杨树人工林碳循环对淹水的响应

本文采用涡度相关方法对安徽怀宁杨树人工林的碳通量进行监测,得到微气象数据和通量数据。通过坐标旋转、密度校正和数据插补处理提高数据质量。选取2005年9月淹水期间的数据。分六个阶段进行分析。得到以下结果：（1）在半小时尺度上,在淹水期表观量子效率α随着土壤含水量SWC上升而降低;在未淹水期最大。为-0．0023μmolphoton^-1;退水期最低,为-0．00122mgCO2μmolphoton^-1,退水后恢复到-0,00168mgCO2μmolphoton^-1;α在淹水中2期降低速率最大,在整个淹水期间α的恢复速率是大于降低速率的。（2）生态系统总初级生产力（GEP）在淹水后不断降低．耒淹水期为8．0gCm^-2day^-1．退水后为4．7gCm^-2day^-1,在淹水中2期降幅是最大的达到了16％。（3）生态系统呼吸（Reco）在完全被水淹的第一天降到最低,仅为2．2gCm^-2day^-1;由于温度的影响其变化规律比较复杂。在淹水中2期的变化幅度是最大的;退水后Reco为4．2gCm^-2day^-1恢复到来淹水前的87％。（4）淹水期净生态系统CO2交换量（NEE）的变化表现为先上升后下降的趋势,在淹水中1期最大为-4．2gCm^-2day^-1。退水后最低。为0．6gCm^-2day^-1是未淹水前的17％。     

New policies to deal with climate change and other drivers impacting on resilience to flooding in urban areas: the CORFU approach

In the context of urban flood management, resilience is equal to resisting, recovering, reflecting and responding. The variety of causes of flooding and their consequences underpin the need for increased and internationally coordinated efforts to enhance technologies and policies for dealing with floods. This paper addresses this issue and presents some novel research ideas related to resilience to flooding in urban areas, which are under development within the EU FP7 project ‘Collaborative research on flood resilience in urban areas’ (CORFU). The approach adopted in this project aims to quantify the cost-effectiveness of resilience measures and integrative and adaptable flood management plans for different scenarios of relevant drivers: urban development, socio-economic trends and climate changes. It is believed that the way in which the different models are being put together, combined with the variability of conditions in case study areas in Asia and in Europe, will ultimately enable more scientifically sound policies for the management of the consequences of urban flooding.     

Risk,vulnerability, and pragmatic inevitability: the conflict–disaster nexus and urban governance in Johannesburg,South Africa

This paper spotlights post-disaster relief provision in Johannesburg, South Africa, following the floods of 2016 in a bid to explore how local government and non-governmental actors in the country conceive of compounding vulnerability and conflict within urban disaster governance. It reveals the diverse strategies employed to navigate violent conflict during the cyclical occurrence of disaster and reconstruction that the predominantly migrant population experiences in the Setswetla informal settlement, adjacent to the Alexandra township in northern Johannesburg. Rendered visible in moments of disaster and recovery are the spatial politics and multidimensional nature of conflict. These phenomena unfold across various levels of urban governance and in the affected community and effectively construct a disaster citizenship that makes risk reduction and community cohesion impossible in the eyes of disaster managers. This research, based on a set of expert interviews, integrates conflict and disaster studies to shed light on how the conflict–disaster interface materialises, and is operationalised, in an urban setting.     

Streamflow Hydrology Estimate Using Machine Learning (SHEM)

Continuity and accuracy of near real‐time streamflow gauge (streamgage) data are critical for flood forecasting, assessing imminent risk, and implementing flood mitigation activities. Without these data, decision makers and first responders are limited in their ability to effectively allocate resources, implement evacuations to save lives, and reduce property losses. The Streamflow Hydrology Estimate using Machine Learning (SHEM) is a new predictive model for providing accurate and timely proxy streamflow data for inoperative streamgages. SHEM relies on machine learning (“training”) to process and interpret large volumes (“big data”) of historic complex hydrologic information. Continually updated with real‐time streamflow data, the model constructs a virtual dataset index of correlations and groups (clusters) of relationship correlations between selected streamgages in a watershed and under differing flow conditions. Using these datasets, SHEM interpolates estimated discharge and time data for any indexed streamgage that stops transmitting data. These estimates are continuously tested, scored, and revised using multiple regression analysis processes and methodologies. The SHEM model was tested in Idaho and Washington in four diverse watersheds, and the model's estimates were then compared to the actual recorded data for the same time period. Results from all watersheds revealed a high correlation, validating both the degree of accuracy and reliability of the model.     

Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

Reservoir management is a critical component of flood management, and information on reservoir inflows is particularly essential for reservoir managers to make real‐time decisions given that flood conditions change rapidly. This study's objective is to build real‐time data‐driven services that enable managers to rapidly estimate reservoir inflows from available data and models. We have tested the services using a case study of the Texas flooding events in the Lower Colorado River Basin in November 2014 and May 2015, which involved a sudden switch from drought to flooding. We have constructed two prediction models: a statistical model for flow prediction and a hybrid statistical and physics‐based model that estimates errors in the flow predictions from a physics‐based model. The study demonstrates that the statistical flow prediction model can be automated and provides acceptably accurate short‐term forecasts. However, for longer term prediction (2 h or more), the hybrid model fits the observations more closely than the purely statistical or physics‐based prediction models alone. Both the flow and hybrid prediction models have been published as Web services through Microsoft's Azure Machine Learning (AzureML) service and are accessible through a browser‐based Web application, enabling ease of use by both technical and nontechnical personnel.     

River Channel Geometry and Rating Curve Estimation Using Height above the Nearest Drainage

River channel geometry is an important input to hydraulic and hydrologic models. Traditional approaches to quantify river geometry have involved surveyed river cross sections, which cannot be extended to ungaged basins. In this paper, we describe a method for developing a synthetic rating curve to relate flow to water level in a stream reach based on reach‐averaged channel geometry properties developed using the Height above Nearest Drainage (HAND) method. HAND uses a digital elevation model (DEM) of the terrain and computes the elevation difference between each land surface cell and the stream bed cell to which it drains. Taking increments in water level in the stream, HAND defines the inundation zone and a water depth grid within this zone, and the channel characteristics are defined from this water depth grid. We apply our method to the Blanco River (Texas) and the Tar River (North Carolina) using 10‐m terrain data from the United States Geological Survey (USGS) 3D Elevation Program (3DEP) dataset. We evaluate the method's performance by comparing the reach‐average stage‐river geometry relationships and rating curves to those from calibrated Hydrologic Engineering Center's River Analysis System (HEC‐RAS) models and USGS gage observations. The results demonstrate that after some adjustment, the river geometry information and rating curves derived from HAND using national‐coverage datasets are comparable to those obtained from hydraulic models or gage measurements. We evaluate the inundation extent and show our approach is able to capture the majority of the Federal Emergency Management Agency (FEMA) 100‐year floodplain.     

Seasonal and Flood‐Induced Variations in Groundwater–Surface Water Exchange in a Northern Coldwater Fishery

Groundwater upwelling is important to coldwater fisheries survival. This study used stable isotopes to identify upwelling zones within a watershed, then combined isotope analyses with reach‐scale monitoring to measure surface water–groundwater exchange over time. Research focused on Amity Creek, Minnesota, a basin that exemplifies conditions limiting coldwater species survival along Lake Superior's North Shore where shallow bedrock limits groundwater capacity, lowering baseflows and increasing temperatures. Groundwater‐fed reaches were identified through synoptic isotope sampling, with results highlighting the importance of isolated shallow surficial aquifers (glacially derived sands and gravels) for providing cold baseflow waters. In an alluvial reach, monitoring well results show groundwater was stored in two reservoirs: one that reacts quickly to changes in stream levels, and one that remained isotopically isolated under most flow conditions, but which helps sustain summer baseflows for weeks to months. A 500‐year flood demonstrated the capacity of high‐flow events to alter surface water–groundwater connectivity. The previously isolated reservoir was exchanged or mixed during the flood pulse, while incision lowered the water table for years. The results here provide insight for streams that lack substantial groundwater inputs yet maintain coldwater species at risk in a warming climate and an approach for managers seeking to protect cold baseflow sources.     

Social Dimensions of Urban Flood Experience,Exposure, and Concern

With growing urban populations and climate change, urban flooding is an important global issue, even in dryland regions. Flood risk assessments are usually used to identify vulnerable locations and populations, flooding experience patterns, or levels of concern about flooding, but rarely are all of these approaches combined. Furthermore, the social dynamics of flood concerns, exposure, and experience are underexplored. We combined geographic and survey data on household‐level measures of flood experience, concern, and exposure in Utah's urbanizing Wasatch Front. We asked: (1) Are socially vulnerable groups more likely to be exposed to flood risk? (2) How common are flooding experiences among urban residents, and how are these experiences related to sociodemographic characteristics and exposure? and (3) How concerned are urban residents about flooding, and does concern vary by exposure, flood experience, and sociodemographic characteristics? Although floodplain residents were more likely to be White and have higher incomes, respondents who were of a racial/ethnic minority, were older, had less education, and were living in floodplains were more likely to report flood experiences and concern about flooding. Flood risk management approaches need to address social as well as physical sources of vulnerability to floods and recognize social sources of variation in flood experiences and concern.     

From disaster management to adaptive governance? Governance challenges to achieving resilient food systems in Australia

Building resilient food systems in the context of climate change and increased natural disasters depends on governance being more ‘adaptive’. Through a case study of events surrounding the extensive flooding that occurred in Queensland, Australia, in 2011, this paper examines how governance settings and processes affected food system actors’ engagement with three aspects of adaptive governance – responsibility, participation and collaboration – as those actors sought to ensure food availability and access during the crisis. We found that, despite the existence of formal governance instruments committed to disaster management, food security and resilience at local, state and national levels, responsibilities for ensuring food supply during a disaster were not clearly articulated. Responsibility was largely assumed by supermarkets, who in turn increased the influence of retailer–government coalitions. The participation of non-supermarket food system actors in governance was low, and there was limited collaboration between local, and other, levels of governance. The policy challenge is to ensure that responsibility, participation and collaboration become a stronger foci for adaptive governance during and after a disaster such as flooding.     

Arc StormSurge: Integrating Hurricane Storm Surge Modeling and GIS

Arc StormSurge is a data model that integrates geographic information systems and the hurricane wave and surge model SWAN+ADCIRC, which is the coupling of the Simulating WAves Nearshore (SWAN) wave model and the Advanced Circulation (ADCIRC) hydrodynamic model. The Arc StormSurge data model is a geodatabase, which is a relational database that can contain georeferenced information. It includes feature classes in feature datasets and tables, all related among them through relationship classes, and raster catalogs and grids. In addition to the data model schema, Arc StormSurge includes a number of pre‐ and post‐processing tools that help integrate spatial data and numerical modeling. As an illustration, Arc StormSurge was used to support the modeling of Hurricane Bret, which made landfall in the Corpus Christi area in Texas in 1999. By using Arc StormSurge, it was possible to take advantage of already available geo‐referenced information (e.g., base maps, land cover datasets, and monitoring station locations) for the model setup, and for identifying spatial patterns in the model results by presenting them in map format.