Di Long

Uncertainty in evapotranspiration from land surface modeling, remote sensing, and GRACE satellites

Proliferation of evapotranspiration (ET) products warrants comparison of these products. The study objective was to assess uncertainty in ET output from four land surface models (LSMs), Noah, Mosaic, VIC, and SAC in NLDAS-2, two remote sensing-based products, MODIS and AVHRR, and GRACE-inferred ET from a water budget with precipitation from PRISM, monitored runoff, and total water storage change (TWSC) from GRACE satellites. The three cornered hat method, which does not require a priori knowledge of the true ET value, was used to estimate ET uncertainties. In addition, TWSC or total water storage anomaly (TWSA) from GRACE was compared with water budget estimates of TWSC from a flux-based approach or TWSA from a storage-based approach. The analyses were conducted using data from three regions (humid-arid) in the South Central United States as case studies. Uncertainties in ET are lowest in LSM ET (∼5 mm/mo), moderate in MODIS or AVHRR-based ET (10–15 mm/mo), and highest in GRACE-inferred ET (20–30 mm/month). There is a trade-off between spatial resolution and uncertainty, with lower uncertainty in the coarser-resolution LSM ET (∼14 km) relative to higher uncertainty in the finer-resolution (∼1–8 km) RS ET. Root-mean-square (RMS) of uncertainties in water budget estimates of TWSC is about half of RMS of uncertainties in GRACE-derived TWSC for each of the regions. Future ET estimation should consider a hybrid approach that integrates strengths of LSMs and satellite-based products to constrain uncertainties.

Ground referencing GRACE satellite estimates of groundwater storage changes in the California Central Valley, USA

There is increasing interest in using Gravity Recovery and Climate Experiment (GRACE) satellite data to remotely monitor groundwater storage variations; however, comparisons with ground-based well data are limited but necessary to validate satellite data processing, especially when the study area is close to or below the GRACE footprint. The Central Valley is a heavily irrigated region with large-scale groundwater depletion during droughts. Here we compare updated estimates of groundwater storage changes in the California Central Valley using GRACE satellites with storage changes from groundwater level data. A new processing approach was applied that optimally uses available GRACE and water balance component data to extract changes in groundwater storage. GRACE satellites show that groundwater depletion totaled ∼31.0 ± 3.0 km3 for Groupe de Recherche de Geodesie Spatiale (GRGS) satellite data during the drought from October 2006 through March 2010. Groundwater storage changes from GRACE agreed with those from well data for the overlap period (April 2006 through September 2009) (27 km3 for both). General correspondence between GRACE and groundwater level data validates the methodology and increases confidence in use of GRACE satellites to monitor groundwater storage changes.

Global analysis of approaches for deriving total water storage changes from GRACE satellites

Increasing interest in use of GRACE satellites and a variety of new products to monitor changes in total water storage (TWS) underscores the need to assess the reliability of output from different products. The objective of this study was to assess skills and uncertainties of different approaches for processing GRACE data to restore signal losses caused by spatial filtering based on analysis of 1°×1° grid scale data and in 60 river basins globally. Results indicate that scaling factors from six LSMs, including GLDAS-1 four models (Noah2.7, Mosaic, VIC, and CLM 2.0), CLM 4.0, and WGHM, are similar over most of humid, sub-humid, and high-latitude regions but can differ by up to 100% over arid and semi-arid basins and areas with intensive irrigation. Temporal variability in scaling factors is generally minor at the basin scale except in arid and semi-arid regions, but can be appreciable at the 1°×1° grid scale. Large differences in TWS anomalies from three processing approaches (scaling factor, additive, and multiplicative corrections) were found in arid and semi-arid regions, areas with intensive irrigation, and relatively small basins (e.g.,u2009≤u2009200,000km2). Furthermore, TWS anomaly products from gridded data with CLM4.0 scaling factors and the additive correction approach more closely agree with WGHM output than the multiplicative correction approach. This comprehensive evaluation of GRACE processing approaches should provide valuable guidance on applicability of different processing approaches with different climate settings and varying levels of irrigation.

How sensitive is SEBAL to changes in input variables, domain size and satellite sensor?

[1]xa0Estimation of evapotranspiration (ET) over large heterogeneous areas using numerous satellite-based algorithms is increasing; however, further analysis of uncertainties is limited. The objective of this study was to evaluate impacts of varying input variables, size of the modeling domain, and spatial resolution of satellite sensors on sensible heat flux (H) estimates from the Surface Energy Balance Algorithm for Land (SEBAL). First, sensitivity analysis of SEBAL is conducted by varying its input variables using Moderate Resolution Imaging Spectroradiometer (MODIS) data for 29 cloud-free days in 2007 covering the Baiyangdian watershed in North China. Domain dependence of the H estimates is quantified by estimating H for subwatersheds of different sizes and the entire watershed using MODIS data for 4 cloud-free days in May 2007. Landsat Thematic Mapper (TM) and MODIS based H estimates are compared to evaluate the effect of spatial resolution of satellite sensors. Results of sensitivity analysis indicate that the H estimates from SEBAL are most sensitive to temperatures of hot and cold pixels and available energy of the hot pixel. Results of domain dependence show that the mean absolute percentage difference (MAPD) and root mean square deviation (RMSD) in the H estimates between different domain sizes up to 53.9% and 75.7 W m−2, respectively. Although areally averaged H estimates from MODIS and Landsat TM sensors are similar, the MODIS-based H estimates show an RMSD of 52.3 W m−2 and a bias of 26.5 W m−2 relative to Landsat TM-based counterparts. Unlike other models, the standard deviation of H estimates from SEBAL using high spatial resolution images can be smaller than that using low spatial resolution images. Furthermore, H estimates from the input upscaling scheme (aggregating input variables) are generally consistent with those from the output upscaling scheme (aggregating the output) for the same sensor, given similar differences between hot and cold pixels for low and high spatial resolution. The resulting H flux and ET estimates from SEBAL can therefore vary with differing extreme pixels selected by the operator, domain size, and spatial resolution of satellite sensors. This study provides insights into various factors that should be considered when applying SEBAL to estimate ET and helps correctly interpret the SEBAL outputs.

A modified surface energy balance algorithm for land (M-SEBAL) based on a trapezoidal framework

[1]xa0The surface energy balance algorithm for land (SEBAL) has been designed and widely used (and misused) worldwide to estimate evapotranspiration across varying spatial and temporal scales using satellite remote sensing over the past 15 yr. It is, however, beset by visual identification of a hot and cold pixel to determine the temperature difference (dT) between the surface and the lower atmosphere, which is assumed to be linearly correlated with surface radiative temperature (Trad) throughout a scene. To reduce ambiguity in flux estimation by SEBAL due to the subjectivity in extreme pixel selection, this study first demonstrates that SEBAL is of a rectangular framework of the contextual relationship between vegetation fraction (fc) and Trad, which can distort the spatial distribution of heat flux retrievals to varying degrees. End members of SEBAL were replaced by a trapezoidal framework of the fc-Trad space in the modified surface energy balance algorithm for land (M-SEBAL). The warm edge of the trapezoidal framework is determined by analytically deriving temperatures of the bare surface with the largest water stress and the fully vegetated surface with the largest water stress implicit in both energy balance and radiation budget equations. Areally averaged air temperature (Ta) across a study site is taken to be the cold edge of the trapezoidal framework. Coefficients of the linear relationship between dT and Trad can vary with fc but are assumed essentially invariant for the same fc or within the same fc class in M-SEBAL. SEBAL and M-SEBAL are applied to the soil moisture-atmosphere coupling experiment (SMACEX) site in central Iowa, U.S. Results show that M-SEBAL is capable of reproducing latent heat flux in terms of an overall root-mean-square difference of 41.1 W m−2 and mean absolute percentage difference of 8.9% with reference to eddy covariance tower-based measurements for three landsat thematic mapper/enhanced thematic mapper plus imagery acquisition dates in 2002. The retrieval accuracy of SEBAL is generally lower than M-SEBAL, depending largely on the selected extremes. Spatial distributions of heat flux retrievals from SEBAL are distorted to a certain degree due to its intrinsic rectangular framework.

Deriving theoretical boundaries to address scale dependencies of triangle models for evapotranspiration estimation

[1]xa0Satellite-based triangle models for evapotranspiration estimation are unique in interpreting the relationship between the normalized difference vegetation index (NDVI)/factional vegetation cover (fc) and surface radiative temperature (Trad) across large heterogeneous areas. However, output and performance of triangle models may depend on the size of the domain being studied and resolution of the satellite images being used. The objective of this study was to assess domain and resolution dependencies of triangle models using progressively larger domains and Landsat Thematic Mapper/Enhanced Thematic Mapper Plus and Moderate Resolution Imaging Spectroradiometer sensors at the Soil Moisture-Atmosphere Coupling Experiment site in central Iowa on days of year 174 and 182 in 2002. Results show domain and resolution dependencies of the triangle models with large uncertainties in evaporative fraction (EF) estimates in terms of a mean absolute percentage difference (MAPD) up to ∼50%. A trapezoid model which requires derivation of theoretical limiting edges of the NDVI-Trad space is proposed to constrain domain and resolution dependencies of triangle models. The theoretical warm edge can be derived by solving for temperatures of the driest bare surface and the fully vegetated surface with the largest water stress implicit in both radiation budget and energy balance equations. Areal average air temperature can be taken as the theoretical cold edge. The triangle model appears to perform well across large areas (∼104 km2) but fails to predict EF over small areas (∼102 km2). The trapezoid model can effectively reduce domain and resolution dependencies and constrain errors of the EF estimates with an MAPD of ∼10%.

GRACE satellite observed hydrological controls on interannual and seasonal variability in surface greenness over mainland Australia

Water-limited ecosystems, covering ~50% of the global land, are controlled primarily by hydrologic factors. Because climate change is predicted to markedly alter current hydroclimatic conditions later this century, a better hydrological indicator of ecosystem performance is warranted to improve understanding of hydrological controls on vegetation and to predict changes in the future. Here we show that the observed total water storage anomaly (TWSA) from the Gravity Recovery and Climate Experiment (GRACE) can serve as this indicator. Using the Australian mainland as a case study, where ecosystems are generally water limited, we found that GRACE-observed TWSA can explain changes in surface greenness (as measured by the normalized difference vegetation index, NDVI) both interannually and seasonally. In addition, we found that TWSA shows a significant decreasing trend during the millennium drought from 1997 through 2009 in the region. However, decline in annual mean NDVI during the same period was mainly driven by decline in annual minimum monthly NDVI, whereas annual maximum monthly NDVI remained relatively constant across biomes. This phenomenon reveals an intrinsic sensitivity of ecosystems to water availability that drought-induced reductions in surface greenness are more likely expressed through its influence on vegetation during lower NDVI months, whereas ecosystem activities tend to recover to their maximum level during periods when the combined environmental conditions favor vegetation growth within a year despite the context of the prolonged drought.

Estimation of daily actual evapotranspiration from remotely sensed data under complex terrain over the upper Chao river basin in North China

Daily actual evapotranspiration over the upper Chao river basin in North China on 23 June 2005 was estimated based on the Surface Energy Balance Algorithm for Land (SEBAL), in which the parameterization schemes for calculating the instantaneous solar radiation and daily integrated radiation were improved by accounting for the variations in slope and azimuth of land surface and terrain shadow in mountainous areas. The evapotranspiration (ET) estimated from satellite data in this study for the whole watershed ranges from 0 mm to 7.3 mm day−1 with a mean of 3.4 mm day−1, which was validated by Penman–Monteith approaches for water body and paddy land. The comparison of ET estimates for a wide range of land cover types reflected distinct mechanisms of energy partition and water removal of various land cover types, showing differences in the spatial distribution pattern of ET, which could be not only the reflection but also the driving force of advection and local circulation that may violate the surface energy balance equation in the vertical direction. The spatial variation in daily solar radiation and ET estimates under the complex terrain of forest land were elaborated and evaluated by exploring the relationship between ET estimates and elevations for wood land and grass land. In addition, the utility and limitations of SEBALs applicability to watersheds with various land cover types and complex terrain were analysed.

Integration of the GG model with SEBAL to produce time series of evapotranspiration of high spatial resolution at watershed scales

[1]xa0Lack of good quality satellite images because of cloud contamination or long revisit time severely degrades predictions of evapotranspiration (ET) time series at watershed/regional scales from satellite-based surface flux models. We integrate the feedback model developed by Granger and Gray (the GG model) with the Surface Energy Balance Algorithm for Land (SEBAL), with the objective to generate ET time series of high spatial resolution and reliable temporal distribution at watershed scales. First, SEBAL is employed to yield estimates of ET for the Baiyangdian watershed in a semihumid climatic zone in north China on cloud-free days, where there exists the complementary relationship (CR) between actual ET and pan ET. These estimates constitute input to the GG model to inversely derive the relationship between the relative evaporation and the relative drying power of the air. Second, the modified GG model is used to yield ET time series on a daily basis simply by using routine meteorological data and Moderate Resolution Imaging Spectroradiometer (MODIS) albedo and leaf area index products. Results suggest that the modified GG model that has incorporated remotely sensed ET can effectively extend remote sensing based ET to days without images and improve spatial representation of ET at watershed scales. Utility of the evaporative fraction method and the crop coefficients approaches to extrapolate ET time series depends largely on the number and interval of good quality satellite images. Comparison of ET time series from the two techniques and the proposed integration method for days with daily net radiation larger than 100 W m−2 and corresponding pan ET clearly shows that only the integration method can exhibit an asymmetric CR at the watershed scale and daily time scale. Validation performed using hydrologic budget calculations indicate that the proposed method has the highest accuracy in terms of annual estimates of ET for both watersheds in north China.

Toward the Use of the MODIS ET Product to Estimate Terrestrial GPP for Nonforest Ecosystems

Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary production (GPP) data (MOD17), based on the light-use-efficiency algorithm, have been widely used to assess large-scale carbon budgets. However, systemic errors of this product have been reported, particularly for nonforest ecosystems. Here, we test a simple and operational way to estimate GPP in nonforest ecosystems by inverting the MODIS evapotranspiration (ET) product (MOD16) using ecosystem water use efficiency (WUE = GPP/ET) . Field measurements from 17 nonforest AmeriFlux sites of GPP were used for validation. Results show that the inverted GPP from MOD16 (MOD16 GPP) agrees better with the observed GPP than MOD17 does. The overall root-mean-square error (RMSE) and mean bias of MOD16 GPP are 19.63 g C/m2/8-day and -4.06 g C/m2 /8-day, respectively, which are lower than the corresponding values of MOD17 GPP ( RMSE = 23.82 g C/ m2/8-day and mean bias = -9.07 g C/m2/8-day). This finding suggests the potential to achieve a better assessment of GPP for nonforest ecosystems with a fine resolution.