Institute for Environmental Sciences, University Koblenz-Landau, Fortstraße 7, 76829 Landau, Germany
Faculty of Resources and Environment, University of Thu Dau Mot, 06 Tran Van On street, Thu Dau Mot City, Binh Duong 820000, Vietnam.
Philos Trans R Soc Lond B Biol Sci. 2018 Dec 3;374(1764):20180004. doi: 10.1098/rstb.2018.0004.
Salinization of surface waters is a global environmental issue that can pose a regional risk to freshwater organisms, potentially leading to high environmental and economic costs. Global environmental change including climate and land use change can increase the transport of ions into surface waters. We fit both multiple linear regression (LR) and random forest (RF) models on a large spatial dataset to predict Ca (266 sites), Mg (266 sites), and [Formula: see text] (357 sites) ion concentrations as well as electrical conductivity (EC-a proxy for total dissolved solids with 410 sites) in German running water bodies. Predictions in both types of models were driven by the major factors controlling salinity including geologic and soil properties, climate, vegetation and topography. The predictive power of the two types of models was very similar, with RF explaining 71-76% of the spatial variation in ion concentrations and LR explaining 70-75% of the variance. Mean squared errors for predictions were all smaller than 0.06. The factors most strongly associated with stream ion concentrations varied among models but rock chemistry and climate were the most dominant. The RF model was subsequently used to forecast the changes in EC that were likely to occur for the period of 2070 to 2100 in response to just climate change-i.e. no additional effects of other anthropogenic activities. The future forecasting shows approximately 10% and 15% increases in mean EC for representative concentration pathways 2.6 and 8.5 (RCP2.6 and RCP8.5) scenarios, respectively.This article is part of the theme issue 'Salt in freshwaters: causes, ecological consequences and future prospects'.
地表水的盐化是一个全球性的环境问题，可能对淡水生物构成区域性风险，从而带来高昂的环境和经济代价。包括气候和土地利用变化在内的全球环境变化会增加离子向地表水的输送。我们使用大量的空间数据集拟合了多元线性回归（LR）和随机森林（RF）模型，以预测德国流水体中的 Ca（266 个站点）、Mg（266 个站点）和 [Formula: see text]（357 个站点）离子浓度以及电导率（EC-具有 410 个站点的总溶解固体的代理）。这两种模型的预测都受到控制盐分的主要因素的驱动，包括地质和土壤特性、气候、植被和地形。这两种类型的模型的预测能力非常相似，RF 解释了离子浓度空间变化的 71-76%，LR 解释了 70-75%的方差。预测的均方误差都小于 0.06。与溪流离子浓度最密切相关的因素因模型而异，但岩石化学和气候是最主要的因素。随后，RF 模型被用于预测 2070 年至 2100 年期间由于气候变化（即没有其他人为活动的额外影响）可能发生的 EC 变化。未来的预测显示，代表性浓度途径 2.6 和 8.5（RCP2.6 和 RCP8.5）情景下，平均 EC 分别增加约 10%和 15%。本文是主题为“淡水中的盐分：成因、生态后果和未来展望”的一部分。
