[1] There is a strong demand from policy makers for predictions about the potential impacts of climate change on water resources. Integrated environmental models, combining climatic and hydrologic models, are often used for this purpose. This paper examines the impact of uncertainties related to GCMs in hydrological impact studies in the tropical Andes. A conceptual hydrological model is calibrated on data from four mesoscale, mountainous catchments in south Ecuador. The model inputs are then perturbed with anomalies projected by 20 GCMs available from the IPCC Data Distribution Centre. The results show that on average, the average monthly discharge is not expected to change dramatically. However, the simulated discharges driven by different global climate model forcing data can diverge widely, with prediction ranges often surpassing current discharge.

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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.

Abstract. Stream and soil waters were collected on a weekly basis in a tropical montane cloud forest catchment for two years and analyzed for stable water isotopes in order to infer transit time distribution functions and to define the mean transit times. Depending on the water type (stream or soil water), lumped distribution functions such as Exponential-Piston flow, Linear-Piston flow and Gamma models using temporal isotopic variations of precipitation event samples as input, were fitted. Samples were aggregated to daily, weekly, biweekly, monthly and bimonthly time scales in order to check the sensitivity of temporal sampling on model predictions. The study reveals that the effect of decreasing sampling frequency depends on the water type. For soil waters with transit times in the order of weeks to months, there was a clear trend of over prediction. In contrast, the trend of prediction for stream waters, with a dampened isotopic signal and mean transit times in the order of 2 to 4 years, was less clear and depending 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, while at the same time predictions can reach better fitting efficiencies, lesser uncertainties, errors and biases. For both water types an optimal sampling frequency seems to be one or at most two weeks. The results of our analyses provide information for the planning (in particular in terms of cost-benefit and time requirements) of future fieldwork in similar Andean or other catchments.

The Ecuadorian river system is comprised of more than 2,000 rivers and streams formed primarily in the Andes Mountains and discharging into the Pacific and Atlantic Oceans. Steep channels with coarse bed material constitute the main components of mountainous drainage systems (Buckalew et al., 1998). These channels are the principal source of sediment to lower gradient channels downstream. Historically, lower gradient rivers have received more attention due, in part, to their proximity to large population centers. Prior studies often did not consider slope explicitly or assumed slope to be a flow dependent variable (Andrews, 1984; Palucis & Lamb, 2017; Parker et al., 2007; Pitlick & Cress, 2002). Inclusion of slope as a determining parameter in the evolution of a river has been strengthened with the study of high gradient channels (Comiti et al., 2007; David et al., 2010; Ferguson, 2012; Tominaga & Nezu, 1992). Slope is an important controlling variable for fluvial characteristics such as sediment transport, development of stream habitat, and bed roughness (Palucis & Lamb, 2017). Hydraulic geometry (HG) theory is applied here to characterize mountainous rivers in Ecuador and determine the relation between slope and channel characteristics. HG was first presented by Leopold and Maddock (1953) and has been incorporated in a wide range of studies including habitat assessment, basin characterization, and flow resistance analysis (