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Georgia Papacharalampous

@georgiapapachar

I am an engineer, PhD, MSc working mainly on the intersection of geoscience and statistical modelling with a focus on machine learning algorithms, their combination with physics-based models and predictive uncertainty estimation.

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13.12.2023
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Latest posts by Georgia Papacharalampous @georgiapapachar

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Estimating low and reverse sap flux density with the temperature ratio heat pulse (TRHP) method The compensation heat pulse (CHP) method is widely-used for monitoring sap flux density, but it has a limited measurement range and often underestimat…

Hydrology Paper of the Day @kinarnicholas.bsky.social and colleagues on extending the range of the compensation heat pulse (CHP) method: the use of two probes in conjunction with novel mathematics; numerical and lab sand column experiments; and comparisons.
www.sciencedirect.com/science/arti...

12.03.2026 00:45 πŸ‘ 5 πŸ” 1 πŸ’¬ 0 πŸ“Œ 0

Hydrology Paper of the Day @vesnazupanc.bsky.social on engineered soils used to remediate lignite fly ash landfills: a study site in Slovenia; chemical and physical sampling; lab analyses indicating chemical changes over a 15 to 30-year period; and a framework for monitoring and measurement.

11.03.2026 01:52 πŸ‘ 4 πŸ” 1 πŸ’¬ 0 πŸ“Œ 0

Hydrology Paper of the Day @emde.science on understanding carbon changes in soils: a more precise definition of the concepts associated with carbon sequestration; understanding use of terminology in journals; loss mitigation and emissions; and climate change mitigation in the context of management.

10.03.2026 02:49 πŸ‘ 9 πŸ” 1 πŸ’¬ 0 πŸ“Œ 0

Thank you so much, @kinarnicholas.bsky.social!!

09.03.2026 15:54 πŸ‘ 1 πŸ” 0 πŸ’¬ 0 πŸ“Œ 0

Hydrology Paper of the Day @christost.bsky.social @georgiapapachar.bsky.social on the Nash‑Sutcliffe loss: a measure with clear theoretical underpinnings, and the concept of Nash-Sutcliffe linear regression in context of an estimation framework in hydrology, machine learning and geosciences.

09.03.2026 01:37 πŸ‘ 7 πŸ” 3 πŸ’¬ 0 πŸ“Œ 1

Hydrology Paper of the Day @gescilam.bsky.social and colleagues on communication in an age of water scarcity: issues and challenges identified in conjunction with the scientific community at EGU24; data from interviews and surveys; and the need for trust, appropriate audiences, equity, and clarity

08.03.2026 01:35 πŸ‘ 12 πŸ” 7 πŸ’¬ 2 πŸ“Œ 0

Hristos Tyralis, Georgia Papacharalampous: Learning with the Nash-Sutcliffe loss https://arxiv.org/abs/2603.00968 https://arxiv.org/pdf/2603.00968 https://arxiv.org/html/2603.00968

03.03.2026 06:53 πŸ‘ 3 πŸ” 2 πŸ’¬ 0 πŸ“Œ 1
The Nash-Sutcliffe efficiency ($\text{NSE}$) is a widely used, positively oriented relative measure for evaluating forecasts across multiple time series. However, it lacks a decision-theoretic foundation for this purpose. To address this, we examine its negatively oriented counterpart, which we refer to as Nash-Sutcliffe loss, defined as $L_{\text{NS}} = 1 - \text{NSE}$. We prove that $L_{\text{NS}}$ is strictly consistent for an elicitable and identifiable multi-dimensional functional, which we name the Nash-Sutcliffe functional. This functional is a data-weighted component-wise mean. The common practice of maximizing the average NSE across multiple series is the sample analog of minimizing the expected $L_{\text{NS}}$. Consequently, this operation implicitly assumes that all series originate from a single non-stationary, stochastic process. We introduce Nash-Sutcliffe linear regression, a multi-dimensional model estimated by minimizing the average $L_{\text{NS}}$, which reduces to a data-weighted least squares formulation. By reorienting the sample average loss function, we extend the previously proposed evaluation and estimation framework to forecasting multiple stationary dependent time series with differing stochastic properties. This constitutes a more natural empirical implementation of the $\text{NSE}$ than the earlier formulation. Our results establish a decision-theoretic foundation for $\text{NSE}$-based model estimation and forecast evaluation in large datasets, while further clarifying the benefits of global over local machine learning models.

The Nash-Sutcliffe efficiency ($\text{NSE}$) is a widely used, positively oriented relative measure for evaluating forecasts across multiple time series. However, it lacks a decision-theoretic foundation for this purpose. To address this, we examine its negatively oriented counterpart, which we refer to as Nash-Sutcliffe loss, defined as $L_{\text{NS}} = 1 - \text{NSE}$. We prove that $L_{\text{NS}}$ is strictly consistent for an elicitable and identifiable multi-dimensional functional, which we name the Nash-Sutcliffe functional. This functional is a data-weighted component-wise mean. The common practice of maximizing the average NSE across multiple series is the sample analog of minimizing the expected $L_{\text{NS}}$. Consequently, this operation implicitly assumes that all series originate from a single non-stationary, stochastic process. We introduce Nash-Sutcliffe linear regression, a multi-dimensional model estimated by minimizing the average $L_{\text{NS}}$, which reduces to a data-weighted least squares formulation. By reorienting the sample average loss function, we extend the previously proposed evaluation and estimation framework to forecasting multiple stationary dependent time series with differing stochastic properties. This constitutes a more natural empirical implementation of the $\text{NSE}$ than the earlier formulation. Our results establish a decision-theoretic foundation for $\text{NSE}$-based model estimation and forecast evaluation in large datasets, while further clarifying the benefits of global over local machine learning models.

arXivπŸ“ˆπŸ€–
Learning with the Nash-Sutcliffe loss
By Tyralis, Papacharalampous

03.03.2026 18:17 πŸ‘ 3 πŸ” 2 πŸ’¬ 0 πŸ“Œ 0
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Happy to contribute to this community paper on science communication in hydrology within the IAHS HELPING decade.

We propose the FUSS framework: messages should be Few, Unambiguous, Short, and Structured.

Link to paper: www.tandfonline.com/doi/full/10....

#WaterScience #ScienceCommunication

06.03.2026 17:52 πŸ‘ 6 πŸ” 2 πŸ’¬ 0 πŸ“Œ 1

Hydrology Paper of the Day @hollykirk.bsky.social on a Nature-Water Design framework for ensuring urban and landscape design in the context of ecological services, systems, and climate change: a combination of frameworks; design principles; ensuring connectivity and stewardship; and five principles.

04.03.2026 03:44 πŸ‘ 11 πŸ” 7 πŸ’¬ 1 πŸ“Œ 0

Hydrology Paper of the Day @grosseguido.bsky.social on how ice-wedges and permafrost are affected by fire on the Alaskan tundra: remote sensing image analyses; LIDAR surveys to obtain digital terrain models; polygonal terrain modelling by computational geometry and graphs; and network analyses.

05.03.2026 02:27 πŸ‘ 12 πŸ” 1 πŸ’¬ 0 πŸ“Œ 0

Hydrology Paper of the Day @hydrologywsl.bsky.social on the HydCHeck screening tool for showing anthropogenic controls on rivers in Switzerland: assessment categories and a spider chart; application of the HYDMOD-F method for flow regimes to NAWA Catchment Areas; and a regional overview.

06.03.2026 03:06 πŸ‘ 6 πŸ” 5 πŸ’¬ 1 πŸ“Œ 0

Hydrology Paper of the Day @alexzivkovic.bsky.social on the films of Georges Méliès: explorations of water in the context of human understanding; an aquarium representation of hydrological processes and underwater worlds; real and imagined infrastructures; and artificial environments.

07.03.2026 00:23 πŸ‘ 8 πŸ” 1 πŸ’¬ 0 πŸ“Œ 0

Hydrology Paper of the Day @lytarasova.bsky.social on a framework that allows for an understanding of hydrological model performance and error analysis: explainable machine learning applied to modelling; data from 340 catchments in Germany; and calibration and validation of a rainfall-runoff model.

02.03.2026 03:24 πŸ‘ 11 πŸ” 4 πŸ’¬ 1 πŸ“Œ 0

Hydrology Paper of the Day @gbrunohydro.bsky.social @manuelaibrunner.bsky.social on the hydrological processes of streamflow droughts in the Anthropocene: time scales, temporal changes, and identification; coupling with atmospheric and land-surface processes; soils and groundwater; and modelling.

03.03.2026 03:59 πŸ‘ 8 πŸ” 5 πŸ’¬ 1 πŸ“Œ 0

Hydrology Paper of the Day @mcmillanhydro.bsky.social on an overview of forested catchment-scale hydrology: the rarity and regularity of some processes; the importance of soils and antecedent soil moisture; preferential flow paths; and quantification of fluxes in the context of location.

28.02.2026 04:50 πŸ‘ 13 πŸ” 3 πŸ’¬ 0 πŸ“Œ 0

Hydrology Paper of the Day @lindamariathompson.bsky.social on the illusion of natural flows at NΓ€mforsen on the Γ…ngermanΓ€lven River, Sweden: dams and the idea of water release to ensure place-marketing; exploring place through photography and film; dry riverbeds; and human-environmental control.

01.03.2026 03:55 πŸ‘ 5 πŸ” 2 πŸ’¬ 1 πŸ“Œ 0

Hydrology Paper of the Day @peatofmind.bsky.social on how mosses and lichens are affected by the hydrology of rock barren depressions and wetlands: soil moisture and biomass; moisture in lichens and mosses; productivity; and linkages between these environments and turtle nesting habitats.

27.02.2026 02:55 πŸ‘ 8 πŸ” 2 πŸ’¬ 0 πŸ“Œ 0

Hydrology Paper of the Day @catchmentsci.bsky.social on appraisal of the FOSS KLT-IV image velocimetry software to determine river surface velocity: experiments on the River Dart (Devon, UK); comparison with flow gauging data; calibration and processing pipelines; uncertainties; and applications.

25.02.2026 23:37 πŸ‘ 10 πŸ” 5 πŸ’¬ 1 πŸ“Œ 0

Hydrology Paper of the Day @lmcampbell.bsky.social on reactive amendment remediation of wetlands with Hg and As gold mine tailings in Nova Scotia: design of a protective capping; testing on samples from the Muddy Pond Waverley Gold District; and physical and biological verification of remediation.

24.02.2026 02:12 πŸ‘ 8 πŸ” 2 πŸ’¬ 1 πŸ“Œ 0

Hydrology Paper of the Day @richard-fewster.bsky.social on quantifying how Arctic peatlands change due to global warming: 12 peatlands and transects in Europe and Canada; drone aerial surface photography and 91 soil monoliths; quantitative methods of dating; and how peatlands have expanded.

25.02.2026 03:26 πŸ‘ 8 πŸ” 4 πŸ’¬ 0 πŸ“Œ 0

Hydrology Paper of the Day @iceybethan.bsky.social on the hydroclimatology of the Antarctic Peninsula in the context of various warming scenarios: increasing surface melt and eventual ice shelf collapses under high warming scenarios; changes in biogeography; and a need to reduce global temperatures.

23.02.2026 04:09 πŸ‘ 5 πŸ” 1 πŸ’¬ 0 πŸ“Œ 0
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Striking picture.

23.02.2026 01:25 πŸ‘ 68 πŸ” 13 πŸ’¬ 1 πŸ“Œ 2
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How do #climate, #antecedent #moisture, #soils, #topography, #geology, and #vegetation control #runoff #processes in #forested #catchments worldwide? Find this out here rdcu.be/eXTtd! @natwater.nature.com
Thanks a lot @mcmillanhydro.bsky.social for this commentary
www.nature.com/articles/s44...!

07.01.2026 09:15 πŸ‘ 16 πŸ” 3 πŸ’¬ 2 πŸ“Œ 1
Client Challenge

A new Analysis in Nature Water by Daniele Penna @danielepenna.bsky.social performs a comprehensive study of 691 forested catchments worldwide examines the interplay of biotic and abiotic factors in runoff generation. www.nature.com/articles/s44...

14.01.2026 18:45 πŸ‘ 5 πŸ” 3 πŸ’¬ 1 πŸ“Œ 0
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Somw fun with #discharge measurents today in the Re della Pietra #experimental #catchment danielepenna.wixsite.com/redellapietr... #rainfall #water #precipitation #forest #streamflow

28.01.2026 18:33 πŸ‘ 9 πŸ” 1 πŸ’¬ 0 πŸ“Œ 1
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Thank you @ Marco Borga and Giulia Zuecco for inviting me to @unipd.bsky.social TESAF for a seminar on controls on #runoff #processes in #forested #catchments www.nature.com/articles/s44...

20.02.2026 07:26 πŸ‘ 3 πŸ” 1 πŸ’¬ 0 πŸ“Œ 0

Hydrology Paper of the Day @danielepenna.bsky.social on why trees cause soil to have a dual-permeability effect on forested hillslopes: processes of throughfall and stemflow; spatial scales and soil measurements in the Re della Pietra catchment of Italy; modelling; and geophysical observations.

22.02.2026 04:01 πŸ‘ 11 πŸ” 4 πŸ’¬ 0 πŸ“Œ 0

Hydrology Paper of the Day @cambup-archaeology.cambridge.org on the water reservoirs and hydrology of Northern Belize during the Mayan empire: Indigenous civil engineering; urban forest gardens and spatial patterns of settlement; possible wetland management; and mapping drainage by LiDAR techniques.

21.02.2026 02:26 πŸ‘ 8 πŸ” 2 πŸ’¬ 0 πŸ“Œ 0

Hydrology Paper of the Day @forestecosyst.bsky.social on water use and plant traits in the karst landscapes of the Maolan Nature Reserve, Guizhou Province, China: gradients of biogeochemical cycles; numerical plant importance values; plant trait spectrums; and utilizing stable isotope analyses.

17.02.2026 00:40 πŸ‘ 6 πŸ” 1 πŸ’¬ 0 πŸ“Œ 0