Intercomparison of Meteorological Forcing Data from Empirical and Mesoscale Model Sources in the North Fork American River Basin in Northern Sierra Nevada, California*

Water Resources Research

Kavetski

et al. 2015

Self Cite

Estimating basin-mean precipitation in complex terrain is difficult due to uncertainty in the topographical representativeness of precipitation gauges relative to the basin. To address this issue, we use Bayesian methodology coupled with a multimodel framework to infer basin-mean precipitation from streamflow observations, and we apply this approach to snow-dominated basins in the Sierra Nevada of California. Using streamflow observations, forcing data from lower-elevation stations, the Bayesian Total Error Analysis (BATEA) methodology and the Framework for Understanding Structural Errors (FUSE), we infer basin-mean precipitation, and compare it to basin-mean precipitation estimated using topographically informed interpolation from gauges (PRISM, the Parameter-elevation Regression on Independent Slopes Model). The BATEA-inferred spatial patterns of precipitation show agreement with PRISM in terms of the rank of basins from wet to dry but differ in absolute values. In some of the basins, these differences may reflect biases in PRISM, because some implied PRISM runoff ratios may be inconsistent with the regional climate. We also infer annual time series of basin precipitation using a two-step calibration approach. Assessment of the precision and robustness of the BATEA approach suggests that uncertainty in the BATEAinferred precipitation is primarily related to uncertainties in hydrologic model structure. Despite these limitations, time series of inferred annual precipitation under different model and parameter assumptions are strongly correlated with one another, suggesting that this approach is capable of resolving year-to-year variability in basin-mean precipitation.

Section: Discussionmentioning

confidence: 98%

“…weather models now produce spatially distributed precipitation fields at sufficient resolution to resolve the study basins [Wayand et al, 2013]. Thus, approaches exist to reduce uncertainty in basin-mean precipitation.…”

Section: 1002/2014wr016736mentioning

confidence: 99%

Estimating mountain basin‐mean precipitation from streamflow using Bayesian inference

Henn

Water Resources Research

Kavetski

et al. 2015

Self Cite

“…Forcing uncertainty is enhanced in complex terrain where meteorological variables exhibit high spatial variability Flint and Childs, 1987;Herrero and Polo, 2012;Lundquist and Cayan, 2007). As a result, the choice of forcing data can yield substantial differences in calibrated model parameters and in modeled hydrologic processes, such as snowmelt and evapotranspiration Wayand et al, 2013). Thus, forcing uncertainty demands more attention in snow-affected watersheds.…”

Section: S Raleigh Et Al: Physical Model Sensitivity To Forcing mentioning

confidence: 99%

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

Raleigh

Lundquist

Hydrol. Earth Syst. Sci.

2015

162

128

Abstract. Physically based models provide insights into key hydrologic processes but are associated with uncertainties due to deficiencies in forcing data, model parameters, and model structure. Forcing uncertainty is enhanced in snowaffected catchments, where weather stations are scarce and prone to measurement errors, and meteorological variables exhibit high variability. Hence, there is limited understanding of how forcing error characteristics affect simulations of cold region hydrology and which error characteristics are most important. Here we employ global sensitivity analysis to explore how (1) different error types (i.e., bias, random errors), (2) different error probability distributions, and (3) different error magnitudes influence physically based simulations of four snow variables (snow water equivalent, ablation rates, snow disappearance, and sublimation). We use the Sobol' global sensitivity analysis, which is typically used for model parameters but adapted here for testing model sensitivity to coexisting errors in all forcings. We quantify the Utah Energy Balance model's sensitivity to forcing errors with 1 840 000 Monte Carlo simulations across four sites and five different scenarios. Model outputs were (1) consistently more sensitive to forcing biases than random errors, (2) generally less sensitive to forcing error distributions, and (3) critically sensitive to different forcings depending on the relative magnitude of errors. For typical error magnitudes found in areas with drifting snow, precipitation bias was the most important factor for snow water equivalent, ablation rates, and snow disappearance timing, but other forcings had a more dominant impact when precipitation uncertainty was due solely to gauge undercatch. Additionally, the relative importance of forcing errors depended on the model output of interest. Sensitivity analysis can reveal which forcing error characteristics matter most for hydrologic modeling.

“…Several past studies have evaluated how different climate forcing datasets affect the hydrologic simulations (Guo et al 2006;Materia et al 2010;Mo et al 2012;Wayand et al 2013). The results from the previous studies show that differences in precipitation affect the magnitude of runoff while other forcing variables such as radiative fluxes can affect estimates of evaporation and rates of snowmelt, changing both the overall partitioning of precipitation to evapotranspiration (ET) and runoff and changing the timing of spring snowmelt runoff (Materia et al 2010;Gao et al 2011;Nasonova et al 2011;Haddeland et al 2012;Wayand et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Hydrologic Implications of Different Large-Scale Meteorological Model Forcing Datasets in Mountainous Regions

Mizukami

Journal of Hydrometeorology

Slater

et al. 2014

Process-based hydrologic models require extensive meteorological forcing data, including data on precipitation, temperature, shortwave and longwave radiation, humidity, surface pressure, and wind speed. Observations of precipitation and temperature are more common than other variables; consequently, radiation, humidity, pressure, and wind speed often must be either estimated using empirical relationships with precipitation and temperature or obtained from numerical weather prediction models. This study examines two climate forcing datasets using different methods to estimate radiative energy fluxes and humidity and investigates the effects of the choice of forcing data on hydrologic simulations over the mountainous upper Colorado River basin (293 472 km 2 ). Comparisons of model simulations forced by two climate datasets illustrate that the methods used to estimate shortwave radiation impact hydrologic states and fluxes, particularly at high elevation (e.g., ;20% difference in runoff above 3000-m elevation), substantially altering the timing of snowmelt and runoff (;20 days difference) and the partitioning of precipitation between evapotranspiration and runoff. The different forcing datasets also exhibit differences in hydrologic sensitivity to interannual temperature at high elevation. The results suggest that the choice of forcing dataset is an important consideration when conducting climate impact assessments and the subsequent applications of these assessments for water resources planning and management.