986 citations as recorded by crossref.

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53. Evaluating methods for reconstructing large gaps in historic snow depth time series J. Aschauer & C. Marty https://doi.org/10.5194/gi-10-297-2021
54. Climate change impacts on multi-objective reservoir management: case study on the Seine River basin, France D. Dorchies et al. https://doi.org/10.1080/15715124.2013.865636
55. Characterizing and avoiding physical inconsistency generated by the application of univariate quantile mapping on daily minimum and maximum temperatures over Hudson Bay M. Agbazo & P. Grenier https://doi.org/10.1002/joc.6432
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78. Projected effects of climate change on shallow landslides, North Shore Mountains, Vancouver, Canada M. Jakob & T. Owen https://doi.org/10.1016/j.geomorph.2021.107921
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860. Towards the development of an isotope‐enabled rainfall‐runoff model: Improving the ability to capture hydrological and anthropogenic change A. Watson et al. https://doi.org/10.1002/hyp.14819
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924. Possible NPP changes and risky ecosystem region identification in China during the 21st century based on BCC-CSM2 C. Zhang et al. https://doi.org/10.1007/s11442-020-1778-8
925. Future changes in crop yield over Poland driven by climate change, increasing atmospheric CO2 and nitrogen stress P. Marcinkowski & M. Piniewski https://doi.org/10.1016/j.agsy.2023.103813
926. Future exposure of rainfall and temperature extremes to the most populous island of Indonesia: A projection based on CORDEX simulation V. Hendrawan et al. https://doi.org/10.1002/joc.8537
927. Uncertainty of Rainfall Products: Impact on Modelling Household Nutrition from Rain-Fed Agriculture in Southern Africa R. Luetkemeier et al. https://doi.org/10.3390/w10040499
928. Machine-learning-enhanced multi-model climate ensemble for future rainfall, temperature and drought assessment in the semi-arid Papagni River Basin, India C. Naresh & A. Mathew https://doi.org/10.1016/j.pce.2026.104555
929. Inter‐decadal variability in potential glacier surface melt energy at Vestari Hagafellsjökull (Langjökull, Iceland) and the role of synoptic circulation T. Matthews et al. https://doi.org/10.1002/joc.4191
930. Behaviour of energy piles under climate-change scenarios: a case study in Southern Italy M. Adinolfi et al. https://doi.org/10.1680/jenge.19.00093
931. Investigating statistical bias correction with temporal subsample of the upper Ping River Basin, Thailand S. Wuthiwongtyohtin https://doi.org/10.2166/wcc.2020.021
932. Should We Use Quantile-Mapping-Based Methods in a Climate Change Context? A “Perfect Model” Experiment M. Vrac et al. https://doi.org/10.3390/cli13070137
933. Assessing multivariate bias corrections of climate simulations on various impact models under climate change D. Allard et al. https://doi.org/10.5194/hess-29-4711-2025
934. Understanding the Uncertainty of the Lim River Basin Response to Changing Climate M. Stojkovic & S. Simonovic https://doi.org/10.1061/(ASCE)HE.1943-5584.0001964
935. Projecting Health Impacts of Future Temperature: A Comparison of Quantile-Mapping Bias-Correction Methods W. Qian & H. Chang https://doi.org/10.3390/ijerph18041992
936. Assessment of CMIP5 and CORDEX-SA experiments in representing multiscale temperature climatology over central India A. Vishwakarma et al. https://doi.org/10.2166/wpt.2022.098
937. Achieving effective calibration of precipitation forecasts over a continental scale Q. Yang et al. https://doi.org/10.1016/j.ejrh.2021.100818
938. Revisiting the bias correction of climate models for impact studies T. Dinh & F. Aires https://doi.org/10.1007/s10584-023-03597-y
939. The influence of natural variability and interpolation errors on bias characterization in RCM simulations N. Addor & E. Fischer https://doi.org/10.1002/2014JD022824
940. Precipitation Bias Correction: A Novel Semi‐parametric Quantile Mapping Method C. Rajulapati & S. Papalexiou https://doi.org/10.1029/2023EA002823
941. Multivariate—Intervariable, Spatial, and Temporal—Bias Correction* M. Vrac & P. Friederichs https://doi.org/10.1175/JCLI-D-14-00059.1
942. Does Quantile Mapping of Simulated Precipitation Correct for Biases in Transition Probabilities and Spell Lengths? J. Rajczak et al. https://doi.org/10.1175/JCLI-D-15-0162.1
943. Long-term changes in offshore wind power density and wind turbine capacity factor in the Iberian Peninsula (1900–2010) S. Carreno-Madinabeitia et al. https://doi.org/10.1016/j.energy.2021.120364
944. Performance Evaluation of Integrated System to Model the Climate Change Impacts on Hydro-Geological Hazard R. Guido et al. https://doi.org/10.2139/ssrn.2526731
945. Comparison of bias correction methods in the arid region of Pakistan Z. Ali et al. https://doi.org/10.1088/1755-1315/1467/1/012026
946. Validation and Projections of Climate Characteristics in the Saginaw Bay Watershed, MI, for Hydrologic Modeling Applications D. Kluver & W. Robertson https://doi.org/10.3389/frwa.2021.779811
947. A strategy to effectively make use of large volumes of climate data for climate change adaptation R. Benestad et al. https://doi.org/10.1016/j.cliser.2017.06.013
948. Does applying quantile mapping to subsamples improve the bias correction of daily precipitation? P. Reiter et al. https://doi.org/10.1002/joc.5283
949. Hybrid approaches enhance hydrological model usability for local streamflow prediction Y. Du & I. Pechlivanidis https://doi.org/10.1038/s43247-025-02324-y
950. Impact of surface-net solar radiation and soil temperature on tea production in India: a study of the Dooars region in West Bengal P. Mallik & T. Ghosh https://doi.org/10.1007/s10113-021-01844-5
951. Rainfall Variability and Droughts in the Sebou Basin (Morocco): ERA5 Bias Correction by Quantile Mapping and Trend Analysis over 1980–2024 K. Rezkallah et al. https://doi.org/10.3390/app16147023
952. Spatio-temporal distribution of water availability in Karnali-Mohana Basin, Western Nepal: Climate change impact assessment (Part-B) V. Pandey et al. https://doi.org/10.1016/j.ejrh.2020.100691
953. Combined Dynamical and Statistical Downscaling for High-Resolution Projections of Multiple Climate Variables in the Beijing–Tianjin–Hebei Region of China Z. Han et al. https://doi.org/10.1175/JAMC-D-19-0050.1
954. Trends, Patterns, and Persistence of Rainfall, Streamflow, and Flooded Area in the Upper Paraguay Basin (Brazil) M. Coelho et al. https://doi.org/10.3390/w17101549
955. Modeling climate change impacts on streamflow dynamics in the Melka Wakana catchment, Ethiopia T. Geresu et al. https://doi.org/10.2166/wcc.2026.251
956. Possible future changes in South East Australian frost frequency: an inter-comparison of statistical downscaling approaches S. Crimp et al. https://doi.org/10.1007/s00382-018-4188-1
957. Combining literature-based and data-driven fuzzy models to predict brown trout (Salmo trutta L.) spawning habitat degradation induced by climate change R. Muñoz-Mas et al. https://doi.org/10.1016/j.ecolmodel.2018.08.012
958. New multivariate composite remote sensing drought index based on machine learning and geospatial techniques, insights from Northern Iraq K. Qaraghuli et al. https://doi.org/10.1016/j.ejrh.2026.103211
959. Climate change increases the number of landslides at the juncture of the Alpine, Pannonian and Mediterranean regions M. Jemec Auflič et al. https://doi.org/10.1038/s41598-023-50314-x
960. Efficient photovoltaic power prediction to achieve carbon neutrality in China J. Gao et al. https://doi.org/10.1016/j.enconman.2025.119653
961. Uni- and multivariate bias adjustment methods in Nordic catchments: Complexity and performance in a changing climate F. Tootoonchi et al. https://doi.org/10.1016/j.scitotenv.2022.158615
962. Climate change impacts on wind power generation for the Italian peninsula R. Bonanno et al. https://doi.org/10.1007/s10113-022-02007-w
963. Identifying hotspots cities vulnerable to climate change in Pakistan under CMIP5 climate projections S. Ali et al. https://doi.org/10.1002/joc.6638
964. Enhancing climate change impact assessment in viticulture by resolving microclimates A. Fonseca et al. https://doi.org/10.1016/j.eja.2025.127740
965. Impact of climate change on potential evapotranspiration and crop water requirement in Upper Wabe Bridge watershed, Wabe Shebele River Basin, Ethiopia M. Gurara et al. https://doi.org/10.1016/j.jafrearsci.2021.104223
966. Projected changes in days with zero‐crossings for Norway I. Nilsen et al. https://doi.org/10.1002/joc.6913
967. Modeling climate change impacts on blue and green water of the Kobo-Golina River in data-scarce upper Danakil basin, Ethiopia B. Abate et al. https://doi.org/10.1016/j.ejrh.2024.101756
968. Hydrological response of Chamelia watershed in Mahakali Basin to climate change V. Pandey et al. https://doi.org/10.1016/j.scitotenv.2018.09.053
969. Projected cryospheric and hydrological impacts of 21st century climate change in the Ötztal Alps (Austria) simulated using a physically based approach F. Hanzer et al. https://doi.org/10.5194/hess-22-1593-2018
970. Climate Change Adaptation Strategies for Canadian Asphalt Pavements; Part 1: Adaptation strategies S. Swarna et al. https://doi.org/10.1016/j.jclepro.2022.132313
971. Assessing future climate trends and their impact on different fractured rock aquifers in Northern Togo M. Akara et al. https://doi.org/10.2166/wcc.2022.264
972. Climate Change Impact on Hydrological Extremes: Preliminary Results from the Polish-Norwegian Project R. Romanowicz et al. https://doi.org/10.1515/acgeo-2016-0009
973. Climate change impact assessment on hydrological fluxes based on ensemble GCM outputs: a case study in eastern Indian River Basin J. Padhiary et al. https://doi.org/10.2166/wcc.2019.080
974. Aridity shifts in the MENA region under the Paris Agreement climate change scenarios M. Hamed et al. https://doi.org/10.1016/j.gloplacha.2024.104483
975. Watershed vulnerability assessment in the context of climate change, land use change, and water use change in the Nan River Basin, northern Thailand S. Talampas & S. Shrestha https://doi.org/10.2166/wcc.2025.546
976. Regression Recalibration by Learning PIT Map Values C. Qian et al. https://doi.org/10.1080/00401706.2025.2464004
977. Modelling high-resolution climate datasets for Djebel Aïssa National park (Southwestern Algeria) by downscaling worldclim data using GAM and RF I. Habibi et al. https://doi.org/10.1007/s40808-025-02593-8
978. Changes in storm surges based on a bias-adjusted reconstruction dataset from 1900 to 2010 J. Feng et al. https://doi.org/10.1016/j.jhydrol.2022.128759
979. Future changes of summer monsoon rainfall and temperature over Bangladesh using 27 CMIP6 models A. Bhattacharjee et al. https://doi.org/10.1080/10106049.2023.2285342
980. Estimating pros and cons of statistical downscaling based on EQM bias adjustment as a complementary method to dynamical downscaling A. Reder et al. https://doi.org/10.1038/s41598-024-84527-5
981. Integrating Climate Modelling, Downscaling, and Bias Correction for Drought Studies: Current Approaches and Future Directions C. Naresh & A. Mathew https://doi.org/10.1007/s11269-026-04843-5
982. Risks and opportunities for a Swiss hydroelectricity company in a changing climate K. Hakala et al. https://doi.org/10.5194/hess-24-3815-2020
983. Sensitivities of heat-wave mortality projections: Moving towards stochastic model assumptions L. Abadie & J. Polanco-Martínez https://doi.org/10.1016/j.envres.2021.111895
984. Extreme precipitation events under climate change in the Iberian Peninsula S. Cardoso Pereira et al. https://doi.org/10.1002/joc.6269
985. An approach to identify the best climate models for the assessment of climate change impacts on meteorological and hydrological droughts A. Collados-Lara et al. https://doi.org/10.5194/nhess-22-599-2022
986. Assessment of meteorological drought change in the 21st century based on CMIP6 multi-model ensemble projections over mainland China Z. Song et al. https://doi.org/10.1016/j.jhydrol.2021.126643