Albert I. J. M. van Dijk

Changing Climate and Overgrazing Are Decimating Mongolian Steppes

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.

Global‐scale regionalization of hydrologic model parameters

Current state-of-the-art models typically applied at continental to global scales (hereafter called macroscale) tend to use a priori parameters, resulting in suboptimal streamflow (Q) simulation. For the first time, a scheme for regionalization of model parameters at the global scale was developed. We used data from a diverse set of 1787 small-to-medium sized catchments ( 10–10,000 km2) and the simple conceptual HBV model to set up and test the scheme. Each catchment was calibrated against observed daily Q, after which 674 catchments with high calibration and validation scores, and thus presumably good-quality observed Q and forcing data, were selected to serve as donor catchments. The calibrated parameter sets for the donors were subsequently transferred to 0.5° grid cells with similar climatic and physiographic characteristics, resulting in parameter maps for HBV with global coverage. For each grid cell, we used the 10 most similar donor catchments, rather than the single most similar donor, and averaged the resulting simulated Q, which enhanced model performance. The 1113 catchments not used as donors were used to independently evaluate the scheme. The regionalized parameters outperformed spatially uniform (i.e., averaged calibrated) parameters for 79% of the evaluation catchments. Substantial improvements were evident for all major Koppen-Geiger climate types and even for evaluation catchmentsu2009>u20095000 km distant from the donors. The median improvement was about half of the performance increase achieved through calibration. HBV with regionalized parameters outperformed nine state-of-the-art macroscale models, suggesting these might also benefit from the new regionalization scheme. The produced HBV parameter maps including ancillary data are available via www.gloh2o.org.

Global Maps of Streamflow Characteristics Based on Observations from Several Thousand Catchments

AbstractStreamflow Q estimation in ungauged catchments is one of the greatest challenges facing hydrologists. Observed Q from 3000 to 4000 small-to-medium-sized catchments (10–10 000 km2) around the globe were used to train neural network ensembles to estimate Q characteristics based on climate and physiographic characteristics of the catchments. In total, 17 Q characteristics were selected, including mean annual Q, baseflow index, and a number of flow percentiles. Testing coefficients of determination for the estimation of the Q characteristics ranged from 0.55 for the baseflow recession constant to 0.93 for the Q timing. Overall, climate indices dominated among the predictors. Predictors related to soils and geology were relatively unimportant, perhaps because of their data quality. The trained neural network ensembles were subsequently applied spatially over the entire ice-free land surface, resulting in global maps of the Q characteristics (at 0.125° resolution). These maps possess several unique feat...

Natural hazards in Australia: droughts

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.

Natural hazards in Australia: floods

Floods are caused by a number of interacting factors, making it remarkably difficult to explain changes in flood hazard. This paper reviews the current understanding of historical trends and variability in flood hazard across Australia. Links between flood and rainfall trends cannot be made due to the influence of climate processes over a number of spatial and temporal scales as well as landscape changes that affect the catchment response. There are also still considerable uncertainties in future rainfall projections, particularly for sub-daily extreme rainfall events. This is in addition to the inherent uncertainty in hydrological modelling such as antecedent conditions and feedback mechanisms.Research questions are posed based on the current state of knowledge. These include a need for high-resolution climate modelling studies and efforts in compiling and analysing databases of sub-daily rainfall and flood records. Finally there is a need to develop modelling frameworks that can deal with the interaction between climate processes at different spatio-temporal scales, so that historical flood trends can be better explained and future flood behaviour understood.

River gauging at global scale using optical and passive microwave remote sensing

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 (>0.05°) 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 (Ru2009 u20090.6. The best results (Ru2009>u20090.9) 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.

Environmental reporting and accounting in Australia: progress, prospects and research priorities.

Despite strong demand for information to support the sustainable use of Australias natural resources and conserve environmental values and despite considerable effort and investment, nation-wide environmental data collection and analysis remains a substantially unmet challenge. We review progress in producing national environmental reports and accounts, identify challenges and opportunities, and analyse the potential role of research in addressing these. Australias low and concentrated population density and the short history since European settlement contribute to the lack of environmental data. There are additional factors: highly diverse data requirements and standards, disagreement on information priorities, poorly measurable management objectives, lack of coordination, over-reliance on researchers and businesses for data collection, lack of business engagement, and short-term, project-based activities. New opportunities have arisen to overcome some of these challenges: enhanced monitoring networks, standardisation, data management and modelling, greater commitment to share and integrate data, community monitoring, increasing acceptance of environmental and sustainability indicators, and progress in environmental accounting practices. Successes in generating climate, water and greenhouse gas information appear to be attributable to an unambiguous data requirement, considerable investment, and legislative instruments that enhance data sharing and create a clearly defined role for operational agencies. Based on the analysis presented, we suggest six priorities for research: (1) common definitions and standards for information that address management objectives, (2) ecological measures that are scalable from local to national level, (3) promotion of long-term data collection and reporting by researchers, (4) efficient satellite and sensor network technologies and data analysis methods, (5) environmental modelling approaches that can reconcile multiple data sources, and (6) experimental accounting to pursue consistent, credible and relevant information structures and to identify new data requirements. Opportunities exist to make progress in each of these areas and help secure a more sustainable future.

How do Spatial Scale, Noise, and Reference Data affect Empirical Estimates of Error in ASAR-Derived 1 km Resolution Soil Moisture?

The performance of the advanced synthetic aperture radar (ASAR) global mode (GM) surface soil moisture (SSM) data was studied over Australia by means of two widely used bivariate measures, the root-mean-square error (RMSE) and the Pearson correlation coefficient (R). By computing RMSE and at multiple spatial scales and for different data combinations, we assessed how, and at which scales, the spatial sampling error, noise, and the choice of the reference data impact on RMSE and . The results reveal large changes in RMSE and with continental average values of 8% and 18% for the RMSE of relative soil moisture saturation and between 0.4 and 0.7 for depending on the spatial scale of aggregation and the choice of reference data. The combined effect of noise and spatial sampling error accounted for a 79% RMSE increase at 1 km and predominated over the error due to the choise of the reference data also at 5 km scale. The effect of noise on RMSE strongly diminished at spatial scales ≥2 km. By contrast, the impact of uncertainties in the reference data was larger on than on RMSE. This highlights the better potential of to estimate the benefit of observations prior to data assimilation. Based on our results, it is further suggested that a potential way for an improved ASAR GM SSM error assessment is to: 1) aggregate the data to ≥2 km resolution to minimize the noise; 2) subtract the spatial sampling error within the coarse resolution footprint; and 3) remove the reference uncertainty using advanced techniques such as triple collocation.

Estimating fire severity and carbon emissions over Australian tropical savannahs based on passive microwave satellite observations

ABSTRACT We investigated the use of a recently developed satellite-based vegetation optical depth (VOD) data set to estimate fire severity and carbon emission over Australian tropical savannahs. VOD is sensitive to the dynamics of all aboveground vegetation and available nearly every two days. For areas burned during 2003–2010, we calculated the VOD change (ΔVOD) pre- and post-fire and the associated loss in the above ground biomass carbon. ΔVOD agreed well with the Normalized Burn Ratio change (ΔNBR) which is the metric used to estimate fire severity and carbon loss compared well with modelled emissions from the Global Fire Emissions Database (GFED). We found that the ΔVOD and ΔNBR are generally linearly related. The Pearson correlation coefficients (r) between VOD- and GFED-based fire carbon emissions for monthly and annual total estimates are very high, 0.92 and 0.96, respectively. A key feature of fire carbon emissions is the strong inter-annual variation, ranging from 21.1 Mt in 2010 to 84.3 Mt in 2004. This study demonstrates that a reasonable estimate of fire severity and carbon emissions can be achieved in a timely manner based on multiple satellite observations over Australian tropical savannahs, which can be complementary to the currently used approaches.

Using alternative soil moisture estimates in the McArthur Forest Fire Danger Index

McArthur’s Forest Fire Danger Index (FFDI) incorporates the Keetch–Byram Drought Index (KBDI) estimate of soil dryness. Improved approaches for estimating soil moisture now exist, with potential for informing the calculation of FFDI. We evaluated the effect, compared with KBDI, of two alternative methods of estimating soil moisture: the rainfall-based Antecedent Precipitation Index and soil moisture from the Soil Moisture Ocean Salinity satellite mission. These methods were used to calculate FFDI over a sample period of 5years (2010–14) at seven locations around Australia. The effect of substituting the alternatives for KBDI, and of entirely replacing the Drought Factor (DF) (a measure of fuel availability in FFDI) with the alternatives was explored by studying the effect on magnitude, distribution and timing of FFDI and associated Fire Danger Rating (FDR). Both approaches predicted drier soil conditions than KBDI, resulting in fewer Low–Moderate FDR days and more days of High FDR and above. The alternative methods replacing KBDI had little effect on seasonal patterns of FDR. Of all approaches, replacing DF entirely with the soil moisture alternatives most closely mimicked McArthur’s FFDI. Overall, if alternative measures of soil moisture are adopted for FFDI, the entire replacement of the DF term should be considered.