Sampling frequency trade-offs in the assessment of mean transit times of tropical montane catchment waters under semi-steady-state conditions

Abstract

Abstract. Precipitation event samples and weekly based water samples from streams and soils were collected in a tropical montane cloud forest catchment for 2 years and analyzed for stable water isotopes in order to understand the effect of sampling frequency in the performance of three lumped-parameter distribution functions (exponential-piston flow, linear-piston flow and gamma) which were used to estimate mean transit times of waters. Precipitation data, used as input function for the models, were aggregated to daily, weekly, bi-weekly, monthly and bi-monthly sampling resolutions, while analyzed frequencies for outflows went from weekly to bi-monthly. By using different scenarios involving diverse sampling frequencies, this study reveals that the effect of lowering the sampling frequency depends on the water type. For soil waters, with transit times on the order of few weeks, there was a clear trend of over predictions. In contrast, the trend for stream waters, which have a more damped isotopic signal and mean transit times on the order of 2 to 4 years, was less clear and showed a dependence on the type of model used. The trade-off to coarse data resolutions could potentially lead to misleading conclusions on how water actually moves through the catchment, notwithstanding that these predictions could reach better fitting efficiencies, fewer uncertainties, errors and biases. For both water types an optimal sampling frequency seems to be 1 or at most 2 weeks. The results of our analyses provide information for the planning of future fieldwork in similar Andean or other catchments.

Mean transit times in headwater catchments: insights from the Otway Ranges, Australia

“…The EPM model accords with the expected geometry of flow in the catchment (vertical recharge through the unsaturated zone followed by flow along flow paths of varying length), and EPM models with these EPM ratios have reproduced the 3 H time series in headwater catchments with similar geometries elsewhere (Maloszewski and Zuber, 1982;Morgenstern and Daughney, 2012;Blavoux et al, 2013;Morgenstern et al, 2010). For the DM, D p values of 0.05 and 0.5 were adopted, which are appropriate for kilometre-scale flow systems (Zuber and Maloszewski, 2001;Gelhar et al, 1992). Utilisation of a variety of LPMs allows the impact of the assumed model on the MTTs to be assessed.…”

“…Determining MTTs from stable isotope ratios or major ion concentrations relies on tracking the delay and dampening of the seasonal variations between precipitation and discharge. However, use of these tracers typically requires sub-weekly sampling over time periods equal to or exceeding that of the transit times (Timbe et al, 2015). In addition, these tracers become ineffective when transit times exceed 4 to 5 years as the initial variations in rainfall are progressively dampened to below the point at which they can be detected .…”

Linking tracers, water age and conceptual models to identify dominant runoff processes in a sparsely monitored humid tropical catchment

“…Exceptions of work in tropical ecosystems included, e.g. Barnes and Bonell (1996), Roa-García and Weiler (2010), Muñoz-Villers and McDonnell (2012) and Timbe et al (2015) ranging from seasonally dry/wet pristine and mixed land use to tropical montane cloud forest and high-elevation paramo, respectively. While this allowed assessment of catchment functioning in terms of how water and solutes are transported, mixed and stored and the associated transit times, the northern temperate focus is geographically restricted.…”

Multicriteria assessment of water dynamics reveals subcatchment variability in a seemingly homogeneous tropical cloud forest catchment

“…The gamma and exponential piston models provided the best predictions. The effect of the sampling resolution on those predictions was analyzed by Timbe et al (2015). The MTT estimates of both models were similar and among subcatchments, they varied between 2.0 and 4.2 years, indicating a predominance of shallow aquifers .…”

“…Besides, new information, such as the reassessment of the MTT by Timbe et al (2015), using a more robust approach and a denser dataset, yielded results that differ from previous published data (e.g., Crespo et al, 2012). To further improve results, we used in this study in parallel a set of statistical methods such as: bivariate, multiple regression, principal component, and cluster analysis, enabling the reconstruction of a more complete and precise picture of the hydrological processes at hand.…”