Martijn Westhoff

Distributed fiber‐optic temperature sensing for hydrologic systems

Instruments for distributed fiber-optic measurement of temperature are now available with temperature resolution of 0.01°C and spatial resolution of 1 m with temporal resolution of fractions of a minute along standard fiber-optic cables used for communication with lengths of up to 30,000 m. We discuss the spectrum of fiber-optic tools that may be employed to make these measurements, illuminating the potential and limitations of these methods in hydrologic science. There are trade-offs between precision in temperature, temporal resolution, and spatial resolution, following the square root of the number of measurements made; thus brief, short measurements are less precise than measurements taken over longer spans in time and space. Five illustrative applications demonstrate configurations where the distributed temperature sensing (DTS) approach could be used: (1) lake bottom temperatures using existing communication cables, (2) temperature profile with depth in a 1400 m deep decommissioned mine shaft, (3) air-snow interface temperature profile above a snow-covered glacier, (4) air-water interfacial temperature in a lake, and (5) temperature distribution along a first-order stream. In examples 3 and 4 it is shown that by winding the fiber around a cylinder, vertical spatial resolution of millimeters can be achieved. These tools may be of exceptional utility in observing a broad range of hydrologic processes, including evaporation, infiltration, limnology, and the local and overall energy budget spanning scales from 0.003 to 30,000 m. This range of scales corresponds well with many of the areas of greatest opportunity for discovery in hydrologic science.

Fiber optics opens window on stream dynamics

A new approach to monitoring surface waters using distributed fiber optic temperature sensing is presented, allowing resolutions of temperature of 0.01°C every meter along a fiber optic cable of up to 10,000 m in length. We illustrate the potential of this approach by quantifying both stream temperature dynamics and groundwater inflows to the Maisbich, a first-order stream in Luxembourg (49°47?N, 6°02?E). The technique provides a very rich dataset, which may be of interest to many types of environmental research, notably that of stream ecosystems.

Quantifying hyporheic exchange at high spatial resolution using natural temperature variations along a first?order stream

Hyporheic exchange is an important process that underpins stream ecosystem function, and there have been numerous ways to characterize and quantify exchange flow rates and hyporheic zone size. The most common approach, using conservative stream tracer experiments and 1?D solute transport modeling, results in oversimplified representations of the system. Here we present a new approach to quantify hyporheic exchange and the size of the hyporheic zone (HZ) using high?resolution temperature measurements and a coupled 1?D transient storage and energy balance model to simulate in?stream water temperatures. Distributed temperature sensing was used to observe in?stream water temperatures with a spatial and temporal resolution of 2 and 3 min, respectively. The hyporheic exchange coefficient (which describes the rate of exchange) and the volume of the HZ were determined to range between 0 and 2.7 × 10?3 s?1 and 0 and 0.032 m3 m?1, respectively, at a spatial resolution of 1–10 m, by simulating a time series of in?stream water temperatures along a 565 m long stretch of a small first?order stream in central Luxembourg. As opposed to conventional stream tracer tests, two advantages of this approach are that exchange parameters can be determined for any stream segment over which data have been collected and that the depth of the HZ can be estimated as well. Although the presented method was tested on a small stream, it has potential for any stream where rapid (in regard to time) temperature change of a few degrees can be obtained.

Stream Temperature Response to Three Riparian Vegetation Scenarios by Use of a Distributed Temperature Validated Model

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 Switzerlands 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 Merges 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 set-aside and grow very quickly, appear to provide substantial thermal control, potentially useful for land-use management.

Maximum entropy production: can it be used to constrain conceptual hydrological models?

In recent years, optimality principles have been proposed to constrain hydrological models. The principle of maximum entropy production (MEP) is one of the proposed principles and is subject of this study. It states that a steady state system is organized in such a way that entropy pro- duction is maximized. Although successful applications have been reported in literature, generally little guidance has been given on how to apply the principle. The aim of this paper is to use the maximum power principle - which is closely related to MEP - to constrain parameters of a simple con- ceptual (bucket) model. Although, we had to conclude that conceptual bucket models could not be constrained with re- spect to maximum power, this study sheds more light on how to use and how not to use the principle. Several of these is- sues have been correctly applied in other studies, but have not been explained or discussed as such. While other studies were based on resistance formulations, where the quantity to be optimized is a linear function of the resistance to be identified, our study shows that the approach also works for formulations that are only linear in the log- transformed space. Moreover, we showed that parameters de- scribing process thresholds or influencing boundary condi- tions cannot be constrained. We furthermore conclude that, in order to apply the principle correctly, the model should be (1) physically based; i.e. fluxes should be defined as a gra- dient divided by a resistance, (2) the optimized flux should have a feedback on the gradient; i.e. the influence of bound- ary conditions on gradients should be minimal, (3) the tem- poral scale of the model should be chosen in such a way that the parameter that is optimized is constant over the modelling period, (4) only when the correct feedbacks are implemented the fluxes can be correctly optimized and (5) there should be a trade-off between two or more fluxes. Although our ap- plication of the maximum power principle did not work, and although the principle is a hypothesis that should still be thor- oughly tested, we believe that the principle still has potential in advancing hydrological science.

Importance of temporal variability for hydrological predictions based on the maximum entropy production principle

This work builds on earlier work by Kleidon and Schymanski (2008) who explored the use of the maximum entropy production (MEP) principle for modeling hydrological systems. They illustrated that MEP can be used to determine the partitioning of soil water into runoff and evaporation—which determines hydroclimatic conditions around the Globe—by optimizing effective soil and canopy conductances in a way to maximize entropy production by these fluxes. In the present study, we show analytically that under their assumption of constant rainfall, the proposed principle always yields an optimum where the two conductances are equal, irrespective of rainfall rate, evaporative demand, or gravitational potential. Subsequently, we show that under periodic forcing or periodic variations in one resistance (e.g., vegetation seasonality), the optimal conductance does depend on climatic drivers such as the length of dry spells or the time of closure of stomata.

Maximum energy dissipation to explain velocity fields in shallow reservoirs

ABSTRACT Shallow reservoirs are often used as sediment traps or storage basins, in which sedimentation depends on the flow pattern. Short rectangular reservoirs reveal a straight jet from inlet to outlet with identical recirculation zones on both sides. In longer reservoirs, the main jet reattaches to the side of the reservoir leading to small and large recirculation zones. Previous studies have found an empirical geometric relation describing the switch between these two flow patterns. In this study, we demonstrate, with a simple analytical model, that this switch coincides with a maximization of energy dissipation in the shear layer between the main jet and recirculation zones: short reservoirs dissipate more energy when the flow pattern is symmetric, while longer reservoirs dissipate more energy with an asymmetric pattern. This approach enables the prediction of the flow patterns without detailed knowledge of small scale processes, potentially useful in the early phase of reservoir design.