环境科学
濒危物种
气候变化
资源(消歧)
人口
出院手续
水文学(农业)
地理
水资源管理
生态学
栖息地
流域
人口学
计算机网络
地图学
岩土工程
社会学
计算机科学
工程类
生物
作者
Wei Zhi,Hubert Baniecki,Jiangtao Liu,Elizabeth W. Boyer,Chaopeng Shen,Gary W. Shenk,Xiaofeng Liu,Li Li
标识
DOI:10.1073/pnas.2402028121
摘要
The loss of phosphorous (P) from the land to aquatic systems has polluted waters and threatened food production worldwide. Systematic trend analysis of P, a nonrenewable resource, has been challenging, primarily due to sparse and inconsistent historical data. Here, we leveraged intensive hydrometeorological data and the recent renaissance of deep learning approaches to fill data gaps and reconstruct temporal trends. We trained a multitask long short-term memory model for total P (TP) using data from 430 rivers across the contiguous United States (CONUS). Trend analysis of reconstructed daily records (1980–2019) shows widespread decline in concentrations, with declining, increasing, and insignificantly changing trends in 60%, 28%, and 12% of the rivers, respectively. Concentrations in urban rivers have declined the most despite rising urban population in the past decades; concentrations in agricultural rivers however have mostly increased, suggesting not-as-effective controls of nonpoint sources in agriculture lands compared to point sources in cities. TP loss, calculated as fluxes by multiplying concentration and discharge, however exhibited an overall increasing rate of 6.5% per decade at the CONUS scale over the past 40 y, largely due to increasing river discharge. Results highlight the challenge of reducing TP loss that is complicated by changing river discharge in a warming climate.
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