Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters. The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.

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Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

Nowadays an accurate modeling of the system to be controlled is essential for reliable autopilot. This paper presents a non-linear model of the autonomous underwater vehicle “CWolf”. Matrices and the corresponding coefficients generate a parameterized representation for added mass, Coriolis and centripetal forces, damping, gravity and buoyancy, using the equations of motion, for all six degrees of freedom. The determination of actuator behaviour by surge tests allows the conversion of propeller revolutions to the respective forces and moments. Based on geometric approximations, the coefficients of the model can be specified by optimization algorithms in “open loop” sea trials. The realistic model is the basis for the subsequent design of the autopilot. The reference variables used in the four decoupled adaptive PID controllers for surge, heading, pitch and heave are provided a “Line of Sight” - guidance system. A constraint criteria optimization determines the required controller parameters. The verification by “closed loop” sea trials ensures the results.

Model identification and controller parameter optimization for an autopilot design for autonomous underwater vehicles

This paper presents a review on flood modeling and rainfall-runoff relationships. It was found many methods have been used by previous researches to solve the problem in flood modeling. The suitable techniques were applied based on flood modeling parameters and watershed intensity. Understanding on the rainfall-runoff relationships is one of the requirement and necessity in hydrometeorology. Thus, the studies carried out previously provide the significant result contributed to flood modeling. Reviews in this study also contain analyses and comparison between advantages and disadvantages of method used. It also reveals that the characteristics of rainfall-runoff data influence the successful of model application in flood prediction.

A review on flood modelling and rainfall-runoff relationships

Flood is one of the major of the natural disasters that cause casualties and infrastructure damage. The flood often happens in several regions in Indonesia caused by high rainfall during the rainy season. This paper describes the design and deployment of the system to detect the elevation of water level in the river automatically. Alert system of the surface water has been completed by employing an ultrasonic sensor-based microcontroller with communication via wireless, which will determine the water heights whether are below, normal or above states. The results of the implemented system have an accuracy of the ultrasonic sensor that generates mean error obtained of 0.97% and RMSE is 0.36 for each increase in 1 cm.

Design and Deployment of Wireless Sensor Networks for Flood Detection in Indonesia

This paper presents a computationally efficient method to forecast floods stochastically The main purpose of the method is to encapsulate prior knowledge as off-line calculations leading to earliest possible warnings The computational efficiency is improved through exploiting the stereotypical features of hydrology and its dependence on topography by combining the parallel water-flow processes into parallel calculation through a probability transition matrix Efficiency is improved further with the use of properties of Markov matrices in the general equation of the model Extensive simulations on real rainfall data over parts of Queensland, Australia, during January 2012, revealed that this method was capable of improving the calculation efficiency by over 18 times with respect to gradient based calculations

A computationally efficient framework for stochastic prediction of flood propagation

Rainfall-runoff models are used as an integral part of flood warning systems. Especially in small catchment areas with a fast response to intense rain, an early enough triggering of warnings requires high quality weather forecasts as well as sufficiently accurate models. A conceptual rainfall-runoff model proposed by Lorent/Gevers serves as core routine of a pilot flash flood warning system for the Truse catchment in Thuringia (Germany). A moving horizon state estimator is used in order to enable this model for online application.

Flash flood prediction for small rivers

Die Führung von Unterwasserfahrzeugen stellt erhebliche Ansprüche an die einzusetzenden Algorithmen. Die Schwierigkeiten liegen im nichtlinearen Systemverhalten des Fahrzeugs, der aufwendigen Positionsermittlung, der robusten Auslegung des Autopiloten sowie der übersichtlichen Darstellung der eingehenden Informationen für den Operator.

Modellierung, Regelung und Navigation von Teilautonomen, Ferngesteuerten Unterwasserfahrzeugen (Modelling, Control and Navigation of Semiautonomous, Remotely Operated Underwater Vehicles)

Editorial of the Special issue of underwater robotics

Abstract Rainfall–runoff models are used as an integral part of flood warning systems. Especially in small river catchment areas with a fast response to intense rain, an early enough triggering of warnings requires high quality weather forecasts as well as sufficiently accurate models. A conceptual rainfall–runoff model proposed by Lorent/Gevers serves as core routine of a pilot flash flood warning system for the Truse catchment in Thuringia (Germany). A moving horizon state estimator is used in order to enable this model for online application.

Torsten Pfützenreuter

Papers

Flash flood prediction for small rivers

Modellierung, Regelung und Navigation von Teilautonomen, Ferngesteuerten Unterwasserfahrzeugen (Modelling, Control and Navigation of Semiautonomous, Remotely Operated Underwater Vehicles)

Editorial of the Special issue of underwater robotics

State estimation in flash flood prediction for small rivers