Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.     

Exploring the spatial dimension of community-level flood risk perception: a cognitive mapping approach

Environmental perceptions are central to individuals’ behavioural interactions with the environment. Cognitive maps, portraying a spatial representation of an individual’s environmental perception, can be aggregated to gain insight into the collective environmental perception of groups and populations. This paper uses cognitive mapping techniques to examine one aspect of environmental perception, flood risk perception, within a residential population (n?=?305). Flood risk perception was examined for the whole sample and six subgroup pairs. Using subgroups allowed examination of how factors previously shown to influence flood risk perception influence the cognitive map production in this population. We use a novel technique (slope analysis) to examine how the population’s perception of flood risk compares with expert assessments of flood risk, and compare the results of this novel technique with a commonly used cognitive map analysis technique (majority threshold method). Both methods identify areas where there is consensus within the population as to which areas are at risk of flooding. However, slope analysis usefully identifies areas where the population’s perception of flood risk lacks consensus, and is at odds with expert assessments of flood risk, without the loss of information inherent in the majority threshold method. Thus, this technique provides a novel approach to studies of environmental perception that can be widely applied within many fields.     

硅肥耦合水分管理对复合污染稻田土壤As-Cd生物有效性及稻米累积阻控

选取湖南某矿区重金属镉、砷复合污染稻田土,以泰优390号为材料,通过盆栽试验研究湿润灌溉(CK)、农艺措施淹水(WF)、速溶硅肥结合淹水措施(FYsi)、矿物硅肥结合淹水措施(FKsi)及2种硅肥结合淹水(FYK)对土壤中As、Cd生物有效性及水稻糙米中As、Cd累积效应的影响.结果表明,淹水处理土壤p H值上升幅度为0.12~0.72个单位,均呈现先升高再下降,最后趋于中性的规律.淹水后,土壤Eh显著降低,FYsi、FKsi、FYK这3种配合硅肥的淹水处理土壤Eh值显著高于WF处理,与对照相比,淹水处理糙米中Cd含量降低了38.83%~65.05%,其中,WF、FYK处理对糙米Cd累积抑制效果最佳,糙米中Cd含量为0.98 mg·kg~(-1)、0.72 mg·kg~(-1);除WF处理糙米中As含量增加了36.64%外,FYsi、FKsi、FYK处理糙米中As的含量与对照相比分别降低了23.80%、38.10%、47.62%.FYsi、FYK处理对糙米As累积抑制效果最佳,糙米中As含量为0.13 mg·kg~(-1)、0.11 mg·kg~(-1).Cd各提取态含量与其在水稻糙米中的含量均呈现出极显著的正相关关系(P0.01);可交换态、TCLP提取态As与水稻糙米中As含量无显著相关关系.因此,FYsi、FKsi、FYK这3种施硅耦合淹水处理均可有效阻控稻米镉砷复合污染,其中FYK效果最佳.     

黄河口孤东油田灾害环境评价

孤东油田系新开发的油田工程之一。 位于黄河入海口的滨海地带,自然环境复杂。其中,风暴潮、黄泛和河口蚀退等不利的灾害因素,对油田安全生产的威胁比较突出。在掌握这些灾害因素时空分布规律及其影响程度的基础上,相应地提出了加固油田堤防工程、河口海岸工程等环境保护对策的意见。     

闽江河口芦苇潮汐湿地甲烷通量及主要影响因子

2007年利用静态箱-气相色谱仪法对闽江河口区最大的鳝鱼滩芦苇湿地和入侵种互花米草斑块涨潮前、涨落潮过程及落潮后甲烷通量季节动态进行了原位测定,并利用室内培养-气相色谱仪法测定了芦苇湿地不同土层土壤甲烷产生潜力.结果表明,鳝鱼滩芦苇湿地全年均属于大气甲烷的排放源,排放通量具有明显的季节变化;涨潮前、涨落潮过程和落潮后甲烷通量大小并无一致的规律,平均排放通量分别为5.13、5.06和4.74 mg·m-2·h-1,差异不显著,其中涨落潮过程排放到潮水和大气环境的甲烷通量分别为2.98和2.08 mg·m-2·h-1.互花米草入侵斑块年均甲烷排放通量(11.02mg·m-2·h-1)明显高于芦苇湿地年均甲烷排放通量(4.98mg·m-2·h-1),互花米草入侵明显增加了闽江河口区湿地的甲烷排放通量.芦苇湿地0～40 cm土壤中甲烷产生潜力范围为0.029～0.123μg·g-1·d-1,0～5 cm土层的甲烷产生潜力最大,且与其它土层差异显著(P<0.05).气温、土壤温度和地上生物量对芦苇湿地甲烷排放影响显著(P<0.05),落潮后芦苇湿地甲烷排放通量与盐度有负相关关系.     

Error Assessment for Height Above the Nearest Drainage Inundation Mapping

Real‐time flood inundation mapping is vital for emergency response to help protect life and property. Inundation mapping transforms rainfall forecasts into meaningful spatial information that can be utilized before, during, and after disasters. While inundation mapping has traditionally been conducted on a local scale, automated algorithms using topography data can be utilized to efficiently produce flood maps across the continental scale. The Height Above the Nearest Drainage method can be used in conjunction with synthetic rating curves (SRCs) to produce inundation maps, but the performance of these inundation maps needs to be assessed. Here we assess the accuracy of the SRCs and calculate statistics for comparing the SRCs to rating curves obtained from hydrodynamic models calibrated against observed stage heights. We find SRCs are accurate enough for large‐scale approximate inundation mapping while not as accurate when assessing individual reaches or cross sections. We investigate the effect of terrain and channel characteristics and observe reach length and slope predict divergence between the two types of rating curves, and SRCs perform poorly for short reaches with extreme slope values. We propose an approach to recalculate the slope in Manning’s equation as the weighted average over a minimum distance and assess accuracy for a range of moving window lengths.     

A Rapid Response Survey to Characterize the Impacts of the 2017 High Water Event on Lake Ontario

In the spring and summer of 2017, communities along the Lake Ontario shoreline suffered from the worst flood event on record. In late May, daily water levels reached their highest point in over 100 years, and flooding continued throughout much of the summer as lake levels slowly declined, with inundation and erosion significantly impacting shoreline homes and businesses. In this work, we present results from a rapid response online survey of property owners along the New York Lake Ontario shoreline to quantify the perceived flood impacts of the 2017 extended high water event. The survey focused on the degree and spatial distribution of inundation and erosion; the duration and drivers of inundation; the associated damages to different property features, with an emphasis on shoreline protection; and the degree of disruption to business and other activities and services. Photographic documentation of inundation extent and property damage also was provided by survey respondents. We demonstrate the potential utility of this dataset by characterizing key features of inundation and erosion impacts across the shoreline, and by using classification and regression trees to explore the predictability of inundation and erosion based on property characteristics. This work is part of a larger effort to develop models of inundation and erosion that can support flood impact assessments across the shoreline and help communities better prepare for future extended high water events.     

Runoff Storage Potential of Drained Upland Depressions on the Des Moines Lobe of Iowa

We present estimates of the volumetric storage capacities of currently drained upland depressions and catchment depressional specific storage and runoff storage indices for the Des Moines Lobe of Iowa (DML‐IA) subregion of the Prairie Pothole Region of North America. Storage capacities were determined using hydrologically enforced Light Detection and Ranging‐derived digital elevation models, and a unique geoprocessing algorithm. Depressional specific storage was estimated for each 12‐digit Hydrologic Unit Code (HUC12) watershed in the region from total catchment‐specific depressional storage volume and catchment area. Runoff storage indices were calculated using catchment depressional specific storage values and estimates of the amount of rainfall likely to fall within each watershed during sub‐annual and 1‐, 2‐, 5‐, and 10‐year 24‐h events. The 173,171 identified drained depressions in the DML‐IA can store up to 903.5 Mm³ of runoff. Most of this capacity is in depressions located in the north of the region. Specific storage varies from nearly 109 mm in the younger landscapes to <10 mm in older more eroded areas. For 95% of the HUC12 watersheds comprising the region, depressional storage will likely be exhausted by rainfall‐derived runoff in excess of a 1‐year 24‐h event. Rainfall amounts greater than a 5‐year 24‐h event will exceed all available depressional storage. Therefore, the capacity of drained depressions in the DML‐IA to mitigate flooding resulting from infrequent, but large, storm events is limited.     

Estimating Floodwater Depths from Flood Inundation Maps and Topography

Information on flood inundation extent is important for understanding societal exposure, water storage volumes, flood wave attenuation, future flood hazard, and other variables. A number of organizations now provide flood inundation maps based on satellite remote sensing. These data products can efficiently and accurately provide the areal extent of a flood event, but do not provide floodwater depth, an important attribute for first responders and damage assessment. Here we present a new methodology and a GIS‐based tool, the Floodwater Depth Estimation Tool (FwDET), for estimating floodwater depth based solely on an inundation map and a digital elevation model (DEM). We compare the FwDET results against water depth maps derived from hydraulic simulation of two flood events, a large‐scale event for which we use medium resolution input layer (10 m) and a small‐scale event for which we use a high‐resolution (LiDAR; 1 m) input. Further testing is performed for two inundation maps with a number of challenging features that include a narrow valley, a large reservoir, and an urban setting. The results show FwDET can accurately calculate floodwater depth for diverse flooding scenarios but also leads to considerable bias in locations where the inundation extent does not align well with the DEM. In these locations, manual adjustment or higher spatial resolution input is required.     

Challenges and Opportunities for Creating Intelligent Hazard Alerts: The “FloodHippo” Prototype

Disasters evolving from hazards are a persistent and deadly occurrence in the United States. Despite this, hazard alerts have remained spatially vague, temporally imprecise, and lack actionable information. These deficiencies indicate a divide between the status quo and what is possible given modern environmental models, geographic information systems (GIS), and smartphone capabilities. This work describes an alternative, prototype system, “FloodHippo,” which integrates operational model outputs, cloud‐based GIS, and expanded communication channels to provide personal and interactive disaster alerts for floods. The precepts and methods underpinning FloodHippo apply equally to other disasters that evolve over space and time, presenting the opportunity for a more intelligent disaster response system. The development of such a system would not only minimize current shortcomings in disaster alerts but also improve resilience through individual action, along with community, academic, and federal cooperation.