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.
The “Millennium Drought” (2001–2009) can be described as the worst drought on record for southeast Australia. Adaptation to future severe droughts requires insight into the drivers of the drought and its impacts. These were analyzed using climate, water, economic, and remote sensing data combined with biophysical modeling. Prevailing El Niño conditions explained about two thirds of rainfall deficit in east Australia. Results for south Australia were inconclusive; a contribution from global climate change remains plausible but unproven. Natural processes changed the timing and magnitude of soil moisture, streamflow, and groundwater deficits by up to several years, and caused the amplification of rainfall declines in streamflow to be greater than in normal dry years. By design, river management avoided impacts on some categories of water users, but did so by exacerbating the impacts on annual irrigation agriculture and, in particular, river ecosystems. Relative rainfall reductions were amplified 1.5–1.7 times in dryland wheat yields, but the impact was offset by steady increases in cropping area and crop water use efficiency (perhaps partly due to CO2 fertilization). Impacts beyond the agricultural sector occurred (e.g., forestry, tourism, utilities) but were often diffuse and not well quantified. Key causative pathways from physical drought to the degradation of ecological, economic, and social health remain poorly understood and quantified. Combined with the multiple dimensions of multiyear droughts and the specter of climate change, this means future droughts may well break records in ever new ways and not necessarily be managed better than past ones.
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 Köppen‐Geiger climate types and even for evaluation catchments > 5000 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.
Key Points:
For the first time, a scheme for regionalization of model parameters at global scale was developed
The improvement in model performance was about half of that achieved through calibration
The regionalized model outperformed nine state‐of‐the‐art models including their ensemble mean
Vegetation optical depth (VOD) retrievals from three satellite‐based passive microwave instruments were merged to produce the first long‐term global microwave‐based vegetation product. The resulting ...VOD product spans more than two decades and shows seasonal cycles and inter‐annual variations that generally correspond with those observed in the Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI). Some notable differences exist in the long‐term trends: the NDVI, operating in the optical regime, is sensitive to chlorophyll abundance and photosynthetically active biomass of the leaves, whereas the microwave‐based VOD is an indicator of the vegetation water content in total above‐ground biomass, i.e., including wood and leaf components. Preliminary analyses indicate that the fluctuations in VOD typically correlated to precipitation variations, and that the mutually independent VOD and NDVI do not necessarily respond in identical manners. Considering both products together provides a more robust structural characterization and assessment of long‐term vegetation dynamics at the global scale.
Key Points
First long‐term global microwave‐based vegetation product (VOD) was developed
Notable similarities and differences exist between VOD and NDVI product
Mutually independent products will provide a more comprehensive analysis
Numerous previous studies have constructed models to estimate base flow characteristics from climatic and physiographic characteristics of catchments and applied these to ungauged regions. However, ...these studies generally used streamflow observations from a relatively small number of catchments (<200) located in small, homogeneous study areas, which may have led to less reliable models with limited applicability elsewhere. Here, we use streamflow observations from a highly heterogeneous set of 3394 catchments (<10,000 km2) worldwide to construct reliable, widely applicable models based on 18 climatic and physiographic characteristics to estimate two important base flow characteristics: (1) the base flow index (BFI), defined as the ratio of long‐term mean base flow to total streamflow; and (2) the base flow recession constant (k), defined as the rate of base flow decay. Regression analysis results revealed that BFI and k were related to several climatic and physiographic characteristics, notably mean annual potential evaporation, mean snow water equivalent depth, and abundance of surface water bodies. Ensembles of artificial neural networks (ANNs; obtained by subsampling the original set of catchments) were trained to estimate the base flow characteristics from climatic and physiographic data. The catchment‐scale estimation of the base flow characteristics demonstrated encouraging performance with R2 values of 0.82 for BFI and 0.72 for k. The connection weights of the trained ANNs indicated that climatic characteristics were more important for estimating k than BFI. Global maps of estimated BFI and k were obtained using global climatic and physiographic data as input to the derived models. The resulting global maps are available for free download at http://www.hydrology-amsterdam.nl.
Key Points
We trained neural networks to estimate baseflow index and recession rate
Streamflow observations from 3394 catchments were used for the training
First global maps of baseflow index and recession rate
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