Eddy Covariance Stations Research Articles

Spatial patterns of evaporation in a small catchment

Abstract In this study a network of three eddy covariance stations rotated between five measurement sites is used to measure evaporation (E) within the Hydrological Open Air Laboratory (HOAL) in Petzenkirchen, Austria for 8 years. Discharge measurements at the tributaries and outlet of the main catchment allow for E to be estimated for 6 subcatchments using the water balance method. Year to year variability in monthly E measured by the eddy covariance stations is found to be driven primarily by net radiation and temperature and annual E by net radiation. Year to year variability in the water balance-based E estimate was driven by precipitation. The two methods are found to be consistent, when storage and leakage are accounted for. Daily and seasonal patterns can be seen resulting from the agricultural land use cycle, due to the variations in land cover during the growing season.

Predicting the fundamental fluxes of an eddy-covariance station using machine learning methods

Monitoring tools are needed to maximise living systems' ability to mitigate emissions and adapt to changing environmental conditions. Therefore, it is important to be able to predict the fundamental fluxes in crops, in this case vineyards, such as sensible heat flux (H), latent heat flux (LE) and carbon dioxide flux (CO2), in order to know their capacity to adapt to the environmental effects of climate change. In this study, Linear Regression (LR), Elastic Net (EN) regression, K-Nearest Neighbours (KNN), Gaussian-Process (GP), Decision Tree (TREE) Regression, Random Forest (RF) Regression, XGBoost (XGB) Regression, Support Vector Regression (SVR) and Multi-layer Perceptron (MLP) models have been applied to predict fundamental fluxes of an eddy-covariance station from conventional meteorological parameters. These models reproduced well the estimations of three output parameters from the eddy-covariance station. The performance of each predictive model was evaluated using Root-Mean-Squared Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE) and the coefficient of determination (R2). The findings indicate that for the variable H, the GP model outperformed the SVR and all other models, achieving an R2 value of 0.99. Conversely, the SVR demonstrated superior performance for the variables LE and CO2, with R2 values of 0.96 for both. In summary, these findings suggest that the three models proposed show a robust performance in the prediction of the studied fluxes, underlining their versatility and adaptability to the various environmental conditions of the vineyard.

A Novel Method for Characterizing the Inter‐ and Intra‐Lake Variability of CH4 Emissions: Validation and Application Across a Latitudinal Transect in the Alpine Region

AbstractLakes in the Alpine region are recognized as critical methane (CH4) emitters, but a robust characterization of the magnitude and variability of CH4 fluxes is missing. We developed a mobile platform for CH4 eddy covariance (EC) flux measurements to tackle this gap. The mobile system was deployed at nine lakes across a latitudinal transect in the Alps and validated by comparing the measured fluxes with fixed on‐shore EC station and to chamber and boundary layer flux estimates. Our approach was shown to be well suited to capture different CH4 emission pathways and to integrate the within lake variability, therefore, overcoming the limitations of other methods (e.g., boundary layer method). Additionally, this mobile system offers a tool to characterize inter‐lake variability of fluxes with consistent measurements. Methane fluxes were explained by dissolved nitrogen, total phosphorus, dissolved oxygen, seston and lake size. The highest fluxes and most substantial seasonal variability were found in a shallow low‐altitude lake in the Southern Alps. We suggest that characterizing the intra‐lake emission heterogeneity and consistent measurements for a better understanding of inter‐lake emission differences is fundamental for a solid estimate of freshwater CH4 budgets.

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

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

Uncertainty assessment of satellite remote-sensing-based evapotranspiration estimates: a systematic review of methods and gaps

Abstract. Satellite remote sensing (RS) data are increasingly being used to estimate total evaporation, often referred to as evapotranspiration (ET), over large regions. Since RS-based ET (RS-ET) estimation inherits uncertainties from several sources, many available studies have assessed these uncertainties using different methods. However, the suitability of methods and reference data subsequently affects the validity of these evaluations. This study summarizes the status of the various methods applied for uncertainty assessment of RS-ET estimates, discusses the advances and caveats of these methods, identifies assessment gaps, and provides recommendations for future studies. We systematically reviewed 676 research papers published from 2011 to 2021 that assessed the uncertainty or accuracy of RS-ET estimates. We categorized and classified them based on (i) the methods used to assess uncertainties, (ii) the context where uncertainties were evaluated, and (iii) the metrics used to report uncertainties. Our quantitative synthesis shows that the uncertainty assessments of RS-ET estimates are not consistent and comparable in terms of methodology, reference data, geographical distribution, and uncertainty presentation. Most studies used validation methods using eddy-covariance (EC)-based ET estimates as a reference. However, in many regions such as Africa and the Middle East, other references are often used due to the lack of EC stations. The accuracy and uncertainty of RS-ET estimates are most often described by root-mean-squared errors (RMSEs). When validating against EC-based estimates, the RMSE of daily RS-ET varies greatly among different locations and levels of temporal support, ranging from 0.01 to 6.65 mm d−1, with a mean of 1.18 mm d−1. We conclude that future studies need to report the context of validation, the uncertainty of the reference datasets, the mismatch in the temporal and spatial scales of reference datasets to those of the RS-ET estimates, and multiple performance metrics with their variation in different conditions and their statistical significance to provide a comprehensive interpretation to assist potential users. We provide specific recommendations in this regard. Furthermore, extending the application of RS-ET to regions that lack validation will require obtaining additional ground-based data and combining different methods for uncertainty assessment.

The Performance of GRAMM-SCI and WRF in Simulating the Surface-Energy Budget and Thermally Driven Winds in an Alpine Valley

Using WRF as a benchmark, GRAMM-SCI simulations are performed for a case study of thermally driven valley- and slope winds in the Inn Valley, Austria. A clear-sky, synoptically undisturbed day was selected when large spatial heterogeneities occur in the components of the surface-energy budget driven by local terrain and land-use characteristics. The models are evaluated mainly against observations from four eddy-covariance stations in the valley. While both models are able to capture the main characteristics of the surface-energy budget and the locally driven wind field, a few overall deficiencies are identified: (i) Since the surface-energy budget is closed in the models, whereas large residuals are observed, the models generally tend to overestimate the daytime sensible and latent heat fluxes. (ii) The partitioning of the available energy into sensible and latent heat fluxes remains relatively constant in the simulations, whereas the observed Bowen ratio decreases continuously throughout the day because of a temporal shift between the maxima in sensible and latent heat fluxes, which is not captured by the models. (iii) The comparison between model results and observations is hampered by differences between the real land use and the vegetation type in the model. Recent modifications of the land-surface scheme in GRAMM-SCI improve the representation of nighttime katabatic winds over forested areas, reducing the modeled wind speeds to more realistic values.

A double two-sources energy-water balance model for improving evapotranspiration estimates and irrigation management in fruit trees fields

Improving the use of water in irrigated agriculture is meaningful in a period of increasing water scarcity conditions. A more accurate estimate of evapotranspiration (ET) and its components thus becomes fundamental to better quantify the irrigation water volumes. Many existing models, based on different remote sensing data, provide daily estimates of latent heat flux (LE) from correlation between net radiation and instantaneous estimates of LE, computed as residual component of the energy balance equation or from a correlation of land surface temperature (LST) with vegetation indices., However, they mainly lack of solutions which are continuous in time (e.g. hourly), independent from satellite LST availability and a simultaneous estimation of soil moisture. Addressing this gap, a double two-sources energy-water balance model (FEST‐2×2‐EWB) is developed based on the decoupling of both water and energy fluxes between the bare soil or grass interrow and the trees rows. This novel parameterization approach enables the differentiation of water uptake from the root zone, which varies between tall trees and grass, considering the dynamics of soil moisture (SM) in the superficial and deep layers. Additionally, it provides partitioned values for transpiration and evaporation. The new model has been evaluated in two irrigated trees fields in the North of Italy, a walnut trees field from 2019 to 2021 and a pear trees field, for the year 2022. Results of the study showcased a root-mean-square-error (RMSE) of about 55 W m− 2 and a bias of about 40 W m− 2 for hourly latent and sensible heat fluxes when compared to the eddy covariance stations located in the fields, while a RMSE of 2 °C (bias of 1.5 °C) for LST and of 0.04 for SM. The FEST‐2×2‐EWB model significantly enhances the accuracy of ET simulation in fruits trees areas in respect to the original one source and one-layer version of the same model. Finally, the application of an irrigation optimization strategy with this new model, allowed to demonstrate its potentiality in water saving (about 90 mm in a year) in respect to farmers applied irrigation, and with a difference of about 60 mm between using the double two-sources model and single source one.

New data‐driven method for estimation of net ecosystem carbon exchange at meteorological stations effectively increases the global carbon flux data

Abstract The eddy covariance (EC) flux stations have great limitations in the evaluation of the global net ecosystem carbon exchange (NEE) and in the uncertainty reduction due to their sparse and uneven distribution and spatial representation. If the EC stations are linked with widely distributed meteorological stations using machine learning (ML) and remote sensing, it will play a big role in effectively improving the accuracy of the global NEE assessment and reducing uncertainty. In this study, we developed a framework for estimating NEE at meteorological stations. We first optimized the hyperparameters and input variables of the ML model based on the optimization method called an adaptive genetic algorithm. Then, we developed 566 random forest (RF)‐based NEE estimation models by the strategy of spatial leave‐out‐one cross‐validation. We innovatively established the Euclidean distance‐based accuracy projection algorithm of the R square (R2), which could test the accuracy of each model to estimate the NEE of the specific flux at the weather station. Only the model with the highest R2 was selected from the models with a prediction accuracy of R2 > 0.5 for the specific meteorological stations to estimate its NEE. 4674 out of 10,289 weather stations around the world might match at least one of the 566 NEE estimation models with a projected accuracy of R2 > 0.5. The NEE estimation models we screened for the meteorological stations showed a reliable performance and a higher accuracy than the former studies. The NEE values of the most (96.9%) screened meteorological stations around the world are negative (carbon sink) and most (65.3%) of those showed an increasing trend in the mean annual NEE (carbon sink). The NEE dataset produced at the meteorological stations could be used as a supplement to the EC observations and quasi‐observation data to assess the NEE products of the global grid. The NEE dataset is publicly available via the figshare with https://doi.org/10.6084/m9.figshare.20485563.v1.

The land–atmosphere feedback observatory: a new observational approach for characterizing land–atmosphere feedback

Abstract. Important topics in land–atmosphere (L–A) feedback research are water and energy balances and heterogeneities of fluxes at the land surface and in the atmospheric boundary layer (ABL). To target these questions, the Land–Atmosphere Feedback Observatory (LAFO) has been installed in southwestern Germany. The instrumentation allows comprehensive and high-resolution measurements from the bedrock to the lower free troposphere. Grouped into three components, atmosphere, soil and land surface, and vegetation, the LAFO observation strategy aims for simultaneous measurements in all three compartments. For this purpose the LAFO sensor synergy contains lidar systems to measure the atmospheric key variables of humidity, temperature and wind. At the land surface, eddy covariance stations are operated to record the energy distribution of radiation, sensible, latent and ground heat fluxes. Together with a water and temperature sensor network, the soil water content and temperature are monitored in the agricultural investigation area. As for vegetation, crop height, leaf area index and phenological growth stage values are registered. The observations in LAFO are organized into operational measurements and intensive observation periods (IOPs). Operational measurements aim for long time series datasets to investigate statistics, and we present as an example the correlation between mixing layer height and surface fluxes. The potential of IOPs is demonstrated with a 24 h case study using dynamic and thermodynamic profiles with lidar and a surface layer observation that uses the scanning differential absorption lidar to relate atmospheric humidity patterns to soil water structures. Both IOPs and long-term observations will provide new insight into exchange processes and their statistics for improving the representation of L–A feedbacks in climate and numerical weather prediction models. The lidar component in particular will support the investigation of coupling to the atmosphere.

Globally Scalable Approach to Estimate Net Ecosystem Exchange Based on Remote Sensing, Meteorological Data, and Direct Measurements of Eddy Covariance Sites

Despite a rapid development of Nature-Based Solutions (NBS) for carbon removal in recent years, the methods for evaluating NBS still have certain gaps. We propose an approach based on a combination of remote sensing data and meteorological variables to reconstruct the spatiotemporal variation of net ecosystem exchange from eddy-covariance stations. A Lagrangian particle dispersion model was used for upscaling satellite images and flux towers. We trained data-driven models based on kernel methods separately for each selected land-cover class. The results suggest that the proposed approach to quantifying carbon exchange on a medium-to-large scale by blending eddy covariance flux data with moderate resolution satellite and weather data provides a set of key advantages over previously deployed methods: (1) scalability, achieved via the validation design based on a separate set of eddy covariance stations; (2) high spatial and temporal resolution thanks to the use of Landsat imagery; and (3) robust and accurate predictions due to improved data quality control, advanced machine learning techniques, and rigorous validation. The machine learning models yielded high cross-validation results. Stratification that uses separate Fluxnet stations for each fold of validation ensures that the models are accurate across the area covered by the Fluxnet sites. Overall, we present here a globally scaled technology for the land sector based on high resolution remote sensing imagery, meteorological variables, and direct carbon flux measurements of eddy covariance flux stations.

Estimation of hourly actual evapotranspiration over the Tibetan Plateau from multi-source data

Actual evapotranspiration (ETa) is an important component of the global energy exchange process and hydrological cycle. ETa data with high accuracy are of great significance to the understanding of hydrological processes and the regulation of local climate. In this paper, an ETa product with hourly temporal resolution for the entire Tibetan Plateau (TP) was proposed by using Fengyun-4A geostationary satellite data and a random forest (RF) model. Validation between RF-based ETa and in situ observations from six eddy-covariance stations over the TP showed a root mean square error (RMSE) of 0.991 mm day−1. Compared with the ETa results from the surface energy balance system (SEBS), maximum entropy production (MEP) and the European Centre for Medium-Range Weather Forecasts Reanalysis Fifth Generation (ERA5) with RMSE values of 1.997 mm day−1, 2.182 mm day−1 and 1.555 mm day−1, respectively, the RF model showed the best performance. The annual RF-based ETa over the entire TP was approximately 365.60 mm. Thus, the total water amount evapotranspired from the TP surface was approximately 9811.01×108 t yr−1. Additionally, diurnal, monthly and seasonal ETa variations in different land cover types and climate zones over the TP and their contributions to the total ETa were clearly quantified.

UAS-based high resolution mapping of evapotranspiration in a Mediterranean tree-grass ecosystem

Understanding the impact of land use and land cover change on surface energy and water budgets is increasingly important in the context of climate change research. Eddy covariance (EC) methods are the gold standard for high temporal resolution measurements of water and energy fluxes, but cannot resolve spatial heterogeneity and are limited in scope to the tower footprint (few hundred meter range). Satellite remote sensing methods have excellent coverage, but lack spatial and temporal resolution. Long-range unmanned aerial systems (UAS) can complement these other methods with high spatial resolution over larger areas. Here we use UAS thermography and multispectral data as inputs to two variants of the Two Source Energy Balance Model to accurately map surface energy and water fluxes over a nutrient manipulation experiment in a managed semi-natural oak savanna from peak growing season to senescence. We use energy flux measurements from 6 EC stations to evaluate the performance of our method and achieve good accuracy (RMSD ≈ 60 W m−2 for latent heat flux). We use the best performing latent heat estimates to produce very high-resolution evapotranspiration (ET) maps, and investigate the drivers of ET change over the transition to the senescence period. We find that nitrogen and nitrogen plus phosphorus treatments lead to significant increases in ET (P < 0.001) for both trees (4 and 6%, respectively) and grass (12 and 9%, respectively) compared to the control. These results highlight that the high sensitivity and spatial and temporal resolution of a UAS system allows the precise estimation of relative water and energy fluxes over heterogeneous vegetation cover.

Building a UAV Based System to Acquire High Spatial Resolution Thermal Imagery for Energy Balance Modelling

High spatial resolution and geolocation accuracy canopy evapotranspiration (ET) maps are well suited tools for evaluation of small plot field trials. While creating such a map by use of an energy balance model is routinely performed, the acquisition of the necessary imagery at a suitable quality is still challenging. An UAV based thermal/RGB integrated imaging system was built using the RaspberryPi (RPi) microcomputer as a central unit. The imagery served as input to the two-source energy balance model pyTSEB to derive the ET map. The setup’s flexibility and modularity are based on the multiple interfaces provided by the RPi and the software development kit (SDK) provided for the thermal camera. The SDK was installed on the RPi and used to trigger cameras, retrieve and store images and geolocation information from an onboard GNSS rover for PPK processing. The system allows acquisition of 8 cm spatial resolution thermal imagery from a 60 m height of flight and less than 7 cm geolocation accuracy of the mosaicked RGB imagery. Modelled latent heat flux data have been validated against latent heat fluxes measured by eddy covariance stations at two locations with RMSE of 75 W/m2 over a two-year study period.

Ecosystem Productivity and Evapotranspiration Dynamics of a Seasonally Dry Tropical Forest of the Yucatan Peninsula

AbstractTropical forests are among the most threatened ecosystems despite their important role in the global carbon and water cycles. In Mexico, the Yucatan Peninsula (YP) hosts near to 30% of these ecosystems. We established an ecohydrological monitoring site in a relatively pristine tropical dry forest (TDF) within “El Palmar” state reserve at the northwest of the YP, to study the seasonal dynamics of its carbon and water fluxes and to evaluate their main controls as they respond to climatic variability. Spatio‐temporal analyses using Bio‐meteorological data from an Eddy Covariance station (from January 2017 to August 2019) and remotely sensed data (from 2001 to 2019), reveal the inter and intra‐annual dynamics of carbon and water fluxes between this ecosystem and the atmosphere. The TDF's greenness, net exchange of carbon, gross primary production, ecosystem respiration, and evapotranspiration are strongly controlled by the seasonality in precipitation pulses and their legacy from recharged moisture in the shallow aquifer observed through three marked seasons: dry, rainy, and post‐rainy. The study site continuously behaves like a carbon sink with annual rates ranging between −1.09 and −7.06 , whose performance responds to a precipitation threshold above which its productivity declines. Differences in the ecosystem productivity among years result from a combination of water and energy availability during the growing season and variable access to groundwater during the post‐rainy and dry seasons. This highlights the ecosystem sensitivity to rainfall modulation of energy and water availability throughout the year and its impact on the ecosystem's functioning and resilience.

SEASONAL AND DIURNAL VARIATION OF SURFACE ENERGY FLUXES AND SURFACE CHARACTERISTICS OVER A SEMIARID GRASSLAND IN WEST AFRICA

Radiative fluxes and surface energy fluxes computed using the eddy covariance method from January to December 2013 were used to investigate the partition of energy and energy exchange over degraded grassland in the Sudanian savannah region of West Africa. In Sumbrungu Agunsi, (10.841°N, 0.918°W), in Ghana’s upper east area, an Eddy Covariance station was erected near to the Ghana–Burkina-Faso border. The fluctuations in radiation components, energy fluxes, and surface characteristics components were studied on a seasonal and daily basis. The Incoming shortwave radiation, out-going shortwave radiation, incoming long wave radiation and out-going long wave radiation all varied annually, resulting in a seasonal variation in net radiation. The soil moisture content, evaporative fraction, and the surface albedo were found to be low during the dry season but high during the wet season, while, the Bowen ratio was found to be high during the dry season but low during the wet season. During the wet season, majority of the available energy were converted to latent heat, but, during the dry season, majority of the available energy was transformed to sensible heat. The station’s energy balance closure was investigated; slope of the regression found was 0.67 with an intercept of 33 Wm-2 . The reasons for the station’s energy balance not being closed were investigated.

Gap-Filling Eddy Covariance Latent Heat Flux: Inter-Comparison of Four Machine Learning Model Predictions and Uncertainties in Forest Ecosystem

Environmental monitoring using satellite remote sensing is challenging because of data gaps in eddy-covariance (EC)-based in situ flux tower observations. In this study, we obtain the latent heat flux (LE) from an EC station and perform gap filling using two deep learning methods (two-dimensional convolutional neural network (CNN) and long short-term memory (LSTM) neural networks) and two machine learning (ML) models (support vector machine (SVM), and random forest (RF)), and we investigate their accuracies and uncertainties. The average model performance based on ~25 input and hysteresis combinations show that the mean absolute error is in an acceptable range (34.9 to 38.5 Wm−2), which indicates a marginal difference among the performances of the four models. In fact, the model performance is ranked in the following order: SVM &gt; CNN &gt; RF &gt; LSTM. We conduct a robust analysis of variance and post-hoc tests, which yielded statistically insignificant results (p-value ranging from 0.28 to 0.76). This indicates that the distribution of means is equal within groups and among pairs, thereby implying similar performances among the four models. The time-series analysis and Taylor diagram indicate that the improved two-dimensional CNN captures the temporal trend of LE the best, i.e., with a Pearson’s correlation of &gt;0.87 and a normalized standard deviation of ~0.86, which are similar to those of in situ datasets, thereby demonstrating its superiority over other models. The factor elimination analysis reveals that the CNN performs better when specific meteorological factors are removed from the training stage. Additionally, a strong coupling between the hysteresis time factor and the accuracy of the ML models is observed.

The Water Balance of Wet Grassland Sites with Shallow Water Table Conditions in the North-Eastern German Lowlands in Extreme Dry and Wet Years

In recent years, Germany has experienced an increasing number of extreme wet and dry years. In the North German lowlands, wet grassland sites with shallow water table conditions are widespread landscape elements. They are characterized by a special water and nutrient balance that reacts very sensitively to changes in the hydrological system. Studies on evapotranspiration (ETa) and the development of groundwater levels were carried out at two typical wet grassland sites with shallow water table conditions. A weighable groundwater lysimeter system in the Spreewald wetland (SPW) and an eddy covariance station in Havelländisches Luch (HL) were used to measure ETa. The results show that even these shallow water table sites cannot sufficiently meet the vegetation’s water demands in extreme dry conditions. The groundwater levels drop to values deeper than 1 m below the surface. As a result, water supply to the vegetation is temporarily limited. The mean crop coefficients (Kc) of these wet grassland sites reach values of 1.1 in the vegetation period with a sufficient water supply, but drop to around 0.8 in dry years when the water supply is limited. Areas with small catchment areas, such as HL, are more seriously affected by the dry meteorological conditions than areas with sufficient inflows from larger catchment areas, such as SPW.

Physical connection of sensible and ground heat flux

Characterizing physical relationships between the sensible, latent and ground heat flux at the Earth surface is of crucial importance in studying the global energy, water, and carbon cycles. Here we demonstrate an analogy between the daily mean turbulent heat transfer in the atmospheric surface layer and conductive-advective heat transfer in soil. This finding, in conjunction with temperature continuity at the land–atmosphere interface, uncovers a physical linkage between sensible and ground heat flux. The proposed relationship and its ramification for estimation of latent heat flux are validated based on daily averaged data from six AmeriFlux eddy covariance stations in North America over a wide range of land covers and climate regimes.

Long-term year-round observations of magmatic CO2 emissions on Mammoth Mountain, California, USA

Diffuse emission of magmatic CO2 is one of the main indicators of volcanic unrest at Mammoth Mountain, but the presence of deep seasonal snowpack at the site has hindered year-round CO2 flux observations. A permanent eddy covariance station was established at the largest area of diffuse CO2 degassing on Mammoth Mountain (Horseshoe Lake tree kill) that measured CO2 fluxes (Fc) and meteorological parameters on a half-hourly basis. From July 22, 2014 to May 24, 2020, Fc ranged from −35 to 10,546 g m−2 d−1. Fc decreased on average by 53% over the study period, tracking the long-term decline in CO2 emissions following the last major increase that occurred at the Horseshoe Lake tree kill area from 2009 to 2011. Statistical and spectral analyses were applied to the Fc and ancillary meteorological parameter time series to understand (1) relationships between these parameters, (2) their dominant periodicities, and (3) changes in Fc that may be unexplained by meteorological forcing. Variations in detrended Fc (Fcdt) were most strongly correlated with wind direction and atmospheric temperature, followed by atmospheric pressure on diurnal to annual time scales, but wind direction likely exerted the most direct control on Fcdt. Comparison of the smoothed (180-d span) Fcdt time series to the time series of average-daily snow water equivalent measured ~1 km away suggested that snowpack may have suppressed CO2 emissions. No evidence of a change in CO2 emissions related to the last major seismic swarm beneath Mammoth Mountain on February 2–18, 2014 was observed.

Prediction of Potential and Actual Evapotranspiration Fluxes Using Six Meteorological Data-Based Approaches for a Range of Climate and Land Cover Types

Evapotranspiration is the major component of the water cycle, so a correct estimate of this variable is fundamental. The purpose of the present research is to assess the monthly scale accuracy of six meteorological data-based models in the prediction of evapotranspiration (ET) losses by comparing the modelled fluxes with the observed ones from eight sites equipped with eddy covariance stations which differ in terms of vegetation and climate type. Three potential ET methods (Penman-Monteith, Priestley-Taylor, and Blaney-Criddle models) and three actual ET models (the Advection-Aridity, the Granger and Gray, and the Antecedent Precipitation Index method) have been proposed. The findings show that the models performances differ from site to site and they depend on the vegetation and climate characteristics. Indeed, they show a wide range of error values ranging from 0.18 to 2.78. It has been not possible to identify a single model able to outperform the others in each biome, but in general, the Advection-Aridity approach seems to be the most accurate, especially when the model calibration in not carried out. It returns very low error values close to 0.38. When the calibration procedure is performed, the most accurate model is the Granger and Gray approach with minimum error of 0.13 but, at the same time, it is the most impacted by this process, and therefore, in a context of data scarcity, it results the less recommended for ET prediction. The performances of the investigated ET approaches have been furthermore tested in case of lack of measured data of soil heat fluxes and net radiation considering using empirical relationships based on meteorological data to derive these variables. Results show that, the use of empirical formulas to derive ET estimates increases the errors up to 200% with the consequent loss of model accuracy.