Articles | Volume 14, issue 11
https://doi.org/10.5194/essd-14-4949-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-4949-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
11 Nov 2022
Data description paper |
| 11 Nov 2022
| 11 Nov 2022
Downscaled hyper-resolution (400 m) gridded datasets of daily precipitation and temperature (2008–2019) for the East–Taylor subbasin (western United States)
Energy Geosciences Division, Lawrence Berkeley National Laboratory,
Berkeley, CA 94720, USA
Dipankar Dwivedi
Energy Geosciences Division, Lawrence Berkeley National Laboratory,
Berkeley, CA 94720, USA
James B. Brown
Environmental Genomics and System Biology, Lawrence Berkeley National
Laboratory, Berkeley, CA 94720, USA
Department of Statistics, University of California, Berkeley, CA 94720, USA
Carl I. Steefel
Energy Geosciences Division, Lawrence Berkeley National Laboratory,
Berkeley, CA 94720, USA
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We created the first comprehensive, high-resolution, and easily-accessible bathymetry dataset for the three main branches of the Danube Delta. By combining four data sources, we obtained a detailed representation of the riverbed, with resolutions ranging from 2 to 100 m. This dataset will support future studies on water and nutrient exchanges between the Danube and the Black Sea, and provide insights into the Delta’s buffer role within the understudied Danube-Black Sea continuum.
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Juliette Godet, Pierre Nicolle, Nabil Hocini, Eric Gaume, Philippe Davy, Frederic Pons, Pierre Javelle, Pierre-André Garambois, Dimitri Lague, and Olivier Payrastre
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This paper describes a dataset that includes input, output, and validation data for the simulation of flash flood hazards and three specific flash flood events in the French Mediterranean region. This dataset is particularly valuable as flood mapping methods often lack sufficient benchmark data. Additionally, we demonstrate how the hydraulic method we used, named Floodos, produces highly satisfactory results.
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Fanny J. Sarrazin, Sabine Attinger, and Rohini Kumar
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Nitrogen (N) and phosphorus (P) contamination of water bodies is a long-term issue due to the long history of N and P inputs to the environment and their persistence. Here, we introduce a long-term and high-resolution dataset of N and P inputs from wastewater (point sources) for Germany, combining data from different sources and conceptual understanding. We also account for uncertainties in modelling choices, thus facilitating robust long-term and large-scale water quality studies.
Shanti Shwarup Mahto, Simone Fatichi, and Stefano Galelli
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-441, https://doi.org/10.5194/essd-2024-441, 2024
Revised manuscript accepted for ESSD
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The MSEA-Res database offers an open-access dataset tracking absolute water storage for 185 large reservoirs across Mainland Southeast Asia from 1985–2023. It provides valuable insights into how reservoir storage has grown by 130 % between 2008 and 2017, driven by dams in key river basins. Our data also reveal how droughts, like the 2019–2020 event, significantly impacted water reservoirs. This resource can aid water management, drought planning, and research globally.
Rohit Mukherjee, Frederick Policelli, Ruixue Wang, Elise Arellano-Thompson, Beth Tellman, Prashanti Sharma, Zhijie Zhang, and Jonathan Giezendanner
Earth Syst. Sci. Data, 16, 4311–4323, https://doi.org/10.5194/essd-16-4311-2024, https://doi.org/10.5194/essd-16-4311-2024, 2024
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Global water resource monitoring is crucial due to climate change and population growth. This study presents a hand-labeled dataset of 100 PlanetScope images for surface water detection, spanning diverse biomes. We use this dataset to evaluate two state-of-the-art mapping methods. Results highlight performance variations across biomes, emphasizing the need for diverse, independent validation datasets to enhance the accuracy and reliability of satellite-based surface water monitoring techniques.
Lei Huang, Yong Luo, Jing M. Chen, Qiuhong Tang, Tammo Steenhuis, Wei Cheng, and Wen Shi
Earth Syst. Sci. Data, 16, 3993–4019, https://doi.org/10.5194/essd-16-3993-2024, https://doi.org/10.5194/essd-16-3993-2024, 2024
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Timely global terrestrial evapotranspiration (ET) data are crucial for water resource management and drought forecasting. This study introduces the VISEA algorithm, which integrates satellite data and shortwave radiation to provide daily 0.05° gridded near-real-time ET estimates. By employing a vegetation index–temperature method, this algorithm can estimate ET without requiring additional data. Evaluation results demonstrate VISEA's comparable accuracy with accelerated data availability.
Sibylle Kathrin Hassler, Rafael Bohn Reckziegel, Ben du Toit, Svenja Hoffmeister, Florian Kestel, Anton Kunneke, Rebekka Maier, and Jonathan Paul Sheppard
Earth Syst. Sci. Data, 16, 3935–3948, https://doi.org/10.5194/essd-16-3935-2024, https://doi.org/10.5194/essd-16-3935-2024, 2024
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Agroforestry systems (AFSs) combine trees and crops within the same land unit, providing a sustainable land use option which protects natural resources and biodiversity. Introducing trees into agricultural systems can positively affect water resources, soil characteristics, biomass and microclimate. We studied an AFS in South Africa in a multidisciplinary approach to assess the different influences and present the resulting dataset consisting of water, soil, tree and meteorological variables.
Keirnan J. A. Fowler, Ziqi Zhang, and Xue Hou
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-263, https://doi.org/10.5194/essd-2024-263, 2024
Revised manuscript accepted for ESSD
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This paper presents Version 2 of the Australian edition of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) series of datasets. CAMELS-AUS v2 comprises data for an increased number (561) of catchments, each with with long-term monitoring, combining hydrometeorological time series with attributes related to geology, soil, topography, land cover, anthropogenic influence and hydroclimatology. It is freely downloadable from https://zenodo.org/doi/10.5281/zenodo.12575680.
Kaihao Zheng, Peirong Lin, and Ziyun Yin
Earth Syst. Sci. Data, 16, 3873–3891, https://doi.org/10.5194/essd-16-3873-2024, https://doi.org/10.5194/essd-16-3873-2024, 2024
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We develop a globally applicable thresholding scheme for DEM-based floodplain delineation to improve the representation of spatial heterogeneity. It involves a stepwise approach to estimate the basin-level floodplain hydraulic geometry parameters that best respect the scaling law while approximating the global hydrodynamic flood maps. A ~90 m resolution global floodplain map, the Spatial Heterogeneity Improved Floodplain by Terrain analysis (SHIFT), is delineated with demonstrated superiority.
Yuzhong Yang, Qingbai Wu, Xiaoyan Guo, Lu Zhou, Helin Yao, Dandan Zhang, Zhongqiong Zhang, Ji Chen, and Guojun Liu
Earth Syst. Sci. Data, 16, 3755–3770, https://doi.org/10.5194/essd-16-3755-2024, https://doi.org/10.5194/essd-16-3755-2024, 2024
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We present the temporal data of stable isotopes in different waterbodies in the Beiluhe Basin in the hinterland of the Qinghai–Tibet Plateau (QTP) produced between 2017 and 2022. In this article, the first detailed stable isotope data of 359 ground ice samples are presented. This first data set provides a new basis for understanding the hydrological effects of permafrost degradation on the QTP.
Bernhard Lehner, Mira Anand, Etienne Fluet-Chouinard, Florence Tan, Filipe Aires, George H. Allen, Pilippe Bousquet, Josep G. Canadell, Nick Davidson, C. Max Finlayson, Thomas Gumbricht, Lammert Hilarides, Gustaf Hugelius, Robert B. Jackson, Maartje C. Korver, Peter B. McIntyre, Szabolcs Nagy, David Olefeldt, Tamlin M. Pavelsky, Jean-Francois Pekel, Benjamin Poulter, Catherine Prigent, Jida Wang, Thomas A. Worthington, Dai Yamazaki, and Michele Thieme
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-204, https://doi.org/10.5194/essd-2024-204, 2024
Revised manuscript accepted for ESSD
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The Global Lakes and Wetlands Database (GLWD) version 2 distinguishes a total of 33 non-overlapping wetland classes, providing a static map of the world’s inland surface waters. It contains cell fractions of wetland extents per class at a grid cell resolution of ~500 m. The total combined extent of all classes including all inland and coastal waterbodies and wetlands of all inundation frequencies—that is, the maximum extent—covers 18.2 million km2, equivalent to 13.4 % of total global land area.
Hordur Bragi Helgason and Bart Nijssen
Earth Syst. Sci. Data, 16, 2741–2771, https://doi.org/10.5194/essd-16-2741-2024, https://doi.org/10.5194/essd-16-2741-2024, 2024
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LamaH-Ice is a large-sample hydrology (LSH) dataset for Iceland. The dataset includes daily and hourly hydro-meteorological time series, including observed streamflow and basin characteristics, for 107 basins. LamaH-Ice offers most variables that are included in existing LSH datasets and additional information relevant to cold-region hydrology such as annual time series of glacier extent and mass balance. A large majority of the basins in LamaH-Ice are unaffected by human activities.
Chengcheng Hou, Yan Li, Shan Sang, Xu Zhao, Yanxu Liu, Yinglu Liu, and Fang Zhao
Earth Syst. Sci. Data, 16, 2449–2464, https://doi.org/10.5194/essd-16-2449-2024, https://doi.org/10.5194/essd-16-2449-2024, 2024
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To fill the gap in the gridded industrial water withdrawal (IWW) data in China, we developed the China Industrial Water Withdrawal (CIWW) dataset, which provides monthly IWWs from 1965 to 2020 at a spatial resolution of 0.1°/0.25° and auxiliary data including subsectoral IWW and industrial output value in 2008. This dataset can help understand the human water use dynamics and support studies in hydrology, geography, sustainability sciences, and water resource management and allocation in China.
Nehar Mandal, Prabal Das, and Kironmala Chanda
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-109, https://doi.org/10.5194/essd-2024-109, 2024
Revised manuscript accepted for ESSD
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Optimal features among hydroclimatic variables and land surface model (LSM) outputs are selected using a novel Bayesian network (BN) approach for simulating Terrestrial Water Storage Anomalies (TWSA). TWSA is simulated using ML models (CNN, SVR, ETR, and Stacking Ensemble Regression), and gridwise leader models are identified globally. TWSA is reconstructed (BNML_TWSA) with the selected leader models from January 1960 to December 2022 to generate a continuous global gridded dataset.
Pierre-Antoine Versini, Leydy Alejandra Castellanos-Diaz, David Ramier, and Ioulia Tchiguirinskaia
Earth Syst. Sci. Data, 16, 2351–2366, https://doi.org/10.5194/essd-16-2351-2024, https://doi.org/10.5194/essd-16-2351-2024, 2024
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Nature-based solutions (NBSs), such as green roofs, have appeared as relevant solutions to mitigate urban heat islands. The evapotranspiration (ET) process allows NBSs to cool the air. To improve our knowledge about ET assessment, this paper presents some experimental measurement campaigns carried out during three consecutive summers. Data are available for three different (large, small, and point-based) spatial scales.
Ralph Bathelemy, Pierre Brigode, Vazken Andréassian, Charles Perrin, Vincent Moron, Cédric Gaucherel, Emmanuel Tric, and Dominique Boisson
Earth Syst. Sci. Data, 16, 2073–2098, https://doi.org/10.5194/essd-16-2073-2024, https://doi.org/10.5194/essd-16-2073-2024, 2024
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The aim of this work is to provide the first hydroclimatic database for Haiti, a Caribbean country particularly vulnerable to meteorological and hydrological hazards. The resulting database, named Simbi, provides hydroclimatic time series for around 150 stations and 24 catchment areas.
Changming Li, Ziwei Liu, Wencong Yang, Zhuoyi Tu, Juntai Han, Sien Li, and Hanbo Yang
Earth Syst. Sci. Data, 16, 1811–1846, https://doi.org/10.5194/essd-16-1811-2024, https://doi.org/10.5194/essd-16-1811-2024, 2024
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Using a collocation-based approach, we developed a reliable global land evapotranspiration product (CAMELE) by merging multi-source datasets. The CAMELE product outperformed individual input datasets and showed satisfactory performance compared to reference data. It also demonstrated superiority for different plant functional types. Our study provides a promising solution for data fusion. The CAMELE dataset allows for detailed research and a better understanding of land–atmosphere interactions.
Yuhan Guo, Hongxing Zheng, Yuting Yang, Yanfang Sang, and Congcong Wen
Earth Syst. Sci. Data, 16, 1651–1665, https://doi.org/10.5194/essd-16-1651-2024, https://doi.org/10.5194/essd-16-1651-2024, 2024
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We have provided an inaugural version of the hydrogeomorphic dataset for catchments over the Tibetan Plateau. We first provide the width-function-based instantaneous unit hydrograph (WFIUH) for each HydroBASINS catchment, which can be used to investigate the spatial heterogeneity of hydrological behavior across the Tibetan Plateau. It is expected to facilitate hydrological modeling across the Tibetan Plateau.
Lingbo Li, Hong-Yi Li, Guta Abeshu, Jinyun Tang, L. Ruby Leung, Chang Liao, Zeli Tan, Hanqin Tian, Peter Thornton, and Xiaojuan Yang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-43, https://doi.org/10.5194/essd-2024-43, 2024
Revised manuscript accepted for ESSD
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We have developed a new map that reveals how organic carbon from soil leaches into headwater streams over the contiguous United States. We use advanced artificial intelligence techniques and a massive amount of data, including observations at over 2,500 gauges and a wealth of climate and environmental information. The map is a critical step in understanding and predicting how carbon moves through our environment, hence a useful tool for tackling climate challenges.
Ziyun Yin, Peirong Lin, Ryan Riggs, George H. Allen, Xiangyong Lei, Ziyan Zheng, and Siyu Cai
Earth Syst. Sci. Data, 16, 1559–1587, https://doi.org/10.5194/essd-16-1559-2024, https://doi.org/10.5194/essd-16-1559-2024, 2024
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Large-sample hydrology (LSH) datasets have been the backbone of hydrological model parameter estimation and data-driven machine learning models for hydrological processes. This study complements existing LSH studies by creating a dataset with improved sample coverage, uncertainty estimates, and dynamic descriptions of human activities, which are all crucial to hydrological understanding and modeling.
Pierluigi Claps, Giulia Evangelista, Daniele Ganora, Paola Mazzoglio, and Irene Monforte
Earth Syst. Sci. Data, 16, 1503–1522, https://doi.org/10.5194/essd-16-1503-2024, https://doi.org/10.5194/essd-16-1503-2024, 2024
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FOCA (Italian FlOod and Catchment Atlas) is the first systematic collection of data on Italian river catchments. It comprises geomorphological, soil, land cover, NDVI, climatological and extreme rainfall catchment attributes. FOCA also contains 631 peak and daily discharge time series covering the 1911–2016 period. Using this first nationwide data collection, a wide range of applications, in particular flood studies, can be undertaken within the Italian territory.
Wei Jing Ang, Edward Park, Yadu Pokhrel, Dung Duc Tran, and Ho Huu Loc
Earth Syst. Sci. Data, 16, 1209–1228, https://doi.org/10.5194/essd-16-1209-2024, https://doi.org/10.5194/essd-16-1209-2024, 2024
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Dams have burgeoned in the Mekong, but information on dams is scattered and inconsistent. Up-to-date evaluation of dams is unavailable, and basin-wide hydropower potential has yet to be systematically assessed. We present a comprehensive database of 1055 dams, a spatiotemporal analysis of the dams, and a total hydropower potential of 1 334 683 MW. Considering projected dam development and hydropower potential, the vulnerability and the need for better dam management may be highest in Laos.
Chuanqi He, Ci-Jian Yang, Jens M. Turowski, Richard F. Ott, Jean Braun, Hui Tang, Shadi Ghantous, Xiaoping Yuan, and Gaia Stucky de Quay
Earth Syst. Sci. Data, 16, 1151–1166, https://doi.org/10.5194/essd-16-1151-2024, https://doi.org/10.5194/essd-16-1151-2024, 2024
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The shape of drainage basins and rivers holds significant implications for landscape evolution processes and dynamics. We used a global 90 m resolution topography to obtain ~0.7 million drainage basins with sizes over 50 km2. Our dataset contains the spatial distribution of drainage systems and their morphological parameters, supporting fields such as geomorphology, climatology, biology, ecology, hydrology, and natural hazards.
Jingyu Lin, Peng Wang, Jinzhu Wang, Youping Zhou, Xudong Zhou, Pan Yang, Hao Zhang, Yanpeng Cai, and Zhifeng Yang
Earth Syst. Sci. Data, 16, 1137–1149, https://doi.org/10.5194/essd-16-1137-2024, https://doi.org/10.5194/essd-16-1137-2024, 2024
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Our paper provides a repository comprising over 330 000 observations encompassing daily, weekly, and monthly records of surface water quality spanning the period 1980–2022. It included 18 distinct indicators, meticulously gathered at 2384 monitoring sites, ranging from inland locations to coastal and oceanic areas. This dataset will be very useful for researchers and decision-makers in the fields of hydrology, ecological studies, climate change, policy development, and oceanography.
Ana M. Ricardo, Rui M. L. Ferreira, Alberto Rodrigues da Silva, Jacinto Estima, Jorge Marques, Ivo Gamito, and Alexandre Serra
Earth Syst. Sci. Data, 16, 375–385, https://doi.org/10.5194/essd-16-375-2024, https://doi.org/10.5194/essd-16-375-2024, 2024
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Floods are among the most common natural disasters responsible for severe damages and human losses. Agueda.2016Flood, a synthesis of locally sensed data and numerically produced data, allows complete characterization of the flood event that occurred in February 2016 in the Portuguese Águeda River. The dataset was managed through the RiverCure Portal, a collaborative web platform connected to a validated shallow-water model.
Cited articles
Abatzoglou, J. T.: Development of gridded surface meteorological data for
ecological applications and modelling, Int. J. Climatol., 33, 121–131,
https://doi.org/10.1002/joc.3413, 2013.
Baño-Medina, J., Manzanas, R., and Gutiérrez, J. M.: Configuration and intercomparison of deep learning neural models for statistical downscaling, Geosci. Model Dev., 13, 2109–2124, https://doi.org/10.5194/gmd-13-2109-2020, 2020.
Barnes, R.: RichDEM: Terrain Analysis Software, gitHub [software], http://github.com/r-barnes/richdem (last access: 15 January 2022), 2016.
Behnke, R., Vavrus, S., Allstadt, A., Albright, T., Thogmartin, W. E., and
Radeloff, V. C.: Evaluation of downscaled, gridded climate data for the
conterminous United States, Ecol. Appl., 26, 1338–1351,
https://doi.org/10.1002/15-1061, 2016.
Beven, K., Cloke, H., Pappenberger, F., Lamb, R., and Hunter, N.:
Hyperresolution information and hyperresolution ignorance in modelling the
hydrology of the land surface, Sci. China Earth Sci., 58, 25–35,
https://doi.org/10.1007/s11430-014-5003-4, 2015.
Bierkens, M. F. P.: Global hydrology 2015: State, trends, and directions,
Water Resour. Res., 51, 4923–4947, https://doi.org/10.1002/2015WR017173,
2015.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001.
Bürger, G., Murdock, T. Q., Werner, A. T., Sobie, S. R., and Cannon, A.
J.: Downscaling Extremes–An Intercomparison of Multiple Statistical
Methods for Present Climate, J. Climate, 25, 4366–4388,
https://doi.org/10.1175/JCLI-D-11-00408.1, 2012.
Clow, D. W.: Changes in the Timing of Snowmelt and Streamflow in Colorado: A
Response to Recent Warming, J. Climate, 23, 2293–2306,
https://doi.org/10.1175/2009JCLI2951.1, 2010.
Cotter, A. S., Chaubey, I., Costello, T. A., Soerens, T. S., and Nelson, M.
A.: Water quality model output uncertainty as affected by spatial resolution
of input data, J. Am. Water Resour. As., 39, 977–986,
https://doi.org/10.1111/j.1752-1688.2003.tb04420.x, 2003.
Coulibaly, P., Dibike, Y. B., and Anctil, F.: Downscaling Precipitation and
Temperature with Temporal Neural Networks, J. Hydrometeorol., 6, 483–496,
https://doi.org/10.1175/JHM409.1, 2005.
Crespi, A., Matiu, M., Bertoldi, G., Petitta, M., and Zebisch, M.: A high-resolution gridded dataset of daily temperature and precipitation records (1980–2018) for Trentino-South Tyrol (north-eastern Italian Alps), Earth Syst. Sci. Data, 13, 2801–2818, https://doi.org/10.5194/essd-13-2801-2021, 2021.
Daly, C.: Guidelines for assessing the suitability of spatial climate data
sets, Int. J. Climatol., 26, 707–721, https://doi.org/10.1002/joc.1322,
2006.
Daly, C., Neilson, R. P., and Phillips, D. L.: A Statistical-Topographic
Model for Mapping Climatological Precipitation over Mountainous Terrain, J.
Appl. Meteorol., 33, 140–158,
https://doi.org/10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2, 1994.
Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor,
G. H., Curtis, J., and Pasteris, P. P.: Physiographically sensitive mapping
of climatological temperature and precipitation across the conterminous
United States, Int. J. Climatol., 28, 2031–2064,
https://doi.org/10.1002/joc.1688, 2008.
Dwivedi, D., Mital, U., Faybishenko, B., Dafflon, B., Varadharajan, C.,
Agarwal, D., Williams, K. H., Steefel, C., and Hubbard, S.: Imputation of
Contiguous Gaps and Extremes of Subhourly Groundwater Time Series Using
Random Forests, J. Mach. Learn. Model Comput., 3, 1–22,
https://doi.org/10.1615/JMachLearnModelComput.2021038774, 2022.
Feldman, D., Aiken, A., Boos, W., Carroll, R., Chandrasekar, V., Collins,
W., Collis, S., Deems, J., DeMott, P., Fan, J., Flores, A., Gochis, D.,
Harrington, J., Kumjian, M., Leung, L., O'Brien, T., Raleigh, M., Rhoades,
A., McKenzie Skiles, S., Smith, J., Sullivan, R., Ullrich, P., Varble, A.,
and Williams, K.: Surface Atmosphere Integrated Field Laboratory (SAIL)
Science Plan, Technical Report, https://doi.org/10.2172/1781024, 2021.
Fiddes, J. and Gruber, S.: TopoSCALE v.1.0: downscaling gridded climate data in complex terrain, Geosci. Model Dev., 7, 387–405, https://doi.org/10.5194/gmd-7-387-2014, 2014.
Friedman, J. H.: Greedy Function Approximation: A Gradient Boosting Machine,
Ann. Stat., 29, 1189–1232, 2001.
Gallaudet, T. and Petty, T. R.: Federal action plan for improving forecasts
of water availability: Prepared pursuant to Section 3 of the October 19,
2018 Presidential Memorandum on Promoting the Reliable Supply and Delivery
of Water in the West, https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/UCOL-BriefingSheet final.pdf (last access: 6 November 2021), 2018.
Gillies, S. and others: Rasterio: geospatial raster I/O for Python
programmers, GitHub [code], https://github.com/rasterio/rasterio (last access: 15 January 2022), 2013.
Giorgi, F.: Thirty Years of Regional Climate Modeling: Where Are We and
Where Are We Going next?, J. Geophys. Res.-Atmos., 124, 5696–5723,
https://doi.org/10.1029/2018JD030094, 2019.
Groenke, B., Madaus, L., and Monteleoni, C.: ClimAlign: Unsupervised
statistical downscaling of climate variables via normalizing flows, in:
Proceedings of the 10th International Conference on Climate Informatics,
CI2020, United Kingdom (virtual), 22–25 September 2020, 60–66, https://doi.org/10.1145/3429309.3429318, 2020.
Gu, C.: Smoothing spline ANOVA models, Springer, New York, London, https://doi.org/10.1007/978-1-4757-3683-0, 2002.
Gutiérrez, J. M., Maraun, D., Widmann, M., Huth, R., Hertig, E.,
Benestad, R., Roessler, O., Wibig, J., Wilcke, R., Kotlarski, S., San
Martín, D., Herrera, S., Bedia, J., Casanueva, A., Manzanas, R.,
Iturbide, M., Vrac, M., Dubrovsky, M., Ribalaygua, J., Pórtoles, J.,
Räty, O., Räisänen, J., Hingray, B., Raynaud, D., Casado, M. J.,
Ramos, P., Zerenner, T., Turco, M., Bosshard, T., Štěpánek, P.,
Bartholy, J., Pongracz, R., Keller, D. E., Fischer, A. M., Cardoso, R. M.,
Soares, P. M. M., Czernecki, B., and Pagé, C.: An intercomparison of a
large ensemble of statistical downscaling methods over Europe: Results from
the VALUE perfect predictor cross-validation experiment, Int. J. Climatol., 39,
3750–3785, https://doi.org/10.1002/joc.5462, 2019.
Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P.
D., and New, M.: A European daily high-resolution gridded data set of
surface temperature and precipitation for 1950–2006, J. Geophys.
Res.-Atmos., 113, D20119, https://doi.org/10.1029/2008JD010201, 2008.
Hubbard, S. S., Williams, K. H., Agarwal, D., Banfield, J., Beller, H.,
Bouskill, N., Brodie, E., Carroll, R., Dafflon, B., Dwivedi, D., Falco, N.,
Faybishenko, B., Maxwell, R., Nico, P., Steefel, C., Steltzer, H., Tokunaga,
T., Tran, P. A., Wainwright, H., and Varadharajan, C.: The East River,
Colorado, Watershed: A Mountainous Community Testbed for Improving
Predictive Understanding of Multiscale Hydrological–Biogeochemical
Dynamics, Vadose Zone J., 17, 1–25,
https://doi.org/10.2136/vzj2018.03.0061, 2018.
James, T., Evans, A., Madly, E., and Kelly, C.: The economic importance of
the Colorado River to the basin region, L William Seidman Research
Institute, W. P. Carey School of Business, Arizona State University, https://businessforwater.org/wp-content/uploads/2016/12/PTF-Final-121814.pdf (last access: 4 February 2021), 2014.
Kunkel, K. E.: Simple Procedures for Extrapolation of Humidity Variables in
the Mountainous Western United States, J. Climate, 2, 656–670,
https://doi.org/10.1175/1520-0442(1989)002<0656:SPFEOH>2.0.CO;2, 1989.
Liston, G. E. and Elder, K.: A Meteorological Distribution System for
High-Resolution Terrestrial Modeling (MicroMet), J.
Hydrometeorol., 7, 217–234, https://doi.org/10.1175/JHM486.1, 2006.
Liu, Y., Ganguly, A. R., and Dy, J.: Climate Downscaling Using YNet: A Deep
Convolutional Network with Skip Connections and Fusion, in: Proceedings of
the 26th ACM SIGKDD International Conference on Knowledge Discovery &
Data Mining, KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery
and Data Mining, Virtual Event CA USA, 6–10 July 2020, 3145–3153,
https://doi.org/10.1145/3394486.3403366, 2020.
Livneh, B., Rosenberg, E. A., Lin, C., Nijssen, B., Mishra, V., Andreadis,
K. M., Maurer, E. P., and Lettenmaier, D. P.: A Long-Term Hydrologically
Based Dataset of Land Surface Fluxes and States for the Conterminous United
States: Update and Extensions, J. Climate, 26, 9384–9392,
https://doi.org/10.1175/JCLI-D-12-00508.1, 2013.
Lussana, C., Tveito, O. E., Dobler, A., and Tunheim, K.: seNorge_2018, daily precipitation, and temperature datasets over Norway, Earth Syst. Sci. Data, 11, 1531–1551, https://doi.org/10.5194/essd-11-1531-2019, 2019.
Maina, F. Z., Siirila-Woodburn, E. R., and Vahmani, P.: Sensitivity of meteorological-forcing resolution on hydrologic variables, Hydrol. Earth Syst. Sci., 24, 3451–3474, https://doi.org/10.5194/hess-24-3451-2020, 2020.
Matheron, G.: Principles of geostatistics, Econ. Geol., 58, 1246–1266,
https://doi.org/10.2113/gsecongeo.58.8.1246, 1963.
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., and Houston, T. G.: An
Overview of the Global Historical Climatology Network-Daily Database, J.
Atmos. Ocean. Tech., 29, 897–910,
https://doi.org/10.1175/JTECH-D-11-00103.1, 2012.
Milly, P. C. D. and Dunne, K. A.: Colorado River flow dwindles as
warming-driven loss of reflective snow energizes evaporation, Science, 367,
1252–1255, https://doi.org/10.1126/science.aay9187, 2020.
Misra, S., Sarkar, S., and Mitra, P.: Statistical downscaling of
precipitation using long short-term memory recurrent neural networks, Theor.
Appl. Climatol., 134, 1179–1196, https://doi.org/10.1007/s00704-017-2307-2,
2018.
Mital, U., Dwivedi, D., Brown, J. B., Faybishenko, B., Painter, S. L., and
Steefel, C. I.: Sequential Imputation of Missing Spatio-Temporal
Precipitation Data Using Random Forests, Front. Water, 2, 20,
https://doi.org/10.3389/frwa.2020.00020, 2020.
Mital, U., Dwivedi, D., Brown, J. B., and Steefel, C. I.: Downscaled
precipitation and mean air temperature datasets; East-Taylor subbasin;
2008–2019; daily temporal resolution; 400 m spatial resolution, ESS-DIVE
[data set], https://doi.org/10.15485/1822259, 2021.
Mital, U., Dwivedi, D., Özgen-Xian, I., Brown, J. B., and Steefel, C.
I.: Modeling Spatial Distribution of Snow Water Equivalent by Combining
Meteorological and Satellite Data with Lidar Maps, Artificial Intelligence
for the Earth Systems, early online release, https://doi.org/10.1175/AIES-D-22-0010.1,
2022.
Molnar, C.: Interpretable Machine Learning: A Guide for Making Black Box
Models Explainable, 2nd edn., https://christophm.github.io/interpretable-ml-book (last access: 4 January 2022), 2019.
Mott, R., Vionnet, V., and Grünewald, T.: The Seasonal Snow Cover
Dynamics: Review on Wind-Driven Coupling Processes, Front. Earth Sci., 6,
197, https://doi.org/10.3389/feart.2018.00197, 2018.
Mouatadid, S., Easterbrook, S., and Erler, A. R.: A Machine Learning
Approach to Non-uniform Spatial Downscaling of Climate Variables, in: 2017
IEEE International Conference on Data Mining Workshops (ICDMW), 2017 IEEE
International Conference on Data Mining Workshops (ICDMW), New Orleans, LA, 18–21 November 2017,
332–341, https://doi.org/10.1109/ICDMW.2017.49, 2017.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual
models part I – A discussion of principles, J. Hydrol., 10,
282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
National Research Council: New Strategies for America's Watersheds, National
Academies Press, Washington, D.C., https://doi.org/10.17226/6020, 1999.
Nourani, V., Razzaghzadeh, Z., Baghanam, A. H., and Molajou, A.: ANN-based
statistical downscaling of climatic parameters using decision tree predictor
screening method, Theor. Appl. Climatol., 137, 1729–1746,
https://doi.org/10.1007/s00704-018-2686-z, 2019.
Painter, T. H.: ASO L4 Lidar Snow Water Equivalent 50m UTM Grid, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set],
https://doi.org/10.5067/M4TUH28NHL4Z, 2018.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., and Cournapeau, D.: Scikit-learn: Machine Learning in Python,
J. Mach. Learn. Res., 12, 2825–2830, 2011.
Rouf, T., Mei, Y., Maggioni, V., Houser, P., and Noonan, M.: A Physically
Based Atmospheric Variables Downscaling Technique, J.
Hydrometeorol., 21, 93–108, https://doi.org/10.1175/JHM-D-19-0109.1,
2020.
Sachindra, D. A., Ahmed, K., Rashid, Md. M., Shahid, S., and Perera, B. J.
C.: Statistical downscaling of precipitation using machine learning
techniques, Atmos. Res., 212, 240–258,
https://doi.org/10.1016/j.atmosres.2018.05.022, 2018.
Sen Gupta, A. and Tarboton, D. G.: A tool for downscaling weather data from
large-grid reanalysis products to finer spatial scales for distributed
hydrological applications, Environ. Modell. Softw., 84,
50–69, https://doi.org/10.1016/j.envsoft.2016.06.014, 2016.
Shuai, P., Chen, X., Mital, U., Coon, E. T., and Dwivedi, D.: The effects of spatial and temporal resolution of gridded meteorological forcing on watershed hydrological responses, Hydrol. Earth Syst. Sci., 26, 2245–2276, https://doi.org/10.5194/hess-26-2245-2022, 2022.
Singh, V. P.: Effect of spatial and temporal variability in rainfall and
watershed characteristics on stream flow hydrograph, Hydrol. Process., 11,
1649–1669, https://doi.org/10.1002/(SICI)1099-1085(19971015)11:12<1649::AID-HYP495>3.0.CO;2-1, 1997.
Škrk, N., Serrano-Notivoli, R., Čufar, K., Merela, M., Črepinšek, Z., Kajfež Bogataj, L., and de Luis, M.: SLOCLIM: a high-resolution daily gridded precipitation and temperature dataset for Slovenia, Earth Syst. Sci. Data, 13, 3577–3592, https://doi.org/10.5194/essd-13-3577-2021, 2021.
Strachan, S. and Daly, C.: Testing the daily PRISM air temperature model on
semiarid mountain slopes, J. Geophys. Res.-Atmos., 122, 5697–5715,
https://doi.org/10.1002/2016JD025920, 2017.
Tapiador, F. J., Navarro, A., Moreno, R., Sánchez, J. L., and
García-Ortega, E.: Regional climate models: 30 years of dynamical
downscaling, Atmos. Res., 235, 104785,
https://doi.org/10.1016/j.atmosres.2019.104785, 2020.
Thornton, M. M., Shrestha, R., Wei, Y., Thornton, P. E., and Wilson, B. E.:
Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version
4, ORNL DAAC [data set], https://doi.org/10.3334/ORNLDAAC/1840, 2020.
Thornton, P. E., Shrestha, R., Thornton, M., Kao, S.-C., Wei, Y., and
Wilson, B. E.: Gridded daily weather data for North America with
comprehensive uncertainty quantification, Sci. Data, 8, 190,
https://doi.org/10.1038/s41597-021-00973-0, 2021.
Torre, V. and Poggio, T. A.: On Edge Detection,
IEEE T. Pattern Anal., PAMI-8, 147–163, https://doi.org/10.1109/TPAMI.1986.4767769,
1986.
U.S. Geological Survey: The National Map–New data delivery homepage,
advanced viewer, lidar visualization, Reston, VA,
https://doi.org/10.3133/fs20193032, 2019.
Vandal, T., Kodra, E., Ganguly, S., Michaelis, A., Nemani, R., and Ganguly,
A. R.: DeepSD: Generating High Resolution Climate Change Projections through
Single Image Super-Resolution, in: Proceedings of the 23rd ACM SIGKDD
International Conference on Knowledge Discovery and Data Mining – KDD '17,
the 23rd ACM SIGKDD International Conference, Halifax, NS, Canada, 13–17 August 2017,
1663–1672, https://doi.org/10.1145/3097983.3098004, 2017.
Vandal, T., Kodra, E., and Ganguly, A. R.: Intercomparison of machine
learning methods for statistical downscaling: the case of daily and extreme
precipitation, Theor. Appl. Climatol., 137, 557–570,
https://doi.org/10.1007/s00704-018-2613-3, 2019.
Vörösmarty, C. J., McIntyre, P. B., Gessner, M. O., Dudgeon, D.,
Prusevich, A., Green, P., Glidden, S., Bunn, S. E., Sullivan, C. A.,
Liermann, C. R., and Davies, P. M.: Global threats to human water security
and river biodiversity, Nature, 467, 555–561,
https://doi.org/10.1038/nature09440, 2010.
Xia, Y., Mitchell, K., Ek, M., Cosgrove, B., Sheffield, J., Luo, L., Alonge,
C., Wei, H., Meng, J., Livneh, B., Duan, Q., and Lohmann, D.:
Continental-scale water and energy flux analysis and validation for North
American Land Data Assimilation System project phase 2 (NLDAS-2): 2.
Validation of model-simulated streamflow, J. Geophys. Res.-Atmos., 117, D3110, https://doi.org/10.1029/2011JD016051, 2012.
Xie, P., Chen, M., Yang, S., Yatagai, A., Hayasaka, T., Fukushima, Y., and
Liu, C.: A Gauge-Based Analysis of Daily Precipitation over East Asia,
J. Hydrometeorol., 8, 607–626, https://doi.org/10.1175/JHM583.1,
2007.
Short summary
We present a new dataset that estimates small-scale variations in precipitation and temperature in mountainous terrain. The dataset is generated using a new machine learning framework that extracts relationships between climate and topography from existing coarse-scale datasets. The generated dataset is shown to capture small-scale variations more reliably than existing datasets and constitutes a valuable resource to model the water cycle in the mountains of Colorado, western United States.
We present a new dataset that estimates small-scale variations in precipitation and temperature...
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