• Energy Research

Summary For instantaneous latent heat flux ( λE ) estimates from thermal remote sensing data to be useful in the hydrologic sciences, they require integration over longer time frames (e.g., months to years). This is not trivial because thermal remote sensing data acquired under cloud-free daytime conditions require upscaling to a monthly energy amount that is both relevant over cloudy periods and considers daytime and nighttime. Previous work has compared upscaling approaches, but as yet there is no authoritative comparison that does so under conditions relevant for thermal remote sensing. In this paper we describe, under the conditions relevant for thermal remote sensing, a generic framework for comparing any upscaling approach that assumes self-preservation. Then we use eddy-flux data from two sites in contrasting climates to systematically evaluate the accuracy of different upscaling proposals within the framework. We assumed that the instantaneous estimate of the latent heat flux measured by the eddy-flux technique would have been measured by a satellite sensor. We then scaled this estimate to a monthly period using four approaches and compared the result with the observed monthly integral. This design enabled us to isolate the accuracy of each upscaling method. The four methods upscaled λE by: (i) observed solar irradiance ( S ); (ii) modelled solar irradiance from a sine function ( S SIN ); (iii) modelled top-of-atmosphere solar irradiance ( S TOA ); and (iv) observed available energy ( A E ). We showed that upscaling λE using observed data ( S , A E ) resulted in underestimation of monthly evaporative energy, while the use of modelled data ( S SIN , S TOA ) led to overestimation, primarily due to the relationship between error and both the season (day-of-year) and cloud fraction. Of the two observed fluxes, upscaling with S resulted in lower overall errors than when using A E ( S bias: −1.11 M J m −2 d −1 or −16%; A E bias: −2.15 M J m −2 d −1 or −34%). Of the two modelled fluxes, upscaling with S TOA had lower errors than the widely used S SIN method ( S SIN bias: 1.03 M J m −2 d −1 or 14%; S TOA bias: 0.91 M J m −2 d −1 or 13%). We subsequently developed a simple procedure to minimise bias from all four upscaling approaches, and concluded that modelled data ( S TOA ) can be used to upscale λE to longer timescales for thermal remote sensing applications. This study developed the theory to minimise upscaling bias at two sites with contrasting climates, further work is needed to extend the approach to all global terrestrial climates.

Remotely sensed vegetation and water-balance measurements from 190 river basins across Australia show that sub-humid and semi-arid basins are ‘greening’—as expected under CO2 fertilization—increasing water consumption and reducing streamflow. Global environmental change has implications for the spatial and temporal distribution of water resources, but quantifying its effects remains a challenge. The impact of vegetation responses to increasing atmospheric CO2 concentrations on the hydrologic cycle is particularly poorly constrained1,2,3. Here we combine remotely sensed normalized difference vegetation index (NDVI) data and long-term water-balance evapotranspiration (ET) measurements from 190 unimpaired river basins across Australia during 1982–2010 to show that the precipitation threshold for water limitation of vegetation cover has significantly declined during the past three decades, whereas sub-humid and semi-arid basins are not only ‘greening’ but also consuming more water, leading to significant (24–28%) reductions in streamflow. In contrast, wet and arid basins show nonsignificant changes in NDVI and reductions in ET. These observations are consistent with expected effects of elevated CO2 on vegetation. They suggest that projected future decreases in precipitation4 are likely to be compounded by increased vegetation water use, further reducing streamflow in water-stressed regions.

Summary Practical methods to scale ‘instantaneous’ latent heat flux (λ E ) estimates from thermal remote sensing to values for daily or longer periods normally rely on the assumption that the evaporative fraction ( EF ) – the ratio of λ E over available energy – remains constant during daytime. However, this self-preservation of the EF is only one of three major surface energy balance interactions that might introduce bias. The other two are differences between use of 24-h versus daytime-only available energy and latent heat flux. We used observations from two Australian long-term flux tower sites to assess the magnitude and interaction of the three factors. The difference between 24-h and daytime-only available energy caused the most error at the temperate forest site (median error: −18%) whereas the lack of self preservation of the EF caused the most error at the tropical savanna site (median error: −14%). The difference between 24-h and daytime-only latent heat flux caused the least error at both sites (median error: −3%). The three factors tended to accumulate rather than compensate and were time of day dependant; combined errors of −24% to −38% were found when scaling instantaneous λ E values for mid-morning, reducing to −6% to −21% for mid-afternoon values. A generic correction method reduced bias in daily λE estimates from −1.86 MJ m −2 to −0.02 MJ m −2 at the temperate forest site and from −0.99 MJ m −2 to −0.19 MJ m −2 at the tropical savanna site. This study showed that all three surface energy balance factors should be considered when implementing the EF method and suggested that most improvement in accuracy might be attained by better modelling of available energy.

Key Points Drivers and impacts of Australia's record drought were analyzed Impacts accumulated and propagated through the water cycle at different rates Future droughts may not be managed better than past ones.

AbstractAn integrated understanding of the biogeochemical consequences of climate extremes and land use changes is needed to constrain land-surface feedbacks to atmospheric CO2 from associated climate change. Past assessments of the global carbon balance have shown particularly high uncertainty in Southeast Asia. Here, we use a combination of model ensembles to show that intensified land use change made Southeast Asia a strong source of CO2 from the 1980s to 1990s, whereas the region was close to carbon neutral in the 2000s due to an enhanced CO2 fertilization effect and absence of moderate-to-strong El Niño events. Our findings suggest that despite ongoing deforestation, CO2 emissions were substantially decreased during the 2000s, largely owing to milder climate that restores photosynthetic capacity and suppresses peat and deforestation fire emissions. The occurrence of strong El Niño events after 2009 suggests that the region has returned to conditions of increased vulnerability of carbon stocks.

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 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 pre-instrumental) 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.

Satellite observations identify the Mongolian steppes as a hotspot of global biomass reduction, the extent of which is comparable with tropical rainforest deforestation. To conserve or restore these grasslands, the relative contributions of climate and human activities to degradation need to be understood. Here we use a recently developed 21-year (1988-2008) record of satellite based vegetation optical depth (VOD, a proxy for vegetation water content and aboveground biomass), to show that nearly all steppe grasslands in Mongolia experienced significant decreases in VOD. Approximately 60% of the VOD declines can be directly explained by variations in rainfall and surface temperature. After removing these climate induced influences, a significant decreasing trend still persists in the VOD residuals across regions of Mongolia. Correlations in spatial patterns and temporal trends suggest that a marked increase in goat density with associated grazing pressures and wild fires are the most likely non-climatic factors behind grassland degradation.