Seong Jin Noh

Ensemble Kalman Filtering and Particle Filtering in a Lag-Time Window for Short-Term Streamflow Forecasting with a Distributed Hydrologic Model

AbstractThe performance of the ensemble Kalman filter (EnKF) and the particle filter (PF) is assessed for short-term streamflow forecasting with a distributed hydrologic model, namely, the water and energy transfer processes (WEP) model. To mitigate the drawbacks of conventional filters, the ensemble square root filter (EnSRF) and the regularized particle filter (RPF) are implemented. For both the EnSRF and the RPF, sequential data assimilation is performed within a lag-time window to consider the response times of internal hydrologic processes. The proposed methods are applied to two catchments in Japan and Korea to assess their performance. The model ensembles are perturbed by the noise of the soil moisture content and are assimilated with streamflow observations. The forecasting accuracy of both the EnSRF and the RPF is improved when sufficient lag-time windows are provided. The EnSRF is sensitive to the length of the lag-time window and has a limited ability to forecast within short lead times, wherea...

Development and Application of the Catchment Hydrologic Cycle Assessment Tool Considering Urbanization (I) - Model Development -

The objective of this study is to develop a catchment hydrologic cycle assessment model which can assess the impact of urban development and designing water cycle improvement facilities. Developed model might contribute to minimize the damage caused by urban development and to establish sustainableurban environments. The existing conceptual lumped models have a potential limitation in their capacity to simulate the hydrologic impacts of land use changes and assess diverse urban design. The distributed physics-based models under active study are data demanding; and much time is required to gather and check input data; and the cost of setting up a simulation and computational demand are required. The Catchment Hydrologic Cycle Assessment Tool (hereinafter the CAT) is a water cycle analysis model based on physical parameters and it has a link-node model structure. The CAT model can assess the characteristics of the short/long-term changes in water cycles before and after urbanization in the catchment. It supports the effective design of water cycle improvement facilities by supplementing the strengths and weaknesses of existing conceptual parameter-based lumped hydrologic models and physical parameter-based distributed hydrologic models. the model was applied to Seolma-cheon catchment, also calibrated and validated using 6 years (2002~2007) hourly streamflow data in Jeonjeokbigyo station, and the Nash-Sutcliffe model efficiencies were 0.75 (2002~2004) and 0.89 (2005~2007).

Application of WEP Model to the Cheonggyecheon Watershed

Water cycle analysis in the Cheonggyecheon watershed(river length: 13.75 km, area: ) was performed using WEP model, a physically based distributed rainfall-runoff model. As the application results of the model, the hydrological characteristics of the Cheonggyecheon watershed are significantly consistent with those of a typical urbanized watershed. The direct runoff from the watershed was larger and the evapotranspiration. was lower, and the response of runoff to rainfall was occurred very fast, as compared to forest watersheds. The river channel routing simulation results are similar to the change pattern and scale of the field data. The possible supply period of instream flow from Cheonggyecheoon watershed itself was estimated using WEP. According to the WEP simulation results for the annual water balance of the Cheonggyecheon watershed in 2002, the amount of direct runoff, infiltration and evapotranspiration were 830 mm, 388 mm and 397 mm respectively for an annual precipitation of 1,388 mm. The runoff to rivers was 1,288 mm. And the proportion of direct runoff, intermediate runoff and groundwater runoff were and respectively.

Real-time assimilation of streamflow observations into a hydrological routing model: effects of model structures and updating methods

ABSTRACT This paper comparatively assesses the performance of five data assimilation techniques for three-parameter Muskingum routing with a spatially lumped or distributed model structure. The assimilation techniques used include direct insertion (DI), nudging scheme (NS), Kalman filter (KF), ensemble Kalman filter (EnKF) and asynchronous ensemble Kalman filter (AEnKF), which are applied to river reaches in Texas and Louisiana, USA. For both lumped and distributed routing, results from KF, EnKF and AEnKF are sensitive to the error specification. As expected, DI outperformed the other models in the case of lumped modelling, while in distributed routing, KF approaches, particularly AEnKF and EnKF, performed better than DI or nudging, reflecting the benefit of updating distributed states through error covariance modelling in KF approaches. The results of this work would be useful in setting up data assimilation systems that employ increasingly abundant real-time observations using distributed hydrological routing models.

Hyper-resolution 1D-2D urban flood modelling using LiDAR data and hybrid parallelization

Abstract Coupled 1D-2D modelling is a widely used approach to predict water movement in complicated surface and subsurface drainage systems in urban or peri-urban areas. In this study, a hybrid parallel code, H12, is developed for 1D-2D coupled urban flood modelling. Hybrid-1D-2D, or H12, enables street-resolving hyper-resolution simulation over a large area by combining Open Multi-Processing (OpenMP) and Message Passing Interface (MPI) parallelization. Variable grid sizing is adopted for detailed geometric representation of urban surfaces as well as efficient computation. To assess the capability of H12, simulation experiments were carried for the Johnson Creek Catchment (∼40 km2) in Arlington, Texas. The LiDAR-derived digital elevation model (DEM) and detailed land cover map at 1-m resolution are used to represent the terrain and urban features in flood modelling. Hybrid parallelization achieves up to a 79-fold reduction in simulation time compared to the serial run and is more efficient than either OpenMP or MPI alone especially in hyper-resolution simulations.

Assimilation of Streamflow Observations

Streamflow is arguably the most important predictor in operational hydrologic forecasting and water resources management. Assimilation of streamflow observations into hydrologic models has received growing attention in recent decades as a cost-effective means to improve prediction accuracy. Whereas the methods used for streamflow data assimilation (DA) originated and were popularized in atmospheric and ocean sciences, the nature of streamflow DA is significantly different from that of atmospheric or oceanic DA. Compared to the atmospheric processes modeled in weather forecasting, the hydrologic processes for surface and groundwater flow operate over a much wider range of time scales. Also, most hydrologic systems are severely under-observed. The purpose of this chapter is to provide a review on streamflow measurements and associated uncertainty and to share the latest advances, experiences gained, and science issues and challenges in streamflow DA. Toward this end, we discuss the following aspects of streamflow observations and assimilation methods: (1) measurement methods and uncertainty of streamflow observations, (2) streamflow assimilation applications, and (3) benefits and challenges streamflow DA with regard to large-scale DA, multi-data assimilation, and dealing with timing errors.