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Ember transport and the subsequent development of spot fires is a significant mode of wildfire spread, particularly in extreme conditions. An important simplifying assumption made in early research into ember transport is the terminal-velocity assumption, in which embers are assumed to always fly at their terminal velocity relative to the wind field. With increases in computational power, it is now possible to directly simulate the atmospheric conditions resulting from wildfires and such simulations can resolve the larger of the turbulent processes involved. Because of the time-scales at which these processes occur, the terminal-velocity assumption may not be justified when modelling ember transport using these simulations. In this study we use a large eddy simulation of a turbulent plume to examine the validity of the terminal-velocity assumption when modelling the long-range transport of non-combusting embers. The results indicate that the use of the terminal-velocity assumption significantly overestimates the density of ember landings at long range, particularly for embers with higher terminal fall speeds.

Accurate quantification of land degradation is a global need, particularly in the world’s dryland areas. However, there is a well-documented lack of field data and long-term observational studies for most of these regions. Remotely sensed data offers the only long-term vegetation record that can be used for land degradation assessment at a national, continental or global scale. Both the rainfall and vegetation datasets used for land degradation assessment contain errors and uncertainties, but little work has been done to understand how this may impact results. This study uses the recently developed Time Series Segmented RESidual TREND (TSS-RESTREND) method applied to six rainfall and two vegetation datasets to assess the impact of dataset selection on the estimates of dryland degradation over Australia. Large differences in the data and methods used to produce the precipitation datasets did not significantly impact results with the estimate of average change varying by 95% of regions. On the other hand, the vegetation dataset selection had a much greater impact. Calibration errors in the Global Inventory Monitoring and Modeling System Version 3 NDVI (GIMMSv3.0g) dataset caused significant errors in the trends over some of Australia’s dryland regions. Though identified over Australia, the problematic calibration in the GIMMSv3.0g dataset may have effected dryland NDVI values globally. These errors have been addressed in the updated GIMMSv3.1g which is strongly recommended for use in future studies. Our analysis suggests that using an ensemble composed of multiple runs performed using different datasets allows for the identification of errors that cannot be detected using only a single run or with the data quality flags of the input datasets. A multi-run ensemble made using different input datasets provides more comprehensive quantification of uncertainty and errors in space and time.

This study evaluates the performance of different data sources in representing the spatio-temporal characteristics of aerosol species over Australia, which is one of the primary sources of dust in the Southern Hemisphere and an important contributor to global biomass burning emissions. First, NASA/MERRA2 and ECMWF/CAMS Total AOD550nm are evaluated against 16 AERONET stations during 2003–2018, with CAMS consistently underestimating (−15%) and MERRA2 overestimating (19%) the total AOD. Despite the differences in magnitudes, both reanalyses capture the measured Australian aerosols’ seasonal and interannual variability, mainly modulated by seasonal biomass burning and episodic dust storms. CAMS performs remarkably well in low aerosol conditions, while MERRA2 captures extreme aerosol events better. The intercomparison of the different aerosol species from the two reanalyses confirms that CAMS shows lower mean aerosol species concentrations than MERRA2. Results show the greatest differences (more than 50%) in sea salt and sulfates, while organic matter AOD is similarly represented between both reanalyses, with differences of roughly 3%. The spatial distribution and annual cycle of the aerosol types are also compared. In both reanalyses, carbonaceous AOD (black carbon and organic matter) are predominant in the northern part of the country during austral spring, and are highly correlated with MODIS active fires. Dust aerosols prevail in central Australia in summer, and sulfates in the main urban areas throughout the year. However, unlike the other species, sea salt exhibits opposite annual cycles in the two reanalyses. Finally, both MERRA2 and CAMS are used to evaluate the historical simulations of 16 GCM from CMIP6. The models capture the Australian aerosols’ annual variation, although they tend to overestimate dust and underrepresent biomass burning AOD. IPSL-CM6A-LR and EC-Earth3-AerChem perform particularly well simulating aerosols over Australia. The evaluation of Australian aerosols performed in this study could contribute to reanalysis and climate model improvements, as well as improving long-term solar energy resources assessment.

Summary Addressing systematic biases in regional climate model simulations of extreme rainfall is a necessary first step before assessing changes in future rainfall extremes. Commonly used bias correction methods are designed to match statistics of the overall simulated rainfall with observations. This assumes that change in the mix of different types of extreme rainfall events (i.e. convective and non-convective) in a warmer climate is of little relevance in the estimation of overall change, an assumption that is not supported by empirical or physical evidence. This study proposes an alternative approach to account for the potential change of alternate rainfall types, characterized here by synoptic weather patterns (SPs) using self-organizing maps classification. The objective of this study is to evaluate the added influence of SPs on the bias correction, which is achieved by comparing the corrected distribution of future extreme rainfall with that using conventional quantile mapping. A comprehensive synthetic experiment is first defined to investigate the conditions under which the additional information of SPs makes a significant difference to the bias correction. Using over 600,000 synthetic cases, statistically significant differences are found to be present in 46% cases. This is followed by a case study over the Sydney region using a high-resolution run of the Weather Research and Forecasting (WRF) regional climate model, which indicates a small change in the proportions of the SPs and a statistically significant change in the extreme rainfall over the region, although the differences between the changes obtained from the two bias correction methods are not statistically significant.

Warmer temperatures are expected to increase extreme short-duration rainfall due to the increased moisture-holding capacity of the atmosphere. While attention has been paid to the impacts of climate change on future design rainfalls at daily or longer time scales, the potential changes in short duration design rainfalls have been often overlooked due to the limited availability of sub-daily projections and observations. This study uses a high-resolution regional climate model (RCM) to predict the changes in sub-daily design rainfalls for the Greater Sydney region in Australia. Sixteen methods for predicting changes to sub-daily future extremes are assessed based on different options for bias correction, disaggregation and frequency analysis. A Monte Carlo cross-validation procedure is employed to evaluate the skill of each method in estimating the design rainfall for the current climate. It is found that bias correction significantly improves the accuracy of the design rainfall estimated for the current climate. For 1 h events, bias correcting the hourly annual maximum rainfall simulated by the RCM produces design rainfall closest to observations, whereas for multi-hour events, disaggregating the daily rainfall total is recommended. This suggests that the RCM fails to simulate the observed multi-duration rainfall persistence, which is a common issue for most climate models. Despite the significant differences in the estimated design rainfalls between different methods, all methods lead to an increase in design rainfalls across the majority of the study region.

Dryland degradation is an issue of international significance as dryland regions play a substantial role in global food production. Remotely sensed data provide the only long term, large scale record of changes within dryland ecosystems. The Residual Trend, or RESTREND, method is applied to satellite observations to detect dryland degradation. Whilst effective in most cases, it has been shown that the RESTREND method can fail to identify degraded pixels if the relationship between vegetation and precipitation has broken-down as a result of severe or rapid degradation. This paper presents an extended version of the RESTREND methodology that incorporates the Breaks For Additive Seasonal and Trend method to identify step changes in the time series that are related to significant structural changes in the ecosystem, e.g. land use changes. When applied to Australia, this new methodology, termed Time Series Segmentation and Residual Trend analysis (TSS-RESTREND), was able to detect degradation in 5.25% of pixels compared to only 2.0% for RESTREND alone. This modified methodology was then assessed in two regions with known histories of degradation where it was found to accurately capture both the timing and directionality of ecosystem change.

This study is conducted over the data-poor Limpopo basin centered over southern Africa using reanalysis downscaled to useful resolution. Reanalysis products are of limited value in hydrological applications due to the coarse spatial scales they are available at. Dynamical downscaling of these products over a domain of interest offers a means to convert them to finer spatial scales in a dynamically consistent manner. Additionally, this downscaling also offers a way to resolve dominantatmospheric processes, leading to improved accuracy in the atmospheric variables derived. This study thus evaluates high-resolution downscaling of an objectively chosen reanalysis (ERA-I) over the Limpopo basin using Weather Research and Forecasting (WRF) as a regional climate model. The model generally under-estimates temperature and over-estimates precipitation over the basin, although reasonably consistent with observations. The model does well in simulating observed sustained hydrological extremes as assessed using the Standardized Precipitation Index (SPI) although it consistently under-estimates the severity ofmoisture deficit for the wettest part of the year during the dry years. The basin's aridity index (I) is above the severe drought threshold during summer and is more severe in autumn. This practically restricts rain-fed agriculture to around 3 months in a year over the basin. This study presents possible beneficial use of the downscaled simulations foroptimal hydrologic design and water resources planning in data scarce parts of the world.

Ground observation absence in many parts of the world highlights the importance of merged satellite precipitation products. In this study, we aim to evaluate the effect of different sources of data in the uncertainties of a merged satellite product, by comparing the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final product (V06B) with a ground-based radar product, Multi-Radar Multi-Sensor (MRMS), using both pixel-based and object-based approaches. This study is focused on the eastern United States (land-only) during the hurricane days that occurred in 2016–2018. The results showed that IMERG had better agreement in terms of the average precipitation intensity and area with a bias reduction of 75% and 65%, respectively, when the passive microwave (PMW) sensor overpass is matched instantaneously with MRMS compared to temporally averaged MRMS data (MRMS-Averaged). PMW observations tend to show storms with smaller areas in the IMERG Final product in comparison with MRMS, possibly due to the effect of light precipitation not detected properly by PMW sensors. However, by removing the light precipitation (less than 1 mm/h) in the object-based approach, hurricane objects in the IMERG Final product tend to be larger during the PMW observations, which might be related to different viewing angles of sensors contributing to MRMS and IMERG products. Precipitation estimates have smaller areas with higher average intensity during the PMW observations in the IMERG Final product compared to data estimated by Morphed or IR (morph/IR) observations, which is probably related to the effect of morphing technique, leading to homogenization of the varying rainstorm characteristics. In addition, with the longer absence of PMW observations, the quality of morph/IR estimates in IMERG Final product deteriorates with a decreasing correlation coefficient, a growth in precipitation area and a downward trend in average precipitation intensity. Finally, the inter-comparison of PMW sensors showed the priority of imagers over sounders with GMI as the best among imagers and MHS as the best among sounders in terms of correlation and average intensity compared to MRMS; however, SSMIS was the best in capturing the precipitation area.

The growing demand for nuts and the required diversification of supply are urging to identify additional zones for hazelnut tree cultivation around the world. Given the long-term nature of the investment needed to establish new orchards, an ex-ante evaluation of the future production trends due to global changes is critical to support stakeholders and decision makers. With this motivation, we investigate the physiological response and the attainable yield of hazelnut in Australia, using a process-based model. Simulations examined phenological development, hazelnut growth processes and yield in recent past and near-future climate conditions, using an ensemble of regional climate models bounded by four global climate models (GCMs). While the entire domain of analysis will warm up in the next twenty years, the precipitation patterns are rather erratic across GCMs. The effect of climate change on hazelnut farming is variable across agro-climatic zones, except in the southeasternmost part of Australia, where all simulations agree in predicting a yield increase ranging from 18 to 52%. Elsewhere the hazelnut production potential varies, with some GCMs projecting yield increase and others estimating reductions or no significant changes. Yield increase is associated mainly with higher gross assimilation rates, whereas decrease is related to a delay in chilling requirements fulfilment, caused by the projected increase in minimum temperatures and to sub-optimal conditions for the photosynthetic process. Despite the need of additional field trials to further validate the model, these results may be used by private and public bodies to support new investment plans, and promote legislative measures aimed at encouraging hazelnut cultivation in Australia.

Summary Areal reduction factors (ARFs) are commonly used to transform point design rainfall to represent the average design rainfall for a catchment area. While there has been considerable attention paid in the research and engineering communities to the likely changes in rainfall intensity in future climates, the issue of changes to design areal rainfall has been largely ignored. This paper investigates the impact of climate change on ARFs. A new methodology for estimating changes in ARFs is presented. This method is used to assess changes in ARFs in the greater Sydney region using a high-resolution regional climate model (RCM). ARFs under present (1990–2009) and future (2040–2059) climate conditions were derived and compared for annual exceedance probabilities (AEPs) from 50% to 5% for durations ranging from 1 h to 120 h. The analysis shows two main trends in the future changes in ARFs. For the shortest duration events (1-h) the ARFs are found to increase which implies that these events will tend to have a larger spatial structure in the future than the current climate. In contrast, storms with durations between 6 and 72 h are likely to have decreased ARFs in the future, suggesting a more restricted spatial coverage of storms under a warming climate. The extent of the decrease varies with event frequency and catchment size. The largest decreases are found for large catchments and rare events. Although the results here are based on a single RCM and need to be confirmed in future work with multiple models, the framework that is proposed will be useful for future studies considering changes in the areal extent of rainfall extremes.

Summary The impact of model structure and parameterization on the estimation of evaporation is investigated across a range of Penman–Monteith type models. To examine the role of model structure on flux retrievals, three different retrieval schemes are compared. The schemes include a traditional single-source Penman–Monteith model (Monteith, 1965), a two-layer model based on Shuttleworth and Wallace (1985) and a three-source model based on Mu et al. (2011). To assess the impact of parameterization choice on model performance, a number of commonly used formulations for aerodynamic and surface resistances were substituted into the different formulations. Model response to these changes was evaluated against data from twenty globally distributed FLUXNET towers, representing a cross-section of biomes that include grassland, cropland, shrubland, evergreen needleleaf forest and deciduous broadleaf forest. Scenarios based on 14 different combinations of model structure and parameterization were ranked based on their mean value of Nash–Sutcliffe Efficiency. Results illustrated considerable variability in model performance both within and between biome types. Indeed, no single model consistently outperformed any other when considered across all biomes. For instance, in grassland and shrubland sites, the single-source Penman–Monteith model performed the best. In croplands it was the three-source Mu model, while for evergreen needleleaf and deciduous broadleaf forests, the Shuttleworth–Wallace model rated highest. Interestingly, these top ranked scenarios all shared the simple lookup-table based surface resistance parameterization of Mu et al. (2011), while a more complex Jarvis multiplicative method for surface resistance produced lower ranked simulations. The highly ranked scenarios mostly employed a version of the Thom (1975) formulation for aerodynamic resistance that incorporated dynamic values of roughness parameters. This was true for all cases except over deciduous broadleaf sites, where the simpler aerodynamic resistance approach of Mu et al. (2011) showed improved performance. Overall, the results illustrate the sensitivity of Penman–Monteith type models to model structure, parameterization choice and biome type. A particular challenge in flux estimation relates to developing robust and broadly applicable model formulations. With many choices available for use, providing guidance on the most appropriate scheme to employ is required to advance approaches for routine global scale flux estimates, undertake hydrometeorological assessments or develop hydrological forecasting tools, among many other applications. In such cases, a multi-model ensemble or biome-specific tiled evaporation product may be an appropriate solution, given the inherent variability in model and parameterization choice that is observed within single product estimates.

Summary The stable isotopes of water are useful tracers of water sources and hydrological processes. Stable water isotope-enabled land surface modeling is a relatively new approach for characterizing the hydrological cycle, providing spatial and temporal variability for a number of hydrological processes. At the land surface, the integration of stable water isotopes with other meteorological measurements can assist in constraining surface heat flux estimates and discriminate between evaporation (E) and transpiration (T). However, research in this area has traditionally been limited by a lack of continuous in-situ isotopic observations. Here, the National Centre for Atmospheric Research stable isotope-enabled Land Surface Model (ISOLSM) is used to simulate the water and energy fluxes and stable water isotope variations. The model was run for a period of one month with meteorological data collected from a coastal sub-tropical site near Sydney, Australia. The modeled energy fluxes (latent heat and sensible heat) agreed reasonably well with eddy covariance observations, indicating that ISOLSM has the capacity to reproduce observed flux behavior. Comparison of modeled isotopic compositions of evapotranspiration (ET) against in-situ Fourier Transform Infrared spectroscopy (FTIR) measured bulk water vapor isotopic data (10 m above the ground), however, showed differences in magnitude and temporal patterns. The disparity is due to a small contribution from local ET fluxes to atmospheric boundary layer water vapor (∼1% based on calculations using ideal gas law) relative to that advected from the ocean for this particular site. Using ISOLSM simulation, the ET was partitioned into E and T with 70% being T. We also identified that soil water from different soil layers affected T and E differently based on the simulated soil isotopic patterns, which reflects the internal working of ISOLSM. These results highlighted the capacity of using the isotope-enabled models to discriminate between different hydrological components and add insight into expected hydrological behavior.

summary Current snowmelt parameterisation schemes are largely untested in warmer maritime snowfields, where physical snow properties can differ substantially from the more common colder snow environments. Physical properties such as snow density influence the thermal properties of snow layers and are likely to be important for snowmelt rates. Existing methods for incorporating physical snow properties into temperature-index models (TIMs) require frequent snow density observations. These observations are often unavailable in less monitored snow environments. In this study, previous techniques for end-ofseason snow density estimation (Bormann et al., 2013) were enhanced and used as a basis for generating daily snow density data from climate inputs. When evaluated against 2970 observations, the snow density model outperforms a regionalised density-time curve reducing biases from � 0.027 g cm � 3 to � 0.004 g cm � 3 (7%). The simulated daily densities were used at 13 sites in the warmer maritime snowfields of Australia to parameterise snowmelt estimation. With absolute snow water equivalent (SWE) errors between 100 and 136 mm, the snow model performance was generally lower in the study region than that reported for colder snow environments, which may be attributed to high annual variability. Model performance was strongly dependent on both calibration and the adjustment for precipitation undercatch errors, which influenced model calibration parameters by 150–200%. Comparison of the density-based snowmelt algorithm against a typical temperature-index model revealed only minor differences between the two snowmelt schemes for estimation of SWE. However, when the model was evaluated against snow depths, the new scheme reduced errors by up to 50%, largely due to improved SWE to depth conversions. While this study demonstrates the use of simulated snow density in snowmelt parameterisation, the snow density model may also be of broad interest for snow depth to SWE conversion. Overall, the study responds to recent calls for broader testing of TIMs across different snow environments, improves existing snow modelling in Australia and proposes a new method for introducing physically-based constraints on snowmelt rates in data-poor regions.

a b s t r a c t We evaluated the performance of four commonly applied land surface evaporation models using a high-quality dataset of selected FLUXNET towers. The models that were examined include an energy balance approach (Surface Energy Balance System; SEBS), a combination-type technique (single-source Penman-Monteith; PM), a complementary method (advection-aridity; AA) and a radiation based approach (modified Priestley-Taylor; PT-JPL). Twenty FLUXNET towers were selected based upon sat- isfying stringent forcing data requirements and representing a wide range of biomes. These towers encompassed a number of grassland, cropland, shrubland, evergreen needleleaf forest and deciduous broadleaf forest sites. Based on the mean value of the Nash-Sutcliffe efficiency (NSE) and the root mean squared difference (RMSD), the order of overall performance of the models from best to worst were: ensemble mean of models (0.61, 64), PT-JPL (0.59, 66), SEBS (0.42, 84), PM (0.26, 105) and AA (0.18, 105) (statistics stated as (NSE, RMSD in W m −2 )). Although PT-JPL uses a relatively simple and largely empirical formulation of the evaporative process, the technique showed improved performance com- pared to PM, possibly due to its partitioning of total evaporation (canopy transpiration, soil evaporation, wet canopy evaporation) and lower uncertainties in the required forcing data. The SEBS model showed low performance over tall and heterogeneous canopies, which was likely a consequence of the effects of the roughness sub-layer parameterization employed in this scheme. However, SEBS performed well overall. Relative to PT-JPL and SEBS, the PM and AA showed low performance over the majority of sites, due to their sensitivity to the parameterization of resistances. Importantly, it should be noted that no single model was consistently best across all biomes. Indeed, this outcome highlights the need for further evaluation of each model's structure and parameterizations to identify sensitivities and their appropriate application to different surface types and conditions. It is expected that the results of this study can be used to inform decisions regarding model choice for water resources and agricultural management, as well as providing insight into model selection for global flux monitoring efforts.

The influence of spatial resolution on the estimation of land surface heat fluxes from remote sensing is poorly understood. In this study, the effects of aggregation from fine (

Summary Snow density is a fundamental physical property of snowpacks used in many aspects of snow research. As an integral component in the remote sensing of snow water equivalent and parameterisation of snow models, snow density may be used to describe many important features of snowpack behaviour. The present study draws on a significant dataset of snow density and climate observations from the United States, Australia and the former Soviet Union and uses regression-based techniques to identify the dominant climatological drivers for snow densification rates, characterise densification rate variability and estimate spring snow densities from more readily available climate data. Total winter precipitation was shown to be the most prominent driver of snow densification rates, with mean air temperature and melt-refreeze events also found to be locally significant. Densification rate variance is very high at Australian sites, very low throughout the former Soviet Union and between these extremes throughout much of the US. Spring snow densities were estimated using a statistical model with climate variable inputs and best results were achieved when snow types were treated differently. Given the importance of snow density information in many snow-related research disciplines, this work has implications for current methods of converting snow depths to snow water equivalent, the representation of snow dynamics in snow models and remote sensing applications globally.

Summary This paper describes an algorithm for disaggregating daily rainfall into sub-daily rainfall ‘fragments’ (fine-resolution rainfall sequences) under a future, warmer climate. The algorithm uses a combined generalised additive model (GAM) and method of fragments (MoFs) framework to resample sub-daily rainfall fragments from the historical record conditional on daily rainfall amount and a range of atmospheric covariates. The rationale is that as the atmosphere warms, future rainfall patterns will be more reflective of historical rainfall patterns corresponding to warmer days at the same location, or to locations which have an atmospheric profile more reflective of expected future climate. It was found that the daily to sub-daily scaling relationship varied significantly by season and by location, with rainfall patterns on warmer seasons or at warmer locations typically showing more intense rainfall occurring over shorter periods compared with cooler seasons and stations. Importantly, by regressing against atmospheric covariates such as temperature, this effect was substantially reduced, suggesting that the approach may also be valid when extrapolating to a future climate. The GAM–MoF algorithm was then applied to nine stations around Australia, with the results showing that relative to the daily rainfall amount, the maximum intensity of short duration rainfall increased by between 4.1% and 13.4% per degree change in temperature for the maximum six minute burst, and by between 3.1% and 6.8% for the maximum 1 h burst. The fraction of each wet day with no rainfall also increased by between 1.5% and 3.5%. This highlights that a significant proportion of the change to the distribution of rainfall is likely to occur at sub-daily timescales, with important implications for many hydrological systems.

A new daily snow cover dataset was developed using Moderate resolution Imaging Spectroradiometer (MODIS) Level-1B products for the Australian alpine region over the period 2000–2010 at 500 m resolution. The dataset has been evaluated during clear-sky conditions using snow detection estimates derived from Landsat Thematic Mapper (TM) data and has been compared to the MOD10_L2 snow cover products. The ability to customise the snow detection threshold is one of the benefits of developing the Melt Area Detection Index (MADI) approach for regional conditions. The dataset provides a new satellite based observational record that may be used to characterise spatial and temporal development of Australian snow cover extent and duration. The new snow cover observations provide insight into snow characteristics in this region where significant declines in snow cover extent, season duration and a shift towards earlier snow melt date are observed. Shifts towards early season melt dates are observed for snow at 1580 m and above. This includes areas which are pertinent to snow recreation activities in the region. Season length declines are attributed to earlier seasonal snowmelt rather than later season onset and may be linked to observed warming trends in the area. The MODIS based approach can be applied to other regions and other sensors to assist in evaluating snow modelling efforts and improve water resource management and snow hydrology based investigations.

This paper defines a land cover classification technique based on the annual NDVI cycle. A similarity measure based directly on the components of the Discrete Fourier Transform is used to determine a pixels class membership. This Fourier component similarity measure produces an objective, computationally inexpensive and rapid method of classification that is able to classify rangeland vegetation by dominant shrub type, and which performs favorably compared to previously published classification techniques. By also defining a Fourier component based coverage measure this technique provides an estimate of vegetation coverage.

A new influx of sea-rafted pumice reached the eastern coast of Australia in October 2002, approximately 1 year after a felsic, shallow-marine explosive eruption at a previously unknown volcano (0403-091) along the Tofua volcanic arc (Tonga). The eruption produced floating pumice rafts that first became stranded in Fiji in November 2001, approximately I month after the eruption. Strandings of sea-rafted pumice along shorelines have been the only record of products from this submarine explosive eruption at the remote, submerged volcano. Computed drift trajectories of the sea-rafted pumice using numerical models of southwest Pacific surface wind fields and ocean currents indicate two cyclonic systems disturbed the drift of pumice to eastern Australia, as well as the importance of the combined wave and direct wind effect on pumice trajectory. Pumice became stranded along at least two-thirds (>2000 km) of the coastline of eastern Australia, being deposited on beaches during a sustained period of fresh onshore winds. Typical amounts of pumice initially stranded on beaches were 500-4000 individual clasts per in, and a minimum volume estimate of pumice that arrived to eastern Australia is 1.25 x 10(5) m(3). Pumice was beached below maximum tidal/storm surge levels and was quickly reworked back into the ocean, such that the concentration of beached pumice rapidly dissipated within weeks of the initial stranding, and little record of this stranding event now exists. Most stranded pumice clasts ranged in size from 2 to 5 cm in diameter; the largest measured clasts were 10 cm in Australia and 20 cm in Fiji. The pumice has a low phenocryst content ( 3500 km) and period of pumice floatation (greater than or equal to1 year), confirm the importance of sea-rafted pumice as a long-distance dispersal mechanism for marine organisms including marine pests and harmful invasive species. Billions of individual rafting pumice clasts can be generated in a single small-volume eruption, such as observed here, and the geological implications for the transport of sessile taxa over large distances are significant. An avenue for future research is to examine whether speciation events and volcanicity are linked; the periodic development of globalism for some taxa (e.g., corals, gastropods, bryozoa) may correlate in time and/or space with voluminous silicic igneous events capable of producing >10(6) km(3) of silicic pumice-rich pyroclastic material and emplaced into ocean basins. (C) 2004 Elsevier B.V. All rights reserved.

In this study we present a technique to discriminate between climate or human-induced dryland degradation, based on evaluations of AVHRR NDVI data and rainfall data. Since dryland areas typically have high inter-annual rainfall variations and rainfall has a dominant role in determining vegetation growth, minor biomass trends imposed by human influences are difficult to verify. By performing many linear regression calculations between different periods of accumulated precipitation and the annual NDVImax, we identify the rainfall period that is best related to the NDVImax and by this the proportion of biomass triggered by rainfall. Positive or negative deviations in biomass from this relationship, expressed in the residuals, are interpreted as human-induced. We discuss several approaches that use either a temporally fixed NDVI peaking time or an absolute one, a best mean rainfall period for the entire drylands or the best rainfall period for each individual pixel. Advantages and disadvantages of either approach or one of its combinations for discriminating between climate and human-induced degradation are discussed. Depending on the particular land-use either method has advantages. To locate areas with a high likelihood of human-induced degradation we therefore recommend combining results from each approach.

The introduction of complex land surface parameterization schemes into regional climate models (RCMs) has been focused on improving the modeling of land surface feedbacks to the atmosphere. As such the modeling of streamflow was considered a by-product of the water balance and until recently it received relatively little attention. Comparison of four RCMs (RegCM2, MM5/BATS, MM5/SHEELS and MM5/OSU) and a simple hydrology model, Catchment Moisture Deficit ‐Identification of unit Hydrographs And Component flows from Rainfall, Evaporation and Streamflow data (CMD-IHACRES) demonstrates the improvement in the characteristics of the streamflow prediction, which may be achieved using CMD-IHACRES. The conceptual structure of CMD-IHACRES allows it to be ‘incorporated’ into the RCMs, improving their streamflow predictions, as is demonstrated for the FIFE region of central USA. q 2003 Elsevier B.V. All rights reserved.

Representation of the hydrological interaction between the land surface and the atmosphere requires considerable improvement, particularly for predicting evapotranspiration feedbacks for use in models of the general circulation (GCMs) of the atmosphere. The predictive model developed here attempts to use a water balance approach that extracts information from the masses of catchment-scale time series data available on precipitation, energy-related variables and stream discharge. It begins with a few simple assumptions in order to seek some synthesis of the climate and landscape controls on evapotranspiration and soil moisture feedbacks, and catchment water yields. The model adopts the hydrograph identification approach used in the linear module of the rainfall-runoff model IHACRES but replaces the previous statistically based non-linear evapotranspiration loss module by a catchment moisture deficit accounting scheme. One advantage of this more conceptual approach is that evapotranspiration can be output on the same time step at which precipitation and energy variables are available (such as from GCMs), and this time step can be shorter (e.g. half hourly) than the discharge time step (e.g. daily) used to calibrate the model parameters.