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; Wagener et al. 2001 ; Boyle et al. 2001 ), the Nedbør-Afstrømnings model (NAM; Madsen 2000 ), and the Tank model ( Sugawara 1995 ). The HyMOD accounts for two different components in the hydrology of the watersheds. The fast component comprises surface processes like runoff while the slower component comprises subsoil processes like infiltration and interflow. Hence, the HyMOD uses a nonlinear tank connected to two tanks, each parameterizing the two processes of different rates. NAM ( Madsen 2000
; Wagener et al. 2001 ; Boyle et al. 2001 ), the Nedbør-Afstrømnings model (NAM; Madsen 2000 ), and the Tank model ( Sugawara 1995 ). The HyMOD accounts for two different components in the hydrology of the watersheds. The fast component comprises surface processes like runoff while the slower component comprises subsoil processes like infiltration and interflow. Hence, the HyMOD uses a nonlinear tank connected to two tanks, each parameterizing the two processes of different rates. NAM ( Madsen 2000
, and these maps continue to be periodically updated by the NWS (e.g., Bonnin et al. 2006 ). Fig . 25-1. Intensity–duration–frequency curve for Baltimore, Maryland, from TP-40 ( Hershfield 1961a ). [From Weather Bureau (1955); Source: NOAA/NWS.] The focus of this chapter is progress in hydrology for the last 100 years. As with any effort to track the progress of a field over a century, it is not quite possible to document all the advancements made across all subdisciplines. However, to make our
, and these maps continue to be periodically updated by the NWS (e.g., Bonnin et al. 2006 ). Fig . 25-1. Intensity–duration–frequency curve for Baltimore, Maryland, from TP-40 ( Hershfield 1961a ). [From Weather Bureau (1955); Source: NOAA/NWS.] The focus of this chapter is progress in hydrology for the last 100 years. As with any effort to track the progress of a field over a century, it is not quite possible to document all the advancements made across all subdisciplines. However, to make our
directions rather than the direction determined by the building bottom elevation. Transforming initial spatial rainfall distribution on roofs into a new concentrated pattern at each building downspout is called building-induced rainfall redistribution (BIRR) herein. However, whether and how the BIRR influences urban hydrologic responses are still less understood. The current work aims to figure out the following issues: 1) whether the BIRR is negligible in urban hydrology; 2) if not, what the impact of
directions rather than the direction determined by the building bottom elevation. Transforming initial spatial rainfall distribution on roofs into a new concentrated pattern at each building downspout is called building-induced rainfall redistribution (BIRR) herein. However, whether and how the BIRR influences urban hydrologic responses are still less understood. The current work aims to figure out the following issues: 1) whether the BIRR is negligible in urban hydrology; 2) if not, what the impact of
-generated precipitation is subject to significant errors in amounts and timing, which adversely impact the land surface hydrology estimates. To avoid this problem, the MERRA-2 model-generated precipitation is corrected with gauge- and satellite-based precipitation observations before reaching the surface. Observation-corrected precipitation was also used in MERRA-Land, an offline, land-only reanalysis product that supplements the original MERRA product ( Reichle 2012 ). Because of the precipitation corrections and
-generated precipitation is subject to significant errors in amounts and timing, which adversely impact the land surface hydrology estimates. To avoid this problem, the MERRA-2 model-generated precipitation is corrected with gauge- and satellite-based precipitation observations before reaching the surface. Observation-corrected precipitation was also used in MERRA-Land, an offline, land-only reanalysis product that supplements the original MERRA product ( Reichle 2012 ). Because of the precipitation corrections and
1. Introduction Recent changes in the Arctic region climate ( Wanishsakpong et al. 2016 ; Whitfield et al. 2004 ), vegetation ( Xu et al. 2013 ), and other environmental functions ( Hinzman et al. 2005 ) motivate investigation of the future hydrology of the Arctic. Changes in streamflow discharge ( Mendoza et al. 2015 ; Arheimer and Lindström 2015 ; Gelfan et al. 2017 ), permafrost thaw ( Woo et al. 2007 ), subsurface water storage and flow ( Walvoord et al. 2012 ), and snow accumulation and
1. Introduction Recent changes in the Arctic region climate ( Wanishsakpong et al. 2016 ; Whitfield et al. 2004 ), vegetation ( Xu et al. 2013 ), and other environmental functions ( Hinzman et al. 2005 ) motivate investigation of the future hydrology of the Arctic. Changes in streamflow discharge ( Mendoza et al. 2015 ; Arheimer and Lindström 2015 ; Gelfan et al. 2017 ), permafrost thaw ( Woo et al. 2007 ), subsurface water storage and flow ( Walvoord et al. 2012 ), and snow accumulation and
in situations where the hydrological climate change signal is unclear ( Mudelsee et al. 2003 ; Milly et al. 2002 ). Global climate models (GCMs) are used to investigate possible trends in the past and future global climate. To quantify details of projected changes in the hydrological cycle and their potential impacts on water resources, commonly used global hydrology models (GHMs) or land surface hydrology models (LSHMs) are forced with GCM output. These hydrological simulations largely depend
in situations where the hydrological climate change signal is unclear ( Mudelsee et al. 2003 ; Milly et al. 2002 ). Global climate models (GCMs) are used to investigate possible trends in the past and future global climate. To quantify details of projected changes in the hydrological cycle and their potential impacts on water resources, commonly used global hydrology models (GHMs) or land surface hydrology models (LSHMs) are forced with GCM output. These hydrological simulations largely depend
variability of snow grain size with time and depth can have a large impact on the snowpack brightness temperatures ( Foster et al. 2005 ). Additionally, differences in the dielectric properties between liquid and frozen water lead to decreased volume scattering and amplified absorption, as the liquid water content of the snowpack increases. This means that meaningful derivation of snow parameters from passive microwave satellite observations for wet snowpacks is difficult. Land surface hydrology models
variability of snow grain size with time and depth can have a large impact on the snowpack brightness temperatures ( Foster et al. 2005 ). Additionally, differences in the dielectric properties between liquid and frozen water lead to decreased volume scattering and amplified absorption, as the liquid water content of the snowpack increases. This means that meaningful derivation of snow parameters from passive microwave satellite observations for wet snowpacks is difficult. Land surface hydrology models
1. Introduction Arid/semiarid regions, covering a large fraction of the global land area ( Zeng et al. 2008 ), are affected by high-impact flooding associated with infrequent extreme rainfall events (e.g., Schick 1988 ). The hydrology, hydrometeorology, and hydroclimatology of flooding in arid/semiarid regions are poorly understood (e.g., Higgins et al. 2003 ), and yet are of great societal importance. Our study focuses on the central Arizona region in the southwestern United States. Much of
1. Introduction Arid/semiarid regions, covering a large fraction of the global land area ( Zeng et al. 2008 ), are affected by high-impact flooding associated with infrequent extreme rainfall events (e.g., Schick 1988 ). The hydrology, hydrometeorology, and hydroclimatology of flooding in arid/semiarid regions are poorly understood (e.g., Higgins et al. 2003 ), and yet are of great societal importance. Our study focuses on the central Arizona region in the southwestern United States. Much of
al. (2002 , 2006) , and references therein]. Because this paper deals entirely with hydrologic changes, it is worthwhile to describe in more detail both the precipitation and land surface schemes used in both models. In the R30 model, surface hydrology and precipitation are computed using a geographically uniform 15-cm “bucket” and the “moist convective adjustment” scheme, respectively, which are described by Manabe (1969) . Runoff is computed when the amount of liquid water exceeds the field
al. (2002 , 2006) , and references therein]. Because this paper deals entirely with hydrologic changes, it is worthwhile to describe in more detail both the precipitation and land surface schemes used in both models. In the R30 model, surface hydrology and precipitation are computed using a geographically uniform 15-cm “bucket” and the “moist convective adjustment” scheme, respectively, which are described by Manabe (1969) . Runoff is computed when the amount of liquid water exceeds the field
1. Introduction A correct representation of the soil water buffering in land surface schemes used for weather and climate prediction is essential to accurately simulate surface water fluxes toward both the atmosphere and rivers ( van den Hurk et al. 2005 ; Hirschi et al. 2006a ). Moreover, the energy partition at the surface is largely driven by the soil moisture, which directly influences the Bowen ratio. Differences in the treatment of the surface energy balance and soil hydrology between
1. Introduction A correct representation of the soil water buffering in land surface schemes used for weather and climate prediction is essential to accurately simulate surface water fluxes toward both the atmosphere and rivers ( van den Hurk et al. 2005 ; Hirschi et al. 2006a ). Moreover, the energy partition at the surface is largely driven by the soil moisture, which directly influences the Bowen ratio. Differences in the treatment of the surface energy balance and soil hydrology between
