Abstract. Water resources are essential to the ecosystem and social economy in the desert and oasis of the arid Tarim River basin, northwestern China, and expected to be vulnerable to climate change. It has been demonstrated that regional climate models (RCMs) provide more reliable results for a regional impact study of climate change (e.g., on water resources) than general circulation models (GCMs). However, due to their considerable bias it is still necessary to apply bias correction before they are used for water resources research. In this paper, after a sensitivity analysis on input meteorological variables based on the Sobol' method, we compared five precipitation correction methods and three temperature correction methods in downscaling RCM simulations applied over the Kaidu River basin, one of the headwaters of the Tarim River basin. Precipitation correction methods applied include linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), distribution mapping (DM) and quantile mapping (QM), while temperature correction methods are LS, variance scaling (VARI) and DM. The corrected precipitation and temperature were compared to the observed meteorological data, prior to being used as meteorological inputs of a distributed hydrologic model to study their impacts on streamflow. The results show (1) streamflows are sensitive to precipitation, temperature and solar radiation but not to relative humidity and wind speed; (2) raw RCM simulations are heavily biased from observed meteorological data, and its use for streamflow simulations results in large biases from observed streamflow, and all bias correction methods effectively improved these simulations; (3) for precipitation, PT and QM methods performed equally best in correcting the frequency-based indices (e.g., standard deviation, percentile values) while the LOCI method performed best in terms of the time-series-based indices (e.g., Nash-Sutcliffe coefficient, R 2 ); (4) for temperature, all correction methods performed equally well in correcting raw temperature; and (5) for simulated streamflow, precipitation correction methods have more significant influence than temperature correction methods and the performances of streamflow simulations are consistent with those of corrected precipitation; i.e., the PT and QM methods performed equally best in correcting flow duration curve and peak flow while the LOCI method performed best in terms of the time-series-based indices. The case study is for an arid area in China based on a specific RCM and hydrologic model, but the methodology and some results can be applied to other areas and models.

Abstract. Water resources are essential to the ecosystem and social economy in the desert and oasis of the arid Tarim River Basin, Northwest China, and expected to be vulnerable to climate change. Regional Climate Models (RCM) have been proved to provide more reliable results for regional impact study of climate change (e.g. on water resources) than GCM models. However, it is still necessary to apply bias correction before they are used for water resources research due to often considerable biases. In this paper, after a sensitivity analysis on input meteorological variables based on Sobol' method, we compared five precipitation correction methods and three temperature correction methods to the output of a RCM model with its application to the Kaidu River Basin, one of the headwaters of the Tarim River Basin. Precipitation correction methods include Linear Scaling (LS), LOCal Intensity scaling (LOCI), Power Transformation (PT), Distribution Mapping (DM) and Quantile Mapping (QM); and temperature correction methods include LS, VARIance scaling (VARI) and DM. These corrected precipitation and temperature were compared to the observed meteorological data, and then their impacts on streamflow were also compared by driving a distributed hydrologic model. The results show: (1) precipitation, temperature, solar radiation are sensitivity to streamflow while relative humidity and wind speed are not, (2) raw RCM simulations are heavily biased from observed meteorological data, which results in biases in the simulated streamflows, and all bias correction methods effectively improved theses simulations, (3) for precipitation, PT and QM methods performed equally best in correcting the frequency-based indices (e.g. SD, percentile values) while LOCI method performed best in terms of the time series based indices (e.g. Nash–Sutcliffe coefficient, R2), (4) for temperature, all bias correction methods performed equally well in correcting raw temperature. (5) For simulated streamflow, precipitation correction methods have more significant influence than temperature correction methods and the performances of streamflow simulations are consistent with these of corrected precipitation, i.e. PT and QM methods performed equally best in correcting flow duration curve and peak flow while LOCI method performed best in terms of the time series based indices. The case study is for an arid area in China based on a specific RCM and hydrologic model, but the methodology and some results can be applied to other area and other models.

Stroke is a complex and devastating neurological condition with limited treatment options. Brain edema is a serious complication of stroke. Early edema formation can significantly contribute to infarct formation and thus represents a promising target. Aquaporin (AQP) water channels contribute to water homeostasis by regulating water transport and are implicated in several disease pathways. At least 7 AQP subtypes have been identified in the rodent brain and the use of transgenic mice has greatly aided our understanding of their functions. AQP4, the most abundant channel in the brain, is up-regulated around the peri-infarct border in transient cerebral ischemia and AQP4 knockout mice demonstrate significantly reduced cerebral edema and improved neurological outcome. In models of vasogenic edema, brain swelling is more pronounced in AQP4-null mice than wild-type providing strong evidence of the dual role of AQP4 in the formation and resolution of both vasogenic and cytotoxic edema. AQP4 is co-localized with inwardly rectifying K+-channels (Kir4.1) and glial K+ uptake is attenuated in AQP4 knockout mice compared to wild-type, indicating some form of functional interaction. AQP4-null mice also exhibit a reduction in calcium signaling, suggesting that this channel may also be involved in triggering pathological downstream signaling events. Associations with the gap junction protein Cx43 possibly recapitulate its role in edema dissipation within the astroglial syncytium. Other roles ascribed to AQP4 include facilitation of astrocyte migration, glial scar formation, modulation of inflammation and signaling functions. Treatment of ischemic cerebral edema is based on the various mechanisms in which fluid content in different brain compartments can be modified. The identification of modulators and inhibitors of AQP4 offer new therapeutic avenues in the hope of reducing the extent of morbidity and mortality in stroke.

Brain mitochondrial dysfunction is involved in the development of neurological and neurodegenerative diseases. Mitochondria specifically located at synapses play a key role in providing energy to support synaptic functions and plasticity, thus their defects may lead to synaptic failure, which is a common hallmark of neurodegenerative diseases. High-Fat Diet (HFD) consumption increases brain oxidative stress and impairs brain mitochondrial functions, although the underlying mechanisms are not completely understood. The aim of our study is to analyze neuroinflammation and mitochondrial dysfunctions in brain cortex and synaptosomal fraction isolated from a mouse model of diet-induced obesity. Male C57Bl/6 mice were divided into two groups fed a standard diet or HFD for 18 weeks. At the end of the treatment, inflammation (detected by ELISA), antioxidant state (measured by enzymatic activity), mitochondrial functions and efficiency (detected by oxidative capacity and Seahorse analysis), and brain-derived neurotrophic factor (BDNF) pathway (analyzed by western blot) were determined in brain cortex and synaptosomal fraction. In HFD animals, we observed an increase in inflammatory parameters and oxidative stress and a decrease in mitochondrial oxidative capacity both in the brain cortex and synaptosomal fraction. These alterations parallel with modulation of BDNF, a brain key signaling molecule that is linking synaptic plasticity and energy metabolism. Neuroinflammation HFD-dependent negatively affects BDNF pathway and mitochondrial activity in the brain cortex. The effect is even more pronounced in the synaptic region, where the impaired energy supply may have a negative impact on neuronal plasticity.

Abstract. This paper describes the design and use of a recursive ensemble Kalman filter (REnKF) to assimilate streamflow data in an operational flow forecasting system of seven catchments in New Zealand. The REnKF iteratively updates past and present model states (soil water, aquifer and surface storages), with lags up to the concentration time of the catchment, to improve model initial conditions and hence flow forecasts. We found the REnKF overcame instabilities in the standard EnKF, which were associated with the natural lag time between upstream catchment wetness and flow at the gauging locations. The forecast system performance was correspondingly improved in terms of Nash-Sutcliffe score, persistence index and bounding of the measured flow by the model ensemble. We present descriptions of filter design parameters and explanations and examples of filter behaviour, as an information source for other groups wishing to assimilate discharge observations for operational forecasting.