Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided DL and DA approach to make 7-day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1-day-ahead and 1.4 to 1.9°C for 7-day-ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process-guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision-making.     

Boosting large-scale river connectivity restoration by planning for the presence of unrecorded barriers

Conservation decisions are invariably made with incomplete data on species’ distributions, habitats, and threats, but frameworks for allocating conservation investments rarely account for missing data. We examined how explicit consideration of missing data can boost return on investment in ecosystem restoration, focusing on the challenge of restoring aquatic ecosystem connectivity by removing dams and road crossings from rivers. A novel way of integrating the presence of unmapped barriers into a barrier optimization model was developed and applied to the U.S. state of Maine to maximize expected habitat gain for migratory fish. Failing to account for unmapped barriers during prioritization led to nearly 50% lower habitat gain than was anticipated using a conventional barrier optimization approach. Explicitly acknowledging that data are incomplete during project selection, however, boosted expected habitat gains by 20–273% on average, depending on the true number of unmapped barriers. Importantly, these gains occurred without additional data. Simply acknowledging that some barriers were unmapped, regardless of their precise number and location, improved conservation outcomes. Given incomplete data on ecosystems worldwide, our results demonstrate the value of accounting for data shortcomings during project selection.     

手机定位人口数据在应急救援中的应用

随着智能手机的普及,基于手机定位的人口数据在突发性灾害应急救援领域发挥着日益重要的作用。以近几年地震灾区为主要研究对象,选择灾区中有代表性的旅游景区、县城及乡镇,获取这些区域手机定位的人口数据,同时采用传统方法估算区域内人口数量,并进行差异性对比。研究结果表明:差异性最大的是旅游景区、其次是县城和乡镇;在人口年度变化上,旅游景区的人口在7、8月份达到高峰,乡镇人口高值区出现在2月份,县城人口年度差异无明显规律。     

“三线一单”促进四川省高质量发展

过去40年来我国经济建设取得了举世瞩目的成绩,但是也留下了很多生态环境问题。目前我国经济已经由高速增长阶段转向高质量发展阶段,但高质量发展缺乏指标体系和具体路径。“三线一单”是一套生态环境分区管控体系,其主要目的是统筹地方社会经济与环境保护的矛盾,促进经济结构转型。四川省在“三线一单”编制过程中,通过建立指标体系从生态环境保护的角度为地区高质量发展提供了量化标准,通过差异化管控要求为地区高质量发展提供了具体路径,从生态环境角度助力高质量发展。     

宁波月湖的水质改善与生态修复

随着浙江"五水共治"工作的深化,宁波作为浙江"五水共治"的中坚力量,始终把"五水共治"作为全市工作的首要任务和突破重点。自1999年进行清淤后,月湖水质得到短期改善,然而治标不治本的治理措施,并没有让月湖水质得到根本改善。因此,对月湖进行科学有效的生态修复,针对性地处理水污染问题,使之重新焕发活力具有十分重要的意义。     

土壤监测质量保证与质量控制

质量保证与质量控制措施是土壤监测工作在具体开展过程中确保其监测结果准确性、代表性、全面性的重要措施,同时也是土壤监测工作在具体开展过程中的核心工作内容。在环境保护与可持续发展战略情况下,采取全面有效的质量保证与质量控制措施可以确保并优化土壤监测结果的真实性、准确性和全面性,对促进土壤监测工作的优化发展具有非常重要的意义。基于此,本文针对土壤监测工作开展过程中应当实施的各项质量保证与质量控制措施进行了分析总结。     

国内外总量管理制度发展历程对南京市的启示

本文介绍了国内外总量管理制度的发展历程,特别是"十三五"以来我国总量管理工作新的规定,并由此产生的针对南京市主要污染物总量管理工作的几点启示。     

浅谈爆破振动的控制措施

结合爆破振动的作用机理,分别对减少振源爆炸能量、干扰降振、阻断振动波传播途径、控制爆破规模、合理选择分段间隔时间等爆破振动主要控制措施进行了分析讨论。并根据实际施工情况分析了通过电子雷管实现干扰降振的减振效果,为类似工程提供借鉴作用。     

新型冠状病毒肺炎疫情期间企业生产安全风险管理

本文在分析新型冠状病毒肺炎疫情期间企业安全风险特点的基础上,建立了疫情期间安全风险管理模型,分析了这一特殊时期企业外部变化,以及企业内部人、物、环境、管理变化带来特殊安全风险,提出了针对性的风险管控措施。     

Minimizing opportunity costs to aquatic connectivity restoration while controlling an invasive species

Controlling invasive species is critical for conservation but can have unintended consequences for native species and divert resources away from other efforts. This dilemma occurs on a grand scale in the North American Great Lakes, where dams and culverts block tributary access to habitat of desirable fish species and are a lynchpin of long‐standing efforts to limit ecological damage inflicted by the invasive, parasitic sea lamprey (Petromyzon marinus). Habitat restoration and sea‐lamprey control create conflicting goals for managing aging infrastructure. We used optimization to minimize opportunity costs of habitat gains for 37 desirable migratory fishes that arose from restricting sea lamprey access (0–25% increase) when selecting barriers for removal under a limited budget (US$1–105 million). Imposing limits on sea lamprey habitat reduced gains in tributary access for desirable species by 15–50% relative to an unconstrained scenario. Additional investment to offset the effect of limiting sea‐lamprey access resulted in high opportunity costs for 30 of 37 species (e.g., an additional US$20–80 million for lake sturgeon [Acipenser fulvescens]) and often required ≥5% increase in sea‐lamprey access to identify barrier‐removal solutions adhering to the budget and limiting access. Narrowly distributed species exhibited the highest opportunity costs but benefited more at less cost when small increases in sea‐lamprey access were allowed. Our results illustrate the value of optimization in limiting opportunity costs when balancing invasion control against restoration benefits for diverse desirable species. Such trade‐off analyses are essential to the restoration of connectivity within fragmented rivers without unleashing invaders.