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Floodplains are a key ecological feature of arid and semi-arid regions and flooding is the main source of vegetation productivity. Characterizing the spatiotemporal flood dynamics, such as surface water and changes in subsequent vegetation growth vigour and biomass, is essential in better understanding of ecology and hydrology of these regions. Recent remotely sensed flooding studies of arid zone floodplains have concentrated on improving classification of surface water, particularly in mixed water-vegetation areas; less is known about the effect of surface water flooding pattern on emergent vegetation dynamics. We use an integrated framework for mapping both flood extents and the persistence of floodplain response changes of water, vegetation and moisture in Cooper Creek, Australia. We analysed pixel-based time series of multiple indices generated by daily MODIS data for 14 highly variable flow flood events between 2000 and 2012. Results indicate that for the extremely flat Cooper Creek floodplain, mapping inundation area by changes in vegetation growth vigour and biomass was significantly larger than surface water mapping area (16.2% for the 2004 flood event) and the difference in inundation mapping mainly occurs around the inundated edges. In addition, by studying surface water and subsequent vegetation response together, it is possible to generate new information, such as the lag time between flooding and peak vegetation growth vigour and biomass, and persistence time of surface water and green vegetation, which provide important insights to arid zone floodplain behaviour. The large extent and high frequency of MODIS images provide advantages in characterizing inundation dynamics for large-scale floodplains where instantaneous (daily) inundation extent is considerably smaller than total cumulative inundation extent, compared with sensors with higher spatial resolution but lower temporal resolution; however, the coarse resolution of MODIS (500 m) limits its performance for small flood events. Globally, this approach is suitable for other large, low-gradient floodplains in arid zones that show similar, long duration vegetation responses as observed in Cooper Creek. [ABSTRACT FROM AUTHOR]

[Display omitted] • In asynchronous climates, the forest growth response to rainfall is not well known. • Lagged relationship of forest growth to antecedent rainfall was explored. • Vegetation in the asynchronous climate have a long memory to the rainfall. • The memory of vegetation to rain could indicate soil/rooting depth across the landscape. The out-of-phase rainfall and temperature and deep root system make the sequential connection between past rainfall events, soil water storage, and forest growth response complicated and temporally extended in asynchronous climates with Mediterranean-type settings. Unfortunately, these location-specific deep-soil water stores are rarely measured due to logistic and financial constraints, especially in the forest. Therefore, at a large spatio-temporal scale, forest growth relationship to growth drivers is still unknown in these ecosystems, limiting our knowledge to understand the functioning of these forests and their links with hydrological processes. Although process-based water balance models can analyze vegetation growth response to the input climate forcing, they rely upon some significant assumptions regarding plants access to deep soil water storage. Thus, this study aims to understand how the out-of-phase rainfall events affect the current ecosystem growth response, represented by the observed Normalized Difference Vegetation Index (NDVI), across the landscape. We have used an empirical approach on long term observed data without any assumption on access to deep-soil water stores. We estimated time lags between forest growth and rainfall events using a lagged correlation analysis applied to monthly anomalies of NDVI and rainfall against their climatological averages over 2002–2018. The study found that the forests in asynchronous climates exhibit unexpectedly long (10–25 months) memory to rain, and this memory has a systematic pattern across the landscape, which we contend has highlighted three things: 1) the forest in the middle aridity (∼3–4.5) range are relatively more sensitive to changes in the short-term rainfall than the forest in lower (<3) and higher (>5) aridity regions, could be due to rapid depletion of relatively small soil water storage in between the storms, 2) the variable memory of forest to rain across the landscape can be an indicator of soil depth/rooting depth, and 3) the variable sized location specific antecedent rainfall windows can explain significant variability in forest growth status in asynchronous climates, thus these rainfall windows can be employed to forecast forest growth with a lead time (>4 months). [ABSTRACT FROM AUTHOR]