486 results on '"Eric F. Wood"'
Search Results
2. Satellite Flood Inundation Assessment and Forecast Using SMAP and Landsat
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Jinyang Du, John S. Kimball, Justin Sheffield, Ming Pan, Colby K. Fisher, Hylke E. Beck, and Eric F. Wood
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Flood ,Global Forecast System (GFS) ,Google Earth Engine (GEE) ,Landsat ,Soil Moisture Active Passive (SMAP) ,Ocean engineering ,TC1501-1800 ,Geophysics. Cosmic physics ,QC801-809 - Abstract
The capability and synergistic use of multisource satellite observations for flood monitoring and forecasts is crucial for improving disaster preparedness and mitigation. Here, surface fractional water cover (FW) retrievals derived from Soil Moisture Active Passive (SMAP) L-band (1.4 GHz) brightness temperatures were used for flood assessment over southeast Africa during the Cyclone Idai event. We then focused on five subcatchments of the Pungwe basin and developed a machine learning based approach with the support of Google Earth Engine for daily (24-h) forecasting of FW and 30-m inundation downscaling and mapping. The Classification and Regression Trees model was selected and trained using retrievals derived from SMAP and Landsat coupled with rainfall forecasts from the NOAA Global Forecast System. Independent validation showed that FW predictions over randomly selected dates are highly correlated (R = 0.87) with the Landsat observations. The forecast results captured the flood temporal dynamics from the Idai event; and the associated 30-m downscaling results showed inundation spatial patterns consistent with independent satellite synthetic aperture radar observations. The data-driven approach provides new capacity for flood monitoring and forecasts leveraging synergistic satellite observations and big data analysis, which is particularly valuable for data sparse regions.
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- 2021
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3. A new vector-based global river network dataset accounting for variable drainage density
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Peirong Lin, Ming Pan, Eric F. Wood, Dai Yamazaki, and George H. Allen
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Science - Abstract
Measurement(s) vector river network • river network drainage density Technology Type(s) computational modeling technique • machine learning Sample Characteristic - Environment river • watershed • drainage basin Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13377329
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- 2021
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4. Solar and wind energy enhances drought resilience and groundwater sustainability
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Xiaogang He, Kairui Feng, Xiaoyuan Li, Amy B. Craft, Yoshihide Wada, Peter Burek, Eric F. Wood, and Justin Sheffield
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Science - Abstract
The role of solar and wind energy (SWE) in management of water-food-energy (WFE) nexus is largely neglected. Here the authors developed a trade-off frontier framework to quantify the water sustainability value of SWE and applied it in California, where they found that SWE penetration creates beneficial feedback for the WFE nexus by enhancing drought resilience and benefits groundwater sustainability over long run.
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- 2019
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5. The Reliability of Global Remote Sensing Evapotranspiration Products over Amazon
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Jie Wu, Venkataraman Lakshmi, Dashan Wang, Peirong Lin, Ming Pan, Xitian Cai, Eric F. Wood, and Zhenzhong Zeng
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evapotranspiration ,global product ,Amazon ,consistency ,response ,deforestation ,Science - Abstract
As a key component of terrestrial water cycle, evapotranspiration (ET), specifically over the Amazon River basin, is of high scientific significance. However, due to the sparse observation network and relatively short observational period of eddy covariance data, large uncertainties remain in the spatial-temporal characteristics of ET over the Amazon. Recently, a great number of long-term global remotely sensed ET products have been developed to fill the observation gap. However, the reliabilities of these global ET products over the Amazon are unknown. In this study, we assessed the consistency of the magnitude, trend and spatial pattern of Amazon ET among five global remotely sensed ET reconstructions. The magnitudes of these products are similar but the long-term trends from 1982 to 2011 are completely divergent. Validation from the eddy covariance data and water balance method proves a better performance of a product grounded on local measurements, highlighting the importance of local measurements in the ET reconstruction. We also examined four hypotheses dealing with the response of ET to brightening, warming, greening and deforestation, which shows that in general, these ET products respond better to warming and greening than to brightening and deforestation. This large uncertainty highlights the need for future studies focusing on ET issues over the Amazon.
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- 2020
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6. Effect of Structural Uncertainty in Passive Microwave Soil Moisture Retrieval Algorithm
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Lanka Karthikeyan, Ming Pan, Dasika Nagesh Kumar, and Eric F. Wood
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soil moisture ,equifinality ,uncertainty ,vod ,passive microwave ,retrieval algorithm ,amsr-e ,radiative transfer model ,Chemical technology ,TP1-1185 - Abstract
Passive microwave sensors use a radiative transfer model (RTM) to retrieve soil moisture (SM) using brightness temperatures (TB) at low microwave frequencies. Vegetation optical depth (VOD) is a key input to the RTM. Retrieval algorithms can analytically invert the RTM using dual-polarized TB measurements to retrieve the VOD and SM concurrently. Algorithms in this regard typically use the τ-ω types of models, which consist of two third-order polynomial equations and, thus, can have multiple solutions. Through this work, we find that uncertainty occurs due to the structural indeterminacy that is inherent in all τ-ω types of models in passive microwave SM retrieval algorithms. In the process, a new analytical solution for concurrent VOD and SM retrieval is presented, along with two widely used existing analytical solutions. All three solutions are applied to a fixed framework of RTM to retrieve VOD and SM on a global scale, using X-band Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) TB data. Results indicate that, with structural uncertainty, there ensues a noticeable impact on the VOD and SM retrievals. In an era where the sensitivity of retrieval algorithms is still being researched, we believe the structural indeterminacy of RTM identified here would contribute to uncertainty in the soil moisture retrievals.
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- 2020
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7. Satellite Flood Assessment and Forecasts from SMAP and Landsat.
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Jinyang Du, John S. Kimball, Justin Sheffield, Ming Pan, Colby K. Fisher, Hylke E. Beck, and Eric F. Wood
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- 2020
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8. Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields
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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
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- 2021
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9. A global-scale framework for hydropower development incorporating strict environmental constraints
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Rongrong Xu, Zhenzhong Zeng, Ming Pan, Alan D. Ziegler, Joseph Holden, Dominick V. Spracklen, Lee E. Brown, Xinyue He, Deliang Chen, Bin Ye, Haiwei Xu, Sonia Jerez, Chunmiao Zheng, Junguo Liu, Peirong Lin, Yuan Yang, Junyu Zou, Dashan Wang, Mingyi Gu, Zongliang Yang, Dongfeng Li, Junling Huang, Venkataraman Lakshmi, and Eric. F. Wood
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- 2023
10. Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches
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Peirong Lin, Ming Pan, Hylke E. Beck, Yuan Yang, Dai Yamazaki, Renato Frasson, Cédric H. David, Michael Durand, Tamlin M. Pavelsky, George H. Allen, Colin J. Gleason, and Eric F. Wood
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- 2019
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11. Reduced Moisture Transport Linked to Drought Propagation Across North America
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Julio E. Herrera‐Estrada, J. Alejandro Martinez, Francina Dominguez, Kirsten L. Findell, Eric F. Wood, and Justin Sheffield
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- 2019
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12. Evaluation of 18 Satellite- and Model-Based Soil Moisture Products Using in Situ Measurements From 826 Sensors
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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
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Geosciences (General) - Abstract
Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as “open-loop” models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byråns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3ESWI, SMOSSWI, AMSR2SWI, and ASCATSWI, with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.
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- 2021
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13. PPDIST, global 0.1° daily and 3-hourly precipitation probability distribution climatologies for 1979–2018
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Hylke E. Beck, Seth Westra, Jackson Tan, Florian Pappenberger, George J. Huffman, Tim R. McVicar, Gaby J. Gründemann, Noemi Vergopolan, Hayley J. Fowler, Elizabeth Lewis, Koen Verbist, and Eric F. Wood
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Meteorology And Climatology ,Earth Resources And Remote Sensing - Abstract
We introduce the Precipitation Probability DISTribution (PPDIST) dataset, a collection of global high-resolution (0.1°) observation-based climatologies (1979–2018) of the occurrence and peak intensity of precipitation (P) at daily and 3-hourly time-scales. The climatologies were produced using neural networks trained with daily P observations from 93,138 gauges and hourly P observations (resampled to 3-hourly) from 11,881 gauges worldwide. Mean validation coefficient of determination (R^(2)) values ranged from 0.76 to 0.80 for the daily P occurrence indices, and from 0.44 to 0.84 for the daily peak P intensity indices. The neural networks performed significantly better than current state-of-the-art reanalysis (ERA5) and satellite (IMERG) products for all P indices. Using a 0.1 mm 3 per h threshold, P was estimated to occur 12.2%, 7.4%, and 14.3% of the time, on average, over the global, land, and ocean domains, respectively. The highest P intensities were found over parts of Central America, India, and Southeast Asia, along the western equatorial coast of Africa, and in the intertropical convergence zone.
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- 2020
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14. Global Estimates of Reach‐Level Bankfull River Width Leveraging Big Data Geospatial Analysis
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Peirong Lin, Ming Pan, George H. Allen, Renato Prata de Frasson, Zhenzhong Zeng, Dai Yamazaki, and Eric F. Wood
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- 2020
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15. A Climate Data Record (CDR) for the global terrestrial water budget: 1984–2010
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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
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- 2018
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16. Multiscaling Analysis in Distributed Modeling and Remote Sensing: An Application Using Soil Moisture
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Ralph Dubayah, Eric F. Wood, and Daniel Lavallée
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- 2023
17. Validation of SMAP soil moisture for the SMAPVEX15 field campaign using a hyper‐resolution model
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Xitian Cai, Ming Pan, Nathaniel W. Chaney, Andreas Colliander, Sidharth Misra, Michael H. Cosh, Wade T. Crow, Thomas J. Jackson, and Eric F. Wood
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- 2017
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18. Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS
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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
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Meteorology And Climatology ,Earth Resources And Remote Sensing - Abstract
New precipitation (P) datasets are released regularly, following innovations in weather forecasting models, satellite retrieval methods, and multi-source merging techniques. Using the conterminous US as a case study, we evaluated the performance of 26 gridded (sub-)daily P datasets to obtain insight into the merit of these innovations. The evaluation was performed at a daily timescale for the period 2008–2017 using the Kling–Gupta efficiency (KGE), a performance metric combining correlation, bias, and variability. As a reference, we used the high-resolution (4 km) Stage-IV gauge-radar P dataset. Among the three KGE components, the P datasets performed worst overall in terms of correlation (related to event identification). In terms of improving KGE scores for these datasets, improved P totals (affecting the bias score) and improved distribution of P intensity (affecting the variability score) are of secondary importance. Among the 11 gauge-corrected P datasets, the best overall performance was obtained by MSWEP V2.2, underscoring the importance of applying daily gauge corrections and accounting for gauge reporting times. Several uncorrected P datasets outperformed gauge-corrected ones. Among the 15 uncorrected P datasets, the best performance was obtained by the ERA5-HRES fourth-generation reanalysis, reflecting the significant advances in earth system modeling during the last decade. The (re)analyses generally performed better in winter than in summer, while the opposite was the case for the satellite-based datasets. IMERGHH V05 performed substantially better than TMPA-3B42RT V7, attributable to the many improvements implemented in the IMERG satellite P retrieval algorithm. IMERGHH V05 outperformed ERA5-HRES in regions dominated by convective storms, while the opposite was observed in regions of complex terrain. The ERA5-EDA ensemble average exhibited higher correlations than the ERA5-HRES deterministic run, highlighting the value of ensemble modeling. The WRF regional convection-permitting climate model showed considerably more accurate P totals over the mountainous west and performed best among the uncorrected datasets in terms of variability, suggesting there is merit in using high-resolution models to obtain climatological P statistics. Our findings provide some guidance to choose the most suitable P dataset for a particular application.
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- 2019
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19. FarmCan: a physical, statistical, and machine learning model to forecast crop water deficit for farms
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Sara Sadri, James S. Famiglietti, Ming Pan, Hylke E. Beck, Aaron Berg, and Eric F. Wood
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General Earth and Planetary Sciences ,General Environmental Science - Abstract
In the coming decades, a changing climate, the loss of high-quality land, the slowing in the annual yield of cereals, and increasing fertilizer use indicate that better agricultural water management strategies are needed. In this study, we designed FarmCan, a novel, robust remote sensing and machine learning (ML) framework to forecast farms' needed daily crop water quantity or needed irrigation (NI). We used a diverse set of simulated and observed near-real-time (NRT) remote sensing data coupled with a random forest (RF) algorithm and inputs about farm-specific situations to predict the amount and timing of evapotranspiration (ET), potential ET (PET), soil moisture (SM), and root zone soil moisture (RZSM). Our case study of four farms in the Canadian Prairies Ecozone (CPE) shows that 8 d composite precipitation (P) has the highest correlation with changes (Δ) of RZSM and SM. In contrast, 8 d PET and 8 d ET do not offer a strong correlation with 8 d P. Using R2, root mean square error (RMSE), and Kling–Gupta efficiency (KGE) indicators, our algorithm could reasonably calculate daily NI up to 14 d in advance. From 2015 to 2020, the R2 values between predicted and observed 8 d ET and 8 d PET were the highest (80 % and 54 %, respectively). The 8 d NI also had an average R2 of 68%. The KGE of the 8 d ET and 8 d PET in four study farms showed an average of 0.71 and 0.50, respectively, with an average KGE of 0.62. FarmCan can be used in any region of the world to help stakeholders make decisions during prolonged periods of drought or waterlogged conditions, schedule cropping and fertilization, and address local government policy concerns.
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- 2022
20. Copula-Based Downscaling of Coarse-Scale Soil Moisture Observations With Implicit Bias Correction.
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Niko E. C. Verhoest, Martinus Johannes van den Berg, Brecht Martens, Hans Lievens, Eric F. Wood, Ming Pan, Yann H. Kerr, Ahmad Al Bitar, Sat Kumar Tomer, Matthias Drusch, Hilde Vernieuwe, Bernard De Baets, Jeffrey P. Walker, Gift Dumedah, and Valentijn R. N. Pauwels
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- 2015
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21. Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning
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Lian Feng, Jie Yin, Ganquan Mao, Zhenzhong Zeng, Dalei Hao, Eric F. Wood, Chiyuan Miao, Peirong Lin, Alan D. Ziegler, Junyu Zou, Xin Jiang, Ming Pan, Shijing Liang, Dashan Wang, Xinyue He, and Yelu Zeng
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Synthetic aperture radar ,010504 meteorology & atmospheric sciences ,Flood myth ,Computer science ,business.industry ,Deep learning ,0211 other engineering and technologies ,02 engineering and technology ,Land cover ,01 natural sciences ,Atomic and Molecular Physics, and Optics ,Computer Science Applications ,Statistical classification ,Unsupervised learning ,Satellite imagery ,Artificial intelligence ,Computers in Earth Sciences ,business ,Scale (map) ,Engineering (miscellaneous) ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Remote sensing - Abstract
Synthetic aperture radar (SAR) has great potential for timely monitoring of flood information as it penetrates the clouds during flood events. Moreover, the proliferation of SAR satellites with high spatial and temporal resolution provides a tremendous opportunity to understand the flood risk and its quick response. However, traditional algorithms to extract flood inundation using SAR often require manual parameter tuning or data annotation, which presents a challenge for the rapid automated mapping of large and complex flooded scenarios. To address this issue, we proposed a segmentation algorithm for automatic flood mapping in near-real-time over vast areas and for all-weather conditions by integrating Sentinel-1 SAR imagery with an unsupervised machine learning approach named Felz-CNN. The algorithm consists of three phases: (i) super-pixel generation; (ii) convolutional neural network-based featurization; (iii) super-pixel aggregation. We evaluated the Felz-CNN algorithm by mapping flood inundation during the Yangtze River flood in 2020, covering a total study area of 1,140,300 km2. When validated on fine-resolution Planet satellite imagery, the algorithm accurately identified flood extent with producer and user accuracy of 93% and 94%, respectively. The results are indicative of the usefulness of our unsupervised approach for the application of flood mapping. Meanwhile, we overlapped the post-disaster inundation map with a 10-m resolution global land cover map (FROM-GLC10) to assess the damages to different land cover types. Of these types, cropland and residential settlements were most severely affected, with inundation areas of 9,430.36 km2 and 1,397.50 km2, respectively, results that are in agreement with statistics from relevant agencies. Compared with traditional supervised classification algorithms that require time-consuming data annotation, our unsupervised algorithm can be deployed directly to high-performance computing platforms such as Google Earth Engine and PIE-Engine to generate a large-spatial map of flood-affected areas within minutes, without time-consuming data downloading and processing. Importantly, this efficiency enables the fast and effective monitoring of flood conditions to aid in disaster governance and mitigation globally.
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- 2021
22. High‐Resolution Soil Moisture Data Reveal Complex Multi‐Scale Spatial Variability Across the United States
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Noemi Vergopolan, Justin Sheffield, Nathaniel W. Chaney, Ming Pan, Hylke E. Beck, Craig R. Ferguson, Laura Torres‐Rojas, Felix Eigenbrod, Wade Crow, and Eric F. Wood
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Geophysics ,General Earth and Planetary Sciences - Abstract
Soil moisture (SM) spatiotemporal variability critically influences water resources, agriculture, and climate. However, besides site-specific studies, little is known about how SM varies locally (1–100-m scale). Consequently, quantifying the SM variability and its impact on the Earth system remains a long-standing challenge in hydrology. We reveal the striking variability of local-scale SM across the United States using SMAP-HydroBlocks — a novel satellite-based surface SM data set at 30-m resolution. Results show how the complex interplay of SM with landscape characteristics and hydroclimate is primarily driven by local variations in soil properties. This local-scale complexity yields a remarkable and unique multi-scale behavior at each location. However, very little of this complexity persists across spatial scales. Experiments reveal that on average 48% and up to 80% of the SM spatial information is lost at the 1-km resolution, with complete loss expected at the scale of current state-of-the-art SM monitoring and modeling systems (1–25 km resolution).
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- 2022
23. An initial assessment of SMAP soil moisture retrievals using high‐resolution model simulations and in situ observations
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Ming Pan, Xitian Cai, Nathaniel W. Chaney, Dara Entekhabi, and Eric F. Wood
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- 2016
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24. Deriving Vegetation Phenological Time and Trajectory Information Over Africa Using SEVIRI Daily LAI.
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Kaiyu Guan, David Medvigy, Eric F. Wood, Kelly K. Caylor, Shi Li 0001, and Su-Jong Jeong
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- 2014
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25. An Approach to Constructing a Homogeneous Time Series of Soil Moisture Using SMOS.
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Delphine J. Leroux, Yann H. Kerr, Eric F. Wood, Alok Sahoo, Rajat Bindlish, and Thomas J. Jackson
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- 2014
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26. Reducing Solar Radiation Forcing Uncertainty and Its Impact on Surface Energy and Water Fluxes
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Zhongwang Wei, Eric F. Wood, Zhenzhong Zeng, Peirong Lin, Justin Sheffield, and Liqing Peng
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Atmospheric Science ,Environmental science ,Forcing (mathematics) ,Radiation ,Atmospheric sciences ,Surface energy - Abstract
Downward shortwave radiation Rsd determines the surface energy balance, alters evapotranspiration and hydrological conditions, and feeds back to the regional and global climate. Large-scale Rsd estimates are usually retrieved from satellite-based top-of-atmosphere radiation and cloud parameters. These estimates are subject to biases and temporal inhomogeneity due to errors in atmospheric parameters, algorithms, and sensor changes. We found that three satellite products overestimate Rsd by 8%–10% over Asia for 1984–2006, particularly in high latitudes. We used the model tree ensemble (MTE) machine-learning algorithm and commonly used ensemble averaging methods to integrate ground observations and satellite products. Validations based on test stations and independent networks showed that the MTE approach reduces the median relative biases from 8%–10% to 2%, which is more effective than the ensemble averaging methods. We further evaluated the impacts of uncertainty in radiation forcing on surface energy and water balances using the land surface model Noah-MP. The uncertainty of radiation data affects the prediction of sensible heat the most, and also largely affects latent heat prediction in humid regions. Holding the other variables constant, a 10% positive bias in Rsd can lead to a 20%–60% positive bias in the monthly median sensible heat. The simulated hydrological responses to changing radiation forcing are nonlinear as a result of the interactions among evapotranspiration, snowpack, and soil moisture. Our analysis concludes that reducing uncertainty of radiation data is beneficial for predicting regional energy and water balances, which requires more high-quality ground observations and improved satellite retrieval algorithms.
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- 2021
27. Hydros Soil Moisture Retrieval Algorithms: Status and Relevance to Future Missions.
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Peggy O'Neill, Manfred Owe, Ben T. Gouweleeuw, Eni G. Njoku, Jiancheng Shi 0001, and Eric F. Wood
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- 2006
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28. Development of soil moisture retrieval algorithms for the hydros microwave radiometer.
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Peggy O'Neill, Eni G. Njoku, Jiancheng Shi 0001, and Eric F. Wood
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- 2005
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29. Global Evaluation of Seasonal Precipitation and Temperature Forecasts from NMME
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Xiaogang He, Tirthankar Roy, Eric F. Wood, Hylke E. Beck, Peirong Lin, and Christopher L. Castro
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Atmospheric Science ,010504 meteorology & atmospheric sciences ,Climatology ,Seasonal forecasting ,0207 environmental engineering ,Environmental science ,02 engineering and technology ,Precipitation ,020701 environmental engineering ,01 natural sciences ,Forecast verification ,0105 earth and related environmental sciences - Abstract
We present a comprehensive global evaluation of monthly precipitation and temperature forecasts from 16 seasonal forecasting models within the NMME Phase-1 system, using Multi-Source Weighted-Ensemble Precipitation version 2 (MSWEP-V2; precipitation) and Climate Research Unit TS4.01 (CRU-TS4.01; temperature) data as reference. We first assessed the forecast skill for lead times of 1–8 months using Kling–Gupta efficiency (KGE), an objective performance metric combining correlation, bias, and variability. Next, we carried out an empirical orthogonal function (EOF) analysis to compare the spatiotemporal variability structures of the forecasts. We found that, in most cases, precipitation skill was highest during the first lead time (i.e., forecast in the month of initialization) and rapidly dropped thereafter, while temperature skill was much higher overall and better retained at higher lead times, which is indicative of stronger temporal persistence. Based on a comprehensive assessment over 21 regions and four seasons, we found that the skill showed strong regional and seasonal dependencies. Some tropical regions, such as the Amazon and Southeast Asia, showed high skill even at longer lead times for both precipitation and temperature. Rainy seasons were generally associated with high precipitation skill, while during winter, temperature skill was low. Overall, precipitation forecast skill was highest for the NASA, NCEP, CMC, and GFDL models, and for temperature, the NASA, CFSv2, COLA, and CMC models performed the best. The spatiotemporal variability structures were better captured for precipitation than temperature. The simple forecast averaging did not produce noticeably better results, emphasizing the need for more advanced weight-based averaging schemes.
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- 2020
30. FarmCan: A Physical, Statistical, and Machine Learning Model to Forecast Crop Water Deficit at Farm Scales
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Sara Sadri, James S. Famiglietti, Ming Pan, Hylke E. Beck, Aaron Berg, and Eric F. Wood
- Abstract
In the coming decades, a changing climate, growing global population, and rising food prices will have significant yet uncertain impacts on both water and food security. The loss of high-quality land, the slowing in annual yield of major cereals, and increasing fertilizer use, all indicate that strategies are needed for monitoring and predicting ongoing and future water deficits on farms for better agricultural water management decisions. Most such activities are based on in-situ measurements which are costly, hard to scale, and ignore the wealth of spatial and temporal information from remotely-sensed data. In this study, we designed FarmCan, a novel and robust climate-informed machine learning (ML) framework to predict crop water demand at the farm scale with up to 14 days lead time. We use a diverse set of simulated and observed near-real-time (NRT) remote sensing data coupled with inputs from farmers, a Random Forest (RF) algorithm, and precipitation (P) prediction from MSWEP to predict the amount and timing of evapotranspiration (ET), potential ET (PET), soil moisture (SM), and root zone soil moisture (RZSM). Our study shows that SM and RZSM are the variables that are more correlated with P, while PET and ET do not show a strong correlation with P, SM, and RZSM. Our case study of 4 farms in the Canadian Prairies Ecozone (CPE) using R2, RMSE, and KGE indicators, shows that our algorithm was able to forecast crop water requirements 14 days in advance reasonably well. We also found that during 2020, RF forecasted ET and PET and needed irrigation (NI) with more accuracy than SM and RZSM, although this might vary based on the soil type, location, year of study, and crop type. Due to the forecasting capability and transferability of the mechanism developed, FarmCan is a promising tool for use in any region of the world to help stakeholders make decisions during prolonged periods of drought or waterlogged conditions, schedule cropping and fertilization, and address local government' policy concerns.
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- 2022
31. Mapping field-scale soil moisture and its spatial variability across the United States using SMAP-HydroBlocks
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Noemi Vergopolan, Justin Sheffield, Nathaniel W. Chaney, Ming Pan, Hylke E. Beck, Craig R. Ferguson, Laura Torres-Rojas, Felix Eigenbrod, Wade Crow, and Eric F. Wood
- Abstract
Soil moisture (SM) varies widely in space and time. This variability influences agriculture, land-atmosphere interactions and triggers hazards, such as flooding, landslides, droughts, and wildfires. Yet, current observations are limited to a few regional in situ measurement networks or coarse-scale satellite retrievals (9–36-km resolution). As a result, besides site-specific studies, little is known on how SM varies locally (1–100-m resolution). Consequently, quantifying the impact of this variability remains a critical and long-standing challenge in hydrology. This presentation introduces SMAP-HydroBlocks – a novel 30-m resolution SM dataset (2015–2019) that combines hyper-resolution land surface modeling, satellite, and in-situ observations over the United States. Using this data, we reveal the striking variability of local-scale SM across the United States. By mapping the SM spatial variability and its persistence across spatial scales, we show the complex interplay between the landscape and hydroclimate and how this variability is highly scale-dependent. Results show that up to 80% of SM spatial variability information is lost at the 1-km scale, with further losses expected at the scale of current monitoring systems (5–25-km). This high degree of SM variability has a critical influence on freshwater and land ecosystem dynamics. By mapping its spatial variability locally, we provide a stepping-stone towards understanding SM-dependent hydrological, biogeochemical, and ecological processes at local (and so far unresolved) scales.
- Published
- 2022
32. Doubling of annual forest carbon loss over the tropics during the early twenty-first century
- Author
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Yu Feng, Zhenzhong Zeng, Timothy D. Searchinger, Alan D. Ziegler, Jie Wu, Dashan Wang, Xinyue He, Paul R. Elsen, Philippe Ciais, Rongrong Xu, Zhilin Guo, Liqing Peng, Yiheng Tao, Dominick V. Spracklen, Joseph Holden, Xiaoping Liu, Yi Zheng, Peng Xu, Ji Chen, Xin Jiang, Xiao-Peng Song, Venkataraman Lakshmi, Eric F. Wood, Chunmiao Zheng, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), ICOS-ATC (ICOS-ATC), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and This study was supported by the National Natural Science Foundation of China (grants no. 42071022, 41861124003 and 41890852) and the start-up fund provided by Southern University of Science and Technology (no. 29/Y01296122)
- Subjects
tropical forest ,[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere ,Global and Planetary Change ,Ecology ,LAND-USE ,Renewable Energy, Sustainability and the Environment ,IMPACT ,Geography, Planning and Development ,EXPANSION ,carbon loss ,Management, Monitoring, Policy and Law ,PROTECTED AREAS ,CULTIVATION ,Global Forest Change (GFC) ,Urban Studies ,carbon cycle ,DRIVERS ,MAP ,STOCKS ,DEFORESTATION ,[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment ,DIOXIDE EMISSIONS ,Nature and Landscape Conservation ,Food Science - Abstract
Previous estimates of tropical forest carbon loss in the twenty-first century using satellite data typically focus on its magnitude, whereas regional loss trajectories and associated drivers are rarely reported. Here we used different high-resolution satellite datasets to show a doubling of gross tropical forest carbon loss worldwide from 0.97 ± 0.16 PgC yr−1 in 2001–2005 to 1.99 ± 0.13 PgC yr−1 in 2015–2019. This increase in carbon loss from forest conversion is higher than in bookkeeping models forced by land-use statistical data, which show no trend or a slight decline in land-use emissions in the early twenty-first century. Most (82%) of the forest carbon loss is at some stages associated with large-scale commodity or small-scale agriculture activities, particularly in Africa and Southeast Asia. We find that ~70% of former forest lands converted to agriculture in 2001–2019 remained so in 2020, confirming a dominant role of agriculture in long-term pan-tropical carbon reductions on formerly forested landscapes. The acceleration and high rate of forest carbon loss in the twenty-first century suggest that existing strategies to reduce forest loss are not successful; and this failure underscores the importance of monitoring deforestation trends following the new pledges made in Glasgow.
- Published
- 2022
33. An Initial Assessment of SMOS Derived Soil Moisture over the Continental United States.
- Author
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Ming Pan, Alok Sahoo, Eric F. Wood, Ahmad Al Bitar, Delphine J. Leroux, and Yann H. Kerr
- Published
- 2012
- Full Text
- View/download PDF
34. Evaluation of SMOS Soil Moisture Products Over Continental U.S. Using the SCAN/SNOTEL Network.
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Ahmad Al Bitar, Delphine J. Leroux, Yann H. Kerr, Olivier Merlin, Philippe Richaume, Alok Sahoo, and Eric F. Wood
- Published
- 2012
- Full Text
- View/download PDF
35. Impact of Accuracy, Spatial Availability, and Revisit Time of Satellite-Derived Surface Soil Moisture in a Multiscale Ensemble Data Assimilation System.
- Author
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Ming Pan and Eric F. Wood
- Published
- 2010
- Full Text
- View/download PDF
36. Satellite Microwave Remote Sensing of Daily Land Surface Air Temperature Minima and Maxima From AMSR-E.
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Lucas A. Jones, Craig R. Ferguson, John S. Kimball, Ke Zhang 0004, Steven Tsz K. Chan, Kyle C. McDonald, Eni G. Njoku, and Eric F. Wood
- Published
- 2010
- Full Text
- View/download PDF
37. Projected Seasonal Changes in Large-Scale Global Precipitation and Temperature Extremes Based on the CMIP5 Ensemble
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Eric F. Wood, Wang Zhan, Xiaogang He, and Justin Sheffield
- Subjects
Atmospheric Science ,Global precipitation ,010504 meteorology & atmospheric sciences ,Scale (ratio) ,0208 environmental biotechnology ,Extreme events ,Climate change ,02 engineering and technology ,01 natural sciences ,020801 environmental engineering ,Climatology ,Environmental science ,Precipitation ,0105 earth and related environmental sciences - Abstract
Over the past decades, significant changes in temperature and precipitation have been observed, including changes in the mean and extremes. It is critical to understand the trends in hydroclimatic extremes and how they may change in the future as they pose substantial threats to society through impacts on agricultural production, economic losses, and human casualties. In this study, we analyzed projected changes in the characteristics, including frequency, seasonal timing, and maximum spatial and temporal extent, as well as severity, of extreme temperature and precipitation events, using the severity–area–duration (SAD) method and based on a suite of 37 climate models archived in phase 5 of the Coupled Model Intercomparison Project (CMIP5). Comparison between the CMIP5 model estimated extreme events and an observation-based dataset [Princeton Global Forcing (PGF)] indicates that climate models have moderate success in reproducing historical statistics of extreme events. Results from the twenty-first-century projections suggest that, on top of the rapid warming indicated by a significant increase in mean temperature, there is an overall wetting trend in the Northern Hemisphere with increasing wet extremes and decreasing dry extremes, whereas the Southern Hemisphere will have more intense wet extremes. The timing of extreme precipitation events will change at different spatial scales, with the largest change occurring in southern Asia. The probability of concurrent dry/hot and wet/hot extremes is projected to increase under both RCP4.5 and RCP8.5 scenarios, whereas little change is detected in the probability of concurrent dry/cold events and only a slight decrease of the joint probability of wet/cold extremes is expected in the future.
- Published
- 2020
38. The Global Drought and Flood Catalogue: A Complex Relation of Hydrology and Impact
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Justin Sheffield, Zhongwang Wei, Ming Pan, Eric F. Wood, and Xiaogang He
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Hydrology ,Atmospheric Science ,Hydrology (agriculture) ,Flood myth ,Relation (database) ,Environmental science - Published
- 2020
39. A Global Drought and Flood Catalogue from 1950 to 2016
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Eric F. Wood, Zhongwang Wei, Xiaogang He, Justin Sheffield, and Ming Pan
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Atmospheric Science ,Food security ,010504 meteorology & atmospheric sciences ,Flood myth ,Range (biology) ,0207 environmental engineering ,Environmental science ,Production (economics) ,02 engineering and technology ,020701 environmental engineering ,Water resource management ,01 natural sciences ,0105 earth and related environmental sciences - Abstract
Hydrological extremes, in the form of droughts and floods, have impacts on a wide range of sectors including water availability, food security, and energy production. Given continuing large impacts of droughts and floods and the expectation for significant regional changes projected in the future, there is an urgent need to provide estimates of past events and their future risk, globally. However, current estimates of hydrological extremes are not robust and accurate enough, due to lack of long-term data records, standardized methods for event identification, geographical inconsistencies, and data uncertainties. To tackle these challenges, this article presents the development of the first Global Drought and Flood Catalogue (GDFC) for 1950–2016 by merging the latest in situ and remote sensing datasets with state-of-the-art land surface and hydrodynamic modeling to provide a continuous and consistent estimate of the terrestrial water cycle and its extremes. This GDFC also includes an unprecedented level of detailed analysis of drought and large-scale flood events using univariate and multivariate risk assessment frameworks, which incorporates regional spatial–temporal characteristics (i.e., duration, spatial extent, severity) and global hazard maps for different return periods. This Catalogue forms a basis for analyzing the changing risk of droughts and floods and can underscore national and international climate change assessments and provide a key reference for climate change studies and climate model evaluations. It also contributes to the growing interests in multivariate and compounding risk analysis.
- Published
- 2020
40. Bias Correction of Global High-Resolution Precipitation Climatologies Using Streamflow Observations from 9372 Catchments
- Author
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Mauricio Zambrano-Bigiarini, Tim R. McVicar, Camila Alvarez-Garreton, Oscar M. Baez-Villanueva, Dirk Nikolaus Karger, Eric F. Wood, Justin Sheffield, and Hylke E. Beck
- Subjects
Atmospheric Science ,010504 meteorology & atmospheric sciences ,0208 environmental biotechnology ,High resolution ,02 engineering and technology ,Snow ,01 natural sciences ,020801 environmental engineering ,Streamflow ,Climatology ,Environmental science ,Bias correction ,Precipitation ,0105 earth and related environmental sciences - Abstract
We introduce a set of global high-resolution (0.05°) precipitation (P) climatologies corrected for bias using streamflow (Q) observations from 9372 stations worldwide. For each station, we inferred the “true” long-termPusing a Budyko curve, which is an empirical equation relating long-termP,Q, and potential evaporation. We subsequently calculated long-term bias correction factors for three state-of-the-artPclimatologies [the “WorldClim version 2” database (WorldClim V2); Climatologies at High Resolution for the Earth’s Land Surface Areas, version 1.2 (CHELSA V1.2 ); and Climate Hazards Group Precipitation Climatology, version 1 (CHPclim V1)], after which we used random-forest regression to produce global gap-free bias correction maps for thePclimatologies. Monthly climatological bias correction factors were calculated by disaggregating the long-term bias correction factors on the basis of gauge catch efficiencies. We found that all three climatologies systematically underestimatePover parts of all major mountain ranges globally, despite the explicit consideration of orography in the production of each climatology. In addition, all climatologies underestimatePat latitudes >60°N, likely because of gauge undercatch. Exceptionally high long-term correction factors (>1.5) were obtained for all threePclimatologies in Alaska, High Mountain Asia, and Chile—regions characterized by marked elevation gradients, sparse gauge networks, and significant snowfall. Using the bias-corrected WorldClim V2, we demonstrated that other widely usedPdatasets (GPCC V2015, GPCP V2.3, and MERRA-2) severely underestimatePover Chile, the Himalayas, and along the Pacific coast of North America. MeanPfor the global land surface based on the bias-corrected WorldClim V2 is 862 mm yr−1(a 9.4% increase over the original WorldClim V2). The annual and monthly bias-correctedPclimatologies have been released as the Precipitation Bias Correction (PBCOR) dataset, which is available online (http://www.gloh2o.org/pbcor/).
- Published
- 2020
41. Spatiotemporal assimilation–interpolation of discharge records through inverse streamflow routing
- Author
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Eric F. Wood, Colby K. Fisher, and Ming Pan
- Subjects
lcsh:GE1-350 ,010504 meteorology & atmospheric sciences ,Meteorology ,Discharge ,lcsh:T ,0208 environmental biotechnology ,lcsh:Geography. Anthropology. Recreation ,Sampling (statistics) ,02 engineering and technology ,01 natural sciences ,lcsh:Technology ,lcsh:TD1-1066 ,020801 environmental engineering ,Current (stream) ,Routing (hydrology) ,lcsh:G ,Streamflow ,Environmental science ,Upstream (networking) ,lcsh:Environmental technology. Sanitary engineering ,Surface runoff ,lcsh:Environmental sciences ,0105 earth and related environmental sciences ,Interpolation - Abstract
Poorly monitored river flows in many regions of the world have been hindering our ability to accurately estimate global water budgets as well as the variability of the global water cycle. In situ gauging sites, as well as a number of satellite-based systems, make observations of river discharge throughout the globe; however, these observations are often sparse due to, for example, the sampling frequencies of sensors or a lack of reporting. Recently, efforts have been made to develop methods to integrate these discrete observations to gain a better understanding of the underlying processes. This paper presents an application of a fixed interval Kalman smoother-based model, called inverse streamflow routing (ISR), to generate spatially and temporally continuous river discharge fields from discrete observations. The method propagates the observed information across all reachable parts of the river network (up/downstream from gauging point) and all reachable times (before/after observation time) using a two-sweep procedure that first propagates information backward in time to the furthest upstream locations (inverse routing) and then propagates it forward in time to the furthest downstream locations (forward routing). The ISR methodology advances prediction of streamflow in ungauged basins by accounting for a physical representation of the river system that is not generally handled explicitly in more-commonly applied statistically based models. The key advantages of this approach are that it (1) maintains all the physical consistencies embodied by a diffusive wave routing model (flow confluence relationships on the river network and the resulting mass balance, wave velocity, and diffusivity), (2) updates the lateral influx (runoff) at the pixel level (furthest upstream) to guarantee exhaustive propagation of observed information, and (3) works both with a first guess of initial river discharge conditions from a routing model (assimilation) and without a first guess (pure interpolation of observations). Two sets of experiments are carried out under idealized conditions and under real-world conditions provided by United States Geological Survey (USGS) observations. Results show that the method can effectively reproduce the spatial and temporal dynamics of river discharge in each of the experiments presented. The performance is driven by the density of the gauge network as well as the quality of the data being assimilated. We find that when assimilating the actual USGS observations, the performance decreases relative to our idealized scenario; however, we are still able to produce an improved discharge product at each validation site. With further testing, as well as global application, ISR may prove to be a useful method for extending our current network of global river discharge observations.
- Published
- 2020
42. Spatial and Temporal Scaling Behavior of Surface Shortwave Downward Radiation Based on MODIS and In Situ Measurements.
- Author
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Hongbo Su, Eric F. Wood, H. Wang, and Rachel T. Pinker
- Published
- 2008
- Full Text
- View/download PDF
43. High-performance Earth system modeling with NASA/GSFC's Land Information System.
- Author
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Christa D. Peters-Lidard, Paul R. Houser, Yudong Tian, Sujay V. Kumar, James Geiger, S. Olden, L. Lighty, B. Doty, Paul A. Dirmeyer, J. Adams, K. Mitchell, Eric F. Wood, and Justin Sheffield
- Published
- 2007
- Full Text
- View/download PDF
44. Global terrestrial stilling: does Earth’s greening play a role?
- Author
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Zhenzhong Zeng, Shilong Piao, Laurent Z X Li, Philippe Ciais, Yue Li, Xitian Cai, Long Yang, Maofeng Liu, and Eric F Wood
- Subjects
terrestrial stilling ,Earth’s greening ,surface wind speed ,surface roughness ,leaf area index ,weather station ,Environmental technology. Sanitary engineering ,TD1-1066 ,Environmental sciences ,GE1-350 ,Science ,Physics ,QC1-999 - Abstract
Previous studies have documented that surface wind speed ( u ) has been increasing over the ocean but decreasing over land for the past several decades. The decreasing u at the surface over land has been referred to as terrestrial stilling. A plausible hypothesis for terrestrial stilling is an increase in surface roughness associated with changes in land surface (e.g. enhanced vegetation growth, landscape fragmentation or urbanization). One of the most widespread land surface changes is enhanced vegetation leaf area index (LAI) known as greening, particularly over the middle to high latitudes of the Northern Hemisphere where strong stilling is observed from weather station data. In this study, we examine the hypothesis that enhanced vegetation LAI is a key driver of global terrestrial stilling. We first characterized the trend in u over the ocean using long-term satellite altimeter measurements, and the trend in u over land using continuous wind records from 4305 in situ meteorological stations. We then performed initial condition ensemble Atmospheric Model Intercomparison Project-type simulations using two state-of-the-art Earth system models (IPSL-CM and CESM) to isolate the response of u to the historical increase in LAI (representing the greening) for the period 1982–2011. Both models, forced with observed sea surface temperature and sea ice and with LAI from satellite observation, captured the observed strengthening of Pacific trade winds and Southern Ocean westerly winds. However, these simulations did not reproduce the weakening of surface winds over land as significantly as it appears in the observations (−0.006 m s ^−1 versus −0.198 m s ^−1 during 1982–2011), indicating that enhanced LAI (greening) is not a dominant driver for terrestrial stilling.
- Published
- 2018
- Full Text
- View/download PDF
45. Simulated sensitivity of African terrestrial ecosystem photosynthesis to rainfall frequency, intensity, and rainy season length
- Author
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Kaiyu Guan, Stephen P Good, Kelly K Caylor, David Medvigy, Ming Pan, Eric F Wood, Hisashi Sato, Michela Biasutti, Min Chen, Anders Ahlström, and Xiangtao Xu
- Subjects
Africa ,water stress ,rainfall frequency ,rainfall intensity ,rainy season length ,dynamic vegetation modeling ,Environmental technology. Sanitary engineering ,TD1-1066 ,Environmental sciences ,GE1-350 ,Science ,Physics ,QC1-999 - Abstract
There is growing evidence of ongoing changes in the statistics of intra-seasonal rainfall variability over large parts of the world. Changes in annual total rainfall may arise from shifts, either singly or in a combination, of distinctive intra-seasonal characteristics –i.e. rainfall frequency, rainfall intensity, and rainfall seasonality. Understanding how various ecosystems respond to the changes in intra-seasonal rainfall characteristics is critical for predictions of future biome shifts and ecosystem services under climate change, especially for arid and semi-arid ecosystems. Here, we use an advanced dynamic vegetation model (SEIB-DGVM) coupled with a stochastic rainfall/weather simulator to answer the following question: how does the productivity of ecosystems respond to a given percentage change in the total seasonal rainfall that is realized by varying only one of the three rainfall characteristics (rainfall frequency, intensity, and rainy season length)? We conducted ensemble simulations for continental Africa for a realistic range of changes (−20% ~ +20%) in total rainfall amount. We find that the simulated ecosystem productivity (measured by gross primary production, GPP) shows distinctive responses to the intra-seasonal rainfall characteristics. Specifically, increase in rainfall frequency can lead to 28% more GPP increase than the same percentage increase in rainfall intensity; in tropical woodlands, GPP sensitivity to changes in rainy season length is ~4 times larger than to the same percentage changes in rainfall frequency or intensity. In contrast, shifts in the simulated biome distribution are much less sensitive to intra-seasonal rainfall characteristics than they are to total rainfall amount. Our results reveal three major distinctive productivity responses to seasonal rainfall variability—‘chronic water stress’, ‘acute water stress’ and ‘minimum water stress’ - which are respectively associated with three broad spatial patterns of African ecosystem physiognomy, i.e. savannas, woodlands, and tropical forests.
- Published
- 2018
- Full Text
- View/download PDF
46. Multi-model ensemble projections of European river floods and high flows at 1.5, 2, and 3 degrees global warming
- Author
-
Stephan Thober, Rohini Kumar, Niko Wanders, Andreas Marx, Ming Pan, Oldrich Rakovec, Luis Samaniego, Justin Sheffield, Eric F Wood, and Matthias Zink
- Subjects
climate change ,1.5 degree global warming ,mHM ,Noah-MP ,PCR-GLOBWB ,Europe ,Environmental technology. Sanitary engineering ,TD1-1066 ,Environmental sciences ,GE1-350 ,Science ,Physics ,QC1-999 - Abstract
Severe river floods often result in huge economic losses and fatalities. Since 1980, almost 1500 such events have been reported in Europe. This study investigates climate change impacts on European floods under 1.5, 2, and 3 K global warming. The impacts are assessed employing a multi-model ensemble containing three hydrologic models (HMs: mHM, Noah-MP, PCR-GLOBWB) forced by five CMIP5 general circulation models (GCMs) under three Representative Concentration Pathways (RCPs 2.6, 6.0, and 8.5). This multi-model ensemble is unprecedented with respect to the combination of its size (45 realisations) and its spatial resolution, which is 5 km over the entirety of Europe. Climate change impacts are quantified for high flows and flood events, represented by 10% exceedance probability and annual maxima of daily streamflow, respectively. The multi-model ensemble points to the Mediterranean region as a hotspot of changes with significant decrements in high flows from −11% at 1.5 K up to −30% at 3 K global warming mainly resulting from reduced precipitation. Small changes (< ±10%) are observed for river basins in Central Europe and the British Isles under different levels of warming. Projected higher annual precipitation increases high flows in Scandinavia, but reduced snow melt equivalent decreases flood events in this region. Neglecting uncertainties originating from internal climate variability, downscaling technique, and hydrologic model parameters, the contribution by the GCMs to the overall uncertainties of the ensemble is in general higher than that by the HMs. The latter, however, have a substantial share in the Mediterranean and Scandinavia. Adaptation measures for limiting the impacts of global warming could be similar under 1.5 K and 2 K global warming, but have to account for significantly higher changes under 3 K global warming.
- Published
- 2018
- Full Text
- View/download PDF
47. Soil moisture retrieval over the southern Great Plains: comparisons between experimental remote sensing data and operational products.
- Author
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Eric F. Wood, H. Gao, Matthias Drusch, Thomas J. Jackson, and R. Bindish
- Published
- 2003
- Full Text
- View/download PDF
48. Estimating evaporation from satellite remote sensing.
- Author
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Eric F. Wood, Hongbo Su, Matthew F. McCabe, and Bob Su
- Published
- 2003
- Full Text
- View/download PDF
49. The hydrosphere State (hydros) Satellite mission: an Earth system pathfinder for global mapping of soil moisture and land freeze/thaw.
- Author
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Dara Entekhabi, Eni G. Njoku, Paul R. Houser, Michael W. Spencer, Terence Doiron, Yunjin Kim, Joel Smith, Ralph Girard, Stephane Belair, Wade T. Crow, Thomas J. Jackson, Yann H. Kerr, John S. Kimball, Randy Koster, Kyle McDonald, Peggy O'Neill, Terry Pultz, Steven W. Running, Jiancheng Shi 0001, Eric F. Wood, and Jakob J. van Zyl
- Published
- 2004
- Full Text
- View/download PDF
50. Maximizing spatial congruence of observed and DEM-delineated overland flow networks.
- Author
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Theodore A. Endreny and Eric F. Wood
- Published
- 2003
- Full Text
- View/download PDF
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