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• Sobol' method is used to evaluate the sensitivity of APSIM-Wheat yield predictions. • Yield is most sensitive to initial nitrogen amount, soil parameters, precipitation. • Estimated yield is influenced most by the limiting resource under given conditions. • Scenarios for high fertilization level or dry seasons have lower yield variability. The performance of cropping system models is often limited by uncertainties in model parameters and inputs. This study aims to explore how model yield prediction responds to these uncertainties under various environmental and management conditions. The Sobol' method was used to investigate the sensitivity of the Agricultural Production Systems SIMulator (APSIM)-Wheat yield prediction to the following factors: air temperature (maximum and minimum), precipitation, initial soil nitrogen content, nitrogen fertilization amount, and soil hydraulic parameters. Eighteen scenarios were defined to consider a combination of three weather conditions (wet, normal, and dry growing season, based on the historical climatology of the Wimmera district of Victoria, Australia), three soil types (sandy, loamy, and clayey soils), and two nitrogen fertilization applications (50 and 100 kg N/ha). Eight thousand APSIM simulations were used to calculate the first order and total sensitivity indices for each scenario. The effects of weather, soil water characteristics and nitrogen availability were estimated by measuring their impacts on sensitivity indices. Our results show that yield was more sensitive either to variables that control water availability (precipitation and soil type) or variables that control nitrogen availability, depending on which was more limiting to wheat growth under the scenario. For example, in case of low nitrogen fertilization scenario, the initial nitrogen content sensitivity ranked first. Variation of this factor contributed 64% to 79% to the variance in simulated yield for clayey soils, nearly four times higher than the second-ranked factor (nitrogen fertilization amount). Soil hydraulic parameters and precipitation were most important when the crop growth was more constrained by water availability than by nitrogen availability. In the case of sandy soils in dry years with high nitrogen fertilization level, soil parameters and precipitation accounted for 83% of the yield variability. Minimum temperature consistently ranked as the least important factor under all scenarios. This work will help researchers better understand the model inputs' impacts on the simulated yield variability under various soil, management, and weather conditions. The results also support agronomists and practitioners in the optimal use of the APSIM-Wheat model as a management tool. [Display omitted] [ABSTRACT FROM AUTHOR]

• A Machine Learning method is developed by integrating remotely sensed feedback of vegetation to antecedent rainfall to forecast seasonal soil moisture. • The method showed (>50%) and (>80%) variability in root-zone(0–1 m) and deep (1–6 m) soil can be explained 3 months ahead of time. • Integrating remotely sensed vegetation feedback to antecedent rainfall has improved soil moisture forecast by up to 33.3% • This model could be a steppingstone to developing and deploying a computationally light operational seasonal soil moisture forecast model. Seasonal forecast of soil moisture at large spatial scale over forested landscape has numerous implications in forest hydrology and bushfire risk planning. Remotely sensed plant response to rainfall, input meteorological forcing, and site-specific landscape attributes were integrated into a data-driven Gradient Boosting Machine Learning (ML) model to forecast summer season (December to February) soil moisture equivalent to those from Australian Water Resource Assessment-Landscape (AWRA-L) at root-zone (0–1 m) and deep (1–6 m) layers of the soil. Multispectral and thermal infrared bands from MODIS (Band 1–12, LST (day, night)) and meteorological forcing for 2000 – 2018 during and prior to winter (before August) and site-specific landscape attributes were used to generate the explanatory input variables to the model. Spatial and temporal forecasting skills of the model were evaluated using two types of cross-validation: two-fold cross-validation by space over the entire period (2000–2018) and two-fold cross-validation by time for all grid cells (2160 cells). In the first method, the model showed a high skill to forecast deep soil moisture (R2 = 0.81, NSE = 0.79, RMSE = 58.01 mm) and root-zone soil moisture (R2 = 0.65, NSE = 0.63, RMSE = 24.34 mm). In comparison, the second evaluation resulted in more accurate forecast deep-soil moisture (R2 = 0.88, NSE = 0.86, RMSE = 47.39 mm) but in lower performance for root-zone soil (R2 = 0.55, NSE = 0.47, RMSE = 29.2 mm). These outcomes have highlighted that the (>80%) variability in summers deep (1–6 m) soil moisture and (>50%) variability in root-zone (0–1 m) soil moisture over forested landscape can be explained at the end of winters. Furthermore, integrating remotely sensed observations has improved the results by showing an enhancement in the RMSE by 22.8–33.3% in the deep (1–6 m) soil moisture and 8.8–14.3% in root-zone (0–1 m) soil moisture. Overall, the integrated system using remotely sensed plant response, climate forcing and machine learning exhibit great potential to forecast summer soil moisture three months ahead in the Mediterranean climates. [ABSTRACT FROM AUTHOR]

Accurate specification of spatiotemporal errors of remotely sensed soil moisture (SM) data is essential for a correct assessment of their utility and optimally integrating multiple SM products or assimilating them into hydrological models. Although Triple Collocation Analysis (TCA) has been widely used to provide SM errors, the impact of rescaling technique on the TCA error estimates has not received major attention, which can lead to biased and inaccurate error estimates. Moreover, current knowledge about time-variant SM errors derived from TCA is still very limited, which hampers the advance of applying time-variant errors in data merging and data assimilation studies efficiently. Based on these considerations, this work aims to advance the use of the TCA for characterizing errors with a focus on the rescaling techniques, and validating TCA-based time-variant errors using global ground measurements in 759 grid cells. To this end, the Advanced Scatterometer (ASCAT) and four passive-based SM products, including Soil Moisture and Ocean Salinity Level-3 (SMOS-L3), SMOS INRA-CESBIO (SMOS-IC), Soil Moisture Active Passive Level-3 (SMAP-L3), and SMAP INRAE BORDEAUX (SMAP-IB) SM products, were considered. The time-variant error term here denotes an aggregate error magnitude over a 101-day moving-time-window. It is found that different selection of the rescaling technique considered in TCA led to TCA error estimates with significantly different accuracy when ground-based errors are regarded as the benchmark. The optimal combination strategy to implement TCA is applying TCA to SM anomalies and rescaling the errors by coefficients derived from the TCA model. Pearson's correlation with ground-based time-variant errors is 0.62, 0.72, 0.83, 0.89, and 0.93 for SMAP-IB, SMAP-L3, SMOS-IC, SMOS-L3, and ASCAT SM, respectively. Considering time-variant errors in applications is necessary since time-variant errors deviate from time-invariant errors by 50% when errors are rescaled by the TCA model parameters. Time-invariant errors are greater than time-variant errors when SM products are rescaled against a reference dataset while the opposite conclusion can be drawn when errors are rescaled by the TCA coefficients. TCA- and ground-based methods provide consistent evaluations in 74.7% (77.3%), 75.8% (79.8%), 79.6% (81.1%), and 78.6% (79.7%) of the analysis period on average (median) for the TCA implementations with SMAP-L3, SMAP-IB, SMOS-L3, and SMOS-IC SM, respectively. The error analysis reveals that TCA typically underestimated ASCAT errors while overestimated passive SM errors when considering ground-based evaluation as the benchmark. Moreover, TCA was found to have relatively less power to efficiently characterize SM errors in croplands when compared with other land cover types. This study validated TCA time-variant errors using ground measurements and compared TCA- and ground-based evaluation performances on a global scale. Our work arouses particular attention to the rescaling technique selection considered in TCA, which is crucial for accurately characterizing SM errors and efficiently using them in various hydrometeorological applications. • Rescaling techniques have a great impact on the final TCA error estimates. • Noticeable differences are observed for time-variant and time-invariant errors. • TCA has less power to efficiently characterize SM errors in croplands. • Temporal interpolations have a relatively small impact on the TCA errors. • TCA underestimates ASCAT and overestimates passive-based SM errors. [ABSTRACT FROM AUTHOR]

It is assumed that the ratio of actual evapotranspiration (AET) to potential evapotranspiration (PET) is mostly controlled by the soil water content available for ET. This control is formulated using the soil moisture stress function (SSF), where the evaporative fraction (EF) or the fraction of the AET to PET (fPET) is assumed to be either a linear or a non-linear function of soil moisture. We examine the effectiveness of the soil moisture stress function to quantify soil moisture control on EF or fPET over a dryland wheat field in Victoria, Australia. Micrometeorological observations from two cropping seasons were used for the analysis. The efficacy of a root-density-weighted soil moisture estimate in predicting EF and fPET was investigated as against the commonly assumed fixed-depth root zone soil moisture. However, results indicate a strong relationship between EF and available soil water fraction (AWF) in the root zone only when solar radiation is higher than 5 MJ/m 2 /day. As the rooting depth increases with vegetation growth, SSF exhibits the strongest correlation with AWF for increasing soil profile depth. In the early and harvesting crop growth stages, ET is constrained mostly by surface soil moisture (0–5 cm). In the mid-growth stages, ET is strongly influenced by soil moisture in the root zone (0–60 cm). The shape of SSF, however, changes significantly between the two years (2012 and 2013). It is inferred that different temporal rainfall patterns between the years caused wheat’s different response to water stress. [ABSTRACT FROM AUTHOR]

Background & Aims To date, no pharmacological therapy has been approved for non-alcoholic fatty liver disease (NAFLD). The aim of the present study was to evaluate the therapeutic potential of poly ADP-ribose polymerase (PARP) inhibitors in mouse models of NAFLD. Methods As poly ADP-ribosylation (PARylation) of proteins by PARPs consumes nicotinamide adenine dinucleotide (NAD + ), we hypothesized that overactivation of PARPs drives NAD + depletion in NAFLD. Therefore, we assessed the effectiveness of PARP inhibition to replenish NAD + and activate NAD + -dependent sirtuins, hence improving hepatic fatty acid oxidation. To do this, we examined the preventive and therapeutic benefits of the PARP inhibitor (PARPi), olaparib, in different models of NAFLD. Results The induction of NAFLD in C57BL/6J mice using a high-fat high-sucrose (HFHS)-diet increased PARylation of proteins by PARPs. As such, increased PARylation was associated with reduced NAD + levels and mitochondrial function and content, which was concurrent with elevated hepatic lipid content. HFHS diet supplemented with PARPi reversed NAFLD through repletion of NAD + , increasing mitochondrial biogenesis and β-oxidation in liver. Furthermore, PARPi reduced reactive oxygen species, endoplasmic reticulum stress and fibrosis. The benefits of PARPi treatment were confirmed in mice fed with a methionine- and choline-deficient diet and in mice with lipopolysaccharide-induced hepatitis; PARP activation was attenuated and the development of hepatic injury was delayed in both models. Using Sirt1 hep−/− mice, the beneficial effects of a PARPi-supplemented HFHS diet were found to be Sirt1-dependent. Conclusions Our study provides a novel and practical pharmacological approach for treating NAFLD, fueling optimism for potential clinical studies. Lay summary Non-alcoholic fatty liver disease (NAFLD) is now considered to be the most common liver disease in the Western world and has no approved pharmacological therapy. PARP inhibitors given as a treatment in two different mouse models of NAFLD confer a protection against its development. PARP inhibitors may therefore represent a novel and practical pharmacological approach for treating NAFLD. [ABSTRACT FROM AUTHOR]

Water Energy Balance (WEB) Soil Vegetation Atmosphere Transfer (SVAT) modelling can be used to estimate soil moisture by forcing the model with observed data such as precipitation and solar radiation. Recently, an innovative approach that assimilates remotely sensed thermal infrared (TIR) observations into WEB-SVAT to improve the results has been proposed. However, the efficacy of the model-observation integration relies on the model's realistic representation of soil water processes. Here, we explore methods to improve the soil water processes of a simple WEB-SVAT model by adopting and incorporating an exponential root water uptake model with water stress compensation and establishing a more appropriate soil-biophysical linkage between root-zone moisture content, above-ground states and biophysical indices. The existing WEB-SVAT model is extended to a new Multi-layer WEB-SVAT with Dynamic Root distribution (MWSDR) that has five soil layers. Impacts of plant root depth variations, growth stages and phenological cycle of the vegetation on transpiration are considered in developing stages. Hydrometeorological and biogeophysical measurements collected from two experimental sites, one in Dookie, Victoria, Australia and the other in Ponca, Oklahoma, USA, are used to validate the new model. Results demonstrate that MWSDR provides improved soil moisture, transpiration and evaporation predictions which, in turn, can provide an improved physical basis for assimilating remotely sensed data into the model. Results also show the importance of having an adequate representation of vegetation-related transpiration process for an appropriate simulation of water transfer in a complicated system of soil, plants and atmosphere. [ABSTRACT FROM AUTHOR]

Summary For operational flood forecasting, discharge observations may be assimilated into a hydrologic model to improve forecasts. However, the performance of conventional filtering schemes can be degraded by ignoring the time lag between soil moisture and discharge responses. This has led to ongoing development of more appropriate ways to implement sequential data assimilation. In this paper, an ensemble Kalman smoother (EnKS) with fixed time window is implemented for the GR4H hydrologic model (modèle du Génie Rural à 4 paramètres Horaire) to update current and antecedent model states. Model and observation error parameters are estimated through the maximum a posteriori method constrained by prior information drawn from flow gauging data. When evaluated in a hypothetical forecasting mode using observed rainfall, the EnKS is found to be more stable and produce more accurate discharge forecasts than a standard ensemble Kalman filter (EnKF) by reducing the mean of the ensemble root mean squared error ( MRMSE ) by 13–17%. The latter tends to over-correct current model states and leads to spurious peaks and oscillations in discharge forecasts. When evaluated in a real-time forecasting mode using rainfall forecasts from a numerical weather prediction model, the benefit of the EnKS is reduced as uncertainty in rainfall forecasts becomes dominant, especially at large forecast lead time. [ABSTRACT FROM AUTHOR]

Error characterisation of satellite-retrieved soil moisture (SM) is crucial for maximizing their utility in research and applications in hydro-meteorology and climatology. It can provide insights for retrieval development and validation, and inform suitable strategies for data fusion and assimilation. Su et al. (2013a) proposed a potential Fourier method for quantifying the errors based on the difference between the empirical power spectra of these SM data and a water balance model via spectral fitting (SF), circumventing the need for any ancillary data. This work first evaluates its utility by estimating the errors in two passive and active microwave satellite SM over Australia, and comparing the results against the triple collocation (TC) estimator. The SF estimator shows very good agreement with TC in terms of error standard deviation and signal-to-noise ratio, with strong linear correlations of 0.80–0.92 but with lower error estimates. As the two estimators are not strictly comparable, their strong agreement suggests a strong complementarity between time-domain and frequency-domain analyses of errors. A better understanding of the spectral characteristics of the error is still needed to understand their differences. Next, spatial analyses of the derived (SF and TC) error maps, in terms of error standard deviation and noise-to-signal ratio, for the two satellite data are performed with principal component analysis to identify influence of vegetation/leaf-area index (LAI), rainfall, soil wetness, and spatial heterogeneity in topography and soil type on retrieval errors. Lastly, seasonal analysis of the errors discovers systematic temporal variability in errors due to variability in rainfall amount, and less so with changing LAI. [ABSTRACT FROM AUTHOR]

• Water-level based rainfall-runoff calibration can be improved using discharge index. • Efficacy of regionalized discharge index is comparable with that of observed values. • Water-level based calibration with discharge index works better for wet catchments. • Regionalized Qmean is a better choice than regionalized Q95 as discharge index. Rainfall-runoff models are generally calibrated by using continuous stream discharge data. However, most catchments around the globe remain ungauged due to the difficulty in installing gauges in river channels with complex morphology and due to the high costs of installation and maintenance. Recently, new calibration methods that use water level data instead of gauged discharge to calibrate rainfall-runoff models have been proposed. With emerging low-cost local water-level monitoring technologies or with satellite altimetry, these water-level based calibration methods have the potential to significantly extend runoff prediction capability. However, rainfall-runoff models calibrated from using water-level information alone may lead to biased discharge even when water-level variations are accurately reproduced. It has been shown that this bias problem can be significantly alleviated when just a few discharge measurements, especially of high flows, are available and incorporated into the model calibration. However, it is rare to have discharge measurement data when catchments are ungauged. In this study, we incorporate discharge estimates derived from regionalization into a water-level based calibration using an inverse rating curve (IRC) method. Specifically, discharge indices such as the 95th percentile of daily discharge (Q95) and the mean daily discharge (Qmean) are estimated from catchment and climate characteristics based on regionalized relationships. These estimated discharge indices are then used to constrain the IRC calibration. We evaluate both constrained and unconstrained IRC methods over 130 study catchments where observed discharge time series are available but used only for validation in this study. Modelled discharge time series from the constrained IRC methods have median Nash Sutcliffe Efficiency (NSE) values of 0.61–0.63, compared with 0.54 from the unconstrained IRC method. Improvement is seen in around 75% of the catchments, with more than 0.2 increase in NSE values in 25% of the catchments. Improvement in the modelled discharges from using regionalized discharge indices is comparable to using observed discharge indices. Using regionalized Qmean yields greater improvement than using regionalized Q95. The constrained IRC methods tend to perform better in wetter catchments than in drier ones. This method can significantly extend rainfall-runoff model calibration in a wider range of catchments, including real ungauged catchments. [ABSTRACT FROM AUTHOR]

Process-based crop models provide ways to predict crop growth, evaluate environmental impacts on crops, test various crop management options, and guide crop breeding. They can be used to explore options for mitigating climate change impacts when combined with climate projections and explore mitigation of environmental impacts of production. The Agricultural Production Systems SIMulator (APSIM) is a widely adopted crop model that offers modules for simulation of various crops, soil processes, climate, and grazing within a modelling system that enables robust addition of new components. This study uses APSIM Classic-Wheat as an example to examine yield prediction accuracy of biophysically based crop yield modelling and to analyse the factors influencing the model performance. We analysed yield prediction results of APSIM Classic-Wheat from 76 published studies across thirteen countries on four continents. In addition, a meta-database of modelled and observed yields from 30 studies was established and used to identify factors that influence yield prediction uncertainty. Our analysis indicates that, with site-specific calibration, APSIM predicts yield with a root mean squared error (RMSE) smaller than 1 t/ha and a normalised RMSE (NRMSE) of about 28%, across a wide range of environmental conditions for independent evaluation periods. The results show increasing errors in yield with limited modelling information and adverse environmental conditions. Using soil hydraulic parameters derived from site-specific measurements and/or tuning cultivar parameters improves yield prediction accuracy: RMSE decreases from 1.25 t/ha to 0.64 t/ha and NRMSE from 32% to 14%. Lower model accuracy was found where APSIM overestimates yield under high water deficit condition and when it underestimates yield under nitrogen limitation. APSIM severely over-predicts yield when some abiotic stresses such as heatwaves and frost affect the crop growth. This paper uses APSIM-Wheat as an example to provide perspectives on crop model yield prediction performance under different conditions covering a wide spectrum of management practices, and environments. The findings deepen the understanding of model uncertainty associated with different calibration processes or under various stressed conditions. The results also indicate the need to improve the model's predictive skill by filling functional gaps in the wheat simulations and by assimilating external observations (e.g., biomass information estimated by remote sensing) to adjust the model simulation for stressed crops. [Display omitted] • Crop model is a tool to simulate crop growth, seek solutions for mitigating negative environmental impacts on production. • APSIM Classic has been used as an example to explore wheat yield prediction performance and the influential factors. • Reviewed and established a meta-database from 76 published studies. • Overall: the model predicts wheat yield with RMSE=1 t/ha. Fully calibrated and under stress-free condition: RMSE=0.64 t/ha. • Calibration method, heat, frost, water, and nitrogen stresses were identified as factors causing the model to mis-simulate. [ABSTRACT FROM AUTHOR]

Abstract: The use of satellite-based soil moisture retrievals for hydrologic, meteorological and climatological applications is advancing significantly due to increasing capability and temporal coverage of current and future missions. Characterisation of the relative skill of soil moisture products from different satellite sensors on a common spatial grid is crucial to achieve synergetic applications. This paper therefore evaluates three soil moisture products from AMSR-E (Advanced Microwave Scanning Radiometer — Earth Observing System), ASCAT (Advanced Scatterometer) and SMOS (Soil Moisture and Ocean Salinity) in absolute soil moisture units and on a common grid, against in-situ observations from southeast Australia. Before renormalisation, the three products yield correlations of 0.63–0.71 and a similar root-mean-square difference (RMSD) in the order of 0.1m3 m−3, although showing different levels of error contributions from bias, variance and correlations. The results are compared with land and precipitation data to investigate the sensitivity of their errors to land surface features. Three renormalisation strategies – minimum–maximum matching, mean/standard-deviation (μ–σ) matching and cumulative distribution function (CDF) matching – are considered for correcting systematic differences between ground and satellite data. The renormalised satellite data is found to retain RMSDs of 0.04–0.06m3 m−3 on average. The CDF method produces only marginal further improvements to correlations (0.67–0.75) and RMSDs compared to the μ–σ approach. The renormalisations by μ–σ and CDF methods also bring three products into better agreements with each other, but lead to strong correlations between RMSD and the dynamic range of in-situ soil moisture. [Copyright &y& Elsevier]

Abstract: Accurate coarse-scale soil moisture information is required for robust validation of current- and next-generation soil moisture products derived from spaceborne radiometers. Due to large amounts of land surface and rainfall heterogeneity, such information is difficult to obtain from existing ground-based networks of soil moisture sensors. Using ground-based field data collected during the Soil Moisture Experiment in 2002 (SMEX02), the potential for using distributed modeling predictions of the land surface as an upscaling tool for field-scale soil moisture observations is examined. Results demonstrate that distributed models are capable of accurately capturing a significant level of field-scale soil moisture heterogeneity observed during SMEX02. A simple soil moisture upscaling strategy based on the merger of ground-based observations with modeling predictions is developed and shown to be more robust during SMEX02 than upscaling approaches that utilize either field-scale ground observations or model predictions in isolation. [Copyright &y& Elsevier]

Knowledge about spatiotemporal error characteristics of remotely sensed soil moisture (SM) products is essential for correctly interpreting observational information and optimally assimilating them into hydrological models. This work aims to (i) investigate the relative difference between time-invariant and time-variant daily SM errors of Advanced Scatterometer (ASCAT) and Soil Moisture Active Passive (SMAP) products and (ii) analyze correlation of the daily SM errors with surface biomass quantified by the Leaf Area Index (LAI) and rainfall. The time-invariant error denotes an aggregate error magnitude during the whole investigation period and the time-variant error at daily time scale also refers to an aggregate error magnitude but for the period of the 100-day time window centered at the day to be processed. The time-invariant and daily SM errors are estimated using the Triple Collocation Analysis (TCA) applied to ASCAT and SMAP SM retrievals along with the Global Land Data Assimilation System version 2.1 (GLDAS2) SM product for the period of April 2015 – January 2020. Results indicate the relative difference between time-invariant and time-variant daily errors is notable for both ASCAT and SMAP SM products. The relative difference in units percentage denotes the ratio of difference value between time-invariant and daily errors to time-invariant error itself. The daily TCA error fluctuated at 43% value from time-invariant error for ASCAT SM and at 47% value for SMAP SM on a global average. When averaged globally, temporal mean of time-variant daily errors was relatively smaller than time-invariant error by -27% for ASCAT SM and -18% for SMAP SM, respectively. In tropical areas, the relative difference between time-invariant and daily errors is large during the dry season and becomes small when rainy season comes. When the peak lagged correlation is used, daily errors exhibit a stronger correlation with rainfall than they do with LAI in 61% of landmass pixels for ASCAT SM and 66% of landmass pixels for SMAP SM. LAI cannot be used to predict temporal variability of time-variant SM errors in barren land. Lagged correlation analysis reveals rainfall peak coincides with SM error peak in areas featured with low vegetation cover, including barren land, grasslands, and open shrublands. By contrast, the LAI peak comes after the SM error peak in all cases. Savannas and woody savannas are a special case as SM error peak comes first, followed by rainfall then LAI peaks. In summary, ASCAT and SMAP time-variant error varies with a large deviation from time-invariant errors and its temporal variation shows a stronger association with rainfall than changing LAI. • Time-variant daily error is globally evaluated for ASCAT and SMAP soil moisture. • Lag correlation analysis for daily soil moisture error, LAI, and rainfall. • Rainfall better explains temporal variability of soil moisture errors than LAI. • Soil moisture error peak comes before rainfall and LAI peaks in savannas. [ABSTRACT FROM AUTHOR]

• Eighteen spectral indices are compared for their sensitivity to CNC and biomass. • Of these indices, PRI best correlates with CNC but it also correlates with biomass. • Regression relationships between CNC and all indices shift with growth stages. • CCCI exhibits stronger correlation to biomass than CNC. • A wide range of growth conditions are needed to develop a robust indicator for CNC. In precision farming, accurate estimation of canopy nitrogen concentration (CNC) is valuable for effective crop growth monitoring and nitrogen (N) fertiliser management. To date, many canopy multispectral indices have been proposed as indicators for CNC; however, many of these indices have also shown sensitivity to biomass and their performance drops at high biomass levels. Dependence on growth stage, season, or other environmental conditions limits their efficacy as generalized CNC indices. The objectives of this study were to assess the robustness of popular CNC indices across a wide range of biomass levels and fertiliser application levels; and for two contrasting seasons – winter and summer. To achieve this, we analysed the efficacy of seven canopy nitrogen indices, including canopy chlorophyll content index (CCCI), together with eleven other commonly used spectral indices. We used canopy level solar-induced hyperspectral reflectance data acquired using a hand-held optical spectroradiometer across four growth stages in winter (May-June 2018) and four in summer (January-February 2019) from an experimental field of irrigated perennial ryegrass with variable N application in Victoria, Australia. The field contained 40 plots, each with one of eight different N treatments. Almost all the indices exhibited similar correlation to CNC (%) when applied to individual stages (days) in both winter and summer; however, relationships between CNC and individual indices varied significantly between stages. We obtained similar results for canopy biomass. When the data across the entire range of growth stages and seasons were combined, the correlations between most canopy nitrogen indices and CNC became weak (R2 < 0.25, 0.9% ≤ RMSE ≤ 1.0%). PRI exhibited the highest correlation with CNC (R2 = 0.58, RMSE = 0.7%) for the combined data set. Even so, PRI's association with CNC and canopy biomass changed with the season. Most indices responded to both CNC and biomass simultaneously, and this confounds the estimation of CNC due to strong but growth stage-specific relationships between CNC and canopy biomass. This study shows that it is important to consider a wide range of conditions when evaluating multispectral CNC indices. [ABSTRACT FROM AUTHOR]

• The study proposes and uses a new modelling framework for quantifying blue water scarcity. • This study reconstructs various water uses in India using census datasets. • Blue water scarcity is quantified in India at State and monthly scales. • More than 60% of the population in India faces severe water stress every year. • Groundwater abstraction in Gujarat, Rajasthan and Tamil Nadu exceeds the groundwater recharge. Sustainable water use and water scarcity are major concerns in developing countries such as India. Rapidly growing population along with increasing economic and technological development in India have resulted in increased water use leading to severe water scarcity. The present study aims to quantify and assess sustainable water use and water scarcity in India. A data-intensive approach is employed at a State spatial resolution and monthly temporal scales during the period 1991–1999. The Water Stress Index (WSI), which is defined as the ratio of total water use to available water, is used as a measure of blue water scarcity. The total available water includes surface water and groundwater while the total water uses include irrigation, industrial, domestic and environmental uses. A Community Land Model (CLM 4.0) developed by the National Centre for Atmospheric Research (NCAR), is used to quantify the total available water in India. Water uses in India are reconstructed using a census-based statistical database while the environmental water demand is modelled following the hydrology-based approach, which allocates environmental flows as a proportion of the available water, with seasonally defined environmental flow thresholds. Further, the excess water component, defined as the amount of water remaining after meeting the demands at each time step, is incorporated into the modelling framework by adding it to the available water pool. The study estimates that 60% of the population in India faces severe water stress during the analysis period, and is more predominant in the States of Rajasthan, Gujarat, Uttar Pradesh and Maharashtra. Moreover, non-renewable groundwater abstraction, which is defined as the amount of groundwater abstraction more than the groundwater recharge, is also quantified. This study identifies severe groundwater depletion in the north-western parts of India, consistent with the current estimates from GRACE satellite observations. [ABSTRACT FROM AUTHOR]

Farm-level seasonal irrigation water usage is often highly variable across time and space in an irrigation district. Identifying the driving factors of this variation can help researchers and managers understand the underlying efficiency of water usage, identify the sources of water waste and develop best irrigation practices to facilitate the development of more efficient and sustainable irrigated cropping systems. This study explored driving factors for the seasonal irrigation water usage across an irrigation district in south-eastern Australia, using extensive climate, soil, cropping and water use data from 312 farms (a total of 1099 annual crop water use records). It focused on three irrigated crops (corn/maize, cotton and rice) for 2011–2019. Factors considered included climate, soils, irrigation practices and water allocation, from which key driving factors and their effects on the seasonal irrigation water usage were identified. A Bayesian hierarchical model averaging approach was developed to determine key predictors and estimate seasonal irrigation water usage. The Bayesian hierarchical modelling framework allowed seasonal irrigation water usage to be estimated at all crop-farm-year observation points simultaneously while also enabling different key predictors to be selected for different crop types. Results showed that the seasonal irrigation water usage was mainly driven by irrigation practices and soil, but the water use of different crop types was driven by different subsets of irrigation and soil predictors. Modelling was undertaken in natural log space and the model showed reasonable accuracy in estimating the seasonal irrigation water usage (R2 = 0.62). Our model is considered useful in 1) identifying drivers of variation of the seasonal irrigation water usage across time and space, and 2) suggesting potential adjustments to irrigation practices and quantifying their benefits to reduce irrigation water usage. • Factors driving seasonal irrigation usage were explored in an irrigation district. • A Bayesian hierarchical model (BHM) was developed for 1099 crop water use records. • Seasonal irrigation usage was mainly influenced by soil and irrigation practices. • The BHM showed good accuracy in estimating seasonal irrigation usage (R2 = 0.62). [ABSTRACT FROM AUTHOR]

• The effect of training samples on within-season crop classification was examined. • The sample size should be sufficiently large in within-season crop classification. • Proportions of each crop type should be similar for training and application. • Training using samples from previous years led to a minor decline in accuracy. • Overall classification accuracy exceeded 86 % for the third and later months. Within-season crop classification using multispectral imagery is an effective way to generate timely crop maps that can support water and crop management; however, developing such models is challenging due to limited satellite imagery and ground truth data available during the season. This study investigated ways to optimize the use of multi-year samples in a within-season crop classification model, aiming to enable accurate within-season crop mapping across years. Our study focused on classifying field-scale corn/maize, cotton, and rice in south-eastern Australia from 2013 to 2019. The crop classification model was based on the random forest and support vector machine algorithms applied to Landsat 8 multispectral bands. We designed four experiments to understand the influences of training sample selection on model accuracy. Specifically, we analyzed how the within-season classification accuracies are affected by 1) training sample size; 2) proportions of classification classes; 3) the inclusion of a non-crop class (e.g., fallow land) in the training sample, and 4) training samples collected from different years. We found that 1) the training sample size should be sufficiently large to ensure within-season classification accuracy; 2) using training samples for each crop type in proportion to their occurrence within the landscape results in more accurate multi-year classification; 3) the inclusion of the non-crop class can reduce the accuracy with which crop types are distinguished, so the proportion of the non-crop class should be maintained at a relatively low level, and 4) predicting the current year with training samples from previous years can lead to a minor decline in accuracy compared to using samples only from the current year. These training sample settings were adopted to develop a final model. We found that the model accuracy continues to improve as more input imagery is added as the cropping season progresses, with a rapid rate of initial improvement which then slows. December, the third month of the summer growing season, is the earliest time that reliable maps were generated, with an overall accuracy of 86 % and user's accuracies for all crops exceeding 80 %. Our proposed experiments are robust and transferable to other regions and seasons to assist the development of within-season crop maps, and can thus be valuable tools to support agricultural management. [ABSTRACT FROM AUTHOR]

River discharge is a critical component for understanding hydrological processes and sustainable management of water resources. The importance of discharge observation has increased due to its potential extreme variation resulting from the projected climate change and stronger variability of precipitation and temperature in some large basins. However, inherent difficulties in ground-based observations and decreasing number of gauge stations hinder accurate measurement of global river discharge and its spatio-temporal variations. Various remote sensing methods have been examined as alternatives, however, they require ground measurements to convert their proxy measurements into the actual river discharge. In this study, we estimate the discharge at the Óbidos station and the mouth of the Amazon basin using the water storage variations derived from GRACE gravity data without relying on any auxiliary ground observations. We extract the water mass signal along the main stem of the river by applying the Empirical Orthogonal Function (EOF) for water storage variations over the basin. The relative water storage variations along the main stem derived from the EOF decomposition are highly correlated with in-situ discharge at the Óbidos. However, in high water season, the GRACE-based discharge is estimated larger than the in-situ observations, and the difference is particularly significant during the 2009 extreme flood season. We argue that the in-situ river discharge in 2009 was underestimated due to the missed water volume for the flow detouring around the Óbidos gauge station during the high-flow event. Net river discharge of the Amazon Basin to Atlantic Ocean is also estimated, and its annual discharge is about 23% larger than that of the Óbidos. In particular, 2009 river discharge to Atlantic Oceans is estimated as 1050 Gton. [ABSTRACT FROM AUTHOR]

Floodplains are a key ecological feature of arid and semi-arid regions and flooding is the main source of vegetation productivity. Characterizing the spatiotemporal flood dynamics, such as surface water and changes in subsequent vegetation growth vigour and biomass, is essential in better understanding of ecology and hydrology of these regions. Recent remotely sensed flooding studies of arid zone floodplains have concentrated on improving classification of surface water, particularly in mixed water-vegetation areas; less is known about the effect of surface water flooding pattern on emergent vegetation dynamics. We use an integrated framework for mapping both flood extents and the persistence of floodplain response changes of water, vegetation and moisture in Cooper Creek, Australia. We analysed pixel-based time series of multiple indices generated by daily MODIS data for 14 highly variable flow flood events between 2000 and 2012. Results indicate that for the extremely flat Cooper Creek floodplain, mapping inundation area by changes in vegetation growth vigour and biomass was significantly larger than surface water mapping area (16.2% for the 2004 flood event) and the difference in inundation mapping mainly occurs around the inundated edges. In addition, by studying surface water and subsequent vegetation response together, it is possible to generate new information, such as the lag time between flooding and peak vegetation growth vigour and biomass, and persistence time of surface water and green vegetation, which provide important insights to arid zone floodplain behaviour. The large extent and high frequency of MODIS images provide advantages in characterizing inundation dynamics for large-scale floodplains where instantaneous (daily) inundation extent is considerably smaller than total cumulative inundation extent, compared with sensors with higher spatial resolution but lower temporal resolution; however, the coarse resolution of MODIS (500 m) limits its performance for small flood events. Globally, this approach is suitable for other large, low-gradient floodplains in arid zones that show similar, long duration vegetation responses as observed in Cooper Creek. [ABSTRACT FROM AUTHOR]

The Advanced Microwave Scanning Radiometer 2 (AMSR2), a follow-up microwave sensor to the AMSR for Earth Observing System (AMSR-E), was launched on the Global Change Observation Mission 1 – Water (GCOM-W1) satellite in May 2012. It is as yet unclear if instrumental improvements in AMSR2 over AMSR-E have led to better soil moisture (SM) estimates, especially since there is no overlapping period of data between the sensors. This study focuses on comparing the results of AMSR2 and AMSR-E SM over Australia, distinguishing four Köppen climate zones to determine if AMSR2 is better than AMSR-E. This is achieved by selecting two year-long comparative time periods from the operating periods of AMSR-E and AMSR2, based on their statistical similarities in modeled SM as a proxy, using Modern Era Retrospective-analysis for Research and Applications-Land (MERRA-L). The AMSR2 and AMSR-E C- and X-band SM derived from the Land Parameter Retrieval Model (LPRM) was evaluated. Both AMSR2 C- and X-band SM products were found to show similar temporal patterns and spatial agreement with AMSR-E C- and X-band SM, supported by unbiased root mean square difference (ubRMSD) and R-values with MERRA-L SM, respectively. Using lag-based instrumental variable analysis to estimate the random error component of SM retrievals, the noise-to-signal ratios in AMSR2 X-band SM were found to be slightly higher than their AMSR-E counterparts. The improvements in AMSR2, such as the superior radiometric sensitivity and spatial resolution, have therefore not led to statistically significant differences in performance for LPRM retrievals at 1/2° × 1/2° grid resolution, when compared with AMSR-E. However, similarities in the metrics for AMSR2 and AMSR-E SM suggest that AMSR2 provides a valuable continuation to AMSR-E. [ABSTRACT FROM AUTHOR]

[Display omitted] • In asynchronous climates, the forest growth response to rainfall is not well known. • Lagged relationship of forest growth to antecedent rainfall was explored. • Vegetation in the asynchronous climate have a long memory to the rainfall. • The memory of vegetation to rain could indicate soil/rooting depth across the landscape. The out-of-phase rainfall and temperature and deep root system make the sequential connection between past rainfall events, soil water storage, and forest growth response complicated and temporally extended in asynchronous climates with Mediterranean-type settings. Unfortunately, these location-specific deep-soil water stores are rarely measured due to logistic and financial constraints, especially in the forest. Therefore, at a large spatio-temporal scale, forest growth relationship to growth drivers is still unknown in these ecosystems, limiting our knowledge to understand the functioning of these forests and their links with hydrological processes. Although process-based water balance models can analyze vegetation growth response to the input climate forcing, they rely upon some significant assumptions regarding plants access to deep soil water storage. Thus, this study aims to understand how the out-of-phase rainfall events affect the current ecosystem growth response, represented by the observed Normalized Difference Vegetation Index (NDVI), across the landscape. We have used an empirical approach on long term observed data without any assumption on access to deep-soil water stores. We estimated time lags between forest growth and rainfall events using a lagged correlation analysis applied to monthly anomalies of NDVI and rainfall against their climatological averages over 2002–2018. The study found that the forests in asynchronous climates exhibit unexpectedly long (10–25 months) memory to rain, and this memory has a systematic pattern across the landscape, which we contend has highlighted three things: 1) the forest in the middle aridity (∼3–4.5) range are relatively more sensitive to changes in the short-term rainfall than the forest in lower (<3) and higher (>5) aridity regions, could be due to rapid depletion of relatively small soil water storage in between the storms, 2) the variable memory of forest to rain across the landscape can be an indicator of soil depth/rooting depth, and 3) the variable sized location specific antecedent rainfall windows can explain significant variability in forest growth status in asynchronous climates, thus these rainfall windows can be employed to forecast forest growth with a lead time (>4 months). [ABSTRACT FROM AUTHOR]

• Uncertainties in canopy conductance are affected by vapor pressure deficit mostly. • The maximum stomatal conductance (gsm) shows variation with the leaf age. • gsm shows distinctive trends among the C3 crop/grass, C4 crop and C3 tree groups. • Stomatal conductance shows different trends with radiation among plant species. • Air temperature having little influence on the canopy conductance. Canopy conductance (G c) is a crucial variable in accurately estimating latent heat flux and evapotranspiration (ET) over vegetated surfaces. G c is highly dependent on the plant species and the surrounding environment; consequently, it is difficult to accurately simulate G c using a simplified universal model. A number of empirical parameters and functional forms have been introduced to appropriately account for various plant species. This study examines the efficacy of a widely adopted G c model and Penman-Monteith (PM) approach over a selection of vegetated sites in arid and semi-arid regions of China to investigate the factors that influence the accuracy of the model. Comprehensive micrometeorological and land surface monitoring datasets from eight eddy covariance (EC) stations in the Hei and Hai river basins were used for our analysis. E T calculated from a widely used surface conductance and PM method was compared with EC measurements, and the residuals of the comparison were analyzed with factors potentially influencing the residuals using their correlations and regression coefficients to identify factors accounting for the errors caused via G c. The results showed that the vapor pressure deficit and the maximum stomatal conductance (g s m) are most significantly correlated with uncertainties in G c for all plant species. The G c error vs. vapor pressure deficit exhibited distinctive sensitivities among the C3 crop/grass, C4 crop and C3 tree groups. The maximum stomatal conductance showed variation with the leaf age, reaching the maximum level in the mid-growth stage for most types of plant species except for coniferous trees. The residuals increased with radiation levels for sites covered with trees, which appeared to be caused by the decreasing trend of the stomatal conductance vs. radiation at high radiation levels for trees. Finally, we propose potential methods of improving canopy conductance models based on our error analysis. [ABSTRACT FROM AUTHOR]

Soil water is a critical resource in many rain-fed agricultural systems. Climate variability represents a significant risk in these systems, which has been addressed in the past through seasonal weather outlooks. This study undertakes a pilot assessment of the potential to extend seasonal weather outlooks to plant available soil water (PASW). We analyse 20 sites in the southeast Australian wheat belt using seasonal weather outlooks from the Predictive Ocean-Atmosphere Model for Australia (POAMA; (the operational seasonal model of the Australian Bureau of Meteorology), which were downscaled and used in conjunction with the Agricultural Production Simulator (APSIM). Hindcast rainfall, potential evapotranspiration ( PET ) and PASW outlooks were produced on a monthly basis for 33 years at a point scale. The outlooks were assessed using a range of ensemble verification tools. The results showed hit rates that outperformed climatology for rainfall and PET in the short-term (0–2 months), and for PASW with longer lead times (2–5 months). Continuous rank probability skill scores (CRPSS) were generally statistically worse than climatology for rainfall and PET and statistically better than climatology for PASW over 1–3 months. The influence of initial soil water is seasonally dependent, with longer dependence in low evapotranspiration periods. Improved weather model downscaling approaches would transition to climatology and could improve both weather and PASW outlooks. PASW outlooks were strongly reliant on initial conditions, indicating the importance of understanding current soil water status, which needs to be interpreted in a seasonal context as its influence varies over the year. Expanded operational soil water monitoring would be important if PASW outlooks are to become routine. [ABSTRACT FROM AUTHOR]

Real-time de-noising of satellite-derived soil moisture observations presents opportunities to deliver more accurate and timely satellite data for direct satellite users. So far, the most commonly used techniques for reducing the impact of noise in the retrieved satellite soil moisture observations have been based on moving average filters and Fourier based methods. This paper introduces a new alternative wavelet based approach called Wiener-Wavelet-Based Filter (WiW), which uses an entropy based de-noising method to design a causal version of the filter. WiW is used as a post-retrieval processing tool to enhance the quality of observations derived from one active (the Advanced Scatterometer, ASCAT) and one passive (the Advanced Microwave Scanning Radiometer for Earth Observing System, AMSRE) satellite sensors. The filter is then compared with two candidate de-noising techniques, namely: i) a Wiener causal filter introduced by Su et al. (2013) and ii) a conventional moving average filter. The validation is carried out globally at 173 (for AMSRE) and 243 (for ASCAT) soil moisture stations. Results show that all the three de-noising techniques can increase the agreement between satellite and in situ measurements in terms of correlation and signal-to-noise ratio. The Wiener-based methods show least signal distortion and demonstrate to be conservative in retaining the signal information in de-noised data. Importantly, the Wiener filters can be calibrated with the data at hand, without the need for auxiliary data. [ABSTRACT FROM AUTHOR]

Real-time crop canopy nitrogen concentration (CNC) and aboveground biomass (AGB) sensing capability can enable precision agriculture with significant economic and ecological benefits. Canopy spectral response in visible near-infrared (VNIR, 400–979 nm) has been widely used to estimate CNC and AGB of canopies but is often confounded by the soil background, crop type, phenological changes and environmental conditions. Machine (ML) and deep learning (DL) have emerged as promising techniques for mapping CNC and AGB using canopy spectral reflectance in VNIR. However, ML and DL models' performance relies on extensive labelled training datasets, which are often limited due to being expensive and labour intensive. We estimated CNC and AGB using state-of-the-art hyperspectral (HS) modeling approaches using partial least squares regression (PLSR), random forest (RF), and one- and two-dimensional convolutional neural network (1D and 2D CNN). These models, together with multispectral (MS) approaches, were evaluated in the context of a small (422) dataset for model development collected from diverse fertilization and contrasting growth conditions. A handheld field spectroradiometer was used to collect spectral reflectance samples across a wide range (more than ten-fold difference in AGB) of canopy development stages in two seasons (winter and summer) in ryegrass, and across phenological stages (from GS-25 to GS-60) in barley. Results can be summarized in four components: (1) the median values of performance metrics in 1D CNN (R2 test =0.83, NRMSE test =0.15 for CNC; R2 test =0.79 (ryegrass), 0.61 (barley), NRMSE test = 0.29 (ryegrass), 0.30 (barley) for AGB) and PLSR (R2 test =0.71, NRMSE test = 0.20 for CNC; R2 test =0.62 (ryegrass), 0.70 (barley), NRMSE test = 0.38 (ryegrass), 0.27 (barley) for AGB) showed the best performance for CNC and AGB prediction in the test data; (2) when evaluated with the independent measurements, 1D CNN demonstrated the best (up to 56% decrease in NRMSE) performance for AGB and was comparable to PLSR for CNC (up to 25% decrease in NRMSE than their HS and MS counterparts); (3) predictive models underperformed at low canopy cover for CNC estimation and were adversely affected by the high biomass saturation effects for AGB prediction; and (4) visible spectrum is relatively more informative to CNC models whereas the red edge to NIR spectrum is more informative to AGB models. This study demonstrates the potential and limitations of commonly available field VNIR spectral reflectance data for CNC and AGB mapping across contrasting conditions in a small dataset (<500). • 1D convolutional neural network (CNN) exhibited the best and robust performance. • 1D CNN showed a relatively higher tolerance for biomass saturation effects. • Canopy nitrogen concentration (CNC) models underperformed at low canopy cover. • Visible spectrum was more informative for CNC prediction. • Red edge to NIR spectrum showed more relevance for aboveground biomass prediction. [ABSTRACT FROM AUTHOR]

Background & Aims Mitochondrial dysfunction, oxidative stress, inflammation, and metabolic reprograming are crucial contributors to hepatic injury and subsequent liver fibrosis. Poly(ADP-ribose) polymerases (PARP) and their interactions with sirtuins play an important role in regulating intermediary metabolism in this process. However, there is little research into whether PARP inhibition affects alcoholic and non-alcoholic steatohepatitis (ASH/NASH). Methods We investigated the effects of genetic deletion of PARP1 and pharmacological inhibition of PARP in models of early alcoholic steatohepatitis, as well as on Kupffer cell activation in vitro using biochemical assays, real-time PCR, and histological analyses. The effects of PARP inhibition were also evaluated in high fat or methionine and choline deficient diet-induced steatohepatitis models in mice. Results PARP activity was increased in livers due to excessive alcohol intake, which was associated with decreased NAD + content and SIRT1 activity. Pharmacological inhibition of PARP restored the hepatic NAD + content, attenuated the decrease in SIRT1 activation and beneficially affected the metabolic-, inflammatory-, and oxidative stress-related alterations due to alcohol feeding in the liver. PARP1 −/− animals were protected against alcoholic steatohepatitis and pharmacological inhibition of PARP or genetic deletion of PARP1 also attenuated Kupffer cell activation in vitro . Furthermore, PARP inhibition decreased hepatic triglyceride accumulation, metabolic dysregulation, or inflammation and/or fibrosis in models of NASH. Conclusion Our results suggests that PARP inhibition is a promising therapeutic strategy in steatohepatitis with high translational potential, considering the availability of PARP inhibitors for clinical treatment of cancer. Lay summary Poly(ADP-ribose) polymerases (PARP) are the most abundant nuclear enzymes. The PARP inhibitor olaparib (Lynparza) is a recently FDA-approved therapy for cancer. This study shows that PARP is overactivated in livers of subjects with alcoholic liver disease and that pharmacological inhibition of this enzyme with 3 different PARP inhibitors, including olaparib, attenuates high fat or alcohol induced liver injury, abnormal metabolic alteration, fat accumulation, inflammation and/or fibrosis in preclinical models of liver disease. These results suggest that PARP inhibition is a promising therapeutic strategy in the treatment of alcoholic and non-alcoholic liver diseases. [ABSTRACT FROM AUTHOR]

Soil moisture is one of the essential climate variables for the Global Climate Observing System (GCOS) that has been prioritized by the ESA's Climate Change Initiative to construct its homogeneous long-term climate record. This requires a consistent characterization of the error structures in the individual data sets, which vary due to changes in instrument configuration and calibration, and retrieval algorithm design. In this paper, the random error and systematic differences in nine passive and active microwave satellite soil moisture products over Australia (time coverage: 1978–present) are estimated in a same manner for SM components at subseasonal and seasonal-to-interannual timescales separately. The multi-scale error structures are found to be non-trivial and vary between the products, giving cause for conducting multi-scale merging with awareness of these differences. Noticeable similarities between the error structures of the satellite products derived from same retrieval algorithm and same measuring frequency however suggest transferability of error parameters between them. Using partial rank correlation analysis, the error maps are linked to statistics on vegetation index, digital elevation, soil moisture and soil temperature, and land cover fractions and mixing in order to explain the observed variability and the similarities between the products. [ABSTRACT FROM AUTHOR]

Active and passive microwave satellite remote sensing are enabling sub-daily global observations of surface soil moisture (SM) for hydrological, meteorological and climatological studies. Because the retrieved SM data can be quite noisy, post-retrieval processing such as de-noising can play an important role to aid interpretation of the observed dynamics or enhance their utility for data assimilation. To date, the merits of such techniques have not yet been fully evaluated. Here we consider the applications of Fourier-based de-noising filters of Su et al. (2013a) for improving SM retrieved by AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) and ASCAT (Advanced Scatterometer of MetOp-A) sensors. The filters are calibrated in the frequency domain based on a water-balance model, without the need for ancillary data. The evaluation of the de-noising methods was conducted globally against in situ data distributed via the International Soil Moisture Network (ISMN) at 277 AMSR-E and 385 ASCAT pixels. Systematic improvements were found for all considered metrics, namely root-mean-square deviation, linear correlation and signal-to-noise ratio, for both SM products, with improvements more striking for AMSR-E. However, the originally proposed implementation of the filters can induce undesirable over-smoothing and distortion of SM timeseries. To overcome this, based on a simple heuristic argument, we propose the use of ancillary precipitation data in the filtering process, although at some expense of overall agreements with the in situ data. [ABSTRACT FROM AUTHOR]

Pancreatic beta cell dysfunction is a hallmark of type 2 diabetes. Growth differentiation factor 15 (GDF15), which is an energy homeostasis regulator, has been shown to improve several metabolic parameters in the context of diabetes. However, its effects on pancreatic beta-cell remain to be identified. We, therefore, performed experiments using cell models and histological sectioning of wild-type and knock-out GDF15 mice to determine the effect of GDF15 on insulin secretion and cell viability. A bioinformatics analysis was performed to identify GDF15-correlated genes. GDF15 prevents glucotoxicity-mediated altered glucose-stimulated insulin secretion (GSIS) and connexin-36 downregulation. Inhibition of endogenous GDF15 reduced GSIS in cultured mouse beta-cells under standard conditions while it had no impact on GSIS in cells exposed to glucolipotoxicity, which is a diabetogenic condition. Furthermore, this inhibition exacerbated glucolipotoxicity-reduced cell survival. This suggests that endogenous GDF15 in beta-cell is required for cell survival but not GSIS in the context of glucolipotoxicity. • High glucose and palmitate treatment increase GDF15 in cultured pancreatic beta-cells and INS-1E cells. • GDF15 prevents glucose-stimulated insulin secretion inhibited by glucolipotoxicity in the beta-cell and INS-1E cells. • GDF15 prevent glucotoxicity induced connexin-36 downregulation in INS-1E cells. • GDF15 is a potential therapeutic agent which can delay the need of insulin under glucolipotoxic conditions. [ABSTRACT FROM AUTHOR]

Abstract: This paper focuses on the sensitivity of L-band multi-parameter retrievals across the range of angular measurements available from the SMOS (Soil Moisture and Ocean Salinity) mission. The SMOS core algorithm was used to evaluate two-parameter retrieval scenarios including soil moisture and one of either i) vegetation water content, ii) surface roughness, iii) vegetation temperature, or iv) surface soil temperature. For all pairs a range of parameter value combinations were compiled to run the model in forward mode. Subsequently, the resulting angular brightness temperature simulations with two unknown parameters were compared against the brightness temperature response derived from reference simulations using data from the National Airborne Field Experiment 2005 (NAFE'05) in Australia. This paper showed that the two-parameter retrieval accuracy of soil moisture is strongly affected by the surface moisture conditions, the polarization of the brightness temperature data, and the choice of the secondary ancillary parameter to be retrieved. The synthetic analysis demonstrated a tendency for better retrievals from dual-polarized data at large incidence angles (40–50°). Validation with airborne brightness temperature observations at L-band did not demonstrate such a strong angular dependency, although it confirmed that the simultaneous retrieval of soil moisture and vegetation properties is not preferable as opposed to i) soil moisture and surface roughness or ii) soil moisture and surface soil temperature, especially under dry moisture conditions. [Copyright &y& Elsevier]

Abstract: Large region surface soil moisture estimates are important for both hydrologic modeling and remote sensing applications. For soil moisture monitoring, gravimetric soil moisture sampling is reliable; however, it requires a significant effort to gather and process samples. Portable impedance probes serve as a valuable alternative to destructive gravimetric sampling. These probes measure the dielectric properties of the soil–water–air mixture from which we can infer the volumetric soil moisture. As part of recent large-scale experiments in the summers of 2002 and 2003, three different methods for calibrating impedance probes were investigated with the support of coincident gravimetric samples. Field specific calibration improved the accuracy of the probe from greater than ±5% volumetric soil moisture (using the generalized calibration) to less than ±4%. In addition, a significant amount of bias (∼2%) was eliminated. It was also determined that field specific calibration removes a bias due to bulk density variations. Based upon these results it was concluded that the generalized calibration is adequate for estimation of diverse conditions. For studies with more stringent accuracy requirements, field specific calibration is necessary because of reduced bias and error. [Copyright &y& Elsevier]

• An ensemble flood forecasting framework is developed for use with event-based model. • Ensembles of event loss parameters are generated using historical information. • Synthetic rainfall forecasts are used to test model over wide range of conditions. • Forecast reliability evaluated using 31 largest rainfall events over 20-year period. Extreme floods pose a threat to life and property and accurate and reliable flood forecasts are required to mitigate the consequences of these events. In this paper, we propose a novel Monte-Carlo based framework that utilizes an ensemble-based short-term flood forecasting model. An event-based rainfall-runoff model is selected due to its simplicity and wide use by industry practitioners. However, a challenge with event models is that they are unable to account for the initial condition of the catchment wetness at the commencement of a particular rainfall event. To address this issue, we used independent catchment-average estimates of soil moisture to estimate catchment losses at the commencement and over the duration of the event. The adopted framework enables uncertainty in catchment losses and rainfall depth and patterns to be quantified and incorporated in forecasts. The accuracy and reliability of stochastic components of this framework are evaluated using ensemble verification measures. Later, these measures are used to evaluate four scenarios based on different combinations of deterministic and stochastic conditions. In the stochastic scenarios, the uncertainty of the flood forecasts is reliably quantified (88% reliability on average). The results show that operationally available estimates of catchment wetness can be used to account for uncertainty from event-based loss parameters, and that the uncertainty from average forecast rainfall depth is considerably more important than the uncertainty from forecast losses and the spatio-temporal patterns of the rainfall. This study has shown that event-based models have the potential to be applied within a probabilistic framework to operationally generate probabilistic flood forecasts. [ABSTRACT FROM AUTHOR]

The commencement of SEAS5 model for operational seasonal climate forecasting by the European Centre for Medium-Range Weather Forecasts (ECMWF) is a new development. It replaces the ECMWF System 4, which had a large international community of seasonal climate forecast users. To assist potential users of SEAS5 forecasts, a systematic and detailed evaluation of forecast skill and reliability of climate variables over land areas is valuable. In this regional study, we evaluate SEAS5 performance in forecasting precipitation and daily minimum temperature (Tmin) and daily maximum temperature (Tmax) for the Australian continent, based on 36 years of re-forecast data. We evaluate forecasts after simple mean-corrections, and statistically calibrated forecasts using the Bayesian joint probability (BJP) modelling approach. We also provide a comparison with System 4. A new simpler and more efficient BJP algorithm is introduced to facilitate this study and support wider use of the algorithm in other applications. • Seasonal climate forecasts of the new ECMWF SEAS5 model are evaluated for Australia. • SEAS5 offers considerable skill for forecasting the coming month. • The skill drops significantly beyond one month forecast horizon. • A simpler and more efficient Bayesian joint probability (BJP) algorithm is introduced. • Forecasts are more skillful and reliable after calibration using the BJP algorithm. [ABSTRACT FROM AUTHOR]