Large-sample hydrology: a need to balance depth with breadth
By Hoshin V. Gupta, Charles Perrin, Günter Blöschl, Alberto Montanari, Rohini Kumar, M. Clark, Vazken Andréassian. 2013
301 citations recorded by the source corpus.
A holy grail of hydrology is to understand catchment processes well enough that models can provide detailed simulations across a variety of hydrologic settings at multiple spatiotemporal scales, and under changing environmental conditions. Clearly, this cannot be achieved only through intensive place-based investigation at a small number of heavily instrumented catchments, or by empirical methods that do not fully exploit our understanding of hydrology. In this opinion paper, we discuss the need to actively promote and pursue the use of a "large catchment sample" approach to modeling the rainfall–runoff process, thereby balancing depth with breadth. We examine the history of such investigations, discuss the benefits (improved process understanding resulting in robustness of prediction at ungauged locations and under change), examine some practical challenges to implementation and, finally, provide perspectives on issues that need to be taken into account as we move forward. Ultimately, our objective is to provoke further discussion and participation, and to promote a potentially important theme for the upcoming Scientific Decade of the International Association of Hydrological Sciences entitled Panta Rhei.
Subfields: Global and Planetary Change, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/dffb81137d8b18174230e7945894bd3d172de9de
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Citations in the corpus
- Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets (2019, methodology): “to demonstrate that we can use large-sample hydrology data (Gupta et al., 2014; Peters-Lidard et al., 2017) to develop a regional rainfall–runoff model that capitalizes on observable ancillary data in the form of catchment attributes to produce accurate streamflow estimates over a large number of ba”
- HESS Opinions : The complementary merits of competing modelling philosophies in hydrology (2017, background): “A potential way forward towards achieving this, may be the much advocated large sample, comparative hydrology and similarity analysis to identify pattern and generally applicable, functional relationships (e.g. Sivapalan et al., 2003; McDonnell et al., 2007; Blöschl et al., 2013; Sivakumar et al., 2”
- Moving sociohydrology forward: a synthesis across studies (2015, background): “(2013), although the challenges inherent in this approach have also been highlighted, such as data availability and sharing protocols (Gupta et al., 2014).”