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Periodically inundated floodplain areas are hot spots of biodiversity and provide a broad range of ecosystem services but have suffered alarming declines in recent history. Despite their importance, their long-term surface water (SW) dynamics and hydroclimatic drivers remain poorly quantified on continental scales. In this study, we used a 26 year time series of Landsat-derived SW maps in combination with river flow data from 68 gauges and spatial time series of rainfall, evapotranspiration and soil moisture to statistically model SW dynamics as a function of key drivers across Australia's Murray-Darling Basin (∼1 million km²). We fitted generalized additive models for 18,521 individual modeling units made up of 10 × 10 km grid cells, each split into floodplain, floodplain-lake, and nonfloodplain area. Average goodness of fit of models was high across floodplains and floodplain-lakes (r²>0.65), which were primarily driven by river flow, and was lower for nonfloodplain areas (r²>0.24), which were primarily driven by rainfall. Local climate conditions were more relevant for SW dynamics in the northern compared to the southern basin and had the highest influence in the least regulated and most extended floodplains. We further applied the models of two contrasting floodplain areas to predict SW extents of cloud-affected time steps in the Landsat series during the large 2010 floods with high validated accuracy (r²>0.97). Our framework is applicable to other complex river basins across the world and enables a more detailed quantification of large floods and drivers of SW dynamics compared to existing methods.
Spatiotemporal distribution and systematic quantification of surface water and their drivers of change are critical. However, quantifying this distribution is challenging due to a lack of spatially explicit and temporally dynamic em- pirical data of both surface water and its drivers of change at large spatial scales. We focused on one of the largest dryland basins in the world, Australia's Murray-Darling Basin (MDB), recently identified as a global hotspot of water decline. We used a new remotely sensed time-series of surface water extent dynamics (SWD) data to quantify spatiotemporal patterns in surface water across the entire MDB and catchments and to assess natural and anthropo- genic drivers of SWD, including climate and historical land use change. We show high intra- and inter-annual dy- namics in surface water with a rapid loss during the Millennium Drought, the worst, decade-long drought in SE Australia. We show strong regional and catchment differences in SWD, with the northern basin showing high var- iability compared to the southern basin which shows a steady decline in surface water. Linear mixed effect models including climate and land-use change variables explained up to 70% variability in SWD with climate being more important in catchments of the northwestern MDB, whereas land-use was important primarily in the central MDB. Increase in fraction of dryland agriculture in a catchment and maximum temperature was negatively related to SWD, whereas precipitation and soil moisture were positively related to SWD. The fact that land-use change was an important explanatory variable of SWD in addition to climate is a significant result as land-use can be managed more effectively whereas climate-mitigation actions can be intractable, with global change scenarios predicting drier conditions for the area followed by a further reduction in surface water availability.
The usage of time series of Earth observation (EO) data for analyzing and modeling surface water extent (SWE) dynamics across broad geographic regions provides important information for sustainable management and restoration of terrestrial surface water resources, which suffered alarming declines and deterioration globally. The main objective of this research was to model SWE dynamics from a unique, statistically validated Landsat-based time series (1986–2011) continuously through cycles of flooding and drying across a large and heterogeneous river basin, the Murray–Darling Basin (MDB) in Australia. We used dynamic linear regression to model remotely sensed SWE as a function of river flow and spatially explicit time series of soil moisture (SM), evapotranspiration (ET), and rainfall (P). To enable a consistent modeling approach across space, we modeled SWE dynamics separately for hydrologically distinct floodplain, floodplain-lake, and non-floodplain areas within eco-hydrological zones and 10km × 10km grid cells. We applied this spatial modeling framework to three sub-regions of the MDB, for which we quantified independently validated lag times between river gauges and each individual grid cell and identified the local combinations of variables that drive SWE dynamics. Based on these automatically quantified flow lag times and variable combinations, SWE dynamics on 233 (64 %) out of 363 floodplain grid cells were modeled with a coefficient of determination (r2) greater than 0.6. The contribution of P, ET, and SM to the predictive performance of models differed among the three sub-regions, with the highest contributions in the least regulated and most arid sub-region. The spatial modeling framework presented here is suitable for modeling SWE dynamics on finer spatial entities compared to most existing studies and applicable to other large and heterogeneous river basins across the world.
Context: Despite calls for landscape connectivity research to account for spatiotemporal dynamics, studies have overwhelmingly evaluated the importance of habitats for connectivity at single or limited moments in time. Remote sensing time series represent a promising resource for studying connectivity within dynamic ecosystems. However, there is a critical need to assess how static and dynamic landscape connectivity modelling approaches compare for prioritising habitats for conservation within dynamic environments. Objectives: To assess whether static landscape connectivity analyses can identify similar important areas for connectivity as analyses based on dynamic remotely sensed time series data. Methods: We compared degree and betweenness centrality graph theory metric distributions from four static scenarios against equivalent results from a dynamic 25-year remotely sensed surface-water time series. Metrics were compared at multiple spatial aggregation scales across south-eastern Australia’s 1 million km2 semi-arid Murray–Darling Basin and three sub-regions with varying levels of hydroclimatic variability and development. Results: We revealed large differences between static and dynamic connectivity metric distributions that varied by static scenario, region, spatial scale and hydroclimatic conditions. Static and dynamic metrics showed particularly low overlap within unregulated and spatiotemporally variable regions, although similarities increased at coarse aggregation scales. Conclusions: In regions that exhibit high spatiotemporal variability, static connectivity modelling approaches are unlikely to serve as effective surrogates for more data intensive approaches based on dynamic, remotely sensed data. Although this limitation may be moderated by spatially aggregating static connectivity outputs, our results highlight the value of remotely sensed time series for assessing connectivity in dynamic landscapes.
Detailed information on the number and density of trees is important for conservation and sustainable use of forest resources. In this respect, remote sensing technology is a reliable tool for deriving timely and fine-scale information on forest inventory attributes. However, to better predict and understand the functioning of the forest, fine-scale measures of tree number and density must be extrapolated to the forest plot or stand levels through upscaling. In this study, we compared and combined three sources of remotely sensed data, including low point density airborne laser scans (ALS), synthetic aperture radar (SAR) and very-high resolution WorldView-2 imagery to upscale the total number of trees to the plot level in a structurally complex eucalypt forest using random forest regression. We used information on number of trees previously derived from high point density ALS as training data for a random forest regressor and field inventory data for validation. Overall, our modelled estimates resulted in significant fits (p < 0.05) with goodness-of-fit (R 2) of 0.61, but systematically underestimated tree numbers. The ALS predictor variables (e.g. canopy cover and height) were the best for estimating tree numbers (R 2 = 0.48, nRMSE = 61%), as compared to WorldView-2 and SAR predictor variables (R 2 < 0.35). Overall, the combined use of WorldView-2, ALS and SAR predictors for estimating tree numbers showed substantial improvement in R 2 of up to 0.13 as compared to their individual use. Our findings demonstrate the potential of using low point density ALS, SAR and WorldView-2 imagery to upscale high point density ALS derived tree numbers at the plot level in a structurally complex eucalypt forest.
Australia is a continent subject to high rainfall variability, which has major influences on runoff and vegetation dynamics. However, the resulting spatial-temporal pattern of flooding and its influence on riparian vegetation has not been quantified in a spatially explicit way. Here we focused on the floodplains of the entire Murray-Darling Basin (MDB), an area that covers over 1M km<sup>2</sup>, as a case study. The MDB is the country’s primary agricultural area with scarce water resources subject to competing demands and impacted by climate change and more recently by the Millennium Drought (1999–2009). Riparian vegetation in the MDB floodplain suffered extensive decline providing a dramatic degradation of riparian vegetation. We quantified the spatial-temporal impact of rainfall, temperature and flooding patters on vegetation dynamics at the subcontinental to local scales and across inter to intra-annual time scales based on three decades of Landsat (25k images), Bureau of Meteorology data and one decade of MODIS data. Vegetation response varied in space and time and with vegetation types, densities and location relative to areas frequently flooded. Vegetation degradation trends were observed over riparian forests and woodlands in areas where flooding regimes have changed to less frequent and smaller inundation extents. Conversely, herbaceous vegetation phenology followed primarily a ‘boom’ and ‘bust’ cycle, related to inter-annual rainfall variability. Spatial patters of vegetation degradation changed along the N-S rainfall gradient but flooding regimes and vegetation degradation patterns also varied at finer scale, highlighting the importance of a spatially explicit, internally consistent analysis and setting the stage for investigating further cross-scale relationships. Results are of interest for land and water management decisions. The approach developed here can be applied to other areas globally such as the Nile river basin and Okavango River delta in Africa or the Mekong River Basin in Southeast Asia.