Albert I.J.M. Van Dijk

The accuracy of global water balance estimates is limited by the lack of observations at large scale and the uncertainties of model simulations. Global retrievals of terrestrial water storage (TWS) change and soil moisture (SM) from satellites provide an opportunity to improve model estimates through data assimilation. However, combining these two data sets is challenging due to the disparity in temporal and spatial resolution at both vertical and horizontal scale. For the first time, TWS observations from the Gravity Recovery and Climate Experiment (GRACE) and near-surface SM observations from the Soil Moisture and Ocean Salinity (SMOS) were jointly assimilated into a water balance model using the Ensemble Kalman Smoother from January 2010 to December 2013 for the Australian continent. The performance of joint assimilation was assessed against open-loop model simulations and the assimilation of either GRACE TWS anomalies or SMOS SM alone. The SMOS-only assimilation improved SM estimates but reduced the accuracy of groundwater and TWS estimates. The GRACE-only assimilation improved groundwater estimates but did not always produce accurate estimates of SM. The joint assimilation typically led to more accurate water storage profile estimates with improved surface SM, root-zone SM, and groundwater estimates against in situ observations. The assimilation successfully downscaled GRACE-derived integrated water storage horizontally and vertically into individual water stores at the same spatial scale as the model and SMOS, and partitioned monthly averaged TWS into daily estimates. These results demonstrate that satellite TWS and SM measurements can be jointly assimilated to produce improved water balance component estimates.

Phenology is the study of periodic biological occurrences and can provide important insights into the influence of climatic variability and change on ecosystems. Understanding Australia's vegetation phenology is a challenge due to its diverse range of ecosystems, from savannas and tropical rainforests to temperate eucalypt woodlands, semi-arid scrublands, and alpine grasslands. These ecosystems exhibit marked differences in seasonal patterns of canopy development and plant life-cycle events, much of which deviates from the predictable seasonal phenological pulse of temperate deciduous and boreal biomes. Many Australian ecosystems are subject to irregular events (i.e. drought, flooding , cyclones, and fire) that can alter ecosystem composition, structure, and functioning just as much as seasonal change. We show how satellite remote sensing and ground-based digital repeat photography (i.e. phenocams) can be used to improve understanding of phenology in Australian ecosystems. First, we examine temporal variation in phenology on the continental scale using the enhanced vegetation index (EVI), calculated from MODerate resolution Imaging Spec-troradiometer (MODIS) data. Spatial gradients are revealed, ranging from regions with pronounced seasonality in canopy development (i.e. tropical savannas) to regions where seasonal variation is minimal (i.e. tropical rainforests) or high but irregular (i.e. arid ecosystems). Next, we use time series colour information extracted from phenocam imagery to illustrate a range of phenological signals in four contrasting Australian ecosystems. These include greening and senesc-ing events in tropical savannas and temperate eucalypt un-derstorey, as well as strong seasonal dynamics of individual trees in a seemingly static evergreen rainforest. We also demonstrate how phenology links with ecosystem gross primary productivity (from eddy covariance) and discuss why these processes are linked in some ecosystems but not others. We conclude that phenocams have the potential to greatly improve the current understanding of Australian ecosystems. To facilitate the sharing of this information, we have formed the Australian Phenocam Network (http://phenocam.org.au/).

The revised analytical model to predict rainfall interception by sparse canopies (Journal of Hydrology 170 (1995) 79) is further modified to improve the description of evaporation from wet vegetation whose canopy characteristics vary in time (e.g. agricultural crops, deciduous forest, fast-growing plantation forest, effects of storms, pests or logging, etc.). The main adjustments proposed are based on the following assumptions: (1) the canopy capacity is linearly related to leaf area index; (2) the evaporation rate from a saturated canopy can be expressed as an exponential function of leaf area index; and (3) evaporation from stems during the storms may be treated in a similar manner as that from the canopy. The comparative performance of the revised and the presently proposed version of the analytical model in predicting interception by a mixed cropping system in West Java, Indonesia is discussed in a companion paper (Part 2).

Daily streamflow data for 183 Australian catchments were used to assess the characteristics and main drivers of baseflow and quick flow behaviour, and to find an appropriate balance between simplicity and explanatory performance in modelling. Baseflow separation was performed following the Wittenberg algorithm. A linear reservoir model (one parameter) produced baseflow estimates as good as those obtained using a non-linear reservoir (two parameters) and was therefore considered the more appropriate. The transition from storm flow dominated to baseflow dominated streamflow generally occurred 7 to 10 d after the storm event. The catchments investigated had baseflow half-times of about 12 d, with 80% of stations having half-times between 7 and 34 d. The shortest half-times occurred in the driest catchments and were attributed to intermittent occurrence of fast-draining (possibly perched) groundwater. Median baseflow index (BFI) was 0.45 with considerable variation between stations. Catchment humidity explained 27% of the variation in derived baseflow recession coefficients. Another 53% of variance in recession coefficients as well as in BFI showed spatial correlation lengths of 200 to 300 km, corresponding to terrain factors rather than climate or land use. The remaining 16 to 20% of variance remained unexplained. Most (84%) of the variation between stations in average baseflow could be explained by monthly precipitation in excess of potential evapotranspiration. Most (70%) of the variation in average quick flow could be explained by average rainfall. Another 20% of variation was spatially correlated over spatial scales of 400 km, possibly reflecting a combination of terrain and climate factors; the remaining 10 to 16% remained unexplained.

This study investigated the potential for improvement of Soil Conservation Service (SCS) Curve Number (CN) storm runoff estimates with the implementation of satellite-derived soil moisture. A large data-set (1980-2007) of daily measurements of precipitation and streamflow for 135 Australian catchments ranging in size from 53 to 471 km2 was used. The observed CN, a measure of the soil's maximum potential retention, was calculated using the SCS-CN model from measured precipitation and stormflow data. The observed CN was compared to a soil wetness index (SWI) based on AMSR-E satellite surface moisture and an antecedent precipitation index (API) based on field observations. Significant correlations (p

Near surface soil moisture content inferred from remotely sensed passive microwave emissions can improve hydrological and meteorological modeling, but the interpretation and assimilation of estimates is complicated by the large observation footprint and shallow signal source depth. Here, some aspects are discussed that refer to the sub-grid lateral variability of soil moisture content, more specifically the impact of temporary inundation on the satellite derived estimate. For the regional subset of Oklahoma, West South Central USA, surface soil moisture estimates obtained with a retrieval algorithm, developed jointly at the Vrije Universiteit Amsterdam and NASA/GSFC, from the Advanced Microwave Scanner Radiometer (AMSR) on board NASA's Earth Observing System (EOS) Aqua satellite are evaluated against model output of the Community Noah Land Surface Model and Community Land Model (CLM2) operated within the Land Information System (LIS) forced with atmospheric data of a variety of sources, i.e. the NCEP Global Data Assimilation System (GDAS), the European Centre of Medium Range Weather Forecast (ECMWF) and the North American Data Assimilation System (NLDAS). The surface soil moisture retrievals and LSM output are further evaluated against point measurements from the Mesonet observational grid in Oklahoma. Research results presented here indicate methods to screen data influenced by temporary inundation is a significant way to improve the accuracy and usefulness of satellite derived surface moisture estimates.

Daily streamflow data were analysed to assess which climate and terrain factors best explain streamflow response in 183 Australian catchments. Assessed descriptors of catchment response included the parameters of fitted baseflow models, and baseflow index (BFI), average quick flow and average baseflow derived by baseflow separation. The variation in response between catchments was compared with indicators of catchment climate, morphology, geology, soils and land use. Spatial coherence in the residual unexplained variation was investigated using semi-variogram techniques. A linear reservoir model (one parameter; recession coefficient) produced baseflow estimates as good as those obtained using a non-linear reservoir (two parameters) and for practical purposes was therefore considered an appropriate balance between simplicity and explanatory performance. About a third (27-34%) of the spatial variation in recession coefficients and BFI was explained by catchment climate indicators, with another 53% of variation being spatially correlated over distances of 100-150 km, probably indicative of substrate characteristics not captured by the available soil and geology data. The shortest recession half-times occurred in the driest catchments and were attributed to intermittent occurrence of fast-draining (possibly perched) groundwater. Most (70-84%) of the variation in average baseflow and quick flow was explained by rainfall and climate characteristics; another 20% of variation was spatially correlated over distances of 300-700 km, possibly reflecting a combination of terrain and climate factors. It is concluded that catchment streamflow response can be predicted quite well on the basis of catchment climate alone. The prediction of baseflow recession response should be improved further if relevant substrate properties were identified and measured.

Executive Summary Returning tree cover to reduce groundwater recharge has the potential of reversing some of Australia's salinity problems. Plantation forests in low-to-medium rainfall areas (400-800 mm y-1) are currently not widespread and, at the lower end of this ...

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.

The 500m resolution CSIRO MODIS reflectance scaling evapotranspiration product (CMRSET) was combined with a gridded rainfall product to determine where in the landscape evapotranspiration exceeds rainfall over longer time periods, and by implication, where lateral inflows of river or groundwater are received and evaporated. This procedure produces valuable information for hydrological applications, including the spatial distribution of water use, the temporal distribution, and the absolute magnitude of (net) evaporation across the landscape. Practical uses that have been tested in Australia include evaluating the realism of simulated water use components in river models, attributing apparent losses from river reaches to processes and spatial locations, and identifying river and groundwater dependent ecosystems. Satellite observed inundation patterns have been used to separate surface water from groundwater use. Higher resolution Landsat imagery has been used for image enhancement, allowing smaller irrigation and wetland areas to be detected. Satellite-based land use classification helps to separate agricultural from environmental water use. The information produced is used in the Australian Water Resources Assessment (AWRA) system under development by CSIRO and the Australian Bureau of Meteorology to underpin operational delivery of water resources information.

A formal method was developed to achieve optimal model complexity where alternative conceptual structures exist or there are several free model parameters. The method was applied to alternative lumped model structures for surface runoff prediction and for baseflow recession estimation, respectively. The objective performance measure is based on Aikake's Information Criterion (AIC), which attempts to account for the effect of the number of free parameters in the calculation of model prediction error. Model simplification involves stepwise reduction of the least important free parameters, either by removing it or replacing it by a single prior estimate. The model with the optimal performance among all structures and variants is selected as having the optimal trade-off between model complexity and parsimony. Caveats of the method are associated with the assumptions in the AIC model, how telling the used error statistic is, and how representative the data available for performance assessment.