Active heat pulse sensing of 3-D-flow fields in streambeds

© Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License.Profiles of temperature time series are commonly used to determine hyporheic flow patterns and hydraulic dynamics in the streambed sediments. Although hyporheic flows are 3-D, past research has focused on determining the magnitude of the vertical flow component and how this varies spatially. This study used a portable 56-sensor, 3-D temperature array with three heat pulse sources to measure the flow direction and magnitude up to 200 mm below the water–sediment interface. Short, 1 min heat pulses were injected at one of the three heat sources and the temperature response was monitored over a period of 30 min. Breakthrough curves from each of the sensors were analysed using a heat transport equation. Parameter estimation and uncertainty analysis was undertaken using the differential evolution adaptive metropolis (DREAM) algorithm, an adaption of the Markov chain Monte Carlo method, to estimate the flux and its orientation. Measurements were conducted in the field and in a sand tank under an extensive range of controlled hydraulic conditions to validate the method. The use of short-duration heat pulses provided a rapid, accurate assessment technique for determining dynamic and multi-directional flow patterns in the hyporheic zone and is a basis for improved understanding of biogeochemical processes at the water–streambed interface

LPMLE3 : a novel 1-D approach to study water flow in streambeds using heat as a tracer

We introduce LPMLE3, a new 1-D approach to quantify vertical water flow components at streambeds using temperature data collected in different depths. LPMLE3 solves the partial differential equation for coupled water flow and heat transport in the frequency domain. Unlike other 1-D approaches it does not assume a semi-infinite halfspace with the location of the lower boundary condition approaching infinity. Instead, it uses local upper and lower boundary conditions. As such, the streambed can be divided into finite subdomains bound at the top and bottom by a temperature-time series. Information from a third temperature sensor within each subdomain is then used for parameter estimation. LPMLE3 applies a low order local polynomial to separate periodic and transient parts (including the noise contributions) of a temperature-time series and calculates the frequency response of each subdomain to a known temperature input at the streambed top. A maximum-likelihood estimator is used to estimate the vertical component of water flow, thermal diffusivity, and their uncertainties for each streambed subdomain and provides information regarding model quality. We tested the method on synthetic temperature data generated with the numerical model STRIVE and demonstrate how the vertical flow component can be quantified for field data collected in a Belgian stream. We show that by using the results in additional analyses, nonvertical flow components could be identified and by making certain assumptions they could be quantified for each subdomain. LPMLE3 performed well on both simulated and field data and can be considered a valuable addition to the existing 1-D methods

The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients

Background The Coronavirus disease 2019 (COVID-19) pandemic has underlined the urgent need for reliable, multicenter, and full-admission intensive care data to advance our understanding of the course of the disease and investigate potential treatment strategies. In this study, we present the Dutch Data Warehouse (DDW), the first multicenter electronic health record (EHR) database with full-admission data from critically ill COVID-19 patients. Methods A nation-wide data sharing collaboration was launched at the beginning of the pandemic in March 2020. All hospitals in the Netherlands were asked to participate and share pseudonymized EHR data from adult critically ill COVID-19 patients. Data included patient demographics, clinical observations, administered medication, laboratory determinations, and data from vital sign monitors and life support devices. Data sharing agreements were signed with participating hospitals before any data transfers took place. Data were extracted from the local EHRs with prespecified queries and combined into a staging dataset through an extract-transform-load (ETL) pipeline. In the consecutive processing pipeline, data were mapped to a common concept vocabulary and enriched with derived concepts. Data validation was a continuous process throughout the project. All participating hospitals have access to the DDW. Within legal and ethical boundaries, data are available to clinicians and researchers. Results Out of the 81 intensive care units in the Netherlands, 66 participated in the collaboration, 47 have signed the data sharing agreement, and 35 have shared their data. Data from 25 hospitals have passed through the ETL and processing pipeline. Currently, 3464 patients are included in the DDW, both from wave 1 and wave 2 in the Netherlands. More than 200 million clinical data points are available. Overall ICU mortality was 24.4%. Respiratory and hemodynamic parameters were most frequently measured throughout a patient's stay. For each patient, all administered medication and their daily fluid balance were available. Missing data are reported for each descriptive. Conclusions In this study, we show that EHR data from critically ill COVID-19 patients may be lawfully collected and can be combined into a data warehouse. These initiatives are indispensable to advance medical data science in the field of intensive care medicine.Perioperative Medicine: Efficacy, Safety and Outcome (Anesthesiology/Intensive Care

Relative impacts of key drivers on the response of the water table to a major alley farming experiment

Widespread clearing of native vegetation in Southwest Western Australia has led to land degradation associated with rising groundwater, secondary salinisation and waterlogging. Re-establishing deep-rooted perennial vegetation across parts of the landscape is one technique for managing land degradation. Alley farming is an agroforestry practice where multiple perennial tree belts are planted in alternation with traditional agricultural crops. To identify the best configuration (belt width versus alley width) for controlling rising groundwater levels and providing viable economic returns, a large scale experiment was established in 1995. The experiment contains seven different alley farming designs, each with transects of piezometers running across tree belts into adjacent alleys to monitor changes in the groundwater level. Two control piezometers were also installed in an adjacent paddock. Groundwater at the site is shallow (<3 m) and of poor quality (pH 3–5, Ec 2.1–45.9 mS cm−1) so root water uptake from the saturated zone is limited. Simple hydrograph analysis could not separate treatment effects on the water table response. Subsequent statistical analysis revealed that 20–30% of the variability in the water table data over the 12 year study period was attributable to the alley farming experiment. To futher investigate the effect of the experiment on groundwater response, additional hydrograph analysis was conducted to compare the trends in the control piezometers in relation to those located within the belts. A difference of 0.9 m was observed between the mean groundwater levels in the control piezometers and the mean levels in the perennial belt piezometers. For a mean specific yield of 0.03 m3 m−3 (standard deviation of 0.03 m3 m−3) this equates to an additional average annual water use of 27 mm yr−1 (standard deviation of 33 mm yr−1) by the perennial agroforestry system. It is concluded that declining annual rainfall is the principal control on hydrograph response at the site, whilst perennial biomass development has a lesser impact on water table depth

2004_InfiltrationExperiment

Contains the observation data related to the 2004 Infiltration experiment at the C24 depression at the Spy Hill site, Alberta, Canada

GIS_Data

.zip file containing GIS files for the C24 depression, West Nose Creek catchment and digital elevation model of the area

2006_2011_data

Contains observation data (pond depth, runoff, piezometer level and meteorological data) for the C24 depression from Nov 2006 - Dec 2011