Koray K. Yilmaz

TL;DR: A diagnostically interesting decomposition of NSE is presented, which facilitates analysis of the relative importance of its different components in the context of hydrological modelling, and it is shown how model calibration problems can arise due to interactions among these components.

TL;DR: In this article, a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts is described. But despite the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work.

TL;DR: Yilmaz et al. as discussed by the authors investigated a diagnostic approach to model evaluation that exploits hydrological context and theory to aid in the detection and resolution of watershed model inadequacies, through consideration of three major behavioral functions of any watershed system; overall water balance, vertical redistribution, and temporal redistribution.

TL;DR: In this paper, the authors compared mean areal precipitation (MAP) estimates derived from three sources: an operational rain gauge network (MAPG), a radar/gauge multisensor product (MAPX), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) satellite-based system (MAPS) for the time period from March 2000 to November 2003.

TL;DR: In this article, the authors present a methodology based entirely on satellite remote sensing data to set up and calibrate a hydrologic model, simulate the spatial extent of flooding, and evaluate the probability of detecting inundated areas.