Search Results - "Hydrology and earth system sciences"

Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores by Knoben, Wouter J. M, Freer, Jim E, Woods, Ross A

“…A traditional metric used in hydrology to summarize model performance is the Nash–Sutcliffe efficiency (NSE). Increasingly an alternative metric, the…”
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Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks by Kratzert, Frederik, Klotz, Daniel, Brenner, Claire, Schulz, Karsten, Herrnegger, Mathew

“…Rainfall–runoff modelling is one of the key challenges in the field of hydrology. Various approaches exist, ranging from physically based over conceptual to…”
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Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modelling over North America by Tarek, Mostafa, Brissette, François P, Arsenault, Richard

“…The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released its most advanced reanalysis product, the ERA5 dataset. It was designed and…”
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Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets by Kratzert, Frederik, Klotz, Daniel, Shalev, Guy, Klambauer, Günter, Hochreiter, Sepp, Nearing, Grey

“…Regional rainfall–runoff modeling is an old but still mostly outstanding problem in the hydrological sciences. The problem currently is that traditional…”
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Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling by Beck, Hylke E, Vergopolan, Noemi, Pan, Ming, Levizzani, Vincenzo, van Dijk, Albert I. J. M, Weedon, Graham P, Brocca, Luca, Pappenberger, Florian, Huffman, George J, Wood, Eric F

“…We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen…”
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MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data by Beck, Hylke E, van Dijk, Albert I. J. M, Levizzani, Vincenzo, Schellekens, Jaap, Miralles, Diego G, Martens, Brecht, de Roo, Ad

“…Current global precipitation (P) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present…”
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The International Soil Moisture Network: serving Earth system science for over a decade by Dorigo, Wouter, Himmelbauer, Irene, Aberer, Daniel, Schremmer, Lukas, Petrakovic, Ivana, Zappa, Luca, Preimesberger, Wolfgang, Xaver, Angelika, Annor, Frank, Ardö, Jonas, Baldocchi, Dennis, Bitelli, Marco, Blöschl, Günter, Bogena, Heye, Brocca, Luca, Calvet, Jean-Christophe, Camarero, J. Julio, Capello, Giorgio, Choi, Minha, Cosh, Michael C, van de Giesen, Nick, Hajdu, Istvan, Ikonen, Jaakko, Jensen, Karsten H, Kanniah, Kasturi Devi, de Kat, Ileen, Kirchengast, Gottfried, Kumar Rai, Pankaj, Kyrouac, Jenni, Larson, Kristine, Liu, Suxia, Loew, Alexander, Moghaddam, Mahta, Martínez Fernández, José, Mattar Bader, Cristian, Morbidelli, Renato, Musial, Jan P, Osenga, Elise, Palecki, Michael A, Pellarin, Thierry, Petropoulos, George P, Pfeil, Isabella, Powers, Jarrett, Robock, Alan, Rüdiger, Christoph, Rummel, Udo, Strobel, Michael, Su, Zhongbo, Sullivan, Ryan, Tagesson, Torbern, Varlagin, Andrej, Vreugdenhil, Mariette, Walker, Jeffrey, Wen, Jun, Wenger, Fred, Wigneron, Jean Pierre, Woods, Mel, Yang, Kun, Zeng, Yijian, Zhang, Xiang, Zreda, Marek, Dietrich, Stephan, Gruber, Alexander, van Oevelen, Peter, Wagner, Wolfgang, Scipal, Klaus, Drusch, Matthias, Sabia, Roberto

“…In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised…”
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Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS by Beck, Hylke E., Pan, Ming, Roy, Tirthankar, Weedon, Graham P., Pappenberger, Florian, Dijk, Albert I. J. M. van, Huffman, George J., Adler, Robert F., Wood, Eric F.

“…New precipitation (P) datasets are released regularly, following innovations in weather forecasting models, satellite retrieval methods, and multi-source…”
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Evaluation of 18 Satellite- and Model-Based Soil Moisture Products Using in Situ Measurements From 826 Sensors by Beck, Hylke E., Pan, Ming, Reichle, Rolf H., Dorigo, Wouter A., Hahn, Sebastian, Sheffield, Justin, Karthikeyan, Lanka, Balsamo, Gianpaolo, Parinussa, Robert M., Dijk, Albert I.J.M. van, Du, Jinyang, Kimball, John S., Vergopolan, Noemi, Wood, Eric F.

“…Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation…”
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The 2010–2015 megadrought in central Chile: impacts on regional hydroclimate and vegetation by Garreaud, Rene D, Alvarez-Garreton, Camila, Barichivich, Jonathan, Boisier, Juan Pablo, Christie, Duncan, Galleguillos, Mauricio, LeQuesne, Carlos, McPhee, James, Zambrano-Bigiarini, Mauricio

“…Since 2010 an uninterrupted sequence of dry years, with annual rainfall deficits ranging from 25 to 45 %, has prevailed in central Chile (western South…”
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A review of the (Revised) Universal Soil Loss Equation ((R)USLE): with a view to increasing its global applicability and improving soil loss estimates by Benavidez, Rubianca, Jackson, Bethanna, Maxwell, Deborah, Norton, Kevin

“…Soil erosion is a major problem around the world because of its effects on soil productivity, nutrient loss, siltation in water bodies, and degradation of…”
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The CAMELS data set: catchment attributes and meteorology for large-sample studies by Addor, Nans, Newman, Andrew J, Mizukami, Naoki, Clark, Martyn P

“…We present a new data set of attributes for 671 catchments in the contiguous United States (CONUS) minimally impacted by human activities. This complements the…”
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Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX) by Wunsch, Andreas, Liesch, Tanja, Broda, Stefan

“…It is now well established to use shallow artificial neural networks (ANNs) to obtain accurate and reliable groundwater level forecasts, which are an important…”
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Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management by Slater, Louise J, Anderson, Bailey, Buechel, Marcus, Dadson, Simon, Han, Shasha, Harrigan, Shaun, Kelder, Timo, Kowal, Katie, Lees, Thomas, Matthews, Tom, Murphy, Conor, Wilby, Robert L

“…Hydroclimatic extremes such as intense rainfall, floods, droughts, heatwaves, and wind or storms have devastating effects each year. One of the key challenges…”
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The Future of Earth Observation in Hydrology by McCabe, Matthew F, Rodell, Matthew, Alsdorf, Douglas E, Miralles, Diego G, Uijlenhoet, Remko, Wagner, Wolfgang, Lucieer, Arko, Houborg, Rasmus, Verhoest, Niko E C, Franz, Trenton E, Shi, Jiancheng, Gao, Huilin, Wood, Eric F

“…In just the past five years, the field of Earth observation has progressed beyond the offerings of conventional space agency based platforms to include a…”
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The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset by Alvarez-Garreton, Camila, Mendoza, Pablo A, Boisier, Juan Pablo, Addor, Nans, Galleguillos, Mauricio, Zambrano-Bigiarini, Mauricio, Lara, Antonio, Puelma, Cristóbal, Cortes, Gonzalo, Garreaud, Rene, McPhee, James, Ayala, Alvaro

“…We introduce the first catchment dataset for large sample studies in Chile. This dataset includes 516 catchments; it covers particularly wide latitude (17.8 to…”
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Deep learning methods for flood mapping: a review of existing applications and future research directions by Bentivoglio, Roberto, Isufi, Elvin, Jonkman, Sebastian Nicolaas, Taormina, Riccardo

“…Deep learning techniques have been increasingly used in flood management to overcome the limitations of accurate, yet slow, numerical models and to improve the…”
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ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better? by Albergel, Clement, Dutra, Emanuel, Munier, Simon, Calvet, Jean-Christophe, Munoz-Sabater, Joaquin, de Rosnay, Patricia, Balsamo, Gianpaolo

“…The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released the first 7-year segment of its latest atmospheric reanalysis: ERA-5 over the…”
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Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network by Gauch, Martin, Kratzert, Frederik, Klotz, Daniel, Nearing, Grey, Lin, Jimmy, Hochreiter, Sepp

“…Long Short-Term Memory (LSTM) networks have been applied to daily discharge prediction with remarkable success. Many practical applications, however, require…”
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Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling by Pan, Shufen, Pan, Naiqing, Tian, Hanqin, Friedlingstein, Pierre, Sitch, Stephen, Shi, Hao, Arora, Vivek K., Haverd, Vanessa, Jain, Atul K., Kato, Etsushi, Lienert, Sebastian, Lombardozzi, Danica, Nabel, Julia E. M. S., Ottlé, Catherine, Poulter, Benjamin, Zaehle, Sönke

“…Evapotranspiration (ET) is critical in linking global water, carbon and energy cycles. However, direct measurement of global terrestrial ET is not feasible…”
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