10.3: The history of the water cycle
Hydrology has a long history dating back to several millennia (Biswas 1970 ). However, the birth of hydrologic modeling can be traced to the 1850s when Mulvany ( 1850 ) developed a method for computing the time of concentration and hence the rational method for computing peak discharge which is still used for urban drainage design, Darcy ( 1856 ) who conducted experiments on flow-through sands and developed what is now referred to as Darcy’s law which laid the foundation of quantitative groundwater hydrology, and Fick’s first law which states that under steady-state conditions the diffusive flux is proportional to the concentration gradient (spatial) which laid the foundation of water quality hydrology. About half a century earlier, Dalton ( 1802 ) formulated the law of evaporation which states that the rate of evaporation is directly proportional to the difference between saturation vapor pressure at the water surface and the actual vapor pressure in the air. This law constituted the foundation for developing the physics of evaporation. For a period of over a century until the 1960s, many groundbreaking advances in modeling different components of the hydrologic cycle were made. Some of these advances were based on the laws of mathematical physics and some had their basis in laboratory and/or field experiments. The current state of hydrologic science and engineering owes a great deal to the pre-1960 advances. The handbook of applied hydrology edited by Chow ( 1964 ) provided an up-to-date account of hydrologic advances until the 1960s, whereas the handbook of hydrology edited by Maidment ( 1993 ) and the encyclopedia of hydrology and water resources edited by Hershey and Fairbridge ( 1998 ) dealt with advances that occurred during the intervening period. Singh and Woolhiser ( 2002 ) provided a historical account of developments that occurred in modeling different components of the hydrologic cycle.
The decade of the 1960s witnessed the birth of computer revolution and hydrologic modeling took a giant leap forward. The computer provided the power for doing computations that was not available before. As a result, a new branch of hydrology, called digital or numerical hydrology, was born. Another branch that came into being was statistical or stochastic hydrology that often required analyses of large volumes of data. Then, several major advances ensued. First, simulation of the entire hydrologic cycle became a reality, as illustrated by the development of the Stanford Watershed Model (Crawford and Linsley 1966 ) which was followed in the decades to come by umpteen watershed models that were developed all over the world (Singh 1995 ; Singh and Frevert 2002a , b , 2006 ). Second, optimization or operations research techniques were developed, which formed the basis for reservoir management and operation as well as river basin simulation. Some of these techniques were also used for calibrating hydrologic models (Beven 2001 ; Duan et al. 2003 ). Third, two- and three-dimensional modeling was made possible because of advances in numerical mathematics. Consequently, two- and three-dimensional models of groundwater as well as of infiltration and soil water flow were developed (Bear 1979 ; Pinder and Celia 2006 ; Remson et al. 1971 ). Fourth, simultaneous simulation of water flow and sediment and pollutant transport was undertaken; likewise, simultaneous simulation of different phases of flow, such as liquid and gaseous, was done (Bear and Verruijt 1987 ; Charbeneau 2000 ). Fifth, modeling at large spatial scales, such as a large river basin like the Mississippi, and that at small temporal scales, such as seconds or minutes, was undertaken (Molley and Wesse 2009 ; Sorooshian et al. 2008 ). Sixth, integration of hydrology with allied sciences became possible. For example, it was possible to couple hydrology with climatology for precipitation modeling and forecasting (Sorooshian et al. 2008 ), with geomorphology for river basin geometric representation (Baker et al. 1988 ; Bates and Lane 2002 ; Beven and Kirkby 1993 ), with hydraulics for describing flow characteristics (Singh 1996 ), with soil physics for quantifying soil texture and structure (Bohne 2005 ; Guymon 1994 ; Miyazaki 2006 ; Smith et al. 2002 ; Singh 1997 ), and with geology for aquifer characterization (Delleur 1999 ; Fetter 1980 ; Singh 2017a , b , c ). The coupling of hydrology with ecosystems gave rise to ecohydrology (Eagleson 2002 ; Gordon et al. 2006 ; Rodriguez-Iturbe and Porporato 2004 ). Climate change and global warming became part of hydrologic analysis (Arnell 1997 ). A more detailed account of developments in different components of the hydrologic cycle is given in Singh ( 2013 , 2014 , 2015 , 2017a ).
In the decades that followed, computing prowess increased exponentially and hydrology began maturing and expanding in both depth (vertically) and breadth (horizontally). Tools from fluid mechanics, statistics, information theory, and mathematics were employed and became part of hydrology (Bras and Rodriguez-Iturbe 1985 ; Clarke 1998 ; Gelhar 1993 ; Mays and Tung 1992 ; Singh et al. 2007 ; Tung and Yen 2005 ). Further, computer also made possible the development of user-friendly software, and tools for date acquisition, storage, retrieval, processing, and dissemination (Croley 1980 ; Hoggan 1989 ). Remote sensing tools, such as radar and satellites, came into being that made possible to acquire spatial data for large areas (Engman and Gurney 1991 ; Hogg et al. 2017 ; Lakshmi 2017 ; Lakshmi et al. 2015 ). Likewise, geographical information systems (GIS) were developed for processing huge quantities of raster and vector data (Maidment 2002 ). The past two decades witnessed the development of artificial neural networks, fuzzy logic, genetic programming, and wavelet models (Kumar et al. 2006 ; Ross 2010 ; Sen 2010 ; Tayfur 2012 ). New theories borrowed from other areas were introduced in hydrology. Examples of these theories are entropy theory (Singh 2013 , 2014 , 2015 , 2016 , 2017b ), copula theory (Singh and Zhang 2018 ), chaos theory (Sivakumar 2017 ), network theory (Sivakumar et al. 2017 ), and catastrophe theory (Poston and Stewart 1978 ; Zeeman 1978 ). These theories will find increasing place in hydrologic modeling in the years ahead.
Another area that mushroomed subsequent to the pre-computer era is instrumentation. New instruments which were more accurate and sophisticated were developed for measuring all kinds of hydrologic variables, such as velocity, soil moisture, water and air quality parameters, fluxes in porous media, energy fluxes, and so on. Further, instrumentation for data transmission from place of measurement to place of storage, processing, storage, retrieval, and dissemination became highly robust and accessible (Liang et al. 2013 ; Sivakumar and Berndtsson 2010 ).
Source
Singh, V.P. Hydrologic modeling: progress and future directions. Geosci. Lett. 5 , 15 (2018). https://doi.org/10.1186/s40562-018-0113-z