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Drying of soil impacts land energy and water balance, influences the sustainability of vegetation growth, and modulates hydrological extremes including floods. While satellite soil moisture data are widely used for a range of environmental applications, systematic differences from regional in‐situ data prevent their optimal use as key physical signatures (such as soil moisture recession, also termed drydown) are represented differently. This study investigates differences in drydowns from the Soil Moisture Active Passive (SMAP) level 4 product with reference to in‐situ observations. A bivariate filtering alternative is proposed to minimize the disparity noted by modeling the relationship between the rate of drying and initial soil wetness and representing the same as in‐situ. Considerable improvements are observed in the resulting SMAP soil moisture filtered estimates. Although the algorithm assumes spatial stationarity, improvements exist across different soil properties and climatic conditions, providing a parsimonious alternative to better capture the dynamics of soil moisture loss. Plain Language Summary Soil drying affects the environment by changing how land uses energy and water. It also affects plant growth and can lead to extreme events like floods. Scientists use data from satellites to understand soil moisture, but this data sometimes differs from what's measured directly on the ground. Our study looks at these differences, focusing on how soil dries out, using data from a satellite program called the Soil Moisture Active Passive (SMAP). We suggest a new method to make satellite data closer to what's observed on the ground by adjusting it based on initial soil wetness and drying rates. This new approach showed better results and worked well in different types of soil and weather conditions. It helps us track how soil moisture decreases at ground level more accurately, which is important for understanding and managing our environment. Key Points Coarse‐scale satellite‐derived soil moisture dries faster than in‐situ measurements We propose a bivariate recursive filtering approach to characterize soil moisture drying rates and initial wetness conditions The proposed approach is applied to SMAP L4, eliminating systematic bias in drying rates for varied sand fractions and aridity profiles

In machine learning or scientific computing, model performance is measured with an objective function. But why choose one objective over another? According to the information‐theoretic paradigm, the “best” objective function is whichever minimizes information loss. To evaluate different objectives, transform them into likelihoods. The ratios of these likelihoods represent how strongly we should prefer one objective versus another, and the log of that ratio represents the relative information loss (or gain) from one objective to another. In plain terms, minimizing information loss is equivalent to minimizing uncertainty, as well as maximizing probability and general utility. We argue that this paradigm is well‐suited to models that have many uses and no definite utility like the complex Earth system models used to understand the effects of climate change. Furthermore, the benefits of “maximizing information and general utility” extend beyond model accuracy to other important considerations including how efficiently the model calibrates, how well it generalizes, and how well it compresses data. Key Points A basic problem in modeling is the choice of objective function (or performance metric) According to information theory, the “best” objective function minimizes information loss, which we evaluate using the AIC Like friction or inefficiency in a system, information loss incurs additional cost however the model is used

International virtual water (VW) trade helps to balance water stress across regions. However, it can be questioned whether such trade can remain sustainable as water resources are redistributed across regions resulting from changes in our climate. A conceptual framework to compare VW trade volumes with water fluxes within the water cycle is introduced. We evaluate the distribution of traded water surpluses and deficits associated with crop, animal, and industrial products over 157 countries and 182 global watersheds. About 7% of the countries are identified to conduct VW trade unsustainably. Regions within Africa, North America, central Asia, and Europe exhibit unfeasible VW trading resulting from higher appropriation of freshwater resources than availability influenced by precipitation and evaporation. Assessment at the watershed scale captures overexploitation at finer resolution, generally overlooked in country level analysis. An evaluation into the future reveals more watersheds becoming vulnerable to water storage depletion under future climate trends.

Chondroitin sulfate proteoglycans (CSPGs) act as potent inhibitors of axonal growth and neuroplasticity after spinal cord injury (SCI). Here we reveal that CSPGs also play a critical role in preventing inflammation resolution by blocking the conversion of pro-inflammatory immune cells to a pro-repair phenotype in rodent models of SCI. We demonstrate that enzymatic digestion of CSPG glycosaminoglycans enhances immune cell clearance and reduces pro-inflammatory protein and gene expression profiles at key resolution time points. Analysis of phenotypically distinct immune cell clusters revealed CSPG-mediated modulation of macrophage and microglial subtypes which, together with T lymphocyte infiltration and composition changes, suggests a role for CSPGs in modulating both innate and adaptive immune responses after SCI. Mechanistically, CSPG activation of a pro-inflammatory phenotype in pro-repair immune cells was found to be TLR4-dependent, identifying TLR4 signalling as a key driver of CSPG-mediated immune modulation. These findings establish CSPGs as critical mediators of inflammation resolution failure after SCI in rodents, which leads to prolonged inflammatory pathology and irreversible tissue destruction. Inflammation resolution failure is a pathological hallmark of spinal cord injury. Here, the authors show in rodents that chondroitin sulfate proteoglycans contribute to failed resolution by preventing immune cells at the injury core from converting to a pro-resolution phenotype, and this is mediated by TLR4.

While the human fetal immune system defaults to a program of tolerance, there is concurrent need for protective immunity to meet the antigenic challenges encountered after birth. Activation of T cells in utero is associated with the fetal inflammatory response with broad implications for the health of the fetus and of the pregnancy. However, the characteristics of the fetal effector T cells that contribute to this process are largely unknown. We analyzed primary human fetal lymphoid and mucosal tissues and performed phenotypic, functional, and transcriptional analysis to identify T cells with pro-inflammatory potential. The frequency and function of fetal-specific effector T cells was assessed in the cord blood of infants with localized and systemic inflammatory pathologies and compared to healthy term controls. We identified a transcriptionally distinct population of CD4+ T cells characterized by expression of the transcription factor Promyelocytic Leukemia Zinc Finger (PLZF). PLZF+ CD4+ T cells were specifically enriched in the fetal intestine, possessed an effector memory phenotype, and rapidly produced pro-inflammatory cytokines. Engagement of the C-type lectin CD161 on these cells inhibited TCR-dependent production of IFNγ in a fetal-specific manner. IFNγ-producing PLZF+ CD4+ T cells were enriched in the cord blood of infants with gastroschisis, a natural model of chronic inflammation originating from the intestine, as well as in preterm birth, suggesting these cells contribute to fetal systemic immune activation. Our work reveals a fetal-specific program of protective immunity whose dysregulation is associated with fetal and neonatal inflammatory pathologies.

This study suggests a radical approach to hydrologic predictions in ungauged basins, addressing the long standing challenge of issuing predictions when in-situ river discharge does not exist. A simple but powerful rationale for measuring and modeling river discharge is proposed, using coupled advances in hydrologic modeling and satellite remote sensing. Our approach presents a Surrogate River discharge driven Model (SRM) that infers Surrogate River discharge (SR) from remotely sensed microwave signals with the ability to mimic river discharge in varying topographies and vegetation cover, which is then used to calibrate a hydrological model enabling physical realism in the resulting river discharge profile by adding an estimated mean of river discharge via the Budyko framework. The strength of SRM comes from the fact that it only uses remotely sensed data in prediction. The approach is demonstrated for 130 catchments in the Murray Darling Basin (MDB) in Australia, a region of high economic and environmental importance. The newly proposed SR (SR L , representing L-band microwave) boosts the Nash-Sutcliffe Efficiency (NSE) of modeled flow, showing a mean NSE of 0.54, with 70% of catchments exceeding NSE 0.4. We conclude that SRM effectively predicts high-flow and low-flow events related to flood and drought. Overall, this new approach will significantly improve catchment simulation capacity, enhancing water security and flood forecasting capability not only in the MDB but also worldwide.

Climate warming is expected to increase stream temperatures in mountainous regions of western North America, yet the degree to which future climate change may influence seasonal patterns of stream temperature is uncertain. In this study, a spatially explicit statistical model framework was integrated with empirical stream temperature data (approximately four million bi-hourly recordings) and high-resolution climate and land surface data to estimate monthly stream temperatures and potential change under future climate scenarios in the Crown of the Continent Ecosystem, USA and Canada (72,000 km 2 ). Moderate and extreme warming scenarios forecast increasing stream temperatures during spring, summer, and fall, with the largest increases predicted during summer (July, August, and September). Additionally, thermal regimes characteristic of current August temperatures, the warmest month of the year, may be exceeded during July and September, suggesting an earlier onset and extended duration of warm summer stream temperatures. Models estimate that the largest magnitude of temperature warming relative to current conditions may be observed during the shoulder months of winter (April and November). Summer stream temperature warming is likely to be most pronounced in glacial-fed streams where models predict the largest magnitude (> 50%) of change due to the loss of alpine glaciers. We provide the first broad-scale analysis of seasonal climate effects on spatiotemporal patterns of stream temperature in the Crown of the Continent Ecosystem for better understanding climate change impacts on freshwater habitats and guiding conservation and climate adaptation strategies.

Previous research into public perceptions of live prey feeding has been focused on terrestrial animals. The reasons for this likely relate to the difficulty humans have in being compassionate to animals who are phylogenetically distantly related. In order to test these assumptions, the general public (two groups; one who had just visited an aquarium; and one group who had just visited a zoo), aquarium professionals in the UK/US and terrestrial zoo animal professionals (UK) were investigated to see how they would differ in their responses when asked about feeding various live aquatic animals to one another. Likert based surveys were used to obtain data face to face and via online social media. Demographics in previous research identified a lower acceptance of live prey feeding by females, however in aquatic animals this was not reflected. Instead, separations in perception were seen to exist between participants dependent on whether they had just visited a zoo or aquarium, or worked with animals.

Catastrophic flooding has been noted to occur with greater frequency following deforestation, but limited observations have been available to test this connection over large spatial scales. Here we used the data of mega forest fires impacting a region of 25,000 km 2 in Australia exhibiting rapid loss in forest canopy, where the runoff generation has been carefully observed with minimum anthropogenic influences for more than half a century. This provides a unique opportunity to assess the impact of the forest canopy loss on large-scale fluvial flooding. A state-controlled hypothesis test, with the climate and watershed states controlled to enhance robustness, shows a statistically significant increase in annual maximum flows resulting from the forest loss treatment. The reasoning for this natural experiment is that the forest loss impact on the interception potential of forest canopy, fallen leaves, and root-zone soils in wide region could have a recognizable impact on the fluvial flood. Forest coverage loss from wildfires and deforestation increases flood risk for large-scale river catchments by reducing interception, according to analysis of data from twenty-one watersheds in Australia.

The verification of probabilistic forecasts in hydro-climatology is integral to their development, use, and adoption. We propose here a means of utilizing goodness of fit measures for verifying the reliability of probabilistic forecasts. The difficulty in measuring the goodness of fit for a probabilistic prediction or forecast is that predicted probability distributions for a target variable are not stationary in time, meaning one observation alone exists to quantify goodness of fit for each prediction issued. Therefore, we suggest an additional dissociation that can dissociate target information from the other time variant part—the target to be verified in this study is the alignment of observations to the predicted probability distribution. For this dissociation, the probability integral transformation is used. To measure the goodness of fit for the predicted probability distributions, this study uses the root mean squared deviation metric. If the observations after the dissociation can be assumed to be independent, the mean square deviation metric becomes a chi-square test statistic, which enables statistically testing the hypothesis regarding whether the observations are from the same population as the predicted probability distributions. An illustration of our proposed rationale is provided using the multi-model ensemble prediction for El Niño–Southern Oscillation.