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Understanding long-term trends in streamflow is important for water resource management. In this study, we investigate the long-term streamflow trends at 47 gauging sites within the southern Murray-Darling Basin (MDB), Australia. This study aims to estimate regional streamflow trends while understanding the impact of catchment characteristics on the spatial variation in these trends. To achieve this, we applied a Bayesian hierarchical model (BHM) to make the best use of available streamflow records from multiple sites and catchment characteristics such as climate, terrain, geology, land use and vegetation. The results show that streamflow trends from tested sites are consistently negative, with magnitudes of up to 2.7% per year relative to the annual average flow. We also find that spatial variability in trends can be best linked to differences in average climatic and terrain conditions. This finding can be used to inform future water planning for consumptive and environmental uses in the MDB. [ABSTRACT FROM AUTHOR]

Sodium-ion batteries are widely studied due to their abundant sodium resources, widespread distribution, and environmental friendliness. However, due to the large radius of sodium ions, during the charge and discharge process, the cycling performance and rate performance deteriorated seriously, which severely limited the commercial application of sodium-ion batteries. The positive electrode material plays a vital role in the performance of sodium-ion batteries. NaxFeO2 and NaxMnO2 series positive electrode materials showed high theoretical specific capacity and environmentally friendly. They are considered to be potential positive electrode materials for sodium-ion batteries. This article summarizes the research progress of NaxFeO2 and NaxMnO2 series cathode materials in recent years, and discusses the problems and development directions of cathode materials for sodium-ion batteries. [ABSTRACT FROM AUTHOR]

Mapping irrigated areas using remotely sensed imagery has been widely applied to support agricultural water management; however, accuracy is often compromised by the in-field heterogeneity of and interannual variability in crop conditions. This paper addresses these key issues. Two classification methods were employed to map irrigated fields using normalized difference vegetation index (NDVI) values derived from Landsat 7 and Landsat 8: a dynamic thresholding method (method one) and a random forest method (method two). To improve the representativeness of field-level NDVI aggregates, which are the key inputs in our methods, a Gaussian mixture model (GMM)-based filtering approach was adopted to remove noncrop pixels (e.g., trees and bare soils) and mixed pixels along the field boundary. To improve the temporal transferability of method one we dynamically determined the threshold value to account for the impact of interannual weather variability based on the dynamic range of NDVI values. In method two an innovative training sample pool was designed for the random forest modeling to enable automatic calibration for each season, which contributes to consistent performance across years. The irrigated field mapping was applied to a major irrigation district in Australia from 2011 to 2018, for summer and winter cropping seasons separately. The results showed that using GMM-based filtering can markedly improve field-level data quality and avoid up to 1/3 of omission errors for irrigated fields. Method two showed superior performance, exhibiting consistent and good accuracy (kappa > 0.9) for both seasons. The classified maps in wet winter seasons should be used with caution, because rainfall alone can largely meet plant water requirements, leaving the contribution of irrigation to the surface spectral signature weak. The approaches introduced are transferable to other areas, can support multiyear irrigated area mapping with high accuracy, and significantly reduced model development effort. [ABSTRACT FROM AUTHOR]