scholarly journals High-Resolution, In Situ Monitoring of Stable Isotopes of Water Revealed Insight into Hydrological Response Behavior

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 565
Author(s):  
Amir Sahraei ◽  
Philipp Kraft ◽  
David Windhorst ◽  
Lutz Breuer
Keyword(s):  
Stable Isotopes ◽  
Temporal Resolution ◽  
Response Times ◽  
Stream Water ◽  
Precipitation Intensity ◽  
High Temporal Resolution ◽  
Hydrological Response ◽  
Response Behavior ◽  
Runoff Generation

High temporal resolution (20-min intervals) measurements of stable isotopes from groundwater, stream water and precipitation were investigated to understand the hydrological response behavior and control of precipitation and antecedent wetness conditions on runoff generation. Data of 20 precipitation events were collected by a self-sufficient mobile system for in situ measurements over four months in the Schwingbach Environmental Observatory (SEO, temperate climate), Germany. Isotopic hydrograph separation indicated that more than 79% of the runoff consisted of pre-event water. Short response times of maximum event water fractions in stream water and groundwater revealed that shallow subsurface flow pathways rapidly delivered water to the stream. Macropore and soil pipe networks along relatively flat areas in stream banks were likely relevant pathways for the rapid transmission of water. Event water contribution increased with increasing precipitation amount. Pre-event water contribution was moderately affected by precipitation, whereas, the antecedent wetness conditions were not strong enough to influence pre-event water contribution. The response time was controlled by mean precipitation intensity. A two-phase system was identified, at which the response times of stream water and groundwater decreased after reaching a threshold of mean precipitation intensity of 0.5 mm h−1. Our results suggest that high temporal resolution measurements of stable isotopes of multiple water sources combined with hydrometrics improve the understanding of the hydrological response behavior and runoff generation mechanisms.

The use of high-temporal resolution, in-situ sampling of stable isotopes of water to capture fine-scale hydrological responses

2020 ◽  
Author(s):  
Amirhossein Sahraei ◽  
Philipp Kraft ◽  
David Windhorst ◽  
Lutz Breuer
Keyword(s):  
Stable Isotopes ◽  
Temporal Resolution ◽  
Response Times ◽  
Stream Water ◽  
Precipitation Amount ◽  
High Temporal Resolution ◽  
Fine Scale ◽  
Runoff Generation ◽  
Stable Isotopes Of Water ◽  
Precipitation Events

<p>Hydrological responses to precipitation events in headwater catchments often vary in space and time. Understanding of such patterns leads to constrain runoff generation mechanisms and flow pathways. The use of stable isotopes of water combined with classical hydrometrics have increased in recent years to elucidate the response behavior of runoff components and their drivers in runoff generation. However, most of the previous studies dealing with investigation of catchment responses were limited to daily to monthly data, at which potential fine-scale variations could not be captured. Recently, few studies applied high-temporal resolution sampling of stable isotopes of water to investigate isotopic response variation within precipitation events. Sampling sources were mostly limited to streamflow and precipitation. An important, yet poorly known mechanism is the response of shallow groundwater to precipitation.</p><p>In this study, we used an automated in-situ mobile laboratory to continuously sample stable istopes of multiple sources, including stream water, groundwater and precipitation every 20 mintutes. The study was realized in the Schwingbach Environmetal Observatory (SEO) in Hesse, Germany. Hydrograph seperation technique was applied to quantify the share of event and pre-event water contribution to the stream and to estimate response times of maximum event water fractions in the stream water and the groundwater for 20 events in the dry year 2018. We investigated the control of precipitation and antecedent wetness hydrometrics on response characteristics using Spearman rank correlation analysis.</p><p>High-temporal resolution sampling of multiple sources captured the fine-scale variation of isotope concentrations in stream water and groundwater sources during the precipitation events indicating that the Schwingbach is a highly responsive, pre-event water dominated creek. More than 79% of the runoff consisted of pre-event water. Short response times combined with soil moisture variations of different depths revealed the linkage between shallow groundwater in near-stream zones and the stream itself. As a response of the dry conditions in 2018, an extended crack network developed that acted like adrainage system causing rapid delivering of water to the stream network. Event water contribution increased with increasing precipitation amount. Pre-event water contribution was moderately affected by precipitation amount, while antecedent wetness did not influence the runoff generation. The response time of stream water and groundwater was controlled by mean precipitation intensity. A two-phase system was identified, at which the response times of stream water and groundwater started to decrease after reaching a threshold of mean precipitation intensity (0.5 mm h<sup>1</sup>).</p>

Simulating stable water isotope derived information with the aid of artificial neural network applied on independent multivariate events

2020 ◽  
Author(s):  
Alejandro Chamorro ◽  
Amirhossein Sahraei ◽  
Tobias Houska ◽  
Lutz Breuer
Keyword(s):  
Stable Isotopes ◽  
Stream Water ◽  
High Temporal Resolution ◽  
Runoff Generation ◽  
Water Fraction ◽  
Water Fractions ◽  
Artificial Neural ◽  
Stable Isotopes Of Water ◽  
Water Isotope ◽  
Precipitation Events

<p><strong>Abstract </strong>In recent years, stable isotopes of water have become a well-known tool to investigate runoff generation processes. The proper estimation of stable water isotope concentration dynamics based on a set of independent multivariate variables would allow the quantification of event water fraction in stream water even at times when no direct measurements of isotopes are available. Here we estimate stable water isotope concentrations and derived event water fractions in stream water over 40 precipitation events. A mobile field laboratory was set up to measure high-resolution (20 min) stable isotopes of water by laser spectrometry. Artificial neural networks (ANN) were established to model the same information. We consider precipitation and antecedent wetness hydrometrics such as precipitation depth, precipitation intensity and soil moisture of different depths as independent variables measured in the same high-temporal resolution. An important issue is the reduction of the deviation between observations and simulations in both the training and testing set of the network. In order to minimize this difference, various combinations of variables, dimensionalities of the training and testing sets and ANN architectures are studied. A k-fold cross validation analysis is performed to find the best solution. Further constraints in the iteration procedure are considered to avoid overfitting. The study was carried out in the Schwingbach Environmental Observatory (SEO), Germany. Results indicate a good performance of the optimized model, in which the dynamics of the isotope concentrations and the estimated event water fractions in the stream water were estimated. Compared to a multivariate linear model, the ANN-based model clearly outperformed the estimations showing the smallest deviation. The optimum network consists of 2 hidden nodes with a 5-dimensional input set. This strongly suggests that ANN-based models can be used to estimate and even forecast the dynamics of the isotope concentrations and event water fractions for future precipitation events.</p>

scholarly journals Automated Online Flow Cytometry Advances Microalgal Ecosystem Management as in situ, High-Temporal Resolution Monitoring Tool

2021 ◽  
Vol 9 ◽  
Author(s):  
Iris Haberkorn ◽  
Cosima L. Off ◽  
Michael D. Besmer ◽  
Leandro Buchmann ◽  
Alexander Mathys
Keyword(s):  
Flow Cytometry ◽  
Real Time ◽  
Temporal Resolution ◽  
High Temporal Resolution ◽  
Natural Ecosystems ◽  
Biotechnological Production ◽  
Microalgal Biomass ◽  
Bioprocess Optimization ◽  
Related Population

Microalgae are emerging as a next-generation biotechnological production system in the pharmaceutical, biofuel, and food domain. The economization of microalgal biorefineries remains a main target, where culture contamination and prokaryotic upsurge are main bottlenecks to impair culture stability, reproducibility, and consequently productivity. Automated online flow cytometry (FCM) is gaining momentum as bioprocess optimization tool, as it allows for spatial and temporal landscaping, real-time investigations of rapid microbial processes, and the assessment of intrinsic cell features. So far, automated online FCM has not been applied to microalgal ecosystems but poses a powerful technology for improving the feasibility of microalgal feedstock production through in situ, real-time, high-temporal resolution monitoring. The study lays the foundations for an application of automated online FCM implying far-reaching applications to impel and facilitate the implementation of innovations targeting at microalgal bioprocesses optimization. It shows that emissions collected on the FL1/FL3 fluorescent channels, harnessing nucleic acid staining and chlorophyll autofluorescence, enable a simultaneous assessment (quantitative and diversity-related) of prokaryotes and industrially relevant phototrophic Chlorella vulgaris in mixed ecosystems of different complexity over a broad concentration range (2.2–1,002.4 cells ⋅μL–1). Automated online FCM combined with data analysis relying on phenotypic fingerprinting poses a powerful tool for quantitative and diversity-related population dynamics monitoring. Quantitative data assessment showed that prokaryotic growth phases in engineered and natural ecosystems were characterized by different growth speeds and distinct peaks. Diversity-related population monitoring based on phenotypic fingerprinting indicated that prokaryotic upsurge in mixed cultures was governed by the dominance of single prokaryotic species. Automated online FCM is a powerful tool for microalgal bioprocess optimization owing to its adaptability to myriad phenotypic assays and its compatibility with various cultivation systems. This allows advancing bioprocesses associated with both microalgal biomass and compound production. Hence, automated online FCM poses a viable tool with applications across multiple domains within the biobased sector relying on single cell–based value chains.

How do concentration-discharge relations vary among rainfall-runoff events? An analysis for the Ressi experimental catchment (Italian pre-Alps)

2021 ◽  
Author(s):  
Giulia Zuecco ◽  
Chiara Marchina ◽  
Ylenia Gelmini ◽  
Anam Amin ◽  
Ilja van Meerveld ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Stable Isotopes ◽  
Temporal Dynamics ◽  
Major Ions ◽  
Stream Water ◽  
High Temporal Resolution ◽  
Runoff Coefficient ◽  
Rainfall Runoff ◽  
Antecedent Soil Moisture ◽  
Runoff Events

<p>Understanding discharge and solute responses is pivotal for water resources management and pollution mitigation measures. The few studies that have analysed concentration-discharge relations using high temporal resolution tracer data collected during rainfall-runoff events have shown that these relations may vary for different events and depend on season, event characteristics or antecedent wetness conditions. </p><p>In this study, we used hydrometric and tracer data (stable isotopes, major ions and electrical conductivity (EC)) to i) compare the concentration-discharge relations for different tracers, ii) characterize the hysteretic relations between discharge and tracer concentrations at the event timescale, and iii) determine whether the changes in hysteresis can be explained by event characteristics.</p><p>Data collection was carried out in the Ressi catchment, a 2-ha forested watershed in the Italian pre-Alps. The catchment is characterized by high seasonality in runoff response, due to the seasonality in rainfall (high in fall) and evapotranspiration (high in summer). Discharge and rainfall have been measured continuously since August 2012. Stream water, precipitation, shallow groundwater and soil water samples were collected for tracer analyses during 20 rainfall-runoff events between September 2015 and August 2018. All samples were analyzed for EC, isotopic composition (<sup>2</sup>H and <sup>18</sup>O) and major ion concentrations. To investigate the possible controls on concentration-discharge relations, we determined the main characteristics (e.g., total event rainfall, rainfall intensities, antecedent soil moisture and depth to water table, runoff coefficient) for each selected rainfall-runoff event.</p><p>The EC, calcium, magnesium, sodium and sulfate concentrations in stream water decreased during rainfall events, due to the dilution by rain water. The concentration-discharge relations for these tracers with a dilution behavior were stronger and more significant than for the tracers that were mobilized during the event. Interestingly, nitrate, potassium and chloride, concentrations sometimes increased at the onset of events, likely due to a rapid flushing of solutes from the dry parts of the stream channel and the riparian area, and then decreased during the event. These temporal dynamics in solute concentrations resulted in different hysteretic relations with discharge. Clockwise loops (i.e., discharge peaked later than the tracer concentrations) were common for the isotopes, chloride and potassium, whereas anti-clockwise hysteresis loops were more typical for EC, magnesium, calcium, sulfate, sodium and nitrate. A preliminary correlation analysis suggests that event characteristics alone cannot explain the changes in hysteresis, except for the hysteresis area for the relations between discharge and calcium concentration that depends on the magnitude of the rainfall event (i.e., the larger the rainfall amount and the runoff coefficient, the smaller the hysteresis loop). </p><p>These results highlight the importance of the first flush and indicate that runoff processes and solute sources can change when the catchment becomes wetter and connectivity of the hillslopes to the stream increases.</p><p> </p><p>Keywords: concentration-discharge relation; major ions; electrical conductivity; stable isotopes; hysteresis; forested catchment.</p>

scholarly journals Deep Learning for Isotope Hydrology: The Application of Long Short-Term Memory to Estimate High Temporal Resolution of the Stable Isotope Concentrations in Stream and Groundwater

2021 ◽  
Vol 3 ◽  
Author(s):  
Amir Sahraei ◽  
Tobias Houska ◽  
Lutz Breuer
Keyword(s):  
Electrical Conductivity ◽  
Stable Isotopes ◽  
Deep Learning ◽  
Temporal Resolution ◽  
Short Term Memory ◽  
High Temporal Resolution ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Stable Isotopes Of Water

Recent advances in laser spectroscopy has made it feasible to measure stable isotopes of water in high temporal resolution (i.e., sub-daily). High-resolution data allow the identification of fine-scale, short-term transport and mixing processes that are not detectable at coarser resolutions. Despite such advantages, operational routine and long-term sampling of stream and groundwater sources in high temporal resolution is still far from being common. Methods that can be used to interpolate infrequently measured data at multiple sampling sites would be an important step forward. This study investigates the application of a Long Short-Term Memory (LSTM) deep learning model to predict complex and non-linear high-resolution (3 h) isotope concentrations of multiple stream and groundwater sources under different landuse and hillslope positions in the Schwingbach Environmental Observatory (SEO), Germany. The main objective of this study is to explore the prediction performance of an LSTM that is trained on multiple sites, with a set of explanatory data that are more straightforward and less expensive to measure compared to the stable isotopes of water. The explanatory data consist of meteorological data, catchment wetness conditions, and natural tracers (i.e., water temperature, pH and electrical conductivity). We analyse the model's sensitivity to different input data and sequence lengths. To ensure an efficient model performance, a Bayesian optimization approach is employed to optimize the hyperparameters of the LSTM. Our main finding is that the LSTM allows for predicting stable isotopes of stream and groundwater by using only short-term sequence (6 h) of measured water temperature, pH and electrical conductivity. The best performing LSTM achieved, on average of all sampling sites, an RMSE of 0.7‰, MAE of 0.4‰, R2 of 0.9 and NSE of 0.7. The LSTM can be utilized to predict and interpolate the continuous isotope concentration time series either for data gap filling or in case where no continuous data acquisition is feasible. This is very valuable in practice because measurements of these tracers are still much cheaper than stable isotopes of water and can be continuously conducted with relatively minor maintenance.

Multifrequency ground-based radiometer and in-situ measurements of soil moisture at high temporal resolution

2003 ◽  
Author(s):  
Katrin Schneeberger ◽  
Christian Stamm ◽  
Christian Maetzler ◽  
Hannes Fluehler ◽  
Eberhard Lehmann ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Temporal Resolution ◽  
In Situ Measurements ◽  
High Temporal Resolution
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