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This paper develops a tsunami inundation model, filling the current void between industry applied simplified methods (bathtub and attenuation) and comprehensive numerical modeling. The proposed model utilizes two‐dimensional equations established on hydraulic principles (energy conservation and friction loss) to produce the finite‐difference, two‐dimensional model. While the sophistication of depth‐averaged numerical modeling has not been entirely replicated, the incorporation of hydraulic roughness and gravitational effects (slope) through the Bernoulli energy equation, accompanied by suitable assumptions, significantly improves performance over simplified bathtub and attenuation methods. This improvement not only retains excellent efficiency (completing case studies in under 100 s) but also yields inundation results comparable to traditional numerical modeling, which typically takes multiple hours to days. This is the first application of such equations to a two‐dimensional tsunami inundation model, leading to simulations at Gisborne and Christchurch, New Zealand, for validation against the numerical model, COMCOT. F1 scores produced from inundation extent comparisons were upwards of 84%. Inundation depth discrepancies recorded 77% within a ±$\pm$1 m range, while over 93% within a ±$\pm$2 m range. [ABSTRACT FROM AUTHOR]

There are an estimated 800,000 small reservoirs globally with a range of uses. Given the collective importance of these reservoirs to water resource management and wider society, it is essential that we can monitor and understand the hydrological dynamics of ungauged reservoirs, particularly in a changing climate. However, unlike large reservoirs, continuous and systematic hydrological observations of small reservoirs are often unavailable. In response, this study has developed a retrieval framework for water levels of small reservoirs using a deep learning algorithm and remotely sensed satellite data. Demonstrated at four reservoirs in California, satellite imagery from both Sentinel-1 and Sentinel-2 along with corresponding water level field measurements was collected. Post-processed images were fed into a water level inversion convolutional neural network model for water level inversion, while different combinations of these satellite images, sampling approaches for training/testing data, and attention modules were used to train the model and evaluated for accuracy. The results show that random sampling of training data coupled with Sentinel-2 satellite imagery was generally the most accurate initially. Performance is improved by incorporating a channel attention mechanism, with the average R2 increasing by 8.6% and the average RMSE and MAE decreasing by 15.5% and 36.4%, respectively. The proposed framework was further validated on three additional reservoirs in different regions. In conclusion, the retrieval framework proposed in this study provides a stable and accurate methodology for water level estimation of small reservoirs and can be a powerful tool for small reservoir monitoring over large spatial scales. [ABSTRACT FROM AUTHOR]

This study investigates the direct and indirect impacts of extreme sea level (ESL) flooding on critical infrastructure. While methods to quantify the direct impacts of ESL flooding on coastal areas are well established, the indirect impacts that extend beyond the directly affected population are less well understood. This study assesses the vulnerability of electricity, road, telecommunication, water supply, and wastewater infrastructure to service disruption from coastal flooding in South Dunedin, New Zealand. We quantified the population affected by single and multiple service losses caused by 100-year average recurrence interval (ARI) ESL flooding across 0.1 m sea-level rise (SLR) increments up to 1.5 m. The study identified an ESL "tipping-point" at 0.6 m SLR, where direct loss of road access and electricity services is extensive, and causes indirect loss of telecommunication services. This also impacted the functionality of a key wastewater pumpstation servicing the wider region. As sea levels in the area are projected to rise 0.60 m in the next ~60–90 years, this information can inform assessments of potential adaptation investments in long-term infrastructure management plans in the face of uncertainty. [ABSTRACT FROM AUTHOR]

Land-sea riverine carbon transfer (LSRCT) is one of the key processes in the global carbon cycle. Although natural factors (e.g. climate, soil) influence LSRCT, human water management strategies have also been identified as a critical component. However, few systematic approaches quantifying the contribution of coupled natural and anthropogenic factors on LSRCT have been published. This study presents an integrated framework coupling hydrological modeling, field sampling and stable isotope analysis for the quantitative assessment of the impact of human water management practices (e.g. irrigation, dam construction) on LSRCT under different hydrological conditions. By applying this approach to the case study of the Nandu River, China, we find that carbon (C) concentrations originating from different land-uses (e.g. forest, cropland) are relatively stable and outlet C variations are mainly dominated by controlled runoff volumes rather than by input C concentrations. These results indicate that human water management practices are responsible for a reduction of ∼60% of riverine C at seasonal timescales, with an even greater reduction during drought conditions. Annual C discharges have been significantly reduced (e.g. 77 ± 5% in 2015 and 39 ± 11% in 2016) due to changes in human water extraction coupled with climate variation. In addition, isotope analysis also shows that C fluxes influenced by human activities (e.g. agriculture, aquaculture) could contribute the dominant particulate organic carbon under typical climatic conditions, as well as drought conditions. This research demonstrates the substantial effect that human water management practices have on the seasonal and annual fluxes of LSRCT, especially in such small basins. This work also shows the applicability of this integrated approach, using multiple tools to quantify the contribution of coupled anthropogenic and natural factors on LSRCT, and the general framework is believed to be feasible with limited modifications for larger basins in future research., (Copyright © 2019 The Author(s). Published by Elsevier B.V. All rights reserved.)