Surface Energy Balance Model Research Articles

Evolution of the firn pack of Kaskawulsh Glacier, Yukon: meltwater effects, densification, and the development of a perennial firn aquifer

Abstract. In spring 2018, two firn cores (21 and 36 m in length) were extracted from the accumulation zone of Kaskawulsh Glacier, St. Elias Mountains, Yukon. The cores were analyzed for ice layer stratigraphy and density and compared against historical measurements made in 1964 and 2006. Deep meltwater percolation and refreezing events were evident in the cores, with a total ice content of 2.33±0.26 m in the 36 m core and liquid water discovered below a depth of 34.5 m. Together with the observed ice content, surface energy balance and firn modelling indicate that Kaskawulsh Glacier firn retained about 86 % of its meltwater in the years 2005–2017. For an average surface ablation of 0.38 m w.e. yr−1 over this period, an estimated 0.28 m w.e. yr−1 refroze in the firn, 0.05 m w.e. yr−1 was retained as liquid water, and 0.05 m w.e. yr−1 drained or ran off. The refrozen meltwater is associated with a surface lowering of 0.73±0.23 m between 2005 and 2017 (i.e., surface drawdown that has no associated mass loss). The firn has become denser and more ice-rich since the 1960s and contains a perennial firn aquifer (PFA), which may have developed over the past decade. This illustrates how firn may be evolving in response to climate change in the St. Elias Mountains, provides firn density information required for geodetic mass balance calculations, and is the first documented PFA in the Yukon–Alaska region.

Challenges in Reconciling Satellite-Based and Locally Reported Estimates of Wetland Change: A Case of Topographically Constrained Wetlands on the Eastern Tibetan Plateau

The coupling of rapid warming and wetland degradation on the Tibetan Plateau has motivated studies of climate influence on wetland change in the region. These studies typically examine large, topographically homogeneous regions, whereas conservation efforts sometimes require fine-grained information in rugged terrain. This study addresses topographically constrained wetlands on the Eastern Tibetan, where herders report significant wetland degradation. We used Landsat images to examine changes in wetland areas and Sentinel-1 SAR images to investigate water level and vegetation structure. We also analyzed trends in precipitation, growing season length, and reference evapotranspiration in weather station records. Snow cover and the vegetation growing season were quantified using MODIS observations. We analyzed estimates of actual evapotranspiration using the Atmosphere-Land Exchange Inverse model (ALEXI) and the Simplified Surface Energy Balance model (SSEBop). Satellite-informed analyses failed to confirm herders’ accounts of reduced wetland function, as no coherent trends were found in wetland area, water content, or vegetation structure. An analysis of meteorological records did indicate a warming-induced increase in reference evapotranspiration, and both meteorological records and satellites suggest that the growing season had lengthened, potentially increasing water demand and driving wetland change. The discrepancies between the satellite data and local observations pointed to temporal, spatial, and epistemological gaps in combining scientific data with empirical evidence in understanding wetland change on the Tibetan Plateau.

Development of High Performance Computing Tools for Estimation of High-Resolution Surface Energy Balance Products Using sUAS Information.

sUAS (small-Unmanned Aircraft System) and advanced surface energy balance models allow detailed assessment and monitoring (at plant scale) of different (agricultural, urban, and natural) environments. Significant progress has been made in the understanding and modeling of atmosphere-plant-soil interactions and numerical quantification of the internal processes at plant scale. Similarly, progress has been made in ground truth information comparison and validation models. An example of this progress is the application of sUAS information using the Two-Source Surface Energy Balance (TSEB) model in commercial vineyards by the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment - GRAPEX Project in California. With advances in frequent sUAS data collection for larger areas, sUAS information processing becomes computationally expensive on local computers. Additionally, fragmentation of different models and tools necessary to process the data and validate the results is a limiting factor. For example, in the referred GRAPEX project, commercial software (ArcGIS and MS Excel) and Python and Matlab code are needed to complete the analysis. There is a need to assess and integrate research conducted with sUAS and surface energy balance models in a sharing platform to be easily migrated to high performance computing (HPC) resources. This research, sponsored by the National Science Foundation FAIR Cyber Training Fellowships, is integrating disparate software and code under a unified language (Python). The Python code for estimating the surface energy fluxes using TSEB2T model as well as the EC footprint analysis code for ground truth information comparison were hosted in myGeoHub site https://mygeohub.org/ to be reproducible and replicable.

Estimation and Validation of Actual Evapotranspiration (ETa) of Maize Wheat Cropping System Using SSEBop Model Over IARI Research Farm, New Delhi, India

Evapotranspiration (ET) is an indispensable component of earth surface energy exchange studies and forms basis for various environmental applications. The present study attempts to estimate Landsat-8 satellite-based spatial distribution of crop ET using Operational Simplified Surface Energy Balance (SSEBop) model and validate through ET estimated using multilevel micrometeorological tower-based Bowen Ratio Energy Balance (BREB). The study was conducted over Indian Agricultural Research Institute farm during the summer (kharif maize) and winter (wheat) season of 2017–2018. The result of BREB showed the intra-seasonal variations in actual ET estimation ranging from maximum ET of 3.84 mm day−1 and 2.64 mm day−1 for maize and wheat during mid-stage to minimum ET of 1.88 mm day−1 for maize in maturity and 1.14 mm day−1 in an initial stage of wheat. It was observed that SSEBop slightly overestimated the crop ET; however, it showed the good agreement with BREB estimated ET (R2 = 0.76) with d-index of 0.92 and root-mean-square error of 0.48 mm day−1. It can be concluded that despite slight overestimation of ET by SSEBop, satellite-based SSEBop model can be recommended for crop ET estimation over large area and can be used for various water management studies.

Use of multispectral and thermal satellite imagery to determine crop water requirements using SEBAL, METRIC, and SWAP models in hot and hyper-arid Oman

Hyper-arid regions, such as the Sultanate of Oman, require precise information regarding actual water application to develop efficient irrigation management. Remote sensing techniques with imagery models can be used as practical tools in irrigation practices by estimating the actual evapotranspiration (ETa) at local and regional scales. In this study, spatial and temporal changes in the ETa were evaluated by investigating the performance of three different models, including two surface energy balance models: surface energy balance algorithm for land (SEBAL) and mapping evapotranspiration at high-resolution internalized calibration (METRIC), as well as an agro-hydrological model: soil, water, atmosphere, and plant (SWAP), for the hyper-arid conditions of Oman. This study is unique in its field application, as very few studies have conducted ETa estimations using satellite imagery in the hot and hyper-arid regions of Oman. In situ meteorological data were integrated with Landsat-8 operational land multispectral imagery and thermal infrared sensor satellite images. Further, a sensitivity analysis comparing the three models' estimated ETa with in situ sap flow field measurements (SFM) and the Penman-Monteith (PM) model. The results showed that the SEBAL model overestimated the sensible heat flux compared with the METRIC model. Although the PM model showed a higher correlation (R2 = 0.87) with the SFM, the METRIC model, among all the investigated models, showed the highest correlation of R2 = 0.83. Further, the SEBAL and METRIC models overestimated the ETa, especially in the summer. Conversely, SWAP underestimated the ETa, as compared with the SFM. These results show that the METRIC model estimates the ETa with high spatial and temporal accuracy. Meanwhile, the SWAP model was the least suitable model for estimating the ETa under the hyper-arid condition of Oman. These findings will act as a reference for accurate water requirements, thereby linking water resource management and future irrigation scheduling in local hot and hyper-arid conditions.

Atmosphere Driven Mass-Balance Sensitivity of Halji Glacier, Himalayas

The COupled Snowpack and Ice surface energy and mass balance model in PYthon (COSIPY) was employed to investigate the relationship between the variability and sensitivity of the mass balance record of the Halji glacier, in the Himalayas, north-western Nepal, over a 40 year period since October 1981 to atmospheric drivers. COSIPY was forced with the atmospheric reanalysis dataset ERA5-Land that has been statistically downscaled to the location of an automatic weather station at the Halji glacier. Glacier mass balance simulations with air temperature and precipitation perturbations were executed and teleconnections investigated. For the mass-balance years 1982 to 2019, a mean annual glacier-wide climatic mass balance of −0.48 meters water equivalent per year (m w.e. a−1) with large interannual variability (standard deviation 0.71 m w.e. a−1) was simulated. This variability is dominated by temperature and precipitation patterns. The Halji glacier is mostly sensitive to summer temperature and monsoon-related precipitation perturbations, which is reflected in a strong correlation with albedo. According to the simulations, the climate sensitivity with respect to either positive or negative air temperature and precipitation changes is nonlinear: A mean temperature increase (decrease) of 1 K would result in a change of the glacier-wide climatic mass balance of −1.43 m w.e. a−1 (0.99m w.e. a−1) while a precipitation increase (decrease) of 10% would cause a change of 0.45m w.e. a−1 (−0.59m w.e. a−1). Out of 22 circulation and monsoon indexes, only the Webster and Yang Monsoon index and Polar/Eurasia index provide significant correlations with the glacier-wide climatic mass balance. Based on the strong dependency of the climatic mass balance from summer season conditions, we conclude that the snow–albedo feedback in summer is crucial for the Halji glacier. This finding is also reflected in the correlation of albedo with the Webster and Yang Monsoon index.

Spatial variability of evapotranspiration and pressure on groundwater resources: remote sensing monitoring by crop type in the Bekaa plain, Lebanon

The lack of data on water consumption by agriculture in the context of climate change led the authors to estimate the water balance of the Bekaa Valley. Plots were mapped according to the normalized difference vegetation index profiles of spring and summer crop with the Sentinels-2 images. Surface energy balance model python (PYSEBAL) estimated the seasonal evapotranspiration (ETseason) and net irrigation requirements (NIRseason). Spring and summer crop maps were accurate: 90% and 93%. The results of PYSEBAL were validated by the comparison between averaged daily ET of PYSEBAL and FAO-56 method (root mean square error from 0.38 to 1.93 and the mean average error from 0.33 to 1.64 mm / day). The water balance was negative: 0.16 km3 of groundwater was extracted against 0.10 km3 of rainfall available for aquifer recharge in 2017. This is explained by ET/ha/season (alfalfa: 1094 mm / ha / season, orchards 881, corn 719, tobacco 606, cucumber 558, late potatoes 487, summer vegetables 483, early potatoes 467, vineyard 390, spring vegetables 237, and wheat 313). ETseason varies according to temperature (strong correlation between daily ET and temperature, Pearson: 0.74), relative humidity (Pearson: −0.65), surface radiation (Pearson: 0.75), and percentage of sand in the soil and its depth (strong negative Pearson: −0.62 and −0.57).

Estimating evapotranspiration by coupling Bayesian model averaging methods with machine learning algorithms.

Evapotranspiration (ET) is one of the most important components of global hydrologic cycle and has significant impacts on energy exchange and climate change. Numerous models have been developed to estimate ET so far; however, great uncertainties in models still require considerations. The aim of this study is to reduce model errors and uncertainties among multi-models to improve daily ET estimate. The Bayesian model averaging (BMA) method is used to assemble eight ET models to produce ET with Landsat 8 satellite data, including four surface energy balance models (i.e., SEBS, SEBAL, SEBI, and SSEB) and four machine learning algorithms (i.e., polymars, random forest, ridge regression, and support vector machine). Performances of each model and BMA method were validated through in situ measurements of semi-arid region. Results indicated that the BMA method outperformed all eight single models. The four most important models obtained by the BMA method were ranked by random forest, SVM, SEBS, and SEBAL. The BMA method coupled with machine learning can significantly improve the accuracy of daily ET estimate, reducing uncertainties among models, and taking different intrinsic benefits of empirically and physically based models to obtain a more reliable ET estimate.

Daily Actual Evapotranspiration Estimation in a Mediterranean Ecosystem from Landsat Observations Using SEBAL Approach

Quantifying actual evapotranspiration (ETa) over natural vegetation is crucial in evaluating the water status of ecosystems and the water-use patterns in local or regional hydrological basins. Remote sensing-based surface energy balance models have been used extensively for estimating ETa in agro-environments; however, the application of these models to natural ecosystems is still limited. The surface energy balance algorithm for land (SEBAL) physical-based surface energy balance model was applied to estimate the actual evapotranspiration over a heterogeneous coverage of Mediterranean maquis in a natural reserve in Sardinia, Italy. The model was applied on 19 Landsat 5 and 8 images from 2009 to 2014, and the results were compared to the data of a micrometeorological station with eddy covariance flux measurements. Comparing the SEBAL-based evaporative fraction (ΛS) to the corresponding tower-derived evaporative fractions (ΛT) showed good flux estimations in the Landsat overpass time (Coefficient of determination R2 = 0.77, root mean square error RMSE = 0.05 and mean absolute error MAE = 0.076). Three methods were evaluated for upscaling instantaneous latent heat flux (λE) to daily actual evapotranspiration (ETa,D). The upscaling methods use the evaporative fraction (Λ), the reference evapotranspiration fraction (EFr) and the ratio of daily to instantaneous incoming shortwave radiation (Rs24/Rsi) as upscaling factors under the hypothesis of diurnal self-preservation. A preliminary analysis performed using only in-situ measured data demonstrated that the three factors were relatively self-preserved during the daytime, and can yield good ETa,D estimations, particularly when obtained at near the Landsat scene acquisition time (≈10:00 UTC). The upscaling factors obtained from SEBAL retrieved instantaneous fluxes, and some ancillary measured meteorological data were used to upscale SEBAL-estimated instantaneous actual λ to daily ET. The Λ EFr and Rs24/Rsi methods on average overestimated the measured ETa,D by nearly 20, 61 and 18%, respectively. The performance of the Λ and Rs24/Rsi methods was considered satisfactory, bearing in mind the high variable ground cover and the inherent variability of the biome composition, which cannot be properly represented in the Landsat moderate spatial resolution. In this study, we tested the potential of the SEBAL model application in a complex natural ecosystem. This modeling approach will be used to represent the spatial dynamics of ET, which will be integrated into further environmental and hydrological applications.

Remote sensing-based actual evapotranspiration assessment in a data-scarce area of Brazil: A case study of the Urucuia Aquifer System

The large groundwater reserves of the Urucuia Aquifer System (UAS) enabled agricultural development and economic growth in the western Bahia State, in northeastern Brazil. Over the last several years, concern has grown around the aquifer’s diminishing water levels, and water balance (WB) studies are in demand. Considering the lack of measured actual evapotranspiration (ETa), a major component of the water cycle, this work uses the Operational Simplified Surface Energy Balance (SSEBop) model to estimate ETa, and compares it to basin-scale estimates from the Soil Moisture Accounting Procedure (SMAP) monthly model and from an annual WB closure method, based on gridded meteorological data and the Gravity Recovery and Climate Experiment (GRACE) product. Additionally, a comparative assessment of different versions of the SSEBop parameterization was performed. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery was used to implement eight different versions of the SSEBop algorithm over the UAS between 2000 and 2013. SSEBop and SMAP ETa yielded similar seasonal patterns, with correlation coefficient (r) up to 0.65, mean difference (MD) of 0.8 mm/month and mean absolute difference (MAD) of 18.5 mm/month. Comparison of SSEBop annual ETa estimates to annual SMAP and WB closure estimates yielded low MD (12.1 and −7.3 mm/year, respectively) and MAD (82.5 and 82.8 mm/year, respectively), but also low r values (0.00 and 0.37, respectively). The comparison of the different SSEBop versions indicated the need to incorporate a calibration step of the aerodynamic heat resistance (rah) parameter. SSEBop results were also used for land cover and drought monitoring. Analysis indicates that agriculture, associated with an increasing trend of atmospheric evaporative demand, is responsible for the decrease in groundwater levels and streamflow in the studied time period.

Greenland climate simulations show high Eemian surface melt which could explain reduced total air content in ice cores

Abstract. This study presents simulations of Greenland surface melt for the Eemian interglacial period (∼130 000 to 115 000 years ago) derived from regional climate simulations with a coupled surface energy balance model. Surface melt is of high relevance due to its potential effect on ice core observations, e.g., lowering the preserved total air content (TAC) used to infer past surface elevation. An investigation of surface melt is particularly interesting for warm periods with high surface melt, such as the Eemian interglacial period. Furthermore, Eemian ice is the deepest and most compressed ice preserved on Greenland, resulting in our inability to identify melt layers visually. Therefore, simulating Eemian melt rates and associated melt layers is beneficial to improve the reconstruction of past surface elevation. Estimated TAC, based on simulated melt during the Eemian, could explain the lower TAC observations. The simulations show Eemian surface melt at all deep Greenland ice core locations and an average of up to ∼30 melt days per year at Dye-3, corresponding to more than 600 mm water equivalent (w.e.) of annual melt. For higher ice sheet locations, between 60 and 150 mmw.e.yr-1 on average are simulated. At the summit of Greenland, this yields a refreezing ratio of more than 25 % of the annual accumulation. As a consequence, high melt rates during warm periods should be considered when interpreting Greenland TAC fluctuations as surface elevation changes. In addition to estimating the influence of melt on past TAC in ice cores, the simulated surface melt could potentially be used to identify coring locations where Greenland ice is best preserved.

Vegetation Monitoring Optimization With Normalized Difference Vegetation Index and Evapotranspiration Using Remote Sensing Measurements and Land Surface Models Over East Africa

The majority of people in East Africa rely on the agro-pastoral system for their livelihood, which is highly vulnerable to droughts and flooding. Agro-pastoral droughts are endemic to the region and are considered the main natural hazard that contributes to food insecurity. Drought begins with rainfall deficit, gradually leading to soil moisture deficit, higher land surface temperature, and finally impacts to vegetation growth. Therefore, monitoring vegetation conditions is essential in understanding the progression of drought, potential effects on food security, and providing early warning information needed for drought mitigation decisions. Because vegetation processes couple the land and atmosphere, monitoring of vegetation conditions requires consideration of both water provision and demand. While there is consensus in using either the Normalized Difference Vegetation Index (NDVI) or evapotranspiration (ET) for vegetation monitoring, a comprehensive assessment optimizing the use of both has not yet been done. Moreover, the evaluation methods for understanding the relationships between NDVI and ET for vegetation monitoring are also limited. Taking these gaps into account we have developed a framework to optimize vegetation monitoring using both NDVI and ET by identifying where they perform the best by using triple collocation and cross-correlation methods. We estimated the random error structure in Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI; ET from the Operational Simplified Surface Energy Balance (SSEBop) model; and ET from land surface models (LSMs). LSM ET and SSEBop ET have been found to be better indicators for vegetation monitoring during extreme drought events, while NDVI could provide better information on vegetation condition during wetter than normal conditions. The random error structures of these variables suggest that LSM ET is most likely to provide important information for vegetation monitoring over low and high ends of the vegetation fraction areas. Over moderate vegetative areas, any of these variables could provide important vegetation information for drought characterization and food security assessments. While this study provides a framework for optimizing vegetation monitoring for drought and food security assessments over East Africa, the framework can be adopted to optimize vegetation monitoring over any other drought and food insecure region of the world.

Using climate reanalysis data in conjunction with multi-temporal satellite thermal imagery to derive supraglacial debris thickness changes from energy-balance modelling

AbstractSurface energy-balance models are commonly used in conjunction with satellite thermal imagery to estimate supraglacial debris thickness. Removing the need for local meteorological data in the debris thickness estimation workflow could improve the versatility and spatiotemporal application of debris thickness estimation. We evaluate the use of regional reanalysis data to derive debris thickness for two mountain glaciers using a surface energy-balance model. Results forced using ERA-5 agree with AWS-derived estimates to within 0.01 ± 0.05 m for Miage Glacier, Italy, and 0.01 ± 0.02 m for Khumbu Glacier, Nepal. ERA-5 data were then used to estimate spatiotemporal changes in debris thickness over a ~20-year period for Miage Glacier, Khumbu Glacier and Haut Glacier d'Arolla, Switzerland. We observe significant increases in debris thickness at the terminus for Haut Glacier d'Arolla and at the margins of the expanding debris cover at all glaciers. While simulated debris thickness was underestimated compared to point measurements in areas of thick debris, our approach can reconstruct glacier-scale debris thickness distribution and its temporal evolution over multiple decades. We find significant changes in debris thickness over areas of thin debris, areas susceptible to high ablation rates, where current knowledge of debris evolution is limited.

Energy Imbalance and Evapotranspiration Hysteresis Under an Advective Environment: Evidence From Lysimeter, Eddy Covariance, and Energy Balance Modeling

AbstractSurface energy imbalance problems have been underexplored especially under advective environments. Here, we present a novel analysis of surface energy fluxes from three distinct approaches—weighing lysimeter, eddy covariance (EC), and surface energy balance modeling for the 2014 sorghum and 2016 corn crop seasons in Bushland, Texas. Our results suggest that energy imbalance problems in the EC system were evidently associated with two opposite evapotranspiration (ET) hysteresis patterns with respect to net radiation and water vapor pressure deficit (VPD). The lysimeter ET had the smallest ET hysteresis while the eddy covariance ET results indicated the strongest hysteresis with respect to net radiation. Conversely, the lysimeter ET hysteresis related to water VPD was stronger than that of the eddy covariance method. This study provides understanding of ET hysteresis patterns, energy imbalance, and advective conditions associated with turbulent flux dynamics for both diel and crop growing seasonal timescales in Bushland, Texas.

Modeling Evapotranspiration of Winter Wheat Using Contextual and Pixel-Based Surface Energy Balance Models

HighlightsThree contextual-based (CB) and two pixel-based (PB) models were evaluated to estimate ET of rainfed winter wheat.Instantaneous available energy estimation and ET upscaling impacted model performance.The CB models performed better at instantaneous and daily scales compared to the PB models.ET estimation biases increased during low vegetation and drier conditions, especially for the PB models.Abstract. Surface energy balance (SEB) models based on thermal remote sensing data are widely used in research applications to map evapotranspiration (ET) across various landscapes. However, their ability to capture ET from winter wheat remains underexplored, especially in practical applications such as integrated resource management and drought preparedness. Investigating winter wheat ET dynamics is important in agricultural regions such as the Southern Great Plains of the U.S., where winter wheat is extensively cultivated. The goal of this study was to evaluate the performance of five fully automated SEB models, three contextual-based (CB) and two pixel-based (PB), in estimating instantaneous and daily ET of winter wheat by comparing the model results with flux tower observations. The CB models included Surface Energy Balance Algorithm for Land (SEBAL), Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC), and Triangular Vegetation Temperature (TVT). The PB models included Surface Energy Balance System (SEBS) and Two-Source Energy Balance (TSEB). Model evaluation during two winter wheat growing seasons (2016-2018) using 28 Landsat images showed that the instantaneous ET estimates from METRIC and TSEB had the smallest (RMSE = 0.14 mm h-1) and largest (RMSE = 0.27 mm h-1) errors, respectively. At the daily scale, SEBAL was the best performing model (RMSE = 1.0 mm d-1), followed by TVT (RMSE = 1.1 mm d-1), METRIC (RMSE = 1.2 mm d-1), SEBS (RMSE = 1.3 mm d-1), and TSEB (RMSE = 1.5 mm d-1). Overall, the CB models provided smaller errors than the PB models. Larger errors in daily ET estimation were observed during low vegetation and drier conditions, especially for the PB models. Keywords: Flux tower, Landsat, Southern Great Plains, Water use.

Regional crop water use assessment using Landsat‐derived evapotranspiration

AbstractReliable information on water use and availability at basin and field scales are important to ensure the optimized constructive uses of available water resources. This study was conducted with the specific objective to estimate Landsat‐based actual evapotranspiration (ETa) using the Operational Simplified Surface Energy Balance (SSEBop) model across the state of South Dakota (SD), USA for the 1986–2018 (33‐year) period. Validated ETa estimations (r2 = 0.91, PBIAS = −4%, and %RMSE = 11.8%) were further used to understand the crop water‐use characteristics and existing historic mono‐directional (increasing/decreasing) trends over the eastern (ESD) and western (WSD) regions of SD. The crop water‐use characteristics indicated that the annual cropland water uses across the ESD and WSD were more or less met by the precipitation amounts in the area. The ample water supply and distribution have led to high rainfed and low percentage of irrigated cropland (~2.5%) in the state. The WSD faced greater crop‐water use reductions than the ESD during drought periods. The landscape ETa responses across the state were found to be more sensitive than precipitation for the drought impact assessments. The Mann Kendall trend analysis revealed the absence of a significant trend (p > 0.05) in annual ETa at a regional scale due to the varying weather conditions in the state. However, about 12% and 9% cropland areas in the ESD and WSD, respectively, revealed a significant mono‐directional trend at pixel scale ETa. Most of the pixels under significant trend showed an increasing trend that can be explained by the shift in agricultural practices, increased irrigated cropland area, higher productions, moisture regime shifts, and decreased risk of farming in the dry areas. The decreasing trend pixels were clustered in mid‐eastern SD and could be the result of dynamic conversion of wetlands to croplands and decreased irrigation practices in the region. This study also demonstrates the tremendous potential and robustness of the SSEBop model, Landsat imagery, and remote sensing‐based ETa modelling approaches in estimating consistent spatially distributed evapotranspiration.

Urban surface temperature observations from ground-based thermography: intra- and inter-facet variability

Ground based thermal cameras are used to observe urban surface temperatures (Ts) with an unprecedented combination of: temporal and spatial resolution (5 min and ~ 0.5 m → 2.5 m), spatial extent (3.9 ha), instrument number (6 static cameras) and surface heterogeneity (mixed high rise and vegetation). Unsupervised classification of images by geometry and material properties (surface orientation, albedo, solar irradiance, and shadow history) is facilitated by a detailed three-dimensional surface model (430 m × 430 m extent) and sensor view modelling. From detailed source area analysis, 9.5% of the area is observed by the cameras. Across all camera pixels, the 5th - 95th percentile Ts differences reach 37.5 K around midday. Roofs have the greatest diurnal Ts range (290.6 K → 329.0 K). Ts differences across sunlit sloped roofs reach 23.3 K. Walls of different cardinal orientations consistently differ by >10 K between 10:00 and 15:00. Shadow tracking within images is used to model cooling rates, where recently shaded (<30 min) ground can be 18.6 K warmer than equivalent unshaded Ts. West walls remain warm past sunset and are 1.2 K warmer than north walls at 23:00 (~4 h after sunset). Recently shaded walls cool exponentially to ambient Ts at a similar rate as the ground, but four times slower than roofs. The observed Ts characteristics are anticipated to have a wide range of applications (e.g. evaluation of urban surface energy balance models, ground-truthing of satellite thermal remote sensing).

Application of an improved surface energy balance model to two large valley glaciers in the St. Elias Mountains, Yukon

AbstractGlacier surficial melt rates are commonly modelled using surface energy balance (SEB) models, with outputs applied to extend point-based mass-balance measurements to regional scales, assess water resource availability, examine supraglacial hydrology and to investigate the relationship between surface melt and ice dynamics. We present an improved SEB model that addresses the primary limitations of existing models by: (1) deriving high-resolution (30 m) surface albedo from Landsat 8 imagery, (2) calculating shadows cast onto the glacier surface by high-relief topography to model incident shortwave radiation, (3) developing an algorithm to map debris sufficiently thick to insulate the glacier surface and (4) presenting a formulation of the SEB model coupled to a subsurface heat conduction model. We drive the model with 6 years of in situ meteorological data from Kaskawulsh Glacier and Nàłùdäy (Lowell) Glacier in the St. Elias Mountains, Yukon, Canada, and validate outputs against in situ measurements. Modelled seasonal melt agrees with observations within 9% across a range of elevations on both glaciers in years with high-quality in situ observations. We recommend applying the model to investigate the impacts of surface melt for individual glaciers when sufficient input data are available.

Performance of Satellite-Based Evapotranspiration Models in Temperate Pastures of Southern Chile

Farmers in the temperate zone of southern Chile have started to irrigate historically rainfed pastures during recent years to reduce dairy productivity losses against increasingly severe summer droughts. The lack of information on pasture water requirements (i.e., evapotranspiration), however, hampers the implementation of efficient irrigation programs. Here, we use in-situ observations to evaluate the skill of four remote sensing Surface Energy Balance (SEB) models and two satellite-based global evapotranspiration products (PML_V2 and GLEAM) to estimate actual evapotranspiration (ETa) of pastures in southern Chile during 2014–2017. Daily ETa measured at an evaluation site over the period ranges between 1.2 mm and 6.2 mm day−1 during the growing season (October–March), with an annual maximum of about 4.8 mm day−1 in January and a minimum 0.6 mm day−1 in June. Only the Simplified SEB (SEBS) model and its operational variant (SSEBop) and the PML_V2 global evapotranspiration product perform well, capturing 63–79% of the variance of in-situ evapotranspiration with an error between 0.75 mm day−1 and 1.1 mm day−1. The readily available PML_V2 product can be used as a convenient way to determine average water footprint of pastures and the two SEBs models can be implemented to monitor irrigation requirements in near-real time from field to regional scales. These results demonstrated a high potential of satellite observations for monitoring evapotranspiration and quantify the water footprint of pastures in southern Chile for a sustainable irrigation practice.

Modeling of Mass Balance Variability and Its Impact on Water Discharge from the Urumqi Glacier No. 1 Catchment, Tian Shan, China

Originating in the Tian Shan mountains, Urumqi River plays a key role in terms of water supply to downstream areas. In its headwaters, Urumqi Glacier No. 1 (UG1) is the largest glacier contributing to water discharge. Assessing its response to the changing climatic conditions in the area is of major importance to quantify future water availability. We here apply COSIPY, a COupled Snowpack and Ice surface energy and mass balance model in PYthon, to UG1, implementing a new albedo parameterization which integrates site-specific bare-ice albedo values on a pixel-by-pixel basis observed by remote sensing. We assess model performance threefold: quantitatively based on long-term measurement data of (1) surface mass balance (SMB) and (2) water discharge as well as qualitatively (3) comparing simulated snow line altitudes to such imated on the basis of time-lapse photography. Comparison of the modeled SMB with annually-averaged data from ablation stakes reveals that COSIPY including the new albedo parameterization accounts for 57.6% of the variance observed in the measurements. The original albedo parameterization performs only slightly inferior (57.1%). Glacier-wide comparison between modeled and glaciological SMB shows high agreement. In terms of discharge prediction, COSIPY reproduces onset and duration of the discharge season well. Estimated discharge from the whole catchment shows shortcomings in exactly matching the measured times series, but interannual variability is captured.