Debates—The future of hydrological sciences: A (common) path forward? One water. One world. Many climes. Many souls
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It was a dark and stormy night, when our hero, Columbus, set sail for the storied land of India. He found the bountiful Americas. The natives were henceforth dubbed Indians. An interesting story of prediction and validation—if your prediction goes awry, then a correction by declaration is in order—a bias correction in modern hydrologic lingo. I had learned in kindergarten that Columbus’s voyage disproved the hypothesis of the day that the earth was flat. It turns out that this is a myth perpetuated in the 19th century. The notion of a spherical earth was quite well accepted by 1490. The unknown at the time of Columbus’s voyage was the size of the Earth, and the precise position of Asia. The controversy was as to whether the distance to Asia was as low as Columbus estimated or much larger as the scholars of the day thought. The concern was whether the ships would run out of food and water before they reached Asia. It turns out that Columbus grossly miscalculated the distance to Japan as 5000 km, when it was really 20,000 km. The ships arrived at the shores of the eastern Caribbean, with a mutinous crew, at the edge of starvation. Miraculously, Columbus ‘‘discovered’’ the unknown Americas almost precisely at the distance he had conjectured would be Japan. Today, India struggles to deal with the resource constraints of feeding and productively employing over a billion souls. The Americas continue to be a haven for resources. Perhaps, bravado can overcome poor prediction based on bad science, or sometimes you just have to be very lucky and set the course of history.
[1] J. McDonnell,et al. Debates—The future of hydrological sciences: A (common) path forward? A call to action aimed at understanding velocities, celerities and residence time distributions of the headwater hydrograph , 2014 .
[2] Hoshin Vijai Gupta,et al. Debates—the future of hydrological sciences: A (common) path forward? Using models and data to learn: A systems theoretic perspective on the future of hydrological science , 2014 .