224 citations as recorded by crossref.

1. Effects analysis and probability forecast (EAPF) of real-time management on urban flooding: A novel bidirectional verification framework H. Huang et al. https://doi.org/10.1016/j.scitotenv.2023.166908
2. Cross-timescale interaction of nonstationary hydrological responses in subtropical mountainous watersheds R. Hao et al. https://doi.org/10.1016/j.jhydrol.2023.130167
3. Attribution of the Record‐Breaking Extreme Precipitation Events in July 2021 Over Central and Eastern China to Anthropogenic Climate Change L. Wang et al. https://doi.org/10.1029/2023EF003613
4. Nonstationary flood and low flow frequency analysis in the upper reaches of Huaihe River Basin, China, using climatic variables and reservoir index as covariates M. Wang et al. https://doi.org/10.1016/j.jhydrol.2022.128266
5. Linear, nonlinear, parametric and nonparametric regression models for nonstationary flood frequency analysis M. Chen et al. https://doi.org/10.1016/j.jhydrol.2022.128772
6. Challenges for compound coastal flood risk management in a warming climate: a case study of the Gulf Coast of the United States M. Lewis et al. https://doi.org/10.3389/frwa.2024.1405603
7. The Machine Learning Attribution of Quasi-Decadal Precipitation and Temperature Extremes in Southeastern Australia during the 1971–2022 Period M. Speer et al. https://doi.org/10.3390/cli12050075
8. Climate risk-informed decision analysis (CRIDA): ‘top-down’ vs ‘bottom-up’ decision making for planning water resources infrastructure J. Manous & E. Stakhiv https://doi.org/10.2166/wp.2021.243
9. A comparison of annual streamflow sensitivities to vegetation change and climate variability in fourteen large watersheds along climate zones in China Y. Hou et al. https://doi.org/10.1016/j.catena.2023.107571
10. On the nonstationary identification of climate-influenced loads for the semi-probabilistic approach using measured and projected data R. Arnold & M. Kraus https://doi.org/10.1080/23311916.2022.2143061
11. Have river flow droughts become more severe? A review of the evidence from the UK – a data-rich, temperate environment J. Hannaford et al. https://doi.org/10.5194/hess-29-4371-2025
12. Model Selection Challenges in Non-Stationary Precipitation Estimation: The Role of AIC, BIC, and Covariate Choice M. Yegin et al. https://doi.org/10.1007/s11269-025-04357-6
13. Monthly Ocean Primary Productivity Forecasting by Joint Use of Seasonal Climate Prediction and Temporal Memory L. Xu et al. https://doi.org/10.3390/rs15051417
14. Non-stationary streamflow responses to forest harvesting vary along topographic gradients in a sub-alpine watershed of Southwest China S. Deng et al. https://doi.org/10.1016/j.jhydrol.2025.134262
15. Hybrid deep learning and optimized variational mode decomposition for point-interval runoff prediction H. Ma et al. https://doi.org/10.1371/journal.pone.0343063
16. Estimation of return levels and associated uncertainties of extreme temperatures using a time-varying framework: a case study in Iran S. Anvari & J. Rydén https://doi.org/10.1007/s11600-025-01544-2
17. Nonstationary transition Poisson-Lindley Hidden Markov model for community-based disaster insurance claim H. Surya et al. https://doi.org/10.3934/math.20251040
18. Managing risks associated with water for a sustainable world: a special issue introduction P. Li et al. https://doi.org/10.1080/10807039.2026.2634711
19. Joint estimation of trend in bulk and extreme daily precipitation in Switzerland A. Haruna et al. https://doi.org/10.1016/j.wace.2025.100769
20. Investigating Temporal and Spatial Precipitation Patterns in the Southern Mid-Atlantic United States I. Dollan et al. https://doi.org/10.3389/fclim.2021.799055
21. Climate change impact assessment on a German lowland river using long short-term memory and conceptual hydrological models A. Ley et al. https://doi.org/10.1016/j.ejrh.2025.102426
22. A Global Map for Selecting Stationary and Nonstationary Methods to Estimate Extreme Floods Z. Li et al. https://doi.org/10.3390/w15213835
23. Increasing extreme hourly precipitation risk for New York City after Hurricane Ida C. Mossel et al. https://doi.org/10.1038/s41598-024-78704-9
24. Can precipitation intermittency predict flooding? B. Livneh et al. https://doi.org/10.1016/j.scitotenv.2024.173824
25. Revisiting spatio-temporal variation in compound dry-hot extreme events in China with newly developed nonstationary index C. Zhao et al. https://doi.org/10.1016/j.atmosres.2025.108111
26. State-of-the-art visualization for innovative trend analysis S. Alashan et al. https://doi.org/10.1007/s12665-025-12643-0
27. Non‐Stationary Influences of Large‐Scale Climate Drivers on Low Flow Extremes in Southeast Australia P. Goswami et al. https://doi.org/10.1029/2021WR031508
28. Compound Continental Risk of Multiple Extreme Floods in the United States N. Najibi et al. https://doi.org/10.1029/2023GL105297
29. From short to long-term horizons: Comparative assessment of Machine Learning and Deep Learning for river stage prediction with spatial transferability analysis M. Islam et al. https://doi.org/10.1016/j.rineng.2025.108194
30. Multi-distribution regula-falsi profile likelihood method for nonstationary hydrological frequency analysis C. Vidrio-Sahagún et al. https://doi.org/10.1007/s00477-023-02603-0
31. Evaluating the Non-Stationarity, Seasonality and Temporal Risk to Water Resources in the Wei River Basin X. Yuan & F. O’Loughlin https://doi.org/10.3390/w16172513
32. Quantifying the impact of climate change and human activities on the atmospheric water cycle in the Upper Yellow River Basin Y. Man et al. https://doi.org/10.1016/j.ejrh.2026.103527
33. Climate variability conceals emerging hydrological trends across Great Britain W. Chan et al. https://doi.org/10.1016/j.jhydrol.2025.133414
34. 中国大陆地区水文气象要素非平稳变化对不同类型干旱评估的影响 潇. 张 et al. https://doi.org/10.1360/SSTe-2025-0194
35. Assessing global drought conditions under climate change: A comparison of stationary and non-stationary approaches and identification of hotspot regions D. Wu et al. https://doi.org/10.1016/j.jhydrol.2024.131663
36. A framework for the detection and attribution of biodiversity change A. Gonzalez et al. https://doi.org/10.1098/rstb.2022.0182
37. Impact of Climate Variability and Interventions on Malaria Incidence and Forecasting in Burkina Faso N. Traoré et al. https://doi.org/10.3390/ijerph21111487
38. Differential orographic impact on sub-hourly, hourly, and daily extreme precipitation G. Formetta et al. https://doi.org/10.1016/j.advwatres.2021.104085
39. Compound Hydrometeorological Extremes: Drivers, Mechanisms and Methods W. Zhang et al. https://doi.org/10.3389/feart.2021.673495
40. Tracking a semi-arid Eastern Mediterranean ecotone through integration of terrestrial and atmospheric earth observation data (2000–2024) N. Manspeizer & A. Karnieli https://doi.org/10.1016/j.scitotenv.2026.181620
41. Special Issue for UN HELP: ‘Water infrastructure planning, management and design under climate uncertainty’ E. Stakhiv & K. Hiroki https://doi.org/10.2166/wp.2021.268
42. Statistical Attribution of the Influence of Urban and Tree Cover Change on Streamflow: A Comparison of Large Sample Statistical Approaches B. Anderson et al. https://doi.org/10.1029/2021WR030742
43. Greedy copula segmentation of multivariate non-stationary time series for climate change adaptation T. Heo & L. Manuel https://doi.org/10.1016/j.pdisas.2022.100221
44. Contribution of urbanisation to non-stationary river flow in the UK S. Han et al. https://doi.org/10.1016/j.jhydrol.2022.128417
45. Changes in Mediterranean flood processes and seasonality Y. Tramblay et al. https://doi.org/10.5194/hess-27-2973-2023
46. Season-Ahead Flood Quantile Forecasting with Climate-Informed Models for Indian Catchments A. Ganapathy et al. https://doi.org/10.1061/JHYEFF.HEENG-6750
47. Historical changes of extreme temperature in relation to soil moisture over different climatic zones of Iran S. Anvari & M. Moghaddasi https://doi.org/10.1007/s00477-023-02558-2
48. Past and future change in global river flows L. Gudmundsson et al. https://doi.org/10.1038/s43017-025-00745-z
49. Assessing water storage variations in La Plata basin and sub-basins from GRACE, global models data and connection with ENSO events A. Pereira et al. https://doi.org/10.1080/02626667.2024.2349277
50. Integrating Eco-Index and Hydropower Optimization for Cascade Reservoir Operations in the Lancang–Mekong River Basin C. Li & T. Zhu https://doi.org/10.3390/w17202966
51. Spatial-temporal estimation of maximum temperature high returns periods for annual time series considering stationary/nonstationary approaches in Iran urban area M. Salarijazi et al. https://doi.org/10.1016/j.uclim.2023.101504
52. Performance of different reanalysis rainfall datasets and hydrological models in gap-filling for Mpologoma catchment in Eastern Uganda M. Twinomuhangi et al. https://doi.org/10.1016/j.ejrh.2026.103416
53. Impact of Spatiotemporal Rainfall Distribution and Underlying Surface Changes on Flood Processes in Meijiang River Basin, China X. Lu et al. https://doi.org/10.3390/w17040466
54. Trends in reconstructed monthly, seasonal and annual flows for Irish catchments (1900–2016) P. O'Connor et al. https://doi.org/10.1002/wea.4288
55. Contrasting CESM and ECMWF for predictive modeling of spatial heterogeneity of drought indices across Colorado and Louisiana regions of the USA N. Choudhari et al. https://doi.org/10.1016/j.ejrh.2026.103351
56. Nonstationary Spatiotemporal Projection of Drought Across Seven Climate Regions of China in the 21st Century Based on a Novel Drought Index Z. Yan et al. https://doi.org/10.3390/w17223206
57. Quantifying the Geomorphic Effect of Floods Using Satellite Observations of River Mobility A. Leenman et al. https://doi.org/10.1029/2023GL103875
58. Two Decades of Integrated Flood Management: Status, Barriers, and Strategies N. Grigg https://doi.org/10.3390/cli12050067
59. Climate warming accelerates surface soil moisture drying in the Yellow River Basin, China K. Fan et al. https://doi.org/10.1016/j.jhydrol.2022.128735
60. Assessment of upbasin dam impacts on streamflow at Chiang Saen gauging station during the period 1960–2020 in the context of statistical studies T. Nguyen et al. https://doi.org/10.1002/rra.4011
61. High and dry: A ∼300-year record of hydrologic extremes from the French Broad River in the southeastern U.S. M. Davis et al. https://doi.org/10.1016/j.ejrh.2025.102673
62. Improving model calibrations in a changing world: controlling for nonstationarity after mega disturbance reduces hydrological uncertainty E. Boardman et al. https://doi.org/10.5194/hess-29-6333-2025
63. A data-assimilated SEAS5 forecasting framework for seasonal hydropower inflows in a snow-dominated basin G. Uysal et al. https://doi.org/10.1016/j.jhydrol.2026.135279
64. Water use analysis among the village people in the Can-Agtiw watershed for local area conservation in Malimono, Surigao del Norte, Philippines R. Balanay et al. https://doi.org/10.1007/s43621-025-02021-y
65. Nonstationarity in the global terrestrial water cycle and its interlinkages in the Anthropocene W. Nie et al. https://doi.org/10.1073/pnas.2403707121
66. Hydroclimatic time series features at multiple time scales G. Papacharalampous et al. https://doi.org/10.1016/j.jhydrol.2023.129160
67. NSVineCopula: R package for modeling non-stationary multivariate dependence Q. Zhang et al. https://doi.org/10.1016/j.envsoft.2025.106474
68. Comparing hourly climate change projections for hydrology in a Mediterranean meso-scale catchment N. Hachgenei et al. https://doi.org/10.1016/j.ejrh.2026.103491
69. An open workflow to gain insights about low‐likelihood high‐impact weather events from initialized predictions T. Kelder et al. https://doi.org/10.1002/met.2065
70. Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios M. Buechel et al. https://doi.org/10.1038/s43247-021-00334-0
71. The legacy of STAHY: milestones, achievements, challenges, and open problems in statistical hydrology E. Volpi et al. https://doi.org/10.1080/02626667.2024.2385686
72. Proof of concept for Bayesian inference of dynamic rating curve uncertainty in a sparsely gauged watershed R. Cornelio et al. https://doi.org/10.1080/02626667.2024.2401094
73. Analysis of the responses of surface water resources to climate change in arid and semi-arid area J. Wang et al. https://doi.org/10.1016/j.agwat.2024.108751
74. Expected reliability over a planning horizon of a hydraulic infrastructure and its uncertainty under hydroclimatic future projections A. de la Fuente et al. https://doi.org/10.1016/j.jhydrol.2023.129700
75. Hydrogeomorphic effects of Storm Boris (2024) in the rainfall epicentre (NE Czechia) and a century of extreme rainfall patterns R. Tichavský et al. https://doi.org/10.1007/s11069-025-07960-3
76. Historical archives reveal record rainfall and severe flooding in December 1867 resulting from an atmospheric river and snowmelt, western Washington, USA D. Gavin et al. https://doi.org/10.1371/journal.pclm.0000324
77. Multi-Hazard Extreme Scenario Quantification Using Intensity, Duration, and Return Period Characteristics A. Sfetsos et al. https://doi.org/10.3390/cli11120242
78. Streamflow generation, hydroclimatic changes, and flood mechanisms in the Sheep River basin on the eastern slopes of the Canadian Rockies C. Vidrio-Sahagún et al. https://doi.org/10.1016/j.ejrh.2025.102562
79. Paleo-data is policy relevant: How do we better incorporate it in policy and decision making? K. Allen et al. https://doi.org/10.1016/j.gloplacha.2025.104707
80. Modeling of short-term meteorological drought under changing climate in Gujarat, India S. Renuka et al. https://doi.org/10.1007/s00704-025-05882-5
81. Nonstationary groundwater level responses to coupled human–natural drivers in the Baiyangdian Watershed L. Zhou et al. https://doi.org/10.1016/j.jhydrol.2025.134623
82. Streamflow Simulation and Interpretability Analysis in Multi-Climatic Basins Using Physics-Based and Data-Driven Hybrid Models J. Chen et al. https://doi.org/10.1007/s11269-026-04566-7
83. Non-Stationary Flood Discharge Frequency Analysis in West Africa A. Bossa et al. https://doi.org/10.3390/geohazards4030018
84. Future changes in hydro-climatic extremes of a large transboundary river basin using multi-model bias-corrected CMIP6 projections K. Rahaman et al. https://doi.org/10.1007/s11356-025-36754-0
85. Rainfall Patterns From Multiscale Sample Entropy Analysis X. Zhou et al. https://doi.org/10.3389/frwa.2022.885456
86. Diverging trends in large floods across Europe in a warming climate B. Fang et al. https://doi.org/10.1038/s43247-025-02734-y
87. Integrating non-stationarity into extreme rainfall risk assessment: A GAMLSS-based framework for large-scale region W. Wei et al. https://doi.org/10.1016/j.jhydrol.2026.134934
88. Impacts of spatial-temporal rainfall structures and antecedent wetness on flood variability at the catchment scale W. Yang et al. https://doi.org/10.1016/j.jhydrol.2026.135019
89. The Robustness of the Derived Design Life Levels of Heavy Precipitation Events in the Pre-Alpine Oberland Region of Southern Germany P. Laux et al. https://doi.org/10.3390/atmos14091384
90. A Vine Copula‐Based Ensemble Projection of Precipitation Intensity–Duration–Frequency Curves at Sub‐Daily to Multi‐Day Time Scales B. Zhang et al. https://doi.org/10.1029/2022WR032658
91. Modeling extreme precipitation across Japan using stationary and non-stationary generalized extreme value distributions with different estimation methods J. Ho et al. https://doi.org/10.1007/s13762-026-07273-3
92. A framework of DQN/PPO coupling reinforcement learning for dynamic environmental policy decision assessment via multi-agent government-enterprise interaction A. Feng et al. https://doi.org/10.1016/j.rineng.2026.109884
93. Trends in extreme rainfall over the past 55 years suggest springtime subhourly rainfall extremes have intensified in Mahantango Creek, Pennsylvania A. Buda et al. https://doi.org/10.1038/s41598-024-79196-3
94. Multi-model integration framework for monthly runoff prediction based on variational mode decomposition (VMD) and trend-based modeling S. Wang et al. https://doi.org/10.1007/s00477-026-03170-w
95. A practice-oriented framework for stationary and nonstationary flood frequency analysis C. Vidrio-Sahagún et al. https://doi.org/10.1016/j.envsoft.2024.105940
96. African Q99 prediction model: Hydrological clustering and regression machine learning for extreme flow prediction in African catchments M. El baida et al. https://doi.org/10.1016/j.jafrearsci.2026.106225
97. Broadleaf afforestation impacts on terrestrial hydrology insignificant compared to climate change in Great Britain M. Buechel et al. https://doi.org/10.5194/hess-28-2081-2024
98. Robust Local Likelihood Estimation for Non-stationary Flood Frequency Analysis J. Grego & P. Yates https://doi.org/10.1007/s13253-024-00614-0
99. Machine learning-driven rainfall forecasting model for sustainable and adaptive infrastructure planning H. Alif et al. https://doi.org/10.1007/s43621-026-02699-8
100. Hydrometeorological response to afforestation in the UK: findings from a kilometer-scale climate model M. Buechel et al. https://doi.org/10.1088/1748-9326/ad4bf6
101. Deriving phase-contingent dynamic drought-limited water levels: An adaptive framework for managing megadrought evolution Y. Li et al. https://doi.org/10.1016/j.ejrh.2026.103309
102. Updates to the Design of Buildings for NBCC 2025 for Climate Adaptation, Resilience, and Associated Initial Cost Impact S. Li et al. https://doi.org/10.1061/JSENDH.STENG-14645
103. A Bayesian copula-based nonstationary framework for compound flood risk assessment along US coastlines K. Naseri & M. Hummel https://doi.org/10.1016/j.jhydrol.2022.128005
104. Design flood estimation in flood hazard studies: a three-decade systematic review of practices in Canada C. Vidrio-Sahagún et al. https://doi.org/10.1080/07011784.2025.2462603
105. A structured framework to interpret hydro-climatic and water quality trends in Mediterranean climate zones H. Alilou et al. https://doi.org/10.1016/j.jhydrol.2022.128512
106. The influence of nonstationarity in hydro-meteorological variables on different drought types across the Chinese mainland X. Zhang et al. https://doi.org/10.1007/s11430-025-1836-1
107. Non-Stationarity of Hydroclimatic Memory—Is Hydrological Memory Changing Under Climate Warming? M. Birylo https://doi.org/10.3390/w18070869
108. A long-term view of tropical cyclone risk in Australia T. Mortlock et al. https://doi.org/10.1007/s11069-023-06019-5
109. A novel time-varying threshold level method for evaluating non-stationary hydrological drought M. Wang et al. https://doi.org/10.1016/j.jhydrol.2025.133977
110. Recent hydroclimatic extremes in the Cypress Creek watershed, Texas, USA N. Abeysingha & R. Ray https://doi.org/10.1007/s12040-026-02792-6
111. Hybrid forecasting: blending climate predictions with AI models L. Slater et al. https://doi.org/10.5194/hess-27-1865-2023
112. Modelling non-stationary flood frequency in England and Wales using physical covariates D. Faulkner et al. https://doi.org/10.2166/nh.2024.134
113. On the impossibility of extreme event thresholds in the absence of global warming N. Diffenbaugh & F. Davenport https://doi.org/10.1088/1748-9326/ac2f1a
114. Changing seasonality and advanced mean date of annual maximum daily precipitation in major river basins of China (1960–2018) Y. Mao et al. https://doi.org/10.1088/1748-9326/adf97f
115. Nonstationary Seasonal Design Flood Estimation: Exploring Mixed Copulas for the Nonmonotonic Dependence between Peak Discharge and Timing W. Li et al. https://doi.org/10.1061/JHYEFF.HEENG-6043
116. Cumulative forest disturbances decrease runoff in two boreal forested watersheds of the northern interior of British Columbia, Canada Y. Hou et al. https://doi.org/10.1016/j.jhydrol.2021.127362
117. Estimating return intervals for extreme climate conditions related to winter disasters and livestock mortality in Mongolia M. Haraguchi et al. https://doi.org/10.5194/nhess-22-2751-2022
118. True interval non-stationary Intensity-Duration-Frequency curves under changing climate and effect of temporal discretisation on rainfall extremes A. Ghate & P. Timbadiya https://doi.org/10.1016/j.jhydrol.2023.129136
119. Exposure of the US population to extreme precipitation risk has increased due to climate change J. Kim et al. https://doi.org/10.1038/s41598-023-48969-7
120. Identification of drought-flood abrupt alternation by a new non-stationary framework considering natural-social system impacts H. Cui et al. https://doi.org/10.1016/j.jhydrol.2025.132899
121. A generalized design flood estimation framework under stationary and non-stationary scenarios C. Awasthi et al. https://doi.org/10.1016/j.hydroa.2025.100210
122. Compound Climate Risk: Diagnosing Clustered Regional Flooding at Inter-Annual and Longer Time Scales Y. Amonkar et al. https://doi.org/10.3390/hydrology10030067
123. Nonstationary spatiotemporal evolution of extreme flood and low flow affected by climate change and human activities in the Yellow River basin M. Ren et al. https://doi.org/10.1016/j.ejrh.2025.102640
124. Enhancing public understanding of extreme weather events in a changing climate through ClimaMeter D. Faranda https://doi.org/10.3389/fclim.2025.1688221
125. Collaborative multiscale water resources planning in England A. Leonard et al. https://doi.org/10.1080/07900627.2024.2438209
126. Increased intensity and frequency of extreme precipitation events in Belgium as simulated by the regional climate model MAR J. Brajkovic et al. https://doi.org/10.1016/j.ejrh.2025.102399
127. Spatial-temporal pattern and attribution factors of Yellow River’s streamflow seasonality and inter-annual variability C. Jiao et al. https://doi.org/10.1016/j.ejrh.2025.103019
128. Nonstationarity in Records of Extreme Precipitation Frequency for the Upper Midwest N. Gallagher et al. https://doi.org/10.1061/JHYEFF.HEENG-6830
129. Storylines of extreme precipitation events and flood impacts in alpine and pre-alpine environments under various global warming levels L. Munz et al. https://doi.org/10.1016/j.scitotenv.2024.177791
130. Incorporating alluvial hydrogeomorphic complexities into paleoflood hydrology, magnitude estimation and flood frequency analysis, Tennessee River, Alabama R. Lombardi & M. Davis https://doi.org/10.1016/j.jhydrol.2022.128085
131. Spatiotemporal Variation in Extreme Precipitation in Beijiang River Basin, Southern Coastal China, from 1959 to 2018 Z. Liu et al. https://doi.org/10.3390/jmse11010073
132. Revealing the relation between spatial patterns of rainfall return levels and landslide density S. Mtibaa & H. Tsunetaka https://doi.org/10.5194/esurf-11-461-2023
133. Spatial response of water level and quality shows more significant heterogeneity during dry seasons in large river-connected lakes Y. Yin et al. https://doi.org/10.1038/s41598-024-59129-w
134. Evolving Indo-Pacific teleconnections and regional feedbacks in Indian rainfall extremes: A principal component-wavelet framework S. Dixit & K. Pandey https://doi.org/10.1016/j.atmosres.2026.108900
135. Nonstationary Changes in Annual Rainfall over Indonesia’s Maritime Continent G. Sudarman et al. https://doi.org/10.1155/2024/9392844
136. On the attribution of annual maximum discharge across the conterminous United States H. Kim & G. Villarini https://doi.org/10.1016/j.advwatres.2022.104360
137. The SPIChanges R-package: Improving the interpretation of the standardized precipitation index under changing climate conditions G. Blain et al. https://doi.org/10.1016/j.envsoft.2025.106573
138. Multiscale hydrological concurrence analysis and attribution in the Yellow River Water Conservation Area W. Zhu & K. Liang https://doi.org/10.1007/s11442-026-2474-0
139. The Role of Anthropogenic Climate Change on March–April–May (MAM) Drought Trends Across Global Land H. Yohannes et al. https://doi.org/10.1002/joc.8776
140. Analysis of stationary and non-stationary hydrological extremes under a changing environment: A systematic review M. Twinomuhangi et al. https://doi.org/10.1016/j.hydres.2024.12.007
141. A Process-Conditioned and Spatially Consistent Method for Reducing Systematic Biases in Modeled Streamflow A. Bennett et al. https://doi.org/10.1175/JHM-D-21-0174.1
142. Seasonal and upstream rainfall controls on monsoon flood onset in Bangladesh’s Haor wetlands M. Rahman https://doi.org/10.1007/s11069-026-08075-z
143. Investigation of the trend and cycles using the long-term records of annual precipitation in Shandong, China S. Cao et al. https://doi.org/10.2166/wcc.2022.128
144. Artificial Intelligence-Driven Rainfall Forecasting and GIS-Based Flood Risk Mapping for Climate-Resilient Infrastructure in Bangladesh H. Alif et al. https://doi.org/10.1007/s41748-025-00958-8
145. Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States K. Hunt et al. https://doi.org/10.5194/hess-26-5449-2022
146. Aquaculture-planting system (APS) will enhance flood resilience and agricultural sustainability under global warming Z. Lin et al. https://doi.org/10.1016/j.geosus.2026.100436
147. Dynamic adaptive engineering pathways for mitigating flood risks in Shanghai with regret theory Z. Tian et al. https://doi.org/10.1038/s44221-022-00017-w
148. Graph modeling for verifying and validating reasonable flow quantity information: Evidence from Indonesia’s establishment of good governance in the upstream oil and gas sector I. Gunawan et al. https://doi.org/10.1016/j.eswa.2025.127987
149. Floods and droughts: a multivariate perspective M. Brunner https://doi.org/10.5194/hess-27-2479-2023
150. Integrating Hidden Markov and Multinomial models for hydrological drought prediction under nonstationarity M. Santos & L. Slater https://doi.org/10.1016/j.advwatres.2025.104974
151. SEPARATE: Storm Event Partitioning And Rainfall Analytics for Tipping-bucket rain gauge data Evaluation B. Murphy & S. David https://doi.org/10.21105/joss.08851
152. Identification and evaluation of soil moisture flash drought by a nonstationary framework considering climate and land cover changes X. Zha et al. https://doi.org/10.1016/j.scitotenv.2022.158953
153. Integrated WEAP–Hippopotamus Optimization Framework for Climate-Resilience Reservoir Operation: A Case Study of Ubolrat Reservoir, Thailand H. Prasanchum et al. https://doi.org/10.3390/w18040477
154. The Development of a Nonstationary Standardised Streamflow Index Using Climate and Reservoir Indices as Covariates M. Wang et al. https://doi.org/10.1007/s11269-022-03088-2
155. Hydrological response to long-lasting dry spell at the southern edge of Siberian permafrost L. Han & L. Menzel https://doi.org/10.1016/j.scitotenv.2024.171330
156. Urban Water Consumption Prediction Based on CPMBNIP J. Li & S. Song https://doi.org/10.1007/s11269-023-03601-1
157. Leveraging global climate models to assess multi-year hydrologic drought M. Vieira & T. Stadnyk https://doi.org/10.1038/s41612-023-00496-y
158. Deep Learning–Based Streamflow Forecasting in a Snowmelt-Dominated Basin: A Regime-Aware Evaluation R. Vazirian https://doi.org/10.1061/JHYEFF.HEENG-6651
159. Quantifying and valuing forests as a nature-based solution for ecosystem-based disaster risk reduction: A systematic review E. Ashrafizadeh & R. Yousefpour https://doi.org/10.1016/j.nbsj.2025.100242
160. Impacts of hydrometeorological regime shifts on drought Propagation: The meteorological to hydrological perspective J. Wu et al. https://doi.org/10.1016/j.jhydrol.2024.131476
161. Assessing Future Extreme Precipitation in South Thailand: Trends in Extreme Indices and Non-Stationary GEV Return Levels Under CMIP6 SSP3-7.0 T. Chaithong https://doi.org/10.1007/s41748-025-00752-6
162. Regional Analysis of the Dependence of Peak-Flow Quantiles on Climate with Application to Adjustment to Climate Trends T. Over et al. https://doi.org/10.3390/hydrology12050119
163. A RIC<sub>p</sub> criterion for periodicity analysis of hydrological time series L. Wu et al. https://doi.org/10.1360/TB-2021-1329
164. Integrating heterogeneous information for modeling non-stationarity of extreme precipitation in the Yangtze River Basin Y. Liu et al. https://doi.org/10.1016/j.jhydrol.2024.132159
165. Shifts in Precipitation Variability near the Danube Delta Biosphere Reserve (1965–2019) A. Bărbulescu & C. Dumitriu https://doi.org/10.3390/w17182692
166. Higher emissions scenarios lead to more extreme flooding in the United States H. Kim & G. Villarini https://doi.org/10.1038/s41467-023-44415-4
167. Incorporating extreme event attribution into climate change adaptation for civil infrastructure: Methods, benefits, and research needs Y. Zhang et al. https://doi.org/10.1016/j.rcns.2024.03.002
168. Statistical Modeling of the Ocean Environment E. Vanem et al. https://doi.org/10.1146/annurev-statistics-042424-115755
169. A substantial reduction of total and group-specific population risk to compound heat and drought events in China through achieving carbon neutrality J. Chen et al. https://doi.org/10.1016/j.jclepro.2025.144694
170. Estimating flood recurrence uncertainty for non-stationary regimes Y. Gomes et al. https://doi.org/10.1590/2318-0331.282320230031
171. EarthObsNet: A comprehensive Benchmark dataset for data-driven earth observation image synthesis Z. Li et al. https://doi.org/10.1016/j.envsoft.2024.106292
172. Revealing trends in extreme heatwave intensity: applying the UNSEEN approach to Nordic countries S. Berghald et al. https://doi.org/10.1088/1748-9326/ad2893
173. Identifying potential systemic changes in a regulated river: a diagnostic methodology based on whiteness property of innovation sequences S. Vanda & M. Abedini https://doi.org/10.1080/02626667.2022.2115913
174. Incorporating compound flood events in the weather service value chain J. Su et al. https://doi.org/10.1038/s44304-025-00077-4
175. Evaluating the accuracy of gridded water resources reanalysis and evapotranspiration products for assessing water security in poorly gauged basins E. Nkiaka et al. https://doi.org/10.5194/hess-26-5899-2022
176. Yesterday’s extremes, today’s new normal: flood risk in the Kathmandu Valley, Nepal R. Talchabhadel et al. https://doi.org/10.1007/s11069-025-07524-5
177. Impact of different national ecological programs on land-atmosphere water-energy-carbon exchanges varies significantly in the arid and semi-arid regions of China Y. Man et al. https://doi.org/10.1016/j.agee.2026.110431
178. Research on the Degree of Non-Stationarity in Extreme Precipitation in the Continental United States J. Shao et al. https://doi.org/10.1007/s11269-023-03683-x
179. Non-steady state fluctuations in water levels exacerbate long-term and seasonal degradation of water quality in river-connected lakes Y. Yin et al. https://doi.org/10.1016/j.watres.2023.120247
180. Streamflow prediction using Long Short-Term Memory networks: a case study at the Kratie Hydrological Station, Mekong River Basin N. Nguyen et al. https://doi.org/10.2166/hydro.2025.276
181. Impacts of changing environments on seasonal propagation from precipitation to streamflow W. Li et al. https://doi.org/10.1016/j.jenvman.2025.126907
182. Vegetation greening and reservoir construction as drivers of century-long discharge trends in the Mediterranean Basin J. Eekhout et al. https://doi.org/10.1016/j.jhydrol.2026.135387
183. Spatial patterns and indices for heat waves and droughts over Europe using a decomposition of extremal dependency S. Szemkus & P. Friederichs https://doi.org/10.5194/ascmo-10-29-2024
184. Multivariate Hydrologic Risk Analysis for River Thames R. Gabriel & Y. Fan https://doi.org/10.3390/w14030384
185. Enhanced profile likelihood method for the nonstationary hydrological frequency analysis C. Vidrio-Sahagún & J. He https://doi.org/10.1016/j.advwatres.2022.104151
186. The Treatment of Uncertainty in Hydrometric Observations: A Probabilistic Description of Streamflow Records D. de Oliveira & J. Vrugt https://doi.org/10.1029/2022WR032263
187. Long‐term trends in extreme precipitation indices in Ireland C. Ryan et al. https://doi.org/10.1002/joc.7475
188. On method of regional non-stationary flood frequency analysis under the influence of large reservoir group and climate change H. Cui et al. https://doi.org/10.1016/j.jhydrol.2023.129255
189. Assessing spatiotemporal dynamics and hydroclimatic extremes in humid tropical mountain rivers of the Western Ghats, India C. Krishna & V. K https://doi.org/10.1016/j.pce.2026.104385
190. Attributing European runoff changes to climatic drivers under future conditions I. Clemenzi et al. https://doi.org/10.1016/j.jhydrol.2025.134794
191. Interpretable machine learning for daily precipitation forecasting: spatiotemporal dynamics and extreme event prediction in New Jersey F. Gandhi https://doi.org/10.1080/02626667.2025.2609895
192. Identifying the shift in global wildfire weather conditions over the past four decades: an analysis based on change-points and long-term trends K. Shi & Y. Touge https://doi.org/10.1186/s40562-022-00255-6
193. Resilience of UK crop yields to compound climate change L. Slater et al. https://doi.org/10.5194/esd-13-1377-2022
194. Hydrological characteristics of extreme floods in the Klaserie River, a headwater stream in southern Africa S. Marr & A. Swemmer https://doi.org/10.4081/jlimnol.2023.2102
195. Estimation of return levels with long return periods for extreme sea levels in a time-varying framework J. Rydén https://doi.org/10.1007/s10669-024-09974-x
196. The future intensification of hydrological extremes and whiplashes in the contiguous United States increase community vulnerability S. Maharjan et al. https://doi.org/10.1038/s43247-025-02672-9
197. Extreme heat and mortality in the state of Rio de Janeiro in November 2023: attribution to climate change and ENSO S. Collazo et al. https://doi.org/10.5194/nhess-25-3221-2025
198. Incorporating non-stationarity from climate change into rainfall frequency and intensity-duration-frequency (IDF) curves K. Schlef et al. https://doi.org/10.1016/j.jhydrol.2022.128757
199. How fast is the frequency of precipitation extremes doubling in global land regions? G. Chagnaud et al. https://doi.org/10.1088/2515-7620/ad9f12
200. Optimizing urban water sustainability: Integrating deep learning, genetic algorithm, and CMIP6 GCM for groundwater potential zone prediction within a social-ecological-technological framework M. Rahman et al. https://doi.org/10.1016/j.asr.2024.03.033
201. Forest disturbance thresholds on summer low flows in the interior of British Columbia, Canada Y. Hou et al. https://doi.org/10.1016/j.catena.2024.108173
202. Rolling forecast of soil moisture under non-stationary conditions: a robust framework incorporating time-varying dynamics within and between variables C. Yu et al. https://doi.org/10.1016/j.jhydrol.2025.133832
203. Technical note: High Nash–Sutcliffe Efficiencies conceal poor simulations of interannual variance in seasonal regimes S. Ruzzante et al. https://doi.org/10.5194/hess-30-2337-2026
204. Earth observation informed modelling of flash floods C. Watson et al. https://doi.org/10.1016/j.ijdrr.2025.105977
205. GC Insights: Communicating long-term changes in local climate risk using a physically plausible causal chain E. Hawkins et al. https://doi.org/10.5194/gc-7-161-2024
206. Global hydrological reanalyses: The value of river discharge information for world‐wide downstream applications – The example of the Global Flood Awareness System GloFAS C. Prudhomme et al. https://doi.org/10.1002/met.2192
207. Climate Change Impacts on River Hydraulics: A Global Synthesis of Hydrological Shifts, Ecological Consequences, and Adaptive Strategies B. Nile et al. https://doi.org/10.1007/s41101-025-00375-y
208. Observed trends in timing and severity of streamflow droughts across global tropics A. Raut & P. Ganguli https://doi.org/10.1088/1748-9326/ad25a1
209. Hydrological frequency analysis under nonstationarity using the Metastatistical approach and its simplified version C. Vidrio-Sahagún & J. He https://doi.org/10.1016/j.advwatres.2022.104244
210. Impact of Policy Intensity on Carbon Emission Reductions: Based on the Perspective of China’s Low-Carbon Policy H. Chen et al. https://doi.org/10.3390/su16188265
211. Why people matter in 21st century hydrology: lessons learnt from flood forecasting and warning L. Speight https://doi.org/10.1098/rsta.2024.0293
212. Wet-environment Evapotranspiration and Precipitation Standardized Index (WEPSI) for drought assessment and monitoring A. Khoshnazar et al. https://doi.org/10.2166/nh.2022.062
213. Non-stationary modeling of seasonal precipitation series in Turkey: estimating the plausible range of seasonal extremes F. Tosunoglu et al. https://doi.org/10.1007/s00704-023-04807-4
214. Compound droughts and hot extremes: Characteristics, drivers, changes, and impacts Z. Hao et al. https://doi.org/10.1016/j.earscirev.2022.104241
215. Decreasing flood hazard evaluated in Turkey using nonstationary models F. Tosunoglu & L. Slater https://doi.org/10.1002/rra.3998
216. Detection Time for Nonstationary Reservoir System Performance Driven by Climate and Land-Use Change M. Chen & J. Herman https://doi.org/10.1061/JWRMD5.WRENG-6184
217. ИНТЕГРИРОВАННЫЙ ПОДХОД SNTHERM И MLК МОДЕЛИРОВАНИЮ СНЕЖНОГО ПОКРОВА И ТАЯНИЯ В ВКО А. Муссабек et al. https://doi.org/10.51889/2959-5894.2025.91.3.005
218. A spatio-temporal analysis of the role of climatic drivers influencing extreme precipitation events in a Costa Rican basin C. Huguenin et al. https://doi.org/10.1016/j.wace.2023.100602
219. A Nonstationary Standardized Precipitation Index (NSPI) Using Bayesian Splines J. Stagge & K. Sung https://doi.org/10.1175/JAMC-D-21-0244.1
220. Index for assessment of stationarity in spatiotemporal climatic signals R. Gavas et al. https://doi.org/10.1016/j.patrec.2025.07.025
221. Extreme low‐flow effects on riverine fauna: A perspective on methodological assessments J. White et al. https://doi.org/10.1002/eco.2422
222. Science of forests and floods: The quantum leap forward needed, literally and metaphorically H. Pham & Y. Alila https://doi.org/10.1016/j.scitotenv.2023.169646
223. A deep learning-based hybrid approach for multi-time-ahead streamflow prediction in an arid region of Northwest China J. Fang et al. https://doi.org/10.2166/nh.2024.124
224. Evaluation and Adjustment of Historical Hydroclimate Data: Improving the Representation of Current Hydroclimatic Conditions in Key California Watersheds A. Schwarz et al. https://doi.org/10.3390/hydrology12020022