34 citations as recorded by crossref.

1. On some aspects of peaks-over-threshold modeling of floods under nonstationarity using climate covariates A. Silva et al. https://doi.org/10.1007/s00477-015-1072-y
2. Quantifying the Uncertainty Created by Non‐Transferable Model Calibrations Across Climate and Land Cover Scenarios: A Case Study With SWMM A. Sytsma et al. https://doi.org/10.1029/2021WR031603
3. Assessing the dynamics and trigger factors of drought propagation in the China-Pakistan economic corridor: A three-dimensional perspective L. Xie et al. https://doi.org/10.1016/j.jenvman.2024.121730
4. Simultaneous quantile inference for non-stationary long-memory time series W. Wu & Z. Zhou https://doi.org/10.3150/17-BEJ951
5. The Role of Land Cover in the Propagation of Rainfall Into Streamflow Trends V. Chagas & P. Chaffe https://doi.org/10.1029/2018WR022947
6. Bivariate frequency analysis of nonstationary low‐flow series based on the time‐varying copula C. Jiang et al. https://doi.org/10.1002/hyp.10288
7. Copula-based multivariate flood probability construction: a review S. Latif & F. Mustafa https://doi.org/10.1007/s12517-020-5077-6
8. Bayesian uncertainty assessment of flood predictions in ungauged urban basins for conceptual rainfall-runoff models A. Sikorska et al. https://doi.org/10.5194/hess-16-1221-2012
9. Daily runoff prediction based on the adaptive fourier decomposition method and multiscale temporal convolutional network L. Yu et al. https://doi.org/10.1007/s11356-023-28936-5
10. Climate change and fluvial flood risk in the UK: more of the same? R. Wilby et al. https://doi.org/10.1002/hyp.6847
11. Stochastic modeling of suspended sediment load in alluvial rivers S. Shojaeezadeh et al. https://doi.org/10.1016/j.advwatres.2018.06.006
12. Exploring the combined effects of drought and drought-flood abrupt alternation on vegetation using interpretable machine learning model and r-vine copula function L. Xie et al. https://doi.org/10.1016/j.agrformet.2025.110568
13. Ordinal pattern dependence between hydrological time series S. Fischer et al. https://doi.org/10.1016/j.jhydrol.2017.03.029
14. Non-stationarity of low flows and their relevance to river modelling during drought periods D. Rassam et al. https://doi.org/10.1071/MF16399
15. On peaks-over-threshold modeling of floods with zero-inflated Poisson arrivals under stationarity and nonstationarity A. Silva et al. https://doi.org/10.1007/s00477-013-0813-z
16. Nonstationarities in the occurrence rates of flood events in Portuguese watersheds A. Silva et al. https://doi.org/10.5194/hess-16-241-2012
17. The stationarity paradigm revisited: Hypothesis testing using diagnostics, summary metrics, and DREAM(ABC) M. Sadegh et al. https://doi.org/10.1002/2014WR016805
18. Inconsistent hydrological trends do not necessarily imply spatially heterogeneous drivers G. Lischeid et al. https://doi.org/10.1016/j.jhydrol.2021.126096
19. Detecção de Tendências Monotônicas Temporais e Relação com Erros dos Tipos I e II: Estudo de Caso em Séries de Precipitações Diárias Máximas Anuais do Estado do Acre J. Moreira & M. Naghettini https://doi.org/10.1590/0102-778631231420140155
20. Multi-decadal lake-level dynamics in north-eastern Germany as derived by a combination of gauging, proxy-data and modelling K. Kaiser et al. https://doi.org/10.1016/j.jhydrol.2014.12.057
21. Turbulent inflow characteristics for hydrokinetic energy conversion in rivers V. Neary et al. https://doi.org/10.1016/j.rser.2013.05.033
22. On Certain Stationarity Tests for Hydrologic Series A. Rutkowska & M. Ptak https://doi.org/10.1515/sgem-2017-0022
23. Nonstationarity in the occurrence rate of floods in the Tarim River basin, China, and related impacts of climate indices X. Gu et al. https://doi.org/10.1016/j.gloplacha.2016.04.004
24. 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
25. Continuous simulation for computing design hydrographs for water structures S. Blazkova et al. https://doi.org/10.1002/hyp.11204
26. Improved Rainfall‐Runoff Calibration for Drying Climate: Choice of Objective Function K. Fowler et al. https://doi.org/10.1029/2017WR022466
27. Regional patterns of streamflow persistence, complexity, and chaos indicators within the United States K. Raczyński et al. https://doi.org/10.1016/j.ejrh.2026.103184
28. Nonstationarity in maximum annual daily streamflow series from Southern Brazil D. Bartiko et al. https://doi.org/10.1590/2318-0331.0217170054
29. Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework Z. Pan et al. https://doi.org/10.5194/hess-23-3405-2019
30. 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
31. Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene S. Thompson et al. https://doi.org/10.5194/hess-17-5013-2013
32. Design with nature: Causation and avoidance of catastrophic flooding, Myanmar G. Brakenridge et al. https://doi.org/10.1016/j.earscirev.2016.12.009
33. Methods for detecting change in hydrochemical time series in response to targeted pollutant mitigation in river catchments C. Lloyd et al. https://doi.org/10.1016/j.jhydrol.2014.04.036
34. Assessing residual hydropower potential of the La Plata Basin accounting for future user demands I. Popescu et al. https://doi.org/10.5194/hess-16-2813-2012