[1] We use sensitivity analysis to identify the parameters that are most responsible for controlling land surface model (LSM) simulations and to understand complex parameter interactions in three versions of the Noah LSM: the standard version (STD), a version enhanced with a simple groundwater module (GW), and version augmented by a dynamic phenology module (DV). We use warm season, high-frequency, near-surface states and turbulent fluxes collected over nine sites in the U.S. Southern Great Plains. We quantify changes in the pattern of sensitive parameters, the amount and nature of the interaction between parameters, and the covariance structure of the distribution of behavioral parameter sets. Using Sobol 0 's total and first-order sensitivity indexes, we show that few parameters directly control the variance of the model response. Significant parameter interaction occurs. Optimal parameter values differ between models, and the relationships between parameters also change. GW decreases unwarranted parameter interaction and appears to improve model realism, especially at wetter study sites. DV increases parameter interaction and decreases identifiability, implying it is overparameterized and/or underconstrained. At a wet site, GW has two functional modes: one that mimics STD and a second in which GW improves model function by decoupling direct evaporation and base flow. Unsupervised classification of the posterior distributions of behavioral parameter sets cannot group similar sites based solely on soil or vegetation type, helping to explain why transferability between sites and models is not straightforward. Our results suggest that the a priori assignment of parameters should also consider the climatic conditions of a study location.

[1] Data assimilation is increasingly being used to merge remotely sensed land surface variables such as soil moisture, snow, and skin temperature with estimates from land models. Its success, however, depends on unbiased model predictions and unbiased observations. Here a suite of continental-scale, synthetic soil moisture assimilation experiments is used to compare two approaches that address typical biases in soil moisture prior to data assimilation: (1) parameter estimation to calibrate the land model to the climatology of the soil moisture observations and (2) scaling of the observations to the model's soil moisture climatology. To enable this research, an optimization infrastructure was added to the NASA Land Information System (LIS) that includes gradient-based optimization methods and global, heuristic search algorithms. The land model calibration eliminates the bias but does not necessarily result in more realistic model parameters. Nevertheless, the experiments confirm that model calibration yields assimilation estimates of surface and root zone soil moisture that are as skillful as those obtained through scaling of the observations to the model's climatology. Analysis of innovation diagnostics underlines the importance of addressing bias in soil moisture assimilation and confirms that both approaches adequately address the issue.

[1] Semiarid flash floods pose a significant danger for life and property in many dry regions around the world. One effective way to mitigate flood risk lies in implementing a real-time forecast and warning system based on a rainfall-runoff model. This study used a semiarid, physics-based, and spatially distributed watershed model driven by highresolution radar rainfall input to evaluate such a system. The predictive utility of the model and dominant sources of uncertainty were investigated for several runoff events within the U.S. Department of Agriculture Agricultural Research Service Walnut Gulch Experimental Watershed located in the southwestern United States. Sources of uncertainty considered were rainfall estimates, watershed model parameters, and initial soil moisture conditions. Results derived through a variance-based comprehensive global sensitivity analysis indicated that the high predictive uncertainty in the modeled response was heavily dominated by biases in the radar rainfall depth estimates. Key model parameters and initial model states were identified, and we generally found that modeled hillslope characteristics are more influential than channel characteristics in small semiarid basins. We also observed an inconsistency in the parameter sets identified as behavioral for different events, which suggests that model calibration to historical data is unlikely to consistently improve predictive performance for different events and that real-time parameter updating may be preferable.