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AbstractGroundwater is the primary source of water in Madhesh Province, Nepal, yet the province lacks a reliable forecasting framework. This study develops a provincial-scale groundwater forecasting system by integrating satellite-derived groundwater storage (GWS), machine learning, and CMIP6 climate forcing. A Long Short-Term Memory (LSTM) model was trained using two decades of GRACE–GLDAS anomalies, observed climate data, and land use–land cover (LULC) information, and was forced with bias-corrected MIROC6 projections under the SSP5-8.5 scenario. Results reveal a persistent historical GWS decline of approximately 50 mm over 20 years (2.5 mm yr⁻¹), driven primarily by rainfall variability and intensified by urban expansion and surface water loss. Two LSTM setups were tested: a climate-driven model using precipitation and temperature, which achieved higher predictive accuracy but underrepresented human influences, and a multi-parameter model incorporating LULC, soil moisture, groundwater-irrigated area, and domestic demand, which captured more realistic depletion dynamics despite slightly lower statistical performance. Both the climate-driven and multi-parameter LSTM models indicate a monsoon-dependent, highly seasonal recharge with weakening peaks and continued decline through 2045. Furthermore, scenario-based demand sensitivity showed that mean GWS increased from 635.20 mm to 639.82 mm under demand − 10%, but declined to 623.91 mm under total demand + 30%, showing a mean 95% confidence interval width of 9.24 mm and a mean model spread of 7.78 mm, indicating stable projection responses across model structures. The bias-corrected Machine Learning-driven GWS and predictor dataset provides a reproducible basis for groundwater assessments and model benchmarking in data scare regions. These findings provide actionable insights for groundwater management, supporting climate-resilient water resource planning, sustainable irrigation practices, and policy development in Madhesh Province and similar data-scarce alluvial regions.

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Author informationAuthors and AffiliationsEnvironmental Engineering Program, Department of Civil Engineering, Institute of Engineering, Tribhuvan University, Pulchowk Campus, Lalitpur, NepalAnjana Kumari Mahto & Shukra Raj PaudelDepartment of Civil Engineering, Institute of Engineering, Tribhuvan University, Pulchowk Campus, Lalitpur, NepalVishan Dahal, Ram Krishna Regmi & Prakash Chandra GhimireDepartment of Civil Engineering, Institute of Engineering, Tribhuvan University, Thapathali Campus, Kathmandu, NepalGovinda Prasad Poudel & Ramesh KarkiAuthorsAnjana Kumari MahtoView author publicationsSearch author on:PubMed Google ScholarVishan DahalView author publicationsSearch author on:PubMed Google ScholarRam Krishna RegmiView author publicationsSearch author on:PubMed Google ScholarGovinda Prasad PoudelView author publicationsSearch author on:PubMed Google ScholarRamesh KarkiView author publicationsSearch author on:PubMed Google ScholarPrakash Chandra GhimireView author publicationsSearch author on:PubMed Google ScholarShukra Raj PaudelView author publicationsSearch author on:PubMed Google ScholarCorresponding authorCorrespondence to
Ram Krishna Regmi.Ethics declarations

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We have fully agreed to publish the manuscript “Machine-learning projections of groundwater storage under climate, land-use, and human-driven changes in Madhesh Province, Nepal” after peer review on the Scientific Reports.

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Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleMahto, A.K., Dahal, V., Regmi, R.K. et al. Machine-learning projections of groundwater storage under climate, land-use, and human-driven changes in Madhesh Province, Nepal.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-70415-7Download citationReceived: 16 February 2026Accepted: 02 September 2026Published: 09 September 2026DOI: https://doi.org/10.1038/s41598-026-70415-7Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsGroundwater storageMadhesh ProvinceMachine learningClimate change More

AbstractThis paper investigated the intensity and causes of water hyacinth expansion in Lake Tana, Ethiopia, using machine learning techniques. Water hyacinth poses a significant threat to freshwater ecosystems by disrupting ecological balance, degrading biodiversity, and affecting the livelihoods of communities that depend on lake resources. This research uses 20 years of weekly laboratory-based experimental data collected at 27 sampling sites around Lake Tana to model the relationship between water hyacinth expansion and key environmental drivers. Four conventional machine learning and five deep learning models were evaluated. Their performance was assessed using the coefficient of determination (R2), mean absolute error (MAE), and root mean squared error (RMSE). The random forest machine learning model achieved R2, MAE, and RMSE values of 0.99, 4.70, and 14.95, respectively, which are the lowest among the trained models. Thus, the random forest model has achieved the best predictive accuracy. The most influential environmental predictors, total nitrogen, total phosphorus, chlorophyll-a, and pH, were identified. Consequently, the study concluded that integrating machine learning techniques into freshwater ecosystem monitoring and management can improve early detection, strengthen conservation strategies, and support efforts to mitigate further environmental degradation, including eutrophication, water pollution, invasive weed species, oxygen depletion, and climate change.

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Author informationAuthors and AffiliationsBahir Dar Institute of Technology, Bahir Dar University, Bahir Dar, EthiopiaHaileyesus Amssaya & Tesfa TegegneFaculty of Technology and Society, Sustainable Digitalization Research Centre, Malmö University, Malmö, SwedenFisseha MekuriaAuthorsHaileyesus AmssayaView author publicationsSearch author on:PubMed Google ScholarFisseha MekuriaView author publicationsSearch author on:PubMed Google ScholarTesfa TegegneView author publicationsSearch author on:PubMed Google ScholarCorresponding authorCorrespondence to
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Data will be made available on reasonable request to Bahir Dar Institute of Technology, Bahir Dar university.

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Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleAmssaya, H., Mekuria, F. & Tegegne, T. Predicting water hyacinth expansion and identifying environmental drivers in Lake Tana using machine learning.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-70853-3Download citationReceived: 19 March 2026Accepted: 04 September 2026Published: 09 September 2026DOI: https://doi.org/10.1038/s41598-026-70853-3Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsWater hyacinthMachine learningPerformance metricsWater content parametersEnvironmental monitoring. More

AbstractAssessing associations between dry spell variability and green and blue water dynamics is essential for strengthening the resilience of rainfed agriculture. This study evaluated the associations between dry spell characteristics and green-blue water dynamics in the Upper Awash Basin, Ethiopia, using 35 years (1990–2024) of hydro-meteorological data from 10 meteorological stations, physiographic datasets, and SWAT+ model. Dry spell onset, cessation, duration, frequency, and intensity were integrated with hydrological responses through a hydroclimatic sensitivity framework. Model performance was satisfactory, with NSE = 0.61–0.79, R² = 0.66–0.84, and PBIAS = 1.17–7.01%. Results showed downstream intensification of dry spells, with seasonal duration increasing from 7 to 8 dekads upstream to 12 dekads downstream. ETa, representing green-water flow, accounted for 70.14% of seasonal components, while SW accounted for 5.98% and WYLD 23.88%, respectively. Dry spell duration showed the strongest significant association with evapotranspiration (β = -0.83 to -0.51). Blue water yield showed spatially varying negative associations: DSD dominated Subbasins 1 and 4, DSF Subbasin 2, and DSI Subbasins 6, 7, and 9. These findings indicate that greater dry spell severity is associated with changes in water partitioning and provide basis for integrating dry spell indicators into climate-resilient rainfed agricultural water management in the Upper Awash Basin.

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AcknowledgementsThe authors thank the National Meteorological Institute (NMI) and the Ministry of Water and Energy (MoWE) of Ethiopia for providing the weather and streamflow data records used in this study.Author informationAuthor notesMekonen Ayana Gebul, Abebe Demissie Chukalla and Boja Mekonnen Manyazew contributed equally to this work.Authors and AffiliationsDepartment of Water Resources Engineering, College of Civil Engineering and Architecture, Adama Science and Technology University, P.O. Box 1888, Adama, EthiopiaHabtamu Adenew Weletu, Mekonen Ayana Gebul & Boja Mekonnen ManyazewLand and Water Management Department, IHE Delft Institute for Water Education, P.O. Box 3015, 2601 DA, Delft, The NetherlandsAbebe Demissie ChukallaAuthorsHabtamu Adenew WeletuView author publicationsSearch author on:PubMed Google ScholarMekonen Ayana GebulView author publicationsSearch author on:PubMed Google ScholarAbebe Demissie ChukallaView author publicationsSearch author on:PubMed Google ScholarBoja Mekonnen ManyazewView author publicationsSearch author on:PubMed Google ScholarCorresponding authorCorrespondence to
Habtamu Adenew Weletu.Ethics declarations

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Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleWeletu, H.A., Gebul, M.A., Chukalla, A.D. et al. Associations of dry spell characteristics with green and blue water components under rainfed agriculture in the Upper Awash Basin, Ethiopia.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-70667-3Download citationReceived: 18 June 2026Accepted: 03 September 2026Published: 08 September 2026DOI: https://doi.org/10.1038/s41598-026-70667-3Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsDry spellsGreen and blue waterRainfed agricultureSWAT+ modelUpper Awash BasinWater partitioning More

AbstractAccurate multi-day streamflow forecasts support flood preparedness, but single holdout scores can conceal lead-time dependence, temporal instability, baseline skill, and uncertainty. This study compares Long Short-Term Memory, Random Forest, Multiple Linear Regression, Multi-Layer Perceptron, Support Vector Regression, and AdaBoost using an audited Bangladesh Water Development Board discharge record for the Bahadurabad Transit station on the Brahmaputra River. The source contained 10,314 entries from 1995 to 2023. After 275 excess same-date records were consolidated by date-wise averaging, 553 calendar days were missing; no temporal interpolation was applied. We constructed 9,272 complete samples using 30 antecedent discharges to predict Days 1–30 directly. Leakage-resistant evaluation used three expanding rolling-origin folds, target-end-date training cut-offs, a 2019–2020 calibration period, and an independent 2021–2023 test period. Moving-block bootstrap confidence intervals, split-conformal prediction intervals, flow-regime errors, and lag sensitivity were evaluated. Random Forest had the lowest mean rolling-origin RMSE, whereas Multi-Layer Perceptron had the lowest pooled test RMSE (6,763.47 m³/s; R²/NSE = 0.721); their uncertainty intervals overlapped. Ranking changed with horizon: Multiple Linear Regression was best at Day 1 and AdaBoost at Day 30. All models underestimated the highest-flow decile. The results support an uncertainty-aware discharge-only benchmark, not universal model superiority or operational flood-warning readiness.

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AbbreviationsLSTM:
Long short-term memory
RF:
Random forest
MLR:
Multiple linear regression
MLP:
Multi-layer perceptron
SVR:
Support vector regression
AB:
AdaBoost
ACF:
Autocorrelation function
PACF:
Partial autocorrelation function
BWDB:
Bangladesh Water Development Board
MAE:
Mean absolute error
MSE:
Mean squared error
RMSE:
Root mean squared error
R²:
Coefficient of determination
ANN:
Artificial neural network
CNN:
Convolutional neural network
ARIMA:
AutoRegressive integrated moving average
CART:
Classification and regression tree
XGBoost:
Extreme gradient boosting
LSSVR:
Least squares support vector regression
GSA:
Gravitational search algorithm
ODPC:
One-sided dynamic principal components
GDP:
Gross domestic product
BPTT:
Backpropagation through time
RBF:
Radial basis function
ML:
Machine learning
DL:
Deep learning
MCDA:
Multi-criteria decision analysis
PSO:
Particle swarm optimization
GA:
Genetic algorithm
CI:
Confidence interval
NSE:
Nash–Sutcliffe efficiency
PI:
Prediction interval
PICP:
Prediction interval coverage probability
TFT:
Temporal fusion transformer
UQ:
Uncertainty quantification
XAI:
Explainable artificial intelligence
AcknowledgementsThe authors acknowledge the Bangladesh Water Development Board for supplying the mean daily discharge record used in this study. The authors would like to express their sincere gratitude to the Military Institute of Science and Technology (MIST), Dhaka, Bangladesh, for providing research facilities and data support; to Multimedia University (MMU), Cyberjaya, Malaysia, for supporting the Article Processing Charges (APC) and providing administrative and research facilitation; and to the Deanship of Scientific Research at Shaqra University, Saudi Arabia, for their valuable support.Author informationAuthors and AffiliationsDepartment of Computer Science and Engineering, Military Institute of Science and Technology, Dhaka, 1216, BangladeshKazy Noor-e-Alam Siddiquee & Shohana ChowdhuryFaculty of Artificial Intelligence and Engineering, Multimedia University, Cyberjaya, 63100, MalaysiaMd Tanjil Sarker, Md Sabbir Hossen & Md. Shabiul IslamDepartment of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, 11961, Saudi ArabiaMarran Al QwaidShiga University of Medical Science, Setatsukinowacho, Otsu, Shiga, 520-2192, JapanNick BaruaDepartment of Environmental Water Resources and Coastal Engineering, Military Institute of Science and Technology, Dhaka, 1216, BangladeshKazi Shamima AkterAuthorsKazy Noor-e-Alam SiddiqueeView author publicationsSearch author on:PubMed Google ScholarMd Tanjil SarkerView author publicationsSearch author on:PubMed Google ScholarMarran Al QwaidView author publicationsSearch author on:PubMed Google ScholarNick BaruaView author publicationsSearch author on:PubMed Google ScholarKazi Shamima AkterView author publicationsSearch author on:PubMed Google ScholarShohana ChowdhuryView author publicationsSearch author on:PubMed Google ScholarMd Sabbir HossenView author publicationsSearch author on:PubMed Google ScholarMd. Shabiul IslamView author publicationsSearch author on:PubMed Google ScholarCorresponding authorCorrespondence to
Md. Shabiul Islam.Ethics declarations

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Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleSiddiquee, K.NeA., Sarker, M.T., Al Qwaid, M. et al. Advanced machine learning techniques for daily streamflow forecasting: a case study of the Brahmaputra River.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-69747-1Download citationReceived: 02 February 2026Accepted: 30 August 2026Published: 08 September 2026DOI: https://doi.org/10.1038/s41598-026-69747-1Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsBrahmaputra RiverStreamflow forecastingMulti-horizon predictionRolling-origin validationUncertainty quantificationExplainable machine learning More

AbstractThe ecologically fragile Asian Endorheic Basins (AEB) recently exhibited a pronounced productivity increase despite regional drying—a paradox known as the ‘greening despite drying’ dilemma. To quantify its drivers, we integrated satellite observations with Dynamic Global Vegetation Models (DGVMs) from 2001 to 2023. We show that 25.2% of the AEB experienced significant productivity gains, heavily driven by rapid irrigation expansion (43.2%), while climate change and CO2 fertilization played minor roles. Crucially, Terrestrial Water Storage Anomaly (TWSA) analysis reveals these gains coincide with accelerated water depletion, confirming an unsustainable water-carbon trade-off where productivity gains are achieved at the expense of regional water storage. Furthermore, state-of-the-art DGVMs significantly underestimated this irrigation-driven productivity trend, incorrectly attributing it primarily to climate change. Our findings underscore the growing dominance of anthropogenic influences in shaping dryland landscapes. This model-observation mismatch highlights an urgent need to improve DGVM representations of irrigation activities and the severe sustainability risks posed by groundwater depletion.

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FundingThis work was supported by the Key Program of the Natural Science Foundation of Gansu Province, China (grant number 25JRRA646); the Fengyun Application Pioneering Project (grant number FY-APP-2024.0302); and the National Natural Science Foundation of China (grant numbers 42171305 and 42311540014).Author informationAuthor notesThese authors contributed equally: Xiaoyu Zhu, Chunyan Cao.Authors and AffiliationsMoE Key Laboratory of Western China’s Environmental Systems, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu, ChinaXiaoyu Zhu, Chunyan Cao, Xuanlong Ma, Yi Li, Yu Liang & Kaiyue LuoCenter for Remote Sensing of Ecological Environments in Cold and Arid Regions, Lanzhou University, Lanzhou, Gansu, ChinaXiaoyu Zhu, Chunyan Cao, Xuanlong Ma, Yi Li, Yu Liang & Kaiyue LuoSchool of Life Sciences, Faculty of Science, University of Technology Sydney, Ultimo, AustraliaAlfredo HueteCSIRO Environment, Waite Campus, Adelaide, AustraliaSicong GaoGeospatial Sciences Center of Excellence (GSCE), Department of Geography and Geospatial Sciences, South Dakota State University, Brookings, SD, USAYuxia LiuState Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaSi Gao & Kai YanEuropean Commission, Joint Research Centre (JRC), Ispra, ItalyMirco MigliavaccaSwiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, SwitzerlandYunpeng LuoETH Zürich, Department of Environmental System Science, Zürich, SwitzerlandYunpeng LuoCenter for Environmental Remote Sensing (CEReS), Chiba University, Chiba, JapanWei YangAuthorsXiaoyu ZhuView author publicationsSearch author on:PubMed Google ScholarChunyan CaoView author publicationsSearch author on:PubMed Google ScholarXuanlong MaView author publicationsSearch author on:PubMed Google ScholarAlfredo HueteView author publicationsSearch author on:PubMed Google ScholarSicong GaoView author publicationsSearch author on:PubMed Google ScholarYuxia LiuView author publicationsSearch author on:PubMed Google ScholarSi GaoView author publicationsSearch author on:PubMed Google ScholarMirco MigliavaccaView author publicationsSearch author on:PubMed Google ScholarYunpeng LuoView author publicationsSearch author on:PubMed Google ScholarYi LiView author publicationsSearch author on:PubMed Google ScholarYu LiangView author publicationsSearch author on:PubMed Google ScholarKaiyue LuoView author publicationsSearch author on:PubMed Google ScholarKai YanView author publicationsSearch author on:PubMed Google ScholarWei YangView author publicationsSearch author on:PubMed Google ScholarCorresponding authorCorrespondence to
Xuanlong Ma.Ethics declarations

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Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary informationTransparent Peer Review file (download PDF )Supplementary Information (download DOCX )Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleZhu, X., Cao, C., Ma, X. et al. Irrigation drives dryland productivity gains in Asian endorheic basins.
Commun Earth Environ (2026). https://doi.org/10.1038/s43247-026-03989-9Download citationReceived: 27 August 2025Accepted: 20 August 2026Published: 08 September 2026DOI: https://doi.org/10.1038/s43247-026-03989-9Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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AbstractThis study examines the separate and combined effects of climate variability and land-use change on historical and future runoff dynamics in the Dongwan Watershed, the largest sub-basin of the Yihe River, China. Runoff was simulated using the Soil and Water Assessment Tool (SWAT), future land-use change was projected with a Cellular Automata-Markov (CA-Markov) model, and climate forcing was derived from five CMIP6 GCMs under SSP126, SSP370, and SSP585. The SWAT model performed well during calibration and validation, with Nash–Sutcliffe Efficiency values of 0.87 and 0.85. Attribution results show climate variability accounted for 121.1% of the historical annual runoff increase between 1971–1990 and 1991–2010, with land-use change exerting a compensating effect (−25.4%). Annual runoff increased by only 3.0%, but seasonal shifts were pronounced: summer runoff rose 21.7% while winter (−32.2%), spring (−8.1%), and autumn (−8.9%) all declined. This pattern reflects higher flood risk in wet months alongside increasing water scarcity in dry ones. Future projections indicate further amplification of these seasonal contrasts, with increasing summer runoff dominance and reduced dry-season buffering capacity. Continued forest expansion and the near loss of grassland and shrubland moderately dampen climate-driven runoff increases but cannot offset intensified climatic forcing. Overall, climate change emerges as the primary control on runoff dynamics, while projected land-use change under business-as-usual trajectories plays a secondary but moderating role. These findings offer insights into seasonal water management and climate adaptation in semi-humid watersheds of China.

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FundingThis research received no external funding.Author informationAuthors and AffiliationsInstitute of International Rivers and Eco-Security, Yunnan University, Kunming, 650500, ChinaMuhammad Mannan Afzal, Xin Yang & Shiyin LiuYunnan Key Laboratory of International Rivers and Transboundary Eco-Security, Yunnan University, Kunming, 650500, ChinaMuhammad Mannan Afzal, Xin Yang & Shiyin LiuKey Laboratory of Agricultural Water Resources, Institute of Genetics and Develop-Mental Biology, Centre for Agri-Cultural Resources Research, Chinese Academy of Sciences, Shijiazhuang, 050021, ChinaAdeel Ahmad NadeemCollege of Hydrology and Water Resources, Hohai University, Nanjing, 210098, ChinaQiaoling Li, Anis Ur Rehman Khalil & Zhijia LiDirectorate of Onfarm Water Management, KPK Government, Peshawar, 25000, Islamic Republic of PakistanFazli HameedAuthorsMuhammad Mannan AfzalView author publicationsSearch author on:PubMed Google ScholarAdeel Ahmad NadeemView author publicationsSearch author on:PubMed Google ScholarQiaoling LiView author publicationsSearch author on:PubMed Google ScholarFazli HameedView author publicationsSearch author on:PubMed Google ScholarAnis Ur Rehman KhalilView author publicationsSearch author on:PubMed Google ScholarXin YangView author publicationsSearch author on:PubMed Google ScholarZhijia LiView author publicationsSearch author on:PubMed Google ScholarShiyin LiuView author publicationsSearch author on:PubMed Google ScholarCorresponding authorsCorrespondence to
Muhammad Mannan Afzal or Shiyin Liu.Ethics declarations

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleAfzal, M.M., Nadeem, A.A., Li, Q. et al. Contrasting roles of climate change and land-use change on runoff dynamics in a semi-humid monsoon watershed of China.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-67812-3Download citationReceived: 10 January 2026Accepted: 18 August 2026Published: 07 September 2026DOI: https://doi.org/10.1038/s41598-026-67812-3Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsClimate variabilityLand-use changeRunoff attributionSWAT modelCA–MarkovCMIP6 More

AbstractWater security in Peninsular Malaysia remains a critical policy challenge due to fragmented governance, urban expansion, and the uneven distribution of freshwater resources. This study aims to address these challenges by establishing a state-level consolidated water security index (CWSI) that integrates five dimensions, eleven indicators and twenty-nine variables. The CWSI is used to (i) evaluate the current status of water security in eleven states of Peninsular Malaysia and (ii) identify the key risks and challenges associated with water security. Weights for each dimension, indicator, and variable were calculated using the AHP method to ensure methodological rigour. Additionally, normalisation and sensitivity analysis were conducted to ensure cross-state comparability and index robustness. Spatial disparities are clearly evident in the findings. States, such as Terengganu (CWSI = 0.612) and Pahang (CWSI = 0.608), demonstrate strong performance. Meanwhile, Perlis (CWSI = 0.408) and Selangor (CWSI = 0.414) remain vulnerable to persistent water security issues due to dam storage limitations, non-revenue water losses and insufficient treatment capacity. This study suggests that the CWSI be integrated into state-level governance frameworks to guide infrastructure investment, enhance accountability and align performance monitoring with national policies, such as the Water Sector Transformation 2040 (AIR 2040) and the Sustainable Development Goals. By incorporating evidence-based indicators into policy practice, the CWSI provides a tool to strengthen the coherence, transparency and adaptive capacity of Malaysia’s water governance system. Future efforts should prioritise regular index updates to capture temporal variations and address data gaps, thereby supporting more adaptive and evidence-based water security management.

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AcknowledgementsSpecial thanks to the Ministry of Higher Education of Malaysia for providing financial support towards the Integrated Water Research Synergy Consortium (IWaReS) research programme through Konsortium Kecemerlangan Penyelidikan – KKP/2021/UKM-UKM/1). The authors would like to express gratitude to agencies, such as the Ministry of Energy Transition and Water Transformation (PETRA), Ministry of Natural Resources and Environment Sustainability (NRES), National Water Services Commission (SPAN), Sewerage Services Department (SSD), Indah Water Konsortium Sdn Bhd (IWK), Department of Irrigation and Drainage (DID), National Water Research Institute of Malaysia (NAHRIM), Department of Environment (DOE), Selangor Water Management Authority (LUAS), Department of Statistics Malaysia (DOSM) and the Ministry of Health (MOH) for providing valuable information and data that greatly contribute to our study.FundingThis work was supported by the Konsortium Kecemerlangan Penyelidikan (KKP/2021/UKMUKM/1/1) from the Ministry of Higher Education of Malaysia.Author informationAuthors and AffiliationsInstitute for Environment and Development (LESTARI), Universiti Kebangsaan Malaysia, Bangi, MalaysiaNur Hairunnisa Rafaai & Khai Ern LeeIntegrated Water Research Synergy Consortium (IWaReS), Institute for Environment and Development (LESTARI), Universiti Kebangsaan Malaysia, Bangi, MalaysiaKhai Ern LeeFaculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, MalaysiaThian Lai GohSustainable Development Solutions Network Asia (SDSN Asia), Sunway University, Petaling Jaya, MalaysiaMazlin MokhtarMalaysia Global Water Partnership (GWP), Kuala Lumpur, MalaysiaHanapi Mohamad NoorAuthorsNur Hairunnisa RafaaiView author publicationsSearch author on:PubMed Google ScholarKhai Ern LeeView author publicationsSearch author on:PubMed Google ScholarThian Lai GohView author publicationsSearch author on:PubMed Google ScholarMazlin MokhtarView author publicationsSearch author on:PubMed Google ScholarHanapi Mohamad NoorView author publicationsSearch author on:PubMed Google ScholarCorresponding authorCorrespondence to
Khai Ern Lee.Ethics declarations

Competing interests
The authors declare no competing interests.

Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary InformationBelow is the link to the electronic supplementary material.Supplementary Material 1 (download DOCX )Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleRafaai, N.H., Lee, K.E., Goh, T.L. et al. Developing a state-level water security index to inform sustainable water policy in Peninsular Malaysia.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-68987-5Download citationReceived: 06 February 2026Accepted: 25 August 2026Published: 05 September 2026DOI: https://doi.org/10.1038/s41598-026-68987-5Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsAnalytical hierarchy processEcosystem healthGovernanceSustainable development goalsWater security index More

AbstractMaintaining biological stability is essential for high microbiological water quality in drinking water distribution systems (DWDS). Although treatment without residual disinfectant removes biodegradable organic matter to limit microbial regrowth, it remains unclear how improved biological stability influences microbial communities during distribution. We investigated habitat-specific microbial communities in two full-scale DWDS supplied with the same surface water source but treated differently. Conventionally treated drinking water was compared with water produced by managed aquifer recharge and recovery, and the additional effect of ultrafiltration (UF) post-treatment was evaluated. Microbial communities in habitats (bulk drinking water, biofilms and loose deposits) were characterized using 16S rRNA gene amplicon sequencing and analysed together with biological stability, physicochemical and cultivation-based measurements. Improved biological stability reduced regrowth potential and Aeromonas occurrence while altering microbial communities. Treatment established the initial microbial community, but UF-post treatment and storage reshaped this community, increasing cell numbers and promoting the dominance of Comamonadaceae and Brevundimonas. During distribution, loose deposits increasingly contributed to bulk drinking water communities, although this effect was reduced after UF. Loose deposits showed stronger seasonal variation than bulk drinking water. These findings show that biological stability shapes microbial communities and identifies loose deposits as an important habitat for understanding and monitoring regrowth.

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AcknowledgementsWe would like to thank the staff from Aqualab Zuid B.V. for sampling and routine analysis of samples; Marcelle van der Waals (KWR Water Research, NL) and Fieke Mulders (Evides, NL) for project management; and Roos Nefs, Friso Snijder, and numerous other colleagues (Evides, NL) for practical assistance and coordination of the sampling. The research was supported by the Dutch tax credit for research and development (WBSO).Author informationAuthors and AffiliationsKWR Water Research Institute, Nieuwegein, the NetherlandsPeer H. A. Timmers, Goffe Elsinga & Michiel in ’t ZandtSchool of Science and Technology, IE University, Paseo de la Castellana, Madrid, SpainPeer H. A. TimmersEvides Water Company N.V., Rotterdam, the NetherlandsGiovanni Sandrini, Julia Wunderer, Wim Hijnen & Leonie MarangSoil Biology Group, Wageningen University, Wageningen, the NetherlandsMichiel in ’t ZandtAuthorsPeer H. A. TimmersView author publicationsSearch author on:PubMed Google ScholarGiovanni SandriniView author publicationsSearch author on:PubMed Google ScholarJulia WundererView author publicationsSearch author on:PubMed Google ScholarGoffe ElsingaView author publicationsSearch author on:PubMed Google ScholarMichiel in ’t ZandtView author publicationsSearch author on:PubMed Google ScholarWim HijnenView author publicationsSearch author on:PubMed Google ScholarLeonie MarangView author publicationsSearch author on:PubMed Google ScholarCorresponding authorCorrespondence to
Peer H. A. Timmers.Ethics declarations

Competing interests
The authors declare no competing interests.

Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary informationSupplementary information (download PDF )Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleTimmers, P.H.A., Sandrini, G., Wunderer, J. et al. Biological stability shapes habitat-specific microbial community dynamics from treatment to distribution in non-chlorinated drinking water distribution systems.
npj Biofilms Microbiomes (2026). https://doi.org/10.1038/s41522-026-01145-xDownload citationReceived: 23 April 2026Accepted: 24 August 2026Published: 05 September 2026DOI: https://doi.org/10.1038/s41522-026-01145-xShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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