(3): 975–990 https://doi.org/10.5194/hess-20-975-2016
46
Y, Zeng Z, Su L, Wan J Wen . A simulation analysis of the advective effect on evaporation using a two-phase heat and mass flow model. Water Resources Research, 2011, 47(10): 2011WR010701 https://doi.org/10.1029/2011WR010701
47
Y, Zeng Z, Su L, Wan J Wen . Numerical analysis of air-water-heat flow in unsaturated soil: Is it necessary to consider airflow in land surface models. Journal of Geophysical Research, 2011, 116(D20): D20107 https://doi.org/10.1029/2011JD015835
48
H, Saito J, Šimůnek B P Mohanty . Numerical analysis of coupled water, vapor, and heat transport in the vadose zone. Vadose Zone Journal, 2006, 5(2): 784–800 https://doi.org/10.2136/vzj2006.0007
49
S, Kang B, Gu T, Du J Zhang . Crop coefficient and ratio of transpiration to evapotranspiration of winter wheat and maize in a semi-humid region. Agricultural Water Management, 2003, 59(3): 239–254 https://doi.org/10.1016/S0378-3774(02)00150-6
50
J, Wang H, Cai Y, Kang F Chen . Ratio of soil evaporation to the evapotranspiration for summer maize field. Transactions of the Chinese Society of Agricultural Engineering, 2007, 23(4): 17−22 (in Chinese)
51
B, Zhang Y, Liu D, Xu J, Cai N Zhao . Estimation of summer corn canopy conductance by scaling up leaf stomatal conductance. Transactions of the Chinese Society of Agricultural Engineering, 2011, 27(5): 80−86 (in Chinese)
52
Y, Wang Y, Zeng L, Yu P, Yang Der Tol C, Van Q, Yu X, Lü H, Cai Z Su . Integrated modeling of canopy photosynthesis, fluorescence, and the transfer of energy, mass, and momentum in the soil-plant-atmosphere continuum (STEMMUS-SCOPE v1.0.0). Geoscientific Model Development, 2021, 14(3): 1379–1407 https://doi.org/10.5194/gmd-14-1379-2021
53
R G, Allen L S, Pereira D, Raes M Smith . Crop evapotranspiration: guidelines for computing crop water requirements. Rome: FAO, 1998
54
R A, Feddes P J, Kowalik H Zaradny . Simulation of Field Water use and Crop Yield. Wageningen: Centre for Agricultural Publishing and Documentation, 1978
55
J J, Šimůnek M, Šejna H, Saito M, Sakai Genuchten M van . The HYDRUS-1D software package for simulating the one-dimensional movement of water, heat, and multiple solutes in variably-saturated media. Riverside, California: Department of Environmental Sciences, University of California Riverside, 2008
56
de Griend A A, van M Owe . Bare soil surface resistance to evaporation by vapor diffusion under semiarid conditions. Water Resources Research, 1994, 30(2): 181–188 https://doi.org/10.1029/93WR02747
57
Centre for Medium-Range Weather Forecasts (ECMWF) European . IFS Documentation CY48R1 - Part IV: Physical Processes. ECMWF, 2023
58
N, Katerji A, Perrier D, Renard A K O Aissa . A model of actual evapotranspiration (ETR) for a field of lucerne: the role of a crop coefficient. Agronomie, 1983, 3(6): 513−521 (in French)
59
W J Massman . A surface energy balance method for partitioning evapotranspiration data into plant and soil components for a surface with partial canopy cover. Water Resources Research, 1992, 28(6): 1723–1732 https://doi.org/10.1029/92WR00217
60
F M, Kelliher R, Leuning M R, Raupach E D Schulze . Maximum conductances for evaporation from global vegetation types. Agricultural and Forest Meteorology, 1995, 73(1−2): 1–16 https://doi.org/10.1016/0168-1923(94)02178-M
61
S, Ortega-Farias A, Olioso R, Antonioletti N Brisson . Evaluation of the Penman-Monteith model for estimating soybean evapotranspiration. Irrigation Science, 2004, 23(1): 1–9 https://doi.org/10.1007/s00271-003-0087-1
62
O, Gharsallah A, Facchi C Gandolfi . Comparison of six evapotranspiration models for a surface irrigated maize agro-ecosystem in Northern Italy. Agricultural Water Management, 2013, 130: 119–130 https://doi.org/10.1016/j.agwat.2013.08.009
63
Z, Wei P, Paredes Y, Liu W W, Chi L S Pereira . Modelling transpiration, soil evaporation and yield prediction of soybean in North China Plain. Agricultural Water Management, 2015, 147: 43–53 https://doi.org/10.1016/j.agwat.2014.05.004
64
N, Zhao Y, Liu J, Cai P, Paredes R D, Rosa L S Pereira . Dual crop coefficient modelling applied to the winter wheat-summer maize crop sequence in North China Plain: basal crop coefficients and soil evaporation component. Agricultural Water Management, 2013, 117: 93–105 https://doi.org/10.1016/j.agwat.2012.11.008
65
P P, Harris C, Huntingford P M, Cox J H C, Gash Y Malhi . Effect of soil moisture on canopy conductance of Amazonian rainforest. Agricultural and Forest Meteorology, 2004, 122(3-4): 215–227 https://doi.org/10.1016/j.agrformet.2003.09.006
66
Y, Wang R, Horton X, Xue T Ren . Partitioning evapotranspiration by measuring soil water evaporation with heat-pulse sensors and plant transpiration with sap flow gauges. Agricultural Water Management, 2021, 252: 106883 https://doi.org/10.1016/j.agwat.2021.106883
67
X, Li S, Kang F, Li X, Jiang L, Tong R, Ding S, Li T Du . Applying segmented Jarvis canopy resistance into Penman-Monteith model improves the accuracy of estimated evapotranspiration in maize for seed production with film-mulching in arid area. Agricultural Water Management, 2016, 178: 314–324 https://doi.org/10.1016/j.agwat.2016.09.016
68
Z, Wu N, Cui L, Zhao L, Han X, Hu H, Cai D, Gong L, Xing X, Chen B, Zhu M, Lv S, Zhu Q Liu . Estimation of maize evapotranspiration in semi-humid regions of northern China using Penman-Monteith model and segmentally optimized Jarvis model. Journal of Hydrology, 2022, 607: 127483 https://doi.org/10.1016/j.jhydrol.2022.127483
