Surface Energy Balance Model Research Articles

The influence of föhn winds on annual and seasonal surface melt on the Larsen C Ice Shelf, Antarctica

Abstract. Warm, dry föhn winds are observed over the Larsen C Ice Shelf year-round and are thought to contribute to the continuing weakening and collapse of ice shelves on the eastern Antarctic Peninsula (AP). We use a surface energy balance (SEB) model, driven by observations from two locations on the Larsen C Ice Shelf and one on the remnants of Larsen B, in combination with output from the Antarctic Mesoscale Prediction System (AMPS), to investigate the year-round impact of föhn winds on the SEB and melt from 2009 to 2012. Föhn winds have an impact on the individual components of the surface energy balance in all seasons and lead to an increase in surface melt in spring, summer and autumn up to 100 km away from the foot of the AP. When föhn winds occur in spring they increase surface melt, extend the melt season and increase the number of melt days within a year. Whilst AMPS is able to simulate the percentage of melt days associated with föhn with high skill, it overestimates the total amount of melting during föhn events and non-föhn events. This study extends previous attempts to quantify the impact of föhn on the Larsen C Ice Shelf by including a 4-year study period and a wider area of interest and provides evidence for föhn-related melting on both the Larsen C and Larsen B ice shelves.

Interoperability of ECOSTRESS and Landsat for mapping evapotranspiration time series at sub-field scales

Land-surface temperature retrieved from thermal infrared (TIR) remote sensing has proven to be a valuable constraint in surface energy balance models for estimating evapotranspiration (ET). For optimal utility in agricultural water management applications, frequent thermal imaging (<4-day revisit) at sub-field (100 m or less) spatial resolution is desired. While, the current suite of Landsat satellites (7 and 8) provides the required spatial resolution, the 8-day combined revisit can be inadequate to capture rapid changes in surface moisture status or crop phenology, particularly in areas of persistent cloud cover. The new ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission, with an average 4-day revisit interval and nominal 70-m resolution, provides a valuable research platform for augmenting Landsat TIR sampling and for investigating TIR-based ET mapping mission requirements more broadly. This study investigates the interoperability of Landsat and ECOSTRESS imaging for developing ET image timeseries with high spatial (30-m) and temporal (daily) resolution. A data fusion algorithm is used to fuse Landsat and ECOSTRESS ET retrievals at 30 m with daily 500-m retrievals using TIR data from the Moderate Resolution Imaging Spectroradiometer (MODIS) over target agricultural sites spanning the United States.The added value of the combined multi-source dataset is quantified in comparison with daily flux tower observations collected within these target domains. In addition, we investigate ET model performance as a function of ECOSTRESS view angle, overpass time, and time separation between TIR and Landsat visible to shortwave infrared (VSWIR) data acquisitions used to generate land-surface temperature, leaf area index, and albedo inputs to the surface energy balance model. The results demonstrate the value of the higher temporal sampling provided by ECOSTRESS, especially in areas that are frequently impacted by cloud cover. Limiting usage to ECOSTRESS scenes collected between 9:00 a.m. to 5:00 p.m. and nadir viewing angles <20° yielded daily (24-h) ET retrievals of comparable quality to the well-tested Landsat baseline. We also discuss challenges in using land-surface temperature from a thermal free-flyer system for ET retrieval, which may have ramifications for future TIR water-use mapping missions.

COSIPY v1.3 – an open-source coupled snowpack and ice surface energy and mass balance model

Abstract. Glacier changes are a vivid example of how environmental systems react to a changing climate. Distributed surface mass balance models, which translate the meteorological conditions on glaciers into local melting rates, help to attribute and detect glacier mass and volume responses to changes in the climate drivers. A well-calibrated model is a suitable test bed for sensitivity, detection, and attribution analyses for many scientific applications and often serves as a tool for quantifying the inherent uncertainties. Here, we present the open-source COupled Snowpack and Ice surface energy and mass balance model in PYthon (COSIPY), which provides a flexible and user-friendly framework for modeling distributed snow and glacier mass changes. The model has a modular structure so that the exchange of routines or parameterizations of physical processes is possible with little effort for the user. The framework consists of a computational kernel, which forms the runtime environment and takes care of the initialization, the input–output routines, and the parallelization, as well as the grid and data structures. This structure offers maximum flexibility without having to worry about the internal numerical flow. The adaptive subsurface scheme allows an efficient and fast calculation of the otherwise computationally demanding fundamental equations. The surface energy balance scheme uses established standard parameterizations for radiation as well as for the energy exchange between atmosphere and surface. The schemes are coupled by solving both surface energy balance and subsurface fluxes iteratively such that consistent surface skin temperature is returned at the interface. COSIPY uses a one-dimensional approach limited to the vertical fluxes of energy and matter but neglects any lateral processes. Accordingly, the model can be easily set up in parallel computational environments for calculating both energy balance and climatic surface mass balance of glacier surfaces based on flexible horizontal grids and with varying temporal resolution. The model is made available on a freely accessible site and can be used for non-profit purposes. Scientists are encouraged to actively participate in the extension and improvement of the model code.

Regional Distribution of Net Radiation over Different Ecohydrological Land Surfaces

Net radiation is an important component of the earth’s surface energy balance, which plays a vital role in the evolution of regional climate or climate change. The estimation of this component at regional or global scales is critical and challenging due to the sparse and limited ground-based observations. This paper made an attempt to analyze the feasibility of a remote sensing-based surface energy balance model using satellite (TERRA/MODIS) data to derive the net radiation (Rn). In the present study, MODIS data at 15 different days of the year (DOY) were utilized to visualize the spatial pattern of net radiation flux over three versatile and heterogeneous ecohydrological land surfaces (upstream, midstream, and downstream) of northwest China (Heihe river basin). The results revealed that the estimated net radiation from the satellite data agrees well with the ground-based measurements over three different surfaces, with a mean relative error of 9.33% over the upstream superstation (grasslands), 13.95% over the middle stream superstation (croplands), and 11.63% over the downstream superstation (mixed forests), where the overall relative error was 11.64% with an overall rmse of 29.36 W/m2 in the study area. The regional distribution of net radiation over the versatile land surfaces was validated well at a large scale during the five-month period and over different land surfaces. It was also observed that the spatial pattern of net radiation varies spatially over three different landscape regions during four different days of the year, which might be associated with different climatic conditions and landscape features in these regions. The overall findings of this study concluded that satellite-derived net radiation can rationally be obtained using a single-source remote sensing model over different land surfaces.

Imprints of evaporative conditions and vegetation type in diurnal temperature variations

Abstract. Diurnal temperature variations are strongly shaped by the absorption of solar radiation, but evaporation, or the latent heat flux, also plays an important role. Generally, evaporation cools. Its relation to diurnal temperature variations, however, is unclear. This study investigates the diurnal response of surface and air temperatures to evaporative conditions for different vegetation types. We use the warming rate, defined as the increase in temperature in response to absorbed solar radiation in the morning, and evaluate how it changes with evaporative fraction, which is an indicator of the evaporative conditions. Results for 51 FLUXNET sites show that the warming rate of air temperature carries very weak imprints of evaporative fraction across all vegetation types. However, the warming rate of surface temperature is highly sensitive to evaporative fraction with a value of ∼23×10-3 K (W m-2)-1, indicating stronger evaporative cooling for moister conditions. Contrarily, the warming rates of surface and air temperatures are similar at forest sites and carry literally no imprints of evaporative fraction. We explain these contrasting patterns with an analytical surface energy balance model. The derived expressions reproduce the observed warming rates and their sensitivity to evaporative fraction in all vegetation types. Multiplying the warming rate with daily maximum solar radiation gives an approximation for the diurnal surface temperature range (DTsR). We use our model to compare the individual contributions of solar radiation, evaporative conditions, and vegetation (by its aerodynamic conductance) in shaping DTsR and show that the high aerodynamic conductance of forests reduces DTsR substantially more (−56 %) than evaporative cooling (−22 %). We further show that the strong diurnal variation in aerodynamic conductance (∼2.5 times of the mean across vegetation types) reduces DTsR by ∼35 % in short vegetation and savanna but only by ∼22 % in forests. We conclude that diurnal temperature variations may be useful for predicting evaporation for short vegetation. In forests, however, the diurnal variations in temperatures are mainly governed by their high aerodynamic conductance, resulting in negligible imprints of evaporative conditions.

A Test Study of an Energy and Mass Balance Model Application to a Site on Urumqi Glacier No. 1, Chinese Tian Shan

In this study, energy and mass balance is quantified using an energy balance model to represent the glacier melt of Urumqi Glacier No. 1, Chinese Tian Shan. Based on data from an Automatic Weather Station (4025 m a.s.l) and the mass balance field survey data nearby on the East Branch of the glacier, the “COupled Snowpack and Ice surface energy and Mass balance model” (COSIMA) was used to derive energy and mass balance simulations during the ablation season of 2018. Results show that the modeled cumulative mass balance (−0.67 ± 0.03 m w.e.) agrees well with the in-situ measurements (−0.64 ± 0.16 m w.e.) (r2 = 0.96) with the relative difference within 5% during the study period. The correlation coefficient between modeled and observed surface temperatures is 0.88 for daily means. The main source of melt energy at the glacier surface is net shortwave radiation (84%) and sensible heat flux (16%). The energy expenditures are from net longwave radiation (55%), heat flux for snow/ice melting (32%), latent heat flux of sublimation and evaporation (7%), and subsurface heat flux (6%). The sensitivity testing of mass balance shows that mass balance is more sensitive to temperature increase and precipitation decrease than temperature decrease and precipitation increase.

A novel approach for next generation water-use mapping using Landsat and Sentinel-2 satellite data

ABSTRACT Evapotranspiration (ET) is needed in a range of applications in hydrology, climatology, ecology, and agriculture. Remote sensing-based estimation is the only viable and economical method for ET estimation over large areas. The current Landsat satellites provide images every 16 days limiting the ability to capture biophysical changes affecting ET. Thus, we explored the potential integration of Landsat 8 and Sentinel-2 data for estimating ET using a surface energy balance model. The results indicate the proposed Landsat-Sentinel data fusion approach substantially reduced relative errors from 48% to 10% on area-wide and from 49% to 17% on pixel-wide compared to linear interpolation between two Landsat images. The proposed approach had a better agreement with expected actual ET maps across high-vegetation conditions than in low-vegetation conditions. The finer temporal resolution and better accuracy of ET maps based on Landsat-Sentinel integration is of great importance in managing limited water resources.

Inferring the potential impact of human activities on evapotranspiration in the Tumen River Basin based on LANDSAT imagery and historical statistics

AbstractUnderstanding the spatial and temporal changes in actual evapotranspiration (ETa) caused by different human activities is important for water management in water‐sensitive areas, because ETa plays a major role in regulating the availability of water on land. Thus, ETa observation is particularly important for observing land degradation in the watershed. This study used the operational simplified surface energy balance (SSEBop) model to perform ETa estimations and observations in the Tumen River Basin (TRB), covering the border of China and Democratic People's Republic of Korea (DPRK), over the last 30 years by using eight periods of clear‐sky LANDSAT Thematic Mapper/Operational Land Imager (TM/OLI) and thermal infrared sensor (TIRS) data. To assess the reliability of the ETa estimations, multilayer soil temperatures from the China Meteorological Administration (CMA) Land Data Assimilation System Version 2.0 (CLDAS‐V2.0) were utilized. The highest R2 (.71) between ETa and soil temperature was observed in layers shallower than 10 cm; this value of R2 gradually decreased to .57 at a depth of 200 cm. The average ETa of the TRB declined from 1.65 mm in 1985 to 1.03 mm in 2017. The impact of human activities on water loss (ETa) was determined by comparing land cover changes observed by an object‐based image analysis (OBIA) approach and economic activities between 1994 and 2008. Varying economic activities and water depletion in different soil layers may have led to the variable ETa values between the northern (China) and southern (DPRK) part of the TRB during the last three decades.

Seasonal and interannual variability of melt-season albedo at Haig Glacier, Canadian Rocky Mountains

Abstract. In situ observations of summer (June through August, or JJA) albedo are presented for the period 2002–2017 from Haig Glacier in the Canadian Rocky Mountains. The observations provide insight into the seasonal evolution and interannual variability of snow and ice albedo, including the effects of summer snowfall, the decay of snow albedo through the melt season, and the potential short-term impacts of regional wildfire activity on glacier-albedo reductions. Mean JJA albedo (± 1σ) recorded at an automatic weather station in the upper ablation zone of the glacier was αS=0.55 ± 0.07 over this period, with no evidence of long-term trends in surface albedo. Each summer the surface conditions at the weather station undergo a transition from a dry, reflective spring snowpack (αS∼0.8) to a wet, homogeneous midsummer snowpack (αS∼0.5) to exposed, impurity-rich glacier ice, with a measured albedo of 0.21 ± 0.06 over the study period. The ice albedo drops to ∼ 0.12 during years of intense regional wildfire activity such as 2003 and 2017, but it recovers from this in subsequent years. This seasonal albedo decline is well simulated through a parameterization of snow-albedo decay based on cumulative positive degree days (PDDs), but the parameterization does not capture the impact of summer snowfall events, which cause transient increases in albedo and significantly reduce glacier melt. We introduce this effect through a stochastic parameterization of summer precipitation events within a surface energy balance model. The amount of precipitation and the date of snowfall are randomly selected for each model realization based on a predefined number of summer snow events. This stochastic parameterization provides an improved representation of the mean summer albedo and mass balance at Haig Glacier. We also suggest modifications to conventional degree-day melt factors to better capture the effects of seasonal albedo evolution in temperature-index or positive-degree-day melt models on mountain glaciers. Climate, hydrology, or glacier mass balance models that use these methods typically use a binary rather than continuum approach to prescribing melt factors, with one melt factor for snow and one for ice. As alternatives, monthly melt factors effectively capture the seasonal albedo evolution, or melt factors can be estimated as a function of the albedo where these data are available.

Temperature and precipitation in the southern Central Andes during the last glacial maximum, Heinrich Stadial 1, and the Younger Dryas

Recent developments in terrestrial cosmogenic nuclide (TCN) exposure dating and the reinterpretation of TCN boulder ages from moraines have improved our understanding of the glacial chronology in the Central Andes. According to these records, glacial advances throughout the region correlate with insolation-driven changes in the intensity of the South American Summer Monsoon and millennial-scale climate events such as Heinrich Stadials and the Younger Dryas. Quantifying the temperature and precipitation shifts during these events helps to constrain past moisture pathways and associated changes in atmospheric circulation patterns. Yet, particularly in the southern Central Andes, where a wealth of glacigenic landforms attests to formerly cooler and/or wetter conditions, the magnitudes of past temperature and precipitation changes are only loosely constrained at a few sites. Here, we present results from TCN-dated moraines combined with the reconstruction of former glaciers and paleolakes within the eastern sector of the southern Central Andes (24°–27°S) for the Last Glacial Maximum (LGM), Heinrich Stadial 1 (HS1), and the Younger Dryas (YD). We performed Monte-Carlo simulations with 2-dimensional models of ice flow and lake hydrology that are forced by a spatially-distributed surface energy balance model. Our results indicate that temperatures were 3.0–4.2 °C, 2.0–3.3 °C and 1.3–2.5 °C cooler during the LGM, HS1 and YD, respectively, in agreement with previous estimates elsewhere in the Central Andes. We find that temperature changes during the late glacial are compatible with sea-surface temperature anomalies derived from the tropical Atlantic. Precipitation was only 5–27% greater than today, which contrasts with larger anomalies reconstructed for the Bolivian part of the Andean Plateau (Altiplano). We attribute this discrepancy to the southerly position of our study region with respect to the Bolivian High, supporting the hypothesis that this atmospheric pressure system played a prominent role for South American Summer Monsoon dynamics during glacial episodes.

Dust devil winds: Assessing dry convective vortex intensity limits at planetary surfaces

An estimate of the maximum winds associated with dry convective vortices is determined simply as Vmax = ΔT(2Cp/Ta)0.5, where ΔT is equal to half of the maximum surface-atmosphere temperature contrast, Cp is the specific heat capacity, and Ta the background temperature of the atmosphere. The ΔT estimate, and the resultant winds, have strong support from a wide range of observations on Earth and Mars. The model results in maximum predicted vortex winds of 29, 53, 2.7 and 2.0 m/s for Earth, Mars, Titan and Venus respectively, the ΔT values for the latter two bodies being determined from surface energy balance models. The maximum vortex winds in well-documented observations on Earth and Mars appear to be ~25 m/s and ~ 30 m/s respectively, consistent with the model limits.

Evaluation of different methods of estimating ET for the performance assessment of irrigation schemes

In the assessment of irrigation schemes, the accuracy of performance indicators related to the water balance could be improved by estimating crop evapotranspiration (ETc) using remote sensing techniques. The two main remote sensing approaches to estimating ETc are the surface energy balance and the FAO56-based approach, that uses the ability of vegetation indices (VI) to trace the crop coefficient. Both approaches were evaluated comparatively at the Río Dulce irrigation scheme in Argentina (where the predominant crops are cotton, alfalfa, and maize) using products from the Landsat 7 and 8 sensors provided by the EEFlux application. The first analysis used field-specific, VI-derived basal crop coefficients obtained for 1743 fields using series of 9–29 satellite images along the 2014−15 irrigation campaign. The second analysis used 30 fields (grown with cotton and maize) where the actual irrigation schedules in the 2014−15 irrigation campaign were known. A root zone soil water balance was computed in these fields using the FAO56 dual approach with field-specific, VI-derived basal crop coefficients. The ETc obtained from the water balance was compared with the ETc estimated using a single crop coefficient approach that uses field-specific VI and takes into account soil evaporation (herein called synthetic approach), and with the ETc obtained with the METRIC surface energy balance model as facilitated by the EEFlux application. The third analysis was a simulation analysis of errors in the estimation of the ETc due to the interpolation to daily values of single crop coefficients and basal crop coefficients determined at hypothetical satellite overpass intervals of longer than one day. The VI-derived basal crop coefficient curves obtained for the 1743 fields of the first analysis were below the locally adopted standard (not field-specific) basal crop coefficient. Crop evapotranspiration in the 8005 ha covered by this analysis was about 20 % higher when applying standard non-field specific curves than when applying VI-derived curves. This difference pointed to the importance of using field-specific estimations of ETc. In the analysis carried out on the 30 selected fields, the ETc estimated using the VI-based approach agreed well with the ETc obtained from the water balance except under water deficit conditions. The crop coefficients obtained for these fields using the METRIC model correlated with those obtained by applying the VI-based method, although the former tended to be higher than the latter in the lower value range. The analysis of interpolation errors showed that when satellite overpass frequency is greater than one week and water deficit is mild or inexistent, the interpolation of crop coefficients (for instance, of those derived from an energy balance) gives errors of ETc estimations that are greater than those resulting from the VI-based approach. Under water deficit conditions, the VI-based approach systematically overestimates evapotranspiration.

A Comparison of SSEBop-Model-Based Evapotranspiration with Eight Evapotranspiration Products in the Yellow River Basin, China

Accurate evapotranspiration (ET) estimation is important in understanding the hydrological cycle and improving water resource management. The operational simplified surface energy balance (SSEBop) model can be set up quickly for the routine monitoring of ET. Several studies have suggested that the SSEBop model, which can simulate ET, has performed inconsistently across the United States. There are few detailed studies on the evaluation of ET simulated by SSEBop in other regions. To explore the potential and application scope of the SSEBop model, more evaluation of the ET simulated by SSEBop is clearly needed. We calculated the SSEBop-model-based ET (ETSSEBopYRB) with land surface temperature product of MOD11A2 and climate variables as inputs for the Yellow River Basin (YRB), China. We also compared the ETSSEBopYRB with eight coarse resolution ET products, including China ETMTE, produced using the upscaling energy flux method; China ETCR, which is generated using the non-linear complementary relationship model; three global products based on the Penman–Monteith logic (ETPMLv2, ETMODIS, and ETBESS), two global ET products based on the surface energy balance (ETSEBS, ETSSEBopGlo), and integrated ET products based on the Bayesian model averaging method (ETGLASS), using the annual ET data derived from the water balance method (WB-ET) for fourteen catchments. We found that ETSSEBopYRB and the other eight ET products were able to explain 23 to 52% of the variability in the water balance ET for fourteen small catchments in the YRB. ETSSEBopYRB had a better agreement with WB-ET than ETSEBS, ETMODIS, ETCR, and ETGLASS, with lower RMSE (88.3 mm yr−1 vs. 121.7 mm yr−1), higher R2 (0.49 vs. 0.43), and lower absolute RPE (−3.3% vs. –19.9%) values for the years 2003–2015. We also found that the uncertainties of the spatial patterns of the average annual ET values and the ET trends were still large for different ET products. Third, we found that the free global ET product derived from the SSEBop model (ETSSEBopGlo) highly underestimated the annual total ET trend for the YRB. The poor performance of the land surface temperature product of MOD11A2 in 2015 caused the large ETSSEBopYRB uncertainty at eight-day and monthly scales. Further evaluation of ET based on the SSEBop model for site measurements is needed.

Monthly 10-m evapotranspiration rates retrieved by SEBALI with Sentinel-2 and MODIS LST data

Actual and potential evapotranspiration (ET) are important variables for regional and global environmental modelling. These elements provide better understanding and management of the hydrological cycle, particularly in relation to several environmental stresses affecting ecosystems. With the advancement of airborne techniques, remote sensing approaches have enabled an accurate estimation of crop actual ET with limited field visits. In this study, an enhanced version (i.e. m-SEBALI) of the improved surface energy balance model for land, surnamed SEBALI, will be presented. Main improvements concern the retrieval of 10-m ET values using Sentinel-2 images and MODIS Terra LST 1-km datasets. The other enhancement considers the usage of monthly-only climatic datasets, instead of the required hourly, daily and monthly data in SEBALI. Calibrations and validations were made in China, Belgium, Germany, Lebanon and Italy between 2013 and 2017, yielding adequate RSME (i.e. 20.06 mm/month) and AME (i.e. 15.69 mm/month) values. These countries represent four different climatic regions (i.e. continental semi-arid, Monsoon-influenced warm-summer humid continental, Mediterranean hot summer and Oceanic climates). Thus, Landast-8 thermal bands will be unnecessary to implement SEBALI. Also, the conversion between hourly and daily climatic data and monthly climatic datasets in SEBALI showed a Pearson value of 93.3 %. Thus, seven of the required inputs in SEBALI were eliminated in m-SEBALI, saving on time and resources. Furthermore, the wheat seasonal trend is produced in the Bekaa valley, Lebanon, between November 2018 and July 2019, showing an average ET value of 620 mm in a region with 510 mm of precipitations during the same period. Two peaks were visible in January and May signaling the different wheat phenological stages. The importance of m-SEBALI lies in providing an automated retrieval of 10-m ET, biomass production and water productivity (WP), among other variables, in diverse climatic regions. Such improvements shall improve the assessment of ET and WP towards better management of water resources, particularly in regions lacking some required inputs.

A scale-dependent model to represent changing aerodynamic roughness of ablating glacier ice based on repeat topographic surveys

AbstractTurbulent fluxes make a substantial and growing contribution to the energy balance of ice surfaces globally, but are poorly constrained owing to challenges in estimating the aerodynamic roughness length (z0). Here, we used structure from motion (SfM) photogrammetry and terrestrial laser scanning (TLS) surveys to make plot-scale 2-D and 3-D microtopographic estimations ofz0and upscale these to mapz0across an ablating mountain glacier. At plot scales, we found spatial variability inz0estimates of over two orders of magnitude with unpredictablez0trajectories, even when classified into ice surface types. TLS-derived surface roughness exhibited strong relationships with plot-scale SfMz0estimates. At the glacier scale, a consistent increase inz0of ~0.1 mm d−1was observed. Space-for-time substitution based on time since surface ice was exposed by snow melt confirmed this gradual increase inz0over 60 d. These measurements permit us to propose a scale-dependent temporalz0evolution model where unpredictable variability at the plot scale gives way to more predictable changes ofz0at the glacier scale. This model provides a critical step towards deriving spatially and temporally distributed representations ofz0that are currently lacking in the parameterisation of distributed glacier surface energy balance models.

Where Are White Roofs More Effective in Cooling the Surface?

AbstractBy reflecting more sunlight, white (cool) roofs experience lower surface temperatures and produce less heating of the surrounding air. The effectiveness of cool roofs, quantified by the surface temperature difference between cool and regular roofs (ΔTs), is known to vary spatially. A common perception is that ΔTs is controlled by solar radiation that reaches the roof surface. Here we use an Earth System Model and a surface energy balance model to show that the spatial variability of ΔTs, when normalized by the albedo difference between cool and regular roofs Δα, is also controlled by an energy distribution factor that encodes the efficiencies of surface energy balance components in dissipating heat. Our results suggest that painting the roof white is more effective when the roof has less water‐holding capacity and smaller thermal admittance and is located in places with more solar radiation, less precipitation, and lower wind speed.

Narrow-to-Broadband Conversion for Albedo Estimation on Urban Surfaces by UAV-Based Multispectral Camera

Urban surface albedo is important for investigating urban surface–atmosphere radiative heat exchanges. For modeling surface energy balance (SEB) at local and neighborhood scales, ground or unmanned aerial vehicle (UAV)-based multispectral remote sensing (RS) can be used to obtain high-spatial-resolution multispectral information for both horizontal and vertical urban surfaces. The existing narrow-to-broadband (NTB) conversion models, developed for satellite/high-altitude observation and large homogeneous rural/vegetated/snow zones, may not be suitable for downscaling to the local and neighborhood scales or the urban complex texture. We developed three NTB models following published methodologies for three common UAV-based multispectral cameras according to Sample_D, a sample group of extensive spectral albedos of artificial urban surfaces, and evaluated their performance and sensitivities to solar conditions and surface material class. The proposed models were validated with independent samples (Sample_V). A model considering albedo physics was improved by multiplying different variables with respect to the camera (termed as “Model_phy_reg”), which initially proved to be the most accurate with a root mean square error of up to 0.02 for Sample_D and approximately 0.029 for Sample_V, meeting the required accuracy of total shortwave albedo for SEB modeling. The accuracy of Model_phy_reg was not much prone to the solar conditions.

Using high-spatiotemporal thermal satellite ET retrievals to monitor water use over California vineyards of different climate, vine variety and trellis design

Mapping the spatial variability of actual evapotranspiration (ETa) across vineyards is useful for optimizing irrigation scheduling and efficiency, leading to conservation of water resources and more sustainable wine grape production. To support efficient irrigation strategies, we investigate the utility of thermal infrared-based ETa maps over a range of vineyards located throughout California, each representing a unique local climate, trellis design, grape variety, row orientation and management practice. ETa maps are derived by combining the Disaggregated Atmosphere Land Exchange Inverse (ALEXI/DisALEXI) surface energy balance model and the Spatial Temporal Adaptive Reflectance Fusion Model (STARFM) to generate ETa estimates at high spatial (30 m) and temporal (daily) resolution. Model output is evaluated for years 2017 and 2018 over vineyard sites located in Sonoma, Sacramento, and Madera counties in California that are being monitored as part of the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Overall, modeled daily ET estimates compare well with flux tower observations, with average root mean square error (RMSE), mean absolute error (MAE) and mean bias error (MBE) of 0.88 mm day−1, 0.70 mm day−1, and 0.17 mm day−1 respectively, over all four individual vineyard locations, aligning with past GRAPEX studies. Despite general agreement, record wildfires in northern California during 2018 likely resulted in positive model bias, while misrepresentation of leaf area index within a double-trellis designed canopy at the southern-most vineyard resulted in negative model bias. Spatial analysis of monthly total ET highlights the advantages of utilizing a satellite-based approach to characterize the variability in water use within and surrounding the targeted vineyards. A reliable spatial ET product at scale has the potential to improve water allocation and conservation efforts by identifying areas of uneven water use due to variations in soil texture and composition and other environmental or anthropogenic factors.

Evaluation of the Operational Simplified Surface Energy Balance Model for Pastureland Evapotranspiration Mapping and Drought Monitoring in North Central Kentucky

The use of remotely sensed evapotranspiration (ET) for field applications in drought monitoring and assessment is gaining momentum, but meeting this need has been hampered by the absence of extensive ground-based measurement stations for ground validation across agricultural zones and natural landscapes. This is particularly crucial for regions more prone to recurring droughts with limited ground monitoring stations. A three-year (2016–2018) flux ET dataset from a pastureland in north central Kentucky was used to validate the Operational Simplified Surface Energy Balance (SSEBop) model at monthly and annual scales. Flux and SSEBop ET track each other in a consistent manner in response to seasonal changes. The mean bias error (MBE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were 5.47, 21.49 mm mon−1, 30.94%, and 0.87, respectively. The model consistently underestimated ET values during winter months and overestimated them during summer months. SSEBop’s monthly ET anomaly maps show spatial ET distribution and its accurate representation. This is particularly important in areas where detailed surface meteorological and hydrological data are limited. Overall, the model estimated monthly ET magnitude satisfactorily and captured it seasonally. The SSEBop’s functionality for remote ET estimation and anomaly detection, if properly coupled with ground measurements, can significantly enhance SSEBop’s ability to monitor drought occurrence and prevalence quickly and accurately.

Effect of fires on biophysical parameters, energy balance and evapotranspiration in a protected area in the Brazilian Cerrado

Fires are essential for defining the structure, biodiversity and functioning of the Brazilian Cerrado. Several protected areas have been created in the Cerrado to conserve natural ecosystems by eliminating, minimizing or reducing human pressures and threats operating within their limits. However, impacts of these conservation practices on the local microclimate and change in fire coverage are poorly known. Thus, the aim of this study was to evaluate the impact of the 2017 megafire in the Chapada dos Guimarães National Park (CGNP) on biophysical parameters, energy balance and evapotranspiration. We used data from fire spots between 2000 and 2017 and images from sensors Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) Landsat 8 between July and October 2017. The satellite images provided data to calculate the Normalized Difference Vegetation Index (NDVI), Surface Albedo (α), Normalized Difference Water Index (NDWI), while the Simplified Surface Energy Balance (SSEB) model provided estimates of the radiation balance (Rn), soil heat flux (G), sensible heat flux (H), latent heat flux (LE), and the daily evapotranspiration (ET). Surface reflectance and energy balance were evaluated before and after fire at three time periods throughout the study. All variables analyzed were affected by the combination of climate dynamics and fire occurrence. NDVI, LE and ET decreased, and α, NDWI, G and H increased throughout the dry season in the CGNP. The fire that occurred in the CGNP in 2017 decreased NDVI, LE, LE/Rn and ET and increased α, NDWI, G, H and H/Rn significantly. This study demonstrated that fires decrease surface biomass and surface-to-atmosphere water transfer and increase surface warming on Cerrado.