Thomas G. Van Niel

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,

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.

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.

Remote Sensing-Based Piosphere Analysis

The term piosphere was orginially defined as an indicator of the localized impact of grazing on vegetation and soils. It is a radiating zone of attenuating animal impact away from a concentrator, e.g, water, mineral licks, bedding grounds, etc. Over time there may be increased soil erosion, reductions in vegetation cover and changes in soil chemistry within piospheres. This paper expands this definition to include any concentrated animal or anthropogenic impact that radiates from an area of concentration. Satellite remote sensing instruments are capable of detecting both broad-scale climatic effects and small-scale localized impacts. A remote sensing-based tool for conducting piospheric analysis was developed to help evaluate areas of landscape impact caused by livestock or other concentrators. The program characterizes a piospheric response using three GIS layers: a boundary (e.g., a paddock); a concentrator (e.g., a water source); and a response index (e.g., a remotely sensed vegetation index). Piospheric analysis was demonstrated within a grazing paddock that had obvious piospheres. The objectives of the analysis were to: (1) use a time series of dry-season vegetation index imagery from 1972 to 1997 to characterize the historical vegetation response and relate it to climate and grazing at the paddock spatial scale; (2) characterize vegetation response at water points and streams; (3) determine if piospheres can be detected in sagebrush steppe; and (4) demonstrate the utility of the piospheric analysis program. Evidence of persistent degradation at water sources was detected but not at streams. This type of analysis could be quite useful to land managers for separating the effects of climate from persistent degradation induced by localized disturbances.

Experimental evaluation of positional accuracy estimates from a linear network using point- and line-based testing methods

Positional accuracy estimates of linear features based on their well-defined points can be significantly different than the accuracy estimates determined from their adjoining lines. A case study was conducted to determine both the cause of this difference as well as the relative effect of the number of points used in calculating the point-based accuracy estimate. Results showed that: (1) the difference between accuracy estimates was primarily due to dissimilar criteria for measuring closeness of primitive features; and (2) the current US requirement of using 20 well-defined points provided a reasonable estimation of accuracy for the case study. The differences in accuracy estimates described in this study should impact both the definition of geospatial accuracy standards, and the interpretation of geographical analyses with respect to error propagation.

Estimating the Impact of Projected Climate Change on Runoff across the Tropical Savannas and Semiarid Rangelands of Northern Australia

The majority of the world’s population growth to 2050 is projected to occur in the tropics. Hence, there is a serious need for robust methods for undertaking water resource assessments to underpin the sustainable management of water in tropical regions. This paper describes the largest and most comprehensive assessment of the future impacts of runoff undertaken in a tropical region using conceptual rainfall‐runoff models (RRMs). Five conceptual RRMs were calibrated using data from 115 streamflow gauging stations, and model parameters were regionalized using a combination of spatial proximity and catchment similarity. Future rainfall and evapotranspiration projections (denoted here as GCMES) were transformed to catchment-scale variables by empirically scaling (ES) the historical climate series, informed by 15 global climate models (GCMs), to reflect a 18C increase in global average surface air temperature. Using the best-performing RRM ensemble, approximately half the GCMES used resulted in a spatially averaged increase in mean annual runoff (by up to 29%) and half resulted in a decrease (by up to 26%). However, ;70% of the GCMES resulted in a difference of within 65% of the historical rainfall (1930‐2007). The range in modeled impact on runoff, as estimated by five RRMs (for individual GCMES), was compared to the range in modeled runoff using 15 GCMES (for individual RRMs). For mid- to high runoff metrics, better predictions will come from improved GCMES projections. A new finding of this study is that in the wet‐dry tropics, for extremely large runoff events and low flows, improvements are needed in both GCMES and rainfall‐runoff modeling.

Detection of Harbingers of Catastrophic Regime Shifts in Drylands

Harbingers are early warnings of imminent ecosystem collapse and thus are aids to preventing land degradation. Dynamic systems are hypothesized to exhibit dampening or inflation of critical attributes at or near a threshold, which is a decreasing or increasing spatial or temporal trend, respectively. This behavior is diagnostic of a state change and can be operationalized as an early detection system. Consequently, we used a time series from 1972 to 1997 of seasonal soil-adjusted vegetation index (SAVI) data, a proxy for canopy cover that was derived from Landsat imagery of the Marine Corps Air Ground Combat Center. We used dynamical, trend, and autocorrelation function (ACF) time series analysis to find that the time series had an increasing linear trend that correlated with wet periods of the Pacific Decadal Oscillation (PDO) and the El Nino-Southern Oscillation (ENSO). High and low SAVI values were in wet and dry basins of attraction with a rapid shift from dry to wet period from 1981 to 1982. Mean SAVI dampening appears to have occurred 2–3 years prior to the shift. Consequently, this study suggests that this dampening trend of the mean SAVI can be used as a harbinger of land degradation.