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1. Introduction While it is now very likely that the anthropogenic emissions of greenhouse gases have contributed to the observed twentieth-century global warming, and that this warming will amplify over the twenty-first century, the detection and attribution of the recent evolution of continental runoff is still an open question ( Labat et al. 2004 ; Gedney et al. 2006 , hereafter G06 ; Piao et al. 2007 ; Milliman et al. 2008 ; Gerten et al. 2008 ; Dai et al. 2009 ; Alkama et al. 2010b
1. Introduction While it is now very likely that the anthropogenic emissions of greenhouse gases have contributed to the observed twentieth-century global warming, and that this warming will amplify over the twenty-first century, the detection and attribution of the recent evolution of continental runoff is still an open question ( Labat et al. 2004 ; Gedney et al. 2006 , hereafter G06 ; Piao et al. 2007 ; Milliman et al. 2008 ; Gerten et al. 2008 ; Dai et al. 2009 ; Alkama et al. 2010b
1. Introduction Conceptual rainfall–runoff models have been widely used for catchment water balance studies across the world. The observed runoff used to calibrate and validate these models is recorded at the catchment outlet and as such is an aggregated response of spatially variable rainfall across the catchment. There are uncertainties associated with the rainfall data, and the measured point rainfall data are usually available only at limited locations within a catchment or close to the
1. Introduction Conceptual rainfall–runoff models have been widely used for catchment water balance studies across the world. The observed runoff used to calibrate and validate these models is recorded at the catchment outlet and as such is an aggregated response of spatially variable rainfall across the catchment. There are uncertainties associated with the rainfall data, and the measured point rainfall data are usually available only at limited locations within a catchment or close to the
1. Introduction Changes in spatial and temporal patterns of climate variables associated with global warming will have an effect on regional- and catchment-scale hydrological processes. In particular, changes in the extreme rainfall because of intensified hydrological cycle will be amplified in the runoff response ( Chiew 2006 ). The change in runoff will have significant implications on water resources, and for this reason there have been literally thousands of studies on the effect of climate
1. Introduction Changes in spatial and temporal patterns of climate variables associated with global warming will have an effect on regional- and catchment-scale hydrological processes. In particular, changes in the extreme rainfall because of intensified hydrological cycle will be amplified in the runoff response ( Chiew 2006 ). The change in runoff will have significant implications on water resources, and for this reason there have been literally thousands of studies on the effect of climate
problem in the Arctic where gauge undercatch is often substantial. Precipitation underestimates of 20% to 25% have been determined across North America ( Karl et al. 1993 ), while biases of 80% to 120% (in winter) have been estimated for the terrestrial Arctic north of 45°N ( Yang et al. 2005 ). In regions where precipitation exceeded potential evapotranspiration (PET), uncertainty in precipitation translated to an uncertainty in simulated runoff of roughly similar magnitude ( Fekete et al. 2004 ). To
problem in the Arctic where gauge undercatch is often substantial. Precipitation underestimates of 20% to 25% have been determined across North America ( Karl et al. 1993 ), while biases of 80% to 120% (in winter) have been estimated for the terrestrial Arctic north of 45°N ( Yang et al. 2005 ). In regions where precipitation exceeded potential evapotranspiration (PET), uncertainty in precipitation translated to an uncertainty in simulated runoff of roughly similar magnitude ( Fekete et al. 2004 ). To
1. Introduction More than a century ago, Schreiber (1904) analyzed data of the annual mean discharge Ro versus the annual precipitation totals P of continental European river basins fitted to a polynomial curve. Looking at this curve lead him to assume that it can be presented by the formula Schreiber noted on “the physical meaning of the parameter N [that it] approaches the difference precipitation P minus runoff Ro (=evaporation E ) better for larger precipitation,” which
1. Introduction More than a century ago, Schreiber (1904) analyzed data of the annual mean discharge Ro versus the annual precipitation totals P of continental European river basins fitted to a polynomial curve. Looking at this curve lead him to assume that it can be presented by the formula Schreiber noted on “the physical meaning of the parameter N [that it] approaches the difference precipitation P minus runoff Ro (=evaporation E ) better for larger precipitation,” which
gaps, however, in our knowledge of how SSTs affect continental streamflow on a global scale. Progress toward understanding the historical connections between global runoff and SSTs requires a spatially extensive streamflow dataset. A high-quality and spatially detailed streamflow dataset does not exist. Instead of using measured streamflow, the analysis presented in this paper relies on the 0.5°-resolution climate dataset for the twentieth century ( Mitchell 2005 ) coupled to a water-balance model
gaps, however, in our knowledge of how SSTs affect continental streamflow on a global scale. Progress toward understanding the historical connections between global runoff and SSTs requires a spatially extensive streamflow dataset. A high-quality and spatially detailed streamflow dataset does not exist. Instead of using measured streamflow, the analysis presented in this paper relies on the 0.5°-resolution climate dataset for the twentieth century ( Mitchell 2005 ) coupled to a water-balance model
balance (SMB), calving, and basal melting] and surface runoff increased as temperatures rose. During summer, temperature in Greenland coastal areas increased by 1.7°C from 1991 to 2006 ( Comiso 2003 , 2006 ; Hanna et al. 2008 ). There are significant uncertainties in modeling Greenland ice sheet dynamics (e.g., Parizek and Alley 2004 ; Alley et al. 2007 ; Nick et al. 2009 ), partly related to insufficient knowledge of basal conditions at the ice bed and ice–ocean interface. In contrast, Greenland
balance (SMB), calving, and basal melting] and surface runoff increased as temperatures rose. During summer, temperature in Greenland coastal areas increased by 1.7°C from 1991 to 2006 ( Comiso 2003 , 2006 ; Hanna et al. 2008 ). There are significant uncertainties in modeling Greenland ice sheet dynamics (e.g., Parizek and Alley 2004 ; Alley et al. 2007 ; Nick et al. 2009 ), partly related to insufficient knowledge of basal conditions at the ice bed and ice–ocean interface. In contrast, Greenland
to research. This is a very important challenge that not only hinders the generation of knowledge in the hydrological sciences but also many spheres of development. Data scarcity in both spatial and temporal contexts is a major issue in water resource management in Africa, given the recent uncertain trends of climate change, land cover, and water withdrawal associated with rapid population growth. The present study aimed to improve monthly and annual runoff estimates and to assess runoff trend
to research. This is a very important challenge that not only hinders the generation of knowledge in the hydrological sciences but also many spheres of development. Data scarcity in both spatial and temporal contexts is a major issue in water resource management in Africa, given the recent uncertain trends of climate change, land cover, and water withdrawal associated with rapid population growth. The present study aimed to improve monthly and annual runoff estimates and to assess runoff trend
1. Introduction Improving the accuracy of runoff predictions in ungauged catchments is one of most challenging tasks in hydrology ( Franks et al. 2005 ; Goswami et al. 2007 ; Sivapalan et al. 2003 ). Parameter regionalization in lumped rainfall–runoff models is a commonly used method to transfer optimized parameter values to target ungauged catchments. Various regionalization methods have been developed, such as nearest neighbor, kriging, site similarity, and regression methods ( Kay et al
1. Introduction Improving the accuracy of runoff predictions in ungauged catchments is one of most challenging tasks in hydrology ( Franks et al. 2005 ; Goswami et al. 2007 ; Sivapalan et al. 2003 ). Parameter regionalization in lumped rainfall–runoff models is a commonly used method to transfer optimized parameter values to target ungauged catchments. Various regionalization methods have been developed, such as nearest neighbor, kriging, site similarity, and regression methods ( Kay et al
that need to be addressed in this context: changes in mean annual flow and changes in interannual runoff variability. We deal with both of these issues in this paper. There are at least two approaches that can be used to assess the change in annual mean runoff and interannual runoff variability that may result from climate change. The first approach is based on developing a rainfall-runoff model, calibrated for the catchments under consideration, and using generated projections from general
that need to be addressed in this context: changes in mean annual flow and changes in interannual runoff variability. We deal with both of these issues in this paper. There are at least two approaches that can be used to assess the change in annual mean runoff and interannual runoff variability that may result from climate change. The first approach is based on developing a rainfall-runoff model, calibrated for the catchments under consideration, and using generated projections from general
