3 research outputs found
REMOTE SENSING DATA ASSIMILATION IN WATER QUALITY NUMERICAL MODELS FOR SIMULATION OF WATER COLUMN TEMPERATURE
Indiana University-Purdue University Indianapolis (IUPUI)Numerical models are important tools for simulating processes within complex natural systems, such as hydrodynamics and water quality processes within a water body. From decision makers’ perspectives, such models also serve as useful tools for predicting the impacts of water quality problems or develop early warning systems. However, accuracy of a numerical model developed for a specific site is dependent on multiple model parameters and variables whose values are attained via calibration processes and/or expert knowledge. Real time variations in the actual aquatic system at a site necessitate continuous monitoring of the system so that model parameters and variables are regularly updated to reflect accurate conditions. Multiple sources of observations can help adjust the model better by providing benefits of individual monitoring technology within the model updating process. For example, remote sensing data provide a spatially dense dataset of model variables at the surface of a water body, while in-situ monitoring technologies can provide data at multiple depths and at more frequent time intervals than remote sensing technologies. This research aims to present an overview of an integrated modeling and data assimilation framework that combines three-dimensional numerical model with multiple sources of observations to simulate water column temperature in a eutrophic reservoir in central Indiana. A variational data assimilation approach is investigated for incorporating spatially continuous remote sensing observations and spatially discrete in-situ observations to change initial conditions of the numerical model. This research addresses the challenge of improving the model performance by combining water temperature from multi-spectral remote sensing analysis and in-situ measurements. Results of the approach on a eutrophic reservoir in Central Indiana show that with four images of multi-spectral remote sensing data assimilated, the model results oscillate more from the in-situ measurements during the data assimilation period. For validation, the data assimilation has negative impacts on the root mean square error. According to quantitative analysis, more significant water temperature stratification leads to larger deviations. Sampling depth differences for remote sensing technology, in-situ measurements and model output are considered as possible error source
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Assimilation of Multi-Sensor Data into Numerical Hydrodynamic Models of Inland Water Bodies
Numerical models are effective tools for simulating complex physical processes such as hydrodynamic and water quality processes in aquatic systems. The accuracy of the model is dependent on multiple model parameters and variables that need to be calibrated and regularly updated to reproduce changing aquatic conditions accurately. Multi-sensor water temperature observations, such as remote sensing data and in situ monitoring technologies, can improve model accuracy by providing benefits of individual monitoring technology to the model updating process. In contrast to in-situ temperature sensors, remote sensing technologies (e.g., satellites) provide the benefit of collecting measurements with better X-Y spatial coverage. However, the temporal resolution of satellite data is limited comparing to in-situ measurements. Numerical models and all source of observations have large uncertainty coming from different sources such as errors of approximation and truncation, uncertain model inputs, error in measuring devices and etc. Data assimilation (DA) is able to sequentially update the model state variables by considering the uncertainty in model and observations and estimate the model states and outputs more accurately. Data Assimilation has been proposed for multiple water resources studies that require rapid employment of incoming observations to update and improve accuracy of operational prediction models. The usefulness of DA approaches in assimilating water temperature observations from different types of monitoring technologies (e.g., remote sensing and
in-situ sensors) into numerical models of in-land water bodies (e.g., reservoirs, lakes, and rivers) has, however, received limited attention. Assimilating of water temperature measurements from satellites can introduce biases in the updated numerical model of water bodies because the physical region represented by these measurements do not directly correspond with the numerical model's representation of the water column. The main research objective of this study is to efficiently assimilate multi-sensor water temperature data into the hydrodynamic model of water bodies in order to improve the model accuracy. Four specific objectives were addressed in this work to accomplish the overall goal:
* Objective 1: Propose a novel approach to address the representation challenge of model and measurements by coupling a skin temperature adjustment technique based on available air and in-situ water temperature observations, with an ensemble Kalman filter (EnKF) based data assimilation technique for reservoirs and lakes.
* Objective 2: Investigate whether assimilation of remotely sensed temperature observations using the proposed data fusion approach can improve model accuracy with respect to in-situ temperature observations as well as remote sensing data.
* Objective 3: Investigate a global sensitivity analysis tool that combines Latin-hypercube and one-factor-at-a-time sampling to investigate the most sensitive model inputs and parameters in calculating the water age and water temperature of shallow rivers.
* Objective 4: Propose an efficient data assimilation framework to take the advantage of both monitoring technologies (e.g., remote sensing and in-situ measurements) to improve the model efficiency of shallow rivers.
Results showed that the proposed adjustment approach used in this study for four-dimensional analysis of a reservoir provides reasonably accurate surface layer and water column temperature forecasts, in spite of the use of a fairly small ensemble. Assimilation of adjusted remote sensing data using ensemble Kalman Filter technique improved the overall root mean square difference between modeled surface layer
temperatures and the adjusted remotely sensed skin temperature observations from 5.6 °C to 0.51 °C (i.e., 91% improvement). In addition, the overall error in the water column temperature predictions when compared with in-situ observations also decreased from 1.95 °C (before assimilation) to 1.42 °C (after assimilation), thereby, giving a 27% improvement in errors. In contrast, doing data assimilation without the proposed temperature adjustment would have increased this error to 1.98 °C (i.e., 1.5% deterioration). The most effective parameters to calculate water temperature were investigated and perturbed among the acceptable range to create the ensembles. Results show that water temperature is more sensitive to inflow temperature, air temperature, solar radiation, wind speed, flow rate, and wet bulb temperature respectively. Results also show in contrast to in-situ data assimilation, remote sensing data assimilation was able to effectively improve the spatial error of the model. Assimilation of in-situ observation improved the model efficiency at observation site. However, the model error increased by time and after less than two days, the model predictions of updated model were the same as base model before data assimilation. Hence, a maximum acceptable error between model and measurements was defined based on the application of model. Remote sensing data were assimilated into the model as they become available to improve the model accuracy for the entire river. In-situ data were also assimilated into the model when the error between model and observations exceeds the maximum error. Results showed that by assimilation of in-situ data one to three times per day, the average daily error reduced up to 58% comparing to situation that in-situ data were assimilated only once. In addition, the average spatial error reduced from 2.59 °C to 0.66 °C after assimilation of remote sensing data
光学特性が複雑な水域における超多波長反射率データを使った新しい水質推定手法の構築
広島大学(Hiroshima University)博士(工学)Doctor of Engineeringdoctora