Scientific Results

This catalogue is obtained by conducting a systematic literature review of scientific studies and reviews related to monitoring, forecasting, and simulating the inland water cycle. The analysis maps scientific expertise across research groups and classifies findings by the type of inland water studied, application focus, and geographical scope. A gap analysis will identify missing research areas and assess their relevance to policymaking.

ID â–Č Type Year Authors Title Venue/Journal DOI Research type Water System Technical Focus Abstract Link with Projects Link with Tools Related policies ID
publications-2361 Peer reviewed articles 2021 L. S. Anderson; L. S. Anderson; W. H. Armstrong; W. H. Armstrong; R. S. Anderson; P. Buri Debris cover and the thinning of Kennicott Glacier, Alaska: in situ measurements, automated ice cliff delineation and distributed melt estimates The Cryosphere 10.5194/tc-15-265-2021 Dynamics of greenhouse gases in the river–groundwater interface in a gaining river stretch (Triffoy catchment, Belgium) Groundwater Abstract. Many glaciers are thinning rapidly beneath melt-reducing debris cover, including Kennicott Glacier in Alaska where glacier-wide maximum thinning also occurs under debris. This contradiction has been explained by melt hotspots, such as ice cliffs, scattered within the debris cover. However, melt hotspots alone cannot account for the rapid thinning at Kennicott Glacier. We consider the significance of ice cliffs, debris, and ice dynamics in addressing this outstanding problem. We collected abundant in situ measurements of debris thickness, sub-debris melt, and ice cliff backwasting, allowing for extrapolation across the debris-covered tongue (the study area and the lower 24.2 km2 of the 387 km2 glacier). A newly developed automatic ice cliff delineation method is the first to use only optical satellite imagery. The adaptive binary threshold method accurately estimates ice cliff coverage even where ice cliffs are small and debris color varies. Kennicott Glacier exhibits the highest fractional area of ice cliffs (11.7 %) documented to date. Ice cliffs contribute 26 % of total melt across the glacier tongue. Although the relative importance of ice cliffs to area-average melt is significant, the absolute area-averaged melt is dominated by debris. At Kennicott Glacier, glacier-wide melt rates are not maximized in the zone of maximum thinning. Declining ice discharge through time therefore explains the rapid thinning. There is more debris-covered ice in Alaska than in any other region on Earth. Through this study, Kennicott Glacier is the first glacier in Alaska, and the largest glacier globally, where melt across its debris-covered tongue has been rigorously quantified. 759639
publications-2362 Peer reviewed articles 2018 Dirk Scherler, Hendrik Wulf, Noel Gorelick Global Assessment of Supraglacial Debris-Cover Extents Geophysical Research Letters 10.1029/2018gl080158 Impact of P inputs on source-sink P dynamics of sediment along an agricultural ditch network River Basins AbstractRocky debris on glacier surfaces influences ice melt rates and the response of glaciers to climate change. However, scarce data on the extent and evolution of supraglacial debris cover have so far limited its inclusion in regional to global glacier models. Here we present global data sets of supraglacial debris‐cover extents, based on Landsat 8 and Sentinel‐2 optical satellite imagery. We find that about 4.4% (~26,000 km2) of all glacier areas (excluding the Greenland ice sheet and Antarctica) are covered with debris, but that the distribution is heterogeneous. The largest debris‐covered areas are located in high‐mountain ranges, away from the poles. At a global scale, we find a negative scaling relationship between glacier size and percentage of debris. Therefore, the influence of debris cover on glacier mass balances is expected to increase in the future, as glaciers continue to shrink. 759639
publications-2363 Peer reviewed articles 2019 Jędrzej Byrski, Witold Byrski State estimators and observers for continuous and discrete linear systems.Part 2. Integral observers for exact state reconstruction Science, Technology and Innovation 10.5604/01.3001.0013.2871 Data Management & Analytics Irrigation Systems In the paper, the exact state observers will be presented. The state estimators and observers can be used in technical processes for many purposes like the fault detection and diagnosis, the implementation of the state controllers, and soft reconstruction of inaccessible for measurements variables of the system. As the standard, for continuous systems the differential estimators of Kalman filter or Luenberger type observer are commonly used. However, if the initial conditions of the real state are unknown, both estimators guarantee only an asymptotic quality of the real state tracking. The paper presents another type of the state observers, which for continuous system have the structure given by two integral operators. Based on measurements of the system input and output signals on some predefined finite time interval T, they can reconstruct the initial state exactly. In on-line version, the exact state reconstruction is performed continuously for every t, based on special procedure executed within two moving windows of width T, on sliding time interval [t-T, t]. 824046
publications-2364 Peer reviewed articles 2022 Daipeng Zhang, Jaime A. Moreno, Johann Reger Homogeneous Lp-Stability for Homogeneous Systems IEEE Access 10.1109/access.2022.3195505 Restoration of soil quality using biochar and brown coal waste: A review Precipitation & Ecological Systems No abstract available 824046
publications-2365 Peer reviewed articles 2022 DrapaƂa, MichaƂ; Byrski, Witold Online continuous-time adaptive predictive control of the technological glass conditioning process Archives of Control Sciences 10.24425/acs.2022.143670 Data Management & Analytics River Basins No abstract available 824046
publications-2366 Peer reviewed articles 2022 Byrski, Witold; DrapaƂa, MichaƂ On-line process identification using the Modulating Functions Method and non-asymptotic state estimation Archives of Control Sciences 10.24425/acs.2022.142845 IoT & Sensors Uncategorized No abstract available 824046
publications-2367 Peer reviewed articles 2019 Rivas-Perez, SotomayorMoriano, PérezZuñiga, Soto-Angles Real-Time Implementation of an Expert Model Predictive Controller in a Pilot-Scale Reverse Osmosis Plant for Brackish and Seawater Desalination Applied Sciences 10.3390/app9142932 AI & Machine Learning Uncategorized This article addresses the design and real-time implementation of an expert model predictive controller (Expert MPC) for the control of the brackish and seawater desalination process in a pilot-scale reverse osmosis (RO) plant. This pilot-scale plant is used in order to obtain the optimal operation conditions of the RO desalination process through the implementation of different control strategies, as well as in the training of operators in the new control and management technologies. A dynamical mathematical model of this plant has been developed based on the available field data and system identification procedures. Predictions of the obtained model were in good agreement with the available field data. The designed Expert MPC is distinguished by having a plant identification block and an expert system. The expert system, using a rule-based approach and the evolution of the plant variables, can modify the plant identification block, the plant prediction model, and/or the optimizer in order to improve the performance, robustness and operational safety of the overall control system. The real-time comparison results of the designed Expert MPC and a well-designed model predictive controller (MPC) show that the proposed Expert MPC has a significantly better performance and, therefore, higher accuracy and robustness. 824046
publications-2368 Peer reviewed articles 2020 Gustavo Pérez-Zuñiga, Raul Rivas-Perez, Javier Sotomayor-Moriano, Victor Sånchez-Zurita Fault Detection and Isolation System Based on Structural Analysis of an Industrial Seawater Reverse Osmosis Desalination Plant Processes 10.3390/pr8091100 AI & Machine Learning Knowledge Graphs Currently, the use of industrial seawater reverse osmosis desalination (ISROD) plants has increased in popularity in light of the growing global demand for freshwater. In ISROD plants, any fault in the components of their control systems can lead to a plant malfunction, and this condition can originate safety risks, energy waste, as well as affect the quality of freshwater. This paper addresses the design of a fault detection and isolation (FDI) system based on a structural analysis approach for an ISROD plant located in Lima (Peru). Structural analysis allows obtaining a plant model, which is useful to generate diagnostic tests. Here, diagnostic tests via fault-driven minimal structurally overdetermined (FMSO) sets are computed, and then, binary integer linear programming (BILP) is used to select the FMSO sets that guarantee isolation. Simulations shows that all the faults of interest (sensors and actuators faults) are detected and isolated according to the proposed design. 824046
publications-2369 Peer reviewed articles 2022 David Pumaricra Rojas; Matti Noack; Johann Reger; Gustavo PĂ©rez-ZĂșñiga State Estimation for Coupled Reaction-Diffusion PDE Systems Using Modulating Functions Sensors 2022 10.3390/s22135008 IoT & Sensors Natural Water Bodies Many systems with distributed dynamics are described by partial differential equations (PDEs). Coupled reaction-diffusion equations are a particular type of these systems. The measurement of the state over the entire spatial domain is usually required for their control. However, it is often impossible to obtain full state information with physical sensors only. For this problem, observers are developed to estimate the state based on boundary measurements. The method presented applies the so-called modulating function method, relying on an orthonormal function basis representation. Auxiliary systems are generated from the original system by applying modulating functions and formulating annihilation conditions. It is extended by a decoupling matrix step. The calculated kernels are utilized for modulating the input and output signals over a receding time window to obtain the coefficients for the basis expansion for the desired state estimation. The developed algorithm and its real-time functionality are verified via simulation of an example system related to the dynamics of chemical tubular reactors and compared to the conventional backstepping observer. The method achieves a successful state reconstruction of the system while mitigating white noise induced by the sensor. Ultimately, the modulating function approach represents a solution for the distributed state estimation problem without solving a PDE online. 824046
publications-2370 Peer reviewed articles 2019 Witold Byrski, MichaƂ DrapaƂa, JÈ©drzej Byrski An Adaptive Identification Method Based on the Modulating Functions Technique and Exact State Observers for Modeling and Simulation of a Nonlinear Miso Glass Melting Process International Journal of Applied Mathematics and Computer Science 10.2478/amcs-2019-0055 AI & Machine Learning Irrigation Systems Abstract The paper presents new concepts of the identification method based on modulating functions and exact state observers with its application for identification of a real continuous-time industrial process. The method enables transformation of a system of differential equations into an algebraic one with the same parameters. Then, these parameters can be estimated using the least-squares approach. The main problem is the nonlinearity of the MISO process and its noticeable transport delays. It requires specific modifications to be introduced into the basic identification algorithm. The main goal of the method is to obtain on-line a temporary linear model of the process around the selected operating point, because fast methods for tuning PID controller parameters for such a model are well known. Hence, a special adaptive identification approach with a moving window is proposed, which involves using on-line registered input and output process data. An optimal identification method for a MISO model assuming decomposition to many inner SISO systems is presented. Additionally, a special version of the modulating functions method, in which both model parameters and unknown delays are identified, is tested on real data sets collected from a glass melting installation. 824046