Tim R. McVicar

Wind speed climatology and trends for Australia, 1975–2006: Capturing the stilling phenomenon and comparison with near‐surface reanalysis output

[1] Near-surface wind speeds (u) measured by terrestrial anemometers show declines (a ‘stilling’) at a range of midlatitude sites, but two gridded u datasets (a NCEP/NCAR reanalysis output and a surface-pressure-based u model) have not reproduced the stilling observed at Australian stations. We developed Australia-wide 0.01 resolution daily u grids by interpolating measurements from an expanded anemometer network for 1975–2006. These new grids represented the magnitude and spatialvariability of observed u trends, whereas grids from reanalysis systems (NCEP/NCAR, NCEP/DOE and ERA40) essentially did not, even when minimising the sea-breeze impact. For these new grids, the Australianaveraged u trend for 1975–2006 was 0.009 m s 1 a 1 (agreeing with earlier site-based studies) with stilling over 88% of the land-surface. This new dataset can be used in numerous environmental applications, including benchmarking general circulation models to improve the representation of key parameters that govern u estimation. The methodology implemented here can be applied globally. Citation: McVicar, T. R., T. G. Van Niel, L. T. Li, M. L. Roderick, D. P. Rayner, L. Ricciardulli, and R. J. Donohue (2008), Wind speed climatology and trends for Australia, 1975 – 2006: Capturing the stilling phenomenon and comparison with near-surface reanalysis output, Geophys. Res. Lett., 35, L20403,

The current and potential operational uses of remote sensing to aid decisions on drought exceptional circumstances in Australia: a review

Abstract This paper reviews how remote sensing is being used, and can be used, to assist in providing support for the decision-making process for the declaration of areas experiencing drought exceptional circumstances in Australia. To assess how remotely sensed data can be used, a review of current international uses of remotely sensed data was made and several topics requiring further research by remote sensing specialists identified. Consequently, the review focuses on current techniques used by Australian agencies, and also assesses current international methods, as well as proposing some possible new research directions that may use remote sensing for drought assessment and management. The primary scientific information that remote sensing can provide is the estimation of vegetation cover and condition, soil moisture, and the spatial limits to drought exceptional circumstances.

Decomposition of vegetation cover into woody and herbaceous components using AVHRR NDVI time series

A method is developed to separate Normalised Difference Vegetation Index (NDVI) time series data into contributions from woody (perennial) and herbaceous (annual) vegetation, and thereby to infer their separate leaf area indices and cover fractions. The method is formally consistent with fundamental linearity requirements for such a decomposition, and is capable of rejecting contaminated NDVI data. In this study, estimates of annual averaged woody cover and monthly averaged herbaceous cover over Australia are determined using Pathfinder AVHRR Land series (PAL) Global Area Coverage (GAC) Advanced Very High Resolution Radiometer (AVHRR) NDVI data from 1981 to 1994, together with ground-based measurements of leaf area index (LAI) and foliage projective cover (FPC).

Multi-decadal trends in global terrestrial evapotranspiration and its components

Evapotranspiration (ET) is the process by which liquid water becomes water vapor and energetically this accounts for much of incoming solar radiation. If this ET did not occur temperatures would be higher, so understanding ET trends is crucial to predict future temperatures. Recent studies have reported prolonged declines in ET in recent decades, although these declines may relate to climate variability. Here, we used a well-validated diagnostic model to estimate daily ET during 1981–2012, and its three components: transpiration from vegetation (Et), direct evaporation from the soil (Es) and vaporization of intercepted rainfall from vegetation (Ei). During this period, ET over land has increased significantly (p < 0.01), caused by increases in Et and Ei, which are partially counteracted by Es decreasing. These contrasting trends are primarily driven by increases in vegetation leaf area index, dominated by greening. The overall increase in Et over land is about twofold of the decrease in Es. These opposing trends are not simulated by most Coupled Model Intercomparison Project phase 5 (CMIP5) models, and highlight the importance of realistically representing vegetation changes in earth system models for predicting future changes in the energy and water cycle.

Estimating one-time-of-day meteorological data from standard daily data as inputs to thermal remote sensing based energy balance models

Air temperature, atmospheric water vapor (e.g. relative humidity or vapor pressure), solar radiation and wind speed are required inputs when remotely sensed thermal observations are used with resistance energy balance models (REBM) to estimate specific time-of-day components of the energy balance at the time of remotely sensed data acquisition. Times of interest include the afternoon, when Advanced Very High Resolution Radiometer (AVHRR) data are acquired, and mid-morning, when LANDSAT Thematic Mapper (TM) data are acquired. Using data from sites in Australia and China, it has been shown how: (1) air temperature (Ta) at these times can be estimated effectively using the model of Parton and Logan [Parton, W.J., Logan, J.A., 1981. Agric. For. Meteorol. 23, 205–216]. This model only uses daily maximum (Tx) and minimum (Tn) air temperatures as inputs; (2) to calculate relative humidity (h), dew point has often been equated to Tn. An improvement due to Kimball et al.[ Kimball, J.S., Running, S.W., Nemani, R.R., 1997. Agric. For. Meteorol. 85, 87–98] reduces the bias in the consequent estimates of h, but the root mean squared deviation (RMSD) about the mean does not reduce. Here, the estimate is improved by converting h to vapor pressure (ea) and linearly interpolating the ea between the times of sunrise for consecutive days; (3) shortwave solar radiation (Rs) can be effectively estimated by modifying the model of Hungerford et al. [Hungerford, R.D., Nemani, R.R., Running, S.W., Coughlin, J.C., 1989. INT-414, USDA Forest Service] and locally calibrating the Bristow and Campbell [Bristow, K.L., Campbell, G.S., 1984. Agric. For. Meteorol. 31, 159–166] model which estimates the daily total atmospheric transmittance as a function of a rain modulated difference between Tx and Tn. On clear, rain free days, when good quality remotely sensed data are most likely be acquired, the estimates of Rs using both models were within 13% of observations; (4) for wind speed it was found that the sensitivity to using default assumptions such as locally set average was greater for potential evapotranspiration (λEp) than for actual evapotranspiration (λEa), which affects the resulting moisture availability (the ratio of λEa to λEp). The variations in λEa and net radiation (Rn) estimates, introduced by applying these interpolation methods to provide specific time-of-day meteorological inputs to the REBM, were examined for a site in China. When estimating λEa at AVHRR times on days when no rain fell, the RMSD/Mean of measurement was 27% when all meteorological values were estimated and remained 27% when all values were known. Estimating Rn for the same times and conditions the RMSD/Mean of measurement was 15% when all meteorological values were estimated. This value reduced to 9% when all values were known. The results of this investigation provide support for the approach of estimating meteorological variables from daily air temperature extremes and daily rainfall at the times when clear remotely sensed data are acquired.

Using covariates to spatially interpolate moisture availability in the Murray–Darling Basin: A novel use of remotely sensed data

Abstract Moisture availability is estimated in the 1.1 million km 2 Murray–Darling Basin (MDB) in southeast Australia. Remotely sensed data from the Advanced Very High Resolution Radiometer (AVHRR) are combined with meteorological data to estimate the Normalised Difference Temperature Index (NDTI). The NDTI provides a measure of the moisture availability, the ratio of actual to potential evapotranspiration. Surface temperature minus air temperature, percent vegetation cover and net radiation explained 85% of variation in the modelled NDTI. Using these three covariates across the network of meteorological stations allows NDTI images, which maps changes in moisture availability across the MDB, to be calculated. This method uses a calculate then interpolate (CI) approach that uses the per-pixel variation present in the AVHRR data as the backbone for the spatial interpolation. Using the spatially dense AVHRR-based covariates in a CI approach avoids errors that occur between measurement points when interpolating variables for regional hydrologic modelling, most significantly the spatial pattern of rainfall. The NDTI provides a link into regional water balance modelling which does not require daily rainfall to be spatially interpolated. Assessing spatial and temporal interactions between the NDTI and the Normalised Difference Vegetation Index (NDVI) provides useful information about regional hydroecological processes, including agricultural management, within the context of Australias highly variable climate and sparse network of meteorological stations.

Observational evidence from two mountainous regions that near- surface wind speeds are declining more rapidly at higher elevations than lower elevations: 1960-2006

Coupling recent observed declines of terrestrial mid-latitude near-surface wind speed (u) with knowledge that high-elevation sites rapidly experience climate change led to an assessment of the regional near-surface elevation dependence of u (u(Z)) at two mountainous regions (central China and Switzerland). The monthly u(Z) were calculated from 1960-2006. In both regions u(Z) were higher in winter (similar to 2.25 m s(-1) km(-1)) compared to summer (similar to 1.25 m s(-1) km(-1)). For the first time u(Z) trends were calculated, the results were strongly seasonal, ranging from similar to-0.025 m s(-1) km(-1) a(-1) in winter to similar to-0.005 m s(-1) km(-1) a(-1) in summer. For both regions u(Z) trend results showed that u has declined more rapidly at higher than lower elevations, even though different u dynamics were observed. The u(Z) trends have important implications for climatic changes of coupled land-surface/boundary-layer processes (such as evapotranspiration) at high-elevation regions where much of the globes fresh water is derived. Citation: McVicar, T. R., T. G. Van Niel, M. L. Roderick, L. T. Li, X. G. Mo, N. E. Zimmermann, and D. R. Schmatz (2010), Observational evidence from two mountainous regions that near-surface wind speeds are declining more rapidly at higher elevations than lower elevations: 1960-2006, Geophys. Res. Lett., 37, L06402, doi:10.1029/2009GL042255.

Homogenization and Assessment of Observed Near-Surface Wind Speed Trends over Spain and Portugal, 1961–2011*

Near-surfacewindspeedtrendsrecordedat67land-basedstationsacrossSpainandPortugalfor1961–2011, alsofocusingonthe1979–2008subperiod,wereanalyzed.Windspeedseriesweresubjectedtoqualitycontrol, reconstruction, and homogenization using a novel procedure that incorporated the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5)-simulated seriesasreference.Theresultantseriesshowaslightdownwardtrendforboth1961–2011(20.016ms 21 decade 21 ) and 1979–2008 (20.010ms 21 decade 21 ). However, differences between seasons with declining values in winter and spring, and increasing trends in summer and autumn, were observed. Even though wind stilling affected 77.8% of the stations in winter and 66.7% in spring, only roughly 40% of the declining trends were statistically significant at the p , 0.10 level. On the contrary, increasing trends appeared in 51.9% of the stationsinsummerand57.4%inautumn,withalsoaround40%ofthepositivetrendsstatisticallysignificantat the p , 0.10 level. In this article, the authors also investigated (i) the possible impact of three atmospheric indices on the observed trends and (ii) the role played by the urbanization growth in the observed decline. An accurate homogenization and assessment of the long-term trends of wind speed is crucial for many fields such as wind energy (e.g., power generation) and agriculture–hydrology (e.g., evaporative demand).

Current and potential uses of optical remote sensing in rice-based irrigation systems: a review

For high water usage cropping systems such as irrigated rice, the positive outcomes of producing a staple food source and sustaining the economy often come at the cost of high resource use and environmental degradation. Advances in geospatial technology will play an increasingly important role in raising productivity and resource use efficiency and reducing environmental degradation, both worldwide and within Australia. This paper reviews the current use of one of these technologies, remote sensing, with the rice-growing region in Australia as a case study. Specifically, we review applications of remote sensing in crop identification, area measurement, regional yield forecasting, and on-farm productivity monitoring and management. Within this context, consideration is given to classification algorithms and accuracy assessment, hyperspectral remote sensing, positional and areal accuracy, linear mixture modelling, methane (CH4) emissions, yield forecasting techniques, and precision agriculture. We also discuss the potential for using remote sensing to assess crop water use, which has received little attention in rice-based irrigation systems, even though it is becoming increasingly important in land and water management planning for irrigation areas. Accordingly, special attention is given to the role of remote sensing with respect to the surface energy balance, the relationship between surface temperature and remotely sensed vegetation indices, and water use efficiency. A general discussion of other geospatial issues, namely geographic information systems and spatial interpolation, is provided because earth-science analysis using remote sensing is often intrinsically integrated with other spatially based technologies and aspects of geographical science.

Blending landsat and MODIS data to generate multispectral indices: A comparison of "index-then-blend" and "Blend-Then-Index" approaches

The objective of this paper was to evaluate the accuracy of two advanced blending algorithms, Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to downscale Moderate Resolution Imaging Spectroradiometer (MODIS) indices to the spatial resolution of Landsat. We tested two approaches: (i) “Index-then-Blend” (IB); and (ii) “Blend-then-Index” (BI) when simulating nine indices, which are widely used for vegetation studies, environmental moisture assessment and standing water identification. Landsat-like indices, generated using both IB and BI, were simulated on 45 dates in total from three sites. The outputs were then compared with indices calculated from observed Landsat data and pixel-to-pixel accuracy of each simulation was assessed by calculating the: (i) bias; (ii) R2; and (iii) Root Mean Square Deviation (RMSD). The IB approach produced higher accuracies than the BI approach for both blending algorithms for all nine indices at all three sites. We also found that the relative performance of the STARFM and ESTARFM algorithms depended on the spatial and temporal variances of the Landsat-MODIS input indices. Our study suggests that the IB approach should be implemented for blending of environmental indices, as it was: (i) less computationally expensive due to blending single indices rather than multiple bands; (ii) more accurate due to less error propagation; and (iii) less sensitive to the choice of algorithm.