Fifty years of<i>Water Resources Research</i>: Legacy and perspectives for the science of hydrology

Montanari, Alberto; Bahr, Jean M.; Blöschl, Günter; Cai, Ximing; Mackay, D. S.; Michalak, A. M.; Rajaram, Harihar; Sander, G. C.

doi:10.1002/2015wr017998

Cited by 30 publications

(28 citation statements)

References 58 publications

(93 reference statements)

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“…SRS data complement in situ hydrometeorological data (Balsamo et al, 2018), which are typically scarce and whose unavailability hinders the understanding of environmental systems (Tang et al, 2009). This aspect is particularly relevant for developing countries where research for development initiatives have been increasing in the recent years (Montanari et al, 2015).…”

Section: 1029/2019wr026085mentioning

confidence: 99%

Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets

Dembélé

Hrachowitz

Savenije

et al. 2020

Water Resources Research

122

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Hydrological model calibration combining Earth observations and in situ measurements is a promising solution to overcome the limitations of the traditional streamflow-only calibration. However, combining multiple data sources in model calibration requires a meaningful integration of the data sets, which should harness their most reliable contents to avoid accumulation of their uncertainties and mislead the parameter estimation procedure. This study analyzes the improvement of model parameter selection by using only the spatial patterns of satellite remote sensing data, thereby ignoring their absolute values. Although satellite products are characterized by uncertainties, their most reliable key feature is the representation of spatial patterns, which is a unique and relevant source of information for distributed hydrological models. We propose a novel multivariate calibration framework exploiting spatial patterns and simultaneously incorporating streamflow and three satellite products (i.e., Global Land Evaporation Amsterdam Model [GLEAM] evaporation, European Space Agency Climate Change Initiative [ESA CCI] soil moisture, and Gravity Recovery and Climate Experiment [GRACE] terrestrial water storage). The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature data set is used for model evaluation. A bias-insensitive and multicomponent spatial pattern matching metric is developed to formulate a multiobjective function. The proposed multivariate calibration framework is tested with the mesoscale Hydrologic Model (mHM) and applied to the poorly gauged Volta River basin located in a predominantly semiarid climate in West Africa. Results of the multivariate calibration show that the decrease in performance for streamflow (−7%) and terrestrial water storage (−6%) is counterbalanced with an increase in performance for soil moisture (+105%) and evaporation (+26%). These results demonstrate that there are benefits in using satellite data sets, when suitably integrated in a robust model parametrization scheme.

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Section: 1029/2019wr026085mentioning

confidence: 99%

Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets

Dembélé

Hrachowitz

Savenije

et al. 2020

Water Resources Research

122

View full text Add to dashboard Cite

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“…As an example, Montanari et al (2015) stressed the need for a new vision to help drive new theories, new methods and "new thinking". This comes at a time when enhanced computational power and sophisticated monitoring techniques now enable hydrologists to pursue deeper investigations of hydrologic processes, and to thereby simulate watershed hydrology in ever more detail.…”

Section: Introductionmentioning

confidence: 99%

HESS Opinions: Advocating process modeling and de-emphasizing parameter estimation

Bahremand

2016

Hydrol. Earth Syst. Sci.

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Abstract. Since its origins as an engineering discipline, with its widespread use of "black box" (empirical) modeling approaches, hydrology has evolved into a scientific discipline that seeks a more "white box" (physics-based) modeling approach to solving problems such as the description and simulation of the rainfall-runoff responses of a watershed. There has been much recent debate regarding the future of the hydrological sciences, and several publications have voiced opinions on this subject. This opinion paper seeks to comment and expand upon some recent publications that have advocated an increased focus on process-based modeling while de-emphasizing the focus on detailed attention to parameter estimation. In particular, it offers a perspective that emphasizes a more hydraulic (more physics-based and less empirical) approach to development and implementation of hydrological models.

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“…The total amount of freshwater on Earth can satisfy global demands, but uneven temporal and spatial distribution causes regional scarcity of water resources (Montanari et al . ). For instance, China's water resources are sixth in the world by volume, but per capita water availability is near one‐quarter of the world's average (Jin and Young ).…”

Section: Introductionmentioning

confidence: 97%

A thirst for development: mapping water stress using night‐time stable lights as predictors of province‐level water stress in China

You

Monahan

2017

Area

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Given the rapid development within China, the inequality of available water resources has been increasingly of interest. Current methods for assessing water stress are inadequate for province‐scale rapid monitoring. A more responsive indicator at a finer scale is needed to understand the distribution of water stress in China. This paper selected Defense Meteorological Satellite Program Operational Line‐scan System night‐time stable lights as a proxy for water stress at the province level in China from 2004 to 2012, as night‐time lights are closely linked with population density, electricity consumption and other social, economic and environmental indicators associated with water stress. The linear regression results showed the intensity of night‐time lights can serve as a predictive tool to assess water stress across provinces with an R2 from 0.797 to 0.854. The model worked especially well in some regions, such as East China, North China and South West China. Nonetheless, confounding factors interfered with the predictive relationship, including population density, level of economic development, natural resource endowment and industrial structures, etc. The model was not greatly improved by building a multi‐variable linear regression including agricultural and industrial indicators. A straightforward predictor of water stress using remotely sensed data was developed.

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Fifty years ofWater Resources Research: Legacy and perspectives for the science of hydrology

Cited by 30 publications

References 58 publications

Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets

Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets

HESS Opinions: Advocating process modeling and de-emphasizing parameter estimation

A thirst for development: mapping water stress using night‐time stable lights as predictors of province‐level water stress in China

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