In:
Ecohydrology, Wiley, Vol. 15, No. 6 ( 2022-09)
Abstract:
The stable water isotope method is widely applied to distinguish the evapotranspiration ( ET ) components across various vegetation‐covered surfaces. ET partitioning in the urban woodland area is useful for guiding precision irrigation, thereby promoting in urban water conservation. For the first time, this study partitions ET in urban locust forest areas based on stable water isotope observations for the period 2019–2020. The isotope composition of ET ( δ ET ) and soil evaporation ( δ E ) were determined using the Keeling‐plot method and Craig–Gordon model, respectively. The steady‐state (SS) and the non‐steady‐state (NSS) assumptions were compared for estimating the isotope composition of vegetation transpiration ( δ T ). The NSS outperformed SS in daily bulk leaf water isotopic component ( δ L,b ) simulation and recommended to be used in δ T determination for the urban forest land. Both methods resulted in similar estimates of δ L,b and δ T during the daytime; however, substantial difference was observed during the nighttime. The fraction of vegetation transpiration to evapotranspiration ( FT ) varies between 0.21 and 0.95 with an average value of 0.78 for the SS and varies between 0.22 and 0.97 with an average of 0.82 for the NSS. The FT in the urban forest land is higher than the natural forest land due to the urban heat island effect and higher planting densities. The seasonal FT variation is primarily controlled by the leaf area index ( LAI ) and soil moisture. The predictive uncertainty of δ ET and δ T are much higher than δ E , wherein the uncertainties of δ T decreases as FT increases while the uncertainty of δ ET increases as FT increases. The uncertainty analysis highlights the importance of increasing the sampling frequency under low FT condition. This study revealed the seasonal change patterns of FT and its major governing factors in urban woodland areas, thus providing more insights on effective water management in the urban ecosystems.
Type of Medium:
Online Resource
ISSN:
1936-0584
,
1936-0592
Language:
English
Publisher:
Wiley
Publication Date:
2022
detail.hit.zdb_id:
2418105-5
SSG:
12
