[1] Current land surface models use increasingly complex descriptions of the processes that they represent. Increase in complexity is accompanied by an increase in the number of model parameters, many of which cannot be measured directly at large spatial scales. A Monte Carlo framework was used to evaluate the sensitivity and identifiability of ten parameters controlling surface and subsurface runoff generation in the Variable Infiltration Capacity model (VIC). Using the Monte Carlo Analysis Toolbox (MCAT), parameter sensitivities were studied for four U.S. watersheds along a hydroclimatic gradient, based on a 20-year data set developed for the Model Parameter Estimation Experiment (MOPEX). Results showed that simulated streamflows are sensitive to three parameters when evaluated with different objective functions. Sensitivity of the infiltration parameter (b) and the drainage parameter (exp) were strongly related to the hydroclimatic gradient. The placement of vegetation roots played an important role in the sensitivity of model simulations to the thickness of the second soil layer (thick 2 ). Overparameterization was found in the base flow formulation indicating that a simplified version could be implemented. Parameter sensitivity was more strongly dictated by climatic gradients than by changes in soil properties. Results showed how a complex model can be reduced to a more parsimonious form, leading to a more identifiable model with an increased chance of successful regionalization to ungauged basins. Although parameter sensitivities are strictly valid for VIC, this model is representative of a wider class of macroscale hydrological models. Consequently, the results and methodology will have applicability to other hydrological models.Citation: Demaria, E. M., B. Nijssen, and T. Wagener (2007), Monte Carlo sensitivity analysis of land surface parameters using the Variable Infiltration Capacity model,

Abstract. Commonly used bias correction methods such as quantile mapping (QM) assume the function of error correction values between modeled and observed distributions are stationary or time invariant. This article finds that this function of the error correction values cannot be assumed to be stationary. As a result, QM lacks justification to inflate/deflate various moments of the climate change signal. Previous adaptations of QM, most notably quantile delta mapping (QDM), have been developed that do not rely on this assumption of stationarity. Here, we outline a methodology called scaled distribution mapping (SDM), which is conceptually similar to QDM, but more explicitly accounts for the frequency of rain days and the likelihood of individual events. The SDM method is found to outperform QM, QDM, and detrended QM in its ability to better preserve raw climate model projected changes to meteorological variables such as temperature and precipitation.

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Atmospheric rivers (ARs), narrow atmospheric water vapor corridors, can contribute substantially to winter precipitation in the semiarid Southwest U.S., where natural ecosystems and humans compete for over‐allocated water resources. We investigate the hydrologic impacts of 122 ARs that occurred in the Salt and Verde river basins in northeastern Arizona during the cold seasons from 1979 to 2009. We focus on the relationship between precipitation, snow water equivalent (SWE), soil moisture, and extreme flooding. During the cold season (October through March) ARs contribute an average of 25%/29% of total seasonal precipitation for the Salt/Verde river basins, respectively. However, they contribute disproportionately to total heavy precipitation and account for 64%/72% of extreme total daily precipitation (exceeding the 98th percentile). Excess precipitation during AR occurrences contributes to snow accumulation; on the other hand, warmer than normal temperatures during AR landfallings are linked to rain‐on‐snow processes, an increase in the basins' area contributing to runoff generation, and higher melting lines. Although not all AR events are linked to extreme flooding in the basins, they do account for larger runoff coefficients. On average, ARs generate 43% of the annual maximum flows for the period studied, with 25% of the events exceeding the 10 year return period. Our analysis shows that the devastating 1993 flooding event in the region was caused by AR events. These results illustrate the importance of AR activity on the hydrology of inland semiarid regions: ARs are critical for water resources, but they can also lead to extreme flooding that affects infrastructure and human activities.