Articles | Volume 22, issue 12
https://doi.org/10.5194/hess-22-6611-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-22-6611-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Dec 2018
Research article |
| 21 Dec 2018
| 21 Dec 2018
Developing a drought-monitoring index for the contiguous US using SMAP
Department of Civil and Environmental Engineering, Princeton University, 59 Olden St, Princeton, NJ 08540, USA
Eric F. Wood
Department of Civil and Environmental Engineering, Princeton University, 59 Olden St, Princeton, NJ 08540, USA
Department of Civil and Environmental Engineering, Princeton University, 59 Olden St, Princeton, NJ 08540, USA
Related authors
Sara Sadri, James S. Famiglietti, Ming Pan, Hylke E. Beck, Aaron Berg, and Eric F. Wood
Hydrol. Earth Syst. Sci., 26, 5373–5390, https://doi.org/10.5194/hess-26-5373-2022, https://doi.org/10.5194/hess-26-5373-2022, 2022
Short summary
Short summary
A farm-scale hydroclimatic machine learning framework to advise farmers was developed. FarmCan uses remote sensing data and farmers' input to forecast crop water deficits. The 8 d composite variables are better than daily ones for forecasting water deficit. Evapotranspiration (ET) and potential ET are more effective than soil moisture at predicting crop water deficit. FarmCan uses a crop-specific schedule to use surface or root zone soil moisture.
S. Sadri, J. Kam, and J. Sheffield
Hydrol. Earth Syst. Sci., 20, 633–649, https://doi.org/10.5194/hess-20-633-2016, https://doi.org/10.5194/hess-20-633-2016, 2016
Short summary
Short summary
Low flows are a critical part of the river flow regime but little is known about how they are changing in response to human influences and climate. We analyzed low flow records across the eastern US and identified sites that were minimally influenced by human activities. We found a general increasing trend in low flows across the northeast and decreasing trend across the southeast that are likely driven by changes in climate. The results have implications for how we manage our water resources.
Liqing Peng, Justin Sheffield, Zhongwang Wei, Michael Ek, and Eric F. Wood
Earth Syst. Dynam., 15, 1277–1300, https://doi.org/10.5194/esd-15-1277-2024, https://doi.org/10.5194/esd-15-1277-2024, 2024
Short summary
Short summary
Integrating evaporative demand into drought indicators is effective, but the choice of method and the effectiveness of surface features remain undocumented. We evaluate various methods and surface features for predicting soil moisture dynamics. Using minimal ancillary information alongside meteorological and vegetation data, we develop a simple land-cover-based method that improves soil moisture drought predictions, especially in forests, showing promise for better real-time drought forecasting.
Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 17, 7181–7198, https://doi.org/10.5194/gmd-17-7181-2024, https://doi.org/10.5194/gmd-17-7181-2024, 2024
Short summary
Short summary
Accurate hydrologic modeling is vital to characterizing water cycle responses to climate change. For the first time at this scale, we use differentiable physics-informed machine learning hydrologic models to simulate rainfall–runoff processes for 3753 basins around the world and compare them with purely data-driven and traditional modeling approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
Lu Su, Dennis P. Lettenmaier, Ming Pan, and Benjamin Bass
Hydrol. Earth Syst. Sci., 28, 3079–3097, https://doi.org/10.5194/hess-28-3079-2024, https://doi.org/10.5194/hess-28-3079-2024, 2024
Short summary
Short summary
We fine-tuned the variable infiltration capacity (VIC) and Noah-MP models across 263 river basins in the Western US. We developed transfer relationships to similar basins and extended the fine-tuned parameters to ungauged basins. Both models performed best in humid areas, and the skills improved post-calibration. VIC outperforms Noah-MP in all but interior dry basins following regionalization. VIC simulates annual mean streamflow and high flow well, while Noah-MP performs better for low flows.
Sara Sadri, James S. Famiglietti, Ming Pan, Hylke E. Beck, Aaron Berg, and Eric F. Wood
Hydrol. Earth Syst. Sci., 26, 5373–5390, https://doi.org/10.5194/hess-26-5373-2022, https://doi.org/10.5194/hess-26-5373-2022, 2022
Short summary
Short summary
A farm-scale hydroclimatic machine learning framework to advise farmers was developed. FarmCan uses remote sensing data and farmers' input to forecast crop water deficits. The 8 d composite variables are better than daily ones for forecasting water deficit. Evapotranspiration (ET) and potential ET are more effective than soil moisture at predicting crop water deficit. FarmCan uses a crop-specific schedule to use surface or root zone soil moisture.
Noemi Vergopolan, Sitian Xiong, Lyndon Estes, Niko Wanders, Nathaniel W. Chaney, Eric F. Wood, Megan Konar, Kelly Caylor, Hylke E. Beck, Nicolas Gatti, Tom Evans, and Justin Sheffield
Hydrol. Earth Syst. Sci., 25, 1827–1847, https://doi.org/10.5194/hess-25-1827-2021, https://doi.org/10.5194/hess-25-1827-2021, 2021
Short summary
Short summary
Drought monitoring and yield prediction often rely on coarse-scale hydroclimate data or (infrequent) vegetation indexes that do not always indicate the conditions farmers face in the field. Consequently, decision-making based on these indices can often be disconnected from the farmer reality. Our study focuses on smallholder farming systems in data-sparse developing countries, and it shows how field-scale soil moisture can leverage and improve crop yield prediction and drought impact assessment.
Hylke E. Beck, Ming Pan, Diego G. Miralles, Rolf H. Reichle, Wouter A. Dorigo, Sebastian Hahn, Justin Sheffield, Lanka Karthikeyan, Gianpaolo Balsamo, Robert M. Parinussa, Albert I. J. M. van Dijk, Jinyang Du, John S. Kimball, Noemi Vergopolan, and Eric F. Wood
Hydrol. Earth Syst. Sci., 25, 17–40, https://doi.org/10.5194/hess-25-17-2021, https://doi.org/10.5194/hess-25-17-2021, 2021
Short summary
Short summary
We evaluated the largest and most diverse set of surface soil moisture products ever evaluated in a single study. We found pronounced differences in performance among individual products and product groups. Our results provide guidance to choose the most suitable product for a particular application.
Peng Ji, Xing Yuan, Feng Ma, and Ming Pan
Hydrol. Earth Syst. Sci., 24, 5439–5451, https://doi.org/10.5194/hess-24-5439-2020, https://doi.org/10.5194/hess-24-5439-2020, 2020
Short summary
Short summary
By performing high-resolution land surface modeling driven by the latest CMIP6 climate models, we find both the dry streamflow extreme over the drought-prone Yellow River headwater and the wet streamflow extreme over the flood-prone Yangtze River headwater will increase under 1.5, 2.0 and 3.0 °C global warming levels and emphasize the importance of considering ecological changes (i.e., vegetation greening and CO2 physiological forcing) in the hydrological projection.
Colby K. Fisher, Ming Pan, and Eric F. Wood
Hydrol. Earth Syst. Sci., 24, 293–305, https://doi.org/10.5194/hess-24-293-2020, https://doi.org/10.5194/hess-24-293-2020, 2020
Short summary
Short summary
Poorly monitored river flows in many regions of the world have been hindering our ability to accurately estimate global water usage. In this paper we present a method to derive continuous records of streamflow from a set of in situ gauges. Applying this method to the Ohio River basin, we found that we could reliably generate estimates of streamflow throughout the basin using only a small set of streamflow gauges, which can be useful for global river basins where we do not have good observations.
Hylke E. Beck, Ming Pan, Tirthankar Roy, Graham P. Weedon, Florian Pappenberger, Albert I. J. M. van Dijk, George J. Huffman, Robert F. Adler, and Eric F. Wood
Hydrol. Earth Syst. Sci., 23, 207–224, https://doi.org/10.5194/hess-23-207-2019, https://doi.org/10.5194/hess-23-207-2019, 2019
Short summary
Short summary
We conducted a comprehensive evaluation of 26 precipitation datasets for the US using the Stage-IV gauge-radar dataset as a reference. The best overall performance was obtained by MSWEP V2.2, underscoring the importance of applying daily gauge corrections and accounting for reporting times. Our findings can be used as a guide to choose the most suitable precipitation dataset for a particular application.
Andreas Marx, Rohini Kumar, Stephan Thober, Oldrich Rakovec, Niko Wanders, Matthias Zink, Eric F. Wood, Ming Pan, Justin Sheffield, and Luis Samaniego
Hydrol. Earth Syst. Sci., 22, 1017–1032, https://doi.org/10.5194/hess-22-1017-2018, https://doi.org/10.5194/hess-22-1017-2018, 2018
Short summary
Short summary
Hydrological low flows are affected under different levels of future global warming (i.e. 1.5, 2, and 3 K). The multi-model ensemble results show that the change signal amplifies with increasing warming levels. Low flows decrease in the Mediterranean, while they increase in the Alpine and Northern regions. The changes in low flows are significant for regions with relatively large change signals and under higher levels of warming. Adaptation should make use of change and uncertainty information.
Yu Zhang, Ming Pan, Justin Sheffield, Amanda L. Siemann, Colby K. Fisher, Miaoling Liang, Hylke E. Beck, Niko Wanders, Rosalyn F. MacCracken, Paul R. Houser, Tian Zhou, Dennis P. Lettenmaier, Rachel T. Pinker, Janice Bytheway, Christian D. Kummerow, and Eric F. Wood
Hydrol. Earth Syst. Sci., 22, 241–263, https://doi.org/10.5194/hess-22-241-2018, https://doi.org/10.5194/hess-22-241-2018, 2018
Short summary
Short summary
A global data record for all four terrestrial water budget variables (precipitation, evapotranspiration, runoff, and total water storage change) at 0.5° resolution and monthly scale for the period of 1984–2010 is developed by optimally merging a series of remote sensing products, in situ measurements, land surface model outputs, and atmospheric reanalysis estimates and enforcing the mass balance of water. Initial validations show the data record is reliable for climate related analysis.
Hylke E. Beck, Noemi Vergopolan, Ming Pan, Vincenzo Levizzani, Albert I. J. M. van Dijk, Graham P. Weedon, Luca Brocca, Florian Pappenberger, George J. Huffman, and Eric F. Wood
Hydrol. Earth Syst. Sci., 21, 6201–6217, https://doi.org/10.5194/hess-21-6201-2017, https://doi.org/10.5194/hess-21-6201-2017, 2017
Short summary
Short summary
This study represents the most comprehensive global-scale precipitation dataset evaluation to date. We evaluated 13 uncorrected precipitation datasets using precipitation observations from 76 086 gauges, and 9 gauge-corrected ones using hydrological modeling for 9053 catchments. Our results highlight large differences in estimation accuracy, and hence, the importance of precipitation dataset selection in both research and operational applications.
Matthew F. McCabe, Matthew Rodell, Douglas E. Alsdorf, Diego G. Miralles, Remko Uijlenhoet, Wolfgang Wagner, Arko Lucieer, Rasmus Houborg, Niko E. C. Verhoest, Trenton E. Franz, Jiancheng Shi, Huilin Gao, and Eric F. Wood
Hydrol. Earth Syst. Sci., 21, 3879–3914, https://doi.org/10.5194/hess-21-3879-2017, https://doi.org/10.5194/hess-21-3879-2017, 2017
Short summary
Short summary
We examine the opportunities and challenges that technological advances in Earth observation will present to the hydrological community. From advanced space-based sensors to unmanned aerial vehicles and ground-based distributed networks, these emergent systems are set to revolutionize our understanding and interpretation of hydrological and related processes.
Di Tian, Eric F. Wood, and Xing Yuan
Hydrol. Earth Syst. Sci., 21, 1477–1490, https://doi.org/10.5194/hess-21-1477-2017, https://doi.org/10.5194/hess-21-1477-2017, 2017
Short summary
Short summary
This study evaluated dynamic climate model sub-seasonal forecasts for important precipitation and temperature indices over the contiguous United States. The presence of active Madden-Julian Oscillation (MJO) events improved weekly mean precipitation forecast skill over most regions. Sub-seasonal forecast indices calculated from the daily forecast showed higher skill than temporally downscaled forecasts, suggesting the usefulness of the daily forecast for sub-seasonal hydrological forecasting.
D. G. Miralles, C. Jiménez, M. Jung, D. Michel, A. Ershadi, M. F. McCabe, M. Hirschi, B. Martens, A. J. Dolman, J. B. Fisher, Q. Mu, S. I. Seneviratne, E. F. Wood, and D. Fernández-Prieto
Hydrol. Earth Syst. Sci., 20, 823–842, https://doi.org/10.5194/hess-20-823-2016, https://doi.org/10.5194/hess-20-823-2016, 2016
Short summary
Short summary
The WACMOS-ET project aims to advance the development of land evaporation estimates on global and regional scales. Evaluation of current evaporation data sets on the global scale showed that they manifest large dissimilarities during conditions of water stress and drought and deficiencies in the way evaporation is partitioned into several components. Different models perform better under different conditions, highlighting the potential for considering biome- or climate-specific model ensembles.
D. Michel, C. Jiménez, D. G. Miralles, M. Jung, M. Hirschi, A. Ershadi, B. Martens, M. F. McCabe, J. B. Fisher, Q. Mu, S. I. Seneviratne, E. F. Wood, and D. Fernández-Prieto
Hydrol. Earth Syst. Sci., 20, 803–822, https://doi.org/10.5194/hess-20-803-2016, https://doi.org/10.5194/hess-20-803-2016, 2016
Short summary
Short summary
In this study a common reference input data set from satellite and in situ data is used to run four established evapotranspiration (ET) algorithms using sub-daily and daily input on a tower scale as a testbed for a global ET product. The PT-JPL model and GLEAM provide the best performance for satellite and in situ forcing as well as for the different temporal resolutions. PM-MOD and SEBS perform less well: the PM-MOD model generally underestimates, while SEBS generally overestimates ET.
S. Sadri, J. Kam, and J. Sheffield
Hydrol. Earth Syst. Sci., 20, 633–649, https://doi.org/10.5194/hess-20-633-2016, https://doi.org/10.5194/hess-20-633-2016, 2016
Short summary
Short summary
Low flows are a critical part of the river flow regime but little is known about how they are changing in response to human influences and climate. We analyzed low flow records across the eastern US and identified sites that were minimally influenced by human activities. We found a general increasing trend in low flows across the northeast and decreasing trend across the southeast that are likely driven by changes in climate. The results have implications for how we manage our water resources.
M. F. McCabe, A. Ershadi, C. Jimenez, D. G. Miralles, D. Michel, and E. F. Wood
Geosci. Model Dev., 9, 283–305, https://doi.org/10.5194/gmd-9-283-2016, https://doi.org/10.5194/gmd-9-283-2016, 2016
Short summary
Short summary
In an effort to develop a global terrestrial evaporation product, four models were forced using both a tower and grid-based data set. Comparisons against flux-tower observations from different biome and land cover types show considerable inter-model variability and sensitivity to forcing type. Results suggest that no single model is able to capture expected flux patterns and response. It is suggested that a multi-model ensemble is likely to provide a more stable long-term flux estimate.
W. Zhan, M. Pan, N. Wanders, and E. F. Wood
Hydrol. Earth Syst. Sci., 19, 4275–4291, https://doi.org/10.5194/hess-19-4275-2015, https://doi.org/10.5194/hess-19-4275-2015, 2015
N. W. Chaney, J. D. Herman, P. M. Reed, and E. F. Wood
Hydrol. Earth Syst. Sci., 19, 3239–3251, https://doi.org/10.5194/hess-19-3239-2015, https://doi.org/10.5194/hess-19-3239-2015, 2015
Short summary
Short summary
Land surface modeling is playing an increasing role in global monitoring and prediction of extreme hydrologic events. However, uncertainties in parameter identifiability limit the reliability of model predictions. This study makes use of petascale computing to perform a comprehensive evaluation of land surface modeling for global flood and drought monitoring and suggests paths forward to overcome the challenges posed by parameter uncertainty.
K. Guan, S. P. Good, K. K. Caylor, H. Sato, E. F. Wood, and H. Li
Biogeosciences, 11, 6939–6954, https://doi.org/10.5194/bg-11-6939-2014, https://doi.org/10.5194/bg-11-6939-2014, 2014
Short summary
Short summary
Climate change is expected to modify the way that rainfall arrives, namely the frequency and intensity of rainfall events and rainy season length. Yet, the quantification of the impact of these possible rainfall changes across large biomes is lacking. Our study fills this gap by developing a new modeling framework, applying it to continental Africa. We show that African ecosystems are highly sensitive to these rainfall variabilities, with esp. large sensitivity to changes in rainy season length.
M. Pan and E. F. Wood
Hydrol. Earth Syst. Sci., 17, 4577–4588, https://doi.org/10.5194/hess-17-4577-2013, https://doi.org/10.5194/hess-17-4577-2013, 2013
B. Mueller, M. Hirschi, C. Jimenez, P. Ciais, P. A. Dirmeyer, A. J. Dolman, J. B. Fisher, M. Jung, F. Ludwig, F. Maignan, D. G. Miralles, M. F. McCabe, M. Reichstein, J. Sheffield, K. Wang, E. F. Wood, Y. Zhang, and S. I. Seneviratne
Hydrol. Earth Syst. Sci., 17, 3707–3720, https://doi.org/10.5194/hess-17-3707-2013, https://doi.org/10.5194/hess-17-3707-2013, 2013
S. Shukla, J. Sheffield, E. F. Wood, and D. P. Lettenmaier
Hydrol. Earth Syst. Sci., 17, 2781–2796, https://doi.org/10.5194/hess-17-2781-2013, https://doi.org/10.5194/hess-17-2781-2013, 2013
Related subject area
Subject: Engineering Hydrology | Techniques and Approaches: Uncertainty analysis
A more comprehensive uncertainty framework for historical flood frequency analysis: a 500-year long case study
Bayesian calibration of a flood simulator using binary flood extent observations
Intercomparison of global reanalysis precipitation for flood risk modelling
Seamless streamflow forecasting at daily to monthly scales: MuTHRE lets you have your cake and eat it too
An uncertainty partition approach for inferring interactive hydrologic risks
Predicting discharge capacity of vegetated compound channels: uncertainty and identifiability of one-dimensional process-based models
Uncertainty quantification of floodplain friction in hydrodynamic models
Technical note: Assessment of observation quality for data assimilation in flood models
Sedimentation monitoring including uncertainty analysis in complex floodplains: a case study in the Mekong Delta
Assessing the impact of uncertainty on flood risk estimates with reliability analysis using 1-D and 2-D hydraulic models
The transferability of hydrological models under nonstationary climatic conditions
Why hydrological predictions should be evaluated using information theory
Spatial uncertainty assessment in modelling reference evapotranspiration at regional scale
Confidence intervals for the coefficient of L-variation in hydrological applications
Possibilistic uncertainty analysis of a conceptual model of snowmelt runoff
Mathieu Lucas, Michel Lang, Benjamin Renard, and Jérôme Le Coz
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-50, https://doi.org/10.5194/hess-2024-50, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
This paper proposes Binomial models for the inclusion of historical data into flood frequency analysis, which recognize the uncertain nature of the perception threshold and the starting date of the historical period. The procedure is applied to a long systematic series 1816–2020 and 13 historical floods over 1500–1815 (Rhône River, Beaucaire, France). Inclusion of historical floods reduces the uncertainty flood quantiles, even when only the number of perception threshold exceedances is known.
Mariano Balbi and David Charles Bonaventure Lallemant
Hydrol. Earth Syst. Sci., 27, 1089–1108, https://doi.org/10.5194/hess-27-1089-2023, https://doi.org/10.5194/hess-27-1089-2023, 2023
Short summary
Short summary
We proposed a methodology to obtain useful and robust probabilistic predictions from computational flood simulators using satellite-borne flood extent observations. We developed a Bayesian framework to obtain the uncertainty in roughness parameters, in observations errors, and in simulator structural deficiencies. We found that it can yield improvements in predictions relative to current methodologies and can potentially lead to consistent ways of combining data from different sources.
Fergus McClean, Richard Dawson, and Chris Kilsby
Hydrol. Earth Syst. Sci., 27, 331–347, https://doi.org/10.5194/hess-27-331-2023, https://doi.org/10.5194/hess-27-331-2023, 2023
Short summary
Short summary
Reanalysis datasets are increasingly used to drive flood models, especially for continental and global analysis. We investigate the impact of using four reanalysis products on simulations of past flood events. All reanalysis products underestimated the number of buildings inundated, compared to a benchmark national dataset. These findings show that while global reanalyses provide a useful resource for flood modelling where no other data are available, they may underestimate impact in some cases.
David McInerney, Mark Thyer, Dmitri Kavetski, Richard Laugesen, Fitsum Woldemeskel, Narendra Tuteja, and George Kuczera
Hydrol. Earth Syst. Sci., 26, 5669–5683, https://doi.org/10.5194/hess-26-5669-2022, https://doi.org/10.5194/hess-26-5669-2022, 2022
Short summary
Short summary
Streamflow forecasts a day to a month ahead are highly valuable for water resources management. Current practice often develops forecasts for specific lead times and aggregation timescales. In contrast, a single, seamless forecast can serve multiple lead times/timescales. This study shows seamless forecasts can match the performance of forecasts developed specifically at the monthly scale, while maintaining quality at other lead times. Hence, users need not sacrifice capability for performance.
Yurui Fan, Kai Huang, Guohe Huang, Yongping Li, and Feng Wang
Hydrol. Earth Syst. Sci., 24, 4601–4624, https://doi.org/10.5194/hess-24-4601-2020, https://doi.org/10.5194/hess-24-4601-2020, 2020
Adam Kiczko, Kaisa Västilä, Adam Kozioł, Janusz Kubrak, Elżbieta Kubrak, and Marcin Krukowski
Hydrol. Earth Syst. Sci., 24, 4135–4167, https://doi.org/10.5194/hess-24-4135-2020, https://doi.org/10.5194/hess-24-4135-2020, 2020
Short summary
Short summary
The study compares the uncertainty of discharge curves for vegetated channels, calculated using several methods, including the simplest ones, based on the Manning formula and advanced approaches, providing a detailed physical representation of the channel flow processes. Parameters of each method were identified for the same data sets. The outcomes of the study include the widths of confidence intervals, showing which method was the most successful in explaining observations.
Guilherme Luiz Dalledonne, Rebekka Kopmann, and Thomas Brudy-Zippelius
Hydrol. Earth Syst. Sci., 23, 3373–3385, https://doi.org/10.5194/hess-23-3373-2019, https://doi.org/10.5194/hess-23-3373-2019, 2019
Short summary
Short summary
This study presents how the concept of uncertainty quantification can be applied to river engineering problems and how important it is to understand the limitations of numerical models from a probabilistic point of view. We investigated floodplain friction formulations using different uncertainty quantification methods and estimated their contribution in the final results. The analysis of uncertainties is planned to be integrated in future projects and also extended to more complex scenarios.
Joanne A. Waller, Javier García-Pintado, David C. Mason, Sarah L. Dance, and Nancy K. Nichols
Hydrol. Earth Syst. Sci., 22, 3983–3992, https://doi.org/10.5194/hess-22-3983-2018, https://doi.org/10.5194/hess-22-3983-2018, 2018
N. V. Manh, B. Merz, and H. Apel
Hydrol. Earth Syst. Sci., 17, 3039–3057, https://doi.org/10.5194/hess-17-3039-2013, https://doi.org/10.5194/hess-17-3039-2013, 2013
L. Altarejos-García, M. L. Martínez-Chenoll, I. Escuder-Bueno, and A. Serrano-Lombillo
Hydrol. Earth Syst. Sci., 16, 1895–1914, https://doi.org/10.5194/hess-16-1895-2012, https://doi.org/10.5194/hess-16-1895-2012, 2012
C. Z. Li, L. Zhang, H. Wang, Y. Q. Zhang, F. L. Yu, and D. H. Yan
Hydrol. Earth Syst. Sci., 16, 1239–1254, https://doi.org/10.5194/hess-16-1239-2012, https://doi.org/10.5194/hess-16-1239-2012, 2012
S. V. Weijs, G. Schoups, and N. van de Giesen
Hydrol. Earth Syst. Sci., 14, 2545–2558, https://doi.org/10.5194/hess-14-2545-2010, https://doi.org/10.5194/hess-14-2545-2010, 2010
G. Buttafuoco, T. Caloiero, and R. Coscarelli
Hydrol. Earth Syst. Sci., 14, 2319–2327, https://doi.org/10.5194/hess-14-2319-2010, https://doi.org/10.5194/hess-14-2319-2010, 2010
A. Viglione
Hydrol. Earth Syst. Sci., 14, 2229–2242, https://doi.org/10.5194/hess-14-2229-2010, https://doi.org/10.5194/hess-14-2229-2010, 2010
A. P. Jacquin
Hydrol. Earth Syst. Sci., 14, 1681–1695, https://doi.org/10.5194/hess-14-1681-2010, https://doi.org/10.5194/hess-14-1681-2010, 2010
Cited articles
AMS: Drought, available at: http://glossary.ametsoc.org/wiki/Drought
(last access: 30 April 2018), 2012. a
Brocca, L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner, W.,
Dorigo, W., Matgen, P., Martinez-Fernandez, J., Llorens, P., Latron, J.,
Martin, C., and Bittelli, M.: Soil moisture estimation through ASCAT and
AMSR-E sensors: An intercomparison and validation study across
Europe, Remote Sens. Environ., 115, 3390–3408,
https://doi.org/10.1016/j.rse.2011.08.003, 2010. a
Cai, X., Pan, M., Chaney, N. W., Colliander, A., Misra, S., Cosh, M. H., Crow,
W. T., Jackson, T. J., and Wood, E. F.: Validation of SMAP soil moisture
for the SMAPVEX15 field campaign using a hyper-resolution model, Water
Resour. Res., 53, 3013–3028, 2017. a
California Dept. of Water Resources: Wells, available at:
https://water.ca.gov/Programs/Groundwater-Management/Wells, last
access: 25 April 2018. a
Entekhabi, D., Rodriguez-Iturbe, I., and Castelli, F.: Mutual interaction of
soil moisture state and atmospheric processes, J. Hydrol., 184,
3–17, 1996. a
Entekhabi, D., Njoku, E. G., O'Neill, P. E., Kellogg, K. H., Crow, W. T.,
Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J., Johnson, J.,
Kimball, J., Piepmeier, J. R., Koster, R. D., Martin, N., McDonald, K. C.,
Moghaddam, M., Moran, S., Reichle, R., Shi, J. C., Spencer, M. W., Thurman,
S. W., Tsang, L., and Van Zyl, J.: The Soil Moisture Active Passive
(SMAP) Mission Proc., IEEE, 98, 704–716,
https://doi.org/10.1109/JPROC.2010.2043918, 2010. a, b
Friedman, D. G.: The prediction of long-continuing drought in south and
southwest Texas, Occasional Papers in Meteorology, p. 182, the Travelers
Weather Research Center, Hartford, CT, USA, 1957. a
Howitt, R. E., Medellin-Azuara, J., MacEwan, D., Lund, J. R., and Daniel, A.:
Economic Impact of the 2015 Drought on Farm Revenue and Employment Sumner,
Agricultural and Resource Update, Giannini Foundation of Agricultural
Economics, University of California, Davis, USA, 2015. a
Kerr, Y. H., Waldteufel, P., Richaume, P., Wigneron, J. P., Ferrazzoli, P.,
Mahmoodi, A., Al Bitar, A., Cabot, F., Gruhier, C., Juglea, S. E., Leroux,
D., Mialon, A., and Delwart, S.: The SMOS Soil Moisture Retrieval
Algorithm, IEEE T. Geosci. Remote, 50, 1384–1403,
https://doi.org/10.1109/TGRS.2012.2184548, 2012. a
Kerr, Y. H., Al-Yaari, A., Rodriguez-Fernandez, N., Parrens, M., Molero, B.,
Leroux, D., Bircher, S., Mahmoodi, A., Mialon, A., Richaume, P., Delwart, S.,
Pellarin, A. A. B. T., Bindlish, R., Rudiger, T. J. C., Waldteufel, P.,
Mecklenburg, S., and Wigneron, J.: Overview of SMOS performance in terms of
global soil moisture monitoring after six years in operation, Remote Sens.
Environ., 180, 40–63, https://doi.org/10.1016/j.rse.2016.02.042, 2016. a
Lawston, P. M., Santanello Jr., J. A., and Kumar, S. V.: Irrigation Signals
Detected
From SMAP Soil Moisture Retrievals, Geophys. Res. Lett., 44,
11860–11867, 2017. a
Lindgren, B.: Statistical Theory, Mac-millan, New York, USA, 1962. a
Liu, Q., Reichle, R. H., Bindlish, R., Cosh, M. H., Crow, W. T., de Jeu, R.,
Lannoy, G. J. M. D., Huffman, G. J., and Jackson, T. J.: The contributions of
precipitation and soil moisture observations to the skill of soil moisture
estimates in a land data assimilation system, J. Hydrometeorol.,
12, 750–765, 2011. a
Martinez-Fernandez, J., Gonzalez-Zamora, A., Sanchez, N., and A Gumuzzio, .:
Satellite soil moisture for agricultural drought monitoring: Assessment of
the SMOS derived Soil Water Deficit Index (vol. 177, pg. 277, 2016), Remote
Sens. Environ., 183, 368–368, 2016. a
Mo, K. C. and Chelliah, M.: The modified Palmer Drought Severity Index
based on the NCEP North American Regional Reanalysis, J. Appl.
Meteor. Climatol., 45, 1362–1375, 2006. a
NCAR: The Climate Data Guide: Standardized Precipitation Index (SPI),
available at:
https://climatedataguide.ucar.edu/climate-data/standardized-precipitation-index-spi,
last access: 2 April 2018. a
NDMC: Groundwater and Soil Moisture Conditions from GRACE Data Assimilation,
the National Drought Mitigation Center University of Nebraska-Lincoln,
available at: http://nasagrace.unl.edu/Archive.aspx, last access:
17 December 2018b. a
NIST: Beta Distribution, available at:
http://www.itl.nist.gov/div898/handbook/eda/section3/eda366h.htm (last
access: 2 April 2018), 2013. a
NOAA: NLDAS Drought Monitor Soil Moisture, available at:
http://www.emc.ncep.noaa.gov/mmb/nldas/drought/, last access:
5 February 2018a. a
O'Neill, P., Chan, S., Njoku, E. G., Jackson, T., and Bindlish, R.: SMAP L2
Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture,
Distributed Active Archive Center Version 4, NASA National Snow and Ice Data
Center, Boulder, Colo., USA, https://doi.org/10.5067/XPJTJT812XFY, 2016. a
Pan, M., Li, H., and Wood, E.: Assessing the skill of satellite-based
precipitation estimates in hydrologic applications, Water Resour. Res., 46,
W09535, https://doi.org/10.1029/2009WR008290, 2010. a
Parinussa, R. M., Holmes, T. R. H., Wanders, N., Dorigo, W. A., and de Jeu, R.
A. M.: A Preliminary Study toward Consistent Soil Moisture from AMSR2, J.
Hydromet., 16, 932–947, https://doi.org/10.1175/JHM-D-13-0200.1, 2015. a
Princeton University Hydrology:
http://stream.princeton.edu/CONUSFDM/WEBPAGE/interface.php?locale=en
(last access: 15 December 2018), 2013. a
Reichle, R. H., Koster, R. D., Lannoy, G. J. M. D., Forman, B. A., Liu, Q.,
Mahanama, S. P. P., and Toure, A.: Assessment and enhancement of MERRA land
surface hydrology estimates, J. Climate, 24, 6322–6338, 2011. a
Reichle, R. H., Lucchesi, R., Ardizzone, J. V., Kim, G., Smith, E. B., and
Weiss,
B. H.: Soil Moisture Active Passive (SMAP) Mission Level 4
Surface and Root Zone Soil Moisture (L4SM) Product
Specification Document, Tech. Rep. 10 (Version 1.4), NASA Goddard Space
Flight Center, Greenbelt, MD, USA, 2015. a
Rienecker, M., Suarez, M. J., Todling, R., Bacmeister, J., Takacs, L., Liu,
H.-C., Gu, W., Sienkiewicz, M., Koster, R. D., Gelaro, R., Stajner, I., and
Nielsen, J. E.: The GEOS-5 Data Assimilation System –
Documentation of Versions 5.0.1, 5.1.0, and 5.2.0., NASA Technical Report
Series on Global Modeling and Data Assimilation NASA/TM-2008-104606, NASA,
vol. 28, 101 pp., 2008. a
Robock, A., Vinnikov, K., Srinivasa, G., Entin, J., Hollinger, S., Speranskaya,
N., Liu, S., and Namkhai, A.: The Global Soil Moisture Data Bank, B. Am.
Meteorol. Soc., 81, 1281–1299, 2000. a
Sheffield, J., Livneh, B., and Wood, E. F.: Representation of terrestrial
hydrology and large scale drought of the Continental U.S. from the
North American Regional Reanalysis, J. Hydrometeor., 13, 856–876,
https://doi.org/10.1175/JHM-D-11-065.1, 2012. a
Sheffield, J., Wood, E. F., Chaney, N., Guan, K., Sadri, S., Yuan, X., Olang,
L., Amani, A., Ali, A., and Demuth, S.: A Drought Monitoring and Forecasting
System for Sub-Sahara African Water Resources and Food Security, B.
Am. Meteorol. Soc., 95, 861–882, 2014. a
Shukla, S. and Wood, A. W.: Use of a standardized runoff index for
characterizing hydrologic drought, Geophys. Res. Lett., 35, 1–7,
2008. a
Tallaksen, T. and Van Lanen, H. A.: Hydrological Drought, Processes and
Estimation Methods for Streamflow and Groundwater, vol. 48, Elsevier Science,
Amsterdam, the Netherlands, 2004. a
UN/ISDR: Drought Risk Reduction Framework and Practices: Contributing to the
Implementation of the Hyogo Framework for Action. United Nations
Secretariat of the International Strategy for Disaster Reduction
(UN/ISDR), Tech. Rep. 98+vi pp., United Nations Secretariat of the
International Strategy for Disaster Reduction (UN/ISDR), Geneva, Switzerland,
2007. a
USGS: National Gap Analysis Program, Land Cover Data Viewer,, available at:
https://goo.gl/rntijg, last access: 30 April 2018. a
Wagner, W., Hahn, S., Kidd, R., Melzer, T., Bartalis, Z., Hasenauer, S.,
Figa-Saldaña, J., de Rosnay, P., Jann, A., Schneider, S., Komma, J.,
Kubu, G., Brugger, K., Aubrecht, C., Züger, J., Gangkofner, U.,
Kienberger, S., Brocca, L., Wang, Y., Blöschl, G., Eitzinger, J., and
Steinnocher, K.: The ASCAT Soil Moisture Product: A Review of its
Specifications, Validation Results, and Emerging Applications,
Meteorol. Z., 22, 5–33, https://doi.org/10.1127/0941-2948/2013/0399,
2013. a
Wilhite, D. A.: Drought as a Natural Hazard: Concepts and
Definitions, chap. 1, vol. I, National Drought Mitigation Center at Digital Commons at
University of Nebraska, Lincoln, Routledge, London, UK, 2000. a
Wu, Q., Liu, H., Wang, L., and Deng, C.: Evaluation of AMSR2 soil moisture
products over the contiguous United States using in situ data from the
International Soil Moisture Network, Int. J. Appl. Earth Obs., 45,
187–199, https://doi.org/10.1016/j.jag.2015.10.011, 2015.
a
Zaitchik, B. F., Rodell, M., and Reichle, R. H.: Assimilation of GRACE
Terrestrial Water Storage Data into a Land Surface Model: Results for the
Mississippi River Basin, Am. Meteorol. Soc., 9, 535–548,
2008. a
Short summary
Of particular interest to NASA's SMAP-based agricultural applications is a monitoring product that assesses near-surface soil moisture in terms of probability percentiles for dry and wet conditions. However, the short SMAP record length poses a statistical challenge for the meaningful assessment of its indices. This study presents initial insights about using SMAP Level 3 and Level 4 for monitoring drought and pluvial regions with a first application over the contiguous United States (CONUS).
Of particular interest to NASA's SMAP-based agricultural applications is a monitoring product...
