Xu Liang

VIC+ for water‐limited conditions: A study of biological and hydrological processes and their interactions in soil‐plant‐atmosphere continuum

[1]xa0The Three-Layer Variable Infiltration Capacity (VIC-3L) land surface model is extended to include biological and hydrological processes important to water, energy, and carbon budgets under water-limited climatic conditions: (1) movement of soil water from wet to dry regions through hydraulic redistribution (HR); (2) groundwater dynamics; (3) plant water storage; and (4) photosynthetic process. HR is represented with a process-based scheme and the interaction between HR and groundwater dynamics is explicitly considered. The impact of frozen soil on HR in the cold season is also represented. Transpiration is calculated by combining an Ohms law analogy, where flow from the soil to leaves is buffered by plant water storage, with the Penman-Monteith method, where stomatal conductance is linked with photosynthesis. In this extended model (referred to as VIC+), water flow in plants and in the unsaturated and saturated zones, transpiration and photosynthesis are closely coupled, and multiple constraints are simultaneously applied to the transpiration process. VIC+ is evaluated with an analytical solution under simple conditions and with observed data at two AmeriFlux sites. Scenario simulations demonstrate the following results: (1) HR has significant impacts on water, energy, and carbon budgets during the dry season; (2) Rise of groundwater table, increase of root depth, HR, and plant water storage are favorable to dry-season latent heat flux; (3) Plant water storage can weaken the intensity of upward HR; (4) Frozen soil can restrict downward HR in the wet winter and reduce the soil water reserves for the dry season.

A new multiscale routing framework and its evaluation for land surface modeling applications

[1]xa0A new multiscale routing framework is developed and coupled with the Hydrologically based Three-layer Variable Infiltration Capacity (VIC-3L) land surface model (LSM). This new routing framework has a characteristic of reducing impacts of different scales (both in space and time) on the routing results. The new routing framework has been applied to three different river basins with six different spatial resolutions and two different temporal resolutions. Their results have also been compared to the D8-based (eight direction based) routing scheme, whose flow network is generated from the widely used eight direction (D8) method, to evaluate the new frameworks capability of reducing the impacts of spatial and temporal resolutions on the routing results. Results from the new routing framework show that they are significantly less affected by the spatial resolutions than those from the D8-based routing scheme. Comparing the results at the basins outlets to those obtained from the instantaneous unit hydrograph (IUH) method which has, in principle, the least spatial resolution impacts on the routing results, the new routing framework provides results similar to those by the IUH method. However, the new routing framework has an advantage over the IUH method of providing routing information within the interior locations of a basin and along the river channels, while the IUH method cannot. The new routing framework also reduces impacts of different temporal resolutions on the routing results. The problem of spiky hydrographs caused by a typical routing method, due to the impacts of different temporal resolutions, can be significantly reduced.

Analysis of Spatial Similarities Between NEXRAD and NLDAS Precipitation Data Products

Precipitation is one of the key inputs for hydrological modeling. Although the Multisensor Precipitation Estimator (MPE) from NEXRAD (Next Generation Radar) and the NLDAS (North American Land Data Assimilation System) precipitation data have been extensively used in various hydrological and climatic studies, there has been no systematic investigation of the spatial similarities and differences between them, based on long-term time series data over a large spatial region. In this study, six years of hourly and daily precipitation time series data from NEXRAD and NLDAS were investigated for their spatial similarities, over a subregion of the Ohio River basin. Three spatial metrics were used: Cohens Kappa coefficient, Forecast Quality Index (FQI), and displacement-based Forecast Quality Measure (FQM). The three metrics were also applied to the two data products after stratification by season (warm, cold). Results show that significant differences exist between NEXRAD MPE and NLDAS. Analyses and discussions are presented on possible causes of the dissimilarities. In addition, results show that a single metric cannot adequately represent their spatial characteristics. The three metrics are complementary to each other and, when used jointly, can provide a more complete picture of the similarities and differences between the two precipitation products. However, if a single metric is desired, then a more comprehensive one needs to be developed to effectively account for magnitude, distance, shape, and neighborhood effects.

How much improvement can precipitation data fusion achieve with a Multiscale Kalman Smoother‐based framework?

[1]xa0With advancements in measuring techniques and modeling approaches, more and more precipitation data products, with different spatial resolutions and accuracies, become available. Therefore, there is an increasing need to produce a fused precipitation product that can take advantage of the strengths of each individual precipitation data product. This study systematically and quantitatively evaluates the improvements of the fused precipitation data as a result of using the Mulitscale Kalman Smoother-based (i.e., MKS-based) framework. Impacts of two types of errors, i.e., white noise and bias that are associated with individual precipitation products, are investigated through hypothetical experiments. Two measures, correlation and root-mean-square error, are used to evaluate the improvements of the fused precipitation data. Our study shows that the MKS-based framework can significantly recover the loss of precipitations spatial patterns and magnitudes that are associated with the white noise and bias when the erroneous data at different spatial scales are fused together. Although the erroneous data at a finer resolution are generally more effective in improving the spatial patterns and magnitudes of the erroneous data at a coarser resolution, data at a coarser resolution can also provide valuable information in improving the quality of the data at a finer resolution when they are fused. This study provides insights on the values of the MKS-based framework and a guideline for determining a potentially optimal spatial scale over which improvements in both the spatial patterns and the magnitudes can be maximized based on given data with different spatial resolutions.

Hybridizing Bayesian and variational data assimilation for robusthigh-resolution hydrologic forecasting

The success of real-time estimation and forecasting applications based on geophysical models has been possible thanks to the two main existing frameworks for the determination of the models’ initial conditions: Bayesian data assimilation and variational data assimilation. However, while there have been efforts to unify these two paradigms, existing attempts struggle to fully leverage the advantages of both in order to face the challenges posed by modern high-resolution models— mainly related to model indeterminacy and steep computational requirements. In this article we introduce a hybrid algorithm 10 called OPTIMISTS (Optimized PareTo Inverse Modeling through Integrated STochastic Search) which is targeted at nonlinear high-resolution problems and that brings together ideas from particle filters, 4-dimensional variational methods, evolutionary Pareto optimization, and kernel density estimation in a unique way. Streamflow forecasting experiments were conducted to test which specific configurations/parameterizations of OPTIMISTS can leadled to higher predictive accuracy. The experiments were conducted on two watersheds: the Blue River (low-resolution) using the VIC (Variable Infiltration 15 Capacity) model and the Indiantown Run (high-resolution) using the DHSVM (Distributed Hydrology Soil Vegetation Model). By selecting kernel-based non-parametric sampling, non-sequential evaluation of candidate particles, and through the multiobjective minimization of departures from the streamflow observations and from the background states, OPTIMISTS was shown to efficiently produce probabilistic forecasts with higher or similarcomparable accuracy than those producedto those obtained from using a particle filter. Moreover, the experiments demonstrated that OPTIMISTS scales well in high-resolution 20 cases without imposing a significant computational overhead and that it was successful in mitigating the harmful effects of overfitting.. With the combined advantages of allowing for fast, non-Gaussian, non-linear, high-resolution prediction, the algorithm shows the potential to increase the accuracy and efficiency forof operational prediction systems.

A parameter estimation framework for Multiscale Kalman Smoother algorithm in precipitation data fusion

A new effective parameter estimation approach is presented for the Multiscale Kalman Smoother (MKS) algorithm. As demonstrated, it shows promising potentials in deriving better data products involving sources from different spatial scales and precisions. The proposed approach employs a multiobjective parameter estimation framework, which includes three multiobjective estimation schemes (MO schemes), rather than using the conventional maximum likelihood scheme (ML scheme), to estimate the MKS parameters. Unlike the ML scheme, the MO schemes are not built on strict statistical assumptions related to prediction errors and observation errors, rather, they directly associate the fused data of multiple scales with multiple objective functions. In the MO schemes, objective functions are defined to facilitate consistency among the fused data at multiple scales and the input data at their original scales as well in terms of spatial patterns and magnitudes. Merits of the new approach are evaluated through a Monte Carlo experiment and a series of comparison analyses using synthetic precipitation data that contain noises which follow either the multiplicative error model or the additive error model. Our results show that the MKS fused precipitation performs better using the MO framework. Improvements are particularly significant for the fused precipitation associated with fine spatial resolutions. This is due mainly to the adoption of more criteria and constraints in the MO framework. The weakness of the original ML scheme, arising from its blindly putting more weights into the data associated with finer resolutions, is circumvented in the proposed new MO framework.