Sophie Bachmair

New Dimensions of Hillslope Hydrology

Hillslopes are fundamental landscape units that strongly control the processes whereby precipitation or snowmelt is vertically and laterally transported to the streams. They are, in a sense, microcosms of catchments. Understanding and predicting the hillslope response is highly important in terms of flood prediction, transport of nutrients and sediments into surface water bodies, slope stability, and soil-atmosphere-vegetation exchange processes. Through numerous field experiments and numerical studies, much progress has been made in hillslope hydrology in the past decades. However, our ability to extrapolate these findings to ungauged hillslopes and catchments is still very poor (Sivapalan 2005). A common thread that has evolved recently is to search for the underlying principles of hydrological processes instead of characterizing and cataloging the enormous heterogeneity and complexity of rainfall-runoff processes (McDonnell et al. 2007). The aim of this chapter is to provide an overview on today’s conceptual models of processes at the hillslope scale and to examine the factors driving these mechanisms. The focus will be on compiling recent findings on the dominant controls of hillslope runoff processes to meet the need of identifying underlying principles. The chapter closes with thoughts on new dimensions and directions of hillslope hydrology and research avenues to follow for the future.

Developing drought impact functions for drought risk management

Drought management frameworks are dependent on methods for monitoring and prediction, but quantifying the hazard alone is arguably not sufficient; the negative consequences that may arise from a lack of precipitation must also be predicted if droughts are to be better managed. However, the link between drought intensity, expressed by some hydrometeorological indicator, and the occurrence of drought impacts has only recently begun to be addressed. One challenge is the paucity of information on ecological and socio-economic consequences of drought. This study tests the potential for 15 developing empirical “drought impact functions” based on drought indicators (Standardized Precipitation and Standardized Precipitation Evaporation Index) as predictors, and text-based reports on drought impacts as a surrogate variable for drought damage. While there have been studies exploiting textual evidence of drought impacts, a systematic assessment of the effect of impact quantification method and different functional relationships for modeling drought impacts is missing. Using SouthEast England as a case study we tested the potential of three different data-driven models for predicting drought impacts 20 quantified from text-based reports; logistic regression, zero-altered negative binomial regression (“hurdle model”), and an ensemble regression tree approach (“random forest”). The logistic regression model can only be applied to a binary impact/no impact time series, whereas the other two models can additionally predict the full counts of impact occurrence at each time point. While modeling binary data results in the lowest prediction uncertainty, modeling the full counts has the advantage of also providing a measure of impact severity, and the counts were found to be predictable within reasonable 25 limits. However, there were noticeable differences in skill between modeling methodologies. For binary data the logistic regression and the random forest model performed similarly well based on leave-one-out cross-validation. For count data the random forest outperformed the hurdle model. The between-model differences occurred for total drought impacts as well as for two subsets of impact categories (water supply and freshwater ecosystem impacts). In addition, different ways of defining the impact counts were investigated, and were found to have little influence on the prediction skill. For all models 30 we found a positive effect of including impact information of the preceding month as a predictor in addition to the hydrometeorological indicators. We conclude that, although having some limitations, text-based reports on drought impacts can Nat. Hazards Earth Syst. Sci. Discuss., doi:10.5194/nhess-2017-187, 2017 Manuscript under review for journal Nat. Hazards Earth Syst. Sci. Discussion started: 31 May 2017 c