Operational aspects of asynchronous filtering for flood forecasting

This study investigates the suitability of the asynchronous ensemble Kalman filter (AEnKF) and a partitioned updating scheme for hydrological forecasting. The AEnKF requires forward integration of the model for the analysis and enables assimilation of current and past observations simultaneously at a single analysis step. The results of discharge assimilation into a grid-based hydrological model (using a soil moisture error model) for the Upper Ourthe catchment in the Belgian Ardennes show that including past predictions and observations in the data assimilation method improves the model forecasts. Additionally, we show that elimination of the strongly non-linear relation between the soil moisture storage and assimilated discharge observations from the model update becomes beneficial for improved operational forecasting, which is evaluated using several validation measures

Improving Forecast Skill of Lowland Hydrological Models Using Ensemble Kalman Filter and Unscented Kalman Filter

For operational water management in lowlands and polders (for instance, in the Netherlands), lowland hydrological models are used for flow prediction, often as an input for a real-time control system to steer water with pumps and weirs to keep water levels within acceptable bounds. Therefore, proper initialization of these models is essential. The ensemble Kalman filter (EnKF) has been widely used due to its relative simplicity and robustness, while the unscented Kalman filter (UKF) has received little attention in the operational context. Here, we test both UKF and EnKF using a lowland lumped hydrological model. The results of a reforecast experiment in an operational context using an hourly time step show that when using nine ensemble members, both filters can improve the accuracy of the forecast by updating the state of a lumped hydrological model (Wageningen Lowland Runoff Simulator, WALRUS) based on the observed discharge, while UKF has achieved better performance than EnKF. Additionally, we show that an increase in the ensemble members does not necessarily mean a significant increase in performance. WALRUS model with either UKF or EnKF could be considered for hydrological forecasting for supporting water management of polders and lowlands, with UKF being the computationally leaner option.</p

Assessment of an ensemble-based data assimilation system for a shallow estuary

Data assimilation (DA) is an essential element for the next generation of operational forecast systems for estuaries, to improve estuarine management. With limited resources and prohibitive cost to collect observations for such system, sensor choice and location is of prime importance in improving hydrodynamic model performance. In this study, we examine an optimal ensemble-based DA platform for improving the hydrodynamic modelling of a shallow estuary. Using an ensemble Kalman filter (EnKF), a set of synthetic (twin) experiments was conducted to test different DA scenarios covering observation types (i.e. water level and velocity) and noise modelling. We also evaluated the impact of the observation location on the DA performance by performing an observing system simulation experiment (OSSE). Results revealed that the assimilation of a single variable can significantly enhance the accuracy of the variable being assimilated, while the level of improvement for another variable is smaller. However, the best model estimates were obtained via a multivariate EnKF (i.e. both observations are assimilated). EnKF was robust to under and overestimation of the model errors, although overestimation led to slightly greater improvements. Our analysis showed that model performance is more sensitive to velocity observation location, rather than water level. These findings suggest that locations with strong velocity gradients are the locations where the hydrodynamic model needs to be enhanced, and accordingly, they are the preferable locations to deploy a velocity sensor.</p

Assimilation of GPS-tracked drifter data to improve the Eulerian velocity fields in an estuary

Numerical models are invaluable for the provision of real-time and forecasting information that can be used to examine estuarine hydrodynamics, particularly during times of flood or contaminant release. However, model outputs are associated with uncertainty; this necessitates the use of data assimilation (DA) techniques to improve model accuracy. We used an open-source DA tool to effectively assimilate Lagrangian drifter data into an estuarine hydrodynamic model using an ensemble Kalman filter (EnKF) algorithm. Our aims were to (i) evaluate the potential of drifter data for improving the accuracy of model estimates, and (ii) reduce the challenge and programming effort required for assimilation of such datasets, to make this technique accessible, for a broader range of users. We showed that assimilation of Lagrangian data obtained from prompt deployment of drifters in estuaries can lead to significant improvement (here, up to 54%) in modelled velocity fields.</p

Response of Soil and Peanut (Arachis Hypogaea L.) on the Application of Several Local Microorganism and Manures

Introduction: This research was conducted to determine the effect of several types of local microorganism solutions and manures on the growth and yield of peanut and their impact on the physical properties of ultisols. Materials and Methods: The researches was conducted in Simalingkar B, Medan using Factorial Randomized Block Design within three replications. The first factor was types of microbe sources of local microbial solutions, include un-treated waste (M0), pineapple (M1), orange (M2), and tamarillo (M3) wastes. The second factor was manure types, inluce un-treated manure (P0), chicken (P1), cow (P2), and goat (P3) manures. The parameters were: soil physical characteristics (bulk density, water content and total of pore space), plant height, stem diameter, number of pods, and dry seeds yield. Results: The types of local microorganism solutions only affect to plant height of peanut, but has insignificant effect on the physical properties of ultisols, and stem diameter, the number of filled pods.plant-1, and the dry seeds yield.ha-1 of peanut. The types of manure had significantly effected on plant height and number of filled pods.plant-1, but had insignificant effect on stem diameter, dry seeds yield.ha-1 and the physical properties of ultisols. The interaction of MOL sources and manure did not significantly affect the growth and yield of peanut as well as the physical properties of ultisols. Orange MOL and chicken manure could be increase the plant height of peanut by 18.61% and 6.75%, respectively, compared to un-treated. Goat manure showed the highest number of pods.plant-1 by 6.32% compared to un-treated

Increasing Detection Performance of Surveillance Sensor Networks

We study a surveillance wireless sensor network (SWSN) comprised of small and low-cost sensors deployed in a region in order to detect objects crossing the field of interest. In the present paper, we study two problems concerning the design and performance of an SWSN: optimal sensor placement and algorithms for object detection in the presence of false alarms. For both problems, we propose explicit decision rules and efficient algorithmic solutions. Further, we provide several numerical examples and present a simulation model that combines our placement and detection methods

Increasing Detection Performance of Surveillance Sensor Networks

We study a surveillance wireless sensor network (SWSN) comprised of small and low-cost sensors deployed in a region in order to detect objects crossing the field of interest. In the present paper, we study two problems concerning the design and performance of an SWSN: optimal sensor placement and algorithms for object detection in the presence of false alarms. For both problems, we propose explicit decision rules and efficient algorithmic solutions. Further, we provide several numerical examples and present a simulation model that combines our placement and detection methods