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Information about the carbon cycle potentially constrains the water cycle, and vice versa. This paper explores the utility of multiple observation sets to constrain a land surface model of Australian terrestrial carbon and water cycles, and the resulting mean carbon pools and fluxes, as well as their temporal and spatial variability. Observations include streamflow from 416 gauged catchments, measurements of evapotranspiration (ET) and net ecosystem production (NEP) from 12 eddy-flux sites, litterfall data, and data on carbon pools. By projecting residuals between observations and corresponding predictions onto uncertainty in model predictions at the continental scale, we find that eddy flux measurements provide a significantly tighter constraint on continental net primary production (NPP) than the other data types. Nonetheless, simultaneous constraint by multiple data types is important for mitigating bias from any single type. Four significant results emerging from the multiply-constrained model are that, for the 1990-2011 period: (i) on the Australian continent, a predominantly semi-arid region, over half the water loss through ET (0.64±0.05) occurs through soil evaporation and bypasses plants entirely; (ii) mean Australian NPP is quantified at 2.2±0.4 (1σ) PgCyr-1; (iii) annually cyclic ("grassy") vegetation and persistent ("woody") vegetation account for 0.56±0.14 and 0.43±0.14, respectively of NPP across Australia; (iv) the average interannual variability of Australia's NEP (±0.18 PgCyr-1, 1σ) is larger than Australia's total anthropogenic greenhouse gas emissions in 2011 (0.149 PgCequivalentyr-1), and is dominated by variability in Desert and Savanna regions. [ABSTRACT FROM AUTHOR]

Despite being the driest inhabited continent, Australia has one of the highest per capita water consumptions in the world. In addition, instead of having fit-for-purpose water supplies (using different qualities of water for different applications), highly treated drinking water is used for nearly all of Australia's urban water supply needs, including landscape irrigation. The water requirement of urban landscapes, particularly urban parklands, is of growing concern. The estimation of evapotranspiration (ET) and subsequently plant water requirements in urban vegetation needs to consider the heterogeneity of plants, soils, water, and climate characteristics. This research contributes to a broader effort to establish sustainable irrigation practices within the Adelaide Parklands in Adelaide, South Australia. In this paper, two practical ET estimation approaches are compared to a detailed Soil Water Balance (SWB) analysis over a one year period. One approach is the Water Use Classification of Landscape Plants (WUCOLS) method, which is based on expert opinion on the water needs of different classes of landscape plants. The other is a remote sensing approach based on the Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra satellite. Both methods require knowledge of reference ET calculated from meteorological data. The SWB determined that plants consumed 1084 mm· yr-1 of water in ET with an additional 16% lost to drainage past the root zone, an amount sufficient to keep salts from accumulating in the root zone. ET by MODIS EVI was 1088 mm· yr-1, very close to the SWB estimate, while WUCOLS estimated the total water requirement at only 802 mm· yr-1, 26% lower than the SWB estimate and 37% lower than the amount actually added including the drainage fraction. Individual monthly ET by MODIS was not accurate, but these errors were cancelled out to give good agreement on an annual time step. We conclude that the MODIS EVI method can provide accurate estimates of urban water requirements in mixed landscapes large enough to be sampled by MODIS imagery with 250-m resolution such as parklands and golf courses. [ABSTRACT FROM AUTHOR]

The present study evaluates the capability of a novel optimization method in modeling daily crop reference evapotranspiration (ETo), a critical issue in water resource management. A hybrid predictive model based on the artificial neural network (ANN) algorithm that is embedded within the COOT method (COOT bird natural life model-artificial neural network (COOT-ANN)) is developed and evaluated for its suitability for the prediction of daily ETo at seven meteorological stations in different states of Australia. Accordingly, a daily statistical period of 12 years (01-01-2010 to 31-12-2021) for climatic data of maximum temperature, minimum temperature, and ETo were collected. The results are evaluated using six performance criteria metrics: correlation coefficient (R), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), RMSE-observation standard deviation ratio (RSR), Scatter Index (SI), and mean absolute error (MAE) along with the Taylor diagrams. The performance of the COOT-ANN model was compared with those of the conventional ANN model. The results showed that the COOT-ANN hybrid model outperforms the ANN model at all seven stations by 0.803%, 4.127%, 3.359%, 4.072%, 4.148%, and 3.665% based on the average values of the R, RMSE, NSE, RSR, SI, and MAE criteria, respectively. So, this study provides an innovative method for prediction in agricultural and water resource studies. [ABSTRACT FROM AUTHOR]

Information about the carbon cycle potentially constrains the water cycle, and vice versa. This paper explores the utility of multiple observation sets to constrain a land surface model of Australian terrestrial carbon and water cycles, and the resulting mean carbon pools and fluxes, as well as their temporal and spatial variability. Observations include streamflow from 416 gauged catchments, measurements of evapotranspiration (ET) and net ecosystem production (NEP) from 12 eddy-flux sites, litterfall data, and data on carbon pools. By projecting residuals between observations and corresponding predictions onto uncertainty in model predictions at the continental scale, we find that eddy flux measurements provide a significantly tighter constraint on continental net primary production (NPP) than the other data types. Nonetheless, simultaneous constraint by multiple data types is important for mitigating bias from any single type. Four significant results emerging from the multiplyconstrained model are that, for the 1990-2011 period: (i) on the Australian continent, a predominantly semi-arid region, over half the water loss through ET (0.64±0.05) occurs through soil evaporation and bypasses plants entirely; (ii) mean Australian NPP is quantified at 2.2±0.4 (1σ) PgC yr-1; (iii) annually cyclic ("grassy") vegetation and persistent ("woody") vegetation account for 0.67±0.14 and 0.33±0.14, respectively, of NPP across Australia; (iv) the average interannual variability of Australia's NEP (±0.18 PgC yr-1, 1σ) is larger than Australia's total anthropogenic greenhouse gas emissions in 2011 (0.149 Pg C equivalent yr-1), and is dominated by variability in desert and savanna regions. [ABSTRACT FROM AUTHOR]

Current methods for climate change assessment ignore the significant differences in uncertainty in model projections of the two key constituents of drought, precipitation, and evapotranspiration. We present here a new basis for assessing future drought using climate model simulations that addresses this limitation. The new method estimates the Standardized Precipitation Evapotranspiration Index (SPEI) in a two‐stage process. The first stage of our proposed approach is to derive the Standardized Precipitation Index (SPI) using reliable atmospheric variables, which are filtered with a wavelet‐based spectral transformation. This derived SPI is then converted to an equivalent SPEI by combining it with climate model evapotranspiration simulations. We assess the performance of our proposed approach across Australia. The consistency of general circulation model (GCM) drought projections, in terms of both frequency and severity, is improved using the derived SPI. Incorporating evapotranspiration further improves the consistency of the multiple GCMs and drought time scales. The proposed framework can also be generalized to other water resources applications, where the differences in GCM uncertainty for precipitation and evapotranspiration affect climate change impact assessments. Plain Language Summary: Drought is affected by both rainfall and evapotranspiration. Drought indices represent drought severity compared to normal conditions as a function of time. Some drought indices are based on rainfall alone and some use both rainfall and evapotranspiration in the calculations. To understand future drought risk, simulations from climate models are needed. Unfortunately, different climate models often disagree on amounts and patterns of rainfall in the future, the disagreement being considerably more on rainfall than for evapotranspiration. In this study, we attempt to reduce the impact of these differences by developing a new method to estimate future drought. We used a mathematical method known as wavelets to estimate drought indices based on rainfall. Evapotranspiration is then used directly from the climate model and combined with the rainfall based drought index to create one overall drought index. We used projections from multiple climate models to understand if our new method led to a greater agreement in how often and severe future droughts may be. Our results confirm that the new method offers greater consistency in drought projections for the future. Key Points: Current methods for projecting drought ignore differences in uncertainty between P and ET simulationsA new basis for projecting drought is proposed that explicitly accounts for relative uncertainty between P and ETOur results show that the new method offers greater consistency in drought projections for the future [ABSTRACT FROM AUTHOR]

Evapotranspiration by phreatophytes in riparian zones makes up a large component of the water balance. However, our understanding of the relative importance of controlling factors such as climatic conditions, species type, depth to groundwater and distance to surface water in riparian zones remains a significant knowledge gap. A field experiment was conducted in an irrigated catchment in North Queensland, Australia, to investigate the factors controlling evapotranspiration by groundwater dependent trees. The sap flow of four tree species was measured, along with soil moisture, groundwater levels and local climatic conditions. The relative influence of species, hydrologic and climate factors, and measured variables were investigated with two non-parametric methods: random forest and Principal Component Analysis (PCA). Field monitoring data revealed differences in sap flow rates and diurnal sap flow trends between species. Distance from surface water explained the most variance in sap flow rates, followed by depth to groundwater and species, based on random forest modeling. The sap flow rates for some of the Eucalyptus tessellaris trees at this site reduced as groundwater levels declined. Overall, results demonstrate the value that can be gained from applying non-parametric methods, such as random forest and PCA, to investigate the relative importance of the factors influencing evapotranspiration. [ABSTRACT FROM AUTHOR]

While the calibration-free complementary relationship (CR) has performed excellently in predicting terrestrial evapotranspiration (ET a) , how to determine the Priestley–Taylor coefficient (αe) is a remaining question. In this work, we evaluated this highly utilizable method, which only requires atmospheric data, with in situ flux observations and basin-scale water-balance estimates (ET wb) in Australia, proposing how to constrain it with a traditional Budyko equation for ungauged locations. We found that the CR method with a constant αe transferred from fractional wet areas performed poorly in reproducing the mean annual ET wb in unregulated river basins, and it underperformed advanced physical, machine-learning, and land surface models in closing grid-scale water balance. This problem was remedied by linking the CR method with a traditional Budyko equation that allowed for an upscaling of the optimal αe from gauged basins to ungauged locations. The combined CR–Budyko framework enabled us to reflect climate conditions in αe , leading to more plausible ET a estimates in ungauged areas. The spatially varying αe conditioned by local climates enabled the CR method to outperform the three ET a models in reproducing the grid-scale ET wb across the Australian continent. We argued here that the polynomial CR with a constant αe could result in biased ET a , and it can be constrained by a traditional Budyko equation for improvement. [ABSTRACT FROM AUTHOR]

Terrestrial ecosystems are fully coupled with the climate. The planet has been greening owing to the increased vegetation growth in response to the changing atmosphere, which in turn has feedback on the climate. Greening has slowed down the rise in global land-surface air temperature mainly through a coincident increase of evapotranspiration and precipitation in wet regions. In dry regions, greening intensifies the decrease in soil moisture induced by greening-enhanced transpiration. Uncertain, however, is how the climate effects of greening in semi-arid lands might differ for variable wet and dry conditions. Here, we focus on the biosphere–atmosphere interactions in Australia by modeling the perturbation of vegetation changes under various states of sea surface temperature (SST), including the climatology mean, El Niño, and La Niña conditions. For the dry conditions of El Niño, greening exacerbates water stress and largely depletes the soil moisture, while for the wet conditions of La Niña, greening-enhanced evapotranspiration and precipitation resupply the soil moisture. For the normal conditions using the climatology mean SST, a small decrease in soil moisture occurs but with large spatial contrast because of heterogeneous changes of evapotranspiration and precipitation induced by greening. We emphasize that the alternating dry and wet conditions modulated by the large-scale climate variability are vital to understanding the response of climate to greening. Furthermore, vegetation-based warming mitigation policies need to be cautious when inferring distinct climate effects associated with greening. [ABSTRACT FROM AUTHOR]

There is an increasing interest in using the Internet of Things (IoT) in the agriculture sector to acquire soil- and crop-related parameters that provide helpful information to manage farms more efficiently. One example of this technology is using IoT soil moisture sensors for scheduling irrigation. Soil moisture sensors are usually deployed in nodes. A more significant number of sensors/nodes is recommended in larger fields, such as those found in broadacre agriculture, to better account for soil heterogeneity. However, this comes at a higher and often limiting cost for farmers (purchase, labour costs from installation and removal, and maintenance). Methodologies that enable maintaining the monitoring capability/intensity with a reduced number of in-field sensors would be valuable for the sector and of great interest. In this study, sensor data analysis conducted across two irrigation seasons in three cotton fields from two cotton-growing areas of Australia, identified a relationship between soil matric potential and cumulative satellite-derived crop evapotranspiration (ETcn) between irrigation events. A second-degree function represents this relationship, which is affected by the crop development stage, rainfall, irrigation events and the transition between saturated and non-saturated soil. Two machine learning models [a Dense Multilayer Perceptron (DMP) and Support Vector Regression (SVR) algorithms] were studied to explore these second-degree function properties and assess whether the models were capable of learning the pattern of the soil matric potential-ETcn relation to estimate soil moisture from satellite-derived ETc measurements. The algorithms performance evaluation in predicting soil matric potential applied the k-fold method in each farm individually and combining data from all fields and seasons. The latter approach made it possible to avoid the influence of farm consultants' decisions regarding when to irrigate the crop in the training process. Both algorithms accurately estimated soil matric potential for individual (up to 90% of predicted values within ±10kPa) and combined datasets (73% of predicted values within ±10kPa). The technique presented here can accurately monitor soil matric potential in the root zone of cotton plants with reduced in-field sensor equipment and offers promising applications for its use in irrigation-decision systems. [ABSTRACT FROM AUTHOR]

Reconstructed weather datasets, such as reanalyses based on model output with data assimilation, often show systematic biases in magnitude when compared with observations. Postprocessing approaches can help adjust the distribution so that the reconstructed data resemble the observed data as closely as possible. In this study, we have compared various statistical bias-correction approaches based on quantile–quantile matching to correct the data from the Twentieth Century Reanalysis, version 2c (20CRv2c), with observation-based data. Methods included in the comparison utilize a suite of different approaches: a linear model, a median-based approach, a nonparametric linear method, a spline-based method, and approaches that are based on the lognormal and Weibull distributions. These methods were applied to daily data in the Australian region for rainfall, maximum temperature, relative humidity, and wind speed. Note that these are the variables required to compute the forest fire danger index (FFDI), widely used in Australia to examine dangerous fire weather conditions. We have compared the relative errors and performances of each method across various locations in Australia and applied the approach with the lowest mean-absolute error across multiple variables to produce a reliable long-term bias-corrected FFDI dataset across Australia. The spline-based data correction was found to have some benefits relative to the other methods in better representing the mean FFDI values and the extremes from the observed records for many of the cases examined here. It is intended that this statistical bias-correction approach applied to long-term reanalysis data will help enable new insight on climatological variations in hazardous phenomena, including dangerous wildfires in Australia extending over the past century. [ABSTRACT FROM AUTHOR]

Evapotranspiration plays an important role in the terrestrial water cycle. Reference crop evapotranspiration (ET o) has been widely used to estimate water transfer from vegetation surface to the atmosphere. Seasonal ET o forecasting provides valuable information for effective water resource management and planning. Climate forecasts from general circulation models (GCMs) have been increasingly used to produce seasonal ET o forecasts. Statistical calibration plays a critical role in correcting bias and dispersion errors in GCM-based ET o forecasts. However, time-dependent errors resulting from GCM misrepresentations of climate trends have not been explicitly corrected in ET o forecast calibrations. We hypothesize that reconstructing climate trends through statistical calibration will add extra skills to seasonal ET o forecasts. To test this hypothesis, we calibrate raw seasonal ET o forecasts constructed with climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 model across Australia, using the recently developed Bayesian joint probability trend-aware (BJP-ti) model. Raw ET o forecasts demonstrate significant inconsistencies with observations in both magnitudes and spatial patterns of temporal trends, particularly at long lead times. The BJP-ti model effectively corrects misrepresented trends and reconstructs the observed trends in calibrated forecasts. Improving trends through statistical calibration increases the correlation coefficient between calibrated forecasts and observations (r) by up to 0.25 and improves the continuous ranked probability score (CRPS) skill score by up to 15 (%) in regions where climate trends are misrepresented by raw forecasts. Skillful ET o forecasts produced in this study could be used for streamflow forecasting, modeling of soil moisture dynamics, and irrigation water management. This investigation confirms the necessity of reconstructing climate trends in GCM-based seasonal ET o forecasting and provides an effective tool for addressing this need. We anticipate that future GCM-based seasonal ET o forecasting will benefit from correcting time-dependent errors through trend reconstruction. [ABSTRACT FROM AUTHOR]

Abstract: The idea of ground-based evapotranspiration (ET) is of the most interesting for land–atmosphere interactions, such as water-saving irrigation, the performance of irrigation systems, crop water deficit, drought mitigation strategies and accurate initialization of climate prediction models especially in arid and semiarid catchments where water shortage is a critical problem. The recent year''s drought in Australia and concerns about climate change has prominent the need to manage water resources more sustainably especially in the Murrumbidgee catchment which utilizes bulk water for food security and production. This paper discusses the application of a Surface Energy Balance System (SEBS) model based on Landsat-5 TM data and field observations has been used and tested for deriving ET over Coleambally Irrigation Area (CIA), located in the southwest of NSW, Australia. 16 Landsat-5 TM scenes were selected covering the time period of 2009, 2010 and 2011 for estimating the actual ET in CIA. To do the validation the used methodology, the ground-measured ET was compared to the Landsat-5 TM retrieved actual ET results for CIA. The derived ET value over CIA is much closer to the field measurement. From the remote sensing results and observations, the root mean square error (RMSE) is 0.74 and the mean APD is 7.5%. The derived satellite remote sensing values belong to reasonable range. [Copyright &y& Elsevier]

Abstract: Disturbance of forested catchments by fire, logging, or other natural or human induced events that alter the evapotranspiration regime may be a substantial threat to domestic, environmental and industrial water supplies. This paper describes the physically-based modelling of the long term changes in water yield from two wildfire affected catchments in north-eastern Victoria, Australia, and of fire and climate change scenarios in Melbourne''s principal water supply catchment. The effect of scale, data availability and quality, and of forest species parameterisation are explored. The modelling demonstrates the importance of precipitation inputs, with Nash and Sutcliffe Coefficients of Efficiency of predicted versus observed monthly flows increasing from 0.5 to 0.8 with a higher density of rainfall stations, and where forest types are well parameterised. Total predicted flow volumes for the calibrations were within 1% of the observed for the Mitta Mitta River catchment and <4% for the Thomson River, but almost −10% for the less well parameterised Tambo River. Despite the issues of data availability simulations demonstrated the potential for significant impacts to water supply in SE Australia from wildfire and climate change. For example, for the catchments modelled the moderate climate change impact on water yield was more pronounced than the worst fire scenario. Both modelled cases resulted in long term water yield declines exceeding 20%, with the climate change impact nearing 30%. A simulation using observed data for the first four post-fire years at the Mitta Mitta River catchment showed Macaque was able to accurately predict total flow. [Copyright &y& Elsevier]

Reference crop evapotranspiration (ETo) is calculated using a standard formula with temperature, vapor pressure, solar radiation, and wind speed as input variables. ETo forecasts can be produced when forecasts of these input variables from numerical weather prediction (NWP) models are available. As raw ETo forecasts are often subject to systematic errors, statistical calibration is needed for improving forecast quality. The most straightforward and widely used approach is to directly calibrate raw ETo forecasts constructed with the raw forecasts of input variables. However, the predictable signal in ETo forecasts may not be fully implemented by this approach, which does not deal with error propagation from input variables to ETo forecasts. We hypothesize that correcting errors in input variables as a precursor to forecast calibration will lead to more skillful ETo forecasts. To test this hypothesis, we evaluate two calibration strategies that construct raw ETo forecasts with the raw (strategy i) or bias-corrected (strategy ii) input variables in ETo forecast calibration across Australia. Calibrated ETo forecasts based on bias-corrected input variables (strategy ii) demonstrate lower biases, higher correlation coefficients, and higher skills than forecasts produced by the calibration using raw input variables (strategy i). This investigation indicates that improving raw forecasts of input variables could effectively reduce error propagation and enhance ETo forecast calibration. We anticipate that future NWP-based ETo forecasting will benefit from adopting the calibration strategy developed in this study to produce more skillful ETo forecasts. [ABSTRACT FROM AUTHOR]

One of the critical components in understanding the climate system is the interaction between the land and the atmosphere. Whereas previous studies on land–atmosphere coupling mostly focus on its spatial hotspots, we explore the temporal evolution of land surface coupling strength (LCS) during a large-scale flood event in a semiarid region in northern Australia. The LCS indicates the relationship between soil moisture and latent heat flux, and the spatiotemporal variability in precipitation and soil water strongly affects the variability of LCS. The LCS is usually positive in the semiarid climate, where evapotranspiration (ET) occurs under the soil moisture–limited regime and thus increases with soil moisture. However, our analyses of combined land surface modeling and observational datasets show high temporal variability of LCS in the course of the extreme flood event followed by a drying period. The wet regions transferred the ET regime from the soil moisture–limited to the transition section, weakening the linear growth of ET with soil moisture, which resulted in the decline of LCS. The LCS remained weak until the flood retreated and the soil water approached the prestorm average state. Such temporal variation of the LCS has important implications for realistic parameterization of the land–atmosphere coupling and consequently improving subseasonal to seasonal climate forecast. [ABSTRACT FROM AUTHOR]

The 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. [ABSTRACT FROM AUTHOR]

Irrigation is necessary to help meet the high demand for water by sugarcane in several countries including Brazil and Australia. The crop irrigation requirements are typically estimated using the crop coefficient-reference evapotranspiration (Kc-ETo) procedure. Sugarcane evapotranspiration rates were measured in three different sugarcane-producing regions of the world, and crop coefficients (Kc) were derived for those diverse environments, irrigation methods, and farming systems, therefore representing a robust basis for irrigation management. We also verified the occurrence of the inverse relation between Kc and ETo found in previous studies. Two experiments in Brazil and one in Australia were used for the analysis. Our data showed that Kc for a full canopy cover was lower than 1.0 in the three experimental sites and that sugarcane evapotranspiration (ETc) seems to be limited and exceeds ETo only when this is below 4 mm day−1. In one of the Brazilian experiments, Kc declined at higher rates than in the other two experiments, and for the three sites, average Kc was 0.77 and 0.87, respectively, for initial and full cover phase when ETo > 6 mm day−1. The increase of aerodynamic and other upstream resistances to water transport of plants appears to be one of the reasons for Kc to decrease at high levels of ETo. Based on our data and the literature, the Kc values provided by Allen et al. (1998) could overestimate the irrigation needs of sugarcane under high evaporative demand conditions. Irrigation management based on Kc should use the average ETo from the preceding 3 days before irrigating to save water and energy while maintaining high yield levels. [ABSTRACT FROM AUTHOR]

Northern Australia wet season (November–April) rainfall exhibits strong variability on multiyear timescales. In order to reveal the underlying mechanisms of this variability, we investigate observational records for the period 1900–2017. At multiyear timescales, the rainfall varies coherently across north-western Australia (NW) and north-eastern Australia (NE), but the variability in these two regions is largely independent. The variability in the NE appears to be primarily controlled by the remote influence of low frequency variations of El Niño-Southern Oscillation (ENSO). In contrast, multiyear variations in the NW appear to be largely driven locally and stem from a combination of rainfall-wind-evaporation feedback, whereby enhanced land-based rainfall is associated with westerly wind anomalies to the west that enhance local evaporation over the ocean to feed the enhanced land based rainfall, and soil moisture-rainfall feedback. Soil-moisture and associated evapotranspiration over northern Australia appear to act as sources of memory for sustaining multiyear wet and dry conditions in the NW. Our results imply that predictability of multiyear rainfall variations over the NW may derive from the initial soil moisture state and its memory, while predictability in the NE will be limited by the predictability of the low frequency variations of ENSO. [ABSTRACT FROM AUTHOR]

Determining drivers of species richness is recognised as highly complex, involving many synergies and interactions. We examine the utility of newly available remote sensing representations of vegetation productivity and vegetation structure to examine drivers of species richness at continental and regional scales. We related richness estimates derived from stacked species distribution models for birds, mammals, amphibians, and reptiles to estimates of actual and potential evapotranspiration (AET and PET), forest structure, and forest productivity across Australia as a whole as well as by bioclimatic zones. We used structural equation modeling to partition correlations between climate energy and vegetation attributes and their subsequent associations with species richness. Continentally, vertebrate richness patterns were strongly related to patterns of energy availability. Richness of amphibians, mammals, and birds were positively associated with AET. However, reptile richness was most strongly associated with PET. Regionally, forest structure and productivity associations with bird, mammal, and amphibian richness were strongest. Again, reptile richness associated most strongly with PET. Our results suggest that a hierarchy of drivers of broad‐scale vertebrate richness patterns exist (reptiles excluded): 1) climate energy is most important at the continental scale; next, 2) vegetation productivity and vegetation structure are most important at the regional scale; except 3) at low extremes of climate energy when energy becomes limiting. [ABSTRACT FROM AUTHOR]

This study examines the dynamics and robustness of large-scale evapotranspiration products in water-limited environments. Four types of ET products are tested against rainfall in two large semi-arid to arid Australian basins from 2003 to 2010: two energy balance ET methods which are forced by optical satellite retrievals from MODIS; a newly developed land surface model (AWRA); and one approach based on observations from the Gravity Recovery and Climate Experiment (GRACE) and rainfall data. The two basins are quasi (Murray-Darling Basin: 1.06 million km²) and completely (Lake Eyre Basin: 1.14 million km²) endorheic. During the study period, two extreme climatic events--the Millennium drought and the strongest La Niña event--were recorded in the basins and are used in our assessment. The two remotely-sensed ET products constrained by the energy balance tended to overestimate ET flux over water-stressed regions. They had low sensitivity to climatic extremes and poor capability to close the water balance. However, these two remotely-sensed and energy balance products demonstrated their superiority in capturing spatial features including over small-scale and complicated landscapes. AWRA and GRACE formulated in the water balance framework were more sensitive to rainfall variability and yielded more realistic ET estimates during climate extremes. GRACE demonstrated its ability to account for seasonal and inter-annual change in water storage for ET evaluation. [ABSTRACT FROM AUTHOR]

The usage of time series of Earth observation (EO) data for analyzing and modeling surface water extent (SWE) dynamics across broad geographic regions provides important information for sustainable management and restoration of terrestrial surface water resources, which suffered alarming declines and deterioration globally. The main objective of this research was to model SWE dynamics from a unique, statistically validated Landsat-based time series (1986-2011) continuously through cycles of flooding and drying across a large and heterogeneous river basin, the Murray-Darling Basin (MDB) in Australia. We used dynamic linear regression to model remotely sensed SWE as a function of river flow and spatially explicit time series of soil moisture (SM), evapotranspiration (ET), and rainfall (P). To enable a consistent modeling approach across space, we modeled SWE dynamics separately for hydrologically distinct floodplain, floodplain-lake, and non-floodplain areas within ecohydrological zones and 10km × 10km grid cells. We applied this spatial modeling framework to three sub-regions of the MDB, for which we quantified independently validated lag times between river gauges and each individual grid cell and identified the local combinations of variables that drive SWE dynamics. Based on these automatically quantified flow lag times and variable combinations, SWE dynamics on 233 (64 %) out of 363 floodplain grid cells were modeled with a coefficient of determination (r2) greater than 0.6. The contribution of P, ET, and SM to the predictive performance of models differed among the three sub-regions, with the highest contributions in the least regulated and most arid sub-region. The spatial modeling framework presented here is suitable for modeling SWE dynamics on finer spatial entities compared to most existing studies and applicable to other large and heterogeneous river basins across the world. [ABSTRACT FROM AUTHOR]

Missing data and low data quality are common issues in field observations of actual evapotranspiration (ETa) from eddy-covariance systems, which necessitates the need for gap-filling techniques to improve data quality and utility for further analyses. A number of models have been proposed to fill temporal gaps in ETa or latent heat flux observations. However, existing gap-filling approaches often use multi-variate models that rely on relationships between ETa and other meteorological and flux variables, highlighting a critical lack of parsimonious gap-filling models. This study aims to develop and evaluate parsimonious approaches to fill gaps in ETa observations. We adapted three gap-filling models previously used for other meteorological variables but never applied to infill sub-daily ETa or flux observations from eddy-covariance systems before. All three models are solely based on the observed diurnal patterns in the ETa data, which infill gaps in sub-daily data with sinusoidal functions (Sinusoidal), smoothing functions (Smoothing) and pattern matching (MaxCor) approaches, respectively. We presented a systematic approach for model evaluation, considering multiple patterns of data gaps during different times of the day. The three gap-filling models were evaluated together with another benchmarking gap-filling model, mean diurnal variation (MDV) that has been commonly used and has similar data requirement. We used a case study with field measurements from an EC system over summer 2020–2021, at a maize field in southeastern Australia. We identified the MaxCor model as the best gap-filling model, which informs the diurnal pattern of the day to infill by using another day with similar temporal patterns and complete data. Following the MaxCor model, the MDV and the Sinusoidal models show comparable performances. We further discussed the infilling models in terms of their dependence on data availability and their suitability for different practical situations. The MaxCor model relies on high data availability for both days with complete data and the available records within each day to infill. The Sinusoidal model does not rely on any day with complete data, which makes it the ideal choice in situations where days with complete records are limited. [ABSTRACT FROM AUTHOR]

The feasibility of using vegetation greenness metrics as a proxy for transpiration variability over Australia is demonstrated. Several global evapotranspiration datasets, one of which provides transpiration data and is constructed independently of the vegetation greenness measurements, are compared to four satellite-based observations representative of the state of the vegetation over several regions in Australia. Further estimates of the transpiration are obtained by decomposing the evapotranspiration datasets using an ensemble of land surface model simulations. On monthly time scales, the greenness anomaly metrics show a near one-to-one relationship with the transpiration estimates when the time series are appropriately scaled by the mean. The authors demonstrate that anomalous vegetation greenness metrics, when properly scaled, provide a tool for evaluating transpiration variability simulated by land surface models and observation-based evapotranspiration datasets that include transpiration. These methods provide a new test to help constrain the dynamic behavior of the land surface in climate model simulations. [ABSTRACT FROM AUTHOR]

A rigorous evaluation of future hydro-climatic changes is necessary for developing climate adaptation strategies for a catchment. The integration of future climate projections from general circulation models (GCMs) in the simulations of a hydrologic model, such as the Soil and Water Assessment Tool (SWAT), is widely considered as one of the most dependable approaches to assess the impacts of climate alteration on hydrology. The main objective of this study was to assess the potential impacts of climate alteration on the hydrology of the Yarra River catchment in Victoria, Australia, using the SWAT model. The climate projections from five GCMs under two Representative Concentration Pathway (RCP) scenarios—RCP 4.5 and 8.5 for 2030 and 2050, respectively—were incorporated into the calibrated SWAT model for the analysis of future hydrologic behaviour against a baseline period of 1990–2008. The SWAT model performed well in its simulation of total streamflow, baseflow, and runoff, with Nash–Sutcliffe efficiency values of more than 0.75 for monthly calibration and validation. Based on the projections from the GCMs, the future rainfall and temperature are expected to decrease and increase, respectively, with the highest changes projected by the GFDL-ESM2M model under the RCP 8.5 scenario in 2050. These changes correspond to significant increases in annual evapotranspiration (8% to 46%) and decreases in other annual water cycle components, especially surface runoff (79% to 93%). Overall, the future climate projections indicate that the study area will become hotter, with less winter–spring (June to November) rainfall and with more water shortages within the catchment. [ABSTRACT FROM AUTHOR]

The Murray–Darling Basin Plan is a $AU 13 billion program to return water from irrigation use to the environment. Central to the success of the Plan, commenced in 2012, is the implementation of an Environmentally Sustainable Level of Take (ESLT) and a Sustainable Diversion Limit (SDL) on the volume of water that can be taken for consumptive use. Under the enabling legislation, the Water Act (2007), the ESLT and SDL must be set by the "best available science." In 2009, the volume of water to maintain wetlands and rivers of the Basin was estimated at 3000–7600 GL per year. Since then, there has been a steady step-down in this volume to 2075 GL year due to repeated policy adjustments, including "supply measures projects," building of infrastructure to obtain the same environmental outcomes with less water. Since implementation of the Plan, return of water to the environment is falling far short of targets. The gap between the volume required to maintain wetlands and rivers and what is available is increasing with climate change and other risks, but the Plan makes no direct allowance for climate change. We present policy options that address the need to adapt to less water and re-frame the decision context from contestation between water for irrigation versus the environment. Options include best use of water for adaptation and structural adjustment packages for irrigation communities integrated with environmental triage of those wetlands likely to transition to dryland ecosystems under climate change. [ABSTRACT FROM AUTHOR]

Spatial knowledge of land surface evapotranspiration (ET) is of prime interest for environmental applications, such as optimizing irrigation water use, irrigation system performance, crop water deficit, drought mitigation strategies, and accurate initialization of climate prediction models especially in arid and semiarid catchments where water shortage is a critical problem. The recent drought in Australia and concerns about climate change have highlighted the need to manage water resources more sustainably especially in the Murrumbidgee catchment which utilizes bulk water for food production. This study deals with the application of a Surface Energy Balance System (SEBS) algorithm based on Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) data and field observations has been proposed and tested for deriving ET over Coleambally Irrigation Area, located in the southwest of NSW, Australia. We have used 12 ASTER scenes covering the time period of 2002, 2003, 2004, 2005, 2006, and 2009 for estimating the actual ET over the study area. To validate the proposed methodology, the ground-measured ET was compared to the ASTER-derived actual ET values for the study area. The derived ET value over the study area is much closer to the field measurement. From the remote sensing results and observations, the root mean square error is 0.89 and the mean absolute percentage difference is 2.87 %, which demonstrate the reasonability of SEBS ET estimation for the study area. [ABSTRACT FROM AUTHOR]

Dissolved organic carbon (DOC) and total and inorganic nitrogen and phosphorus concentrations were determined over 3 years in headwater streams draining two adjacent catchments. The catchments are currently under different land use; pasture/grazing vs plantation forestry. The objectives of the work were to quantify C and nutrient export from these landuses and elucidate the factors regulating export. In both catchments, stream water dissolved inorganic nutrient concentrations exhibited strong seasonal variations. Concentrations were highest during runoff events in late summer and autumn and rapidly declined as discharge increased during winter and spring. The annual variation of stream water N and P concentrations indicated that these nutrients accumulated in the catchments during dry summer periods and were flushed to the streams during autumn storm events. By contrast, stream water DOC concentrations did not exhibit seasonal variation. Higher DOC and NO3 − concentrations were observed in the stream of the forest catchment, reflecting greater input and subsequent breakdown of leaf-litter in the forest catchment. Annual export of DOC was lower from the forested catchment due to the reduced discharge from this catchment. In contrast however, annual export of nitrate was higher from the forest catchment suggesting that there was an additional NO3 sink. We hypothesize that the denitrification capacity of the forested catchment has been significantly reduced as a consequence of increased evapotranspiration and subsequent decrease in streamflow and associated reduction in the near stream saturated area. [ABSTRACT FROM AUTHOR]− source or reduction of a NO3 − sink. We hypothesize that the denitrification capacity of the forested catchment has been significantly reduced as a consequence of increased evapotranspiration and subsequent decrease in streamflow and associated reduction in the near stream saturated area. [ABSTRACT FROM AUTHOR]

The adage: "There is no such thing as a free lunch", is relevant to land-use hydrology in Australia. Changes in land use to achieve greater productivity of food and fibre may have an adverse effect on the water balance and hence on the natural resource capital of a catchment. An altered regime of catchment outflow accompanies those land-use changes which, together with land degradation, impairs available water resources in quantity and quality and threatens enterprise sustainability, notwithstanding the initial improvement in productivity. Central to any hydrological change is an altered pattern of seasonal and annual water use by vegetation that has become modified in function with an amended transpiration fraction of daily evapotranspiration. In Australia, since measurement of evapotranspiration became feasible, the hydrological consequences of changes in land use have been determined, allowing the benefits in terms of plant productivity achieved through enhanced water use efficiency to be weighed against changed catchment outflows, diminished in either quantity or quality. Four case studies are presented as examples of ecological and hydrological changes: two deal with the upland forest environment and two with arable lowlands. In an upland eucalypt forest, following wildfire with subsequent regeneration from natural seedling establishment, substantial reduction in water yield occurred throughout a 50-year period of succession in the even-aged stand. In comparison, the effect of converting eucalypt forest to pine plantations was less detrimental to the yield of water from the catchments, with substantial growth increases over 30 years. In the lowlands, agricultural productivity, both as annual pasture and as crop, far exceeds that of natural perennial grassland and woodland. This increase in productivity comes not so much from any change to the yield of total water outflow but at the expense of water quality, compromised with increased material transport in suspension and solution resulting from accelerated erosion in association with outbreaks of soil salinity and acidity. The present study is aimed at optimising management to give plant production outcomes that ensure environmental protection through resource conservation. In the uplands, harvesting of water is the dominant consideration so that conservative management with limited plant productivity is sought. In the lowlands, the objective is to devise novel ecosystems with profitable plant production that exercises due control on outflow in maintaining the chemical and physical integrity of the edaphic environment.. [ABSTRACT FROM AUTHOR]

Data from savannas of northern Australia are presented for net radiation, latent and sensible heat, ecosystem surface conductance (Gs) and stand water use for sites covering a latitudinal range of 5° or 700 km. Measurements were made at three locations of increasing distance from the northern coastline and represent high- (1,750 mm), medium- (890 mm) and low- (520 mm) rainfall sites. This rainfall gradient arises from the weakened monsoonal influence with distance inland. Data were coupled to seasonal estimates of leaf area index (LAI) for the tree and understorey strata. All parameters were measured at the seasonal extremes of late wet and dry seasons. During the wet season, daily rates of evapotranspiration were 3.1–3.6 mm day–1 and were similar for all sites along the rainfall gradient and did not reflect site differences in annual rainfall. During the dry season, site differences were very apparent with evapotranspiration 2–18 times lower than wet season rates, the seasonal differences increasing with distance from coast and reduced annual rainfall. Due to low overstorey LAI, more than 80% of water vapour flux was attributed to the understorey. Seasonal differences in evapotranspiration were mostly due to reductions in understorey leaf area during the dry season. Water use of individual trees did not differ between the wet and dry seasons at any of the sites and stand water use was a simple function of tree density. Gs declined markedly during the dry season at all sites, and we conclude that the savanna water (and carbon) balance is largely determined by Gs and its response to atmospheric and soil water content and by seasonal adjustments to canopy leaf area. [ABSTRACT FROM AUTHOR]

Appropriate representation of the vegetation dynamics is crucial in hydrological modelling. To improve an existing limited vegetation parameterization in a semi-distributed hydrologic model, called the Soil Moisture and Runoff simulation Toolkit (SMART), this study proposed a simple method to incorporate daily leaf area index (LAI) dynamics into the model using mean monthly LAI climatology and mean rainfall. The LAI-rainfall sensitivity is governed by a parameter that is optimized by maximizing the Pearson correlation coefficient (R) between the estimated and satellite-derived LAI time series. As a result, the LAI-rainfall sensitivity is smallest for forest, shrub, and woodland regions across Australia, and increases for grasslands and croplands. The impact of the proposed method on catchment-scale simulations of soil moisture (SM), evapotranspiration (ET) and discharge (Q) in SMART was examined across six eco-hydrologically contrasted upland catchments in Australia. Results showed that the proposed method produces almost identical results compared to simulations by the satellite-derived LAI time series. In addition, the simulation results were considerably improved in nutrient/light limited catchments compared to the cases with the default vegetation parameterization. The results showed promise, with possibilities of extension to other hydrologic models that need similar specifications for inbuilt vegetation dynamics. [ABSTRACT FROM AUTHOR]