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• A paired-watershed experiment provides more understanding via state-controlled test. • Runoff change after deforestation increases for higher rainfall intensity. • Higher antecedent rainfall state decreases the deforestation impact on runoff. Understanding watershed processes is critical to predicting and mitigating flood and drought risk. Over the past century, the capability to isolate the influence of individual factors on hydrologic responses has been enhanced through the introduction and application of paired-watershed experiments. However, the high variability of paired experiment outcomes has been noted to limit conclusive reasoning from the results. Thus, in an effort to resolve this issue, this study assumes that the high variability in paired-watershed experiments is due to the changes in the watershed system states (e.g., soil moisture and groundwater states). A state-controlled hypothesis test is proposed for the paired-watershed experiment to test treatment impact with the control of watershed system states. The application of the state-controlled test to one pre-existing paired-watershed experiments in southeast Australia showed that the variability in the test results can be reduced and provide further understanding about the nature of changes that occur. [ABSTRACT FROM AUTHOR]

• Changes in both soil moisture and extreme rainfall influence historical flood trends. • The influence of soil moisture and extreme rainfall depends on flood severity. • The more extreme a flood event is, the less dependent it is on soil moisture changes. • Changes in soil moisture need to be considered in projections of flooding. Despite the expectation that increases in rainfall with climatic change will result in increases in pluvial flooding, there is more historical evidence for decreases in flood magnitude. In Australia, as in many other parts of the world, flood magnitudes are mostly decreasing, despite increasing rainfall extremes. Here, we show how changes in soil moisture have led to decreasing flood magnitudes while rainfall extremes have been increasing. Using gauged streamflow, catchment average rainfall, and modelled soil moisture data across Australia we confirm that streamflow peaks (or floods) that occur at least once a year are strongly related to both the peak rainfall causing the flood and the antecedent soil moisture conditions preceding the storm event. Regions where the magnitude of the peak flow has decreased are visually and statistically correlated to regions of decreasing soil moisture. Flood magnitudes are more likely to increase only for the rarest events, with increases in rainfall offset by decreases in soil moisture for more frequent events. A recurrence interval dependent tipping point is identified beyond which trends in catchment rainfall outweigh those of soil moisture trends and dominate the flood response. For regions where soil moisture has decreased, this tipping point occurs at an average recurrence interval of ten years or more. As most studies investigating trends in flooding generally use annual maxima, changes in soil moisture are likely to be the dominant mechanism behind observed flood trends. Hence changes in soil moisture conditions need to be considered when predicting catchment flood response due to climatic change. [ABSTRACT FROM AUTHOR]

• Enough water to the sites despite urban surroundings and different water regimes. • Groundwater contributes 30–40% of the total transpiration during the driest periods. • Transpiration is 3–16% of total daily transpiration during night-time heat stress. During the expansion of urban areas, small natural reserves have often been left intact within the built environment as central elements of biodiversity conservation, ecological connectivity, landscape sustainability, and quality of life of urban dwellers. Consequently, the surrounding urbanized landscape may impact the environmental conditions of these reserves (e.g., high temperatures, low moisture conditions), resulting in the need for extensive maintenance. This study presents an estimation of the water balance over two years (2017–2018) in three small urban reserves (between 2 and 30 ha) within the Greater Melbourne metropolitan area in Australia, for the purpose of understanding tree water use. Measurements of micrometeorological variables, soil moisture content profiles, water-table levels, sap flow velocities, and stem diameter variations were used to quantify the water sources of tree transpiration in these reserves. Results revealed that, despite the urban surroundings and the climate variations, these reserves have enough water to sustain tree transpiration. In two of the three reserves, groundwater was pivotal in sustaining transpiration rates; specifically, groundwater was estimated to contribute about 30–40% of the total transpiration amount during the driest periods of the year. Groundwater also played an essential role during nights with temperatures above 25 °C, helping trees to maintain night-time water use from 3 to 16% of the daily water use. In the third reserve, the presence of a shallow layer of heavy clay supplied water to the trees, which were able to maintain relatively constant transpiration rates throughout the year. These results demonstrate the importance of understanding the water regime of each urban reserve in order to support government authorities in preserving these ecosystems. [ABSTRACT FROM AUTHOR]

• Hydrologic impacts of climate change assessed in Manning River basin using XAJ model. • XAJ model performs satisfactorily with R2 and NSE more than 0.94 and 0.92. • Runoff is projected to increase in summer and decrease in winter. • Projected changes in water availability are largely dominated by change in rainfall. Hydrological responses of catchments to climate change require detailed examination to ensure sustainable management of both water resources and natural ecosystems. This study evaluated the impacts of climate change on water resource availability of a catchment in eastern Australia (i.e. the Manning River catchment) and analyzed climate-hydrology relationships. For this evaluation, the Xinanjiang (XAJ) model was used and validated to simulate monthly rainfall-runoff relationships of the catchment. Statistically downscaled climate data based on 28 global climate models (GCMs) under RCP8.5 scenarios were used to assess the impacts of climate changes on the Manning River catchment. Our results showed that the XAJ model was able to reproduce observed monthly rainfall-runoff relationships with an R2 ≥ 0.94 and a Nash-Sutcliffe Efficiency ≥ 0.92. The median estimates from the ensemble of downscaled GCM projections showed a slight decrease in annual rainfall and runoff for the period 2021–2060 and an increase for the period 2061–2100. Annual actual evapotranspiration was projected to increase slightly, while annual soil moisture content was predicted to decrease in the future. Our results also demonstrated that future changes in seasonal and annual runoff, actual evapotranspiration and soil moisture are largely dominated by changes in rainfall, with a smaller influence arising from changes in temperature. An increase in the values of high runoffs and a decrease in the values of low runoffs predicted from the ensemble of the 28 GCMs suggest increased variability of water resources at monthly and seasonal time-scales in the future. A trend of decreasing values in winter runoff and soil moisture content in the future is likely to aggravate possible future reductions in water availability in eastern Australia. These results contribute to the development of adaptive strategies and future policy options for the sustainable management of water resources in eastern Australia. [ABSTRACT FROM AUTHOR]

• The SMAP and SMOS soil moisture products were compared against in-situ observations. • Satellite soil moisture products were downscaled using thermal inertia theory. • A regression tree was developed for downscaling, based on in-situ soil moisture data. • Downscaled SMAP and SMOS products showed ubRMSEs of 0.07 and 0.05 cm3/cm3. • Downscaled airborne soil moisture retrievals showed an accuracy of 0.07 cm3/cm3. High spatial resolution soil moisture information is important for hydrological, climatic and agricultural applications. The lack of high resolution soil moisture data over large areas at the required accuracy is a major impediment for such applications. This study investigates the feasibility of downscaling satellite soil moisture products to 1 km resolution. This study was undertaken in the semi-arid Goulburn River Catchment, located in south-eastern Australia. The Soil Moisture Active Passive (SMAP)-Enhanced 9 km (L3SMP-E) and Soil Moisture and Ocean Salinity (SMOS) 25 km gridded (SMOS CATDS L3 SM 3-DAY) radiometric products were compared with in-situ soil moisture observations and a regression tree model was developed for downscaling based on thermal inertia theory. Observations from a long-term soil moisture monitoring network were employed to develop a regression tree model between the diurnal temperature difference and the daily mean soil moisture for soils with different clay content and vegetation greenness. Moderate-resolution Imaging Spectroradiometer (MODIS) land surface temperatures were used to estimate the soil moisture at high spatial resolution by disaggregating the satellite soil moisture products through the regression model. The downscaled SMAP-Enhanced 9 km and SMOS 25 km gridded soil moisture products showed unbiased root mean square errors (ubRMSE) of 0.07 and 0.05 cm3/cm3, respectively, against the in-situ data. These ubRMSEs include errors caused by measuring instrument and the scale mismatch between downscaled products and in-situ data. An RMSE of 0.07 cm3/cm3 was observed when comparing the downscaled soil moisture against the passive airborne L-band retrievals. The findings here auger well for the use of satellite remote sensing for the assessment of high resolution soil moisture. [ABSTRACT FROM AUTHOR]

• Proactive drought responses are crucial in a drought vulnerable future. • Antecedent conditions need to be considered in context of anticipated conditions. • We combine rainfall, soil moisture and evapotranspiration data with forecasts for early warning. • Validation efforts found promising accuracy in the drought early warning maps. • Proof-of-concept drought early warning system can aid proactive drought management. Droughts are a cyclical feature of Australia's climate that have compounding impacts on agricultural productivity and the wellbeing of drought-affected communities. Understanding anticipated drought conditions in context of antecedent observations is critical to providing early warning of drought. In this study we paired probabilistic seasonal rainfall forecasts with precipitation, soil moisture and evapotranspiration data that were objectively combined using Principal Component Analysis (PCA). The final Drought Early Warning System (DEWS) maps overlay forecasting information with the multi-variate PCA-weighted maps at 1-, 3- and 6-month timescales over the common period of overlap between all datasets (1982–2018). In this study period, the 1982–1983 Ash Wednesday tinder drought and the 1997–2001 Millennium drought were investigated. We validated PCA-weighted maps with satellite vegetation data and found performance was strongest over the Murray Darling Basin region (R = 0.63, p = 0.009) and poorest over Central interior Australia (insignificant correlations). We also validated PCA-weighted maps using agricultural commodity data from ABARES. Significant negative correlations at 95 %, 99 % and 99.9 % confidence intervals were found between %-Area in drought category and crop cultivation area; export volume/value; crop yield; and rural debt. Our findings indicate that early warning of drought can be categorised by concern – wherein dry antecedent conditions and dry forecasted conditions are of the highest concern. The developed proof-of-concept DEWS contributes to the growing body of proactive drought management research. In a drought vulnerable future, operationalising and communicating drought early warnings will be critical to reducing the harmful impacts of drought on communities, economies, and environments. [ABSTRACT FROM AUTHOR]

Accurate models of soil moisture are vital for solving core problems in meteorology, hydrology, agriculture and ecology. The capacity for soil moisture modelling is growing rapidly with the development of high-resolution, continent-scale gridded weather and soil data together with advances in modelling methods. In particular, the GlobalSoilMap.net initiative represents next-generation, depth-specific gridded soil products that may substantially increase soil moisture modelling capacity. Here we present an implementation of Campbell’s infiltration and redistribution model within the NicheMapR microclimate modelling package for the R environment, and use it to assess the predictive power provided by the GlobalSoilMap.net product Soil and Landscape Grid of Australia (SLGA, ∼100 m) as well as the coarser resolution global product SoilGrids (SG, ∼250 m). Predictions were tested in detail against 3 years of root-zone (3–75 cm) soil moisture observation data from 35 monitoring sites within the OzNet project in Australia, with additional tests of the finalised modelling approach against cosmic-ray neutron (CosmOz, 0–50 cm, 9 sites from 2011 to 2017) and satellite (ASCAT, 0–2 cm, continent-wide from 2007 to 2009) observations. The model was forced by daily 0.05° (∼5 km) gridded meteorological data. The NicheMapR system predicted soil moisture to within experimental error for all data sets. Using the SLGA or the SG soil database, the OzNet soil moisture could be predicted with a root mean square error ( rmse ) of ∼0.075 m 3 m −3 and a correlation coefficient ( r ) of 0.65 consistently through the soil profile without any parameter tuning. Soil moisture predictions based on the SLGA and SG datasets were ≈ 17% closer to the observations than when using a chloropleth-derived soil data set (Digital Atlas of Australian Soils), with the greatest improvements occurring for deeper layers. The CosmOz observations were predicted with similar accuracy ( r  = 0.76 and rmse of ∼0.085 m 3 m −3 ). Comparisons at the continental scale to 0–2 cm satellite data (ASCAT) showed that the SLGA/SG datasets increased model fit over simulations using the DAAS soil properties ( r  ∼ 0.63 & rmse 15% vs. r 0.48 & rmse 18%, respectively). Overall, our results demonstrate the advantages of using GlobalSoilMap.net products in combination with gridded weather data for modelling soil moisture at fine spatial and temporal resolution at the continental scale. [ABSTRACT FROM AUTHOR]

• Soil water affects litter moisture predictions. • Vapour and capillary rise from the soil were represented to estimate litter moisture. • Models ignoring soil water effects underestimated litter moisture. • Hydraulic coupling between the litter and soil is important. Litter moisture content plays a critical role in fire danger rating systems and forest ecosystems. Soil moisture content has been proposed to affect litter moisture due to vapour flux and capillary rise from the soil. However, few models consider soil water content when predicting litter moisture, and to our knowledge, no model includes capillary fluxes. In this study, we represented soil moisture coupling in a physics-based litter moisture prediction model by describing the vapour and capillary fluxes from the soil to litter. We aimed to evaluate if litter moisture predictions can be improved by incorporating the hydrological process at the soil-litter interface and explored the possible role of soil moisture in litter moisture simulations. Three model versions were compared against observations at a dry and wet experimental site in Australia: the original physics-based model, a model version extended with soil vapour flux only, and a version that incorporates both soil vapour flux and capillary rise. The simulation results suggest that soil moisture considerably influences litter moisture through soil vapour flux and capillary rise, which can lead to more than 10% of oven-dried weight, particularly under wet soil conditions. The corresponding model showed the best performance in comparison with field observations. The contribution of upward soil moisture fluxes was small after long dry and warm periods but noticeable during more moist periods. Further research is needed to evaluate the revised model for a broader range of weather, soil and litter conditions. [ABSTRACT FROM AUTHOR]

Summary: This paper examines the use of a controlled fuel reduction burn in native woodland to increase recharge to a shallow aquifer system for water supply in south-western Australia. Soil moisture trends from neutron probe measurements and watertable dynamics from piezometers under adjacent burned and unburned plots were compared to determine the difference in recharge resulting from the fire. Remotely sensed evapotranspiration (ET) subtracted from rainfall was used to independently estimate groundwater and soil moisture recharge and compare against a physically based model of soil–water fluxes. The use of naturally occurring chemicals (n-alkanes and cycloalkanes) produced in the fire as tracers of the recharge pulse in the winter following the fire was tested. It was found that in the winter following the fire (annual rainfall 701mm), water storage in the 9m thick unsaturated zone increased by about 300mm but recharge to the watertable was only about 60mm. In the subsequent year (rainfall 630mm), leaf area index in the burned woodland recovered to be about 10% less than the unburned plot, remotely sensed ET rose above that in the unburned plot and recharge was lower in the burned than unburned plot. This trend continued and, after 3years, increased ET negated any early recharge gains, and there was a net decrease in recharge resulting from the fire. The combination of water balance measurements, modelling and remote sensing provides a robust method to gain hydrological understanding of recharge mechanisms. Further work is required to refine the use of burn residue chemicals as soil water tracers, particularly in improving instrument design for groundwater sample collection in fine textured soils. As climate continues to change and regions with growing water shortages seek innovative solutions, approaches such as controlled burning may be needed; however, in this example, the approach was unproductive and further the environmental consequences need to be evaluated to make an appropriate assessment of trade-offs and viability of this approach. [Copyright &y& Elsevier]

Summary: Across southern Australia, a large proportion of urban water supply is sourced from mountainous catchments forested with native eucalypts. Mixed species eucalypt forest (MSEF) is the most common forest type in this region and occurs on relatively dry, fire prone sites, yet factors controlling forest water use and stream flow in response to topography, disturbance and drought are poorly understood. This study investigated the patterns and drivers of water balance over a 4year period in a 1.36km2, MSEF catchment by: quantifying spatial and temporal variability in evapotranspiration (Et ) and its components; evaluating the abiotic, structural and physiological factors controlling water use across the catchment; and testing the effects of antecedent soil water conditions on water fluxes after drought. This was done using a ‘bottom up’ measurement approach that included stream flow and Et (sap flow, interception troughs and evaporation dome) and a simple empirical model of Et to track catchment response to drought. Spatial variability was considerable, with 40% lower rates of Et at an up slope plot compared to mid and bottom slope plots. Tree transpiration was the dominant flux annually and was correlated to reference Et (r 2 =0.35–0.80), implying strong limitation by atmospheric demand across the catchment. Annual Et totals were relatively consistent between years (841±34mm) despite large variation in rainfall (463–1179mmy−1). Annual stream flow represented a very small proportion of the water budget (<2% of rainfall) and showed little recovery from the drought period. The modelling showed that the change in soil water storage following drought was large (up to −330mm) and was responsible for decreased rates of stream flow. These findings show that Et in the MSEF is sensitive to topography and demand limitation and suggests that water yield in this forest type may be particularly sensitive to future climatic change as shown by the sustained effect of drought on catchment water balance. [Copyright &y& Elsevier]

Summary: Transpiration is a major component of the water balance of tropical rainforests and it is important to know how it might change in response to climate and/or land use change. Substantial amounts of transpiration data are now available for Australian rainforests in northern Queensland and this paper presents an analysis of these data for four different rainforest types. Measured transpiration rates are much lower than potential transpiration rates, largely because the rainforest canopy conductances are much lower than that associated with the potential rate. Canopy conductance was found to be strongly related to atmospheric vapour pressure deficit and only weakly to solar radiation; because the forests are highly coupled to the atmosphere, due to their very high aerodynamic conductance. The best predictive model of rainforest transpiration is one based directly on daily average vapour pressure deficit and solar radiation. Models based on canopy conductance can give daytime transpiration rates, but they do not account for the significant amounts (20–50%) of nocturnal transpiration that are observed. We also found little/no evidence for any direct soil moisture influence on canopy conductance or transpiration. [Copyright &y& Elsevier]

Summary: Soil moisture patterns and dynamics are important for numerous applications such as flood forecasting, climate modeling, and management of agricultural lands. Unfortunately, widespread observations of soil moisture are not currently available at the spatial scale of most of these applications. Given these data limitations and the complexity of soil moisture dynamics, there is a need to gain a better understanding of soil moisture patterns and to develop methods that can efficiently estimate these patterns from limited observations. In this paper, we use Empirical Orthogonal Function (EOF) analysis to study the Tarrawarra soil moisture dataset from Australia. EOF analysis partitions the observed variation into a series of time-invariant spatial patterns (EOFs) that can be multiplied by temporal varying (but spatially constant) coefficients and summed to reconstruct observed soil moisture patterns. Using this approach, we identify two spatial patterns underlying soil moisture variability at Tarrawarra, which supports previous contentions that the spatial patterns are controlled by local soil properties in wet and dry conditions and topographic characteristics during intermediate conditions. We also use the EOF analysis to identify points whose variability is most representative of each of the underlying spatial patterns and thus can be used to monitor these distinct modes of variability. Finally, we show that the EOF approach can be used to estimate soil moisture patterns for unobserved times if a field campaign has collected detailed soil moisture observations for a limited time period in the past. [Copyright &y& Elsevier]

This paper assesses the potential for measuring spatial patterns of dielectric constant using AirSAR images at high spatial resolutions (up to 10 m pixel sizes). Dielectric constant is strongly related to soil moisture. Data from the 10 ha Tarrawarra experimental catchment in south-eastern Australia are used. Ground data collected include detailed spatial patterns of dielectric constant measured with time domain reflectometry on a 10 m grid over the 10 ha catchment (∼1000 measurements), as well as surface roughness and vegetation characteristics. The average dielectric constant was 16.9, soil moisture was 29.6% volumetric, rms surface roughness height was 1.7 cm and average pasture biomass was 0.18 kg/m2.At the 30 m pixel scale, empirical comparisons between the ground-measured dielectric constant and various band and polarization combinations result in R2 values of 0.22–0.43 for L- and P-band, but were 0.00 for C-band. Using the Dubois model [IEEE Trans. Geosci. Remote Sensing 33 (1995a) 915; IEEE Trans. Geosci. Remote Sensing 33 (1995b) 1340)] in forward mode and comparing backscatter coefficients improved the R2 for L-band to 0.31 and 0.37 for hh and vv polarizations, respectively. Some systematic underestimation occurred. Using the L-band images, the Dubois model operated in inverse mode produced an acceptable estimate of the spatial mean dielectric constant (and soil moisture) but was unable to extract any useful information on the spatial pattern at the 30 m pixel scale. An analysis of the change in error with pixel scales between 10 and ∼100 m suggests that aggregation up to a pixel size of about 50 m is required to remove most of the random error in the inverted dielectric constant (soil moisture) estimate. [Copyright &y& Elsevier]

• We developed a seasonal hydrological ensemble forecasting system for Australia. • The system predicts root-zone soil moisture, evapotranspiration and runoff nationally. • The forecasts use a climate forecasting system combined with a hydrological model. • We assessed the performance of predicting mean conditions and hydrological extremes. • The forecasts show sufficient skill up to three months ahead for many applications. The seasonal predictability of hydrological variables at high resolution across Australia was assessed using a new national seasonal ensemble forecasting system for soil moisture, evapotranspiration and runoff. The system consists of a gridded water balance model (AWRA-L) forced with downscaled and calibrated seasonal climate forecasts from a climate modelling system, ACCESS-S1. Here, we evaluate the hydrological forecasts at one- to six-months lead time relative to a historical reference simulation forced with observed climate inputs. Deterministic and probabilistic skill, accuracy and reliability of the forecast ensemble were assessed, with a specific focus on forecasts of hydrological extremes. Additionally, we assess the performance of the hindcast for selected use cases, particularly focusing on agriculture and water management. Nationally, our assessment of the hydrological forecasts indicated good skill for all variables at one-month lead time, and up to three-months lead time for soil moisture and actual evapotranspiration in many key regions in Australia. Regarding hydrological extremes, we find particularly high skill at longer lead times for low soil moisture and actual evapotranspiration events, highlighting the potential value for applications such as hydrological drought prediction or bushfire risk assessments. Bias of the hydrological forecasts based on calibrated seasonal climate forecasts was shown to be close to zero for all variables, regions and lead times and insignificant, with a minor exception for potential evapotranspiration. More detailed evaluation across economically important regions and river basins, in particular, the Australian Wheatbelt and the Murray-Darling Basin (the largest river basin in Australia and an economically and environmentally critical region), highlights promising potential for the soil moisture and evapotranspiration forecast to be used in applications in agriculture, such as for optimising irrigation or scheduling sowing and harvesting dates. For hydrologically important headwater regions across the Great Dividing Range and the alpine regions of Victoria, the forecasts of runoff show good skill at longer lead times indicating the value of the forecasts for reservoir management. Overall, we conclude that the forecasting system shows sufficient skill for a wide range of applications and regions up to a lead time of three months. We outline limitations of the presented system and highlight potential future research directions. [ABSTRACT FROM AUTHOR]

• New disaggregation scheme for rainfall-runoff models based on soil moisture. • Assessed ability of disaggregated monthly models to match daily flow statistics. • Disaggregation largely performed better than daily models, even during drought. • Disaggregation performance especially suited to eco-hydrological investigations. • Allows complex models and assessments to be conceptualised in a simpler way. Daily timescale hydrological information is important for many purposes such as flood estimation, predicting the consequences of catchment management and meeting the needs of freshwater ecology. In hydrological assessments, daily timestep modelling is typically used because of the availability of daily data and many of the processes governing impacts occur at this timescale. However, daily models can suffer from poor performance in certain contexts (e.g. drier climates), and their computational requirements can make it difficult to efficiently explore many sources of uncertainty in some situations, such as understanding the impacts of climate change in larger water resource systems. Here, we test an alternate approach based on monthly modelling with a post-processing step involving monthly-to-daily disaggregation using historic flow patterns conditioned on soil moisture estimates. We apply our approach to 214 catchments across Australia representing a wide range of climate and hydrological conditions, and assess outcomes for multiple objectives spanning water supply, flood magnitude and freshwater ecological outcomes, and validate performance over an extreme multi-year drought with substantially different rainfall and streamflow characteristics. Our results show that for many metrics including sustained low flows, annual flow maxima, and high and low flow spells, the results based on monthly hydrologic modelling with daily disaggregation are generally better than those based on daily hydrological modelling. This was especially true for ecologically relevant flow metrics. In addition, the disaggregation approach fared better than the daily model when extrapolating to the multi-year dry period. Our approach also has the potential to greatly reduce the effort required to explore uncertainty in large river systems. [ABSTRACT FROM AUTHOR]

• SPEI/SPI-12 are better predictors of hydrological stores like groundwater (GW). • Influence of global climate teleconnections is better diagnosed with SPEI/SPI-12. • GW loss in Murray Darling Basin occurred about 44% of the time during 2002–2017. • Extreme events (droughts and floods) affect the distribution of surface water extent. • SPEI/SPI-3 are excellent metrics that capture variability in annual crop production. Risk-management solutions that increase opportunities for adaptation plans and resilience to climate change require an understanding of climate change processes and the trickle down effects of droughts and floods on hydrological and socio-economic systems. To guide future expectations about droughts, this study aims to assess large-scale variability of hydrological stores (groundwater, soil moisture, and surface water) and their responses to drought intensities over large semi-arid domains in Australia. Multi-scaled indicators synthesised from in-situ rainfall (SPI-standardised precipitation index), water budget (SPEI-standardised evapotranspiration precipitation index), model (soil moisture-SM), and satellite observations (groundwater storage-GWS, surface water extent-SWE, and terrestrial water storage-TWS) are employed to assess this variability in climatic hotspots identified through statistical rotation. The link between these hotspots and climate modes are diagnosed using gaussian kernel-based SVMR (support vector machine regression), and quantile function of storage (QFS) is used to assess the response of hydrological stores to climate variability. The capability of these indicators to capture impacts of water deficit on agricultural systems is explored by implementing a PLSR (partial least square regression). Results show that (i) drought characteristics and intensities vary greatly across Australia but hotspots of higher drought duration and intensity are predominant in regions below latitude 25 ° S, (ii) hydrological drought indicators (SPEI/SPI-12) are better predictors of hydrological stores such as TWS and GWS, though the latter shows strong but opposite phase relationship with climate data, (iii) influence of climate teleconnections is better diagnosed and predicted with SPEI/SPI-12, and the Murray Darling basin-MDB (r = 0.84 , α = 0.05) and central/east coast (r = 0.80 , 0.79 , α = 0.05) are major teleconnection hotspots, (iv) SM and GWS losses in the MDB occurred 60% and about 44% of the time, respectively, during the 2002 - 2017 period, (v) extreme events (droughts and floods) affect the distribution of SWE, and (vi) meteorological drought indicators (SPEI/SPI-3) are excellent metrics that capture variability in crop production. Choosing appropriate indicators that reflect the response of freshwater ecosystems (including groundwater) to climatic and anthropogenic constraints is part of a key process to guide investments in drought resilience and sustainable management of water resources. [ABSTRACT FROM AUTHOR]

• Two drought Indices were derived by using 1 km SMAP soil moisture. • The drought indices were used to analyze spatial and temporal drought conditions in Australia. • More severe drought conditions occurred during spring and summer in Australia since 2017. • The drought indices were positively correlated to SMAP soil moisture and GPM precipitation. Drought is one of the major hazards that could have a significant impact on agriculture. In this study, two drought indices at high spatial resolution: Soil Water Deficit Index (SWDI) and Soil Moisture Deficit Index (SMDI) were derived by 1 km downscaled Soil Moisture Active Passive (SMAP) soil moisture (SM), Global Land Data Assimilation System (GLDAS) long-term SM and soil attribute products, and used to analyze the drought conditions in Australia in 2015–2019. The SWDI was calculated from SMAP SM estimates and SM at field capacity/wilting point derived from soil attribute data, while the SMDI was calculated by integrating GLDAS and SMAP SM using a temporally incremental based method. We found that in the eastern and western coastal regions, the droughts occurred during spring and summer and were relieved in fall and winter. The temporal change pattern of drought conditions for the northern coastal regions was opposite of the eastern/western coasts. On the other hand, the inland regions always had more severe drought conditions. Additionally, the validation results for the 1 km SMAP SM using International Soil Moisture Network (ISMN) in situ data showed reliable accuracy and the Root Mean Square Deviation (RMSD) ranged from 0.02 to 0.09 m3/m3. Both SWDI and SMDI showed clear seasonal and interannual variability, and the drought conditions worsened in 2017–2019. From the 1 km SWDI/SMDI maps in the Murray-Darling River Basin, terrain and streamflow were found to be two deterministic factors for the drought conditions. [ABSTRACT FROM AUTHOR]

• Australian Landscape Water Balance model is used to evaluate hydrologic trends. • Trends in modelled streamflow are evaluated against observed streamflow. • Rainfall, soil moisture, and streamflow trends are consistent with tropical expansion. • Changes in flood and drought are linked to mean rainfall changes at continental scale. • Trends are more likely to be driven by local factors at the catchment scale. Changes in the hydrologic cycle have far reaching impacts on agricultural productivity, water resources availability, riverine ecosystems, and our ability to manage environmental assets, bushfire risk, and flood hazard. For example, declining rainfall in the southeast of Australia has led to a prolonged period of drought, with serious impacts on agriculture, the environment, and water supply to urban and rural towns. Here, using the continental wide Australian Water Resources Assessment Landscape model (AWRA-L), we evaluate historical trends from 1960 to 2017 in rainfall, soil moisture, evapotranspiration, and runoff to explain changing drought and flooding. Northern parts of Australia have experienced increasing annual rainfall totals, resulting in increased water availability in the tropics with increased soil moisture, evapotranspiration, and runoff, particularly during the hot, wet monsoon season. In contrast, the southwest and southeast coast of Australia have experienced declines in rainfall, particularly in the colder months, corresponding with decreasing evapotranspiration, soil moisture, and runoff. Trends in flooding are aligned with runoff trends, and closely follow trends in rainfall, with changes in soil moisture of secondary influence. Streamflow droughts, measured by the standardised runoff index, are increasing across large parts of Australia, with these increases more widespread than changes in rainfall alone. Increases in rainfall in the tropics of northern Australia appear to be related to decreasing drought occurrence and extent, but this trend is not universal, suggesting changes in rainfall alone are not an indicator of changing drought conditions. [ABSTRACT FROM AUTHOR]

• mean daily evaporative losses from the soil for dry periods varied 0.2–0.7 mm d−1. • mean evaporative daily losses during the wet period varied 0.3–13.0 mm d−1. • Hargreaves-Samani equation overestimated soil evaporative losses by two-fold. • Stage 2 and 3 evaporation from soil can account for up to 80% of precipitation. In terrestrial environments evaporative losses constitute up to 40% of evapotranspiration. While ~ 65% of these evaporative losses are from soil, this component is usually only coarsely estimated for water budget calculations. The most common method of estimating potential maximum evaporative (reference) loss is based on solar radiation and weather parameters but does not consider soil water availability. While such estimates of evaporative loss are usually credible in wet climates, they are likely to be inaccurate under warmer and drier climates characterised by strongly episodic or seasonal rainfall. In this study, we used a weighing lysimeter (Ready-To-Go RTG, Umwelt-Geräte-Technik, Germany) to quantify soil evaporative loss in situ and to assess how evaporative loss varied between wet and dry cycles in the semiarid and subtropical Hamersley Basin of the Pilbara region of northwest Australia. Mean daily evaporative losses from the soil for dry periods varied between 0.2 and 0.7 mm d−1. In contrast, mean daily evaporative losses during the wet period varied between 0.3 and 13.0 mm d−1, averaging 3.7 mm d−1. The commonly used mathematical equations for estimation of potential maximum evaporative (reference) loss, such as the Ventura modified Hargreaves-Samani equation, overestimated soil evaporative losses by about two-fold. The results of this study highlight the importance of accounting for the transition between energy limited Stage 1 evaporation versus water-limited Stage 2 and Stage 3 evaporation, as these latter Stages can dominate for extended periods in semiarid climates. [ABSTRACT FROM AUTHOR]

• Cyclones deliver a unique stable isotope environmental tracer in the hot semi-arid sub-tropics. • Cyclones replenished soil moisture in the top 3.5 m of the unsaturated zone. • Evaporative losses from wet soil varied between 8 and 220 mm × yr−1. • Mean long-term groundwater recharge was <6 mm × yr−1. Evaporative flux from soils in arid and semi-arid climates can be very high and may substantially reduce soil moisture retained between infrequent rainfall events. Direct measurement of the evaporative losses from soils is technically challenging. However, environmental tracers such as water stable hydrogen and oxygen isotope composition can be used to calculate evaporation rates if the initial signature of the infiltrating rainwater is distinct from the signature of residual soil moisture. Large tropical cyclones typically result in rainfall events of large volume and very negative δ18O signatures that are significantly lower than the signatures of more frequent and smaller rainfall events. These very negative stable isotope signatures are retained in the soil and can be used to understand the depth of water infiltration, retention and subsequent rate of evaporation from the soil. At our study site in dry subtropical northwest Australia, we repeatedly sampled rainwater and soil moisture prior to, during and after tropical Cyclones Heidi and Lua in 2012. Site inundation from Cyclone Heidi (rainfall 213 mm, δ18O −17.6‰) replenished soil moisture in the unsaturated zone for several months, completely replacing soil moisture down to depths of ~3.5 m and contributing to groundwater recharge. The transient momentary evaporative losses from wet soil at the time of sampling varied between 0.21 and 0.60 mm × day−1 (equivalent to 76 to 220 mm × yr−1 recalculated as an annual rate). During the prolonged dry period between cyclones, evaporative losses decreased to between 8 and 30 mm × yr−1. Mean long-term groundwater recharge for the study site was low (<6 mm × yr−1). Recharge is primarily driven by infrequent but high-volume cyclones that are an important source of soil moisture and an essential water source for vegetation in this semi-arid environment. However, variation in lithology, position in the landscape and time since the last inundation contribute to highly heterogeneous patterns of δ18O in the vadose zone, which complicates upscaling observations from a local to a regional scale model of evaporative demand. [ABSTRACT FROM AUTHOR]