Surface Energy Balance Components Research Articles

Surface energy fluxes and energy balance closure using large aperture scintillometer-based ET station on heterogeneous agricultural landscape in north India

This study was carried out to understand the pattern of surface energy fluxes over a periodical scale and energy balance closure using Large Aperture Scintillometer and Micrometeorological tower. The standalone technique as ‘Scintillometry’ which observes the structure parameter of refractive index based on Monin-Obukhov Similarity theory, has the potential to measure the sensible heat flux precisely. This paper discusses the surface energy balance components and energy balance closure over a period of August 2017 to June 2018. The maximum mean energy fluxes Rn, G, H and LE were observed in September (98.6 Wm-2), May (13.9 Wm-2), June (53.3 Wm-2) and August (82.1 Wm-2), respectively. The overall mean ET was observed at the rate of 1.36 mm day-1 during the study period. This scintillometry technique may further use in evapotranspiration modelling from polar orbiting satellite to geostationary satellite over a heterogeneous and undulated landscape.

Modeling snowpack dynamics and surface energy budget in boreal and subarctic peatlands and forests

Abstract. The snowpack has a major influence on the land surface energy budget. Accurate simulation of the snowpack energy and radiation budget is challenging due to, e.g., effects of vegetation and topography, as well as limitations in the theoretical understanding of turbulent transfer in the stable boundary layer. Studies that evaluate snow, hydrology and land surface models against detailed observations of all surface energy balance components at high latitudes are scarce. In this study, we compared different configurations of the SURFEX land surface model against surface energy flux, snow depth and soil temperature observations from four eddy-covariance stations in Finland. The sites cover two different climate and snow conditions, representing the southern and northern subarctic zones, as well as the contrasting forest and peatland ecosystems typical for the boreal landscape. We tested different turbulent flux parameterizations implemented in the Crocus snowpack model. In addition, we examined common alternative approaches to conceptualize soil and vegetation, and we assessed their performance in simulating surface energy fluxes, snow conditions and soil thermal regime. Our results show that a stability correction function that increases the turbulent exchange under stable atmospheric conditions is imperative to simulate sensible heat fluxes over the peatland snowpacks and that realistic peat soil texture (soil organic content) parameterization greatly improves the soil temperature simulations. For accurate simulations of surface energy fluxes, snow and soil conditions in forests, an explicit vegetation representation is necessary. Moreover, we demonstrate the high sensitivity of surface fluxes to a poorly documented parameter involved in snow cover fraction computation. Although we focused on models within the SURFEX platform, the results have broader implications for choosing suitable turbulent flux parameterization and model structures depending on the potential use cases for high-latitude land surface modeling.

Evaluation of reanalysis data and dynamical downscaling for surface energy balance modeling at mountain glaciers in western Canada

Abstract. Regional-scale surface energy balance (SEB) models of glacier melt require forcing by coarse-gridded data from reanalysis or global climate models that need to be downscaled to glacier scale. As on-glacier meteorological observations are rare, it generally remains unknown how exact the reanalysis and downscaled data are for local-scale SEB modeling. We address this question by evaluating the performance of reanalysis from the European Centre for Medium-Range Weather Forecasts (ERA5 and ERA5-Land reanalysis), with and without downscaling, at four glaciers in western Canada with available on-glacier meteorological measurements collected over different summer seasons. We dynamically downscale ERA5 with the Weather Research and Forecasting (WRF) model at 3.3 and 1.1 km grid spacing. We find that our SEB model, forced separately with the observations and the two reanalyses, yields less than 10 % difference in simulated total melt energy and shows strong correlations (0.86) in simulated time series of daily melt energy at each site. The good performance of the reanalysis-derived melt energy is partly due to cancellation of biases between overestimated incoming shortwave radiation and substantially underestimated wind speed and subsequently turbulent heat fluxes. Downscaling with WRF improves the simulation of wind speed, while other meteorological variables show similar performance to ERA5 without downscaling. The choice of WRF physics parameterization schemes is shown to have a relatively large impact on the simulations of SEB components but a smaller impact on the modeled total melt energy. The results increase our confidence in dynamical downscaling with WRF for long-term glacier melt modeling in this region.

Spatiotemporal Changes of Evapotranspiration in Cities along the Yellow River Basin in Shandong Province from 2000 to 2018

Steapotranspiration is an important component of land surface water circulation and surface energy balance. The study of the spatial and temporal change of the cities along the Yellow River basin in Shandong Province is conducive to explore the regional water circulation and the response of the river basin to climate change. Based on the evapotranspiration data, the spatiotemporal distribution and change characteristics of evapotranspiration in cities along the Yellow River basin in Shandong Province from 2000 to 2018 were analyzed. The results show that (1), the mean value of evaporation in the Yellow River in Shandong Province from 2000 to 2018 was 469.43 mma-1, with large annual variation; (2) high in spring and summer, low in autumn and winter; (3) the highest spatial distribution in July; (5) the decreasing in Jining, Heze, Taian, Zibo, Jinan, Dongying, Liaocheng, Binzhou and Dezhou; and (6) increasing in eastern cities along the Yellow River; the decrease in the west.

Seasonal variation of surface energy balance over Mubi northeastern Nigeria during 2000-2020

Several studies have been undertaken on surface energy balance (SEB) at various places in the world, but none have been undertaken for Mubi, Northeastern Nigeria. In the effort of consideration, this study aims to evaluate the seasonal variation of SEB in Mubi, with emphasis on the observational data from 2000 to 2020. Evaluation of seasonal variations was executed using time series analysis to find out the impact of precipitation, evapotranspiration, soil, and air temperature changes on SEB components. It was found that the SEB components variations of sensible heat (H) had a maximum value of 1035.13 Wm−2 in the dry season, in the month of December, while a minimum value of -104.13 Wm−2 during the rainy season, in the month of July; latent heat (LH), had a peak value of 5243.46 Wm-2 in the dry season in the month of April, while in the rainy season, the lower value was found to be 2460.6 Wm-2, in the month of August; soil heat (G) had minimum and maximum values of 886.43 Wm-2 in March and 275.25 Wm-2 in August respectively; and net radiative (Rn) varies roughly between the highest month in March with 2809.35 Wm−2 (rainy season months) and lowest month in August with 6879.69 Wm−2 (dry season months). It was also found that precipitation, evapotranspiration, soil, and air temperature follow the same trend with some SEB results, affirming their dependency on each other. Therefore, it is expected that this study will help to understand the amount of energy received or emitted in the Mubi region. Along with the main work, some recommendations were made by researchers on some applications of SEB to the community.

Analytical framework based on thermodynamics to estimate spatially distributed surface energy fluxes from remotely sensed radiations

The Limited surface observations of turbulent heat fluxes result in incomplete knowledge about the surface energy balance that drives the climate system. The highly parameterized surface energy balance models suffer from significant uncertainties. Remote sensing information of incoming and outgoing radiation fluxes are important input variables for turbulent heat flux models, though analytical solutions of surface energy budget from these variables are yet to be derived. Here, we developed a novel, purely physics-based analytical method grounded on the thermodynamic principle of maximum power. The approach derives the total turbulent heat flux only from the four inputs of incoming and outgoing longwave and shortwave radiations at the land surface, which are available from remotely sensed satellite data. The proposed approach does not use any parameterization, unlike the existing surface energy balance models, and hence does not suffer from uncertainty due to the same. We validated our methodology with 102 eddy covariance observation stations around the globe with different land use land covers from FLUXNET2015 and available urban datasets. Based on monthly averages of the total turbulent flux estimates for the eddy covariance sites, we observed root-mean-square error (RMSE) of 23.2 ± 10.9 Wm−2, a mean bias error (MBE) of 11.9 ± 13.1 Wm−2 and R2 value of 0.86 ± 0.15. Using the satellite observations of radiation fluxes from CERES at a spatial resolution of 10, we obtained the global flux field of total turbulent flux (QJ) and land surface heat storage (ΔQs) fluxes. On validation of QJ with FLUXNET sites for six grids for different land use land cover, we found RMSE of 20.1 ± 7 Wm−2, MBE of 14.4 ± 9 Wm−2 and R2 value of 0.96 ± 0.02. Further, from the evaporative stress factor of GLEAM, which is based on microwave remotely sensed vegetation optical depth and root zone soil moisture, we have obtained spatially distributed global estimates of Sensible(H) and latent heat (LE). In addition, our analytical estimates address the distribution of residual energy associated with the surface energy balance closure problem driven by the land use land cover. The theoretical estimates of all surface energy balance components from remote sensing based observations will improve our understanding of surface warming for different land use land covers across the globe.

Crop Response to Disease and Water Scarcity Quantified by Normalized Difference Latent Heat Index

Early detection and quantification of plants’ response to disease and water shortage conditions are very important for the agricultural management. This study represents the first utilization of Normalized Difference Latent Heat Index (NDLI) as a dimensionless indicator to assess plant health. By integrating NDLI with thermal infrared and surface energy balance (SEB) components, we aim to enhance the analysis of crop conditions and water scarcity in rice-growing areas. The integration between NDLI and land surface temperature exhibits a strong correlation (r = −0.82) with crop evapotranspiration (ET) derived from the widely used residual Surface Energy Balance Algorithm for Land model. Besides, the performance of NDLI- and SEB-based ET method proved its ability to provide the precise information of paddy field conditions by showing the significant correlations with the crop canopy biophysical properties that are traditionally represented and inferred by the multispectral remote sensing indices. The correlation coefficients of NDLI- and SEB-derived ET with Normalized Difference Vegetation Index (NDVI), Normalize Difference Water Index (NDWI), and Optimization of the Soil Adjusted Vegetation Index (OSAVI) were 0.84, 0.55, and 0.84, respectively. Also, NDLI- and SEB-derived ET exhibits a high degree of consistency with the ET determined through the SEBAL method, with difference less than 10% of the observations over 98.1% of the paddy fields of concern. Interestingly, the abnormally low ET signatures over the confirmed disease-infected regions of paddy fields are obviously observed in the NDLI- and SEB-derived ET maps, but not in the SEBAL-derived ET map. The findings of this work suggest that NDLI can be considered as a valuable indicator to provide information of the water stress status and health of the crop plants for advanced food-supply management.

Impacts of land cover changes and global warming on climate in Colombia during ENSO events

Colombia is highly vulnerable to climate change which may be intensified due to the climatic effects of regional deforestation. Here, we quantify the impact of historical (1900–2011) land cover changes (LCC) and of global warming during ENSO events (CC) on precipitation, temperature and surface energy balance components by running the Weather Research and Forecasting model WRF v3.9 at 10 km resolution. We find that historical anthropogenic CC causes a mean temperature increase of 0.77 ± 0.02 °C in Colombia, which is more pronounced in high altitudes. Precipitation is enhanced by 0.98 ± 0.30 mm/day (+ 9%), particularly over forested areas and reduced at the Pacific coast. LCC imply a reduction of precipitation particularly above the Andes (− 0.48 ± 0.10 mm/day) and Caribbean Coast (− 0.67 ± 0.12 mm/day), where LCC effects dampen CC effects by 24% and 72%, respectively. La Niña tends to intensify LCC and CC effects in the Andes but dampens them at the Coast, roughly by a factor of two compared to El Niño impacts in both regions. At the subregional level, LCC and CC can have impacts of similar magnitude on precipitation highlighting the need to precisely account for both drivers in hydroclimatic assessments. Contrary to almost all observations and similar simulations with climate models, WRF simulates a cooling bias after historical deforestation in Colombia, even with alternative WRF land surface models. We identify two main sources of biases in the default WRF parametrization to explain this inaccuracy: (1) surface shortwave radiation reflected after deforestation is overestimated; (2) associated evapotranspiration loss is underestimated. Improved model representation and validation of tropical vegetation properties are necessary to provide more robust and confident projections.

Scaled outdoor experimental investigation of thermal environment and surface energy balance in deep and shallow street canyons under various sky conditions

This study investigates the characteristics of the thermal environment and surface energy balance (SEB) in idealized outdoor scaled street canyons with different height-to-width ratios (H/W = 0.5 and 2, H = 0.5 m) in a humid subtropical climate from 2019 to 2020. The surface temperature, air temperature, albedo, and SEB components are compared between the deep and shallow canyons under clear, partially cloudy, and cloudy sky conditions. The interior air of the mimic buildings in both canyons showed significant vertical temperature gradients, and the outdoor air temperature in the shallow canyon showed a significant horizontal difference. The wall surfaces in the deep street canyon exhibited a stronger vertical temperature gradient during the daytime. In winter, higher interior air temperature within mimic buildings and slightly lower outdoor air temperature were found in the deep street canyon compared to the shallow one. Sky conditions played an important role in the SEB. The clear sky days showed slightly higher albedo than the cloudy and partially cloudy days. Shallow canyons showed a dominant contribution from the ground heat storage under clear and partially cloudy skies, while the deep canyon showed a similar contribution from the ground, walls, and roof. Lastly, the dependence of storage heat flux on net-all-wave radiation flux was found stronger under clear skies than under partially cloudy skies in all cases and months studied. These results provide an enhanced understanding of indoor and outdoor thermal environment and the data may help to improve numerical simulations under different sky conditions for urban climate studies.

Alteration in surface energy balance fluxes induced from long-term disk-tilled versus no-till management in maize production

Impacts of tillage-induced surface conditions on modification of the soil-crop-atmospheric interface have not been adequately studied. There is poor quantitative understanding at the intersection of tillage and surface energy balance (SEB), especially for the complete growing season. This research measured and evaluated SEB fluxes, i.e., net radiation (Rn), latent heat (LE), sensible heat (H), and ground heat flux (G) for three growing seasons (2011–2013) in a concurrent fashion in two similarly-managed maize (Zea mays L.) fields, which were under disk-till (DT) and no-till (NT) management for 17 years. To this end, each field was equipped with an identical Bowen Ratio Energy Balance System (BREBS) that collected 30-minute SEB data throughout the growing season. G was the most impacted SEB component, followed by H, LE, and Rn on an average basis, although these comparisons were subject to change during different times in the growing season. Seasonal mean interpretations masked within-season heterogeneity in DT vs. NT SEB dynamics and thus, daily differences for all SEB components, albedo (α), surface soil moisture (θv), and Bowen Ratio (β) were quantified. Greater α in NT field (0.16) was maintained throughout the growing season than DT field (0.14). All SEB components demonstrated greater differences between DT and NT in the early and late growing season. Nevertheless, SEB observations show that tillage impacts during significant crop canopy cover should not be ignored. NT surface (0–0.30 m) θv was as much as 22% greater than that of DT, and held critical importance for regional NT crop production during the extreme 2012 drought. θv also exerted control on albedo, with wetter soils having a lower albedo, which consequently translated into altered Rn across DT and NT fields. The research findings shown here underscore the particular importance of accounting for within-season behavior of albedo and G along with other components of SEB for evaluating soil management-related applications.

Long-term energy balance measurements at three different mountain permafrost sites in the Swiss Alps

Abstract. The surface energy balance is a key factor influencing the ground thermal regime. With ongoing climate change, it is crucial to understand the interactions of the individual heat fluxes at the surface and within the subsurface layers, as well as their relative impacts on the permafrost thermal regime. A unique set of high-altitude meteorological measurements was analysed to determine the energy balance at three mountain permafrost sites in the Swiss Alps (Murtèl–Corvatsch, Schilthorn and Stockhorn), where data have been collected since the late 1990s in the framework of the Swiss Permafrost Monitoring Network (PERMOS). All stations are equipped with sensors for four-component radiation, air temperature, humidity, and wind speed and direction, as well as ground temperatures and snow height. The three sites differ considerably in their surface and ground material composition, as well as their ground ice contents. The energy fluxes were calculated based on two decades of field measurements. While the determination of the radiation budget and the ground heat flux is comparatively straightforward (by the four-component radiation sensor and thermistor measurements within the boreholes), larger uncertainties exist for the determination of turbulent sensible and latent heat fluxes. Our results show that mean air temperature at Murtèl–Corvatsch (1997–2018, 2600 m a.s.l.) is −1.66 ∘C and has increased by about 0.8 ∘C during the measurement period. At the Schilthorn site (1999–2018, 2900 m a.s.l.) a mean air temperature of −2.60 ∘C with a mean increase of 1.0 ∘C was measured. The Stockhorn site (2003–2018, 3400 m a.s.l.) recorded lower air temperatures with a mean of −6.18 ∘C and an increase of 0.5 ∘C. Measured net radiation, as the most important energy input at the surface, shows substantial differences with mean values of 30.59 W m−2 for Murtèl–Corvatsch, 32.40 W m−2 for Schilthorn and 6.91 W m−2 for Stockhorn. The calculated turbulent fluxes show values of around 7 to 13 W m−2 using the Bowen ratio method and 3 to 15 W m−2 using the bulk method at all sites. Large differences are observed regarding the energy used for the melting of the snow cover: at Schilthorn a value of 8.46 W m−2, at Murtèl–Corvatsch 4.17 W m−2 and at Stockhorn 2.26 W m−2 are calculated, reflecting the differences in snow height at the three sites. In general, we found considerable differences in the energy fluxes at the different sites. These differences help to explain and interpret the causes of a warming atmosphere. We recognise a strong relation between the net radiation and the ground heat flux. Our results further demonstrate the importance of long-term monitoring to better understand the impacts of changes in the surface energy balance components on the permafrost thermal regime. The dataset presented can be used to improve permafrost modelling studies aiming at, for example, advancing knowledge about permafrost thaw processes. The data presented and described here are available for download at the following site: https://doi.org/10.13093/permos-meteo-2021-01 (Hoelzle et al., 2021).

Ensemble Machine Learning Outperforms Empirical Equations for the Ground Heat Flux Estimation with Remote Sensing Data

Estimating evapotranspiration at the field scale is a major component of sustainable water management. Due to the difficulty to assess some major unknowns of the water cycle at that scale, including irrigation amounts, evapotranspiration is often computed as the residual of the instantaneous surface energy budget. One of the Surface Energy Balance components with the largest uncertainties in their quantification over bare soils and sparse vegetation areas is the ground heat flux (G). Over the last decades, the estimation of G with remote sensing (RS) data has been mainly achieved with empirical equations, on the basis of the G and net radiation (Rn) ratio, G/Rn. The G/Rn empirical equations generally require vegetation data (Type I empirical equations), in combination with surface temperature (Ts) and albedo (Type II empirical equations). In this article, we aim to evaluate the estimation of G with RS data. Here, we compared eight G/Rn empirical equations against two types of machine learning (ML) methods: an ensemble ML type, the Random Forest (RF), and the Neural Networks (NN). The comparison of each method was evaluated using a wide range of climate and land cover datasets, including data from Eddy-Covariance towers that extend along the mid-latitude areas that encompass the European and African continents. Our results have shown evidence that the driver of G in bare soils and sparse vegetation areas (Fraction of Vegetation, Fv ≤ 0.25) is Ts, instead of vegetation greenness indexes. On the other hand, the accuracy in the estimation of G with Rn, Ts or Fv decreases in densely vegetated areas (Fv ≥ 0.50). There are no significant differences between the most accurate Type I and II empirical equations. For bare soils and sparse vegetation areas the empirical equation which combines the Leaf Area Index (LAI) and Ts (E7) estimates G best. In densely vegetated areas, an exponential empirical equation based on Fv (E4), shows the best performance. However, ML better estimates G than the empirical equations, independently of the Fv ranges. An RF model with Rn, LAI and Ts as predictor variables shows the best accuracy and performance metrics, outperforming the NN model.

Spatiotemporal dynamics of agricultural areas with central pivot using orbital remote sensing in the Brazilian semiarid

It is essential to know the dynamics of agricultural areas through surface energy balance components such as radiation balance, evapotranspiration, and soil moisture for the sustainable management of water use in irrigation. In the present study, we undertook to monitor and evaluate the spatiotemporal dynamics of an irrigated area with a center pivot in a region of the Brazilian semiarid region, based on physical and water parameters of the energy balance, using remote sensing techniques. The work was developed using three Landsat-8 satellite images, implementing the Surface Energy Balance Algorithms for Land (SEBAL), responsible for extracting geospatial information. In addition, surface meteorological data were used for image corrections and calibrations. The results presented, from thematic maps, monitoring of the spatiotemporal dynamics of irrigated areas, specifying mainly the production of irrigated corn with pivot. Thus, because of the physical-water parameters, photosynthetically active radiation irrigated areas and water deficit were identified, with higher and lower values of the radiation balance, evapotranspiration, and soil moisture, respectively. Therefore, spatiotemporal monitoring of the dynamics of irrigated areas can help in planning and managing the proper management of agricultural systems for the sustainable use of water in the Brazilian semiarid region. The MNDWI proved to be sensitive and efficient to analyze the dynamics of the presence of water in center pivot irrigation and can be an effective part of irrigation management in the region.

Influence of short-term surface temperature dynamics on tree orchards energy balance fluxes

Land surface temperature (LST) plays an essential role in developing and applying precision agriculture protocols, especially for calculating crop evapotranspiration (ETc) by surface energy balance (SEB) approaches; and for determining crop water status. However, LST is quite dependent on the meteorological conditions, which can rapidly vary. This variability, together with the limited meterological data acquisition frequency in most weather stations, can lead to the miscalculation of the SEB components, especially relevant when used for irrigation purposes.The present study assessed the temporal dynamic of LST in a very short period of time (20-minutes) through the acquisition of multiple thermal imagery. Additionally, a combination of SEB approach with Eddy Covariance technique was performed for quantifying the effect that LST variations have on the sensible (H) and latent (LE) heat fluxes.Even under steady meteorological conditions, temporal variations in LST of 3.5 and 4.0 K were observed for tree canopy and sunny bare soil surfaces, respectively. These LST oscillations reached values of about 7.8 and 17.9 K for tree canopies and bare soil when heterogeneous meteorological conditions were observed (i.e. cloud presence). Such LST differences translated into H and LE differences of about 26 and 19%, respectively; with variations up to 5 (for H) and 2.7 times (for LE) under fast-varying meteorological conditions.The obtained results suggest the necessity of acquiring thermal imagery when steady meteorological conditions exist or, otherwise, ensuring the collection of instantaneous meteorological data for applying post-processing corrections. This is of importance when incorporating the obtained ETc maps into precision irrigation protocols.

Impact of urbanization on surface energy balance components over metropolitan cities of India during 2000–2018 winter seasons

The present study attempts to evaluate the urban energy balance components concerning increasing urbanization and artificial surfaces over Indian metropolitan cities during the 2000–2018 winter seasons by using Landsat 7 and 8 satellite imageries. The results indicate that the estimated ranges of the energy fluxes are in the typical values reported in the earlier literature over global cities. The sensible heat flux (SHF) increased considerably, and the latent heat flux (LHF) slightly decreased during the study period. The mean SHF over the built-up areas (BA) and the dry lands (DL) of Delhi record a maximum increase of 28.2 Wm−2and 39.7 Wm−2 during the study period. The inland cities have high values of SHF over DL than the coastal cities, and the LHF is high over all the land use classes for the west coast cities. The SHF (LHF) shows a positive (negative) correlation with the land surface temperature. The SHF (LHF) is about 19–33% (1.9–15%) of the net radiation flux, and the residual heat flux is about 60 to 80% of the net radiation flux. The present study advocates that the substantial changes of the surface energy balance parameters have a profound influence on the energy exchange mechanism and could affect regional climatic change.

Characteristic Decrease in the Value of Rapeseed Evapotranspiration after Its Ripening

This paper presents the methodology of taking measurements of active surface energy balance components using the Bowen method. It discusses the applied measurement system, an important part of which are HMD 50U/50Y measurement sensors from Vaisala, adapted to work in the field. Their operation is a source of data for determining vertical profiles of temperature and water vapour pressure. These data are used to determine the turbulence components of the energy balance, i.e., sensible and latent heat. Measurements taken during the vegetative season on rape field showed that intensive evaporation occurred in the period until the end of June and that its decrease coincided with the decrease in the value of the degree of plant development determined on the basis of LAI records. In spring, during the period of intensive plant development, the decade sums of evaporation reached 30 mm, after which their quantities fell to the range of 10–15 mm. They became higher only in the periods of precipitation, when the water from the interception was available.

Assessment of a meteorological mesoscale model's capability to simulate intra-urban thermal variability in a tropical city

Model-based studies investigating the spatial and temporal variability of air temperature are commonly employed to assess heat mitigation strategies in cities. To generate reliable predictions, it is therefore important that models are capable to accurately predict air temperature variability across the city to account for the local climate context. The present study explores the sensitivity of the Weather Research and Forecasting (WRF) model coupled with a multi-layer urban scheme (BEP-BEM) to simulate intra-urban variations of 2-m air temperature during different synoptic conditions in the tropical city-state of Singapore. Two one-month long simulations are conducted during a relatively wet period during the Southwest monsoon and a very dry period during the Northeast monsoon. Model evaluation with micrometeorological data collected at an eddy covariance flux tower shows an overall good performance for turbulence parameters and surface energy balance components, in particular during dry conditions. Difficulties in predicting intermittent cloud cover result in slightly higher errors of modelled 2-m air temperature during the wet period (RMSE<2.3∘C) compared to the dry period (RMSE<1.6∘C) when using data from nine locations with different urban morphologies. The present research demonstrates the model's capability to predict the intra-urban variability across distinct urban morphologies under distinct weather conditions. However, the model fails to accurately capture the magnitude of the observed nocturnal temperature differences across all weather conditions investigated.

Modeling of land–surface interactions in the PALM model system 6.0: land surface model description, first evaluation, and sensitivity to model parameters

Abstract. In this paper the land surface model embedded in the PALM model system is described and evaluated against in situ measurements at Cabauw, Netherlands. A total of 2 consecutive clear-sky days are simulated, and the components of surface energy balance, potential temperature, humidity, and horizontal wind speed are compared to observations. For the simulated period, components of the energy balance are consistent with daytime and nighttime observations, and the daytime Bowen ratio also agrees fairly well with observations. The model simulates a more stably stratified nocturnal boundary layer than the observations, and near-surface potential temperature and humidity agree fairly well during the day. Moreover, a sensitivity analysis is performed to investigate dependence of the model on land surface and soil specifications, as well as atmospheric initial conditions, because they represent a major source of uncertainty in the simulation setup. It is found that an inaccurate estimation of leaf area index, albedo, or initial humidity causes a significant misrepresentation of the daytime turbulent sensible and latent heat fluxes. During the night, the boundary-layer characteristics are primarily affected by surface roughness and the applied radiation schemes.

Estimation of measured evapotranspiration using data-driven methods with limited meteorological variables

Determination of surface energy balance depends on the energy exchange between land and atmosphere. Thus, crop, soil and meteorological factors are crucial, particularly in agricultural fields. Evapotranspiration is derived from latent heat component of surface energy balance and is a key factor to clarify the energy transfer mechanism. Development of the methods and technologies for the aim of determining and measuring of evapotranspiration have been one of the main focus points for researchers. However, the direct measurement systems are not common because of economic reasons. This situation causes that different methods are used to estimate evapotranspiration, particularly in locations where no measurements are made. Thus, in this study, non-linear techniques were applied to make accurate estimations of evapotranspiration over the winter wheat canopy located in the field of Atatürk Soil Water and Agricultural Meteorology Research Institute Directorate, Kırklareli, Turkey. This is the first attempt in the literature which consist of the comparison of different machine learning methods in the evapotranspiration values obtained by the Bowen Ratio Energy Balance system. In order to accomplish this aim, support-vector machine, Adaptive neuro fuzzy inference system and Artificial neural network models have been evaluated for different input combinations. The results revealed that even with only global solar radiation data taken as an input, a high prediction accuracy can be achieved. These results are particularly advantageous in cases where the measurement of meteorological variables is limited. With the results of this study, progress can be made in the efficient use and management of water resources based on the input parameters of evapotranspiration especially for regions with limited data.

Evapotranspiration and energy partitioning across a forest-shrub vegetation gradient in a subarctic, alpine catchment

As a result of altitude and latitude amplified climate change, widespread changes in vegetation composition, density and distribution have been observed across northern regions. Despite wide documentation of shrub proliferation and treeline advance, few field-based studies have evaluated the hydrological implications of these changes. Quantification of total evapotranspiration (ET) across a range of vegetation gradients is essential for predicting water yield, yet challenging in cold alpine catchments due to heterogeneous land cover, including both boreal forest and shrub taiga ecosystems. Here, we present six years of surface energy balance components and ET dynamics at three sites along an elevational gradient in a subarctic, alpine catchment near Whitehorse, Yukon Territory, Canada. These sites span a gradient of thermal and vegetation regimes, providing a space-for-time comparison for future ecosystem shifts: 1) a low-elevation boreal white spruce forest (~12–20 m), 2) a mid-elevation subalpine taiga comprised of tall, dense willow (Salix) and birch (Betula) shrubs (~1–3 m) and 3) a high-elevation subalpine taiga with short, sparse shrub cover (<0.75 m) and moss, lichen, and bare rock. Eddy covariance instrumentation ran year-round at the forest and during the growing season at the two shrub sites. Total ET decreased and interannual variability increased with elevation, with mean May to September ET totals of 349 (±3) mm at the forest, 249 (±10) mm at the tall, dense shrub site, and 240 (±26) mm at the short, sparse shrub site. Comparatively, over the same period, ET:R ratios were the highest and most variable at the forest (2.19 ± 0.37) and similar at the tall, dense shrub (1.22 ± 0.09) and short, sparse shrub (1.14 ± 0.05) sites. Our results suggest that advances in treeline will increase overall ET and lower interannual variability; however, the large growing season water deficit at the forest indicates strong reliance on soil moisture from late fall and snowmelt recharge. In contrast, ET was considerably less at the cooler higher elevation shrub sites, which exhibited similar ET losses over 6 years despite differences in shrub height and abundance. ET rates between the two shrub sites were similar throughout the year, except during the peak growing season. Greater interannual variability in ET at the short, sparse shrub site indicates the reduced influence of vegetation controls on ET. Results suggest that predicted changes in vegetation type and structure in northern regions will have a considerable impact on water partitioning and will vary in a complex way in response to changing precipitation timing, phase and magnitude, growing season length, and vegetation snow and rain interactions.