Water Age in Stormwater Management Ponds and Stormwater Management Pond Treated Catchments

Mapping Intimacies ◽

10.32920/ryerson.14654427 ◽

2021 ◽

Author(s):

Kayla Wong

Keyword(s):

Transit Time ◽

Stormwater Management ◽

Damping Ratio ◽

Mean Transit Time ◽

Sine Wave ◽

Urban Stream ◽

Water Age ◽

Catchment Scale ◽

Water Fraction ◽

Hydrologic Responses

Increasing coverage of impervious surfaces in urban waterways result in 'flashy' hydrologic responses, elevated flood risk, and degraded water quality. Stormwater management ponds (SWMPs) are engineered into urban stream networks to mitigate this response. However, little is known about how SWMPs affect hydrological transit time at the catchment scale. This study aims to examine water age in SWMPs and catchments of varying SWMP control. Grab samples of ∂¹⁸O and ∂²H were collected bi-weekly from two SWMPs and five stream sites with varying land cover and stormwater control in their catchments. The damping ratio (DR), young water fraction (Fyw) and mean transit time (MTT) by sine-wave fitting were calculated for each sampled site. SWMP inlet water was consistently older than water arriving at SWMP outlets. MTT decreased as catchments SWMP control increased. Surficial geology was found to have the greatest influence on catchment MTT.

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Water Age in Stormwater Management Ponds and Stormwater Management Pond Treated Catchments

10.32920/ryerson.14654427.v1 ◽

2021 ◽

Author(s):

Kayla Wong

Keyword(s):

Transit Time ◽

Stormwater Management ◽

Damping Ratio ◽

Mean Transit Time ◽

Sine Wave ◽

Urban Stream ◽

Water Age ◽

Catchment Scale ◽

Water Fraction ◽

Hydrologic Responses

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Aggregation in environmental systems: seasonal tracer cycles quantify young water fractions, but not mean transit times, in spatially heterogeneous catchments

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-12-3059-2015 ◽

2015 ◽

Vol 12 (3) ◽

pp. 3059-3103 ◽

Cited By ~ 7

Author(s):

J. W. Kirchner

Keyword(s):

Transit Time ◽

Mean Transit Time ◽

Strong Nonlinearity ◽

Isotopic Tracers ◽

Water Fraction ◽

Benchmark Tests ◽

Environmental Systems ◽

Transit Times ◽

Event Based ◽

The Relationship

Abstract. Environmental heterogeneity is ubiquitous, but environmental systems are often analyzed as if they were homogeneous instead, resulting in aggregation errors that are rarely explored and almost never quantified. Here I use simple benchmark tests to explore this general problem in one specific context: the use of seasonal cycles in chemical or isotopic tracers (such as Cl−, δ18O, or δ2H) to estimate timescales of storage in catchments. Timescales of catchment storage are typically quantified by the mean transit time, meaning the average time that elapses between parcels of water entering as precipitation and leaving again as streamflow. Longer mean transit times imply greater damping of seasonal tracer cycles. Thus, the amplitudes of tracer cycles in precipitation and streamflow are commonly used to calculate catchment mean transit times. Here I show that these calculations will typically be wrong by several hundred percent, when applied to catchments with realistic degrees of spatial heterogeneity. This aggregation bias arises from the strong nonlinearity in the relationship between tracer cycle amplitude and mean travel time. I propose an alternative storage metric, the young water fraction in streamflow, defined as the fraction of runoff with transit times of less than roughly 0.2 years. I show that this young water fraction (not to be confused with event-based "new water" in hydrograph separations) is accurately predicted by seasonal tracer cycles within a precision of a few percent, across the entire range of mean transit times from almost zero to almost infinity. Importantly, this relationship is also virtually free from aggregation error. That is, seasonal tracer cycles also accurately predict the young water fraction in runoff from highly heterogeneous mixtures of subcatchments with strongly contrasting transit time distributions. Thus, although tracer cycle amplitudes yield biased and unreliable estimates of catchment mean travel times in heterogeneous catchments, they can be used reliably to estimate the fraction of young water in runoff.

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Assessment of hydrological pathways in East African montane catchments under different land use

Hydrology and Earth System Sciences ◽

10.5194/hess-22-4981-2018 ◽

2018 ◽

Vol 22 (9) ◽

pp. 4981-5000 ◽

Cited By ~ 12

Author(s):

Suzanne R. Jacobs ◽

Edison Timbe ◽

Björn Weeser ◽

Mariana C. Rufino ◽

Klaus Butterbach-Bahl ◽

...

Keyword(s):

Land Use ◽

Transit Time ◽

Stream Water ◽

Mean Transit Time ◽

Natural Forest ◽

Smallholder Agriculture ◽

Tea Plantation ◽

Water Fraction ◽

Transit Times ◽

Water Isotope

Abstract. Conversion of natural forest (NF) to other land uses could lead to significant changes in catchment hydrology, but the nature of these changes has been insufficiently investigated in tropical montane catchments, especially in Africa. To address this knowledge gap, we aimed to identify stream water (RV) sources and flow paths in three tropical montane sub-catchments (27–36 km2) with different land use (natural forest, NF; smallholder agriculture, SHA; and commercial tea and tree plantations, TTP) within a 1021 km2 catchment in the Mau Forest complex, Kenya. Weekly samples were collected from stream water, precipitation (PC) and mobile soil water for 75 weeks and analysed for stable isotopes of water (δ2H and δ18O) for mean transit time (MTT) estimation with two lumped parameter models (gamma model, GM; and exponential piston flow model, EPM) and for the calculation of the young water fraction. Weekly samples from stream water and potential endmembers were collected over a period of 55 weeks and analysed for Li, Na, Mg, K, Rb, Sr and Ba for endmember mixing analysis (EMMA). Solute concentrations in precipitation were lower than in stream water in all catchments (p < 0.05), whereas concentrations in springs, shallow wells and wetlands were generally more similar to stream water. The stream water isotope signal was considerably damped compared to the isotope signal in precipitation. Mean transit time analysis suggested long transit times for stream water (up to 4 years) in the three sub-catchments, but model efficiencies were very low. The young water fraction ranged from 13 % in the smallholder agriculture sub-catchment to 15 % in the tea plantation sub-catchment. Mean transit times of mobile soil water ranged from 3.2–3.3 weeks in forest soils and 4.5–7.9 weeks in pasture soils at 15 cm depth to 10.4–10.8 weeks in pasture soils at 50 cm depth. The contribution of springs and wetlands to stream discharge increased from a median of 16.5 (95 % confidence interval: 11.3–22.9), 2.1 (−3.0–24.2) and 50.2 (30.5–65.5) % during low flow to 20.7 (15.2–34.7), 53.0 (23.0–91.3) and 69.4 (43.0–123.9) % during high flow in the natural forest, smallholder agriculture and tea plantation sub-catchments, respectively. Our results indicate that groundwater is an important component of stream water, irrespective of land use. The results further suggest that the selected transit time models and tracers might not be appropriate in tropical catchments with highly damped stream water isotope signatures. A more in-depth investigation of the discharge dependence of the young water fraction and transit time estimation using other tracers, such as tritium, could therefore shed more light on potential land use effects on the hydrological behaviour of tropical montane catchments.

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Rainstorm Magnitude Likely Regulates Event Water Fraction and Its Transit Time in Mesoscale Mountainous Catchments: Implication for Modelling Parameterization

Water ◽

10.3390/w12041169 ◽

2020 ◽

Vol 12 (4) ◽

pp. 1169

Author(s):

Jun-Yi Lee ◽

Yu-Ting Shih ◽

Chiao-Ying Lan ◽

Tsung-Yu Lee ◽

Tsung-Ren Peng ◽

...

Keyword(s):

Sensitivity Analysis ◽

Transit Time ◽

Preferential Flow ◽

Mean Transit Time ◽

Time Estimation ◽

Parameter Sensitivity ◽

Hydrograph Separation ◽

Water Fraction ◽

Mountainous Catchments ◽

Time Invariant

Event water transit time estimation has rarely been done for violent rainstorms (e.g., typhoons) in steep and fractured mountainous catchments where the range of transit time, potential controlling factors, and the validity of time-invariant parametrization are unclear. Characterized by steep landscape and torrential typhoon rainfall, Taiwan provides great opportunities for inquiring into the above questions. In this study, the hydrometrics and δ18O in rainwater and streamwater were sampled with a ~3-h interval for six typhoon events in two mesoscale catchments. The TRANSEP (transfer function hydrograph separation) model and global sensitivity analysis were applied for estimating mean transit time (MTTew) and fraction (Few) of event water and identifying the chronosequent parameter sensitivity. Results showed that the MTTew and Few varied from 2.0 to 11.0 h and from 0.2 to 0.8, respectively. Our MTTew in the mesoscale catchments is comparable with that in microscale catchments, showing a fast rainfall-runoff transfer in our steep catchments. The average rainfall intensity is a predominant indicator, which negatively affects the MTTew and positively affects the Few, likely activating preferential flow-paths and quickly transferring event water to the stream. Sensitivity analysis among inter- and intra-events demonstrates that parameter sensitivity is event-dependent and time-variant. A quick and massive subsurface flow without distinct mixing with groundwater would be triggered during large rainstorms, suggesting that time-variant parameterization should be particularly considered when estimating the MTTew in steep and fractured catchments at rainstorm scale.

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Aggregation in environmental systems – Part 1: Seasonal tracer cycles quantify young water fractions, but not mean transit times, in spatially heterogeneous catchments

Hydrology and Earth System Sciences ◽

10.5194/hess-20-279-2016 ◽

2016 ◽

Vol 20 (1) ◽

pp. 279-297 ◽

Cited By ~ 137

Author(s):

J. W. Kirchner

Keyword(s):

Transit Time ◽

Mean Transit Time ◽

Strong Nonlinearity ◽

Isotopic Tracers ◽

Water Fraction ◽

Benchmark Tests ◽

Environmental Systems ◽

Transit Times ◽

Event Based ◽

The Relationship

Abstract. Environmental heterogeneity is ubiquitous, but environmental systems are often analyzed as if they were homogeneous instead, resulting in aggregation errors that are rarely explored and almost never quantified. Here I use simple benchmark tests to explore this general problem in one specific context: the use of seasonal cycles in chemical or isotopic tracers (such as Cl−, δ18O, or δ2H) to estimate timescales of storage in catchments. Timescales of catchment storage are typically quantified by the mean transit time, meaning the average time that elapses between parcels of water entering as precipitation and leaving again as streamflow. Longer mean transit times imply greater damping of seasonal tracer cycles. Thus, the amplitudes of tracer cycles in precipitation and streamflow are commonly used to calculate catchment mean transit times. Here I show that these calculations will typically be wrong by several hundred percent, when applied to catchments with realistic degrees of spatial heterogeneity. This aggregation bias arises from the strong nonlinearity in the relationship between tracer cycle amplitude and mean travel time. I propose an alternative storage metric, the young water fraction in streamflow, defined as the fraction of runoff with transit times of less than roughly 0.2 years. I show that this young water fraction (not to be confused with event-based "new water" in hydrograph separations) is accurately predicted by seasonal tracer cycles within a precision of a few percent, across the entire range of mean transit times from almost zero to almost infinity. Importantly, this relationship is also virtually free from aggregation error. That is, seasonal tracer cycles also accurately predict the young water fraction in runoff from highly heterogeneous mixtures of subcatchments with strongly contrasting transit-time distributions. Thus, although tracer cycle amplitudes yield biased and unreliable estimates of catchment mean travel times in heterogeneous catchments, they can be used to reliably estimate the fraction of young water in runoff.

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‘Teflon Basin’ or Not? A High-Elevation Catchment Transit Time Modeling Approach

Hydrology ◽

10.3390/hydrology6040092 ◽

2019 ◽

Vol 6 (4) ◽

pp. 92 ◽

Cited By ~ 1

Author(s):

Jan Schmieder ◽

Stefan Seeger ◽

Markus Weiler ◽

Ulrich Strasser

Keyword(s):

Transit Time ◽

Amplitude Ratio ◽

Mean Transit Time ◽

High Elevation ◽

Subsurface Water ◽

Balance Model ◽

Water Volume ◽

Time Model ◽

Water Fraction ◽

Ice Melt

We determined the streamflow transit time and the subsurface water storage volume in the glacierized high-elevation catchment of the Rofenache (Oetztal Alps, Austria) with the lumped parameter transit time model TRANSEP. Therefore we enhanced the surface energy-balance model ESCIMO to simulate the ice melt, snowmelt and rain input to the catchment and associated δ18O values for 100 m elevation bands. We then optimized TRANSEP with streamflow volume and δ18O for a four-year period with input data from the modified version of ESCIMO at a daily resolution. The median of the 100 best TRANSEP runs revealed a catchment mean transit time of 9.5 years and a mobile storage of 13,846 mm. The interquartile ranges of the best 100 runs were large for both, the mean transit time (8.2–10.5 years) and the mobile storage (11,975–15,382 mm). The young water fraction estimated with the sinusoidal amplitude ratio of input and output δ18O values and delayed input of snow and ice melt was 47%. Our results indicate that streamflow is dominated by the release of water younger than 56 days. However, tracers also revealed a large water volume in the subsurface with a long transit time resulting to a strongly delayed exchange with streamflow and hence also to a certain portion of relatively old water: The median of the best 100 TRANSEP runs for streamflow fraction older than five years is 28%.

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A statistical learning approach to extract information from hydrologic tracer timeseries

10.5194/egusphere-egu2020-11765 ◽

2020 ◽

Author(s):

Ciaran Harman

Keyword(s):

Statistical Learning ◽

Transit Time ◽

Ad Hoc ◽

Mean Transit Time ◽

Time Variability ◽

Computational Techniques ◽

Full Time ◽

Water Fraction ◽

Different Ages ◽

Extract Information

Hydrologic tracer timeseries data (e.g. of stable water isotopes in rainfall and streamflow) have often been analyzed by extracting summary metrics (like the mean transit time) that provide some information about the storage and turnover of water in a watershed, but are laden with ad hoc, implicit, and questionable assumptions. Consequently, inferences about water age and runoff generation processes may be artifacts of the methods, rather than true implications of the tracer data. Potentially more reliable metrics have been suggested recently (e.g. the &#8216;young water fraction&#8217;) but these do not make full use of the information content of the data. The StorAge Selection (SAS) approach relaxes the common (highly questionable) assumption of steady-state flow, and thus allows the full time-variability of the transit time distribution to be captured. However until now its application has required ad hoc functional forms and relationships to be chosen for the underlying SAS function and its time-variability. This introduces artifacts that can skew estimates of the volume and sensitivity of water turnover rates within the catchment, inhibit inference of complex or multi-modal distributions, and is a subjective complication that presents a barrier to use of the approach.Is it possible to make extract information about catchment water storage, turnover, and transit times without imposing ad-hoc assumptions, and instead allow the data to guide us? Can we obtain a clearer view of how these systems retain and release water of different ages at different rates, and vary how they do so over time? Can doing so allow us to better test hypotheses, tell richer stories about transport in dynamic hydrologic systems?&#160;Three recent advances toward doing so have recently been developed. The first is to unify the analysis of flux quantity and age (or water celerity and velocity) in the form of an &#8216;age-ranked storage-discharge relationship&#8217;. This relationship captures how the discharge of water of different ages changes when there is a change in the overall discharge. It thus provides a clearer view of the catchment mechanics driving streamflow generation and thus discharge age dynamics.The second is Multiresolution Estimation of StorAge Selection (MESAS), a non-parametric statistical learning method for determining this relationship. This method avoids the need to specify a functional form &#8211; instead the shape of the function is iteratively determined from a coarse to a fine resolution, up to a limit at which the capacity of the data to meaningfully constrain the form is maximized.The third is the development of computational techniques to accelerate the statistical learning implementation using an explicit Jacobian formulation and GPU acceleration.

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