Alexandra G. Konings

Comparative hydrology across AmeriFlux sites: The variable roles of climate, vegetation, and groundwater

Watersheds can be characterized as complex space?time filters that transform incoming fluxes of energy, water, and nutrients into variable output signals. The behavior of these filters is driven by climate, geomorphology, and ecology and, accordingly, varies from site to site. We investigated this variation by exploring the behavior of evapotranspiration signals from 14 different AmeriFlux sites. Evapotranspiration is driven by water and energetic forcing and is mediated by ecology and internal redistribution of water and energy. As such, it integrates biological and physical controls, making it an ideal signature to target when investigating watershed filtering. We adopted a paradigmatic approach (referred to as the null model) that couples the Penman?Monteith equation to a soil moisture model and explored the deviations between the predictions of the null model and the observed AmeriFlux data across the sites in order to identify the controls on these deviations and their commonalities and differences across the sites. The null model reproduced evapotranspiration fluxes reasonably well for arid, shallow?rooted systems but overestimated the effects of water limitation and could not reproduce seasonal variation in evapotranspiration at other sites. Accounting for plant access to groundwater (or deep soil moisture) reserves and for the effects of soil temperature on limiting evapotranspiration resolved these discrepancies and greatly improved prediction of evapotranspiration at multiple time scales. The results indicate that site?specific hydrology and climatic factors pose important controls on biosphere?hydrosphere interactions and suggest that plant–water table interactions and early season phenological controls need to be incorporated into even simple models to reproduce the seasonality in evapotranspiration.

Extended triple collocation: Estimating errors and correlation coefficients with respect to an unknown target

Calibration and validation of geophysical measurement systems typically require knowledge of the true value of the target variable. However, the data considered to represent the true values often include their own measurement errors, biasing calibration, and validation results. Triple collocation (TC) can be used to estimate the root-mean-square-error (RMSE), using observations from three mutually independent, error-prone measurement systems. Here, we introduce Extended Triple Collocation (ETC): using exactly the same assumptions as TC, we derive an additional performance metric, the correlation coefficient of the measurement system with respect to the unknown target, rho(t,Xi). We demonstrate that rho(2)(t,Xi) is the scaled, unbiased signal-to-noise ratio and provides a complementary perspective compared to the RMSE. We apply it to three collocated wind data sets. Since ETC is as easy to implement as TC, requires no additional assumptions, and provides an extra performance metric, it may be of interest in a wide range of geophysical disciplines.

Global variations in ecosystem-scale isohydricity

Droughts are expected to become more frequent and more intense under climate change. Plant mortality rates and biomass declines in response to drought depend on stomatal and xylem flow regulation. Plants operate on a continuum of xylem and stomatal regulation strategies from very isohydric (strict regulation) to very anisohydric. Coexisting species may display a variety of isohydricity behaviors. As such, it can be difficult to predict how to model the degree of isohydricity at the ecosystem scale by aggregating studies of individual species. This is nonetheless essential for accurate prediction of ecosystem drought resilience. In this study, we define a metric for the degree of isohydricity at the ecosystem scale in analogy with a recent metric introduced at the species level. Using data from the AMSR-E satellite, this metric is evaluated globally based on diurnal variations in microwave vegetation optical depth (VOD), which is directly related to leaf water potential. Areas with low annual mean radiation are found to be more anisohydric. Except for evergreen broadleaf forests in the tropics, which are very isohydric, and croplands, which are very anisohydric, land cover type is a poor predictor of ecosystem isohydricity, in accordance with previous species-scale observations. It is therefore also a poor basis for parameterizing water stress response in land-surface models. For taller ecosystems, canopy height is correlated with higher isohydricity (so that rainforests are mostly isohydric). Highly anisohydric areas show either high or low underlying water use efficiency. In seasonally dry locations, most ecosystems display a more isohydric response (increased stomatal regulation) during the dry season. In several seasonally dry tropical forests, this trend is reversed, as dry-season leaf-out appears to coincide with a shift toward more anisohydric strategies. The metric developed in this study allows for detailed investigations of spatial and temporal variations in plant water behavior.

Effect of Radiative Transfer Uncertainty on L-Band Radiometric Soil Moisture Retrieval

Microwave radiometry soil moisture retrieval methods suffer from uncertainties about the representation of several effects, including dielectric mixing, surface roughness, and vegetation opacity. These uncertainties lead to two major types of error: systematic bias and random errors. The effect of the uncertainties is studied using the Soil Moisture Active Passive Algorithm Testbed, a simulation environment for evaluating error propagation in retrieval algorithms, and two different common retrieval algorithms (single and dual polarizations). The two types of errors are simulated by using different representations for each factor in the forward and retrieval parts. For both algorithms, this approach introduces a spatially variable bias, which is particularly large when using a single-polarization retrieval algorithm. This paper illustrates the emergence of both this bias and the random error due to uncertainty in the representation of vegetation and soil texture effects in retrieval algorithms. The dependence of these two types of error on vegetation and soil texture properties is shown through mapping them over the simulation region. The relative contribution of these errors to the total error is strongly dependent on the simulation conditions and is not necessarily indicative of what may be experienced during actual observations. Uncertainty due to roughness representation causes a lower error than uncertainty in vegetation opacity and dielectric mixing parameterizations in the simulated soil moisture retrieval. Summation and compensation of multiple errors can cause the estimate error to increase with improved radiative transfer knowledge, even after bias removal. The retrieval of soil moisture from microwave measurements depends on several other parameterizations that are also uncertain. This paper is limited to only three parameterizations that are considered to be among the larger contributors to bias.

How Many Parameters Can Be Maximally Estimated From a Set of Measurements

Remote sensing algorithms often invert multiple measurements simultaneously to retrieve a group of geophysical parameters. In order to create a robust retrieval algorithm, it is necessary to ensure that there are more unique measurements than parameters to be retrieved. If this is not the case, the inversion might have multiple solutions and be sensitive to noise. In this letter, we introduce a methodology to calculate the number of (possibly fractional) “degrees of information” in a set of measurements, representing the number of parameters that can be retrieved robustly from that set. Since different measurements may not be mutually independent, the amount of duplicate information is calculated using the information-theoretic concept of total correlation (a generalization of mutual information). The total correlation is sensitive to the full distribution of each measurement and therefore accounts for duplicate information even if multiple measurements are related only partially and nonlinearly. The method is illustrated using several examples, and applications to a variety of sensor types are discussed.

Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux (H), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed Solar-Induced Fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimates of LE, H and GPP from 2007 to 2015 at 1° × 1° spatial resolution and on monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analysing WECANN retrievals across three extreme drought and heatwave events demonstrates the capability of the retrievals in capturing the extent of these events. Uncertainty estimates of the retrievals are analysed and the inter-annual variability in average global and regional fluxes show the impact of distinct climatic events - such as the 2015 El Niño - on surface turbulent fluxes and GPP.

Tall Amazonian forests are less sensitive to precipitation variability

Climate change is altering the dynamics, structure and function of the Amazon, a biome deeply connected to the Earth’s carbon cycle. Climate factors that control the spatial and temporal variations in forest photosynthesis have been well studied, but the influence of forest height and age on this controlling effect has rarely been considered. Here, we present remote sensing observations of solar-induced fluorescence (a proxy for photosynthesis), precipitation, vapour-pressure deficit and canopy height, together with estimates of forest age and aboveground biomass. We show that photosynthesis in tall Amazonian forests, that is, forests above 30 m, is three times less sensitive to precipitation variability than in shorter (less than 20 m) forests. Taller Amazonian forests are also found to be older, have more biomass and deeper rooting systems1, which enable them to access deeper soil moisture and make them more resilient to drought. We suggest that forest height and age are an important control of photosynthesis in response to interannual precipitation fluctuations. Although older and taller trees show less sensitivity to precipitation variations, they are more susceptible to fluctuations in vapour-pressure deficit. Our findings illuminate the response of Amazonian forests to water stress, droughts and climate change.Tall trees are less sensitive to variation in precipitation than short trees, according to analyses of photosynthetic sensitivity to drought in tall and short Amazon forests. The results demonstrate higher resilience of tall trees to drought.

Active microwave observations of diurnal and seasonal variations of canopy water content across the humid African tropical forests

A higher frequency of severe droughts under warmer temperatures is expected to lead to large impacts on global water and carbon fluxes and on vegetation cover—including possible widespread mortality. Monitoring the hydraulic state of vegetation as represented by the canopy water content will allow rapid assessment of vegetation water stress. Here we show the potential of active microwave backscatter observations at Ku band for monitoring the diurnal and seasonal variations of top-of-canopy water content. We focus on the humid tropical forests of Central Africa and examine spatiotemporal variations of radar backscatter from QuikSCAT (2001–2009) and RapidScat (2014–2016). Diurnal variations in RapidScat backscatter demonstrate the occurrence of widespread midday stomatal closure in this region. Increases in backscatter during the dry seasons in humid forests could be explained by both dry season leaf flushing (as supported by canopy structure) and vapor pressure deficit-driven increases in evapotranspiration rates.

L-Band Radar Soil Moisture Retrieval Without Ancillary Information

A radar-only retrieval algorithm for soil moisture mapping is applied to L-band scatterometer measurements from the Aquarius. The algorithm is based on a nonlinear relation between L-band backscatter and volumetric soil moisture and does not require ancillary information on the surface, e.g., land classification, vegetation canopy, surface roughness, etc. It is based on the definition of three limiting cases or end-members: 1) smooth bare soil; 2) rough bare soil; and 3) maximum vegetation condition. In this study, an estimation method is proposed for the end-member parameters that is iterative and only uses space-borne measurements. The major advantages of the algorithm include an analytic formulation (direct inversion possible), and the fact that there is no requirement for ancillary information. Ancillary data often misclassify the surface and the parameterizations linking surface classification to model parameter values are often highly uncertain. The retrieval algorithm is tested using 3 years of space-borne scatterometer observations from the Aquarius/SAC-D. Retrieved soil moisture accuracy is assessed in several ways: comparison of global spatial patterns with other available soil moisture products (two reanalysis modeling products and retrievals based on the Aquarius radiometer), extended triple collocation (ETC) and time series analysis over selected target areas. In general, low bias and standard deviation are observed with levels comparable to independent radiometer-based retrievals. The errors, however, increase across areas with high vegetation density. The results are promising and applicable to forthcoming L-band radar missions such as SMAP-NASA (2015) and SAOCOM-CONAE (2016).

Relationship Between Vegetation Microwave Optical Depth and Cross-Polarized Backscatter From Multiyear Aquarius Observations

Soil moisture retrieval algorithms based on passive microwave remote sensing observations need to account for vegetation attenuation and emission, which is generally parameterized as vegetation optical depth (VOD). This multisensor study tests a new method to retrieve VOD from cross-polarized radar backscattering coefficients. Three years of Aquarius/SAC-D data were used to establish a relationship between the cross-polarized backscattering coefficient σHV and VOD derived from a multitemporal passive dual-channel algorithm (VODMT). The dependence of the correspondence is analyzed for different land use classes. There are no systematic differences in the slope for woody versus nonwoody vegetation, resulting in a strong correlation (80% explained-variance) and a global linear relationship when all classes are combined. The relationship is stable over the years of observations. The comparison of the Aquarius-derived VODMT to Soil Moisture and Ocean Salinitys multi-angular VOD estimates shows similar spatial patterns and temporal behavior, evident in high correlations. However, VODMT has considerably higher mean values, but lower dynamic range globally. Most of the differences can be attributed to differences in instrument sampling. The main result of this study, a relationship between backscatter and VOD, will permit high-resolution mapping of VOD with synthetic aperture radar measurements. These maps allow future studies of scaling and heterogeneity effects of vegetation on soil moisture retrieval at the coarser scales of land microwave radiometry. The study shows that VOD based on passive measurements and predicted by active measurements are comparable globally and that the breakdown by land cover classification does not affect the relationship appreciably.