An integrated model for the assessment of global water resources – Part 1: Model description and input meteorological forcing

Hanasaki, Naota; Kanae, Shinjiro; Oki, Taikan; Masuda, Kooiti; Motoya, Ken; Shirakawa, Naoki; Shen, Yanjun; Tanaka, K.

doi:10.5194/hess-12-1007-2008

Cited by 514 publications

(442 citation statements)

References 46 publications

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“…For rainfall, as in previous work [Motoya et al, 2002;Hirabayashi et al, 2008b;Hanasaki et al, 2008], the equation of Kondo [1994] was used:…”

Section: 1002/2017jd026613mentioning

confidence: 99%

“…U values were truncated at a certain value (6 m s À1 ) to avoid unrealistically large rainfall values after the correction. For snowfall, the gauge-typespecific correction factors of Motoya et al [2002] were used as in previous work [Hirabayashi et al, 2008b;Hanasaki et al, 2008]: …”

Section: 1002/2017jd026613mentioning

confidence: 99%

“…All the forcing data sets used in this study, including S14FD, would commonly have this problem. There is a difference in the correction method for wind speed because this study corrected the 12 monthly climatologies of the climatic variable, as in Iizumi et al [2014], whereas many other forcing data sets use reanalysis wind speed data without any correction [Zhao and Dirmeyer, 2003;Ngo-Duc et al, 2005;Sheffield et al, 2006;Weedon et al, 2011Weedon et al, , 2014 or with elevation correction based on the logarithm profile [Hirabayashi et al, 2008b;Hanasaki et al, 2008].…”

Section: A3 Differences Between S14fd and Other Major Forcing Data Setsmentioning

confidence: 99%

“…However, there are large differences in the wet-day frequency correction, rainfall/snowfall partition, and wind-induced undercatch correction. There is no wet-day frequency correction in some studies [Zhao and Dirmeyer, 2003;Ngo-Duc et al, 2005;Hanasaki et al, 2008]. However, many studies account for some wet-day frequency correction [Sheffield et al, 2006;Hirabayashi et al, 2008a;Weedon et al, 2011Weedon et al, , 2014Iizumi et al, 2014], although the methodological details vary by study.…”

Section: A3 Differences Between S14fd and Other Major Forcing Data Setsmentioning

confidence: 99%

“…Although bias correction is an unavoidable procedure for using GCM outputs as the inputs of impact models, it is an additional source of uncertainty in climate risk assessments because different bias-correction methods and reference daily weather data sets often lead to different impact outcomes Hawkins et al, 2013;Seaby et al, 2015;Baker et al, 2016]. Global retrospective meteorological forcing data sets, a hybrid of reanalysis data and gridded observations [e.g., Sheffield et al, 2006;Hanasaki et al, 2008;Hirabayashi et al, 2008a;Weedon et al, 2011Weedon et al, , 2014Iizumi et al, 2014], are used as the reference climatology in impact communities when bias correction is conducted at the global scale [e.g., Hempel et al, 2013]. Earlier work examined the uncertainties in the projected climate extremes associated with Representative Concentration Pathways IIZUMI ET AL.…”

Section: Introductionmentioning

confidence: 99%

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Contributions of different bias‐correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes

et al. 2017

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The use of different bias‐correction methods and global retrospective meteorological forcing data sets as the reference climatology in the bias correction of general circulation model (GCM) daily data is a known source of uncertainty in projected climate extremes and their impacts. Despite their importance, limited attention has been given to these uncertainty sources. We compare 27 projected temperature and precipitation indices over 22 regions of the world (including the global land area) in the near (2021–2060) and distant future (2061–2100), calculated using four Representative Concentration Pathways (RCPs), five GCMs, two bias‐correction methods, and three reference forcing data sets. To widen the variety of forcing data sets, we developed a new forcing data set, S14FD, and incorporated it into this study. The results show that S14FD is more accurate than other forcing data sets in representing the observed temperature and precipitation extremes in recent decades (1961–2000 and 1979–2008). The use of different bias‐correction methods and forcing data sets contributes more to the total uncertainty in the projected precipitation index values in both the near and distant future than the use of different GCMs and RCPs. However, GCM appears to be the most dominant uncertainty source for projected temperature index values in the near future, and RCP is the most dominant source in the distant future. Our findings encourage climate risk assessments, especially those related to precipitation extremes, to employ multiple bias‐correction methods and forcing data sets in addition to using different GCMs and RCPs.

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“…For rainfall, as in previous work [Motoya et al, 2002;Hirabayashi et al, 2008b;Hanasaki et al, 2008], the equation of Kondo [1994] was used:…”

Section: 1002/2017jd026613mentioning

confidence: 99%

Section: 1002/2017jd026613mentioning

confidence: 99%

Section: A3 Differences Between S14fd and Other Major Forcing Data Setsmentioning

confidence: 99%

Section: A3 Differences Between S14fd and Other Major Forcing Data Setsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Contributions of different bias‐correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes

et al. 2017

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Modeling U.S. water resources under climate change

et al. 2014

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Water is at the center of a complex and dynamic system involving climatic, biological, hydrological, physical, and human interactions. We demonstrate a new modeling system that integrates climatic and hydrological determinants of water supply with economic and biological drivers of sectoral and regional water requirement while taking into account constraints of engineered water storage and transport systems. This modeling system is an extension of the Massachusetts Institute of Technology (MIT) Integrated Global System Model framework and is unique in its consistent treatment of factors affecting water resources and water requirements. Irrigation demand, for example, is driven by the same climatic conditions that drive evapotranspiration in natural systems and runoff, and future scenarios of water demand for power plant cooling are consistent with energy scenarios driving climate change. To illustrate the modeling system we select "wet" and "dry" patterns of precipitation for the United States from general circulation models used in the Climate Model Intercomparison Project (CMIP3). Results suggest that population and economic growth alone would increase water stress in the United States through mid-century. Climate change generally increases water stress with the largest increases in the Southwest. By identifying areas of potential stress in the absence of specific adaptation responses, the modeling system can help direct attention to water planning that might then limit use or add storage in potentially stressed regions, while illustrating how avoiding climate change through mitigation could change likely outcomes.

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Global-scale analysis on future changes in flow regimes using Gini and Lorenz asymmetry coefficients

et al. 2014

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By introducing two scalar quantities, namely, the Gini and Lorenz asymmetry coefficients, we examined their characteristics and applicability to the global analysis of changes in river flow regimes under future climate change. First, by applying these coefficients to river discharge data, we showed that various types of flow-duration curves can be interpreted quantitatively in terms of the seasonal inequality in the discharge (i.e., the unevenness of the temporal distribution of river discharge). Their statistical characteristics, based on five theoretical distribution functions frequently used in hydrological analysis, were also shown. Next we used these coefficients to evaluate the seasonal inequality of major global rivers using the global hydrological model H08 for four 30 year time spans (1960-1989, 2010-2039, 2040-2069, and 2070-2099) under four climate-change scenarios. We used ensembles of hydrological simulation results with five general circulation models. From the analysis of the Gini coefficient, future changes in seasonal inequality show a contrasting geographical pattern: a decreasing trend at high northern latitudes and an increasing trend in most other areas. The Lorenz asymmetry coefficient shows large changes at high northern latitudes, attributable to major shifts in the flow regime accompanied by different snow-melting properties under different future climate scenarios. Although a flow-duration curve is a pictorial representation of river discharge suitable for one specific site, by depicting the geographical distribution of these two coefficients along river channels, different characteristics of flow-duration curves at different sites can be detected, even within the same river basin.

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An integrated model for the assessment of global water resources – Part 1: Model description and input meteorological forcing

Cited by 514 publications

References 46 publications

Contributions of different bias‐correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes

Contributions of different bias‐correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes

Modeling U.S. water resources under climate change

Global-scale analysis on future changes in flow regimes using Gini and Lorenz asymmetry coefficients

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