Water resource monitoring systems and the role of satellite observations

Spatial water resource monitoring systems (SWRMS) can provide valuable information in support of water management, but current operational systems are few and provide only a subset of the information required. Necessary innovations include the explicit description of water redistribution and water use from river and groundwater systems, achieving greater spatial detail (particularly in key features such as irrigated areas and wetlands), and improving accuracy as assessed against hydrometric observations, as well as assimilating those observations. The Australian water resources assessment (AWRA) system aims to achieve this by coupling landscape models with models describing surface water and groundwater dynamics and water use. A review of operational and research applications demonstrates that satellite observations can improve accuracy and spatial detail in hydrological model estimation. All operational systems use dynamic forcing, land cover classifications and a priori parameterisation of vegetation dynamics that are partially or wholly derived from remote sensing. Satellite observations are used to varying degrees in model evaluation and data assimilation. The utility of satellite observations through data assimilation can vary as a function of dominant hydrological processes. Opportunities for improvement are identified, including the development of more accurate and higher spatial and temporal resolution precipitation products, and the use of a greater range of remote sensing products in a priori model parameter estimation, model evaluation and data assimilation. Operational challenges include the continuity of research satellite missions and data services, and the need to find computationally-efficient data assimilation techniques. The successful use of observations critically depends on the availability of detailed information on observational error and understanding of the relationship between remotely-sensed and model variables, as affected by conceptual discrepancies and spatial and temporal scaling

Use of Gravity Recovery and Climate Experiment terrestrial water storage retrievals to evaluate model estimates by the Australian water resources assessment system

Terrestrial water storage (TWS) estimates retrieved from the Gravity Recovery and Climate Experiment (GRACE) satellite mission were compared to TWS modeled by the Australian Water Resources Assessment (AWRA) system. The aim was to test whether differences could be attributed and used to identify model deficiencies. Data for 2003-2010 were decomposed into the seasonal cycle, linear trends and the remaining de-trended anomalies before comparing. AWRA tended to have smaller seasonal amplitude than GRACE. GRACE showed a strong (>15 mm yr -1) drying trend in northwest Australia that was associated with a preceding period of unusually wet conditions, whereas weaker drying trends in the southern Murray Basin and southwest Western Australia were associated with relatively dry conditions. AWRA estimated trends were less negative for these regions, while a more positive trend was estimated for areas affected by cyclone Charlotte in 2009. For 2003-2009, a decrease of 7-8 mm yr -1 (50-60 km 3 yr -1) was estimated from GRACE, enough to explain 6%-7% of the contemporary rate of global sea level rise. This trend was not reproduced by the model. Agreement between model and data suggested that the GRACE retrieval error estimates are biased high. A scaling coefficient applied to GRACE TWS to reduce the effect of signal leakage appeared to degrade quantitative agreement for some regions. Model aspects identified for improvement included a need for better estimation of rainfall in northwest Australia, and more sophisticated treatment of diffuse groundwater discharge processes and surface-groundwater connectivity for some regions

High-Dimensional Satellite Image Compositing and Statisticsfor Enhanced Irrigated Crop Mapping

Accurate irrigated area maps remain difficult to generate, as smallholder irrigation schemes often escape detection. Efforts to map smallholder irrigation have often relied on complex classification models fitted to temporal image stacks. The use of high-dimensional geometric median composites (geomedians) and high-dimensional statistics of time-series may simplify classification models and enhance accuracy. High-dimensional statistics for temporal variation, such as the spectral median absolute deviation, indicate spectral variability within a period contributing to a geomedian. The Ord River Irrigation Area was used to validate Digital Earth Australia’s annual geomedian and temporal variation products. Geomedian composites and the spectral median absolute deviation were then calculated on Sentinel-2 images for three smallholder irrigation schemes in Matabeleland, Zimbabwe, none of which were classified as areas equipped for irrigation in AQUASTAT’s Global Map of Irrigated Areas. Supervised random forest classification was applied to all sites. For the three Matabeleland sites, the average Kappa coefficient was 0.87 and overall accuracy was 95.9% on validation data. This compared with 0.12 and 77.2%, respectively, for the Food and Agriculture Organisation’s Water Productivity through Open access of Remotely sensed derived data (WaPOR) land use classification map. The spectral median absolute deviation was ranked among the most important variables across all models based on mean decrease in accuracy. Change detection capacity also means the spectral median absolute deviation has some advantages for cropland mapping over indices such as the Normalized Difference Vegetation Index. The method demonstrated shows potential to be deployed across countries and regions where smallholder irrigation schemes account for large proportions of irrigated area.This research was undertaken while supported by the Australian National University (ANU) University Research Scholarship and a Commonwealth Scientific and Industrial Research Organisation (CSIRO) and ANU Digital Agriculture Supplementary Scholarship through the Centre for Entrepreneurial AgriTechnology

Forecasting dryland vegetation condition months in advance through satellite data assimilation

Dryland ecosystems are characterised by rainfall variability and strong vegetation response to changes in water availability over a range of timescales. Forecasting dryland vegetation condition can be of great value in planning agricultural decisions, drought relief, land management and fire preparedness. At monthly to seasonal time scales, knowledge of water stored in the system contributes more to predictability than knowledge of the climate system state. However, realising forecast skill requires knowledge of the vertical distribution of moisture below the surface and the capacity of the vegetation to access this moisture. Here, we demonstrate that contrasting satellite observations of water presence over different vertical domains can be assimilated into an eco-hydrological model and combined with vegetation observations to infer an apparent vegetation-accessible water storage (hereafter called accessible storage). Provided this variable is considered explicitly, skilful forecasts of vegetation condition are achievable several months in advance for most of the world’s drylands.This research was supported through ARC Discovery grant DP140103679. We thank Professor Michael L. Roderick and Professor Jeffery P. Walker for their kind help and suggestions in data analysis. This research was undertaken with the assistance of resources and services from the National Computational Infrastructure (NCI), which is supported by the Australian Government

Multiscale soil moisture estimates using static and roving cosmic-ray soil moisture sensors

Soil moisture plays a critical role in land surface processes and as such there has been a recent increase in the number and resolution of satellite soil moisture observations and the development of land surface process models with ever increasing resolution. Despite these developments, validation and calibration of these products has been limited because of a lack of observations on corresponding scales. A recently developed mobile soil moisture monitoring platform, known as the "rover", offers opportunities to overcome this scale issue. This paper describes methods, results and testing of soil moisture estimates produced using rover surveys on a range of scales that are commensurate with model and satellite retrievals. Our investigation involved static cosmic-ray neutron sensors and rover surveys across both broad (36 x 36 km at 9 km resolution) and intensive (10 x 10 km at 1 km resolution) scales in a cropping district in the Mallee region of Victoria, Australia. We describe approaches for converting rover survey neutron counts to soil moisture and discuss the factors controlling soil moisture variability. We use independent gravimetric and modelled soil moisture estimates collected across both space and time to validate rover soil moisture products. Measurements revealed that temporal patterns in soil moisture were preserved through time and regression modelling approaches were utilised to produce time series of property-scale soil moisture which may also have applications in calibration and validation studies or local farm management. Intensive-scale rover surveys produced reliable soil moisture estimates at 1 km resolution while broad-scale surveys produced soil moisture estimates at 9 km resolution. We conclude that the multiscale soil moisture products produced in this study are well suited to future analysis of satellite soil moisture retrievals and finer-scale soil moisture models

Continental scale downscaling of AWRA-L analysed soil moisture using random forest regression

The Australian Water Resource Assessment Landscape (AWRA-L) model as used by the Bureau of Meteorology (BoM) provides daily continental scale soil moisture (SM) estimates (among other landscape water variables) at ~5-km resolution. At such a coarse scale these data cannot represent the high spatiotemporal variability of SM across heterogeneous land surfaces. Downscaling of coarse SM products based on machine learning (ML) has become increasingly popular due to its robust predictions and potential for large-scale applications. As a first step towards high-resolution daily Australia-wide SM estimation, a downscaling framework was developed to generate monthly SM with 500-m spatial resolution using analysed SM from AWRA-L and multisource geospatial predictors in random forest (RF) regression. Candidate predictors include digital elevation model (DEM), soil properties from the Australian soil and landscape grids, and several retrievals from the MODerate-resolution Imaging Spectroradiometer (MODIS). Ten experiments were conducted to decide the best combination of predictors. In the chosen model, DEM and available water capacity (AWC) were consistently identified as the most important predictors based on the ranking of variable importance. The downscaled SM shows greatly enhanced spatial details at the local scale while maintaining consistent patterns with AWRA-L analysis at the continental scale. Validations against in-situ measurement networks using Pearson correlation coefficient (R) show that there is very little difference in the performance between the downscaled and AWRA-L SM. Average R values for the downscaled SM against CosmOz, OzFlux and OzNet were 0.87, 0.68 and 0.75, respectively, while the original AWRA-L SM average R were 0.86, 0.68 and 0.76, respectively. Furthermore, the time series comparison based on a wetness unit shows that the downscaled SM can well catch up the fluctuations of in-situ SM. In general, this study explores the potential of ML approach for the SM downscaling applications at the continental scale. It could be a promising direction to exploit the modelling capability of integrating multisource geospatial data including satellite retrievals, land surface models (LSM) and interpolated ground observation data. Future directions should concentrate on integrating this approach into an operational framework with a daily frequency. Exploration of the relationships between SM and auxiliaries under difference scales would be essential, in order to better understand the dominant physical controls on spatial variability of SM.This research was undertaken while supported by the Australian National University (ANU) University Research Scholarship and the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and ANU Digital Agriculture Supplementary Scholarship through the Centre for Entrepreneurial Agri-Technology (CEAT). This research was supported with funds from the University of Sydney (USYD) and Grains Research and Development Corporation (GRDC) project SoilWaterNow

Evaluation of precipitation estimation accuracy in reanalyses, satellite products, and an ensemble method for regions in Australia and south and east Asia

Precipitation estimates from reanalyses and satellite observations are routinely used in hydrologic applications, but their accuracy is seldom systematically evaluated. This study used high-resolution gauge-only daily precipitation analyses for Australi

Evaluation of precipitation estimation accuracy in reanalyses, satellite products, and an ensemble method for regions in Australia and south and east Asia

Precipitation estimates from reanalyses and satellite observations are routinely used in hydrologic applications, but their accuracy is seldom systematically evaluated. This study used high-resolution gauge-only daily precipitation analyses for Australia (SILO) and South and East Asia [Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE)] to calculate the daily detection and accuracy metrics for three reanalyses [ECMWF Re-Analysis Interim (ERA-Interim), Japanese 25-yr Reanalysis (JRA-25), and NCEP-Department of Energy (DOE) Global Reanalysis 2] and three satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) 3B42V6, Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)]. A depthfrequency- adjusted ensemble mean of the reanalyses and satellite products was also evaluated. Reanalyses precipitation from ERA-Interim in southern Australia (SAu) and northern Australasia (NAu) showed higher detection performance. JRA-25 had a better performance in South and East Asia (SEA) except for the monsoon period, in which satellite estimates from TRMM and CMORPH outperformed the reanalyses. In terms of accuracy metrics (correlation coefficient, root-mean-square difference, and a precipitation intensity proxy, which is the ratio of monthly precipitation amount to total days with precipitation) and over the three subdomains, the depth-frequency-adjusted ensemble mean generally outperformed or was nearly as good as any of the single members. The results of the ensemble show that additional information is captured from the different precipitation products. This finding suggests that, depending on precipitation regime and location, combining (re)analysis and satellite products can lead to better precipitation estimates and, thus,more accurate hydrological applications than selecting any single product

Preliminary results demonstrating the impact of Mediterranean diet on bone health

Nutrition is an environmental factor affecting bone health. Nutrition is considered essential to achieve and maintain optimal bone mass. Mediterranean diet (MD) has shown to prevent bone disease. Aim of this study is to investigate the relationship between bone health status and adherence the MD