ORCID Profile
0000-0003-3056-4109
Current Organisation
Australian National University
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Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-1501
Abstract: High spatial resolution land surface temperature (LST) ( = 100 m) has a considerable significance for small scale studies like agricultural applications and urban heat island studies. Originally developed for optical data, spatiotemporal fusion methods, such as the widely used Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), are gradually becoming promising approaches to generate high resolution thermal variables but still have shortcomings, such as an invalid assumption in thermal fields and the accumulation of systematic biases. Hence, we proposed a variant of the ESTARFM algorithm, referred as the unbiased ESTARFM (ubESTARFM), aiming to better accommodate the spatiotemporal approach to thermal studies. We evaluated the results derived from our method and the typical ESTARFM against both in-situ LST and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LST over a continental scale of Australia. The results show that the ubESTARFM has a bias of 2.55 K, unbiased RMSE (ubRMSE) of 2.57 K, and Pearson correlation coefficient (R) of 0.95 against the in-situ LST over 11290 s les at 12 sites, all of which are significantly better than that of the ESTARFM, with a bias of 4.73 K, ubRMSE of 3.80 K and R of 0.92. In the cross-satellite comparison, the ubESTARFM LST has a bias of -1.69 K, ubRMSE of 2.00 K, and R of 0.70 over 43 near clear-sky scenes, while the ESTARFM LST has a bias of 1.79 K, ubRMSE of 2.68 K, and R of 0.59. Overall, the ubESTARFM is able to avoid the accumulation of systematic bias, considerably reduce the deviation of uncertainty, and maintain a good level of correlation with validation datasets compared to the typical ESTARFM algorithm. It is a promising method to integrate reliable numeric values from coarse resolution LST and spatial heterogeneity from fine resolution LST, and may be further coupled with energy balance or radiative transfer models to better enable farm- or regional-scale water management strategy or decision making.
Publisher: Copernicus GmbH
Date: 12-10-2020
Abstract: Abstract. A simple and effective two-step data assimilation framework was developed to improve soil moisture representation in an operational large-scale water balance model. The first step is the sequential state updating process that exploits temporal covariance statistics between modelled and satellite-derived soil moisture to produce analysed estimates. The second step is to use analysed surface moisture estimates to impart mass conservation constraints (mass redistribution) on related states and fluxes of the model in a post-analysis adjustment after the state updating at each time step. In this study, we apply the data assimilation framework to the Australian Water Resources Assessment Landscape model (AWRA-L) and evaluate its impact on the model's accuracy against in-situ observations across water balance components. We show that the correlation between simulated surface soil moisture and in-situ observation increases from 0.54 (open-loop) to 0.77 (data assimilation). Furthermore, indirect verification of root-zone soil moisture using remotely sensed vegetation time series across cropland areas results in significant improvements of 0.11 correlation units. The improvements gained from data assimilation can persist for more than one week in surface soil moisture estimates and one month in root-zone soil moisture estimates, thus demonstrating the efficacy of this data assimilation framework.
Publisher: Copernicus GmbH
Date: 24-01-2018
Abstract: Abstract. A portion of globally generated surface and groundwater resources evaporates from wetlands, water bodies and irrigated areas. This secondary evaporation of blue water directly affects the remaining water resources available for ecosystems and human use. At the global scale, a lack of detailed water balance studies and direct observations limits our understanding of the magnitude and spatial and temporal distribution of secondary evaporation. Here, we propose a methodology to assimilate satellite-derived information into the landscape hydrological model W3 at an unprecedented 0.05° or c. 5 km resolution globally. The assimilated data are all derived from MODIS observations, including surface water extent, surface albedo, vegetation cover, leaf area index, canopy conductance, and land surface temperature (LST). The information from these products is imparted on the model in a simple but efficient manner, through a combination of direct insertion of surface water extent, evaporation flux adjustment based on LST, and parameter nudging for the other observations. The resulting water balance estimates were evaluated against river basin discharge records and the water balance of closed basins and demonstrably improved water balance estimates compared to ignoring secondary evaporation (e.g., bias improved from +38 mm/d to +2 mm/d). The evaporation estimates derived from assimilation were combined with global mapping of irrigation crops to derive a minimum estimate of irrigation water requirements (I0), representative of optimal irrigation efficiency. Our I0 estimates were lower than published country-level estimates of irrigation water use produced by alternative estimation methods, for reasons that are discussed. We estimate that 16 % of globally generated water resources evaporate before reaching the oceans, enhancing total terrestrial evaporation by 6.1 • 1012 m3 y−1 or 8.8 %. Of this volume, 5 % is evaporated from irrigation areas, 58% from terrestrial water bodies and 37 % from other surfaces. Model-data assimilation at even higher spatial resolutions can achieve a further reduction in uncertainty but will require more accurate and detailed mapping of surface water dynamics and areas equipped for irrigation.
Publisher: Copernicus GmbH
Date: 08-07-2019
DOI: 10.5194/ESSD-11-1003-2019
Abstract: Abstract. 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. The dataset is available at 0.25914/5c637a7449353 (Hou et al., 2019).
Publisher: Copernicus GmbH
Date: 27-08-2018
Abstract: Abstract. The lack of direct measurement of root-zone soil moisture poses a challenge to the large-scale prediction of ecosystem response to variation in soil water. Microwave remote sensing capability is limited to measuring moisture content in the uppermost few centimetres of soil. In contrast, GRACE (Gravity Recovery and Climate Experiment) mission detected the variability in storage within the total water column, which is often dominated by groundwater variation. However, not all vegetation communities can access groundwater. In this study, satellite-derived water content from GRACE and SMOS were jointly assimilated into an ecohydrological model to better predict the impact of changes in root-zone soil moisture on vegetation vigour. Overall, the accuracy of root-zone soil moisture prediction though the joint assimilation of surface soil moisture and total water storage retrievals showed improved consistency with ground-based soil moisture measurements and satellite-observed greenness when compared to open-loop estimates (i.e. without assimilation). For ex le, the correlation between modelled and in-situ measurements of root-zone moisture increased by 0.1 on average over grasslands and croplands. Improved correlations were found between vegetation greenness and soil water storage derived from the joint assimilation with an increase up to 0.47 over grassland compared to open-loop estimates. Joint assimilation results show a more severe deficit in soil water in eastern Australia, western North America and eastern Brazil over the period of 2010 to 2015 than the open-loop, consistent with the satellite-observed vegetation greenness. The assimilation of satellite-observed water content contributes to more accurate knowledge of soil water availability, providing new insights for monitoring hidden water stress and vegetation response.
Publisher: Copernicus GmbH
Date: 20-02-2019
DOI: 10.5194/ESSD-2019-26
Abstract: Abstract. Hydromorphological attributes such as flow width, water extent and gradient play an important role in river hydrological, biogeochemical and ecological processes helping 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. Proposed scaling rules relating river width to gradient and contributing catchment area and discharge were investigated, demonstrating that such rules capture relatively little of 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 GIS, which should be useful for research and practical applications such as water resource management, aquatic habitat enhancement, and river engineering and management. The dataset is available at 0.25914/5c637a7449353 (Hou et al. 2019).
Publisher: Copernicus GmbH
Date: 19-07-2021
Publisher: Copernicus GmbH
Date: 31-05-2018
Abstract: Abstract. River discharge measurements have proven invaluable to monitor the global water cycle, assess flood risk, and guide water resource management. However, there is a delay and overall decline in the availability of gauging data and stations are highly unevenly distributed globally. While not a substitute for river discharge measurement, remote sensing is a cost-effective technology to acquire information on river dynamics. The general approach has been to relate satellite observation to discharge measured in situ, which prevents its use for ungauged rivers. Alternatively, hydrological models are now available that can be used to estimate river discharge globally. While subject to greater errors and biases than measurements, model estimates of river discharge do expand the options for applying satellite-based discharge monitoring in ungauged rivers. Our aim was to test this approach. We used gridded surface water extent information from two sources: (1) Global Flood Detection System (GFDS) passive microwave data and (2) MODIS optical data. The data were available for the common period of 2000–2014. The hydrological model used was the World-Wide Water (W3) model version 2, providing river discharge from 1980 to 2014. We designed and compared two methods to relate simulated storage and discharge to MODIS and GFDS surface water extent fraction for developing satellite gauging reaches (SGRs), and applied the best performing method to construct SGRs across the Amazon Basin. River discharge estimates from MODIS SGRs, GFDS SGRs, and the W3 model were evaluated with in situ river discharge measurements. The results showed SGRs can be successfully established over a large area using MODIS and GFDS water extent and modelled discharge, and used to estimate river discharge at both gauged and ungauged sites.
Publisher: Copernicus GmbH
Date: 27-09-2018
DOI: 10.5194/HESS-22-4959-2018
Abstract: Abstract. A portion of globally generated surface and groundwater resources evaporates from wetlands, waterbodies and irrigated areas. This secondary evaporation of “blue” water directly affects the remaining water resources available for ecosystems and human use. At the global scale, a lack of detailed water balance studies and direct observations limits our understanding of the magnitude and spatial and temporal distribution of secondary evaporation. Here, we propose a methodology to assimilate satellite-derived information into the landscape hydrological model W3 at an unprecedented 0.05∘, or ca. 5 km resolution globally. The assimilated data are all derived from MODIS observations, including surface water extent, surface albedo, vegetation cover, leaf area index, canopy conductance and land surface temperature (LST). The information from these products is imparted on the model in a simple but efficient manner, through a combination of direct insertion of the surface water extent, an evaporation flux adjustment based on LST and parameter nudging for the other observations. The resulting water balance estimates were evaluated against river basin discharge records and the water balance of closed basins and demonstrably improved water balance estimates compared to ignoring secondary evaporation (e.g., bias improved from +38 to +2 mm yr−1). The evaporation estimates derived from assimilation were combined with global mapping of irrigation crops to derive a minimum estimate of irrigation water requirements (I0), representative of optimal irrigation efficiency. Our I0 estimates were lower than published country-level estimates of irrigation water use produced by alternative estimation methods, for reasons that are discussed. We estimate that 16 % of globally generated water resources evaporate before reaching the oceans, enhancing total terrestrial evaporation by 6.1×1012 m3 yr−1 or 8.8 %. Of this volume, 5 % is evaporated from irrigation areas, 58 % from terrestrial waterbodies and 37 % from other surfaces. Model-data assimilation at even higher spatial resolutions can achieve a further reduction in uncertainty but will require more accurate and detailed mapping of surface water dynamics and areas equipped for irrigation.
Publisher: Springer Science and Business Media LLC
Date: 28-01-2019
DOI: 10.1038/S41467-019-08403-X
Abstract: 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.
Publisher: Copernicus GmbH
Date: 24-08-2021
DOI: 10.5194/HESS-25-4567-2021
Abstract: Abstract. A simple and effective two-step data assimilation framework was developed to improve soil moisture representation in an operational large-scale water balance model. The first step is a Kalman-filter-type sequential state updating process that exploits temporal covariance statistics between modelled and satellite-derived soil moisture to produce analysed estimates. The second step is to use analysed surface moisture estimates to impart mass conservation constraints (mass redistribution) on related states and fluxes of the model using tangent linear modelling theory in a post-analysis adjustment after the state updating at each time step. In this study, we assimilate satellite soil moisture retrievals from both Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) missions simultaneously into the Australian Water Resources Assessment Landscape model (AWRA-L) using the proposed framework and evaluate its impact on the model's accuracy against in situ observations across water balance components. We show that the correlation between simulated surface soil moisture and in situ observation increases from 0.54 (open loop) to 0.77 (data assimilation). Furthermore, indirect verification of root-zone soil moisture using remotely sensed Enhanced Vegetation Index (EVI) time series across cropland areas results in significant improvements from 0.52 to 0.64 in correlation. The improvements gained from data assimilation can persist for more than 1 week in surface soil moisture estimates and 1 month in root-zone soil moisture estimates, thus demonstrating the efficacy of this data assimilation framework.
Publisher: Copernicus GmbH
Date: 21-02-2019
DOI: 10.5194/HESS-23-1067-2019
Abstract: Abstract. The lack of direct measurement of root-zone soil moisture poses a challenge to the large-scale prediction of ecosystem response to variation in soil water. Microwave remote sensing capability is limited to measuring moisture content in the uppermost few centimetres of soil. The GRACE (Gravity Recovery and Climate Experiment) mission detected the variability in storage within the total water column. However, root-zone soil moisture cannot be separated from GRACE-observed total water storage anomalies without ancillary information on surface water and groundwater changes. In this study, GRACE total water storage anomalies and SMOS near-surface soil moisture observations were jointly assimilated into a hydrological model globally to better estimate the impact of changes in root-zone soil moisture on vegetation vigour. Overall, the accuracy of root-zone soil moisture estimates through the joint assimilation of surface soil moisture and total water storage retrievals showed improved consistency with ground-based soil moisture measurements and satellite-observed greenness when compared to open-loop estimates (i.e. without assimilation). For ex le, the correlation between modelled and in situ measurements of root-zone moisture increased by 0.1 (from 0.48 to 0.58) and 0.12 (from 0.53 to 0.65) on average for grasslands and croplands, respectively. Improved correlations were found between vegetation greenness and soil water storage on both seasonal variability and anomalies over water-limited regions. Joint assimilation results show a more severe deficit in soil water anomalies in eastern Australia, southern India and eastern Brazil over the period of 2010 to 2016 than the open-loop, consistent with the satellite-observed vegetation greenness anomalies. The assimilation of satellite-observed water content contributes to more accurate knowledge of soil water availability, providing new insights for monitoring hidden water stress and vegetation conditions.
Publisher: Copernicus GmbH
Date: 19-07-2022
DOI: 10.5194/HESS-26-3785-2022
Abstract: Abstract. Many thousands of large dam reservoirs have been constructed worldwide during the last 70 years to increase reliable water supplies and support economic growth. Because reservoir storage measurements are generally not publicly available, so far there has been no global assessment of long-term dynamic changes in reservoir water volumes. We overcame this by using optical (Landsat) and altimetry remote sensing to reconstruct monthly water storage for 6695 reservoirs worldwide between 1984 and 2015. We relate reservoir storage to resilience and vulnerability and investigate interactions between precipitation, streamflow, evaporation, and reservoir water storage. This is based on a comprehensive analysis of streamflow from a multi-model ensemble and as observed at ca. 8000 gauging stations, precipitation from a combination of station, satellite and forecast data, and open water evaporation estimates. We find reservoir storage has diminished substantially for 23 % of reservoirs over the three decades, but increased for 21 %. The greatest declines were for dry basins in southeastern Australia (−29 %), southwestern USA (−10 %), and eastern Brazil (−9 %). The greatest gains occurred in the Nile Basin (+67 %), Mediterranean basins (+31 %) and southern Africa (+22 %). Many of the observed reservoir changes could be explained by changes in precipitation and river inflows, emphasizing the importance of multi-decadal precipitation changes for reservoir water storage. Uncertainty in the analysis can come from, among others, the relatively low Landsat imaging frequency for parts of the Earth and the simple geo-statistical bathymetry model used. Our results also show that there is generally little impact from changes in net evaporation on storage trends. Based on the reservoir water balance, we deduce it is unlikely that water release trends dominate global trends in reservoir storage dynamics. This inference is further supported by different spatial patterns in water withdrawal and storage trends globally. A more definitive conclusion about the impact of changes in water releases at the global or local scale would require data that unfortunately are not publicly available for the vast majority of reservoirs globally.
Publisher: Copernicus GmbH
Date: 19-07-2021
Abstract: Abstract. Many thousands of large dam reservoirs have been constructed worldwide during the last seventy years to increase reliable water supplies and support economic growth. Because reservoir storage measurements are generally not publicly available, so far there has been no global assessment of long-term dynamic changes in reservoir water volume. We overcame this by using optical (Landsat) and altimetry remote sensing to reconstruct monthly water storage for 6,743 reservoirs worldwide between 1984 and 2015. We relate reservoir storage to resilience and vulnerability and analyse their response to precipitation, streamflow and evaporation. We find reservoir storage has diminished substantially for 23 % of reservoirs over the three decades but increased for 21 %. The greatest declines were for dry basins in southeastern Australia (−29 %), the USA (−10 %), and eastern Brazil (−9 %). The greatest gains occurred in the Nile Basin (+67 %), Mediterranean basins (+31 %) and southern Africa (+22 %). Many of the observed reservoir changes were explained well by changes in precipitation and river inflows, emphasising the importance of multi-decadal precipitation changes for reservoir water storage, rather than changes in net evaporation or (demand-driven) dam water releases.
Publisher: Copernicus GmbH
Date: 13-09-2022
Abstract: Abstract. Measurements of the spatiotemporal dynamics of lake and reservoir water storage are fundamental in the assessment of the influence of climate variability and anthropogenic activities on water quantity and quality, as well as wetland ecology and the estimating greenhouse gas emissions from lakes. Previous studies estimated relative water volume changes for lakes where both satellite-derived extent and radar altimetry data are available. This approach is limited to only few hundreds of lakes worldwide. In this study, the number of measured lakes was increased by a factor 400 using high-resolution Landsat and Sentinel-2 optical remote sensing and ICESat-2 laser altimetry in addition to radar altimetry from the Topex/Poseidon, Jason-1, -2 and -3, and Sentinel-3 instruments. Time series of relative (i.e., storage change) or absolute (i.e., total stored volume) storage for more than 170,000 lakes globally with a surface area of at least 1 km2 (representing 99 % of the total volume of all water stored in lakes and reservoirs globally) were retrieved. Within these, we were able to develop an automated workflow for near real-time global lake monitoring of more than 27,000 lakes. The historical and near real-time lake storage dynamics data for 1984 to current are publicly available through 0.25914/K8ZF-6G46 (Hou et al., 2022).
Publisher: Copernicus GmbH
Date: 11-12-2018
DOI: 10.5194/HESS-22-6435-2018
Abstract: Abstract. River discharge measurements have proven invaluable to monitor the global water cycle, assess flood risk, and guide water resource management. However, there is a delay, and ongoing decline, in the availability of gauging data and stations are highly unevenly distributed globally. While not a substitute for river discharge measurement, remote sensing is a cost-effective technology to acquire information on river dynamics in situations where ground-based measurements are unavailable. The general approach has been to relate satellite observation to discharge measured in situ, which prevents its use for ungauged rivers. Alternatively, hydrological models are now available that can be used to estimate river discharge globally. While subject to greater errors and biases than measurements, model estimates of river discharge do expand the options for applying satellite-based discharge monitoring in ungauged rivers. Our aim was to test whether satellite gauging reaches (SGRs), similar to virtual stations in satellite altimetry, can be constructed based on Moderate Resolution Imaging Spectroradiometer (MODIS) optical or Global Flood Detection System (GFDS) passive microwave-derived surface water extent fraction and simulated discharge from the World-Wide Water (W3) model version 2. We designed and tested two methods to develop SGRs across the Amazon Basin and found that the optimal grid cell selection method performed best for relating MODIS and GFDS water extent to simulated discharge. The number of potential river reaches to develop SGRs increases from upstream to downstream reaches as rivers widen. MODIS SGRs are feasible for more river reaches than GFDS SGRs due to its higher spatial resolution. However, where they could be constructed, GFDS SGRs predicted discharge more accurately as observations were less affected by cloud and vegetation. We conclude that SGRs are suitable for automated large-scale application and offer a possibility to predict river discharge variations from satellite observations alone, for both gauged and ungauged rivers.
Publisher: Elsevier BV
Date: 03-2020
Publisher: American Geophysical Union (AGU)
Date: 04-2022
DOI: 10.1029/2021WR031649
Abstract: Accurate initial conditions play a critical role in improving predictive accuracy of hydrological models for quantities such as streamflow generation. Streamflow observations from in situ gauging sites have been assimilated in a wide range of past research to improve lumped catchment streamflow. However, spatio‐temporal state updating in distributed hydrological models through streamflow data assimilation (DA) remains a challenge due to the large dimensional disparity between model state space and observation space. This study explores the use of model spatio‐temporal variance‐covariance from simulated climatology as a surrogate for model error, as a way of distributing constraint from gauge measurements spatially. In the approach developed here, grid cells within and surrounding gauged catchments were updated simultaneously. The proposed DA method leads to a substantial improvement in performance and reliability compared to open‐loop (OL) estimates of streamflow. In particular, for “ungauged” catchments updated using streamflow observations from neighboring catchments, the mean absolute error (MAE) was reduced by 20% on average after the assimilation. In addition, improvements from using the analyzed states as initial conditions were found to persist for more than 1 week, and even longer for catchments with high annual runoff. The improvement in streamflow forecasts using initial conditions from DA can be more than 40% in terms of MAE compared to OL results for 1 day lead time, and more than 10% for 5 days lead time.
Publisher: American Geophysical Union (AGU)
Date: 12-2022
DOI: 10.1029/2022JB024669
Abstract: Global Positioning System (GPS) deformation measurements were combined with groundwater level data to examine the spatiotemporal variability of groundwater storage in the Lachlan catchment located in central New South Wales (Australia). After correcting for effects of glacial isostatic adjustment, non‐tidal oceanic and atmospheric loading as well as hydrologic loading using existing models, we show that the seasonal and interannual variability of ground deformation and hydraulic head level data, extracted using wavelet time‐frequency analysis, exhibits an in‐phase behavior, indicating that the observed surface deformation is the poroelastic response to groundwater pressure change in aquifer system. Combination of GPS displacement and groundwater level change enables the estimation of elastic skeletal specific storage coefficients, which were then used for estimating groundwater storage changes. The estimated groundwater storage changes clearly reflect the four climate events of the Lachlan catchment since 1996: (a) the Millennium drought over 1996–2009, (b) the 2011–2012 La Nina and two significant floods in 2012 and 2016, (c) the drought conditions from mid‐2017 to late‐2019, and (d) the return of La Nina conditions since early 2020. We also found annual and long‐term groundwater storage variations of respectively and over the period 2012–2021. Moreover, we show that groundwater level fluctuations can be predicted from GPS displacement measurements and storage coefficients with sufficient accuracy (80% correlation and 70% RMS reduction when compared in terms of seasonal cycle). This study provides essential information that can contribute to future groundwater planning, management, and control over the Australian continent.
No related grants have been discovered for Luigi Renzullo.