Abstract. Data assimilation techniques have received growing attention due to their capability to improve prediction. Among various data assimilation techniques, sequential Monte Carlo (SMC) methods, known as "particle filters", are a Bayesian learning process that has the capability to handle non-linear and non-Gaussian state-space models. In this paper, we propose an improved particle filtering approach to consider different response times of internal state variables in a hydrologic model. The proposed method adopts a lagged filtering approach to aggregate model response until the uncertainty of each hydrologic process is propagated. The regularization with an additional move step based on the Markov chain Monte Carlo (MCMC) methods is also implemented to preserve sample diversity under the lagged filtering approach. A distributed hydrologic model, water and energy transfer processes (WEP), is implemented for the sequential data assimilation through the updating of state variables. The lagged regularized particle filter (LRPF) and the sequential importance resampling (SIR) particle filter are implemented for hindcasting of streamflow at the Katsura catchment, Japan. Control state variables for filtering are soil moisture content and overland flow. Streamflow measurements are used for data assimilation. LRPF shows consistent forecasts regardless of the process noise assumption, while SIR has different values of optimal process noise and shows sensitive variation of confidential intervals, depending on the process noise. Improvement of LRPF forecasts compared to SIR is particularly found for rapidly varied high flows due to preservation of sample diversity from the kernel, even if particle impoverishment takes place.
We estimated the flux of caesium-137 adsorbed to suspended sediment in the Kusaki Dam reservoir in the Fukushima region of eastern Japan, which was contaminated by the Fukushima Nuclear Power Plant accident. The amount and rate of reservoir sedimentation and the caesium-137 concentration were validated based on the mixed-particle distribution and a sediment transport equation. The caesium-137 and sediment flux data suggested that wash load, suspended load sediment, and caesium-137 were deposited and the discharge and transport processes generated acute pollution, especially during extreme rainfall-runoff events. Additionally, we qualitatively assessed future changes in caesium-137 and sediment fluxes in the reservoir. The higher deposition and discharge at the start of the projection compared to the 2090s are most likely explained by the radioactive decay of caesium-137 and the effects of reservoir sedimentation. Predictions of the impacts of future climate on sediment and caesium-137 fluxes are crucial for environmental planning and management.
Abstract:In order to evaluate cumulus parameterization (CP) schemes for hydrological applications, the Pennsylvania State University-National Center for Atmospheric Research's fifth-generation mesoscale model (MM5) was used to simulate a summer monsoon in east China. The performances of five CP schemes (Anthes-Kuo, Betts-Miller, Fritsch-Chappell, Kain-Fritsch, and Grell) were evaluated in terms of their ability to simulate amount of rainfall during the heavy, moderate, and light phases of the event. The Grell scheme was found to be the most robust, performing well at all rainfall intensity and spatial scales. The Betts-Miller scheme also performed well, particularly at larger scales, but its assumptions may make it inapplicable to non-tropical environments and at smaller scales. The Kain-Fritsch scheme was the best at simulating moderate rainfall rates, and was found to be superior to the Fritsch-Chappell scheme on which it was based. The Anthes-Kuo scheme was found to underpredict precipitation consistently at the mesoscale. Simulation performance was found to improve when schemes that included downdrafts were used in conjunction with schemes that did not include downdrafts.
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