Information Content Of Data Research Articles

Automated Grasp Labeling and Detection Framework with Pixel-Level Precision

Due to the differences in shape, material, and color of objects, the detection of planar grasps by robots remains challenging. Traditional methods rely on discrete grasp configurations for annotation, ignoring many possible grasp configurations. This leads to poor network generalization, making it challenging to handle a diverse range of grasped objects. Manual reannotation to continuous labels can effectively address this issue but comes with a significant cost. Therefore, this paper proposes a Pixel-level Grasp framework. Firstly, APGLG can automatically generate pixel-level grasp labels and discrete labels into continuous labels, effectively increasing the information content of individual data and improving the network generalization performance. Then, we propose the Max-Grasp-Net, built on the Multi-axis Vision Transformer and Dynamic Convolution Decomposition, to construct a U-shaped Network structure. A grasp decoder is incorporated, and deep supervision is applied to enhance network generalization. On the Cornell dataset, we achieve the best results, a grasping accuracy of 99.55%, an average success rate of 97.92% in single-object grasping, and 95.83% in multi-object grasping. We verified the effectiveness of our label generation algorithms and network innovation through actual grasp experiments.

Optimization of machine learning model training procedure on multi-gpu systems to enhance cyber security in telecommunication networks

This paper analyzes the optimization features of machine learning (ML) model training procedures using multi-GPU systems to enhance cyber security in telecommunication networks. A key aspect of the study is the use of data parallelism, which allows the distribution of the training load across multiple GPUs, significantly reducing training time and improving model accuracy-critical factors for rapid threat detection in cyberspace. A novel approach for optimizing data batch size using Mutual Information (MI) is proposed, which harmonizes the utilization of computational resources with the information content of the training data. MI helps to determine the optimal data batch size that minimizes training errors and improves model accuracy without a significant increase in training time. Experimental results demonstrate the substantial advantages of multi-GPU configurations compared to single-GPU setups, providing faster training and improved model accuracy. It was particularly emphasized that MI-guided batch size tuning significantly outperforms traditional manual tuning methods, ensuring higher validation accuracy and reducing training time. The study showed that the MI-based approach is an effective tool for addressing the problem of optimizing ML model training processes in real-world scenarios where cyber security is critical. The proposed methods allow ML models to train faster and more accurately identify potential threats, making them particularly relevant for telecommunication networks where a rapid response to new threats in real time is required. The implementation of modern computational technologies such as multi-GPU systems and MI-optimized training enhances the efficiency and accuracy of machine learning models. This, in turn, improves cyber security measures and ensures a more reliable defence of telecommunication networks against malicious attacks. It is noted that the proposed approaches can be adapted not only for cyber security but also for other areas where high model accuracy and fast training are important. Future research prospects include the development of new machine learning methods, particularly deep neural networks, the exploration of alternative computational architectures such as quantum computing or distributed systems, and their integration into real-time systems. Special attention should be paid to the ethical aspects of implementing automated cyber security systems, particularly in preventing bias in algorithms and ensuring fairness in their application.

Fisher information contained in incomplete observations

The accuracy of statistical estimates of unknown distribution parameters depends not only on the bulk of sampling data but also on the method of data acquisition. The information content of experimental data is one of the basic requirements. Problems of mathematical statistics, in particular parametric estimation based on censored observations, have specific features. Typical representatives of models of incomplete observations on a straight line are models of random censoring, competing risks of (single, multiple) random censoring. The purpose of this study is to show that censoring does not always lead to loss of (Fisher) information. It is shown that if censoring is informative, i.e., the distribution of censoring random variables depends on the same parameter, it is possible to specify a model where information can be preserved due to censoring. On the contrary, if the censoring is not informative, then the loss of information is inevitable. The Cramer – Rao efficiency was taken as a criterion for the quality of the assessment, whereas the Fisher information was taken as the criterion for information about the unknown parameter.

Recent advances in vertical temperature profiler instrumentation and flux estimation methods facilitate groundwater – Surface water exchange studies in environments with strong discharge zones

Groundwater fluxes to many surface water systems are spatially heterogeneous with discharge focused into discrete, high-flux zones. Quantifying fluxes in these preferential discharge zones is critical to a range of surface water habitat and water quality processes, but characterization can be difficult due to short-scale spatial and temporal variability. Passive heat-as-a-tracer methods employing vertical temperature profiler (VTP) data can provide the necessary spatial and temporal resolution, but upward fluid flow strongly attenuates the thermal signals used for estimating fluxes. In preferential discharge zones it becomes difficult to measure the signals in the subsurface and the flux parameter can become insensitive in the analysis models, leading to large uncertainties. We use data from a high-flux site of contaminant-loaded groundwater discharge to the Quashnet River on Cape Cod, Massachusetts, USA, to demonstrate how recent advances in VTP instrumentation that allow for the acquisition of high-resolution (0.001 °C) temperature data at short (1 cm) offsets near the ground surface, combined with advances in flux estimation methods that exploit the information content of the high-resolution data, facilitate heat-as-a-tracer approaches for characterizing groundwater-surface water exchanges and make it possible to obtain accurate and statistically robust results in a preferential discharge zone with a specific discharge of ∼1 m/d.

A unified description of ion exchange and complete adsorption isotherms, including cluster growth and cavity filling

In the context of micro- and mesoporous materials, the interpretation of ion exchange and gas adsorption isotherms rarely covers the full information content of the experimental data. Based on our earlier work regarding the role of entropy in multiple equilibria, we have revisited cation exchange data of zeolite A in more detail and addressed the analysis of Ar and N2 adsorption isotherms of nonporous, microporous and mesoporous silicates. The previously reported analysis is thereby extended and improved. The theory is presented in a unified and compact manner through rigorous application of the concept of fractional amount, including particle distribution, cluster growth, and cavity filling. A result is that the structure of mesoporous silica MCM-41 can be described as consisting of the main channel and two types of cavities, which can be interpreted as surface roughness. In a further step, the information delivered by the inflection points in type I, II and IV gas adsorption isotherms is considered. The adsorption of simple gases begins with the formation of a monolayer on the pristine surface. The inflection point thereby represents the start of a new process, indicating that the adsorption isotherms must be understood as the signature of several sequential chemical equilibrium steps. Information can be extracted by inspecting this signature more closely. The results demonstrate the ability of the reported procedure to elicit additional insight including structural features from experimental equilibrium isotherm data.

Improvement of Aerosol Coarse-Mode Detection through Additional Use of Infrared Wavelengths in the Inversion of Arctic Lidar Data

An Nd:YAG-based Raman lidar provides a mature technology to derive profiles of the optical properties of aerosols over a wide altitude range. However, the derivation of micro-physical parameters is an ill-posed problem. Hence, increasing the information content of lidar data is desirable. Recently, ceilometers and wind lidar systems, both operating in the near-infrared region, have been successfully employed in aerosol research. In this study, we demonstrate that the inclusion of additional backscatter coefficients from these two latter instruments clearly improves the inversion of micro-physical parameters such as volume distribution function, effective radius, or single-scattering albedo. We focus on the Arctic aerosol and start with the typical volume distribution functions of Arctic haze and boreal biomass burning. We forward calculate the optical coefficients that the lidar systems should have seen and include or exclude the backscatter coefficients of the ceilometer (910 nm) and wind lidar data (1500 nm) to analyze the value of these wavelengths in their ability to reproduce the volume distribution function, which may be mono- or bimodal. We found that not only the coarse mode but also the properties of the accumulation mode improved when the additional wavelengths were considered. Generally, the 1500 nm wavelength has greater value in correctly reproducing the aerosol properties.

ESTAN—A toolbox for standardized and effective global sensitivity-based estimability analysis

The development of accurate and reliable mathematical models is the cornerstone for the modeling and optimization of processes. However, most of the existing models suffer from weak prediction capabilities due to poor data information content and poor parameter estimation methodologies. Several estimability approaches have been developed and increasingly implemented to address some of these issues. However, the wider adoption of these methods is still hampered by the lack of standardized and robust methodologies. In this paper, we present a Matlab Toolbox, called ESTAN, designed and developed to make estimability analysis accessible to non-specialist users. It uses a Quasi-Monte Carlo sequence to sample the main unknown parameters within their variation spaces. Then, depending on whether the studied model is computationally cheap or expensive, sensitivity indices are calculated either using the Sobol method or Fourier Amplitude Sensitivity Test (FAST). The calculated sensitivities are finally used within an orthogonalization algorithm to rank the parameters from the most to less estimable ones and to determine the estimable and non-estimable ones based on an estimability cut-off criterion. Various case studies are used to validate the toolbox and guide the users. The first one deals with the non-dynamic Toth adsorption model, while the second one deals with a dynamic batch cooling crystallization model. The main challenge with these two case studies is to show the importance of estimability analysis in the interpolation/extrapolation of model prediction capabilities. The last case addresses a computationally expensive thermodynamic model. The results for all the case studies are found to be promising, showing how the presented toolbox simplifies the investigation of the estimability analysis.

Particle filtering supported probability density estimation of mobility patterns

This paper presents a methodology that aims to enhance the accuracy of probability density estimation in mobility pattern analysis by integrating prior knowledge of system dynamics and contextual information into the particle filter algorithm. The quality of the data used for density estimation is often inadequate due to measurement noise, which significantly influences the distribution of the measurement data. Thus, it is crucial to augment the information content of the input data by incorporating additional sources of information beyond the measured position data. These other sources can include the dynamic model of movement and the spatial model of the environment, which influences motion patterns. To effectively combine the information provided by positional measurements with system and environment models, the particle filter algorithm is employed, which generates discrete probability distributions. By subjecting these discrete distributions to exploratory techniques, it becomes possible to extract more certain information compared to using raw measurement data alone. Consequently, this study proposes a methodology in which probability density estimation is not solely based on raw positional data but rather on probability-weighted samples generated through the particle filter. This approach yields more compact and precise modeling distributions. Specifically, the method is applied to process position measurement data using a nonparametric density estimator known as kernel density estimation. The proposed methodology is thoroughly tested and validated using information-theoretic and probability metrics. The applicability of the methodology is demonstrated through a practical example of mobility pattern analysis based on forklift data in a warehouse environment.

Trans-dimensional inversion for seafloor properties for three mud depocenters on the New England shelf under dynamical oceanographic conditionsa).

This paper presents inversion results for three datasets collected on three spatially separated mud depocenters (hereafter called mud ponds) during the 2022 Seabed Characterization Experiment (SBCEX). The data considered here represent modal time-frequency (TF) dispersion as estimated from a single hydrophone. Inversion is performed using a trans-dimensional (trans-D) Bayesian inference method that jointly estimates water-column and seabed properties along with associated uncertainties. This enables successful estimation of the seafloor properties, consistent with in situ acoustic core measurements, even when the water column is dynamical and mostly unknown. A quantitative analysis is performed to (1) compare results with previous modal TF trans-D studies for one mud pond but under different oceanographic condition, and (2) inter-compare the new SBCEX22 results for the three mud ponds. Overall, the estimated mud geoacoustic properties show no significant temporal variability. Further, no significant spatial variability is found between two of the mud ponds while the estimated geoacoustic properties of the third are different. Two hypotheses, considered to be equally likely, are explored to explain this apparent spatial variability: it may be the result of actual differences in the mud properties, or the mud properties may be similar but the inversion results are driven by difference in data information content.

Codon language embeddings provide strong signals for use in protein engineering

Protein representations from deep language models have yielded state-of-the-art performance across many tasks in computational protein engineering. In recent years, progress has primarily focused on parameter count, with recent models’ capacities surpassing the size of the very datasets they were trained on. Here we propose an alternative direction. We show that large language models trained on codons, instead of amino acid sequences, provide high-quality representations that outperform comparable state-of-the-art models across a variety of tasks. In some tasks, such as species recognition, prediction of protein and transcript abundance or melting point estimation, we show that a language model trained on codons outperforms every other published protein language model, including some that contain over 50 times more parameters. These results indicate that, in addition to commonly studied scale and model complexity, the information content of biological data provides an orthogonal direction to improve the power of machine learning in biology.

Environmental Risk Assessment with Energy Budget Models: A Comparison Between Two Models of Different Complexity.

The extrapolation of effects from controlled standard laboratory tests to real environmental conditions is a major challenge facing ecological risk assessment (ERA) of chemicals. Toxicokinetic-toxicodynamic (TKTD) models, such as those based on dynamic energy budget (DEB) theory, can play an important role in filling this gap. Through the years, different practical TKTD models have been derived from DEB theory, ranging from the full "standard" DEB animal model to simplified "DEBtox" models. It is currently unclear what impact a different level of model complexity can have on the regulatory risk assessment. In the present study, we compare the performance of two DEB-TKTD models with different levels of complexity, focusing on model calibration on standard test data and on forward predictions for untested time-variable exposure profiles. The first model is based on the standard DEB model with primary parameters, whereas the second is a reduced version with compound parameters, based on DEBkiss. After harmonization of the modeling choices, we demonstrate that these two models can achieve very similar performances both in the calibration step and in the forward prediction step. With the data presented in the present study, selection of the most suitable TKTD model for ERA therefore cannot be based alone on goodness-of-fit or on the precision of model predictions (within current ERA procedures for pesticides) but would likely be based on the trade-off between ease of use and model flexibility. We also stress the importance of modeling choices, such as how to fill gaps in the information content of experimental toxicity data and how to accommodate differences in growth and reproduction between different data sets for the same chemical-species combination. Environ Toxicol Chem 2024;43:440-449. © 2023 ibacon GmbH. Bayer AG and The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

The suitability of atmospheric oxygen measurements to constrain western European fossil-fuel CO2 emissions and their trends

Abstract. Atmospheric measurements of the O2/N2 ratio and the CO2 mole fraction (combined into the conceptual tracer “Atmospheric Potential Oxygen”, APO) over continents have been proposed as a constraint on CO2 emissions from fossil-fuel burning. Here we assess the suitability of such APO data to constrain anthropogenic CO2 emissions in western Europe, with particular focus on their decadal trends. We use an inversion of atmospheric transport to estimate spatially and temporally explicit scaling factors on a bottom-up fossil-fuel emissions inventory. Based on the small number of currently available observational records, our CO2 emissions estimates show relatively large apparent year-to-year variations, exceeding the expected uncertainty of the bottom-up inventory and precluding the calculation of statistically significant trends. We were not able to trace the apparent year-to-year variations back to particular properties of the APO data. Inversion of synthetic APO data, however, confirms that data information content and degrees of freedom are sufficient to successfully correct a counterfactual prior. Larger sets of measurement stations, such as the recently started APO observations from the Integrated Carbon Observation System (ICOS) European research infrastructure, improve the constraint and may ameliorate possible problems with local signals or with measurement or model errors at the stations. We further tested the impact of uncertainties in the O2:CO2 stoichiometries of fossil-fuel burning and land biospheric exchange and found they are not fundamental obstacles to estimating decadal trends in fossil-fuel CO2 emissions, though further work on fossil-fuel O2:CO2 stoichiometries seems necessary.

New approach to monitoring a wastewater irrigation system controlled by the artificial neural network (ANN)

In this paper, a cost-effective and scalable wireless sensor network (WSN) system has been developed, targeting monitoring and observation applications. The system specifically emphasises wastewater control and management in the context of irrigation practises. An artificial neural network (ANN) technique, incorporating the Well Node approach, has been employed to process a one-year dataset collected from sensor nodes in an actual wastewater treatment station in Morocco. Thus, the acquired data include a range of parameters, such as electrical conductivity (EC), total dissolved solids (TDS), chlorine levels (Cl), nitrogen content (N), hydrogen carbonate concentrations (HCO3), and pH values. The proposed hybrid system incorporates the Kohonen self-regulating characteristic map for data validation and reconstruction, along with multilayer perceptrons for sample prediction. The selected ANN demonstrates impressive performance, with recognition rates exceeding 95% and an F1 score of approximately 96.81% in terms of wastewater reusability. In particular, the system exhibits robustness in handling uncertainties, deviations, and low information content in input data, allowing for informed decisions on usage or reprocessing through its self-learning capabilities. The study highlights the adaptability of the WSN system to different farming requirements and geographical areas, contributing to improved resource management, enhanced water utilization efficiency, and protection of plants, soil, and the environment. The research findings advance the understanding of ANN architecture and its unique modifications for effective wastewater control. The results also emphasise the effectiveness of the system during the adaptation and implementation phases, offering valuable insights for algorithmic improvements.

Social response and Disaster management: Insights from twitter data Assimilation on Hurricane Ian

During natural disasters, people use social media platforms to share their opinions and general information about the event. Here, we investigate the public response to large and destructive hurricane Ian in late September 2022 by examining the textual content of tweets shared on Twitter across the contiguous United States (CONUS). We mined and processed over twenty million tweets for discovering the main topics of discussion and relationship between them, and classifying tweets into humanitarian topics and categories to help disaster management with thorough sentiment analysis. We employed a variety of algorithms in Artificial Intelligence for Natural Language Processing (NLP) including sentiment analysis, topic modeling, and text classification to assimilate the information content in massive Twitter data. The findings of this study provide insights on how people utilize social media to learn and disseminate information about hurricane events, which accordingly aid emergency responders and disaster managers in mitigating the negative consequences of such catastrophes and improving community preparedness.

Coherence analysis in electroencephalography. Prospects and problems of using coherent analysis in EEG practice (lecture one)

In the lecture, the author analyzes in detail the legitimacy of using the coherence function to analyze the bioelectrical activity of the brain in practical electroencephalography from the standpoint of mathematical and clinical validity. The author touches upon the issues of the informational significance of EEG coherence indicators in the disclosure in the assessment of the functional state of the brain about the mental characteristics of a person. As an evidence base, the author demonstrates examples of the information content of EEG coherence analysis data in terms of predicting the outcome in patients in an unconscious state. The author describes the stages of the formation of intracerebral connections according to the data of coherent analysis of the EEG at the stages of age-related development on the model of infants of age, after term birth and in preterm infants with a favorable course of development, and also the formation of spatio- temporal relations of the EEG of premature infants with periventricular leukomalacia on the 1st — 2 nd years of life.

Distributed Optimal Frequency Control under communication packet loss in multi-agent electric energy systems

This paper investigates the impact of communication packet loss on Distributed Optimal Frequency Control (DOFC) in Alternating Current (AC) electric energy systems, populated with multiple clusters of hybrid producer–consumer (prosumer) agents. The paper first establishes rigorous relationships between the communication packet delivery ratio and the convergence rate of the proposed DOFC algorithm. This provides a foundation for resource allocation on communication systems to enhance the convergence speed of distributed optimization and control algorithms, such as DOFC, under noisy and disrupted communication systems. The paper develops a systematic approach to identify the best possible convergence rate over all possible algorithms, by introducing an algorithm that can achieve asymptotically the Cramér-Rao (CR) lower bound. This fundamental result links the information contents of data to the best possible mean-square estimation error. Simulation studies on an electric energy system validate the theoretical results.

Forecasting changes of the flow regime at deep geothermal wells based on high resolution sensor data and low resolution chemical analyses

Abstract. Geothermal waters provide a great resource to generate clean energy, however, there is a notorious lack of high quality data on these waters. The scarcity of deep geothermal aquifer information is largely due to inaccessibility and high analysis costs. However, multiple operators use geothermal wells in Lower Bavaria and Upper Austria for balneological (medical and wellness) applications as well as for heat mining purposes. The state of the art sampling strategy budgets for a sampling frequency of 1 year. Previous studies have shown that robust groundwater data requires sampling intervals of 1–3 months, however, these studies are based on shallow aquifers which are more likely to be influenced by seasonal changes in meteorological conditions. This study set out to assess whether yearly sampling adequately represents sub-yearly hydrochemical fluctuations in the aquifer by comparing yearly with quasi-continuous hydrochemical data at two wells in southeast Germany by assessing mean, trend and seasonality detection among the high and low temporal resolution data sets. Furthermore, the ability to produce reliable forecasts based on yearly data was examined. In order to test the applicability of virtual sensors to elevate the information content of yearly data, correlations between the individual parameters were assessed. The results of this study show that seasonal hydrochemical variations take place in deep aquifers, and are not adequately represented by yearly data points, as they are typically gathered at similar production states of the well and do not show varying states throughout the year. Forecasting on the basis of yearly data does not represent the data range of currently measured continuous data. The limited data availability did not allow for strong correlations to be determined. We found that annual measurements, if taken at regular intervals and roughly the same production rates, represent only a snapshot of the possible hydrochemical compositions. Neither mean values, trends nor seasonality was accurately captured by yearly data. This could lead to a violation of stability criteria for mineral water, or to problems in the geothermal operation (scalings, degassing). We thus recommend a new testing regime of at least 3 samples a year. While not a replacement for the detailed analyses, under the right circumstances, and when trained with more substantial data sets, viertual sensors provide a robust method in this setting to trigger further actions.

Topological Generality and Spectral Dimensionality in the Earth Mineral Dust Source Investigation (EMIT) Using Joint Characterization and the Spectral Mixture Residual

NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) mission seeks to use spaceborne imaging spectroscopy (hyperspectral imaging) to map the mineralogy of arid dust source regions. Here we apply recent developments in Joint Characterization (JC) and the spectral Mixture Residual (MR) to explore the information content of data from this novel mission. Specifically, for a mosaic of 20 spectrally diverse scenes, we find: (1) a generalized three-endmember (Substrate, Vegetation, Dark; SVD) spectral mixture model is capable of capturing the preponderance (99% in three dimensions) of spectral variance with low misfit (99% pixels with <3.7% RMSE); (2) manifold learning (UMAP) is capable of identifying spatially coherent, physically interpretable clustering relationships in the spectral feature space; (3) UMAP yields results that are at least as informative when applied to the MR as when applied to raw reflectance; (4) SVD fraction information usefully contextualizes UMAP clustering relationships, and vice-versa (JC); and (5) when EMIT data are convolved to spectral response functions of multispectral instruments (Sentinel-2, Landsat 8/9, Planet SuperDove), SVD fractions correlate strongly across sensors, but UMAP clustering relationships for the EMIT hyperspectral feature space are far more informative than for simulated multispectral sensors. Implications are discussed for both the utility of EMIT data in the near-term and for the potential of high signal-to-noise (SNR) spaceborne imaging spectroscopy more generally, to transform the future of optical remote sensing in the years and decades to come.

Application of Statistical Inference Using Entropy to Characterize the Transfer of Data Across Financial Systems

This paper explores the application of statistical inference using entropy to characterize the transfer of data across financial systems. The transfer of data between financial systems is a critical process that affects the accuracy and reliability of financial information. In this study, we propose a new approach to quantify the amount of information transferred between two financial systems based on entropy measures. Specifically, we use Transfer entropy to measure the information content of data transferred between financial systems. The Kullback-Leibler separation of conditional probabilities is a design statistic known as Transfer entropy. This method enables the determination, measurement, and testing of information transmission without being limited to linear movements. We demonstrate the effectiveness of our approach by analyzing using this empirical technique, the importance of the borrowing exchange industry in comparison to the exchanges for debt securities. In addition to this, this paper investigates the greater connectivity that exists among operational risks, as represented, respectively, by iTraxx and VIX Europe. We show that our entropy-based approach can detect changes in the information content of data transferred between the two exchanges, which can be indicative of changes in market conditions or the behavior of market participants. Overall, this study highlights the potential of entropy-based statistical inference methods for characterizing the transfer of data across financial systems. This approach has the potential to provide valuable insights into the behavior of financial markets and the efficiency of financial systems, which can inform policy decisions and improve the accuracy and reliability of financial information.

Upscaling Tracer‐Aided Ecohydrological Modeling to Larger Catchments: Implications for Process Representation and Heterogeneity in Landscape Organization

AbstractStable isotopes of water are ideal tracers to integrate into process‐based models, advancing ecohydrological understanding. Current tracer‐aided ecohydrological modeling is mostly conducted in relatively small‐scale catchments, due to limited tracer data availability and often highly damped stream isotope signals in larger catchments (>100 km2). Recent model developments have prioritized better spatial representation, offering new potential for advancing upscaling in tracer‐aided modeling. Here, we adapted the fully distributed EcH2O‐iso model to the Selke catchment (456 km2, Germany), incorporating monthly sampled isotopes from seven sites between 2012 and 2017. Parameter sensitivity analysis indicated that the information content of isotope data was generally complementary to discharge and more sensitive to runoff partitioning, soil water and energy dynamics. Multi‐criteria calibrations revealed that inclusion of isotopes could significantly improve discharge performance during validations and isotope simulations, resulting in more reasonable estimates of the seasonality of stream water ages. However, capturing isotopic signals of highly non‐linear near‐surface processes remained challenging for the upscaled model, but still allowed for plausible simulation of water ages reflecting non‐stationarity in transport and mixing. The detailed modeling also helped unravel spatio‐temporally varying patterns of water storage‐flux‐age interactions and their interplay under severe drought conditions. Embracing the upscaling challenges, this study demonstrated that even coarsely sampled isotope data can be of value in aiding ecohydrological modeling and consequent process representation in larger catchments. The derived innovative insights into ecohydrological functioning at scales commensurate with management decision making, are of particular importance for guiding science‐based measures for tackling environmental changes.