Australian Water Resources Assessment Landscape Research Articles

What is the probability that a drought will break in Australia?

Large scale modes of climate variability influence rainfall and soil moisture in southeastern Australia, and extended dry conditions have been associated with a lack of climate mode phases conducive to wetter conditions. However, the role of large-scale climate variability in breaking ongoing soil moisture droughts has not been well quantified, and the utility of large-scale signals in drought recovery assessments have not been explored. Here, we study the influence of El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) on the probability of soil moisture drought breaking in eastern and southeastern Australia, using logistic regression modelling. A long-term historical dataset from the Australian Water Resources Assessment Landscape (AWRA-L) model is used for the assessment. The probability estimates from the logistic regression modelling validate well against observed probability of a drought ending. We then use model estimates to understand the probability contributions from different climate modes. We show that there is a seasonal pattern in soil moisture drought breaking probabilities with higher probabilities in austral summer in eastern Australia and summer/autumn in southeastern Australia. ENSO has the largest influence on probabilities in winter with extreme opposite phases of the mode resulting in regional average probability differences of 15–26%. The IOD exhibits the largest influences during spring and winter and opposite phases result in differences of about 18%. The method can be used to estimate soil moisture drought breaking probabilities in near real-time during drought events, and may assist decision making by managers engaged in drought risk and water resources planning.

Seasonal forecast of soil moisture over Mediterranean-climate forest catchments using a machine learning approach

Seasonal forecast of soil moisture at large spatial scale over forested landscape has numerous implications in forest hydrology and bushfire risk planning. Remotely sensed plant response to rainfall, input meteorological forcing, and site-specific landscape attributes were integrated into a data-driven Gradient Boosting Machine Learning (ML) model to forecast summer season (December to February) soil moisture equivalent to those from Australian Water Resource Assessment-Landscape (AWRA-L) at root-zone (0–1 m) and deep (1–6 m) layers of the soil.Multispectral and thermal infrared bands from MODIS (Band 1–12, LST (day, night)) and meteorological forcing for 2000 – 2018 during and prior to winter (before August) and site-specific landscape attributes were used to generate the explanatory input variables to the model. Spatial and temporal forecasting skills of the model were evaluated using two types of cross-validation: two-fold cross-validation by space over the entire period (2000–2018) and two-fold cross-validation by time for all grid cells (2160 cells). In the first method, the model showed a high skill to forecast deep soil moisture (R2 = 0.81, NSE = 0.79, RMSE = 58.01 mm) and root-zone soil moisture (R2 = 0.65, NSE = 0.63, RMSE = 24.34 mm). In comparison, the second evaluation resulted in more accurate forecast deep-soil moisture (R2 = 0.88, NSE = 0.86, RMSE = 47.39 mm) but in lower performance for root-zone soil (R2 = 0.55, NSE = 0.47, RMSE = 29.2 mm). These outcomes have highlighted that the (>80%) variability in summers deep (1–6 m) soil moisture and (>50%) variability in root-zone (0–1 m) soil moisture over forested landscape can be explained at the end of winters. Furthermore, integrating remotely sensed observations has improved the results by showing an enhancement in the RMSE by 22.8–33.3% in the deep (1–6 m) soil moisture and 8.8–14.3% in root-zone (0–1 m) soil moisture.Overall, the integrated system using remotely sensed plant response, climate forcing and machine learning exhibit great potential to forecast summer soil moisture three months ahead in the Mediterranean climates.

Application of multi-criteria decision-making methods to identification of soil moisture monitoring sites in an urban catchment in South Australia

When choosing sites for monitoring of soil moisture for hydrological purposes, a suitable process that considers the factors influencing soil moisture level should be followed. In this study, two multi-criteria decision-making (MCDM) methods, the multi-influencing factor (MIF) method and the analytical hierarchy process (AHP) method, were used to identify the optimal soil moisture monitoring (SMM) sites in the Dry Creek Catchment in South Australia. The most representative areas for nine SMM sites were obtained using the MIF method, considering the factors of rainfall, soil type, land use, catchment slope, elevation, and upslope accumulated area (UAA). The AHP method was used to select the optimal sites using the site-specific criteria. 30.3% of the catchment area in the Australian Water Resources Assessment Landscape (AWRA-L) Grid_DC2 can be considered acceptable as representative area with the MIF method. Four potential sites were evaluated for each AWRA-L grid using the relative weights of the site-specific criteria with the AHP method. The Grid_DC2 required two sites that had the highest overall weight chosen with the AHP analysis. The procedure was repeated for the remaining four AWRA-L grids within the study area to select the required SMM sites.

Evaluating Satellite Soil Moisture Datasets for Drought Monitoring in Australia and the South-West Pacific

Soil moisture (SM) is critical in monitoring the time-lagged impacts of agrometeorological drought. In Australia and several south-west Pacific Small Island Developing States (SIDS), there are a limited number of in situ SM stations that can adequately assess soil-water availability in a near-real-time context. Satellite SM datasets provide a viable alternative for SM monitoring and agrometeorological drought provision in these regions. In this study, we investigated the performance of Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Operational Products System (SMOPS), SM from the Advanced Microwave Scanning Radiometer 2 (AMSR-2) and SM from the Advanced Scatterometer (ASCAT) over Australia and south-west Pacific SIDS. Products were first evaluated in Australia, given the presence of several in-situ SM monitoring stations and a state-of-the-art hydrological model—the Australian Water Resources Assessment Landscape modelling system (AWRA-L). We further investigated the accuracy of SM satellite datasets in Australia and the south-west Pacific through Triple Collocation analysis with two other SM reference datasets—ERA5 reanalysis SM data and model data from the Global Land Data Assimilation System (GLDAS) dataset. All datasets have differing observation periods ranging from 1911-now, with a common period of observations between 2015–2021. Results demonstrated that ASCAT and SMOS were consistently superior in their performance. Analysis in the six south-west Pacific SIDS indicated reduced performance for all products, with ASCAT and SMOS still performing better than others for most SIDS with median R values ranging between 0.3–0.9. We conducted a case study of the 2015 El Niño and Positive Indian Ocean Dipole-induced drought in Papua New Guinea. It was shown that ASCAT is a valuable dataset indicative of agrometeorological drought for the nation, highlighting the value of using satellite SM products to provide early warning of drought in data-sparse regions in the south-west Pacific.

Modeling Vegetation Water Stress over the Forest from Space: Temperature Vegetation Water Stress Index (TVWSI)

The conventional Land Surface Temperature (LST)–Normalized Difference Vegetation Index (NDVI) trapezoid model has been widely used to retrieve vegetation water stress. However, it has two inherent limitations: (1) its complex and computationally intensive parameterization for multi-temporal observations and (2) deficiency in canopy water content information. We tested the hypothesis that an improved water stress index could be constructed by the representation of canopy water content information to the LST–NDVI trapezoid model. Therefore, this study proposes a new index that combines three indicators associated with vegetation water stress: canopy temperature through LST, canopy water content through Surface Water Content Index (SWCI), and canopy fractional cover through NDVI in one temporally transferrable index. Firstly, a new optical space of SWCI–NDVI was conceptualized based on the linear physical relationship between shortwave infrared (SWIR) and soil moisture. Secondly, the SWCI–NDVI feature space was parameterized, and an index d(SWCI, NDVI) was computed based on the distribution of the observations in the SWCI–NDVI spectral space. Finally, standardized LST (LST/long term mean of LST) was combined to d(SWCI, NDVI) to give a new water stress index, Temperature Vegetation Water Stress Index (TVWSI). The modeled soil moisture from the Australian Water Resource Assessment—Landscape (AWRA-L) and Soil Water Fraction (SWF) from four FLUXNET sites across Victoria and New South Wales were used to evaluate TVWSI. The index TVWSI exhibited a high correlation with AWRA-L soil moisture (R2 of 0.71 with p < 0.001) and the ground-based SWF (R2 of 0.25–0.51 with p < 0.001). TVWSI predicted soil moisture more accurately with RMSE of 21.82 mm (AWRA-L) and 0.02–0.04 (SWF) compared to the RMSE ranging 28.98–36.68 mm (AWRA-L) and 0.03–0.05 (SWF) were obtained for some widely used water stress indices. The TVWSI could also be a useful input parameter for other environmental models.

A New Drought Index for Soil Moisture Monitoring Based on MPDI-NDVI Trapezoid Space Using MODIS Data

The temperature vegetation dryness index (TVDI) has been commonly implemented to estimate regional soil moisture in arid and semi-arid regions. However, the parameterization of the dry edge in the TVDI model is performed with a constraint to define the maximum water stress conditions. Mismatch of the spatial scale between visible and thermal bands retrieved from remotely sensed data and terrain variations also affect the effectiveness of the TVDI. Therefore, this study proposed a new drought index named the condition vegetation drought index (CVDI) to monitor the temporal and spatial variations of soil moisture status by substituting the land surface temperature (LST) with the modified perpendicular drought index (MPDI). In situ soil moisture observations at crop and pasture sites in Victoria were used to validate the effectiveness of the CVDI. The results indicate that the dry and wet edges in the parameterization scheme of the CVDI formed a better-defined trapezoid shape than that of the TVDI. Compared with the MPDI and TVDI for soil moisture monitoring at crop sites, the CVDI exhibited a performance superior to the MPDI and TVDI in most days where the coefficients of determination (R2) achieved can reach to 0.67 on DOY023, 137, 274 and 0.71 on DOY 322 and reproduced more accurate spatial and seasonal variation of soil moisture. Moreover, the CVDI showed higher correlation with the Australian Water Resource Assessment Landscape (AWRA-L) soil moisture product on temporal scales. The R2 can reach to 0.69 and the root mean square error (RMSE) is also much better than that of the MPDI and TVDI. Overall, it can be concluded that the CVDI appears to be a feasible method and can be successfully used in regional soil moisture monitoring.

Understanding trends in hydrologic extremes across Australia

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.

Spatio-Temporal Evaluation of Water Storage Trends from Hydrological Models over Australia Using GRACE Mascon Solutions

The Gravity Recovery and Climate Experiment (GRACE) data have been extensively used to evaluate the total terrestrial water storage anomalies (TWSA) from hydrological models. However, which individual water storage components (i.e., soil moisture storage anomalies (SMSA) or groundwater water storage anomalies (GWSA)) cause the discrepancies in TWSA between GRACE and hydrological models have not been thoroughly investigated or quantified. In this study, we applied GRACE mass concentration block (mascon) solutions to evaluate the spatio-temporal TWSA trends (2003–2014) from seven prevailing hydrological models (i.e., Noah-3.6, Catchment Land Surface Model (CLSM-F2.5), Variable Infiltration Capacity macroscale model (VIC-4.1.2), Water—Global Assessment and Prognosis (WaterGAP-2.2d), PCRaster Global Water Balance (PCR-GLOBWB-2), Community Land Model (CLM-4.5), and Australian Water Resources Assessment Landscape model (AWRA-L v6)) in Australia and, more importantly, identified which individual water storage components lead to the differences in TWSA trends between GRACE and hydrological models. The results showed that all of the hydrological models employed in this study, except for CLM-4.5 model, underestimated the GRACE-derived TWSA trends. These underestimations can be divided into three categories: (1) ignoring GWSA, e.g., Noah-3.6 and VIC-4.1.2 models; (2) underrating both SMSA and GWSA, e.g., CLSM-F2.5, WaterGAP-2.2d, and PCR-GLOBWB-2 models; (3) deficiently modeling GWSA, e.g., AWRA-L v6 model. In comparison, CLM-4.5 model yielded the best agreement with GRACE but overstated the GRACE-derived TWSA trends due to the overestimation of GWSA. Our results underscore that GRACE mascon solutions can be used as a valuable and efficient validation dataset to evaluate the spatio-temporal performance of hydrological models. Confirming which individual water storage components result in the discrepancies in TWSA between GRACE and hydrological models can better assist in further hydrological model development.

Climatology and Variability of the Evaporative Stress Index and Its Suitability as a Tool to Monitor Australian Drought

AbstractThe seasonal cycle of the evaporative stress index (ESI) over Australia, and its relationship to observed rainfall and temperature, is examined. The ESI is defined as the standardized anomaly of the ratio of actual evapotranspiration to potential evapotranspiration, and as such, is a measure of vegetation moisture stress associated with agricultural or ecological drought. The ESI is computed using the daily output of version 6 of the Bureau of Meteorology’s landscape water balance model [Australian Water Resource Assessment Landscape (AWRA-L)] on a 5-km horizontal grid over a 45-yr period (1975–2019). Here we show that the ESI exhibits marked spatial and seasonal variability and can be used to accurately monitor drought across Australia, where ESI values less than negative one indicate drought. While the ESI is highly correlated with rainfall as expected, its relationship with temperature only becomes significant during the warmer seasons, suggesting a threshold above which temperature may affect vegetation stress. Our analysis also shows that the ESI tends to be strongly negative (i.e., indicating drought) during El Niño and positive phases of the Indian Ocean dipole (IOD), when conditions tend to be anomalously hot and dry. A negative phase of the southern annular mode also tends to drive negative ESI values during austral spring with a one-month delay.

Anomaly Kriging Helps to Remove Bias in Spatial Model Runoff Estimates

AbstractThe low spatial density of streamflow gauging stations limits the accuracy of spatial streamflow estimates in many parts of the world. Strategies to improve runoff estimates in the absence of dense measurements have tended to focus on estimating parameters of runoff models in ungauged regions, through so‐called parameter regionalization methods. However, parameter regionalization can be affected by overdependence on calibration at gauged sites, model parameter equifinality, and ensuing estimation errors. As a result, spatial model runoff estimates typically exhibit spatially correlated biases. This analysis attempts to enhance the use of observations in spatial runoff estimation. Specifically, we assessed the potential to reduce systematic errors by spatially interpolating residuals (i.e., errors) between prior grid‐based streamflow estimates for Australia at 0.05° × 0.05° grid from the Australian Bureau of Meteorology's calibrated, operational Australian Water Resources Assessment Landscape model (AWRA‐L) and streamflow gauging records from 780 unimpeded, relatively small catchments. We analyzed spatial autocorrelation in residuals and tested an efficient two‐step correction approach involving a uniform correction and subsequent kriging of residuals. The approach removed an average of 41% of systematic bias in the model estimates and also improved other model performance measures. Further reduction in errors at shorter timescales may be achievable through a temporally hierarchical correction scheme.

Agricultural drought assessment based on multiple soil moisture products

In this study, we evaluated three soil moisture (SM) products (Advanced Microwave Scanning Radiometer-2 [AMSR2], Advanced SCATterometer [ASCAT], and European Reanalysis Interim [ERA-interim]) across Australia in four climate zones by comparing against the Australian Water Resources Assessment-Landscape (AWRA-L) SM products from July 2012 to June 2017. The ASCAT SM indicated better performance than other SM products over Australia. To evaluate the applicability and reliability for monitoring agricultural drought, an agricultural drought index, the Soil Water Deficit Index, was estimated from three SM products and compared with three commonly-used drought indices (atmospheric water deficit [AWD], Evaporative Stress Index, and Reconnaissance Drought Index). Volumetric contingency tables were compiled to quantitatively assess the performance of agricultural drought detection using various SM products compared with the AWD. All products had reliable drought detection capability over Australia based on the results of temporal evolution and contingency tables with a mean volumetric hit index of 0.700, 0.728, and 0.787 for AMSR2, ASCAT, and ERA-interim, respectively. The slight incapability of drought detection capability of SWDI in tropical region was low due to the variation in persistence times of moisture in the atmosphere and soil. Except arid zone, in all climate zones, the reliability of SM products for drought detection followed the following order ASCAT > ERA-interim > AMSR2.

Assessment of satellite- and reanalysis-based evapotranspiration products with two blending approaches over the complex landscapes and climates of Australia

Numerous evapotranspiration (ET) products with different spatio-temporal resolutions at continental and global scales have been developed by utilizing commonly available satellite imagery and ground-based observations. However, the developed ET products present large uncertainties that limit their operational hydro-meteorological applications, specifically in water limited regions where water consumption is competing. This study presents the uncertainties among four widely available ET (GLDAS, GLEAM, MOD16, and MERRA) products and then assesses the performance of two blending approaches (Maximize R and simple Taylor skill’s score; STS)) for generating a fused ET product using combinations of the above ET datasets in a dry continent (Australia) during the period of 2005–2014. The accuracy for all four ET products compared with Australian Water Resource Assessment Landscape (AWRA-L) ET dataset demonstrated large uncertainties across seven different land cover classifications and four different climate zones, with a coefficient of correlation (R) ranging between 0.1–0.85. GLEAM and GLDAS showed better agreement (R ∼ 0.8) over forest and cropland areas, respectively, while better performance of all products was noted in tropical regions compared with other climate zones in the region. Similarly, implementation of the two blending approaches to generate merged ET products revealed an overall higher contribution (>25%) from GLEAM followed by the GLDAS, while that of MOD16 was lowest (<20%) over various climatic zones as well as all of Australia. The accuracy assessment of the two merged ET products exhibited relatively better performance by reducing the Bias toward 0 and Root Mean Square Error (RMSE) of 0.2-0.4 mm/8day with an index of agreement (IOA) >0.8 compared with individual ET products under all climatic and land cover conditions. These statistical indicators explained the relatively large differences among the spatial distributions of fused ET and those of individual products. Among the two blending approaches, the STS method produced more reliable results compared to the accuracy of fused ET generated by the Maximize R method. This reason for this was the discrepancy in the number of datasets used to derive each merging method. The STS method allows combining all given datasets by providing their corresponding weights, whereas the Maximize R method employed the weights only based on the two best of the given products. Overall, the assessments made in the current study provide comprehensive insights on the quality and integration of four globally available ET products to benefit regional water users and the hydro-meteorological community on the basis of major landscape and climatic conditions over long time scales.

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

Abstract 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.

Evaluation of forest interception estimation in the continental scale Australian Water Resources Assessment – Landscape (AWRA-L) model

summary The AWRA-L model is a core component of the joint Bureau of Meteorology (BOM) and CSIRO Australian Water Resources Assessment (AWRA) system which integrates hydrological models and a variety of observations and satellite products to produce a National Water Accounting system for Australia. AWRA-L is a continental scale sub-model which provides surface water balance component estimates for all landscape types, including forests ranging from sparse woody vegetation in dry climates to dense tropical rainforest in wet climates. This paper describes the AWRA-L interception sub-routine, which is based on the widely used Gash model, and considers how its predictions compare with measurements of interception in tropical rainforests in north Queensland and sparse jarrah forests in Western Australia. The results demonstrate the importance of having accurate estimates of the three parameters which dominate the interception loss (as a fraction of rainfall), i.e. canopy water holding capacity, S, the average wet canopy evaporation rate, E, and the average rainfall rate, R. The current ‘default’ values of these parameters used in AWRA-L lead to significant errors in interception, which will also be reflected in the AWRA stream flow estimates. Analysis of how E and R values vary across the Australian continent have led to recommendations for how the interception calculations made by the AWRA-L model can be improved using values of E and R calculated from forest height and latitude. Revisions of the canopy storage parameters based on regressions derived from measured values at the rainforest and jarrah sites are also evaluated. Overall, the adoption of both updated canopy storage and E/R ratios outlined in this paper does provide a measurable improvement to the performance of the AWRA-L interception model. The approach developed in this study also has direct relevance to other applications of interception models for water balance modelling at large spatial scales.