Inundation is a primary driver of floodplain ecology. Understanding temporal and spatial variability of inundation patterns is critical for optimum resource management, particularly in striking an appropriate balance between environmental water application and extractive use. Nevertheless, quantifying inundation at the fine resolution required of ecological modeling is an immense challenge in these environments. In this study, Random Forest, a machine learning technique, was implemented to predict the inundation pattern in a section of the Darling River Floodplain, Australia, at a spatial scale of 30 m and daily temporal resolution. The model achieved very good performance with an average accuracy of 0.915 based on the area under the receiver operating characteristic curve over 10 runs of the model in testing data sets. Six variables explained 70% of the total contribution to inundation occurrence, with the most influential being landscape shape (local deviation from global mean elevation), elevation‐weighted distance to the river, the magnitude of river flow (10‐ and 30‐day accumulated river discharge), local rainfall, and soil moisture. This approach is applicable to other floodplains across the world where understanding of fine‐scale inundation pattern is for operational ecological management and scenario testing.
Environmental flow assessment and maintenance are relatively new practices, especially in developing countries. This paper describes the desktop assessment of environmental flows in a river with insufficient data on ecological features and values. In this study, the potential environmental flows in a typical river reach of the Shahr Chai River in Iran were investigated using a newly developed hydrological method (flow duration curve (FDC) shifting) and Global Environmental Flow Calculator software. This approach uses monthly flow data to develop an environmental FDC and to generate flow requirements corresponding to different features of the river ecosystem. Results were compared with those from four alternative hydrological methods: the desktop reserve model (DRM), Tennant, low-flow index, and flow duration curve analysis (FDCA). Comparisons of these methods indicated that to maintain the basic function of the river ecosystem, the river flows should be managed within an acceptable environmental level. The predictions from the Tennant method and the low-flow index (7-day low flow with a 10-year return period), and from the FDCA (for flows exceeding 90 % of occurrence) are not as reliable as those from the FDC shifting technique and DRM. Comparative results indicate that a minimum flow rate of 1.2 m 3 /s (equivalent to 23 % of the natural mean annual runoff, or flow with 80 % occurrence depicted from the FDC) is required for the Shahr Chai River to run toward the internationally recognized Urmia Lake in Iran.
Nano zero-valent iron (nZVI) is one of the most prominent examples of a rapidly emerging technology for the treatment of contaminated soil and groundwater. These particles were used successfully to absorb various heavy metals (e.g., Pb 2+ , Ba 2+ , As 5+ , As 3+ , Cr 6+ , Co 2+ , Cd 2+ , Cu 2+ ), chlorinated organic contaminants (e.g., solvents, pesticides), nitrate and so on. nZVI particles have a large surface area and high surface reactivity. However, the agglomeration, oxidation by non-target compounds and high mobility in the aqueous solutions are the major challenges of nZVI use in environmental remediation. In general, there are several gaps concerning fate, transport and toxicity of nZVI. In recent years, considerable efforts have been made to overcome these problems. These efforts include synthetic methods, surface property modification, trapping of nZVI in different beads and enhancement for field delivery and reactions. This paper provides an overview of research on reclamation of unconventional water with nZVI.
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