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For areas that are urbanized rapidly, the practice of low impact development (LID) has gained an important place in stormwater management and urban planning due to its capability and beneficial effects in restoring the original hydrological cycle. The performances of LID alternatives can vary substantially due to different climate conditions. This study investigated the performances of five LID alternatives under a semi-arid climate in northern China onwater balance and flood control. A numerical model, Storm Water Management Model version 5 (US Environmental Protection Agency), was employed to run 10 years' rainfall events for these objectives. Twoevaluationmethodswere proposed in this study: the efficiency index forwater balance and a performance radar chart. The investigation of the five LID alternatives revealed that these LID alternatives functioned differently in flood control andwater balance, and porous pavement performed best in all indices except the lag time. The two evaluation methods, in conjunction with the long-term numerical simulation, can facilitate design and decision making by providing a clear picture of the performance and functions for these LID alternatives. [ABSTRACT FROM AUTHOR]

• A four-source energy balance model for estimation of urban evapotranspiration (ET) is proposed. • Urban ET, soil evaporation (E), and vegetation transpiration (T) exhibited a declining trend with the urbanization. • The decline in the urban ET, E and T can be explained by changes in urban land use. • The urban heat island effect is due to the different evaporative cooling effects in urban versus suburban areas. Urbanization substantially changes many aspects including the regional hydrological cycle, energy balance, and microclimate. However, the degree to which urbanization alters urban evapotranspiration (ET) and its components (soil evaporation (E), vegetation transpiration (T), impervious surface evaporation (I) and water body evaporation (W)) remains unclear. A significant obstacle is the absence of a multi-source energy balance model for an urban area. To solve this issue, a customized four-source energy balance model for urban areas (FSU model) is proposed that differentiates between urban E, T, W, and I. The performance of the FSU model was verified using the eddy correlation (EC), stable hydrogen and oxygen isotope observations in a mega city: Tianjin, China. Long-term urban ET and its composition changes were reconstructed using the Landsat image during the period of 1986–2021 in Tianjin. Trend analyses demonstrate that urban ET, E, and T exhibit significant decreases of trend, while urban W, sensible heat flux (H), and Bowen ratio (BR) exhibit significant increases in trends with urbanization. Urban ET decreased at a rate of 1.41 mm/yr, corresponding to a ∼ 13% decrease below the long-term mean value of total urban ET during the period 1986–2021. Correlation analyses revealed a declining trend of urban ET, E, and T primarily caused by urban land use changes, while the increasing trends of urban W, H, and BR were mainly due to the urban microclimate changes. The proposed FSU model aids in assessment of the urban heat island (UHI) effect and facilitates scientific water resources management in urban areas. This research improves the in-depth understanding of the impact of urbanization on urban ET and its components. [ABSTRACT FROM AUTHOR]

• A satellite-based urban three-source evapotranspiration (ET) model is developed. • A complementary relationship-based parameterization scheme for estimation of soil evaporation is proposed. • Three-source remote sensing model for urban areas outperformed the existing single-source urban ET models. Estimation of urban evapotranspiration (ET) is meaningful for evaluating the urban heat island effect and promoting urban water conservation. While multi-source satellite-based ET models for farmland and natural ecosystems have been developed, these models lack the object-oriented solutions for the urban areas. In view of this, the present study proposed a Three-source Remote sensing model for Urban areas (TRU) to simulate the urban ET, which can discriminate between urban soil evaporation, vegetation transpiration, and impervious surface evaporation. The new parameterization scheme proposed herein is characterized by the theory of complementary relationship to estimate the urban soil evaporation. Evaporation losses in the impervious areas were independently estimated in accordance with the distribution of urban land use and land cover (LULC). The developed TRU model was evaluated for 63 cloud-free test days in Tianjin, China by using 30 m Landsat Operational Land Imager (OLI)/Enhanced Thematic Mapper Plus (ETM+) images. The outcomes of this study revealed a root-mean-square-error (RMSE) of 40.2 W/m2 and a bias of 10.8 W/m2 compared with the two eddy correlation (EC) observations for the instantaneous latent heat flux (LE) simulation. Moreover, the RMSE and bias were 0.092 and −0.023, respectively in simulating the vegetation latent heat flux fraction (LE v /LE) when verified with respect to the stable water isotope measurements. Due to limited water availability, the impervious urban surface evaporation magnitude was found quite low with minimal variation on different test days. The results emphasize the importance of coupling LULC information in the urban ET modeling conceptualization. Certainly, the TRU model improved the ET simulation accuracy in urban areas and could facilitate the mapping of urban ET components at high spatial resolution. [ABSTRACT FROM AUTHOR]

• The deep learning method is used to simulate the canopy stomatal resistance (r sc);. • A two-objective monte Carlo-based bayesian framework is proposed to estimate soil surface resistance (r ss);. • Bayesian model evidence (BME) is used to evaluate model performance;. • Evapotranspiration (ET) simulation and partition are evaluated in urban forest lands;. • The enhanced shuttleworth-wallace model has the ability to simulate regional scale ET. The Shuttleworth-Wallace two-source (S-W) model has been widely applied to estimate evapotranspiration (ET) and its components in a variety of vegetation-covered surface conditions. However, significant uncertainties occur in calculation of vegetation canopy resistance (r sc) and soil surface resistance (r ss). In this study, an enhanced version of the S-W model is proposed to simulate and partition ET. The deep learning (DL) approach which combined soil, vegetation, and meteorological observation data, is employed to simulate r sc. A two-objective Monte Carlo-based Bayesian parameter optimization (TOMCBP) is developed to determine the empirical parameters for the r ss calculation. The enhanced S-W model was verified based on the three years of eddy correlation (EC) and stable water isotope observations in an urban forest land located in Tianjin, China. Results suggest the machine learning-based r sc parametric scheme effectively improved the performance of the S-W model for the ET simulation. The TOMCBP-based r ss parameterization scheme can improve the performance of the S-W model for ET partition. Furthermore, this study used the Bayesian model evidence (BME) to evaluate the performance of different models. BME is shown to balance the model complexity and fitting accuracy when compared with traditional statistical parameters, thus showing superiority in model evaluation and selection. This study improves the physical mechanism and performance of the S-W model and proposes a new method for r sc and ET components simulation based on machine learning. The enhanced S-W model is more precise and provides guidance for irrigation measures in urban woodland areas. [ABSTRACT FROM AUTHOR]