We examined the effect of land cover on stream discharge in hilly catchment streams during extreme rain events. Three years of rainfall-runoff observations, between January 2014 and December 2016, were collected in eleven neighbouring catchments. Each catchment was dominated by a different land cover, namely natural shola forests, natural grasslands and wattle (Acacia mearnsii). Rain intensities between percentiles 25-90, 90-95 and over 95 were categorised as light, heavy and extreme and were used to study stream discharge responses. Land cover significantly influenced the hydrologic response to extreme rain events. During light rains (< 38 mm/day), grassland dominated catchments showed higher discharge than shola (0.01 mm/s) and wattle (0.004 mm/s). However, during extreme rain events (> 71 mm/day) discharge was significantly higher in wattle dominated catchments when compared to the natural shola (0.033 mm/s) and grasslands (0.023 mm/s). Antecedent moisture conditions played a major role in determining peak flows along with rainfall, catchment shape and drainage density.

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.

The representation of rainfall is important for hydrological modelling, particularly for spatially distributed models. Accurate estimation of rainfall is particularly challenging in mountainous regions where observations are often sparse relative to the spatial variability of rainfall, making interpolation challenging. In these regions, orographic processes lead to complex patterns of rainfall enhancement and rain shadow depletion. This study tests one deterministic method, Natural Neighbour Interpolation (NNI), and two geostatistical methods, ordinary kriging (OK) and ordinary cokriging (CK), to determine if CK improves rainfall interpolation during three extreme rainfall events that occurred in the north west of England. Preliminary analysis using long-term annual average rainfall totals, including additional high elevation rainfall observations, showed that CK with an effective elevation index as a secondary variable performed better than NNI and OK with an overall improvement of around 40%. Using rainfall totals for long-term wind direction and wind speed rainfall classes, CK performance was variable across classes but provided an improvement of approximately 15% for wind direction classes without an easterly wind component. For 15-minute timesteps during extreme rainfall events, there were comparatively small differences between interpolation methods, attributed to having only relatively low elevation rainfall observations for cross-validation, providing weak constraint. Importantly, cross-variogram estimation (that controls the strength of the correlation between rainfall magnitude and the secondary variable) provided differing cross-validation results when estimated for different rainfall total periods: 15-minutes, hourly, daily and long-term. Variograms and cross variograms estimated at a 15-minute timestep frequency were robust for many timesteps, but were difficult to fit automatically for others. Variograms estimated from longer periods were more reliably estimated, but tended to have lower variance and cross-variance and longer correlation ranges producing a smoother interpolated rainfall field. Given the weak cross-validation constraint, care must be taken in identifying the most appropriate method and variogram estimation period.

There is increased interest in the potential of tree planting to help mitigate flooding using nature-based solutions or natural flood management. However, many publications based upon catchment studies conclude that, as flood magnitude increases, benefit from forest cover declines and is insignificant for extreme flood events. These conclusions conflict with estimates of evaporation loss from forest plot observations of gross rainfall, throughfall and stem flow. This study explores data from existing studies to assess the magnitudes of evaporation and attempts to identify the meteorological conditions under which they would be supported. This is achieved using rainfall event data collated from publications and data archives from studies undertaken in temperate environments around the world. The meteorological conditions required to drive the observed evaporation losses are explored theoretically using the Penman-Monteith equation. The results of this theoretical analysis are compared with the prevailing meteorological conditions during large and extreme rainfall events in mountainous regions of the UK to assess the likely significance of wet canopy evaporation loss. The collated dataset showed that Ewc losses between approximately 2 and 38% of gross rainfall (1.5 to 39.4 mm d-1) have been observed during large rainfall events (up to 118 mm d-1) and limited data for extreme events (> 150 mm d-1). Event data greater than 150 mm, where duration was not reported, showed similarly high percentage evaporation losses. Theoretical estimates of wet-canopy evaporation indicated that, to reproduce these high losses, relative humidity and the aerodynamic resistance for vapour transport needed to be within an envelope of approximately 90 to 97.5% and 0.5 to 2 s m-1 respectively. Surface meteorological data during large and extreme rainfall events in the UK suggest that conditions favourable for high wet-canopy evaporation are not uncommon and indicate that significant evaporation losses during large and extreme events are possible but not for all events and not at all locations. Thus the disparity with the results from catchment studies remains.