Energy Balance Index Research Articles

Assessing and analysing energy system balance: A decision tree approach

Due to the impacts of climate change, the global energy system must undergo a green transformation. However, this transition process may entail both positive and negative impacts. Therefore, governments must formulate balanced energy policies to establish a secure, accessible, affordable, and environmentally sustainable energy system. To assist decision-makers in evaluating the status of energy systems and formulating rational energy policies to address the complex and dynamic challenges, this study focuses on proposing a method to assess and analyze the equilibrium of a nation's energy system. To achieve these objectives, we propose the Energy Balance Index as a comprehensive method for assessing the equilibrium of a country's energy system. Subsequently, we employ a decision tree-based model to analyze the impact of different factors on the Energy Balance Index. The findings reveal that environmental sustainability holds the most significant influence, followed by energy equity, while energy security has a minor impact. Moreover, the relative influence of these factors varies over time, with energy equity exerting a greater influence prior to 2015, and environmental sustainability emerging as the foremost determinant since then. Through development status analysis of China, this study provides valuable references and recommendations for decision-makers in designing energy policies.

Insights into biomass mild gasification based on mass-energy balance: Establishing condition, thermodynamic mechanisms, and product properties

To alleviate the challenge of gas and char quality competition arising from over oxidation during the biomass gasification. A new concept of biomass mild gasification based on mass and energy balance was proposed, wherein the energy produced during the oxidation stage precisely equals the energy consumed in the pyrolysis and reduction stages. An energy balance index (EBI), obtained through the combined use of TG-DSC-MS-FTIR and Gaussian model fitting to represent the relationship between mass loss and endothermic/exothermic properties among different reaction stages, was optimized by response surface methodology (RSM) to obtain the critical conditions of biomass mild gasification. Under two typical mild gasification conditions, the quality characteristics of mild gasification biochar were analyzed and the gas evolution characteristics and sequence were determined through the combination of two-dimensional correlation spectroscopy (2D COS). The results indicated that mild gasification can be achieved within a heating rate range of 21 °C/min to 26 °C/min, with the oxidation temperature ranging from 776 °C to 869 °C. Under mild gasification conditions, the biochar produced at 850 °C-20 °C/min had higher COOH content, which can regulate soil pH, promote organic matter degradation, and enhance biological activity. Under the 800 °C-25 °C/min mild gasification condition, more CO and CH4 were generated during the pyrolysis stage, but slightly more CO2 was also produced during both the pyrolysis and oxidation stages. This work provides comprehensive thinking on the precious control of gasification process, which lays a foundation for co-generation of high-quality char and gas.

Development of prediction model for body weight and energy balance indicators from milk traits in lactating dairy cows based on deep neural networks

To develop a body weight (BW) prediction model using milk production traits and present a useful indicator for energy balance (EB) evaluation in dairy cows. Data were collected from 30 Holstein cows using an automatic milking system. BW prediction models were developed using multiple linear regression (MLR), local regression (LOESS), and deep neural networks (DNN). Milk production traits readily available on commercial dairy farms, such as energy-corrected milk (ECM), fat-to-protein ratio, days in milk (DIM), and parity, were used as input variables for BW prediction. The EB was evaluated as the difference between energy intake and energy demand. The DNN model showed the greatest predictive accuracy for BW compared with the LOESS and MLR models. The BW predicted using the DNN model was used to calculate the energy demand. Our results revealed that the day on which the EB status transitioned from negative to positive differed among cows. The cows were assigned to one of the three EB index groups. EB index 1 indicated that the day of EB transition was within DIM ≤ 70. The EB indexes 2 and 3 were 70 < DIM ≤ 140 and 140 < DIM ≤ 305, respectively. EB index 3 had the lowest EB, which is the slowest to transition from a negative to a positive energy balance compared with EB indexes 1 and 2. The highest ECM and feed efficiency were observed for EB index 3. The calving interval was the shortest for EB index 1. EB of individual cows during lactation can be estimated and monitored with moderately high accuracy using EB indexes.

Commercial Level Analysis of P2P vs. Net-Metering Comparing Economic and Technical Indexes

As photovoltaics (PV), also known as solar electricity, has been growing over the years, the energy markets have been gradually moving toward decentralization. However, recent media accusations suggest that decentralized renewable energy is slowly becoming unpopular because of the hidden fees being charged to owners of installed PV systems. In response, this paper investigates the potential for alternative approaches to incentivize owners using peer-to-peer (P2P) sharing. This study provides an analytical comparison between the use of the P2P mechanism, the net-metering mechanism, and a combination of these in the commercial sector. Through the use of a simulation, this case study presents the possible outcomes of the implementation of these models in a microgrid. Using technical and economic indexes the comparison was made by looking at the following indexes: peak power, energy balance, economic benefit, and transaction index. Based on a microgrid of 28 commercial buildings, readings of consumption were taken at intervals of one hour, and a Python model was made to find PV size and compare trading mechanisms. It was found that the combination of P2P and net-metering had the best overall performance, followed by net-metering itself, with the best season being all for both, and summer for net-metering by itself. This shows that a P2P model implemented in a microgrid helps create more energy balance, although the combination would achieve the highest performance. This study can be used by policymakers for proposing renewable energy policies and regulations that are more beneficial to all prosumers and consumers.

Estimation of actual evapotranspiration using the simplified-surface energy balance index model on an irrigated agricultural farm

Evapotranspiration (ET) plays a crucial role in the energy and water balance of agricultural ecosystems and is a vital component of the hydrological cycle. Efficient irrigation water management relies on accurate spatiotemporal coverage of crop ET across a farm. Thanks to the availability of multi-temporal high-resolution satellite datasets and remote sensing-based surface energy balance models, near-real-time estimation of ET is now possible. This study utilized Landsat 8/9 data to estimate ET using the simplified surface energy balance index (S-SEBI) model, which was then compared to eddy covariance measurements over a semi-arid agricultural farm in New Delhi, India during the post-monsoon periods of 2021-22 and 2022-23. The S-SEBI model predicted daily ET from Landsat 8/9 data with an average correlation coefficient and RMSE of 0.89 and 0.79 mm/day, respectively. The spatiotemporal map was also used to evaluate the model's performance, and it could accurately differentiate between ET over dryland crops and well-irrigated wheat fields on the farm. Despite underestimating ET (0.51 mm/day) during the initial growing season (Nov-Dec) and overestimating it (0.73 mm/day) during mid-season (Feb-Mar), the S-SEBI model can still be an operational tool for mapping ET with high accuracy and sufficient variation across pixels, making it an ideal option for incorporating into irrigation scheduling.

Evapotranspiration in Semi-Arid Climate: Remote Sensing vs. Soil Water Simulation

Estimating crop evapotranspiration (ETa) is an important requirement for a rational assessment and management of water resources. The various remote sensing products allow the determination of crops' biophysical variables integrated in the evaluation of ETa by using surface energy balance (SEB) models. This study compares ETa estimated by the simplified surface energy balance index (S-SEBI) using Landsat 8 optical and thermal infra-red spectral bands and transit model HYDRUS-1D. In semi-arid Tunisia, real time measurements of soil water content (θ) and pore electrical conductivity (ECp) were made in the crop root zone using capacitive sensors (5TE) for rainfed and drip irrigated crops (barley and potato). Results show that HYDRUS model is a fast and cost-effective assessment tool for water flow and salt movement in the crop root layer. ETa estimated by S-SEBI varies according to the available energy resulting from the difference between the net radiation and soil flux G0, and more specifically according to the assessed G0 from remote sensing. Compared to HYDRUS, the ETa from S-SEBI was estimated to have an R2 of 0.86 and 0.70 for barley and potato, respectively. The S-SEBI performed better for rainfed barley (RMSE between 0.35 and 0.46 mm·d-1) than for drip irrigated potato (RMSE between 1.5 and 1.9 mm·d-1).

Performance evaluation of Satellite-based actual evapotranspiration technique

Estimating crop evapotranspiration is vital for the calculation of irrigation water requirements. Remote sensing data have proven to be a valuable and efficient tool for estimating evapotranspiration. It has been used intensively over the past decade due to free, high temporal and spectral resolution data availability. The main aim of this study was to estimate the evapotranspiration (ET) over a selected area in El-Beheira governorate, Egypt based on the Simplified Surface Energy Balance Index (S-SEBI) using nine Landsat-8 images acquired from January to December 2020. The performance of the studied method was compared with the CROPWAT-8 model. The results revealed the acceptable accuracy of the ET estimated from S-SEBI algorithms with Landsat 8 images according to the coefficient of determination (r2 = 0.82) and root mean square error (RMSE = 0.53 mm/day). Therefore, it is recommended to use the S-SEBI to calculate the spatial evapotranspiration distribution using Landsat-8 images to provide the required information for determining irrigation water requirements and suggesting an efficient water management strategy.

Assessing uncertainties in estimating surface energy fluxes from remote sensing over natural grasslands in Brazil

Evapotranspiration (ET) is one of the main fluxes in the global water cycle. As the Brazilian Pampa biome carries a rich biodiversity, accurate information on the ET dynamics is essential to support its proper monitoring and establish conservation strategies. In this context, we assessed an operational methodology based on the Simplified Surface Energy Balance Index (S-SEBI) model to estimate energy fluxes over the natural grasslands of the Pampa between 2014 and 2019. The S-SEBI is an ET model that requires a minimum of meteorological inputs and has demonstrated reasonable accuracy worldwide. Therefore, we investigated the model performance considering radiation data from both ERA5 reanalysis and Eddy Covariance measurements from a flux tower. Furthermore, comparisons from satellite-based estimates with in situ measurements were performed with and without energy balance closure (EBC). Results indicated that the meteorological inputs have low sensitivity on daily ET estimates from the S-SEBI model. In contrast, the instantaneous energy balance components are more affected. The strong seasonality impacts the evaporative fraction, which is more evident in late summer and autumn and may compromise the performance of the model in the biome. The effects in the daily ET are lower when in situ data without EBC are considered as ground truth. However, they are less correlated with the remote sensing-based estimates. These insights are useful to monitor water and energy fluxes from local to regional scale and provide the opportunity to capture ET trends over the natural grasslands of the Pampa.

Aleksandr Bogdanov and Sergei Eisenstein on Emotions: The Affectional, the Theory of Expressiveness, and the Emotional Script

Abstract In Empiriomonism, Aleksandr Bogdanov introduces the term ‘affectional’ that he borrowed from Richard Avenarius but revised in the light of William James’s theory of emotions. The ‘affectional’ is an index of energy balance between suffering and pleasure. Employing Bogdanov’s revised notions of affectional as an element of any organization or complex, Sergei Eisenstein develops the principles of expressivity. He sees emotions as an organism’s embodied reaction to its interaction with the environment. Eisenstein proposes a notion of an emotional script, which is a narrative of a prospective viewer telling what has impressed him. Aleksandr Rzheshevskiy, a scriptwriter of Eisenstein’s never completed ‘Bezhin Meadow’ (1937), became an ‘emotional scriptwriter’ in practice. The paper investigates the relations between Bogdanov’s notions of the affectional, Eisenstein’s theory of expressiveness, and the emotional script as conceived by Eisenstein and realized by Rzheshevskiy.

Comparative Evaluation of Simplified Surface Energy Balance Index-Based Actual ET against Lysimeter Data in a Tropical River Basin

In the past decades, multispectral and multitemporal remote sensing has been popularly used for estimating actual evapotranspiration (ETc) across the globe. It has been proven to be a cost-effective tool for understanding agricultural practices in a region. Today, because of the availability of different onboard sensors on an increasing number of different satellites, land surface activity can be captured at fine spatial and time scales. In the present study, three multi-date satellite imageries were used for the evaluation of remote sensing-based estimation of actual evapotranspiration in paddy in the command area of the tropical Kangsabati river basin. A surface energy balance model, the Simplified-Surface Energy Balance Index (S-SEBI), was applied for all three dates of the Rabi season (2014–2015) for the estimation of actual evapotranspiration. The crop coefficient was calculated using the exhaustive survey data collected from the command area and adjusted to local conditions. The ETc estimated using the S-SEBI-based model was compared with the Food and Agriculture Organization Penman–Monteith (FAO-56 PM) method multiplied by the adjusted local crop coefficient and lysimeter data in the command area. The coefficient of determination (r2) was applied to examine the accuracy of the S-SEBI model with respect to lysimeter data and the FAO-56 PM-based ETc. The results showed that the S-SEBI model performed well with the lysimeter (r2 = 0.90) in comparison with FAO-56 PM, with an r2 of 0.65. In addition to this, the S-SEBI-based ET estimates correlated well with the FAO-56 PM, with r and RMSE values of 0.06 and 1.13 mm/day (initial stage), 0.85 and 0.48 mm/day (development stage), and 0.77 and 0.52 (maturity stage) for paddy, respectively. The S-SEBI-based ETc estimate varied with different stages of crop growth and successfully captured the spatial heterogeneity within the command area. In general, this study showed that the S-SEBI method has the potential to calculate spatial evapotranspiration and provide useful information for efficient water management. The results revealed the applicability and accuracy of remote sensing-based ET for managing water resources in a command area with scarce data.

Do restinga cnemidophorine lizards run on empty along the Brazilian coast?

The number of individuals with empty stomachs in a population (i.e. the proportion of individuals "running on empty") can be used as a simple index of instantaneous energy balance of some organisms such as lizards and fishes. In this study, we aimed to analyze the proportion of empty stomachs in 16 populations of five cnemidophorine species (Glaucomastix abaetensis, Ameivula ocellifera, Glaucomastix littoralis, Contomastix lacertoides and Ameivula nativo) along approximately 5,000 km the Brazilian coast. A total of 550 individuals had their stomach contents removed and identified in the laboratory. Our results showed that the proportion of individuals "running on empty" varied from 0 to 11.1% among the different populations and species. These proportions are suggestive that the five studied species would be in an overall positive energy balance at the time of the study.

Plasma Level of Macromolecules and Mathematical Calculation of Potential Energy in Type 2 Diabetic Individuals at NAUTH, Nnewi, Nigeria

Diabetes mellitus is associated with neutered metabolism and higher Energy Expenditure. This study aimed on the use of Adenosine diphosphate (ADP), Flavin adenine dinucleotide (FAD), AcetylCo-enzyme A (ACA) and Nicotinamide Adenine Dinucleotide (NADH) as an index of energy utilization, storage and energy balance in Diabetic individuals. This is a longitudinal, prospective, case-controlled study involving seventy seven (77) diabetic individuals newly diagnosed attending diabetic clinic of Nnamdi Azikiwe University University Teaching Hospital (NAUTH) aged 18-60 years both male and female not on anti-diabetic drug, were enrolled in the study as test subjects and thirty six (36) apparently healthy non-diabetic individuals both male and female as control subjects. ADP, FAD, ACA and NADH were estimated by enzyme linked immunosorbent assay (ELISA), while, energy balance from macromolecules was determined by calculation. The data obtained were subjected to statistical analysis using SPSS software application (version 21.0) and the results expressed as mean ± standard deviation. The Plasma Adenosine diphosphate (ADP), Flavin adenine dinucleotide (FAD), AcetylCo-enzymeA (ACA) and Nicotinamide Adenine Dinucleotide (NADH), were significantly lower (P&lt;0.05) in both Diabetic pre-treatment and diabetic post-treatment group compared with control groups. Furthermore, the plasma level of ACA and NADH were significantly lower (P&lt;0.05) in DM pre-treatment group compared with DM post-treatment group. While, the plasma concentration of ADP was significantly lower in DM post-treatment groups compared with DM pre-treatment groups. However, the Calculated energy from Macromolecules was lower (P&lt;0.05) in DM groups compared with control group. Meanwhile, the calculated energy from Macromolecules in DM pre-treatment was significantly lower (P&lt;0.05) compared with DM post-treatment. In conclusion, the significant changes in the biochemical parameters measured suggest altered metabolism, increased energy expenditure and energy deficit/energy imbalance in diabetic subjects resulting from increased energy expenditure. Hence, energy from macromolecules such as ADP, FAD, ACA and NADH can be used to predict early energy deficit and manage energy imbalance in diabetic individuals.

Use of ATP, GTP, ADP and AMP as an Index of Energy Utilization and Storage in HIV Infected Individuals at NAUTH, Nigeria: A Longitudinal, Prospective, Case-Controlled Study

Human immunodeficiency virus (HIV) is associated with altered metabolism and increased energy expenditure, this energy requirement increases significantly as the HIV disease progresses. This study aimed on the use of Adenosine triphosphate (ATP), Guanosine triphosphate (GTP), Adenosine diphosphate (ADP) and Adenosine monophosphate (AMP) as an index of energy utilization, storage and energy balance in HIV infected individuals. This is a longitudinal, prospective, case-controlled study involving seventy seven (77) HIV Sero-positive individuals newly diagnosed attending retroviral disease treatment centre of Nnamdi Azikiwe University University Teaching Hospital (NAUTH) aged 18-60 years both male and female not on highly active antiretroviral therapy (HAART), were enrolled in the study as test subjects and thirty six (36) apparently healthy HIV Sero-negative individuals both male and female as control subjects. ATP, GTP, ADP and AMP were estimated by enzyme linked immunosorbent assay (ELISA), while, total Energy Balance was determined by calculation. The data obtained were subjected to statistical analysis using SPSS software application (version 21.0) and the results expressed as mean ± standard deviation. The plasma ATP and GTP were significantly lower (P&lt;0.05) in both HIV pre-treatment and post-treatment group compared with control group. Meanwhile, the plasma level of ADP and AMP were significantly lower (P&lt;0.05) in HIV post-treatment group compared with HIV pre-treatment and control group. There was also a significant difference (P&lt;0.05) in ATP, ADP, AMP and GTP level between HIV pre-treatment and post-treatment group. Meanwhile, the energy balance was lower (P&lt;0.05) in HIV groups compared with control group. However, the energy balance in HIV post-treatment group was significantly lower (P&lt;0.05) compared to HIV pre- treatment group. In conclusion, the significant changes in the biochemical parameters measured suggest altered metabolism, increased energy expenditure and energy deficit/negative energy balance in HIV subjects resulting from increased energy expenditure. Hence, High energy molecules such ATP, ADP, GTP and AMP can be used to predict early energy deficit and manage energy imbalance in HIV infected individuals.

Evapotranspiration Estimation with the S-SEBI Method from Landsat 8 Data against Lysimeter Measurements at the Barrax Site, Spain

Evapotranspiration (ET) is a variable of the climatic system and hydrological cycle that plays an important role in biosphere–atmosphere–hydrosphere interactions. In this paper, remote sensing-based ET estimates with the simplified surface energy balance index (S-SEBI) model using Landsat 8 data were compared with in situ lysimeter measurements for different land covers (Grass, Wheat, Barley, and Vineyard) at the Barrax site, Spain, for the period 2014–2018. Daily estimates produced superior performance than hourly estimates in all the land covers, with an average difference of 12% and 15% for daily and hourly ET estimates, respectively. Grass and Vineyard showed the best performance, with an RMSE of 0.10 mm/h and 0.09 mm/h and 1.11 mm/day and 0.63 mm/day, respectively. Thus, the S-SEBI model is able to retrieve ET from Landsat 8 data with an average RMSE for daily ET of 0.86 mm/day. Some model uncertainties were also analyzed, and we concluded that the overpass of the Landsat missions represents neither the maximum daily ET nor the average daily ET, which contributes to an increase in errors in the estimated ET. However, the S-SEBI model can be used to operationally retrieve ET from agriculture sites with good accuracy and sufficient variation between pixels, thus being a suitable option to be adopted into operational ET remote sensing programs for irrigation scheduling or other purposes.

Comparison of Satellite Driven Surface Energy Balance Models in Estimating Crop Evapotranspiration in Semi-Arid to Arid Inter-Mountain Region

The regional-scale estimation of crop evapotranspiration (ETc) over a heterogeneous surface is an important tool for the decision-makers in managing and allocating water resources. This is especially critical in the arid to semi-arid regions that require supplemental water due to insufficient precipitation, soil moisture, or groundwater. Over the years, various remote sensing-based surface energy balance (SEB) models have been developed to accurately estimate ETc over a regional scale. However, it is important to carry out the SEB model assessment for a particular geographical setting to ensure the suitability of a model. Thus, in this study, four commonly used and contrasting remote sensing models viz. METRIC (mapping evapotranspiration at high resolution with internalized calibration), SEBAL (surface energy balance algorithm for land), S-SEBI (simplified surface energy balance index), and SEBS (surface energy balance system) were compared and used to quantify and map the spatio-temporal variation of ETc in the semi-arid to arid inter-mountain region of Big Horn Basin, Wyoming (Landsat Path/Row: 37/29). Model estimates from 19 cloud-free Landsat 7 and 8 images were compared with the Bowen ratio energy balance system (BREBS) flux stationed in a center pivot irrigated field during 2017 (sugar beet), 2018 (dry bean), and 2019 (barley) growing seasons. The results indicated that all SEB models are effective in capturing the variation of ETc with R2 ranging in between 0.06 to 0.95 and RMSD between 0.07 to 0.15 mm h−1. Pooled data over three vegetative surfaces for three years under irrigated conditions revealed that METRIC (NSE = 0.9) performed better across all land cover types, followed by SEBS (NSE = 0.76), S-SEBI (NSE = 0.73), and SEBAL (NSE = 0.65). In general, all SEB models substantially overestimated ETc and underestimated sensible heat (H) fluxes under dry conditions when only crop residue was available at the surface. A mid-season density plot and absolute difference maps at image scale between the models showed that models involving METRIC, SEBAL, and S-SEBI are close in their estimates of daily crop evapotranspiration (ET24) with pixel-wise RMSD ranged from 0.54 to 0.76 mm d−1 and an average absolute difference across the study area ranged from 0.47 to 0.56 mm d−1. Likewise, all the SEB models underestimated the seasonal ETc, except SEBS.

Artificial neural networks model based on remote sensing to retrieve evapotranspiration over the Brazilian Pampa

Evapotranspiration (ET) quantification improves the comprehension of the water, heat, and carbon interactions and the feedback to the climate, which is essential for global change research. We aimed to model ET using artificial neural networks (ANNs) based on Landsat-8 and reanalysis data from the National Centers for Environmental Prediction over the grasslands of the Pampa biome. The output variable was the ET trained by eddy covariance (EC) measurements acquired from a flux tower located in Santa Maria, Brazil. ANN was performed using the backpropagation algorithm with four remote sensing input variables (albedo, normalized difference vegetation index, land surface temperature, and surface net radiation). In addition, four meteorological variables from the Environmental Prediction Climate Forecast System Version 2 hourly product were included in the model (air temperature, atmospheric pressure, relative humid, and wind speed). We analyzed 67 clear-sky scenes between 2014 and 2019. Results produced very robust daily ET estimates. ANN exhibited a correlation of 0.88 relative to in situ EC data, demonstrating a good linear relationship between ET estimated and measured and producing a root-mean-square error (mean absolute error) of 0.75 (0.58) mm/day. The ANN model was also compared with the widely known simplified surface energy balance index (S-SEBI) model. S-SEBI exhibited lower correlation with the ET in situ compared to the ANN model. Furthermore, the ANN model had a superior performance in summer and winter seasons in which S-SEBI was found to outperform the ET in situ. The model developed in our research is an alternative to approaches that need a great number of input variables or in situ data since it is only dependent on freely available data. Therefore, it should support future integrated strategies of water resources allocation over the natural grasslands of the Brazilian Pampa.

Evaluation of Simplified Surface Energy Balance Index (S-SEBI) Method for Estimating Actual Evapotranspiration in Kangsabati Reservoir Command Using Landsat 8 Imagery

Evapotranspiration (ET) is an important hydrological variable for better irrigation management, water budgeting, and runoff estimation which should be estimated as precisely as possible both in space and time. However, most of the available crop coefficient-based ET computation methods provide point-scale estimates which need upscaling to apply at the catchment or command area scale. This study evaluates the applicability of the simplified surface energy balance index (S-SEBI) method to compute the spatially distributed daily ET in the Kangsabati reservoir command in eastern India considering the crop coefficient-based coupled Hargreaves–Samani (ETc_HG) method as the benchmark. The study is based on two major crops of paddy and potato in the Rabi season of 2015 at 100 surveyed ground truth locations in the selected command area having different crop growth stages and using the site-specific Landsat 8 images on three cloud-free dates. The S-SEBI method shows improved ET estimates during the crop development stage characterized by higher canopy cover than that during the initial crop development stage with lesser canopy cover that traps less radiation. Results revealed that S-SEBI-based ET estimates correlated well with ETc_HG with r and RMSE value of 0.06 and 1.13 mm/day (initial stage), 0.71 and 0.52 mm/day (development stage) and 0.77 and 0.52 (maturity stage) for paddy. The r and RMSE value for potato is found to be better during the development stage (0.43, 0.69 mm/day) than the initial stage (0.17, 0.64 mm/day) in a similar trend with paddy. Therefore, the crop coefficient-based method could be advantageous at point-scale with adequate data availability conditions, whereas the S-SEBI method could be used in data-scarce areas to estimate the spatially distributed ET values.

Calculating the surface melt rate of Antarctic glaciers using satellite-derived temperatures and stream flows.

Melt rate models are fundamental for understanding the impacts of climate change on glaciers and the subsequent effects on habitats and sea level rise. Ice melt models have mostly been derived from energy balance or air temperature index calculations. This research demonstrates that satellite-derived land surface temperature (LST) measurements provide a simpler method for estimating surface melt rate that substitutes for energy balance models. Since these satellite images are continuous (distributed) across space, they do not need calibration for topography. Antarctic glacier melt discharge data from nearby stream gauges were used to calibrate an LST-derived melt model. The model calculations are simplified by the fact that groundwater flow is assumed to be minimal due to permafrost, and the glaciers are assumed to only melt on the surface. A new method called the Temperature Area Sum model is developed, which builds on an existing Temperature Area Index model. A daily melt rate model is developed using 77 Landsat 8 images and calculates the volume of meltwater produced per hectare for any given LST between - 7 and 0°C. A seasonal average daily melt rate model is also developed that uses 1660 MODIS images. The utility of the seasonal MODIS model is demonstrated by calculating melt rates, water flows and wetness across the entire Ross Sea Region. An unexpected large wet area to the southwest of the Ross Ice Shelf requires further investigation and demonstrates the usefulness of these models for large remote areas. Surface melt rate and wetness can now be calculated for different climate change scenarios.

Evapotranspiration Estimation in the Sahel Using a New Ensemble-Contextual Method

In many tropical areas, evapotranspiration is the most important but least known component of the water cycle. An innovative method, named E3S (for EVASPA S-SEBI Sahel), was developed to provide spatially-distributed estimates of daily actual evapotranspiration (ETd) from remote sensing data in the Sahel. This new method combines the strengths of a contextual approach that is used to estimate the evaporative fraction (EF) from surface temperature vs. albedo scatterograms and of an ensemble approach that derives ETd estimates from a weighted average of evapotranspiration estimated from several EF methods. In this work, the two combined approaches were derived from the simplified surface energy balance index (S-SEBI) model and the EVapotranspiration Assessment from SPAce (EVASPA) tool. Main innovative aspects concern (i) ensemble predictions of ETd through the implementation of a dynamic weighting scheme of several evapotranspiration estimations, (ii) epistemic uncertainty of the estimation of ETd from the analysis of the variability of evapotranspiration estimates, and (iii) a new cloud filtering method that significantly improves the detection of cloud edges that negatively affect EF determination. E3S was applied to MODIS/TERRA and AQUA datasets acquired during the 2005–2008 period over the mesoscale AMMA-CATCH (Analyse Multidisciplinaire de la Mousson Africaine—Couplage de l’Atmosphère Tropicale et du Cycle Hydrologique) observatory in South-West Niger. E3S estimates of instantaneous and daily available energy, evaporative fraction, and evapotranspiration were evaluated at a local scale based on two field-monitored plots representing the two main ecosystem types in the area—a millet crop and a fallow savannah bush. In addition to these ground-based observations, the local scale evaluation was performed against continuous simulations by a locally-calibrated soil-vegetation-atmosphere transfer model for the two plots. The RMSE (root mean square error) from this comparison for E3S’s ETd estimates from combined AQUA/TERRA sources was 0.5 mm·day−1, and the determination coefficient was 0.90. E3S significantly improved representation of the evapotranspiration seasonality, compared with a classical implementation of S-SEBI or with the original EVASPA’s non-weighted ensemble scheme. At the mesoscale, ETd estimates were obtained with an average epistemic uncertainty of 0.4 mm·day−1. Comparisons with the reference 0.25°-resolution GLEAM (global land evaporation Amsterdam model) product showed good agreement. These results suggested that E3S could be used to produce reliable continuous regional estimations at a kilometric resolution, consistent with land and water management requirements in the Sahel. Moreover, all these innovations could be easily transposed to other contextual approaches.

Evaluating the performance of two SEB models for estimating ET based on satellite images in arid regions

Surface energy balance (SEB) models are one of the most important methods for estimating evapotranspiration (ET). This study was aimed at assessing the applications of two SEB models under arid conditions. This was achieved by integrating in situ meteorological data and Landsat 8 satellite images for two consecutive years (2014 and 2015) for alfalfa cultivation in 2014 and maize cultivation in 2015. The performance of the two SEB models was evaluated by comparing the ET estimated by the SEB models and that measured using an Eddy covariance (EC) instrument. Mapping the ET at high resolution using internalized calibration (METRIC) displayed the best performance in 2014, with a coefficient of determination, R2 of 0.8, followed by the simplified surface energy balance index (S-SEBI) model (with R2 = 0.67). In addition, METRIC showed high efficiency, with a Nash Sutcliffe (NSE) model efficiency coefficient of 0.98, followed by S-SEBI, with an NSE of 0.93. Poor performance was observed in 2015 where the R2 for both METRIC and S-SEBI were 0.3 and 0.45, respectively. In addition, the NSE for METRIC decreased to 0.91. Conversely, the NSE for S-SEBI improved to 0.97. Therefore, the performance of the models was affected by instability due to soil water content (SWC) and low precipitation (62.2 mm in 2015). However, the performance of METRIC was better than that of S-SEBI. In general, METRIC is considered a good SEB model for arid regions. In addition, S-SEBI could be an adequate model for overcoming the difficulty associated with obtaining meteorological data.