Hydromorphological attributes for all Australian river reaches derived from Landsat dynamic inundation remote sensing

Hydromorphological attributes such as flow width, water extent, and gradient play an important role in river hydrological, biogeochemical, and ecological processes and can help to predict river conveyance capacity, discharge, and flow routing. While there are some river width datasets at global or regional scales, they do not consider temporal variation in river width and do not cover all Australian rivers. We combined detailed mapping of 1.4 million river reaches across the Australian continent with inundation frequency mapping from 27 years of Landsat observations. From these, the average flow width at different recurrence frequencies was calculated for all reaches, having a combined length of 3.3 million km. A parameter γ was proposed to describe the shape of the frequency–width relationship and can be used to classify reaches by the degree to which flow regime tends towards permanent, frequent, intermittent, or ephemeral. Conventional scaling rules relating river width to gradient and contributing catchment area and discharge were investigated, demonstrating that such rules capture relatively little of the real-world variability. Uncertainties mainly occur in multi-channel reaches and reaches with unconnected water bodies. The calculated reach attributes are easily combined with the river vector data in a GIS, which should be useful for research and practical applications such as water resource management, aquatic habitat enhancement, and river engineering and management

On agricultural drought monitoring in Australia using Himawari-8 geostationary thermal infrared observations

Monitoring agricultural drought effectively and timely is important to support drought management and food security. Effective drought monitoring requires a suite of drought indices to capture the evolution process of drought. Thermal infrared signals respond rapidly to vegetation water stress, thus being regarded useful for drought monitoring at the early stage. Several temperature-based drought indices have been developed considering the role of land surface temperature (LST) in surface energy and water balance. Here, we compared the recently proposed Temperature Rise Index (TRI) with several agricultural drought indices that also use thermal infrared observations, including Temperature Condition Index (TCI), Vegetation Health Index (VHI) and satellite-derived evapotranspiration ratio anomaly (ΔfRET) for a better understanding of these thermal infrared drought indices. To do so, we developed a new method for calculating TRI directly from the top-of-atmosphere brightness temperatures in the two split-window channels (centered around ∼11 and 12 μm) rather than from LST. TRI calculated using the Himawari-8 brightness temperatures (TRI_BT) and LST retrievals (TRI_LST), along with the other LST-based indices, were calculated for the growing season (July–October) of 2015−2019 over the Australian wheatbelt. An evaluation was conducted by spatiotemporally comparing the indices with the drought indices used by the Australian Bureau of Meteorology in the official drought reports: the Precipitation Condition Index (PCI) and the Soil Moisture Condition Index (SMCI). All the LST-based drought indices captured the wet conditions in 2016 and dry conditions in 2019 clearly. Ranking of Pearson correlations of the LST-based indices with regards to PCI and SMCI produced very similar results. TRI_BT and TRI_LST showed the best agreement with PCI and SMCI (r > 0.4). TCI and VHI presented lower consistency with PCI and SMCI compared with TRI_BT and TRI_LST. ΔfRET had weaker correlations than the other LST-based indices in this case study, possibly because of outliers affecting the scaling procedure. The capability of drought early warning for TRI was demonstrated by comparing with the monthly time series of the greenness index Vegetation Condition Index (VCI) in a case study of 2018 considering the relatively slow response of the greenness index to drought. TRI_BT and TRI_LST had a lead of one month in showing the changing dryness conditions compared with VCI. In addition, the LST-based indices were correlated with annual wheat yield. Compared to wheat yields, all LST-based indices had a peak correlation in September. TRI_BT and TRI_LST had strong peak and average correlations with wheat yield (r ≥ 0.8). We conclude that TRI has promise for agricultural drought early warning, and TRI_BT appears to be a good candidate for efficient operational drought early warning given the readily accessible inputs and simple calculation approach

Monitoring agricultural drought in Australia using MTSAT-2 land surface temperature retrievals

Drought indices based on thermal remote sensing have been developed and have merit for effective early warning of agricultural droughts, but approaches so far are relatively complex or sensitive to land surface temperature (LST) estimation uncertainties. Here, we propose the temperature rise index (TRI), a drought index that is comparatively robust and easy to calculate, as the anomaly of the intrinsic morning rise of LST. The underlying principle is that the rate of LST rise between 1.5 and 3.5 h after the sunrise is approximately linear and occurs more rapidly under dry conditions than under wet conditions over vegetated surfaces as a consequence of stomatal control. TRI during the growing seasons of 2010–2014 was calculated over the Australian wheatbelt from LST retrievals from the geostationary Multifunction Transport Satellite-2 (MTSAT-2) instrument. The calculated TRI was compared with indices based on precipitation integrated over 1-, 3- and 6-month time scales, on Soil Moisture and Ocean Salinity (SMOS) soil moisture derived from passive microwave remote sensing, and on vegetation condition (normalized difference vegetation index, NDVI) derived from optical remote sensing. The various indices were also compared to annual wheat yield over large areas. The correlation coefficient between TRI and precipitation anomaly that serves as an operational drought index in Australia was above 0.6 in general with 3-month integrative time scale for precipitation. TRI produced spatiotemporal dryness patterns that were very similar to those in soil moisture, but with more detail due to its finer resolution. A time lag of >1 month was found between TRI and observed vegetation condition, supporting the use of TRI in early warning. Among the compared drought indices, TRI explained the largest fraction (35%) of wheat yield variations. TRI correlations with wheat yields peaked higher and earlier by almost one month in comparison to other indices. We conclude that the thermal drought index proposed here shows considerable potential for use in drought early warning as an effective complement

Comparison of remotely sensed and modelled soil moisture data sets across Australia

This study compared surface soil moisture from 11 separate remote sensing and modelled products across Australia in a common framework. The comparison was based on a correlation analysis between soil moisture products and in situ data collated from three separate ground-based networks: OzFlux, OzNet and CosmOz. The correlation analysis was performed using both original data sets and temporal anomalies, and was supported by examination of the time series plots. The interrelationships between the products were also explored using cluster analyses. The products considered in this study include: Soil Moisture Ocean Salinity (SMOS; both Land Parameter Retrieval Model (LPRM) and L-band Microwave Emission of the Biosphere (LMEB) algorithms), Advanced Microwave Scanning Radiometer 2 (AMSR2; both LPRM and Japan Aerospace Exploration Agency (JAXA) algorithms) and Advanced Scatterometer (ASCAT) satellite-based products, and WaterDyn, Australian Water Resource Assessment Landscape (AWRA-L), Antecedent Precipitation Index (API), Keetch-Byram Drought Index (KBDI), Mount's Soil Dryness Index (MSDI) and CABLE/BIOS2 model-based products. The comparison of the satellite and model data sets showed variation in their ability to reflect in situ soil moisture conditions across Australia owing to individual product characteristics. The comparison showed the satellite products yielded similar ranges of correlation coefficients, with the possible exception of AMSR2_JAXA. SMOS (both algorithms) achieved slightly better agreement with in situ measurements than the alternative satellite products overall. Among the models, WaterDyn yielded the highest correlation most consistently across the different locations and climate zones considered. All products displayed a weaker performance in estimating soil moisture anomalies than the original data sets (i.e. the absolute values), showing all products to be more effective in detecting interannual and seasonal soil moisture dynamics rather than individual events. Using cluster analysis we found satellite products generally grouped together, whereas models were more similar to other models. SMOS (based on LMEB algorithm and ascending overpass) and ASCAT (descending overpass) were found to be very similar to each other in terms of their temporal soil moisture dynamics, whereas AMSR2 (based on LPRM algorithm and descending overpass) and AMSR2 (based on JAXA algorithm and ascending overpass) were dissimilar. Of the model products, WaterDyn and CABLE were similar to each other, as were the API/AWRA-L and KBDI/MSDI pairs. The clustering suggests systematic commonalities in error structure and duplication of information may exist between products. This evaluation has highlighted relative strengths, weaknesses, and complementarities between products, so the drawbacks of each may be minimised through a more informed assessment of fitness for purpose by end users