Spatially distributed hydro-chemical data with temporally high-resolution is needed to adequately assess the hydrological functioning of headwater catchments

Correa, Alicia; Breuer, Lutz; Crespo, Patricio; Célleri, Rolando; Feyen, Jan; Birkel, Christian; Silva, Camila; Windhorst, David

doi:10.1016/j.scitotenv.2018.09.189

Cited by 35 publications

(55 citation statements)

References 58 publications

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“…Discharge at the Zhurucay catchment presents a very flashy response to precipitation inputs, particularly during the wettest months (January to April; Mosquera et al, ). Geochemically‐based mixing models have also shown that direct precipitation, leading to fast runoff response, can be an important contributor to discharge to the Zhurucay catchment during rainfall events (Correa, ). Together, these findings suggest that the rapid response of runoff to rainfall events is resembled satisfactorily by the wavelet spectrum.…”

Section: Resultsmentioning

confidence: 99%

Wavelet analyses of neural networks based river discharge decomposition

Campozano

Mendoza

Mosquera

et al. 2020

Hydrological Processes

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The problem of discharge forecasting using precipitation as input is still very active in Hydrology, and has a plethora of approaches to its solution. But, when the objective is to simulate discharge values without considering the phenomenology behind the processes involved, Artificial Neural Networks, ANN give good results. However, the question of how the black box internally solve this problem remains open. In this research, the classical rainfall-runoff problem is approached considering that the total discharge is a sum of components of the hydrological system, which from the ANN perspective is translated to the sum of three signals related to the fast, middle and slow flow. Thus, the present study has two aims (a) to study the time-frequency representation of discharge by an ANN hydrologic model and (b) to study the capabilities of ANN to additively decompose total river discharge.This study adds knowledge to the open problem of the physical interpretability of black-box models, which remains very limited. The results show that total discharge is adequately simulated in the time frequency domain, although less power spectrum is evident during the rainy seasons in the ANN model, due to fast flow underestimation. The wavelet spectrum of discharge represents well the slow, middle and fast flow components of the system with transit times of 256, 12-64 and 2-12 days, respectively. Interestingly, these transit times are remarkably similar to those of the soil water reservoirs of the studied system, a small headwater catchment in the tropical Andes. This result needs further research because it opens the possibility of determining MMT on a fraction of the cost of isotopic based methods. The cross-power spectrum indicates that the error in the simulated discharge is more related to the misrepresentation of the fast and the middle flow components, despite limitations in the recharge period of the slow flow component. With respect to the representation of individual signals of the slow, middle and fast flows components, the three neurons were uncapable to individually represent such flows. However, the combination of pairs of these signals resemble the dynamics and the spectral content of the aforementioned flows signals. These results show some evidence that signal processing techniques may be used to infer information about the hydrological functioning of a basin. K E Y W O R D S ANN, cross wavelet transform, discharge decomposition, fast flow, middle flow, mountain hydrology, slow flow

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Section: Resultsmentioning

confidence: 99%

Wavelet analyses of neural networks based river discharge decomposition

Campozano

Mendoza

Mosquera

et al. 2020

Hydrological Processes

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“…We also collected bi-weekly water samples from 4 potential water source end-members: rainfall (RF), soil water from Andosols (AN) and Histosols (HS) and spring water (SW) (n ~ 30). The above-mentioned waters sources (RF, AN, HS and SW), were previously identified as end-members (Correa et al, 2017(Correa et al, , 2018 (Table 1). A multi-5 tracer (14 tracers) data set of conservative tracers was obtained from each water sample (Na, Mg, Al, Si, K, Ca, Rb, Sr, Ba, Ce, V, Y, Nd) at the Institute for Landscape Ecology and Resource Management of the Justus Liebig University using an ICP-MS (Agilent 7500ce, Agilent Technologies) and the electrical conductivity (EC) was measured in situ.…”

Section: Study Site and Datamentioning

confidence: 99%

“…More detailed information on the study site and data set can be found in Correa et al (2017Correa et al ( , 2018.…”

Section: Study Site and Datamentioning

confidence: 99%

“…Using the classic EMMA approach (Christophersen and Hooper, 1992), end-members (source) and stream (mixture) data were projected into a three-dimensional space (Correa et al, 2018) visualized in Figure 1. The resulting median and standard deviation of end-members and stream coordinates are shown in Table 1.…”

Section: Source Water Uncertainty Estimation 10mentioning

confidence: 99%

“…Such a widespread use of tracers was mainly facilitated by novel analytical techniques that provide high sensitive, rapid multielement analysis at lower cost (Falkner et al, 1995). For example, the use of inductively coupled plasma mass spectrometry (ICP-MS) as one of the leading analytical techniques for elemental analysis (Helaluddin et al, 2016), led to the availability and use of large tracers sets (elements) in hydrological studies (Barthold et al, 2017;Belli et al, 10 2017;Correa et al, 2018;Kirchner and Neal, 2013;Mimba et al, 2017). Trace elements together with water stable isotopes (novel Cavity Ringdown Laser Absorption Spectroscopy paved the way: (Berman et al, 2009;Lis et al, 2008)) as well as physical-chemical water parameters (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Technical note: Uncertainty in multi-source partitioning using large tracer data sets

Correa¹,

Ochoa-Tocachi²,

Birkel³

2019

Preprint

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Abstract. The availability of large tracer data sets opened up the opportunity to investigate multiple source contributions to a mixture. However, the source contributions may be uncertain and apart from Bayesian approaches to estimate such source uncertainty only sound methods for two and three sources. We expand these methods developing an uncertainty estimation method for four sources based on multiple tracers as input data. Taylor series approximation is used to solve the set of linear mass balance equations. We illustrate the method with an example from hydrology, where we use a large tracer set from four water sources contributing to streamflow in a tropical, high-elevation catchment. However, our uncertainty estimation method can be generalized to any number of tracers across a range of disciplines.

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SWAT Simulation of a Small Neotropical Alpine Catchment in the Ecuadorian Andes

Jarrin-Perez¹,

Jeong²,

Célleri

et al. 2022

Preprint

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To support the hydrological assessment of Alpine ecosystems, we studied the suitability of the SWAT model to simulate neotropical alpine grasslands or so-called Andean Paramo. Given the paucity of observational data in paramo catchments, data-driven models are usually underutilized, and their outcomes are arguable. However, our research examined if SWAT can reasonably represent the hydrological response of grassland-dominated paramo catchments under data-abundance conditions.

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Spatially distributed hydro-chemical data with temporally high-resolution is needed to adequately assess the hydrological functioning of headwater catchments

Cited by 35 publications

References 58 publications

Wavelet analyses of neural networks based river discharge decomposition

Wavelet analyses of neural networks based river discharge decomposition

Technical note: Uncertainty in multi-source partitioning using large tracer data sets

SWAT Simulation of a Small Neotropical Alpine Catchment in the Ecuadorian Andes

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