Hydroclimatic time series features at multiple time scales
暂无分享,去创建一个
Georgia Papacharalampous | Yannis Markonis | Martin Hanel | Hristos Tyralis | M. Hanel | Georgia Papacharalampous | Y. Markonis | Hristos Tyralis
[1] Demetris Koutsoyiannis,et al. The scientific legacy of Harold Edwin Hurst (1880–1978) , 2016 .
[2] Edward H. Wiser,et al. Stochastic Models in Hydrology , 1967 .
[3] George Athanasopoulos,et al. Forecasting: principles and practice , 2013 .
[4] M. Taqqu,et al. Fractionally differenced ARIMA models applied to hydrologic time series: Identification, estimation, and simulation , 1997 .
[5] M. Hipsey,et al. “Panta Rhei—Everything Flows”: Change in hydrology and society—The IAHS Scientific Decade 2013–2022 , 2013 .
[6] R. Hirsch,et al. Fragmented patterns of flood change across the United States , 2016, Geophysical research letters.
[7] Georgia Papacharalampous,et al. Explanation and Probabilistic Prediction of Hydrological Signatures with Statistical Boosting Algorithms , 2021, Remote. Sens..
[8] Xiaozhe Wang,et al. Characteristic-Based Clustering for Time Series Data , 2006, Data Mining and Knowledge Discovery.
[9] Irma J. Terpenning,et al. STL : A Seasonal-Trend Decomposition Procedure Based on Loess , 1990 .
[10] Bellie Sivakumar,et al. Chaos in Hydrology: Bridging Determinism and Stochasticity , 2018 .
[11] L. Hay,et al. Hydrometeorological dataset for the contiguous USA , 2014 .
[12] Taha B. M. J. Ouarda,et al. Stochastic simulation of nonstationary oscillation hydroclimatic processes using empirical mode decomposition , 2012 .
[13] O. Ledvinka,et al. Detection of field significant long-term monotonic trends in spring yields , 2015, Stochastic Environmental Research and Risk Assessment.
[14] Gabriele Villarini,et al. On the seasonality of flooding across the continental United States , 2016 .
[15] R. Wilby,et al. Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management , 2020, Hydrology and Earth System Sciences.
[16] Jaewoo Jung,et al. The Interpretation of Spectral Entropy Based Upon Rate Distortion Functions , 2006, 2006 IEEE International Symposium on Information Theory.
[17] Georgia Papacharalampous,et al. How to explain and predict the shape parameter of the generalized extreme value distribution of streamflow extremes using a big dataset , 2018, Journal of Hydrology.
[18] Evaluating Participatory Modeling Methods for Co‐creating Pathways to Sustainability , 2021, Earth's Future.
[19] Khaled H. Hamed. Trend detection in hydrologic data: The Mann–Kendall trend test under the scaling hypothesis , 2008 .
[20] Günter Blöschl,et al. Spatial patterns and characteristics of flood seasonality in Europe , 2017, Hydrology and Earth System Sciences.
[21] A. Zeileis,et al. zoo: S3 Infrastructure for Regular and Irregular Time Series , 2005, math/0505527.
[22] Yongqiang Zhang,et al. Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review , 2020, WIREs Water.
[23] Demetris Koutsoyiannis,et al. A Global-Scale Investigation of Stochastic Similarities in Marginal Distribution and Dependence Structure of Key Hydrological-Cycle Processes , 2021, Hydrology.
[24] Max A. Little,et al. Highly comparative time-series analysis: the empirical structure of time series and their methods , 2013, Journal of The Royal Society Interface.
[25] Sina Khatami,et al. Global-scale massive feature extraction from monthly hydroclimatic time series: Statistical characterizations, spatial patterns and hydrological similarity. , 2020, The Science of the total environment.
[26] Persistent multi-scale fluctuations shift European hydroclimate to its millennial boundaries , 2018, Nature Communications.
[27] Benjamin Letham,et al. Forecasting at Scale , 2018, PeerJ Prepr..
[28] Yihui Xie,et al. knitr: A Comprehensive Tool for Reproducible Research in R , 2018, Implementing Reproducible Research.
[29] J. Kirchner,et al. Growing Spatial Scales of Synchronous River Flooding in Europe , 2019, Geophysical Research Letters.
[30] H. E. Hurst,et al. Long-Term Storage Capacity of Reservoirs , 1951 .
[31] Stefanie Seiler,et al. Finding Groups In Data , 2016 .
[32] The role of stochastic hydrology in dealing with climatic variability , 1987 .
[33] J. R. Wallis,et al. Noah, Joseph, and Operational Hydrology , 1968 .
[34] Yihui Xie,et al. Dynamic Documents with R and knitr, Second Edition , 2015 .
[35] Christoforos Pappas,et al. A cross-scale framework for integrating multi-source data in Earth system sciences , 2021, Environ. Model. Softw..
[36] Marshall E. Moss,et al. Autocorrelation structure of monthly streamflows , 1974 .
[37] Trevor Hastie,et al. An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.
[38] R. B. Leipnik,et al. Stochastic renewal model of low-flow streamflow sequences , 1996 .
[39] Klaus Nordhausen,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman , 2009 .
[40] P. Claps,et al. Changing climate shifts timing of European floods , 2017, Science.
[41] M. P. González-Dugo,et al. Twenty-three unsolved problems in hydrology (UPH) – a community perspective , 2019, Hydrological Sciences Journal.
[42] Georgia Papacharalampous,et al. Boosting algorithms in energy research: A systematic review , 2021, Neural Comput. Appl..
[43] Ronny Berndtsson,et al. Advances in Data-Based Approaches for Hydrologic Modeling and Forecasting , 2010 .
[44] Feng Li,et al. GRATIS: GeneRAting TIme Series with diverse and controllable characteristics , 2019, Stat. Anal. Data Min..
[45] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[46] Rob J. Hyndman,et al. Large-Scale Unusual Time Series Detection , 2015, 2015 IEEE International Conference on Data Mining Workshop (ICDMW).
[47] Rob J Hyndman,et al. Automatic Time Series Forecasting: The forecast Package for R , 2008 .
[48] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[49] Martyn P. Clark,et al. The CAMELS data set: catchment attributes and meteorology for large-sample studies , 2017 .
[50] Bruno Merz,et al. Climate influences on flood probabilities across Europe , 2019, Hydrology and Earth System Sciences.
[51] Nick S. Jones,et al. Highly Comparative Feature-Based Time-Series Classification , 2014, IEEE Transactions on Knowledge and Data Engineering.
[52] T. M. Chui,et al. Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods , 2020, Hydrology and Earth System Sciences.
[53] Demetris Koutsoyiannis,et al. Hydrology and change , 2013 .
[54] Martyn P. Clark,et al. Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance , 2014 .
[55] Demetris Koutsoyiannis,et al. Climatic Variability Over Time Scales Spanning Nine Orders of Magnitude: Connecting Milankovitch Cycles with Hurst–Kolmogorov Dynamics , 2013, Surveys in Geophysics.
[56] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[57] D. G. Watts,et al. Application of Linear Random Models to Four Annual Streamflow Series , 1970 .
[58] J. Kyselý,et al. Revisiting the recent European droughts from a long-term perspective , 2018, Scientific Reports.
[59] Hadley Wickham,et al. Tools to Make Developing R Packages Easier , 2016 .
[60] K. Hipel,et al. Time series modelling of water resources and environmental systems , 1994 .
[61] Assessing changes in US regional precipitation on multiple time scales , 2019, Journal of Hydrology.
[62] A. Langousis,et al. A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources , 2019, Water.
[63] Yaxing Wei,et al. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 2 , 2014 .
[64] Kate Smith-Miles,et al. Visualising forecasting algorithm performance using time series instance spaces , 2017 .
[65] O. Ledvinka. Evolution of low flows in Czechia revisited , 2015 .
[66] Elena Volpi,et al. Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale , 2022, Geoscience Frontiers.
[67] Ben D. Fulcher,et al. Feature-based time-series analysis , 2017, ArXiv.
[68] Michael Leonard,et al. A global-scale investigation of trends in annual maximum streamflow , 2017 .
[69] Vujica Yevjevich,et al. Determinism and stochasticity in hydrology , 1974 .
[70] Martin Hanel,et al. An R package for assessment of statistical downscaling methods for hydrological climate change impact studies , 2017, Environ. Model. Softw..
[71] Kohske Takahashi,et al. Welcome to the Tidyverse , 2019, J. Open Source Softw..
[72] Georgia Papacharalampous,et al. Super ensemble learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithms , 2020, Neural Computing and Applications.
[73] Lei Cao,et al. Hydrological Similarity-Based Parameter Regionalization under Different Climate and Underlying Surfaces in Ungauged Basins , 2021, Water.