[1] This first paper of the two-part series describes the objectives of the community efforts in improving the Noah land surface model (LSM), documents, through mathematical formulations, the augmented conceptual realism in biophysical and hydrological processes, and introduces a framework for multiple options to parameterize selected processes (Noah-MP). The Noah-MP's performance is evaluated at various local sites using high temporal frequency data sets, and results show the advantages of using multiple optional schemes to interpret the differences in modeling simulations. The second paper focuses on ensemble evaluations with long-term regional (basin) and global scale data sets. The enhanced conceptual realism includes (1) the vegetation canopy energy balance, (2) the layered snowpack, (3) frozen soil and infiltration, (4) soil moisture-groundwater interaction and related runoff production, and (5) vegetation phenology. Sample local-scale validations are conducted over the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site, the W3 catchment of Sleepers River, Vermont, and a French snow observation site. Noah-MP shows apparent improvements in reproducing surface fluxes, skin temperature over dry periods, snow water equivalent (SWE), snow depth, and runoff over Noah LSM version 3.0. Noah-MP improves the SWE simulations due to more accurate simulations of the diurnal variations of the snow skin temperature, which is critical for computing available energy for melting. Noah-MP also improves the simulation of runoff peaks and timing by introducing a more permeable frozen soil and more accurate simulation of snowmelt. We also demonstrate that Noah-MP is an effective research tool by which modeling results for a given process can be interpreted through multiple optional parameterization schemes in the same model framework.

[1] Results are presented from the second phase of the multiinstitution North American Land Data Assimilation System (NLDAS-2) research partnership. In NLDAS, the Noah, Variable Infiltration Capacity, Sacramento Soil Moisture Accounting, and Mosaic land surface models (LSMs) are executed over the conterminous U.S. (CONUS) in realtime and retrospective modes. These runs support the drought analysis, monitoring and forecasting activities of the National Integrated Drought Information System, as well as efforts to monitor large-scale floods. NLDAS-2 builds upon the framework of the first phase of NLDAS (NLDAS-1) by increasing the accuracy and consistency of the surface forcing data, upgrading the land surface model code and parameters, and extending the study from a 3-year (1997)(1998)(1999)) to a 30-year (1979-2008) time window. As the first of two parts, this paper details the configuration of NLDAS-2, describes the upgrades to the forcing, parameters, and code of the four LSMs, and explores overall model-to-model comparisons of land surface water and energy flux and state variables over the CONUS. Focusing on model output rather than on observations, this study seeks to highlight the similarities and differences between models, and to assess changes in output from that seen in NLDAS-1. The second part of the two-part article focuses on the validation of model-simulated streamflow and evaporation against observations. The results depict a higher level of agreement among the four models over much of the CONUS than was found in the first phase of NLDAS. This is due, in part, to recent improvements in the parameters, code, and forcing of the NLDAS-2 LSMs that were initiated following NLDAS-1. However, large inter-model differences still exist in the northeast, Lake Superior, and western mountainous regions of the CONUS, which are associated with cold season processes. In addition, variations in the representation of sub-surface hydrology in the four LSMs lead to large differences in modeled evaporation and subsurface runoff. These issues are important targets for future research by the land surface modeling community. Finally, improvement from NLDAS-1 to NLDAS-2 is summarized by comparing the streamflow measured from U.S. Geological Survey stream gauges with that simulated by four NLDAS models over 961 small basins.

For Abstract see ChemInform Abstract in Full Text.

[1] The augmented Noah land surface model described in the first part of the two-part series was evaluated here over global river basins. Across various climate zones, global-scale tests can reveal a model's weaknesses and strengths that a local-scale testing cannot. In addition, global-scale tests are more challenging than local-and catchment-scale tests. Given constant model parameters (e. g., runoff parameters) across global river basins, global-scale tests are more stringent. We assessed model performance against various satellite and ground-based observations over global river basins through six experiments that mimic a transition from the original Noah LSM to the fully augmented version. The model shows transitional improvements in modeling runoff, soil moisture, snow, and skin temperature, despite considerable increase in computational time by the fully augmented Noah-MP version compared to the original Noah LSM. The dynamic vegetation model favorably captures seasonal and spatial variability of leaf area index and green vegetation fraction. We also conducted 36 ensemble experiments with 36 combinations of optional schemes for runoff, leaf dynamics, stomatal resistance, and the b factor. Runoff schemes play a dominant and different role in controlling soil moisture and its relationship with evapotranspiration compared to ecological processes such as the b factor, vegetation dynamics, and stomatal resistance. The 36-member ensemble mean of runoff performs better than any single member over the world's 50 largest river basins, suggesting a great potential of land-based ensemble simulations for climate prediction.

This paper describes a simple, one-pot method that generates dimers of silver nanospheres in one step without any additional assembly steps. The dimers are consisted of single-crystal silver nanospheres ∼30 nm in diameter and separated by a gap of 1.8 nm wide. The key to the success of this method lies in the control of colloidal stability and oxidative etching by optimizing the amount of chloride added to a polyol synthesis. The dimers provide a well-defined system for studying the hot spot phenomenon (hot spot: the gap region of a pair of strongly coupled silver or gold nanoparticles), an extremely important but poorly understood subject in surface-enhanced Raman scattering (SERS). Because of the relatively small size of the silver nanospheres, only those molecules trapped in the hot spot region are expected to contribute to the detected SERS signals. By correlating SERS measurements with SEM imaging, we found that the SERS enhancement factor within the hot spot region of such a dimer was on the order of 2 × 107.

[1] This is the second part of a study on continental-scale water and energy flux analysis and validation conducted in phase 2 of the North American Land Data Assimilation System project (NLDAS-2). The first part concentrates on a model-by-model comparison of mean annual and monthly water fluxes, energy fluxes and state variables. In this second part, the focus is on the validation of simulated streamflow from four land surface models (Noah, Mosaic, Sacramento Soil Moisture Accounting (SAC-SMA), and Variable Infiltration Capacity (VIC) models) and their ensemble mean. Comparisons are made against 28-years (1 October 1979-30 September 2007) of United States Geological Survey observed streamflow for 961 small basins and 8 major basins over the conterminous United States (CONUS). Relative bias, anomaly correlation and Nash-Sutcliffe Efficiency (NSE) statistics at daily to annual time scales are used to assess model-simulated streamflow. The Noah (the Mosaic) model overestimates (underestimates) mean annual runoff and underestimates (overestimates) mean annual evapotranspiration. The SAC-SMA and VIC models simulate the mean annual runoff and evapotranspiration well when compared with the observations. The ensemble mean is closer to the mean annual observed streamflow for both the 961 small basins and the 8 major basins than is the mean from any individual model. All of the models, as well as the ensemble mean, have large daily, weekly, monthly, and annual streamflow anomaly correlations for most basins over the CONUS, implying strong simulation skill. However, the daily, weekly, and monthly NSE analysis results are not necessarily encouraging, in particular for daily streamflow. The Noah and Mosaic models are useful (NSE > 0.4) only for about 10% of the 961 small basins, the SAC-SMA and VIC models are useful for about 30% of the 961 small basins, and the ensemble mean is useful for about 42% of the 961 small basins. As the time scale increases, the NSE increases as expected. However, even for monthly streamflow, the ensemble mean is useful for only 75% of the 961 small basins.

Drought prediction is of critical importance to early warning for drought managements. This review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid methods. Statistical drought prediction is achieved by modeling the relationship between drought indices of interest and a suite of potential predictors, including large‐scale climate indices, local climate variables, and land initial conditions. Dynamical meteorological drought prediction relies on seasonal climate forecast from general circulation models (GCMs), which can be employed to drive hydrological models for agricultural and hydrological drought prediction with the predictability determined by both climate forcings and initial conditions. Challenges still exist in drought prediction at long lead time and under a changing environment resulting from natural and anthropogenic factors. Future research prospects to improve drought prediction include, but are not limited to, high‐quality data assimilation, improved model development with key processes related to drought occurrence, optimal ensemble forecast to select or weight ensembles, and hybrid drought prediction to merge statistical and dynamical forecasts.

Nelun, "Drought onset mechanisms revealed by satellite solar-induced chlorophyll fluorescence: Insights from two contrasting extreme events" (2015). studies to explore the potential of satellite-observed solar-induced chlorophyll fluorescence (SIF) for monitoring drought dynamics. We find that the spatial patterns of negative SIF anomalies from the Global Ozone Monitoring Experiment 2 (GOME-2) closely resembled drought intensity maps from the U.S. Drought Monitor for both events. The drought-induced suppression of SIF occurred throughout 2011 but was exacerbated in summer in the Texas drought. This event was characterized by a persistent depletion of root zone soil moisture caused by yearlong below-normal precipitation. In contrast, for the central Great Plains drought, warmer temperatures and relatively normal precipitation boosted SIF in the spring of 2012; however, a sudden drop in precipitation coupled with unusually high temperatures rapidly depleted soil moisture through evapotranspiration, leading to a rapid onset of drought in early summer. Accordingly, SIF reversed from above to below normal. For both regions, the GOME-2 SIF anomalies were significantly correlated with those of root zone soil moisture, indicating that the former can potentially be used as proxy of the latter for monitoring agricultural droughts with different onset mechanisms. Further analyses indicate that the contrasting dynamics of SIF during these two extreme events were caused by changes in both fraction of absorbed photosynthetically active radiation fPAR and fluorescence yield, suggesting that satellite SIF is sensitive to both structural and physiological/biochemical variations of vegetation. We conclude that the emerging satellite SIF has excellent potential for dynamic drought monitoring.