Predicting catchment stormflow responses after tropical deforestation remains difficult. We used five-minute rainfall and storm runoff data for 30 events to calibrate the Green–Ampt (GA) and the Spatially Variable Infiltration (SVI) model and predict runoff responses for a small, degraded grassland catchment on Leyte Island (the Philippines), where infiltration-excess overland flow is considered the dominant storm runoff generating process. SVI replicated individual stormflow hydrographs better than GA, particularly for events with a small runoff response or multiple peaks. Calibrated parameter values of the SVI model (i.e., spatially averaged maximum infiltration capacity, Im and initial abstraction, F0) varied markedly between events, but exhibited significant negative linear correlations with (mid-slope) soil water content at 10 cm (SWC10) – as did the ‘catchment effective’ hydraulic conductivity (Ke) of the GA model. SWC10-based values of F0 and Im in SVI resulted in satisfactory to good predictions (NSE > 0.50) for 18 out of 26 storms for which data on SWC10 were available, but failed to reproduce the hydrographs for six events (23%) with mostly small runoff responses. Median values of field-measured near-surface Ksat (~2–3 mm h-1, depending on method) were distinctly lower than the median Im (32 mm h-1) and, to a lesser extent, Ke (~8 mm h-1), confirming previously suspected under-estimation of field-measured Ksat. Using pre-storm topsoil moisture content and 5-min rainfall intensities as the driving variables to model infiltration with SVI gave more realistic results than the classic GA approach or the comparison of rainfall intensities with field-measured Ksat.

Crowd-based hydrological observations can supplement existing monitoring networks and allow data collection in regions where otherwise no data would be available. In the citizen science project CrowdWater, repeated water level observations using a virtual staff gauge approach result in time series of water level classes. To investigate the quality of these observations, we compared the water level class data for a number of locations where water levels were also measured and assessed when these observations were submitted. We analysed data for nine locations where citizen scientists reported multiple observations using a smartphone app and stream level data were also available. At twelve other locations, signposts were set up to ask citizens to record observations on a form that could be left in a letterbox. The results indicate that the quality of the data collected with the app was higher than for the forms. A possible explanation is that for each app location, most contributions were made by a single person, whereas at the locations of the forms almost every observation was made by a new contributor. On average, more contributions were made between May and September than during the other months. Observations were submitted for a range of flow conditions, with a higher fraction of high flow observations for the data collected with the app. Overall, the results are encouraging for citizen science approaches in hydrology and demonstrate that the smartphone application with its virtual staff gauge is a promising approach for crowd-based water level class observations.