Long-term Dynamics Research Articles

Decoding the Environmental Synergy in BRI Nations: Analyzing the Influence of Renewable Energy Adoption, Financial Evolution, FDI, and Capital Resilience on Sustainability

In response to escalating global concerns over environmental degradation, this study explores the intricate relationships between financial development (FD), foreign direct investment (FDI), capital adequacy, renewable energy consumption (REC) and environmental sustainability in BRI. We aim to provide nuanced insights into how these economic variables impact environmental quality, contributing to a comprehensive understanding of the complex interplay between economic development and environmental conservation. We aim to explore the associations between FD, FDI, REC, and capital adequacy, and environmental quality, emphasizing short-term and long-term dynamics. We employ the Autoregressive Distributed Lag (ARDL) model and conduct D-H causality tests to discern these variables' temporal and causal relationships. This methodology allows us to capture the complexities of the relationships and provide a robust analysis of their impacts on environmental sustainability. The findings reveal a positive long-run association between FD and environmental quality, suggesting that a well-developed financial sector may contribute positively to environmental outcomes. However, the short-run dynamics introduce complexity, indicating a potential immediate positive impact and raising questions about contextual factors influencing FD's contribution to increased carbon emissions. Shifting the focus to FDI and REC, our research uncovers a potential positive association with environmental quality in the long run. The short-run analysis introduces nuances, suggesting a potential negative impact, reflecting the mixed effects observed in previous studies, which underscores the importance of considering temporal dimensions and policy interventions to enhance the positive contributions of FDI and REC to environmental sustainability. Further, our study delves into the impact of capital adequacy on environmental sustainability, revealing a positive long-run association, which challenges negative associations, underlining the need for tailored policies to balance economic growth and environmental conservation. As a whole, our findings contribute quantitative evidence to guide policymakers in fostering incremental improvements over time, acknowledging the multifaceted nature of the relationships under consideration.

Modelling Inflation Dynamics and Global Oil Price Shocks in OAPEC Countries: TVP-VAR

This paper aims to examine the dynamic pass-through effect of oil price shocks on inflation in OAPEC countries using quarterly data ranging from 1990:Q1 to 2022:Q4. The incorporation of the TVP-VAR model along with the Markov Chain Monte Carlo method with stochastic volatility estimation offers a robust framework for capturing the time-varying nature of the relationship. Additionally, the analysis of the Impulse Response Function provides valuable insights into the short-term and long-term dynamics following oil price shocks. The results demonstrate a significant positive pass-through effect of oil prices on inflation in the sample member states, with heterogeneous magnitude and persistence. These variations are attributed to diversity in oil dependency, exchange rate regimes, monetary policy frameworks and the degree of openness to global markets. The study suggests the need for effective inflation management strategies and appropriate policy responses to oil price fluctuations. Furthermore, policymakers may consider adjusting exchange rates, interest rates, or trade policies to manage inflationary pressures effectively.

Assessing the utility of shellfish sanitation monitoring data for long-term estuarine water quality analysis

Regular testing of coastal waters for fecal coliform bacteria by shellfish sanitation programs could provide data to fill large gaps in existing coastal water quality monitoring, but research is needed to understand the opportunities and limitations of using these data for inference of long-term trends. In this study, we analyzed spatiotemporal trends from multidecadal fecal coliform concentration observations collected by a shellfish sanitation program, and assessed the feasibility of using these monitoring data to infer long-term water quality dynamics. We evaluated trends in fecal coliform concentrations for a 20-year period (1999–2021) using data collected from spatially fixed sampling sites (n = 466) in North Carolina (USA). Findings indicated that shellfish sanitation data can be used for long-term water quality inference under relatively stationary management conditions, and that salinity trends can be used to investigate management-driven bias in fecal coliform observations collected in a particular area.

Modelling the impact of precaution on disease dynamics and its evolution.

In this paper, we introduce the notion of practically susceptible population, which is a fraction of the biologically susceptible population. Assuming that the fraction depends on the severity of the epidemic and the public's level of precaution (as a response of the public to the epidemic), we propose a general framework model with the response level evolving with the epidemic. We firstly verify the well-posedness and confirm the disease's eventual vanishing for the framework model under the assumption that the basic reproduction number . For , we study how the behavioural response evolves with epidemics and how such an evolution impacts the disease dynamics. More specifically, when the precaution level is taken to be the instantaneous best response function in literature, we show that the endemic dynamic is convergence to the endemic equilibrium; while when the precaution level is the delayed best response, the endemic dynamic can be either convergence to the endemic equilibrium, or convergence to a positive periodic solution. Our derivation offers a justification/explanation for the best response used in some literature. By replacing "adopting the best response" with "adapting toward the best response", we also explore the adaptive long-term dynamics.

STOCK MARKET DEVELOPMENT AND ECONOMIC GROWTH: AN ARDL APPROACH

This research project employs the Autoregressive Distributed Lag (ARDL) approach to investigate the intricate relationship between stock market development and economic growth. The primary objective is to systematically analyze the causal links and quantify the impact of key stock market indicators on economic growth, and vice versa. The methodology encompasses a thorough literature review to establish a theoretical framework, followed by the collection and examination of relevant data over a defined timeframe. Unit root tests and cointegration analysis are applied to assess the stationarity and long-term relationships between the variables. The ARDL model is then employed to estimate the dynamic interactions, considering lagged values of the indicators. Diagnostic tests are conducted to ensure the robustness of the findings. The project aims to contribute empirical insights into both short- and long-term dynamics, shedding light on the complex interplay between stock market development and economic growth. The results are expected to offer valuable implications for policymakers and researchers, providing evidence-based guidance for fostering sustainable economic development. Ultimately, this research seeks to advance our understanding of the mechanisms driving economic growth in the context of stock market development. Keywords: Stock Market Development, Economic Growth, ARDL Approach, Causal Relationship, Lagged Variables, Unit Root Tests, Cointegration Analysis, Empirical Insights, Sustainable Economic Development, Policy Implications.

A physiologically-based digital twin for alcohol consumption—predicting real-life drinking responses and long-term plasma PEth

Alcohol consumption is associated with a wide variety of preventable health complications and is a major risk factor for all-cause mortality in the age group 15-47 years. To reduce dangerous drinking behavior, eHealth applications have shown promise. A particularly interesting potential lies in the combination of eHealth apps with mathematical models. However, existing mathematical models do not consider real-life situations, such as combined intake of meals and beverages, and do not connect drinking to clinical markers, such as phosphatidylethanol (PEth). Herein, we present such a model which can simulate real-life situations and connect drinking to long-term markers. The new model can accurately describe both estimation data according to a χ2 -test (187.0 < Tχ2 = 226.4) and independent validation data (70.8 < Tχ2 = 93.5). The model can also be personalized using anthropometric data from a specific individual and can thus be used as a physiologically-based digital twin. This twin is also able to connect short-term consumption of alcohol to the long-term dynamics of PEth levels in the blood, a clinical biomarker of alcohol consumption. Here we illustrate how connecting short-term consumption to long-term markers allows for a new way to determine patient alcohol consumption from measured PEth levels. An additional use case of the twin could include the combined evaluation of patient-reported AUDIT forms and measured PEth levels. Finally, we integrated the new model into an eHealth application, which could help guide individual users or clinicians to help reduce dangerous drinking.

Examining the Effect of Sample Size on the Estimation of Low-Frequency Signals in Tree-Ring Chronologies

Dendrochronology utilizes mean values of individual tree-ring indices to average out noise and strengthen common signals, typically associated with climate. Expressed Population Signal (EPS) is commonly used to assess the degree to which a chronology represents the common signal. However, there is a lack of studies on how EPS reflects low-frequency signals. In this study, we propose a frequency-dependent EPS (FEPS) approach to evaluating the low-frequency signal strength. Extensive tests were conducted using tree rings from Chinese Loess Plateau. We found that as timescales lengthen, EPS decreases due to declining inter-series correlations, thus demanding larger sample sizes. This finding remains robust across different detrending methods, filtering methods, filter orders, and treatments for end effects. Determining sample sizes by standard EPS (common use of EPS with unfiltered series) has a limited impact on multi-year frequencies, but considerably influences timescales exceeding a decade. Our study reveals a prevalent underestimation of sample size requirements for robust multi-year and interdecadal signals estimation, and the FEPS approach is expected to potentially enhance our understanding of long-term climate dynamics.

Dynamic connectedness between FinTech and energy markets: Evidence from fat tails, serial dependence, and Bayesian approach

In a complex global environment, modeling the relationships among several markets and asset classes has become more challenging. Literature strives to provide conclusive evidence due to system complexities. Therefore, we investigate the dynamic relationship between Fintech and energy sectors using fat tails, serial dependence, and the Bayesian approach from May 1, 2019, to October 28, 2022. Results unveil significant temporal variations in network interconnections, with an evident increase in interlinkages across short, medium, and long durations due to transient market events. The COVID-19 pandemic and the onset of the Russia-Ukraine conflict have substantially altered the long-term relational dynamics within these networks. An analysis of net directional linkages reveals a pivotal shift in market roles—from being predominantly shock recipients to becoming shock transmitters—highlighted during the COVID-19 pandemic and the initial phase of the Russia-Ukraine War. Initial observations indicate that, amid the global impact of COVID-19, Fintech markets absorbed shocks through investments in green bonds and renewable energy sources like wind, solar, and clean energy. These findings suggest that while Fintech consistently acts as a shock absorber in long-term scenarios, it assumes the role of a shock transmitter during adverse economic states.

Analyzing GDP Growth Drivers in Saudi Arabia: Investment or Consumption: An Evidence-Based ARDL-Bound Test Approach

This study delves into the intricate interplay of economic growth components, specifically focusing on consumption and investment in Saudi Arabia from 2000 to 2022. Employing vector error correction models and co-integration techniques, we analyze the short- and long-term dynamics within the relationship of consumption, investment, and economic growth. Granger causality analysis is also used to discern these pivotal variables’ causal connections. Our empirical analysis reveals a persistent long-term cointegration relationship among the variables, underscoring the enduring nature of their interdependency. Furthermore, our findings highlight consumption and investment’s statistically significant positive impact on economic growth. Notably, the short-term analysis unveils a stable model characterized by an annual adjustment to equilibrium of 100%. Moreover, the Granger causality study demonstrates unidirectional causal linkages among consumption, investment, and economic growth. These findings hold substantial implications for policy formulation in Saudi Arabia. Policymakers must grasp the ramifications of burgeoning prosperity and evolving private consumption patterns on future environmental outcomes. Achieving sustainable long-term results necessitates equal emphasis on bolstering private consumption and fostering other facets of economic growth.

Threshold dynamics of a reaction–advection–diffusion schistosomiasis epidemic model with seasonality and spatial heterogeneity

Most water-borne disease models ignore the advection of water flows in order to simplify the mathematical analysis and numerical computation. However, advection can play an important role in determining the disease transmission dynamics. In this paper, we investigate the long-term dynamics of a periodic reaction–advection–diffusion schistosomiasis model and explore the joint impact of advection, seasonality and spatial heterogeneity on the transmission of the disease. We derive the basic reproduction number R0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {R}}_0$$\\end{document} and show that the disease-free periodic solution is globally attractive when R0<1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {R}}_0<1$$\\end{document} whereas there is a positive endemic periodic solution and the system is uniformly persistent in a special case when R0>1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {R}}_0>1$$\\end{document}. Moreover, we find that R0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {R}}_0$$\\end{document} is a decreasing function of the advection coefficients which offers insights into why schistosomiasis is more serious in regions with slow water flows.

The biological controls of soil carbon accumulation following wildfire and harvest in boreal forests: A review.

Boreal forests are frequently subjected to disturbances, including wildfire and clear-cutting. While these disturbances can cause soil carbon (C) losses, the long-term accumulation dynamics of soil C stocks during subsequent stand development is controlled by biological processes related to the balance of net primary production (NPP) and outputs via heterotrophic respiration and leaching, many of which remain poorly understood. We review the biological processes suggested to influence soil C accumulation in boreal forests. Our review indicates that median C accumulation rates following wildfire and clear-cutting are similar (0.15 and 0.20 Mg ha-1 year-1, respectively), however, variation between studies is extremely high. Further, while many individual studies show linear increases in soil C stocks through time after disturbance, there are indications that C stock recovery is fastest early to mid-succession (e.g. 15-80 years) and then slows as forests mature (e.g. >100 years). We indicate that the rapid build-up of soil C in younger stands appears not only driven by higher plant production, but also by a high rate of mycorrhizal hyphal production, and mycorrhizal suppression of saprotrophs. As stands mature, the balance between reductions in plant and mycorrhizal production, increasing plant litter recalcitrance, and ectomycorrhizal decomposers and saprotrophs have been highlighted as key controls on soil C accumulation rates. While some of these controls appear well understood (e.g. temporal patterns in NPP, changes in aboveground litter quality), many others remain research frontiers. Notably, very little data exists describing and comparing successional patterns of root production, mycorrhizal functional traits, mycorrhizal-saprotroph interactions, or C outputs via heterotrophic respiration and dissolved organic C following different disturbances. We argue that these less frequently described controls require attention, as they will be key not only for understanding ecosystem C balances, but also for representing these dynamics more accurately in soil organic C and Earth system models.

Total knee and hip replacement in rheumatoid arthritis: А current trend

Total joint replacement (TJR) is a surgical procedure widely used in severe destructive joint damage. TJR of the knee and hip joint (TKR and THR respectively) occupies an important place in the complex treatment of rheumatoid arthritis (RA) and osteoarthritis (OA), and can significantly reduce pain and improve function in patients with severe structural joint damage due to an ineffectiveness of conservative therapy. In recent years, due to the development of the modern concept of active treatment of RA (treat-to-target) and the introduction of new classes of anti-inflammatory drugs, such as biologic disease modifying antirheumatic drugs and JAK inhibitors, the need for TJR in RA has begun to decrease. At the same time, the aging of the population, the increase in the number of patients with OA and the lack of generally approved pathogenetic therapy determine the increase of TKR/THR incidence in this disease. This review presents the long-term dynamics of the incidence of TKR and THR according to the national registers of different countries. The issues of the complication risks in patients with RA and OA, current recommendations for anti-rheumatic therapy in RA in the perioperative period are also considered.

Analysis of public, private and foreign-owned enterprises: Assessment of efficiency and sustainability

Subject. The article addresses the efficiency and sustainability of public, private, and foreign-owned enterprises. Objectives. The aim is to develop and test a methodology for analyzing the said enterprises. Methods. The study rests on general scientific principles and research methods, like abstraction, generalization of approaches to the analysis of enterprises of different forms of business ownership. Results. I developed a methodology, which includes three stages. First, estimating the share of enterprises of different forms of ownership in total assets, revenue, and profit, and analyzing the sectoral and regional sample structure and enterprise size. Second, assessing the short-term and long-term efficiency, with analysis of profitability indicators, long-term dynamics, and investment activity. Finally, analyzing the adaptability of enterprises of different forms of ownership to changed conditions, and evaluating the indicators of financial stability, riskness, and dynamics for 2022. Conclusions. I tested the methodology on data from large Russian consolidated businesses (a total of 593 enterprises for 2018–2022). The calculations and analysis showed that private business prevails in assets, revenue, and net profit. For State-owned enterprises, the most significant industry is oil and gas production, for foreign-owned ones – production and distribution of electricity, gas and water, for private enterprises – retail trade. Higher profitability is typical for foreign and private enterprises. The analysis of adaptability to current conditions showed that revenue growth rate in 2022 did not decrease vs. the annual average for all groups of enterprises, however, profit and profitability indicators deteriorated. In general, large consolidated public, private, and foreign-owned business demonstrated a relatively high degree of stability in 2022 with a decrease in profit and profitability, and now it continues investment activity.

СТРУКТУРА ДИНАМИКИ МАССОВЫХ НЕИНФЕКЦИОННЫХ ЗАБОЛЕВАНИЙ ДЕТЕЙ В ПЕРИОД ПОСТСОЦИАЛИСТИЧЕСКИХ ПРЕОБРАЗОВАНИЙ В СТРАНЕ

The paper discusses the results of the analysis of long-term dynamics of morbidity and its risk in in-dustrial and non-industrial (background) territories of the Irkutsk region using the four-phase structure of the process of adaptation of the organism in inadequate conditions

Liquid-Liquid Dispersion Performance Prediction and Uncertainty Quantification Using Recurrent Neural Networks.

We demonstrate the application of a recurrent neural network (RNN) to perform multistep and multivariate time-series performance predictions for stirred and static mixers as exemplars of complex multiphase systems. We employ two network architectures in this study, fitted with either long short-term memory and gated recurrent unit cells, which are trained on high-fidelity, three-dimensional, computational fluid dynamics simulations of the mixer performance, in the presence and absence of surfactants, in terms of drop size distributions and interfacial areas as a function of system parameters; these include physicochemical properties, mixer geometry, and operating conditions. Our results demonstrate that while it is possible to train RNNs with a single fully connected layer more efficiently than with an encoder-decoder structure, the latter is shown to be more capable of learning long-term dynamics underlying dispersion metrics. Details of the methodology are presented, which include data preprocessing, RNN model exploration, and methods for model performance visualization; an ensemble-based procedure is also introduced to provide a measure of the model uncertainty. The workflow is designed to be generic and can be deployed to make predictions in other industrial applications with similar time-series data.

Long-term dynamics of a stabilized time-space discretization scheme for 2D time-fractional Allen–Cahn equation with double well potential

This paper deals with the long-term discrete dynamics of 2D time-fractional Allen–Cahn equation (TFACE) with double well potential. A stabilized time-space discretization scheme is proposed to solve the equation. Under no constraint of the stepsizes, the proposed scheme is proved to be maximum-principle-preserving, energy-stability-preserving and variational-energy-dissipation-preserving in the discrete sense. Moreover, an error estimate of the numerical scheme is also derived. In the end, a series of numerical experiments are carried out to illustrate the obtained theoretical results.

Synergistic Impacts of Built-Up Characteristics and Background Climate on Urban Vegetation Phenology: Evidence from Beijing, China

Vegetation is an important strategy for mitigating heat island effects, owed to its shading and evaporative cooling functions. However, urbanization has significantly affected regional vegetation phenology and can potentially weaken the cooling potential of vegetation. Previous studies have mainly focused on national and regional vegetation phenology, but local-scale vegetation phenology and dynamic variations in built-up areas remain unclear. Therefore, this study characterized the vegetation phenology in the densely built-up area of Beijing, China over the period of 2000–2020 based on high-resolution NDVI data using Savitzky–Golay filtering and explored its spatiotemporal characteristics and drivers. The results indicate that the vegetation phenology exhibits significant spatial heterogeneity and clustering characteristics. Compared with vegetation in peripheral blocks, vegetation in central urban blocks generally has an earlier start in the growing season (SOS), later end in the growing season (EOS), and a longer growing season length (GSL). However, the overall distribution of these parameters has experienced a process of decentralization along with urbanization. In terms of drivers, vegetation phenology indicators are mainly influenced by background climate. Specifically, SOS and GSL are mainly affected by temperature (TEP), whereas EOS is mainly influenced by annual precipitation (PRE). Additionally, local environmental factors, particularly the percentage of water body (WAP), also have an impact. Notably, the local environment and background climate have a synergistic effect on vegetation phenology, which is greater than their individual effects. Overall, this study extends the current knowledge on the response of vegetation phenology to urbanization by investigating long-term vegetation phenology dynamics in dense urban areas and provides new insights into the complex interactions between vegetation phenology and built environments.

Foreign direct investment on economic growth in Nepal: A sector specific analysis

This paper investigates the impact of Foreign Direct Investment (FDI) on the economic development of Nepal through a comprehensive and sector-specific analysis. With a growing interest in attracting foreign investments to foster economic growth, this study aims to provide nuanced insights into the diverse effects of FDI across various sectors in the context of Nepal. Utilizing a robust methodology that incorporates quantitative and qualitative approaches, the research examines key economic indicators and sectoral performance data over a specified period. This paper employs a sophisticated econometric framework that includes unit root tests to assess the stationarity properties of the time series data, ensuring the reliability of subsequent econometric models. The ECM and ARDL models facilitate the examination of both short-term and long-term dynamics between FDI and economic development variables, ARDL Bound test, is applied to investigate the existence of cointegration among the variables, providing insights into the long-term equilibrium relationship. Additionally, Granger causality tests are conducted to discern the directionality of causal relationships, helping to discern whether FDI leads to economic development or vice versa. The empirical results reveal compelling insights into the relationship between FDI and economic development in Nepal. Robustness checks further validate the reliability of the findings, enhancing the overall robustness and credibility of the study's empirical results. The comprehensive analysis provides valuable insights for policymakers and stakeholders aiming to formulate targeted strategies for fostering sustainable economic development in Nepal through FDI.

Framework for Examining the Correlation Between Trade Liberalization and Employment in Nigeria

This research examines the relationship between trade liberalization and employment in Nigeria using a retrospective research approach. The study considers variables such as the employment rate, trade openness index, foreign direct investment, and currency exchange rate. The analytical framework combines classical linear regression and Auto Regressive Distributed Lag (ARDL) models to capture both short-term and long-term dynamics. Diagnostic tests, including descriptive statistics, Augmented Dickey-Fuller (ADF) test, Perron Unit Root Test, ARDL-Bound test, and Error Correction Model (ECM)-ARDL test, are conducted to analyze the collected data. The study utilizes secondary data from the statistical bulletin of the Central Bank of Nigeria (2018) covering the period from 1985 to 2018. The findings of the study are mixed. The error correction estimates indicate a negative relationship between the trade openness index and the employment rate. On the other hand, foreign direct investment shows a negative relationship with employment rate, but this relationship is not statistically significant. The exchange rate demonstrates a positive and significant correlation with the employment rate. It was recommended that, Diversifying Nigeria&amp;apos;s economy by promoting domestic industries like agriculture, manufacturing, and services to create job opportunities and reduce import dependence. Implement effective labor market policies including job training, worker protections, and competitive labor market measures to mitigate negative impacts of trade liberalization on employment. Target FDI to sectors with high job creation potential, incentivizing investments, supporting technology transfer, and fostering partnerships for employment growth in Nigeria.

Hydroclimatic non-stationarity drives stream hydrochemistry dynamics through controls on catchment connectivity and water ages

We used data from 17 years of routine stream flow sampling to characterise hydrochemical dynamics in the Girnock Burn, an internationally important long-term catchment monitoring site. We combined hydrochemical time series analysis with hydrological modelling to understand short- and long-term dynamics in relation to dominant runoff processes. Isotopic tracer data employed within the model enabled flux tracking of water as it transited the catchment, facilitating water age estimates. The stream drains an upland 31 km2 catchment with complex topography and variable geology, which influences soil distributions; peaty soils dominate valley bottoms which receive drainage from more permeable podzolic soils on steeper slopes. Short-term hydrochemical dynamics largely reflect the relative dominance of hydrological sources: summer low flows are dominated by alkaline groundwater high in weathering products (Ca2+, Mg2+, SiO2) whilst high flows generated by overland flow from peaty soils are more acidic, DOC enriched, and dominated by atmospheric solutes (Na+, Cl-, SO42-). Baseflows are dominated by older groundwater (>∼3years) in contrast to storm runoff dominated by younger (<∼0.5 years), near-surface, waters. Usual seasonal distributions of stream flows, with high flows in winter and summer low flows, result in strong seasonality in most solutes. However, coupled effects of hydroclimatic variability and catchment heterogeneity dictates marked scatter in flow-concentration relationships for most determinands. A long-term decreasing trend was evident for SO42-concentrations, reflecting continuing effects of reductions in atmospheric deposition of pollutants from coal burning. Reductions in large storm event frequency post-2016 and protracted periods of low flows in the droughts of 2018 and 2022 have resulted in the catchment becoming dominated by low flows, derived from older groundwater with more alkaline chemistry for longer periods. Changing climatic conditions and stream flow responses seem likely to continue to cause changes in stream water quality and associated ecosystem services of the Girnock Burn and similar upland catchments.