Drought impacts on hydrology and water quality under climate change

Dynamics of streamflow predictability and memory in response to hydrological extremes: insights from the Bandar Abbas watershed

Hydrological extremes, worsened by climate change, disrupt river flow, threatening water resources in arid regions. This study analyzed data from 17 hydrometric stations (1981-2017) across two periods to assess drought impacts on flow behavior in the Bandar Abbas watershed, Iran. Using the Stream Drought Index, noise variance, Lyapunov exponent (LE), Hurst coefficient (H), approximate entropy (ApEn), and the Mann-Kendall trend test, we identified significant changes. Drought severity increased, with noise variance in drought patterns rising by up to 3475% in central zones and dropping by 70% elsewhere, signaling more frequent and intense dry spells. River flow declined at 12 stations, with the Mann-Kendall test confirming negative trends, reducing water availability by up to 150% in volume at northern sites. LE decreased by 1165% across most stations, indicating drought lowered flow sensitivity to initial conditions, while H fell by 50%, weakening long-term flow memory. Meanwhile, ApEn surged by 354%, reflecting increased randomness and reduced predictability, particularly in northern areas. These shifts strain water availability for ecosystems and agriculture, disrupt aquatic habitats, and challenge management strategies reliant on stable flow. This multi-tool approach, novel in this context, merges chaos, memory, and randomness analyses to clarify drought's effects. Focused on Bandar Abbas, the findings offer insights for arid regions globally, providing a framework for adaptive water management to address scarcity and unpredictability in river systems under climate stress.

Cluster-based downscaling of precipitation using Kolmogorov-Arnold Neural Networks and CMIP6 models: Insights from Oman

Accurate precipitation predictions are crucial for addressing climate change impacts on water resources, especially in arid regions like Oman. Therefore, this study evaluates three machine learning models-Random Forest (RF), Multilayer Perceptron Neural Networks (MLP-ANN), and Kolmogorov-Arnold Neural Networks (KANNs)-to downscale and predict precipitation patterns under climate scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5. We assessed each model's ability to reproduce past trends and predict future precipitation using historical data from 1995 to 2014 and projections from 2020 to 2099. The KANN model demonstrated exceptional proficiency in forecasting extreme precipitation occurrences, especially in the most severe scenario (SSP5-8.5). The MLP-ANN model offered a balanced methodology, yielding dependable forecasts that are adaptive to fluctuating situations, even amongst small increases in precipitation and uncertainty. The RF model generated the most reliable forecasts, suggesting small increases in future precipitation while closely correlating with historical data. The study indicates distinct seasonal patterns, with peak precipitation occurring during the monsoon season from June to August. The RF model predicted more intense and uniformly distributed precipitation during this period, demonstrating its advanced data processing capabilities. The geographical patterns predicted by each model exhibited temporal stability, highlighting their consistent reliability, which is essential for precise climate predictions. This comparative research highlights the significance of choosing a suitable machine learning model according to distinct forecasting requirements. Effective downscaling methodologies are essential for informed water resources management, particularly in areas susceptible to cyclones and water shortages. These results provide essential direction for policymakers to improve climate resilience, optimize water infrastructure, and formulate adaptation measures in Oman and other dry locations.

Integrating deep learning algorithms for forecasting evapotranspiration and assessing crop water stress in agricultural water management

The increasing impacts of climate change on global agriculture necessitate the development of advanced predictive models for efficient water management in crop fields. This study aims to enhance the forecasting of evapotranspiration (ET), potential evapotranspiration (PET), and crop water stress index (CWSI) using state-of-the-art deep learning techniques. This research integrates high-resolution climatic data from the ACCESS-ESM model and incorporates four shared socioeconomic pathways (SSPs) to represent a wide range of future climate scenarios. We employ feed forward neural networks (FFNNs), convolutional neural networks (CNNs), gated recurrent units (GRUs), and long short-term memory networks (LSTMs) to predict ET, PET, and CWSI. These findings reveal significant improvements in prediction accuracy, offering valuable insights for agricultural water management in Bangladesh. This approach provides a robust framework for optimizing irrigation practices and enhancing crop resilience against climate-induced water stress.

The Role of Artificial Lakes Located in Forests in the Context of Small Retention, Biodiversity and Climatic Changes-Evidence From Southern Poland

Drought has an effect on hydrologic conditions and water quality under climate change. Small water retention in forests is one of the priority investment programs implemented in recent years, supported by the European Union. This study aimed to assess the ecological conditions of forest lakes using macrophytes and benthos organisms diversity as an ecological indicator of ecosystem conditions under climatic changes. The study was carried out in forest artificial lakes serving as surface water retention in the context of biodiversity in climatic changes and its role in the retention of water. Despite systematic maintenance activities, a long period of lake existence significantly determines the natural biological processes occurring in lakes and riparian habitats. The analysis showed low values of salinity indicators and the concentration of nitrogen and phosphorus. The pH ranged from 6.2 to 7.6; showing slightly acidic conditions or within the limits of neutral. The model of plant associations showed the occurrence of 24 species of plants within nine plant assemblages in the Phragmitetea and Potametea classes of associations (Biocenotic index 1.007-1.692). Despite human activities, lake condition, as assessed by the ESMI index or the biocenotic diversity indices, is good (0.416-0.648). Climate change, expressed by an increase in the frequency of dry years, creates a situation of changes in filling lakes with water, which, taking into account their small depth, results in dynamically changing conditions for the development of phytolittoral. Along with the phytolittoral changes, benthos communities change, their density and the number of taxa also fluctuate. It should be assumed that with ongoing climate change, these phenomena will probably intensify, which will lead to changes in entire ecosystems at plant and animal levels.

Lamiaceae family-derived endophytic fungi: induced tolerance to drought stress in Thymus vulgaris plants

BACKGROUND

Thymus vulgaris is a valuable medicinal plant widely cultivated for its aromatic and medicinal properties. However, like many plants, T. vulgaris faces challenges such as drought stress, which significantly affects its growth, morphological, physiological, and biochemical processes. Understanding how endophytic fungi isolated from Lamiaceae family influence T. vulgaris under varying watering regimes can enhance its resilience against drought stress. This study aims to assess the impact of individual and co-inoculation of three native endophytic species, i.e., Fusarium sp. (F1), Cladosporium puyae (F2), and Curvularia australiensis (F3), on T. vulgaris growth parameters under different irrigation regimes in greenhouse conditions.

RESULTS

It has been discovered that using fungal endophytes as a biological tool can benefits T. vulgaris under drought stress. The results indicated that drought stress significantly reduced the growth, chlorophyll, and carotenoid content of plants lacking endophytes. Combinatory applications with fungal endophytes significantly improved the above-mentioned parameters under drought stress. Lipid peroxidation levels were significantly reduced in plants inoculated with bacterial endophytes. Drought stress significantly increased the activities of ascorbate peroxidase (APX), superoxide dismutase (SOD), glutathione reductase (GR), peroxidase (POD), and catalase (CAT) in drought conditions.

CONCLUSIONS

The findings suggested that the addition of fungal endophytes to the inoculum enhances drought tolerance in T. vulgaris by mitigating the harmful impact of drought stress on plant growth and physiological functions. The higher activity of antioxidant enzymes and improved redox state of glutathione are responsible for plants' greater resistance to drought.

Global terrestrial drought and its projected socioeconomic implications under different warming targets

Droughts are increasingly frequent as the Earth warms, presenting adaptation challenges for ecosystems and human communities worldwide. A strategic environmental assessment (SEA) and the integration of adaptation strategies into policies, plans, and programs (PPP) are two important approaches for enhancing climate resilience and fostering sustainable development. This study developed an innovative approach to strengthen the SEA of droughts by quantifying the impacts of future temperature increases. A novel method for projecting drought events was integrated into the SEA process by leveraging multiple data sources, including atmospheric reanalysis, reconstructions, satellite-based observations, and model simulations. We identified drought conditions using terrestrial water storage (TWS) anomalies and applied a random forest (RF) model for disentangling the drivers behind drought events. We then set two global warming targets (2.0 °C and 2.5 °C) and analyzed drought changes under three shared socioeconomic pathways (SSP126, SSP370, SSP585). In a 2.0 °C warming world, over 50 % of the global surface will face increased drought risk. With an additional 0.5 °C increase, >60 % of the land will be prone to further drought escalation. We utilized copulas to build the joint distribution for drought duration and severity, estimating the joint return periods (JRP) for bivariate drought hazard. In tropical and subtropical regions, JRP reductions exceeding half are projected for >33 % of the regional land surface under 2.0 °C warming and for >50 % under 2.5 °C warming. Finally, we projected the impacts of drought events on population and gross domestic product (GDP). Among the three SSPs, under SSP370, population exposure is highest and GDP exposure is minimal under 2.0 °C warming. Global GDP and population risks from drought are projected to increase by 37 % and 24 %, respectively, as warming continues. This study enhances the accuracy of SEA in addressing drought risks and vulnerabilities, supporting climate-resilient planning and adaptive strategies.

Global insights into intermediate metabolites: Signaling, metabolic divergence and stress response modulation in plants

Climate change-induced environmental stresses pose significant challenges to plant survival and agricultural productivity. In response, many plants undergo genetic reprogramming, resulting in profound alterations in metabolic pathways and the production of diverse secondary metabolites. As a critical molecular junction, intermediate metabolites by targeted intensification or suppression of subpathways channel cell resources into a multifaceted array of functions such as cell signals, photosynthesis, energy metabolism, ROS homeostasis, producing defensive and protective molecules, epigenetic regulation and stress memory, phytohormones biosynthesis and cell wall architecture under stress conditions. Unlike the well-established functions of end products, intermediate metabolites are context-dependent and produce enigmatic alternatives during stress. As key components of signal transduction pathways, intermediate metabolites with relay and integration of stress signals ensure responses to stress combinations. Investigating efficient metabolic network pathways and their role in regulating unpredictable paths from upstream to downstream levels can unlock their full potential to shape the future of agriculture and ensure global food security. Here, we summarized the activity of some intermediate metabolites, from the perception step to tolerance responses to stress factors.

Plant growth promoting rhizobacteria (PGPR) application for coping with salinity and drought: a bibliometric network multi-analysis

Rhizobacteria play a crucial role in plant growth and yield, stimulating primary production and improving stress resistance. Climate change has several consequences worldwide that affect arable land and agriculture. Studies on plant-soil-microorganism interactions to enhance plant productivity and/or resistance to abiotic stress may open new perspectives. This strategy aims to make agricultural-relevant plant species able to complete their biological cycle in extreme soils with the help of inoculated or primed plant growth-promoting rhizobacteria (PGPR). We provide an overview of the evolution of interest in PGPR research in the last 30 years through: (i) a quantitative search on the Scopus database; (ii) keyword frequencies and clustering analysis, and (iii) a keyword network and time-gradient analysis. The review of scientific literature on PGPR highlighted an increase in publications in the last 15 years, and a specific time gradient on subtopics, such as abiotic stresses. The rise in PGPR as a keyword co-occurring with salinity and drought stresses aligns with the growing number of papers from countries directly or partly affected by climate change. The study of PGPR, its features, and related applications will be a key challenge in the next decades, considering climate change effects on agriculture. The increased interest in PGPR leads to deeper knowledge focused specifically on researching agriculturally sustainable solutions for soils affected by salinity and drought.

Demo-scale up-flow anaerobic sludge blanket reactor coupled with hybrid constructed wetlands for energy-carbon efficient agricultural wastewater reuse in decentralized scenarios

The impact of climate change on water availability and quality has affected agricultural irrigation. The use of treated wastewater can alleviate water in agriculture. Nevertheless, it is imperative to ensure proper treatment of wastewater before reuse, in compliance with current regulations of this practice. In decentralized agricultural scenarios, the lack of adequate treatment facilities poses a challenge in providing treated wastewater for irrigation. Hence, there is a critical need to develop and implement innovative, feasible, and sustainable treatment solutions to secure the use of this alternative water source. This study proposes the integration of intensive treatment solutions and natural treatment systems, specifically, the combination of up-flow anaerobic sludge blanket reactor (UASB), anaerobic membrane bioreactor (AnMBR), constructed wetlands (CWs), and ultraviolet (UV) disinfection. For this purpose, a novel demo-scale plant was designed, constructed and implemented to test wastewater treatment and evaluate the capability of the proposed system to provide an effluent with a quality in compliance with the current European wastewater reuse regulatory framework. In addition, carbon-sequestration and energy analyses were conducted to assess the sustainability of the proposed treatment approach. This research confirmed that UASB rector can be employed for biogas production (2.5 L h-1) and energy recovery from organic matter degradation, but its effluent requires further treatment steps to be reused in agricultural irrigation. The AnMBR effluent complied with class A standards for E. coli, boasting a concentration of 0 CFU 100 mL-1, and nearly negligible TSS levels. However, further reduction of BOD5 (35 mg L-1) is required to reach water quality class A. CWs efficiently produced effluent with BOD5 below 10 mg L-1 and TSS close to 0 mg L-1, making it suitable for water reuse and meeting class A standards. Furthermore, CWs demonstrated significantly higher energy efficiency compared to intensive treatment systems. Nonetheless, the inclusion of a UV disinfection unit after CWs was required to attain water class B standards.

Impact of hydrological drought occurrence, duration, and severity on Murray-Darling basin water quality

The severity and frequency of droughts are projected to increase globally due to climate change, but the effects of this on water quality are uncertain. The Murray-Darling Basin (MDB) is the largest river system in Australia and has been impacted by droughts of varying severity within recent decades. In this study, we assessed the influence of hydrological droughts and their characteristics (severity and duration) on water quality, utilising a long-term (1980-2017) dataset from two monitoring sites. The main drought periods, and their duration and severity, were identified using the calculated Standardised Drought Index values (SDI) from averaged monthly streamflow data. While several hydrological drought periods were identified, the longest duration and greatest severity were during the Millennium Drought (1998-2010). Nutrient loads and concentrations of Total Nitrogen and Total Phosphorus of drought and post-drought periods were significantly different. The drought period showed the lowest median and interquartile range of nutrient (total nitrogen, TN; oxidised nitrogen, NOX; total phosphorus, TP; and soluble reactive phosphorus, SRP) concentrations and loads for both sites, whereas the highest nutrient loads and concentrations were reported during the post-drought period (approx. 1 × 103 to 1 × 105 kg day-1 increase in nutrient loads). Our analysis found significant relationships between nutrient loads and SDI during droughts. The load of N and P in the initial flush post-drought increased with drought at both sites. This suggests that nutrients were retained in the landscape during the drought and released in higher loads post-drought when the catchment became wetter, the hydrology was activated, and nutrients were mobilised. Hydrology is a key driver controlling the water quality within the inter-drought period and the peak nutrient loads post-drought. The duration and the severity of droughts had a significant (p = 0.01) influence on peak TN and TP monthly loads but not cumulative loads over a 12-month period. Hydrological droughts are important factors in controlling the water quality of the MDB. Therefore, management efforts should be focused on reducing the occurrence and duration of these events, along with the implementation of catchment nutrient control measures.

Theoretical design of transition metal-doped oxo-triarylmethyl as a disposable platform for adsorption of ibuprofen

Emerging environmental contaminants have become a crucial environmental issue because of the highly toxic effluents emitted by factories. Ibuprofen (IBP), as a typical anti-inflammatory drug, is frequently detected in water sources. Therefore, its removal using various adsorbents has drawn great interest. Herein, the structural, electronic, energetic, and optical properties of pristine oxo-triarylmethyl (oxTAM) and transition metal-doped oxo-triarylmethyl (TM@oxTAM, TM = Sc, Ti, V, Cr, and Mn) for adsorption of the IBU drug were investigated using density functional theory (DFT) calculations implemented in Gaussian and VASP codes. Frontier molecular orbital (FMO), density of states (DOS), and electronic band structure results demonstrated that transition metal-doped oxTAM causes a significant reduction in the energy band gap (Eg) value of pristine oxTAM, with the highest decrease (30.14 %) in the case of Mn@oxTAM. It was found that transition metal doping onto oxTAM leads to an increase in the adsorption energies (1.20-2.64 eV) and charge density between transition metal and IBU. Natural bond orbital (NBO) analysis revealed that charge was effectively transferred from the IBU towards the transition metal, which was further analyzed by charge decomposition analysis (CDA). Furthermore, quantum theory of atoms in molecules (QTAIM), interaction region indicator (IRI), electron localization function (ELF), and radial distribution function (RDF) analyses revealed that the IBU is adsorbed on the Sc@oxTAM surface via covalent interactions, while electrostatic with partially covalent interactions are dominated in other IBU/TM@oxTAM complexes. The results suggest that TM doping on the oxTAM provides a new insight for developing photocatalyst-based covalent organic frameworks (COFs) to remove emerging pollutants in wastewater.

Effect of climate change on the water quality of Mediterranean rivers and alternatives to improve its status

Surface water (SW) quality is particularly vulnerable to increased concentrations of nutrients, and this issue may be exacerbated by climate change. Knowledge of the effects of temperature and rainfall on SW quality is required to take the necessary measures to achieve good SW status in the future. To address this, the aims of this study were threefold: (1) to assess how a changing climate may alter the nitrate, ammonium, phosphorus and biological oxygen demand status (BOD5) of SW; (2) assess the relationship between water quality and flow; and (3) simulate diffuse and point source pollution reduction scenarios in the Júcar River Basin District in the Mediterranean region. A regionalised long-term climate scenario was used following one Representative Concentration Pathway (RCP8.5) with the data incorporated into the coupling of hydrological and water quality models. According to these climate change scenarios, SW with poor nitrate, ammonium, phosphorus and BOD5 status are expected to increase in the future by factors of 1.3, 1.9, 4 and 4, respectively. Furthermore, median ammonium and phosphorus concentration may be doubled in months with low flows. Additional measures are required to maintain current status in the water bodies, and it is necessary to reduce at least 25% of diffuse nitrate pollution, and 50% of point loads of ammonium, phosphorus, and BOD5.

Natural radionuclides behaviour in drinking groundwaters from Castilla y León (Spain); radiological implications

Since the coming into force of the European Council Directive 51/2013 EURATOM and its transposition into the Spanish legislation, the presence of radioactive substances in drinking waters must be kept under surveillance to ensure that the health protection requirements are met. Driven by this regulatory framework, in an attempt to know the starting point from which to design surveillance plans, the groundwaters intended for human consumption of Castilla y León (Spain) have been radiologically characterised by using both low-level γ-ray and α-particle spectrometry to determine the activity concentration of the natural radionuclides needed to account for the indicative dose estimation. This extensive research has comprised the radiological characterisation of more than 400 drinking water samples from one of the European Union's largest regions. Furthermore, the gross α and gross β activities have been analysed. Results showed a high geographical variability that can be related to the hydrogeological formations where the groundwaters come from. The uranium isotopes, 234U and 238U, are the main radionuclides present in the analysed drinking waters reaching values up to 2000 mBq/L, in the southwestern and western of Castilla y León, where U-rich minerals are part of the host rock. High 210Pb and 226,228Ra occurrences are found in the low permeability igneous and metasedimentary hydrogeological formations of Salamanca province. From a public health protection point of view, 4.4% of the total drinking water samples from intakes exceeded the Indicative Dose parametric value of 0.1 mSv, which is a not negligible number of samples, being very likely related to granitic and metamorphosed host rock under specific local conditions. This fact highlights the need for research and consideration of special surveillance of the groundwaters from these areas.

Modeling, challenges, and strategies for understanding impacts of climate extremes (droughts and floods) on water quality in Asia: A review

The increasing frequency and intensity of extreme climate events are among the most expected and recognized consequences of climate change. Prediction of water quality parameters becomes more challenging with these extremes since water quality is strongly related to hydro-meteorological conditions and is particularly sensitive to climate change. The evidence linking the influence of hydro-meteorological factors on water quality provides insights into future climatic extremes. Despite recent breakthroughs in water quality modeling and evaluations of climate change's impact on water quality, climate extreme informed water quality modeling methodologies remain restricted. This review aims to summarize the causal mechanisms across climate extremes considering water quality parameters and Asian water quality modeling methods associated with climate extremes, such as floods and droughts. In this review, we (1) identify current scientific approaches to water quality modeling and prediction in the context of flood and drought assessment, (2) discuss the challenges and impediments, and (3) propose potential solutions to these challenges to improve understanding of the impact of climate extremes on water quality and mitigate their negative impacts. This study emphasizes that one crucial step toward enhancing our aquatic ecosystems is by comprehending the connections between climate extreme events and water quality through collective efforts. The connections between the climate indices and water quality indicators were demonstrated to better understand the link between climate extremes and water quality for a selected watershed basin.