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.g., Zamora et al. 2014 ). Therefore, flash flood prediction in these semiarid domains is particularly challenging and is further complicated by the dry antecedent conditions of ephemeral channel beds, resulting in large transmission losses ( Goodrich et al. 1997 ). Previous efforts to improve the physical process representation in hydrologic forecasts within these environments have focused on the implementation of spatially distributed hydrologic models, making use of gridded high
.g., Zamora et al. 2014 ). Therefore, flash flood prediction in these semiarid domains is particularly challenging and is further complicated by the dry antecedent conditions of ephemeral channel beds, resulting in large transmission losses ( Goodrich et al. 1997 ). Previous efforts to improve the physical process representation in hydrologic forecasts within these environments have focused on the implementation of spatially distributed hydrologic models, making use of gridded high
1. Introduction Hydrologic models are used to predict and manage water resources and provide insights into key hydrologic processes. Models are thus a leading strategy for understanding catchment dynamics and the impacts of variability in climate and land use, among other factors. There are numerous hydrologic models available ( Hrachowitz and Clark 2017 ), ranging from simple conceptual models (e.g., the Monthly Water Balance Model; McCabe and Markstrom 2007 ) to more complex physically
1. Introduction Hydrologic models are used to predict and manage water resources and provide insights into key hydrologic processes. Models are thus a leading strategy for understanding catchment dynamics and the impacts of variability in climate and land use, among other factors. There are numerous hydrologic models available ( Hrachowitz and Clark 2017 ), ranging from simple conceptual models (e.g., the Monthly Water Balance Model; McCabe and Markstrom 2007 ) to more complex physically
applicability of satellite products to hydrological modeling. The model, as the surrogate of natural watersheds, can diminish (or amplify) and propagate input errors into other simulated fluxes or states, reflecting useful information to the hydrologists who use them to drive the simulation of terrestrial water cycle ( Pan et al. 2010 ). With this application-oriented view, many studies have investigated the performance of satellite precipitation products by hydrological modeling in specific watersheds
applicability of satellite products to hydrological modeling. The model, as the surrogate of natural watersheds, can diminish (or amplify) and propagate input errors into other simulated fluxes or states, reflecting useful information to the hydrologists who use them to drive the simulation of terrestrial water cycle ( Pan et al. 2010 ). With this application-oriented view, many studies have investigated the performance of satellite precipitation products by hydrological modeling in specific watersheds
1. Introduction The basic theories of hydrologic systems—for example, Darcy’s law and Horton runoff scheme—were developed on the basis of multitudinous laboratory and in situ experiments. Since the 1950s, lumped conceptual hydrologic models (e.g., Burnash et al. 1973 ; Zhao 1992 ) have been widely used for many basins. Up to the present time, such models still play important roles in the study and application of rainfall–runoff calculation, hydraulic engineering design, and basin
1. Introduction The basic theories of hydrologic systems—for example, Darcy’s law and Horton runoff scheme—were developed on the basis of multitudinous laboratory and in situ experiments. Since the 1950s, lumped conceptual hydrologic models (e.g., Burnash et al. 1973 ; Zhao 1992 ) have been widely used for many basins. Up to the present time, such models still play important roles in the study and application of rainfall–runoff calculation, hydraulic engineering design, and basin
1. Introduction For many years, calibration of hydrologic models has been a crucial step in identifying parameters used to represent mechanisms that are either poorly understood, too computationally expensive to resolve, or even unnecessary for a given application. Calibration of hydrologic models has traditionally been performed by adjusting model parameters such that the simulated hydrograph best fits an observed hydrograph. This framework is often limited in that the observed outlet
1. Introduction For many years, calibration of hydrologic models has been a crucial step in identifying parameters used to represent mechanisms that are either poorly understood, too computationally expensive to resolve, or even unnecessary for a given application. Calibration of hydrologic models has traditionally been performed by adjusting model parameters such that the simulated hydrograph best fits an observed hydrograph. This framework is often limited in that the observed outlet
1. Introduction Floods have resulted in tremendous risks and damages to our society. It is now widely recognized that such events will become more frequent and intensive with the changing climate ( Hirabayashi et al. 2013 ). To predict flood risks for future conditions, scientists have relied heavily on coupled climate and hydrological models. The climate models are classified to general circulation models (GCMs) and regional climate models (RCMs) crossing over the world. Hydrological models
1. Introduction Floods have resulted in tremendous risks and damages to our society. It is now widely recognized that such events will become more frequent and intensive with the changing climate ( Hirabayashi et al. 2013 ). To predict flood risks for future conditions, scientists have relied heavily on coupled climate and hydrological models. The climate models are classified to general circulation models (GCMs) and regional climate models (RCMs) crossing over the world. Hydrological models
1. Introduction Precipitation is the key driver of the terrestrial hydrological system and the most important atmospheric input in land surface hydrological models ( Beven and Hornberger 1982 ; Su et al. 2008 ; Tong et al. 2014b ). However, direct meteorological observations are either sparse or nonexistent in many remote high mountainous areas because of their high elevation, complex terrain, and inaccessibility. This is especially true for the Third Pole (TP) ( Qiu 2008 ), which is the high
1. Introduction Precipitation is the key driver of the terrestrial hydrological system and the most important atmospheric input in land surface hydrological models ( Beven and Hornberger 1982 ; Su et al. 2008 ; Tong et al. 2014b ). However, direct meteorological observations are either sparse or nonexistent in many remote high mountainous areas because of their high elevation, complex terrain, and inaccessibility. This is especially true for the Third Pole (TP) ( Qiu 2008 ), which is the high
1. Introduction Global warming is expected to lead to considerable hydrological change ( Huntington 2006 ). A large number of hydrological modeling studies have been carried out in order to quantify the magnitude of the future hydrological change (e.g., van Roosmalen et al. 2007 ; Andrésson et al. 2004 ; Lettenmaier et al. 1999 ). Typically, the forcing data for hydrological model simulations under future climate conditions are derived from simulations with general circulation models (GCMs
1. Introduction Global warming is expected to lead to considerable hydrological change ( Huntington 2006 ). A large number of hydrological modeling studies have been carried out in order to quantify the magnitude of the future hydrological change (e.g., van Roosmalen et al. 2007 ; Andrésson et al. 2004 ; Lettenmaier et al. 1999 ). Typically, the forcing data for hydrological model simulations under future climate conditions are derived from simulations with general circulation models (GCMs
1. Introduction a. The growing number of gridded meteorological datasets: Opportunities and caveats for hydrological studies Over the past two decades, gridded meteorological datasets have been increasingly used as inputs to hydrological studies ( Habets et al. 2008 ; Coustau et al. 2015 ; Soci et al. 2016 ). Such datasets result from the combination of short-term weather model forecasts with a quite diverse number of observation sources (weather stations, radar, buoys, and satellite products
1. Introduction a. The growing number of gridded meteorological datasets: Opportunities and caveats for hydrological studies Over the past two decades, gridded meteorological datasets have been increasingly used as inputs to hydrological studies ( Habets et al. 2008 ; Coustau et al. 2015 ; Soci et al. 2016 ). Such datasets result from the combination of short-term weather model forecasts with a quite diverse number of observation sources (weather stations, radar, buoys, and satellite products
to produce good agreement between the calculated and observed runoff in some glacierized catchments ( Hagg et al. 2007 ; Gao et al. 2012 ). For simulating hydrologic processes in alpine glacierized catchments, a hydrologic model should contain snow and ice and frozen soil schemes. Unfortunately, most popular large-scale models do not consider mountain glacier–related hydrologic processes and the effect of frozen soil on discharge. Thus, for catchments where these processes are relevant, it is
to produce good agreement between the calculated and observed runoff in some glacierized catchments ( Hagg et al. 2007 ; Gao et al. 2012 ). For simulating hydrologic processes in alpine glacierized catchments, a hydrologic model should contain snow and ice and frozen soil schemes. Unfortunately, most popular large-scale models do not consider mountain glacier–related hydrologic processes and the effect of frozen soil on discharge. Thus, for catchments where these processes are relevant, it is
