Elevated in-stream temperature has led to a surge in the occurrence of parasitic intrusion proliferative kidney disease and has resulted in fish kills throughout Switzerland's waterways. Data from distributed temperature sensing (DTS) in-stream measurements for three cloud-free days in August 2007 over a 1260 m stretch of the Boiron de Morges River in southwest Switzerland were used to calibrate and validate a physically based one-dimensional stream temperature model. Stream temperature response to three distinct riparian conditions were then modeled: open, in-stream reeds, and forest cover. Simulation predicted a mean peak stream temperature increase of 0.7°C if current vegetation was removed, an increase of 0.1°C if dense reeds covered the entire stream reach, and a decrease of 1.2°C if a mature riparian forest covered the entire reach. Understanding that full vegetation canopy cover is the optimal riparian management option for limiting stream temperature, in-stream reeds, which require no riparian setaside and grow very quickly, appear to provide substantial thermal control, potentially useful for land-use management.

[1] The characterization of temporal and spatial distribution of sunlight is essential for understanding energy transport in natural ecosystems. Fiber-optic distributed temperature sensing (DTS) allows meter resolution measurements of temperature at subminute resolution. The difference in temperature due to absorption and reflection of a pair of helically twisted black and white fiber-optic cables was measured with a DTS to document areas exposed to sunlight over the Walla Walla River. A high correlation (R 2 = 0.99) was found between DTS-based results and manual field observations of effective shade. These preliminary results provide proof of the concept that this method can be used for estimating the effective shade at fine spatial resolutions. Potential shortcomings and the need for a more quantitative physical model are suggested for further research.

Identifying or ruling out groundwater discharges into sediment and surface waters is often critical for evaluating impacts and for planning remedial actions. Information about subsurface structure and groundwater can be helpful, but imperfect information, heterogeneous materials, and the likelihood of preferential pathways make it difficult to locate seeps without direct seep monitoring. We present the practical application of a method that uses fiber optic temperature measurement to provide high-resolution, sensitive, and dynamic monitoring of seepage from sediments over large areas: distributed temperature sensing to identify groundwater discharge (DTSID). First, we introduce a stochastic Monte Carlo method for designing DTSID installation based on site characteristics and the required probability of detecting particular size seeps. We then present practical methods for analysing DTSID results to prioritize locations for further investigation used at three industrial locations. Summer conditions generally presented greater difficulty in the method due to stronger environmentally-driven temperature fluctuations and thermal stratification of surface water. Tidal fluctuations were shown to be helpful in seepage detection at some locations by creating a dynamic temperature pattern that likely reflects changing seepage with varying water levels. At locations with suitable conditions for the application of DTSID, it can provide unique information regarding likely seep locations, enhancing an integrated site investigation.