Abstract. Weekly samples from surface waters, springs, soil water and rainfall were collected in a 76.9 km2 mountain rain forest catchment and its tributaries in southern Ecuador. Time series of the stable water isotopes δ18O and δ2H were used to calculate mean transit times (MTTs) and the transit time distribution functions (TTDs) solving the convolution method for seven lumped-parameter models. For each model setup, the generalized likelihood uncertainty estimation (GLUE) methodology was applied to find the best predictions, behavioral solutions and parameter identifiability. For the study basin, TTDs based on model types such as the linear–piston flow for soil waters and the exponential–piston flow for surface waters and springs performed better than more versatile equations such as the gamma and the two parallel linear reservoirs. Notwithstanding both approaches yielded a better goodness of fit for most sites, but with considerable larger uncertainty shown by GLUE. Among the tested models, corresponding results were obtained for soil waters with short MTTs (ranging from 2 to 9 weeks). For waters with longer MTTs differences were found, suggesting that for those cases the MTT should be based at least on an intercomparison of several models. Under dominant baseflow conditions long MTTs for stream water ≥ 2 yr were detected, a phenomenon also observed for shallow springs. Short MTTs for water in the top soil layer indicate a rapid exchange of surface waters with deeper soil horizons. Differences in travel times between soils suggest that there is evidence of a land use effect on flow generation.

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The relative importance of catchment's water provenance and flow paths varies in space and time, complicating the conceptualization of the rainfall‐runoff responses. We assessed the temporal dynamics in source areas, flow paths, and age by End Member Mixing Analysis (EMMA), hydrograph separation, and Inverse Transit Time Proxies (ITTPs) estimation within a headwater catchment in the Ecuadorian Andes. Twenty‐two solutes, stable isotopes, pH, and electrical conductivity from a stream and 12 potential sources were analyzed. Four end‐members were required to satisfactorily represent the hydrological system, i.e., rainfall, spring water, and water from the bottom layers of Histosols and Andosols. Water from Histosols in and near the riparian zone was the highest source contributor to runoff throughout the year (39% for the drier season, 45% for the wetter season), highlighting the importance of the water that is stored in the riparian zone. Spring water contributions to streamflow tripled during the drier season, as evidenced by geochemical signatures that are consistent with deeper flow paths rather than shallow interflow through Andosols. Rainfall exhibited low seasonal variation in this contribution. Hydrograph separation revealed that 94% and 84% is preevent water in the drier and wetter seasons, respectively. From low‐flow to high‐flow conditions, all the sources increased their contribution except spring water. The relative age of stream water decreased during wetter periods, when the contributing area of the riparian zone expands. The multimethod and multitracer approach enabled to closely study the interchanging importance of flow processes and water source dynamics from an interannual perspective.

In this study, the Mean Transit Time and Mixing Model Analysis methods are combined to unravel the runoff generation process of the San Francisco River basin (73.5 km2) situated on the Amazonian side of the Cordillera Real in the southernmost Andes of Ecuador. The montane basin is covered with cloud forest, sub‐páramo, pasture and ferns. Nested sampling was applied for the collection of streamwater samples and discharge measurements in the main tributaries and outlet of the basin, and for the collection of soil and rock water samples. Weekly to biweekly water grab samples were taken at all stations in the period April 2007–November 2008. Hydrometric data, Mean Transit Time and Mixing Model Analysis allowed preliminary evaluation of the processes controlling the runoff in the San Francisco River basin. Results suggest that flow during dry conditions mainly consists of lateral flow through the C‐horizon and cracks in the top weathered bedrock layer, and that all subcatchments have an important contribution of this deep water to runoff, no matter whether pristine or deforested. During normal to low precipitation intensities, when antecedent soil moisture conditions favour water infiltration, vertical flow paths to deeper soil horizons with subsequent lateral subsurface flow contribute most to streamflow. Under wet conditions in forested catchments, streamflow is controlled by near surface lateral flow through the organic horizon. Exceptionally, saturation excess overland flow occurs. By absence of the litter layer in pasture, streamflow under wet conditions originates from the A horizon, and overland flow. Copyright © 2011 John Wiley & Sons, Ltd.

Abstract. The Pacific-Andean region in western South America suffers from rainfall data scarcity, as is the case for many regions in the South. An important research question is whether the latest satellite-based and numerical weather prediction (NWP) model outputs capture well the temporal and spatial patterns of rainfall over the region, and hence have the potential to compensate for the data scarcity. Based on an interpolated gauge-based rainfall data set, the performance of the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 and its predecessor V6, and the North Western South America Retrospective Simulation (OA-NOSA30) are evaluated over 21 sub-catchments in the Pacific-Andean region of Ecuador and Peru (PAEP).In general, precipitation estimates from TRMM and OA-NOSA30 capture the seasonal features of precipitation in the study area. Quantitatively, only the southern sub-catchments of Ecuador and northern Peru (3.6-6 • S) are relatively well estimated by both products. The accuracy is considerably less in the northern and central basins of Ecuador (0-3.6 • S). It is shown that the probability of detection (POD) is better for light precipitation (POD decreases from 0.6 for rates less than 5 mm day −1 to 0.2 for rates higher than 20 mm day −1 ). Compared to its predecessor, 3B42 V7 shows modest regionwide improvements in reducing biases. The improvement is specific to the coastal and open ocean sub-catchments. In view of hydrological applications, the correlation of TRMM and OA-NOSA30 estimates with observations increases with time aggregation. The correlation is higher for the monthly time aggregation in comparison with the daily, weekly, and 15-day time scales. Furthermore, it is found that TRMM performs better than OA-NOSA30 in generating the spatial distribution of mean annual precipitation.

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Abstract. This study focuses on the investigation of the mean transit time (MTT) of water and its spatial variability in a tropical high-elevation ecosystem (wet Andean páramo). The study site is the Zhurucay River Ecohydrological Observatory (7.53 km 2 ) located in southern Ecuador. A lumped parameter model considering five transit time distribution (TTD) functions was used to estimate MTTs under steadystate conditions (i.e., baseflow MTT). We used a unique data set of the δ 18 O isotopic composition of rainfall and streamflow water samples collected for 3 years (May 2011 to May 2014) in a nested monitoring system of streams. Linear regression between MTT and landscape (soil and vegetation cover, geology, and topography) and hydrometric (runoff coefficient and specific discharge rates) variables was used to explore controls on MTT variability, as well as mean electrical conductivity (MEC) as a possible proxy for MTT. Results revealed that the exponential TTD function best describes the hydrology of the site, indicating a relatively simple transition from rainfall water to the streams through the organic horizon of the wet páramo soils. MTT of the streams is relatively short (0.15-0.73 years, 53-264 days). Regression analysis revealed a negative correlation between the catchment's average slope and MTT (R 2 = 0.78, p < 0.05). MTT showed no significant correlation with hydrometric variables, whereas MEC increases with MTT (R 2 = 0.89, p < 0.001). Overall, we conclude that (1) baseflow MTT confirms that the hydrology of the ecosystem is dominated by shallow subsurface flow; (2) the interplay between the high storage capacity of the wet páramo soils and the slope of the catchments provides the ecosystem with high regulation capacity; and (3) MEC is an efficient predictor of MTT variability in this system of catchments with relatively homogeneous geology.