Albert I.J.M. Van Dijk | The Australian National University (original) (raw)

Papers by Albert I.J.M. Van Dijk

Observations or products from a range of satellite missions have been used to parameterize or eva... more Observations or products from a range of satellite missions have been used to parameterize or evaluate the Australian Water Resources Assessment (AWRA) system, a high resolution water resources monitoring system that is currently being made operational and will underpin the daily delivery of water balance information across Australia by the Bureau of Meteorology. Satellite data used to develop or parameterize

Journal of Hydrology: Regional Studies, 2017

Droughts are a recurrent and natural part of the Australian hydroclimate, with evidence of drough... more Droughts are a recurrent and natural part of the Australian hydroclimate, with evidence of drought dating back thousands of years. However, our ability to monitor, attribute, forecast and manage drought is exposed as insufficient whenever a drought occurs. This paper summarises what is known about drought hazard, as opposed to the impacts of drought, in Australia and finds that, unlike other hydroclimatic hazards, we currently have very limited ability to tell when a drought will begin or end. Understanding, defining, monitoring, forecasting and managing drought is also complex due to the variety of temporal and spatial scales at which drought occurs and the diverse direct and indirect causes and consequences of drought. We argue that to improve understanding and management of drought, three key Climatic Change research challenges should be targeted: (1) defining and monitoring drought characteristics (i.e. frequency, start, duration, magnitude, and spatial extent) to remove confusion between drought causes, impacts and risks and better distinguish between drought, aridity, and water scarcity due to over-extractions; (2) documenting historical (instrumental and preinstrumental) variation in drought to better understand baseline drought characteristics, enable more rigorous identification and attribution of drought events or trends, inform/evaluate hydrological and climate modelling activities and give insights into possible future drought scenarios; (3) improving the prediction and projection of drought characteristics with seasonal to multidecadal lead times and including more realistic modelling of the multiple factors that cause (or contribute to) drought so that the impacts of natural variability and anthropogenic climate change are accounted for and the reliability of long-term drought projections increases.

Phenology is the study of periodic biological occurrences and can provide important insights into... more Phenology is the study of periodic biological occurrences and can provide important insights into the influence of climatic variability and change on ecosystems. Understanding Australia's vegetation phenology is a challenge due to its diverse range of ecosystems, from savannas and tropical rainforests to temperate eucalypt woodlands, semi-arid scrublands, and alpine grasslands. These ecosystems exhibit marked differences in seasonal patterns of canopy development and plant life-cycle events, much of which deviates from the predictable seasonal phenological pulse of temperate deciduous and boreal biomes. Many Australian ecosystems are subject to irregular events (i.e. drought, flooding , cyclones, and fire) that can alter ecosystem composition, structure, and functioning just as much as seasonal change. We show how satellite remote sensing and ground-based digital repeat photography (i.e. phenocams) can be used to improve understanding of phenology in Australian ecosystems. First, we examine temporal variation in phenology on the continental scale using the enhanced vegetation index (EVI), calculated from MODerate resolution Imaging Spec-troradiometer (MODIS) data. Spatial gradients are revealed, ranging from regions with pronounced seasonality in canopy development (i.e. tropical savannas) to regions where seasonal variation is minimal (i.e. tropical rainforests) or high but irregular (i.e. arid ecosystems). Next, we use time series colour information extracted from phenocam imagery to illustrate a range of phenological signals in four contrasting Australian ecosystems. These include greening and senesc-ing events in tropical savannas and temperate eucalypt un-derstorey, as well as strong seasonal dynamics of individual trees in a seemingly static evergreen rainforest. We also demonstrate how phenology links with ecosystem gross primary productivity (from eddy covariance) and discuss why these processes are linked in some ecosystems but not others. We conclude that phenocams have the potential to greatly improve the current understanding of Australian ecosystems. To facilitate the sharing of this information, we have formed the Australian Phenocam Network (http://phenocam.org.au/).

Recent discharge observations are lacking for most rivers globally. Discharge can be estimated fr... more Recent discharge observations are lacking for most rivers globally. Discharge can be estimated from remotely sensed floodplain and channel inundation area, but there is currently no method that can be automatically extended to many rivers. We examined whether automated monitoring is feasible by statistically relating inundation estimates from moderate to coarse (>0.058) resolution remote sensing to monthly station discharge records. Inundation extents were derived from optical MODIS data and passive microwave sensors, and compared to monthly discharge records from over 8000 gauging stations and satellite altimetry observations for 442 reaches of large rivers. An automated statistical method selected grid cells to construct ''satellite gauging reaches'' (SGRs). MODIS SGRs were generally more accurate than passive microwave SGRs, but there were complementary strengths. The rivers widely varied in size, regime, and morphology. As expected performance was low (R < 0.7) for many (86%), often small or regulated, rivers, but 1263 successful SGRs remained. High monthly discharge variability enhanced performance: a standard deviation of 100-1000 m 3 s 21 yielded ca. 50% chance of R > 0.6. The best results (R > 0.9) were obtained for large unregulated lowland rivers, particularly in tropical and boreal regions. Relatively poor results were obtained in arid regions, where flow pulses are few and recede rapidly, and in temperate regions, where many rivers are modified and contained. Provided discharge variations produce clear changes in inundated area and gauge records are available for part of the satellite record, SGRs can retrieve monthly river discharge values back to around 1998 and up to present.

The advent of mobile laser scanning has enabled time efficient and cost effective collection of f... more The advent of mobile laser scanning has enabled time efficient and cost effective collection of forest structure information. To make use of this technology in calibrating or evaluating models of forest and landscape dynamics, there is a need to systematically and reproducibly automate the processing of LiDAR point clouds into quantities of forest structural components. Here we propose a method to classify vegetation structural components of an open-understorey eucalyptus forest, scanned with a 'Zebedee' mobile laser scanner. It detected 98% of the tree stems (N ¼ 50) and 80% of the elevated understorey components (N ¼ 15). Automatically derived DBH values agreed with manual field measurements with r 2 ¼ 0.72, RMSE ¼ 3.8 cm, (N ¼ 27), and total basal area agreed within 1.5%. Though this methodological study was restricted to one ecosystem, the results are promising for use in applications such as fuel load, habitat structure, and biomass estimations.

Understanding hydrological processes in water-limited systems requires consideration of temporal ... more Understanding hydrological processes in water-limited systems requires consideration of temporal and spatial vegetation water use patterns at the landscape scale. We used data derived from the MODerate Resolution Imaging Spectroradiometer (MODIS) satellite instrument and interpolated climate data covering a ten-year period to contrast the spatio-temporal patterns of actual evapotranspiration (AET) from known phreatophytic and non-phreatophytic vegetation overlying a large superficial aquifer. We assessed shallow to deeper groundwater habitats and compared AET responses to seasonal and inter-annual variation in precipitation. Overall, vegetation in shallow groundwater habitats had higher AET rates during the growth season (spring and summer) than vegetation growing in deeper groundwater habitats, suggesting that the former was not physiologically constrained by water deficit. Vegetation in areas of consistently high (ground-)water availability maintained higher AET, reaching a peak of 95 mm in mid-summer. In contrast, plantation maritime pines had the highest AET rates at deep groundwater habitats. Interannual variability in AET correlated with rainfall and AET rates peaked two months after the majority of effective rainfall had fallen. During low rainfall years, maximum AET peaked one month earlier relative to higher rainfall years. The results of this study suggest that remote sensing of AET can give a conditional indication of where groundwater is important in supporting vegetation and can be a valuable tool in identifying management focus areas where vegetation is variably sensitive to water deficit.

Current global precipitation (P ) datasets do not take full advantage of the complementary nature... more Current global precipitation (P ) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1, a global P dataset for the period 1979-2015 with a 3hourly temporal and 0.25 • spatial resolution, specifically designed for hydrological modeling. The design philosophy of MSWEP was to optimally merge the highest quality P data sources available as a function of timescale and location. The long-term mean of MSWEP was based on the CHPclim dataset but replaced with more accurate regional datasets where available. A correction for gauge under-catch and orographic effects was introduced by inferring catchment-average P from streamflow (Q) observations at 13 762 stations across the globe. The temporal variability of MSWEP was determined by weighted averaging of P anomalies from seven datasets; two based solely on interpolation of gauge observations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH, GSMaP-MVK, and TMPA 3B42RT), and two on atmospheric model reanalysis (ERA-Interim and JRA-55). For each grid cell, the weight assigned to the gauge-based estimates was calculated from the gauge network density, while the weights assigned to the satellite-and reanalysis-based estimates were calculated from their comparative performance at the surrounding gauges. The quality of MSWEP was compared against four state-of-the-art gauge-adjusted P datasets (WFDEI-CRU, GPCP-1DD, TMPA 3B42, and CPC Unified) using independent P data from 125 FLUXNET tower stations around the globe. MSWEP obtained the highest daily correlation coefficient (R) among the five P datasets for 60.0 % of the stations and a median R of 0.67 vs. 0.44-0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments (< 50 000 km 2 ) across the globe. Specifically, we calibrated the simple conceptual hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning) against daily Q observations with P from each of the different datasets. For the 1058 sparsely gauged catchments, representative of 83.9 % of the global land surface (excluding Antarctica), MSWEP obtained a median calibration NSE of 0.52 vs. 0.29-0.39 for the other P datasets. MSWEP is available via http://www.gloh2o.org.

The accuracy of global water balance estimates is limited by the lack of observations at large sc... more 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.

Satellite and on-ground observations over Australia were analysed and compared with output from l... more Satellite and on-ground observations over Australia were analysed and compared with output from land surface models to investigate changes in the interaction between atmosphere, water cycle and vegetation. Observations included top soil water content and vegetation vigour derived from passive microwave satellite observations since 1979, remotely sensed gravity anomalies (indicative of changes in soil water and groundwater storage) since 2002,

Abstract This one-day workshop will be held prior to the ISRSE 34 symposium. It will bring togeth... more Abstract This one-day workshop will be held prior to the ISRSE 34 symposium. It will bring together the Earth Observation community, modeling, and other water management communities to look at issues of drought and management approaches in various regions (Asia/Australia, America, Europe and Africa) and the needs of the community for GEOSS-derived information. The workshop will consist of a series of presentations, breakout sessions and discussions. A report will be written with recommendations for GEOSS.

Bulletin of the American Meteorological Society, 2013

We compared estimates of actual evapotranspiration (ET) produced with six different vegetation me... more We compared estimates of actual evapotranspiration (ET) produced with six different vegetation measures derived from the MODerate resolution Imaging Spectroradiometer (MODIS) and three contrasting estimation approaches using measurements from eddy covariance flux towers at 16 FLUXNET sites located over six different land cover types. The aim was to assess optimal approaches in using optical remote sensing to estimate ET. The first two approaches directly regressed various MODIS vegetation indices (VIs) and products such as leaf area index (LAI) and fraction of photosynthetically active radiation (fPAR) with ET and evaporative fraction (EF). In the third approach, the Penman-Monteith (PM) equation was inverted to obtain surface conductance (G s ), for dry plant canopies. The G s values were then regressed against the MODIS data products and used to parameterize the PM equation for retrievals of ET. Jack-Knife cross-validation was used to evaluate the various regression models against observed ET. The PM-G s approach provided the lowest root mean square error (RMSE), and highest determination coefficients (R 2 ) across all sites, with an average RMSE = 38 W m −2 and R 2 = 0.72. Direct regressions of observed ET against the VIs resulted in an average RMSE = 60 W m −2 and R 2 = 0.22, while the EF regressions an average RMSE = 42 W m −2 and R 2 = 0.64. The MODIS LAI and fPAR product produced the poorest estimates of ET (RMSE > 44 W m −2 and R 2 b 0.6); while the VIs each performed best for some of the land cover types. The enhanced vegetation index (EVI) produced the best ET estimates for evergreen needleleaf forest (RMSE = 28.4 W m −2 , R 2 = 0.66). The normalized difference vegetation index (NDVI) best estimated ET in grassland (RMSE = 23.8 W m −2 and R 2 = 0.68), cropland (RMSE = 29.2 W m −2 and R 2 = 0.86) and woody savannas (RMSE = 25.4 W m −2 and R 2 = 0.82), while the VI-based crop coefficient (K c ) yielded the best estimates for evergreen and deciduous broadleaf forests (RMSE = 27 W m −2 and R 2 = 0.7 in both cases). Using the ensemble-average of ET as estimated using NDVI, EVI and K c we computed global grids of dry canopy conductance (G c ) from which annual statistics were extracted to characterise different functional types. The resulting G c values can be used to parameterize land surface models.

Remote Sensing of Environment, 2015

Climate and physiological controls of vegetation gross primary production (GPP) vary in space and... more Climate and physiological controls of vegetation gross primary production (GPP) vary in space and time. In many ecosystems, GPP is primary limited by absorbed photosynthetically-active radiation; in others by canopy conductance. These controls further vary in importance over daily to seasonal time scales. We propose a simple but effective conceptual model that estimates GPP as the lesser of a conductance-limited (F c ) and radiationlimited (Fr) assimilation rate. F c is estimated from canopy conductance while Fr is estimated using a light use efficiency model. Both can be related to vegetation properties observed by optical remote sensing. The model has only two fitting parameters: maximum light use efficiency, and the minimum achieved ratio of internal to external CO 2 concentration. The two parameters were estimated using data from 16 eddy covariance flux towers for six major biomes including both energy-and water-limited ecosystems. Evaluation of model estimates with flux tower-derived GPP compared favourably to that of more complex models, for fluxes averaged; per day (r 2 = 0.72, root mean square error, RMSE = 2.48 μmol C m 2 s −1 , relative percentage error, RPE = −11%), over 8-day periods (r 2 = 0.78 RMSE = 2.09 μmol C m 2 s −1 ,RPE = −10%), over months (r 2 = 0.79, RMSE = 1.93 μmol C m 2 s −1 , RPE = −9%) and over years (r 2 = 0.54, RMSE = 1.62 μmol C m 2 s −1 , RPE = −9%). Using the model we estimated global GPP of 107 Pg C y −1 for 2000-2011. This value is within the range reported by other GPP models and the spatial and inter-annual patterns compared favourably. The main advantages of the proposed model are its simplicity, avoiding the use of uncertain biome-or land-cover class mapping, and inclusion of explicit coupling between GPP and plant transpiration.

Journal of Hydrology: Regional Studies, 2017

Bulletin of the American Meteorological Society, 2013

Remote Sensing of Environment, 2015