As a follow-up to my previous post, I'd like to share some additional reflections on the experience of doing hydrology in academia. I've attempted to classify different types of researchers, and below, you'll find the first part of this classification.
There are those who discover. This process involves observing data, identifying unexpected aspects of the water cycle and related sciences, and expanding the empirical base as progress is made. In this sense, science is also about keen observation and m uch of scientific work is conducted in this way. To better grasp this, consider the field of natural sciences. Hydrology is also a natural science and phenomena discovery, observation, classification is an important part of it. Today the field is positively contaminating a lot considering hydrology feedbacks with biology and geochemistry, ecosystems behavior. [[This does not mean that what has to be discovered is all in the interdisciplinary studies, many historical hydrological issues not having being solved yet.]]
Examples of discoveries in data present in literature are, for instance: the observation of self similarity (fractality) in many geophysical sciences which revealed a scale-free response of catchments and hydrologic systems which is not fully explained. Shifts in precipitation timing and intensity, increased frequency of droughts, or altered snowmelt patterns that significantly impact water availability and hydrological cycles in ways not previously anticipated. Anomalous runoff coefficients caused by the presence of karst or melting glaciers. Effects of human activities at various scales. Effects of groundwater water distribution and redistribution in the overall cycle and at various spatial scales. Different stress response of plants with respect to droughts. Unexpected old age of water in runoff challenging the understanding of runoff production. Missing rainfall events, due to lack of space-time resolution of observations. Fill and spill and other non intuitive and sometimes counterintuive phenomena in runoff.
If what you see is new, you can easily publish it. But even if it is a confirmation of something new, you still can. I would distinguish "discover" from "measure". Measures, experiments and field observations have their own place and a different literature.
It is clear that to discover in data the simple and literal observation is not enough. The help of some tools and some mathematics is necessary, even if researcher who excel in narrative capabilities and metaphor production exist and sometimes do without (do not follow their example if you are not a very gifted writer).
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