Albert Van Dijk

Global long-term passive microwave satellite-based retrievals of vegetation optical depth

[1]xa0Vegetation optical depth (VOD) retrievals from three satellite-based passive microwave instruments were merged to produce the first long-term global microwave-based vegetation product. The resulting VOD product spans more than two decades and shows seasonal cycles and inter-annual variations that generally correspond with those observed in the Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI). Some notable differences exist in the long-term trends: the NDVI, operating in the optical regime, is sensitive to chlorophyll abundance and photosynthetically active biomass of the leaves, whereas the microwave-based VOD is an indicator of the vegetation water content in total above-ground biomass, i.e., including wood and leaf components. Preliminary analyses indicate that the fluctuations in VOD typically correlated to precipitation variations, and that the mutually independent VOD and NDVI do not necessarily respond in identical manners. Considering both products together provides a more robust structural characterization and assessment of long-term vegetation dynamics at the global scale.

Toward global drought early warning capability: Expanding international cooperation for the development of a framework for monitoring and forecasting

Drought is a global problem that has far-reaching impacts, especially on vulnerable populations in developing regions. This paper highlights the need for a Global Drought Early Warning System (GDEWS), the elements that constitute its underlying framework (GDEWF), and the recent progress made toward its development. Many countries lack drought monitoring systems, as well as the capacity to respond via appropriate political, institutional, and technological frameworks, and these have inhibited the development of integrated drought management plans or early warning systems. The GDEWS will provide a source of drought tools and products via the GDEWF for countries and regions to develop tailored drought early warning systems for their own users. A key goal of a GDEWS is to maximize the lead time for early warning, allowing drought managers and disaster coordinators more time to put mitigation measures in place to reduce the vulnerability to drought. To address this, the GDEWF will take both a top-down approach...

Global patterns in base flow index and recession based on streamflow observations from 3394 catchments

[1]xa0Numerous previous studies have constructed models to estimate base flow characteristics from climatic and physiographic characteristics of catchments and applied these to ungauged regions. However, these studies generally used streamflow observations from a relatively small number of catchments (<200) located in small, homogeneous study areas, which may have led to less reliable models with limited applicability elsewhere. Here, we use streamflow observations from a highly heterogeneous set of 3394 catchments (<10,000 km2) worldwide to construct reliable, widely applicable models based on 18 climatic and physiographic characteristics to estimate two important base flow characteristics: (1) the base flow index (BFI), defined as the ratio of long-term mean base flow to total streamflow; and (2) the base flow recession constant (k), defined as the rate of base flow decay. Regression analysis results revealed that BFI and k were related to several climatic and physiographic characteristics, notably mean annual potential evaporation, mean snow water equivalent depth, and abundance of surface water bodies. Ensembles of artificial neural networks (ANNs; obtained by subsampling the original set of catchments) were trained to estimate the base flow characteristics from climatic and physiographic data. The catchment-scale estimation of the base flow characteristics demonstrated encouraging performance with R2 values of 0.82 for BFI and 0.72 for k. The connection weights of the trained ANNs indicated that climatic characteristics were more important for estimating k than BFI. Global maps of estimated BFI and k were obtained using global climatic and physiographic data as input to the derived models. The resulting global maps are available for free download at http://www.hydrology-amsterdam.nl.

An analysis of spatiotemporal variations of soil and vegetation moisture from a 29-year satellite-derived data set over mainland Australia

[1]xa0The spatiotemporal behavior of soil and vegetation moisture over mainland Australia was analyzed using passive microwave observations by four satellites going back to late 1978. Differences in measurement specifications prevented merging the data directly. A continuous product was developed for Australia by scaling percentiles of the cumulative moisture distribution within each grid cell to the percentiles of a reference sensor. The coefficient of correlation and root-mean-square error between rescaled values and the reference generally suggest good agreement. Using the merged data product, a strong El Nino–Southern Oscillation signal in near-surface hydrology across Australia was confirmed. Spatial patterns of trends in annual averages show that western and northwestern Australia have experienced an increase in vegetation moisture content, while the east and southeast experienced a decrease. Soil moisture showed a similar spatial pattern but with larger regions experiencing a decrease. This could be explained by decreasing rainfall and increasing potential evapotranspiration during the extended winter period (May–September). The results give us reasonable confidence in the time series of soil and vegetation moisture derived by the scaling method developed in this study. Development of a global data set along these lines should enable better estimation of hydrological variables and should increase understanding of the impacts of ocean circulations on terrestrial hydrology and vegetation dynamics.

Evaluation of precipitation estimation accuracy in reanalyses, satellite products, and an ensemble method for regions in Australia and south and east Asia

Precipitation estimates from reanalyses and satellite observations are routinely used in hydrologic applications, but their accuracy is seldom systematically evaluated. This study used high-resolution gauge-only daily precipitation analyses for Australia (SILO) and South and East Asia [Asian Precipitation—Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE)] to calculate the daily detection and accuracy metrics for three reanalyses [ECMWF Re-Analysis Interim (ERA-Interim), Japanese 25-yr Reanalysis (JRA-25), and NCEP‐Department of Energy (DOE) Global Reanalysis 2] and three satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) 3B42V6, Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)]. A depthfrequency-adjusted ensemble mean of the reanalyses and satellite products was also evaluated. Reanalyses precipitation from ERA-Interim in southern Australia (SAu) and northern Australasia (NAu) showed higher detection performance. JRA-25 had a better performance in South and East Asia (SEA) except for the monsoon period, in which satellite estimates from TRMM and CMORPH outperformed the reanalyses. In terms of accuracy metrics (correlation coefficient, root-mean-square difference, and a precipitation intensity proxy, which is the ratio of monthly precipitation amount to total days with precipitation) and over the three subdomains, the depth-frequency-adjusted ensemble mean generally outperformed or was nearly as good as any of the single members. The results of the ensemble show that additional information is captured from the different precipitation products. This finding suggests that, depending on precipitation regime and location, combining (re)analysis and satellite products can lead to better precipitation estimates and,thus, more accurate hydrological applications than selecting any single product.

Radiation, temperature, and leaf area explain ecosystem carbon fluxes in boreal and temperate European forests

[1]xa0We analyzed measurements of net ecosystem exchange of CO2 (NEE) over 15 European forests (the EuroFlux data set) to investigate which climate and forest characteristics explain temporal and intersite variations in NEE and its components, gross primary production (GPP) and respiration (R). Informed stepwise regression was used to derive a parameter-efficient, empirical model that was consistent with process knowledge. The resulting model required seven site-specific parameters to describe flux behavior at different temporal scales as a function of radiation, temperature, and air humidity. The interpretation appeared robust despite method and data uncertainties, although the data set was probably biased toward well-watered boreal and temperate European forests. Radiation, temperature, and leaf area (through forest assimilation capacity) appear to be the main drivers of the observed temporal and intersite variation in gross primary production, ecosystem respiration, and net ecosystem exchange.

TRMM-TMI satellite observed soil moisture and vegetation density (1998-2005) show strong connection with El Nino in eastern Australia

Oscillation Index (SOI) in spring (r 2 = 0.90), and to a progressively lesser extent autumn, summer and winter. The Indian Ocean Dipole (IOD) index also explained part of the variation in spring q and t. Correlation analysis suggested that the regions most affected by El Nino are mainly located in eastern Australia. The results suggest that the drought conditions experienced in eastern Australia since 2000 and clearly expressed in these satellite observations have a strong connection with El Nino. Citation: Liu, Y., R. A. M. de Jeu, A. I. J. M. van Dijk, and M. Owe (2007), TRMM-TMI satellite observed soil moisture and vegetation density (1998 - 2005) show strong connection with El Nino in eastern Australia, Geophys. Res. Lett., 34, L15401, doi:10.1029/2007GL030311.

Improved water balance component estimates through joint assimilation of GRACE water storage and SMOS soil moisture retrievals

The accuracy of global water balance estimates is limited by the lack of observations at large scale and the uncertainties of model simulations. Global retrievals of terrestrial water storage (TWS) change and soil moisture (SM) from satellites provide an opportunity to improve model estimates through data assimilation. However, combining these two data sets is challenging due to the disparity in temporal and spatial resolution at both vertical and horizontal scale. For the first time, TWS observations from the Gravity Recovery and Climate Experiment (GRACE) and near-surface SM observations from the Soil Moisture and Ocean Salinity (SMOS) were jointly assimilated into a water balance model using the Ensemble Kalman Smoother from January 2010 to December 2013 for the Australian continent. The performance of joint assimilation was assessed against open-loop model simulations and the assimilation of either GRACE TWS anomalies or SMOS SM alone. The SMOS-only assimilation improved SM estimates but reduced the accuracy of groundwater and TWS estimates. The GRACE-only assimilation improved groundwater estimates but did not always produce accurate estimates of SM. The joint assimilation typically led to more accurate water storage profile estimates with improved surface SM, root-zone SM, and groundwater estimates against in situ observations. The assimilation successfully downscaled GRACE-derived integrated water storage horizontally and vertically into individual water stores at the same spatial scale as the model and SMOS, and partitioned monthly averaged TWS into daily estimates. These results demonstrate that satellite TWS and SM measurements can be jointly assimilated to produce improved water balance component estimates.

Impacts of fire on forest age and runoff in mountain ash forests

Runoff from mountain ash (Eucalyptus regnans F.Muell.) forested catchments has been shown to decline significantly in the few decades following fire -- returning to pre-fire levels in the following centuries -- owing to changes in ecosystem water use with stand age in a relationship known as Kuczeras model. We examined this relationship between catchment runoff and stand age by measuring whole-ecosystem exchanges of water using an eddy covariance system measuringforestevapotranspiration(ET)combinedwithsap-flowmeasurementsoftreewateruse,withmeasurementsmade acrossachronosequenceofthreesites(24,80and296yearssince fire).Atthe296-yearoldsiteeddycovariancesystemswere installed above the E. regnans overstorey and above the distinct rainforest understorey. Contrary to predictions from the Kuczera curve, we found that measurements of whole-forest ET decreased by far less across stand age between 24 and 296 years. Although the overstorey tree water use declined by 1.8mmday 1 with increasing forest age (an annual decrease of 657mm) the understorey ET contributed between 1.2 and 1.5mmday 1 , 45% of the total ET (3mmday 1 ) at the old growth forest.

Streamflow rating uncertainty

Common streamflow gauging procedures require assumptions about the stage-discharge relationship (the rating curve) that can introduce considerable uncertainties in streamflow records. These rating uncertainties are not usually considered fully in hydrological model calibration and evaluation yet can have potentially important impacts. We analysed streamflow gauge data and conducted two modelling experiments to assess rating uncertainty in operational rating curves, its impacts on modelling and possible ways to reduce those impacts. We found clear evidence of variance heterogeneity (heteroscedasticity) in streamflow estimates, with higher residual values at higher stage values. In addition, we confirmed the occurrence of streamflow extrapolation beyond the highest or lowest stage measurement in many operational rating curves, even when these were previously flagged as not extrapolated. The first experiment investigated the impact on regional calibration/evaluation of: (i) using two streamflow data transformations (logarithmic and square-root), compared to using non-transformed streamflow data, in an attempt to reduce heteroscedasticity and; (ii) censoring the extrapolated flows, compared to no censoring. Results of calibration/evaluation showed that using a square-root transformed streamflow (thus, compromising weight on high and low streamflow) performed better than using non-transformed and log-transformed streamflow. Also, surprisingly, censoring extrapolated streamflow reduced rather than improved model performance. The second experiment investigated the impact of rating curve uncertainty on catchment calibration/evaluation and parameter estimation. A Monte-Carlo approach and the nonparametric Weighted Nadaraya-Watson (WNW) estimator were used to derive streamflow uncertainty bounds. These were later used in calibration/evaluation using a standard Nash-Sutcliffe Efficiency (NSE) objective function (OBJ) and a modified NSE OBJ that penalised uncertain flows. Using?square-root transformed flows and the modified NSE OBJ considerably improved calibration and predictions, particularly for mid and low flows, and there was an overall reduction in parameter uncertainty. Residual heteroscedasticity for many streamflow gauging stations in Australia.Censoring extrapolated flows reduced model performance in regional calibration.Calibrating parameters against uncensored data fared better in prediction mode.Weighting on uncertain flows using a modified NSE improved calibration/prediction.Weighting on uncertain flows also reduced parameter uncertainty.