Caleb Matthew DeChant

Improving Soil Moisture Profile Prediction With the Particle Filter-Markov Chain Monte Carlo Method

Satellite soil moisture estimates have received increasing attention over the past decade. This paper examines the applicability of estimating soil moisture states and soil hydraulic parameters through two particle filter (PF) methods: The PF with commonly used sampling importance resampling (PF-SIR) and the PF with recently developed Markov chain Monte Carlo sampling (PF-MCMC) methods. In a synthetic experiment, the potential of assimilating remotely sensed near-surface soil moisture measurements into a 1-D mechanistic soil water model (HYDRUS-1D) using both the PF-SIR and PF-MCMC algorithms is analyzed. The effects of satellite temporal resolution and accuracy, soil type, and ensemble size on the assimilation of soil moisture are analyzed. In a real data experiment, we first validate the Advanced Microwave Scanning Radiometer--Earth Observing System (AMSR-E) soil moisture products in the Oklahoma Little Washita Watershed. Aside from rescaling the remotely sensed soil moisture, a bias correction algorithm is implemented to correct the deep soil moisture estimate. Both the ascending and descending AMSR-E soil moisture data are assimilated into the HYDRUS-1D model. The synthetic assimilation results indicated that, whereas both updating schemes showed the ability to correct the soil moisture state and estimate hydraulic parameters, the PF-MCMC scheme is consistently more accurate than PR-SIR. For real data case, the quality of remotely sensed soil moisture impacts the benefits of their assimilation into the model. The PF-MCMC scheme brought marginal gains than the open-loop simulation in RMSE at both surface and root-zone soil layer, whereas the PF-SIR scheme degraded the open-loop simulation.

On the assessment of reliability in probabilistic hydrometeorological event forecasting

Probabilistic forecasts are commonly used to communicate uncertainty in the occurrence of hydrometeorological events. Although probabilistic forecasting is common, conventional methods for assessing the reliability of these forecasts are approximate. Among the most common methods for assessing reliability, the decomposed Brier Score and Reliability Diagram treat an observed string of events as samples from multiple Binomial distributions, but this is an approximation of the forecast reliability, leading to unnecessary loss of information. This article suggests testing the hypothesis of reliability via the Poisson-Binomial distribution, which is a generalized solution to the Binomial distribution, providing a more accurate model of the probabilistic event forecast verification setting. Further, a two-stage approach to reliability assessment is suggested to identify errors in the forecast related to both bias and overly/insufficiently sharp forecasts. Such a methodology is shown to more effectively distinguish between reliable and unreliable forecasts, leading to more robust probabilistic forecast verification.

Examining the Reliability of Hydrologic Drought-Risk Forecasting at Seasonal Timescales

Understanding the risk of seasonal hydrologic drought is essential to managing our interactions with watersheds. Droughts pose a number of risks to society and the environment, which may be mitigated if forecasted accurately and with enough lead time. To effectively manage such risk, probabilistic forecasts of drought must be available and properly utilized. This study takes a holistic approach to forecasting drought probability by accounting for all possible sources of uncertainty. Within the context of seasonal hydrologic predictions, these uncertainties can be attributed to three causes: our imperfect characterization of initial conditions, an incomplete knowledge of future climate, and errors within computational models. Understandably, research and operational hydrologic forecast systems have emphasized climate uncertainties in seasonal predictions. Forecasted climate is arguably the dominant source of uncertainty in a hydrologic forecasting system, but the other sources of uncertainty are significant. In this study, accounting of initial condition errors is performed with ensemble-based data assimilation, while model error is quantified/reduced with sequential Bayesian combination. Through this framework, the reliability of a forecast is increased, providing a more realistic depiction of drought risk.