Abstract. Assessing the impacts of climate change on hydrologic systems is critical for developing adaptation and mitigation strategies for water resource management, risk control and ecosystem conservation practices. Such assessments are commonly accomplished using outputs from a hydrologic model forced with future precipitation and temperature projections. The algorithms used in the hydrologic model components (e.g., runoff generation) can introduce significant uncertainties in the simulated hydrologic variables, yet the identification and quantification of such uncertainties is rarely studied. Here, a modeling framework is developed that integrates multiple runoff generation algorithms with a routing model and associated parameter optimizations. This framework is able to identify uncertainties from both hydrologic model components and climate forcings as well as associated parameterization. Three fundamentally different runoff generation approaches: runoff coefficient method (RCM, conceptual), variable infiltration capacity (VIC, physically-based, infiltration excess) and simple-TOPMODEL (STP, physically-based, saturation excess), are coupled with Hillslope River Routing model to simulate streamflow. A case study conducted in Santa Barbara County, California, reveals that the median changes are 1–10 % increases in mean annual discharge (Qm) and 10–40 % increases in annual maximum daily discharge (Qp) and 100-yr flood discharge (Q100). The Bayesian Model Averaging analysis indicates that the probability of increase in streamflow can be up to 85 %. However, the simulated discharge uncertainties are large (i.e., 230 % for Qm and 330 % for Qp and Q100) with general circulation models (GCMs) and emission scenarios accounting for more than half of the total uncertainty. Hydrologic process models contribute 10–30 % of the total uncertainty, while uncertainty due to hydrologic model parameterization is almost negligible (
Abstract. The study of the relationship between water storage and runoff generation has long been a focus of the hydrological sciences. NASA’s Gravity Recovery and Climate Experiment (GRACE) mission provides monthly depth-integrated information on terrestrial water storage anomalies derived from time-variable gravity observations. As the first basin-scale storage measurement technique, these data offer potentially novel insight into the storage-discharge relationship. Here, we apply GRACE data in a streamflow recession analysis with river discharge measurements across several subdomains of the Mississippi River Basin. Non-linear regression analysis was used for 12 watersheds to determine that the fraction of baseflow in streams during non-winter months varies from 52 to 75 % regionally. Additionally, the first quantitative estimate of absolute drainable water storage was estimated. For the 2002–2014 period, the drainable storage in the Mississippi River Basin ranged from 2,900 ± 400 km3 to 3,600 ± 400 km3.
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