Headwaters are generally assumed to contribute the majority of water to downstream users, but how much water, of what quality and where it is generated are rarely known in the humid tropics. Here, using monthly monitoring in the data scarce (2,370 km2) San Carlos catchment in northeastern Costa Rica, we determined runoff‐area relationships linked to geochemical and isotope tracers. We established 46 monitoring sites covering the full range of climatic, land use and geological gradients in the catchment. Regression and cluster analysis revealed unique spatial patterns and hydrologically functional landscape units. These units were used for seasonal and annual Bayesian tracer mixing models to assess spatial water source contributions to the outlet. Generally, the Bayesian mixing analysis showed that the chemical and isotopic imprint at the outlet is throughout the year dominated by the adjacent lowland catchments (68%) with much less tracer influence from the headwaters. However, the headwater catchments contributed the bulk of water and tracers to the outlet during the dry season (>50%) despite covering less than half of the total catchment area. Additionally, flow volumes seemed to be linearly scaled by area maintaining a link between the headwaters and the outlet particularly during high flows of the rainy season. Stable isotopes indicated mean recharge elevations above the mean catchment altitude, which further supports that headwaters were the primary source of downstream water. Our spatially detailed “snap‐shot” sampling enabled a viable alternative source of large‐scale hydrological process knowledge in the humid tropics with limited data availability.

Abstract. Streamflow simulation across the tropics is limited by the lack of data to calibrate and validate large-scale hydrological models. Here, we applied the process-based, conceptual HYPE (Hydrological Predictions for the Environment) model to quantitatively assess Costa Rica's water resources at a national scale. Data scarcity was compensated for by using adjusted global topography and remotely sensed climate products to force, calibrate, and independently evaluate the model. We used a global temperature product and bias-corrected precipitation from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) as model forcings. Daily streamflow from 13 gauges for the period 1990–2003 and monthly Moderate Resolution Imaging Spectroradiometer (MODIS) potential evapotranspiration (PET) and actual evapotranspiration (AET) for the period 2000–2014 were used to calibrate and evaluate the model applying four different model configurations (M1, M2, M3, M4). The calibration consisted of step-wise parameter constraints preserving the best parameter sets from previous simulations in an attempt to balance the variable data availability and time periods. The model configurations were independently evaluated using hydrological signatures such as the baseflow index, runoff coefficient, and aridity index, among others. Results suggested that a two-step calibration using monthly and daily streamflow (M2) was a better option than calibrating only with daily streamflow (M1), with similar mean Kling–Gupta efficiency (KGE ∼ 0.53) for daily streamflow time series, but with improvements to reproduce the flow duration curves, with a median root mean squared error (RMSE) of 0.42 for M2 and a median RMSE of 1.15 for M1. Additionally, including AET (M3 and M4) in the calibration statistically improved the simulated water balance and better matched hydrological signatures, with a mean KGE of 0.49 for KGE in M3–M4, in comparison to M1–M2 with mean KGE < 0.3. Furthermore, Kruskal–Wallis and Mann–Whitney statistical tests support a similar model performance for M3 and M4, suggesting that monthly PET-AET and daily streamflow (M3) represents an appropriate calibration sequence for regional modeling. Such a large-scale hydrological model has the potential to be used operationally across the humid tropics informing decision-making at relatively high spatial and temporal resolution.

No abstract

Abstract. Streamflow simulation across the tropics is limited by the lack of data to calibrate and validate large-scale hydrological models. Here, we applied the process-based, conceptual HYPE (Hydrological Predictions for the Environment) model to quantitively assess Costa Rica’s water resources at a national scale. Data scarcity was compensated using adjusted global topography and remotely-sensed climate products to force, calibrate, and independently evaluate the model. We used a global temperature product and bias-corrected precipitation from CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) as model forcings. Daily streamflow from 13 gauges for the period 1990–2003 and monthly MODIS (Moderate Resolution Imaging Spectroradiometer) potential evapotranspiration (PET) and actual evapotranspiration (AET) for the period 2000–2014 were used to calibrate and evaluate the model applying four different model configurations. The calibration consisted in step-wise parameter constraints preserving the best parameter sets from previous simulations in an attempt to balance the variable data availability and time periods. The model configurations were independently evaluated using hydrological signatures such as the baseflow index, runoff coefficient, and aridity index, among others. Results suggested that a two-step calibration using monthly and daily streamflow was a better option instead of calibrating only with daily streamflow. Additionally, including PET and AET in the calibration improved the simulated water balance and better matched hydrological signatures. Thus, the constrained parameter uncertainty increased the confidence in the simulation results. Such a large-scale hydrological model has the potential to be used operationally across the humid tropics informing decision making at relatively high spatial and temporal resolution.