Supplemental Material: Disequilibrium river networks dissecting the western slope of the Sierra Nevada, California, USA, record significant late Cenozoic tilting and associated surface uplift

Figures S1–S12; Deviations of equations for knickpoint locations; kmz file.

Supplemental Material: Disequilibrium river networks dissecting the western slope of the Sierra Nevada, California, USA, record significant late Cenozoic tilting and associated surface uplift

Figures S1–S12; Deviations of equations for knickpoint locations; kmz file.

Side Tributary Distribution of Quasi-Uniform Iterative Binary Tree Networks for River Networks

Self-similarity and plane-filling are intrinsic structure properties of natural river networks. Statistical data indicates that most natural river networks are Tokunaga trees. Researchers have explored to use iterative binary tree networks (IBTNs) to simulate natural river networks. However, the characteristics of natural rivers such as Tokunaga self-similarity and plane-filling cannot be easily guaranteed by the configuration of the IBTN. In this paper, the generator series and a quasi-uniform iteration rule are specified for the generation of nonstochastic quasi-uniform iterative binary tree networks (QU-IBTNs). First, we demonstrate that QU-IBTNs definitely satisfy self-similarity. Second, we show that the constraint for a QU-IBTN to be a Tokunaga tree is that the exterior links must be replaced in the generator series with a neighboring generator that is larger than the interior links during the iterative process. Moreover, two natural river networks are examined to reveal the inherent consistency with QU-IBTN at low Horton-Strahler orders.

Securing Biodiversity, Functional Integrity, and Ecosystem Services in Drying River Networks (DRYvER)

River networks are among Earth’s most threatened hot-spots of biodiversity and provide key ecosystem services (e.g., supply drinking water and food, climate regulation) essential to sustaining human well-being. Climate change and increased human water use are causing more rivers and streams to dry, with devastating impacts on biodiversity and ecosystem services. Currently, more than a half of the global river networks consist of drying channels, and these are expanding dramatically. However, drying river networks (DRNs) have received little attention from scientists and policy makers, and the public is unaware of their importance. Consequently, there is no effective integrated biodiversity conservation or ecosystem management strategy of DRNs. A multidisciplinary team of 25 experts from 11 countries in Europe, South America, China and the USA will build on EU efforts to assess the cascading effects of climate change on biodiversity, ecosystem functions and ecosystem services of DRNs through changes in flow regimes and water use. DRYvER (DRYing riVER networks) will gather and upscale empirical and modelling data from nine focal DRNs (case studies) in Europe (EU) and Community of Latin American and Caribbean States (CELAC) to develop a meta-system framework applicable to Europe and worldwide. It will also generate crucial knowledge-based strategies, tools and guidelines for economically-efficient adaptive management of DRNs. Working closely with stakeholders and end-users, DRYvER will co-develop strategies to mitigate and adapt to climate change impacts in DRNs, integrating hydrological, ecological (including nature-based solutions), socio-economic and policy perspectives. The end results of DRYvER will contribute to reaching the objectives of the Paris Agreement and placing Europe at the forefront of research on climate change.

Open-Source, Low-Cost, In-Situ Turbidity Sensor for River Network Monitoring

Fine sediment transport in rivers is important for catchment nutrient fluxes, global biogeochemical cycles, water quality and pollution in riverine, coastal and marine ecosystems. Monitoring of suspended sediment in rivers with current sensors is challenging and expensive and most monitoring setups are restricted to few single site measurements. To better understand the spatial heterogeneity of fine sediment sources and transport in river networks there is a need for new smart water turbidity sensing that is multi-site, accurate and affordable. In this work, we have created such a sensor, which detects scattered light from an LED source using two detectors in a control volume, and can be placed in a river. We compare several replicates of our sensor to different commercial turbidity probes in a mixing tank experiment using two sediment types over a wide range of typical concentrations observed in rivers. Our results show that we can achieve precise and reproducible turbidity measurements in the 0-4000 NTU or 0-16g/L range. Our sensor can also be used directly as a suspended sediment sensor and bypass an unnecessary calibration to Formazin. The developed turbidity sensor is much cheaper than existing options of comparable quality and is especially intended for distributed sensing across river networks.