ALBERT

Description: Numerous regionalisation studies have been conducted to predict the runoff time series in ungauged catchments. However, there are few studies investigating their benefits for predicting runoff time series on a continental scale. This study uses four regionalisation approaches, including spatial proximity (SP), gridded SP, integrated similarity (IS) and gridded IS, to regionalise two rainfall–runoff models (SIMHYD and Xinanjiang) for 605 unregulated catchments distributed across Australia. The SP and IS approaches are used for directly predicting catchment streamflow; the gridded SP and gridded IS approaches are used for predicting runoff at each 0.05° × 0.05° grid cell for continental Australia, which is then aggregated for each catchment. The IS and gridded IS approaches use five properties to build similarity indices, including three physical properties (an aridity index, a fraction of forest ratio and the mean annual air temperature) and two rainfall indices (rainfall seasonality and the standard deviation of daily rainfall). The two rainfall–runoff models show consistent regionalisation results, and there is a marginal difference among the four regionalisation approaches in the wet and densely located catchments. However, the gridded IS approach outperforms the other three in the dry and sparsely located catchments, and it overcomes the unnatural tessellated effect obtained from the gridded SP approach. Use of the gridded IS approach together with rainfall–runoff modelling for predicting runoff on a continental scale is highly recommended. Extra predictors should be included to build similarity indices in other regions, such as the high latitude northern hemisphere or high elevation regions.

Description: The increasing conflicts for water resources between upstream and downstream regions appeal to chronological insight across the world. While the negative consequence of downstream water scarcity has been widely analyzed, the quantification of influence of upstream water use on downstream water scarcity has received little attention. Here non-anthropologically intervened runoff (natural runoff) was first reconstructed in upstream, middle stream and downstream regions in China's 12 large basins in the 1970s to 2000s time period using the Fu–Budyko framework, and then compared to the observed data to obtain the developmental trajectories of water scarcity, including the ratio of water use to availability (WTA) and the per capita water availability (FI; Falkenmark Index) on a decadal scale. Furthermore, a contribution analysis was used to investigate the main drivers of water scarcity trajectories in those basins. The results show that China as a whole has experienced a rapid increase of WTA stress with surface water use rapidly increasing from 161 billion cubic meters (12 % of natural runoff) in the 1970s to 256 billion cubic meters (18 %) in the 2000s, with approximately 65 % increase occurring in northern China. In the 2000s, the increase of upstream WTA stress and the decrease of downstream WTA stress occurred simultaneously for semi-arid and arid basins, which was caused by the increasing upstream water use and the consequent decreasing surface water use in downstream regions. The influence of upstream surface water use on downstream water scarcity was less than 10 % in both WTA and FI for humid and semi-humid basins during the study period, but with an average of 26 % in WTA and 32 % in FI for semi-arid and arid basins. The ratio increased from 10 % in the 1970s to 37 % in the 2000s for WTA and from 22 % in the 1980s to 37 % in the 2000s for FI. The contribution analysis shows that the WTA contribution greatly increases in the 2000s mainly in humid and semi-humid basins, while it decreases mainly in semi-arid and arid basins. The trajectories of China's water scarcity are closely related to socioeconomic development and water policy changes, which provide valuable lessons and experiences for global water resources management.

Description: Gap-filling streamflow data is a critical step for most hydrological studies, such as streamflow trend, flood, and drought analysis and hydrological response variable estimates and predictions. However, there is a lack of quantitative evaluation of the gap-filled data accuracy in most hydrological studies. Here we show that when the missing data rate is less than 10 %, the gap-filled streamflow data obtained using calibrated hydrological models perform almost the same as the benchmark data (less than 1 % missing) when estimating annual trends for 217 unregulated catchments widely spread across Australia. Furthermore, the relative streamflow trend bias caused by the gap filling is not very large in very dry catchments where the hydrological model calibration is normally poor. Our results clearly demonstrate that the gap filling using hydrological modelling has little impact on the estimation of annual streamflow and its trends.

Description: Gap-filling streamflow data is a critical step for most hydrological studies, such as streamflow trend, flood and drought analysis and hydrological response variable estimates and predictions. However, there is lack of quantitative evaluation of the gap-filled data accuracy in most hydrological studies. Here we show that when the missing rate is less than 10%, the gap-filled streamflow data obtained using calibrated hydrological models perform almost as same as the benchmark data (less than 1% missing) for estimating annual trends for 217 unregulated catchments widely spread in Australia. Furthermore, the relative streamflow trend bias caused by the gap-filling is not very large in very dry catchments where the hydrological model calibration is normally poor. Our results clearly demonstrate that the gap-filling using hydrological modelling has little impact on the estimation of annual streamflow and its trends.

Description: The partitioning of precipitation into runoff (R) and evapotranspiration (E), governed by the controlling parameter in the Budyko framework (i.e., n parameter in the Choudhury and Yang equation), is critical to assessing the water balance at global scale. It is widely acknowledged that the spatial variation in this controlling parameter is affected by landscape characteristics, but characterizing its temporal variation remains yet to be done. Considering effective precipitation (Pe), the Budyko framework was extended to the annual water balance analysis. To reflect the mismatch between water supply (precipitation, P) and energy (potential evapotranspiration, E0), we proposed a climate seasonality and asynchrony index (SAI) in terms of both phase and amplitude mismatch between P and E0. Considering streamflow changes in 26 large river basins as a case study, SAI was found to the key factor explaining 51 % of the annual variance of parameter n. Furthermore, the vegetation dynamics (M) remarkably impacted the temporal variation in n, explaining 67 % of the variance. With SAI and M, a semi-empirical formula for parameter n was developed at the annual scale to describe annual runoff (R) and evapotranspiration (E). The impacts of climate variability (Pe, E0 and SAI) and M on R and E changes were then quantified. Results showed that R and E changes were controlled mainly by the Pe variations in most river basins over the globe, while SAI acted as the controlling factor modifying R and E changes in the East Asian subtropical monsoon zone. SAI, M and E0 have larger impacts on E than on R, whereas Pe has larger impacts on R.

Description: The partitioning of water and energy, governed by the controlling parameter in the Budyko framework (i.e., n parameter in the Choudhury and Yang equation), is critical to assess the water balance at global scale. It is widely acknowledged that the spatial variation of this controlling parameter is affected by landscape characteristics, but characterizing its temporal variation remains yet to be done. Considering effective precipitation (Pe), the Budyko framework was extended to the annual water balance analysis. To reflect the mismatch between water supply (precipitation, P) and energy (potential evapotranspiration, E0), a climate seasonality and asynchrony index (SAI) were proposed in terms of both phase and amplitude mismatch between P and E0. Considering streamflow changes in 26 large river basins as a case study, SAI was found to the key factor explaining 46% of the annual variance of parameter n. Furthermore, the vegetation dynamics (M) remarkably impacted the temporal variation of n, explaining 67% of the variance. With SAI and M, a semi-empirical formula for parameter n was developed at the annual scale to describe annual runoff (R) and evapotranspiration (E). The impacts of climate variability (Pe, E0 and SAI) and M on R and E changes were then quantified. Results showed that R and E changes were controlled mainly by the Pe variations in most river basins over the globe, while SAI acted as the controlling factor modifying R and E changes in the East Asian subtropical monsoon zone, E0 in the temperate maritime climate of Europe, and M in the temperate grassland zone of South America.

Description: Estimating baseflow at a large spatial scale is critical for water balance budget, water resources management, and environmental evaluation. To predict baseflow index (BFI, the ratio of baseflow to total streamflow), this study introduces a multilevel regression approach, which is compared to two traditional approaches: hydrological modelling (SIMHYD, a simplified version of the HYDROLOG model, and Xinanjiang models) and classic linear regression. All of the three approaches were evaluated against ensemble average estimates from four well-parameterised baseflow separation methods (Lyne–Hollick, UKIH (United Kingdom Institute of Hydrology), Chapman–Maxwell and Eckhardt) at 596 widely spread Australian catchments in 1975–2012. The two hydrological models obtain BFI from three modes: calibration and two regionalisation schemes (spatial proximity and integrated similarity). The classic linear regression estimates BFI using linear regressions established between catchment attributes and the ensemble average estimates in four climate zones (arid, tropics, equiseasonal and winter rainfall). The multilevel regression approach not only groups the catchments into the four climate zones, but also considers variances both within all catchments and catchments in each climate zone. The two calibrated and regionalised hydrological models perform similarly poorly in predicting BFI with a Nash–Sutcliffe Efficiency (NSE) of −8.44 ~ −2.58 and an absolute percenrate bias (Bias) of 81 146; the classic linear regression is intermediate with the NSE of 0.57 and bias of 25; the multilevel regression approach is best with the NSE of 0.75 and bias of 19. Our study indicates the multilevel regression approach should be used for predicting large-scale baseflow index such as Australian continent where sufficient catchment predictors are available.