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1. Introduction Operational streamflow prediction for water resources management in the western United States depends on winter and spring forecasts of runoff volumes for the relatively dry late-spring and summer period. Target forecast periods vary for different end use applications, and also regionally, with April–July common to the southern half of the domain and April–September more common to the northern half (where the peak runoff, a response to melting snowpack, occurs later), although
1. Introduction Operational streamflow prediction for water resources management in the western United States depends on winter and spring forecasts of runoff volumes for the relatively dry late-spring and summer period. Target forecast periods vary for different end use applications, and also regionally, with April–July common to the southern half of the domain and April–September more common to the northern half (where the peak runoff, a response to melting snowpack, occurs later), although
1. Introduction The El Niño–Southern Oscillation (ENSO) phenomenon is now recognized as a primary mode of seasonal climatic variability, particularly in the Tropics ( Ropelewski and Halpert 1987 ). Sensitivity of streamflow to ENSO has been investigated for a number of rivers ( Wang and Eltahir 1999 ; Whitaker et al. 2001 ; Dettinger et al. 2000 ). These relationships provide a basis to develop streamflow predictions to aid water resources management. The work reported here is part of such an
1. Introduction The El Niño–Southern Oscillation (ENSO) phenomenon is now recognized as a primary mode of seasonal climatic variability, particularly in the Tropics ( Ropelewski and Halpert 1987 ). Sensitivity of streamflow to ENSO has been investigated for a number of rivers ( Wang and Eltahir 1999 ; Whitaker et al. 2001 ; Dettinger et al. 2000 ). These relationships provide a basis to develop streamflow predictions to aid water resources management. The work reported here is part of such an
1. Introduction The use of computational models of hydrologic systems has become a nearly ubiquitous way to forecast streamflow and plan for the allocation of water resources. However, these predictions are often biased, because they are subject to systematic errors in the model inputs, model parameter values, and process representations. Regardless of the source of these errors, which are often difficult to determine, the introduction of such biases in predictions degrades their quality
1. Introduction The use of computational models of hydrologic systems has become a nearly ubiquitous way to forecast streamflow and plan for the allocation of water resources. However, these predictions are often biased, because they are subject to systematic errors in the model inputs, model parameter values, and process representations. Regardless of the source of these errors, which are often difficult to determine, the introduction of such biases in predictions degrades their quality
1. Introduction In the western United States, over half of the water supply comes from mountain snowpacks, and over the past 50 yr, warmer winters and springs have led to earlier snowmelt ( Stewart et al. 2005 ). The fraction of annual streamflow that runs off during late spring and early summer has declined by 10% to 25% ( Roos 1991 ; Wahl 1992 ; Dettinger and Cayan 1995 ). Snowmelt runoff timing has advanced by approximately one to three weeks in the large majority of mountainous catchments
1. Introduction In the western United States, over half of the water supply comes from mountain snowpacks, and over the past 50 yr, warmer winters and springs have led to earlier snowmelt ( Stewart et al. 2005 ). The fraction of annual streamflow that runs off during late spring and early summer has declined by 10% to 25% ( Roos 1991 ; Wahl 1992 ; Dettinger and Cayan 1995 ). Snowmelt runoff timing has advanced by approximately one to three weeks in the large majority of mountainous catchments
1. Introduction Flooding and drought are the most frequent natural hazards, and water resource management is one of the most challenging problems the world is facing. Therefore, hydrological forecast, especially streamflow forecast, is of great interest, and it is a major application of numerical weather prediction (NWP) output. NWP forecasts of precipitation and temperature can be incorporated into a flood warning system, and the forecast lead time can be significantly increased (e
1. Introduction Flooding and drought are the most frequent natural hazards, and water resource management is one of the most challenging problems the world is facing. Therefore, hydrological forecast, especially streamflow forecast, is of great interest, and it is a major application of numerical weather prediction (NWP) output. NWP forecasts of precipitation and temperature can be incorporated into a flood warning system, and the forecast lead time can be significantly increased (e
uncertainties. Meanwhile, climate variations, human activities such as the construction of dams and sluice gates, water withdrawal for agricultural, industrial and urban needs, and land use–land cover changes ( Ye et al. 2003 ; Isik et al. 2008 ) bring new challenges in the spatiotemporal variation analysis of the water cycle and distributed modeling at the basin scale ( Xia and Zhang 2008 ). Some observed hydrologic data—for example, streamflow and water table—become less representative to natural
uncertainties. Meanwhile, climate variations, human activities such as the construction of dams and sluice gates, water withdrawal for agricultural, industrial and urban needs, and land use–land cover changes ( Ye et al. 2003 ; Isik et al. 2008 ) bring new challenges in the spatiotemporal variation analysis of the water cycle and distributed modeling at the basin scale ( Xia and Zhang 2008 ). Some observed hydrologic data—for example, streamflow and water table—become less representative to natural
detect and attribute those hydrometeorological changes to anthropogenic effects. The present article is one of a series of papers describing detection and attribution of the causes of hydroclimatological change in the western United States ( Barnett et al. 2008 ; Bonfils et al. 2008 ; Pierce et al. 2008 ). In particular, this paper focuses on shifts in the timing of streamflow. We investigate whether the shifts in streamflow over the past 50 yr are unlikely to have come about by natural
detect and attribute those hydrometeorological changes to anthropogenic effects. The present article is one of a series of papers describing detection and attribution of the causes of hydroclimatological change in the western United States ( Barnett et al. 2008 ; Bonfils et al. 2008 ; Pierce et al. 2008 ). In particular, this paper focuses on shifts in the timing of streamflow. We investigate whether the shifts in streamflow over the past 50 yr are unlikely to have come about by natural
1. Introduction Hydrological processes reflect combined effects of climate, vegetation, and soil ( Rodriguez-Iturbe 2000 ; Rodriguez-Iturbe et al. 2001 ; Rodriguez-Iturbe and Porporato 2005 ), resulting in changes of streamflow at the basin scale ( Guo et al. 2002 ; Chen et al. 2007 ). Changes of climate combined with human activities (e.g., land reclamation and soil and water conservation engineering) have led to massive changes in hydrological processes in many basins globally. These
1. Introduction Hydrological processes reflect combined effects of climate, vegetation, and soil ( Rodriguez-Iturbe 2000 ; Rodriguez-Iturbe et al. 2001 ; Rodriguez-Iturbe and Porporato 2005 ), resulting in changes of streamflow at the basin scale ( Guo et al. 2002 ; Chen et al. 2007 ). Changes of climate combined with human activities (e.g., land reclamation and soil and water conservation engineering) have led to massive changes in hydrological processes in many basins globally. These
balance. Therefore, the issue of how to use forcing data with large errors to calibrate land surface models is not a trivial one. As indicated by Milly and Dunne (2002a) , most observed streamflow has a 5% error and some has up to 10%–15% error in mountainous regions. However, precipitation errors are usually 30% or higher in cold regions. Therefore in these regions, optimization algorithms, observed streamflow, and available land surface models can be used to inversely estimate precipitation
balance. Therefore, the issue of how to use forcing data with large errors to calibrate land surface models is not a trivial one. As indicated by Milly and Dunne (2002a) , most observed streamflow has a 5% error and some has up to 10%–15% error in mountainous regions. However, precipitation errors are usually 30% or higher in cold regions. Therefore in these regions, optimization algorithms, observed streamflow, and available land surface models can be used to inversely estimate precipitation
1. Introduction Accurate forecasts of seasonal streamflow volumes assist a broad array of water (and other) resource decision makers ( Pagano et al. 2004 ). Therefore, forecasters have a strong interest in the accuracy of seasonal forecasts, and in the potential for improvement of forecast accuracy. Nonetheless, Pagano et al. (2004) reported that the skill of western U.S. seasonal streamflow forecasts generally has not improved since the 1960s. The current skill of seasonal hydrological
1. Introduction Accurate forecasts of seasonal streamflow volumes assist a broad array of water (and other) resource decision makers ( Pagano et al. 2004 ). Therefore, forecasters have a strong interest in the accuracy of seasonal forecasts, and in the potential for improvement of forecast accuracy. Nonetheless, Pagano et al. (2004) reported that the skill of western U.S. seasonal streamflow forecasts generally has not improved since the 1960s. The current skill of seasonal hydrological
