Characterisation of Hydrological Response to Rainfall at Multi Spatio-Temporal Scales in Savannas of Semi-Arid Australia

Land use change in the river basins of the Great Barrier Reef, 1860 to 2019: A foundation for understanding environmental history across the catchment to reef continuum

Land use in the catchments draining to the Great Barrier Reef lagoon has changed considerably since the introduction of livestock grazing, various crops, mining and urban development. Together these changes have resulted in increased pollutant loads and impaired coastal water quality. This study compiled records to produce annual time-series since 1860 of human population, livestock numbers and agricultural areas at the scale of surface drainage river basins, natural resource management regions and the whole Great Barrier Reef catchment area. Cattle and several crops have experienced progressive expansion interspersed by declines associated with droughts and diseases. Land uses which have experienced all time maxima since the year 2000 include cattle numbers and the areas of sugar cane, bananas and cotton. A Burdekin Basin case study shows that sediment loads initially increased with the introduction of livestock and mining, remained elevated with agricultural development, and declined slightly with the Burdekin Falls Dam construction.

Effects of watershed char and climate variables on annual runoff in different climatic zones in China

The complex interactions between climate and watershed characteristics lead to diverse annual runoff responses. Understanding the mechanism by which different climatic and watershed factors affect annual runoff is helpful in understanding the resulting changes in the hydrological process. In this study, the characteristics of 73 watersheds were analyzed. The basins were divided into three categories according to their climatic regions: temperate continental climate (n = 7); temperate monsoon climate(n = 36); and subtropical monsoon climate(n = 30). Correlation analysis, linear regression, and path analysis were used to quantify the effects of selected watershed characteristics and meteorological conditions on long-term runoff. Results showed that the average annual runoff coefficient was strongly correlated with basin area, showing a scale effect. The average annual runoff depth was strongly positively correlated with precipitation for the all watersheds. As the drought index (DI, the ratio of annual evaporation capacity to annual precipitation) increased, the annual runoff depth decreased logarithmically. The average annual rainfall and runoff depth of watersheds in the subtropical monsoon climate zone were significantly higher than those in other climatic zones, and there was no significant difference in potential evaporation between the temperate monsoon climate and subtropical monsoon climate zones. With increases in both the drought index (Ep/P) and moisture index (E/P), the vegetation distribution in the basin showed an increasing trend in farmland area and decreasing trend in forest area. Path analysis showed that rainfall had a positive effect on annual average runoff depth (ranging from 31 to 62%) while actual evapotranspiration had a negative impact (ranging from 17 to 47%). For all basins, a negative effect (13-25%) of basin area on runoff depth was observed, while forestland area had a positive effect (7-39%) on runoff depth. This study further quantified the effects of climatic and geographical factors on the long-term water balance in different climatic regions.

Probability distribution of groundcover for runoff prediction in rangeland in the Burnett–Mary Region, Queensland

Considering the degree of spatial and temporal variation of groundcover in grazing land, it is desirable to use a simple and robust model to represent the spatial variation in cover in order to quantify its effect on runoff and soil loss. The purpose of the study was to test whether a two-parameter beta (β) distribution could be used to characterise cover variation in space at the sub-catchment scale. Twenty sub-catchments (area range 35.8–231km2) in the Burnett–Mary region, Queensland, were randomly selected. Thirty raster layers of groundcover at 30-m resolution were prepared for these 20 sub-catchments with the average cover for the 30 layers ranging from 24% to 91%. Three methods were used to test the appropriateness of the β distribution for characterising the cover variation in space: (i) visual goodness-of-fit assessment and Kolmogorov–Smirnov (K-S) test; (ii) the fractional area with cover ≤53%; and (iii) estimated runoff amount for a given rainfall amount for the area with cover ≤53%. The K-S test on 30×100 samples of groundcover showed that the hypothesis of β distribution for groundcover could not be rejected at P=0.05 for 97.5% of the cases. A comparison of the observed and β distributions in terms of the fractional area with cover ≤53% showed that the discrepancy was ≤8% for the 30 layers considered. A comparison in terms of the estimated runoff showed that results using the observed cover distribution and the β distribution were highly correlated (R2 range 0.91–0.98; Nash–Sutcliffe efficiency measure range 0.88–0.99). The mean absolute error of estimated runoff ranged from 0.98 to 8.10mm and the error relative to the mean was 4–16%. The results indicated that the two-parameter β distribution can be adequately used to characterise the spatial variation of cover and to evaluate the effect of cover on runoff for these predominantly grazing catchments.

Scale effects on runoff and a decomposition analysis of the main driving factors in Haihe Basin mountainous area

Catchment runoff scale relationships comprise an important theoretical support for water resource management. However, previous understandings of the scale effect were mostly based on empirical summaries and quantitative research, while interpretation based on measured data was rare. The purpose of this paper was to quantitatively reveal the causes of runoff scale impacts in the Haihe mountainous area over a 20-year period. Fifty-seven catchments (92-15803 km²) were selected from the available hydrological sites. Multi-year average values for17 environmental variables were calculated in each catchment over the study period, including data on hydrology, meteorology, vegetation, land use, topography, and soil. Based on these data, the quantitative relationship between runoff and catchment area was first established. Then the correlation between environmental factors and runoff scale impacts was assessed. Finally, catchments were divided into three groups by area, and the dominant factors influencing runoff at different scales, as well as the direct and indirect effects of these factors on runoff, were obtained using stepwise regression and path analysis. The results showed that: 1) Runoff coefficients decreased logarithmically as catchment area increased and the scale distribution characteristics of the variables closely related to runoff were an important reason for the runoff scale effect. 2) Larger river basins had fewer sensitivity factors for runoff and the impacts of vegetation and land use factors were mainly reflected in small and medium catchments. 3) Vegetation and land use primarily had indirect effects, which determined the proportion of factors in the total effect. Among these, the indirect effects of farmland were very prominent, which implied that human activities have had an important influence on runoff scale effects. This study emphasized the importance of farmland management in the upstream areas of Haihe mountainous area, and provides important theoretical support for catchment scale effects and water resource management in water-limited regions.

Rainfall-Runoff Modelling Using Hydrological Connectivity Index and Artificial Neural Network Approach

The input selection process for data-driven rainfall-runoff models is critical because input vectors determine the structure of the model and, hence, can influence model results. Here, hydro-geomorphic and biophysical time series inputs, including Normalized Difference Vegetation Index (NDVI) and Index of Connectivity (IC; a type of hydrological connectivity index), in addition to climatic and hydrologic inputs were assessed. Selected inputs were used to develop Artificial Neural Networks (ANNs) in the Haughton River catchment and the Calliope River catchment, Queensland, Australia. Results show that incorporating IC as a hydro-geomorphic parameter and remote sensing NDVI as a biophysical parameter, together with rainfall and runoff as hydro-climatic parameters, can improve ANN model performance compared to ANN models using only hydro-climatic parameters. Comparisons amongst different input patterns showed that IC inputs can contribute to further improvement in model performance, than NDVI inputs. Overall, ANN model simulations showed that using IC along with hydro-climatic inputs noticeably improved model performance in both catchments, especially in the Calliope catchment. This improvement is indicated by a slight increase (9.77% and 11.25%) in the Nash–Sutcliffe efficiency and noticeable decrease (24.43% and 37.89%) in the root mean squared error of monthly runoff from Haughton River and Calliope River, respectively. Here, we demonstrate the significant effect of hydro-geomorphic and biophysical time series inputs for estimating monthly runoff using ANN data-driven models, which are valuable for water resources planning and management.