Evaluation of soil moisture in CMIP5 simulations over the contiguous United States using in situ and satellite observations

Yuan, Shanshui; Quiring, Steven M.

doi:10.5194/hess-21-2203-2017

Cited by 60 publications

(76 citation statements)

References 68 publications

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“…For CMIP5, negative MBE values are mainly found in South America, northern Africa, and southwestern Asia, while positive MBE values are mainly located in western North America and northeastern Asia. The performance of CMIP5 in the U.S. is consistent with that of Yuan and Quiring (). The bar plots of Figure show that the distributions of percentage of areas with certain MBE for GLDAS are very close to normal distributions, while there are more areas with positive MBE than negative MBE for reanalysis and more areas with negative MBE than positive MBE for CMIP5 GCMs, respectively.…”

Section: Resultssupporting

confidence: 74%

“…Based on 26 CMIP5 GCMs, Ruoeteenoja et al () projected substantial decreases in long‐term mean soil moisture in southern Europe in all seasons. The results of previous studies, however, are highly sensitive to the soil moisture data sets used (e.g., Bi et al, ; Dorigo et al, ; Loew et al, ; Yuan & Quiring, ). For example, in Loew et al (), soil moisture from ERA‐Interim showed significant decreasing trends during 1979–2009 in Sahel, while soil moisture from the JSBACH LSM simulations showed trends of significant increase, significant decrease, and insignificant changes in different parts of Sahel.…”

Section: Introductionmentioning

confidence: 96%

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Consistency and Discrepancy of Global Surface Soil Moisture Changes From Multiple Model‐Based Data Sets Against Satellite Observations

Chen

et al. 2019

JGR Atmospheres

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A large population of global soil moisture data sets generated by a variety of models is compared with the latest satellite‐based Essential Climate Variable (ECV) soil moisture product in a common framework. The model‐based surface soil moisture data sets include Global Land Data Assimilation System (GLDAS), reanalysis products, Coupled Model Intercomparison Project Phase 5 Global Climate Models (GCMs), and Inter‐Sectoral Impact Model Intercomparison Project (including observation‐driven outputs ISI‐MIP_OBS and GCM‐driven outputs ISI‐MIP_GCM). We evaluate the model‐based surface soil moisture against ECV with focuses on spatial patterns, temporal correlations, long‐term trends, and relationships with precipitation and Normalized Difference Vegetation Index. The results indicate that all data sets reach a good agreement on the spatial patterns of surface soil moisture, which are also consistent with that of precipitation. However, data sets produced by different techniques have considerable discrepancies in the absolute values of surface soil moisture. Specifically, GCMs tend to underestimate the absolute values of surface soil moisture relative to ECV. In comparisons that remove the influence of absolute values (e.g., unbiased root‐mean‐square error), all model‐based data sets show comparable performances against ECV. GLDAS, reanalysis, and ISI‐MIP_OBS data sets show significant positive temporal correlations with ECV. Model‐based data sets and ECV consistently indicate widespread drying trends during 1980–2005, but the regional trends vary in different data sets. Compared to ECV, GLDAS and reanalysis data sets exhibit more intensive drying trends, while Coupled Model Intercomparison Project Phase 5 and ISI‐MIP_GCM tend to underestimate the drying. In most of the regions, the wetting/drying trends are consistent with the increases/decreases in precipitation and Normalized Difference Vegetation Index.

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Section: Resultssupporting

confidence: 74%

Section: Introductionmentioning

confidence: 96%

Consistency and Discrepancy of Global Surface Soil Moisture Changes From Multiple Model‐Based Data Sets Against Satellite Observations

Chen

et al. 2019

JGR Atmospheres

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“…The lack of CMIP5 model skill in reproduction near‐present observations of hydrological drought has been shown in earlier works (e.g., Santini and Caporaso, ; Ukkola et al ., ). However, other studies also confirmed the capabilities of CMIP5 models to capture the temporal variability and magnitude of observed soil moisture (e.g., Yuan and Quiring, ).…”

Section: Methodsmentioning

confidence: 99%

Global characterization of hydrological and meteorological droughts under future climate change: The importance of timescales, vegetation‐CO₂feedbacks and changes to distribution functions

Vicente‐Serrano

Domínguez‐Castro

McVicar

et al. 2019

Intl Journal of Climatology

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There is a strong scientific debate on how drought will evolve under future climate change. Climate model outputs project an increase in drought frequency and severity by the end of the 21st century. However, there is a large uncertainty related to the extent of the global land area that will be impacted by enhanced climatological and hydrological droughts. Although climate metrics suggest a likely strong increase in future drought severity, hydrologic metrics do not show a similar signal. In the literature, numerous attempts have been made to explain these differences using several physical mechanisms. This study provides evidence that characterization of drought from different statistical perspectives can lead to unreliable detection of climatological/hydrological droughts in model projections and accordingly give a “false alarm” of the impacts of future climate change. In particular, this study analyses future projections based on different drought metrics and stresses that detecting trends in drought behavior in future projections must consider the extreme character of drought events by comparing the percentage change in drought magnitude relative to a reference climatological period and rely on the frequency of events in the tail of the distribution. In addition, the autoregressive character of drought indices makes necessary the use of the same temporal scale when comparing different drought metrics in order to maintain comparability. Taking into consideration all these factors, our study demonstrates that climatological and hydrological drought trends are likely to undergo similar temporal evolution during the 21st century, with almost 30% of the global land areas experiencing water deficit under future greenhouse gas emissions scenarios. As such, a proper characterization of drought using comparable metrics can introduce lower differences and more consistent outputs for future climatic and hydrologic droughts.

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“…Compared to SMAP's limited resolution and time span, land surface models (LSMs), for example, VIC (Nijssen et al, ), Noah (Ek et al, ), CLSM (Koster et al, ), and MOS (Koster & Suarez, ), simulate soil moisture seamlessly over much longer time spans. Despite their frequent use, these models may differ significantly from observations (Dirmeyer et al, ; Leeper et al, ; Xia et al, ; Yuan & Quiring, ). Biases (mean difference from the observed) are notable in all models evaluated in Xia et al ().…”

Section: Introductionmentioning

confidence: 99%

Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network

Fang

Shen

Kifer

et al. 2017

Geophysical Research Letters

229

135

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The Soil Moisture Active Passive (SMAP) mission has delivered valuable sensing of surface soil moisture since 2015. However, it has a short time span and irregular revisit schedules. Utilizing a state-of-the-art time series deep learning neural network, Long Short-Term Memory (LSTM), we created a system that predicts SMAP level-3 moisture product with atmospheric forcings, model-simulated moisture, and static physiographic attributes as inputs. The system removes most of the bias with model simulations and improves predicted moisture climatology, achieving small test root-mean-square errors (<0.035) and high-correlation coefficients >0.87 for over 75% of Continental United States, including the forested southeast. As the first application of LSTM in hydrology, we show the proposed network avoids overfitting and is robust for both temporal and spatial extrapolation tests. LSTM generalizes well across regions with distinct climates and environmental settings. With high fidelity to SMAP, LSTM shows great potential for hindcasting, data assimilation, and weather forecasting. Plain Language Summary Soil moisture is the water content in soil, and it is a critical component of the water cycle. It controls whether crops or wild vegetation can function properly, the risk of wildfire, and the likelihood of floods. The NASA satellite SMAP, launched in 2015, measures soil moisture near the ground surface over Earth at high accuracy. SMAP data are of great value to global communities to which soil moisture is relevant. However, since it was launched only recently, there is only little overlap with other data sets, which limits its use. To extend SMAP's observations in time, we employ a "deep learning" technology called Long Short-Term Memory (LSTM), which is one of the pillars of artificial intelligence and is bringing revolutionary changes in many scientific fields and our daily lives. We use LSTM to learn patterns of soil moisture dynamics and where physics-based models make mistakes in describing moisture changes. This approach shows great promise for projecting SMAP observations into the long past: because soil moisture has a short memory, 2 years of data seem sufficient to train LSTM to successfully capture its dynamics. Because LSTM can handle large and diverse data, it offers valuable alternatives to older statistical methods.

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Evaluation of soil moisture in CMIP5 simulations over the contiguous United States using in situ and satellite observations

Cited by 60 publications

References 68 publications

Consistency and Discrepancy of Global Surface Soil Moisture Changes From Multiple Model‐Based Data Sets Against Satellite Observations

Consistency and Discrepancy of Global Surface Soil Moisture Changes From Multiple Model‐Based Data Sets Against Satellite Observations

Global characterization of hydrological and meteorological droughts under future climate change: The importance of timescales, vegetation‐CO₂feedbacks and changes to distribution functions

Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network

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Evaluation of soil moisture in CMIP5 simulations over the contiguous United States using in situ and satellite observations

Cited by 60 publications

References 68 publications

Consistency and Discrepancy of Global Surface Soil Moisture Changes From Multiple Model‐Based Data Sets Against Satellite Observations

Consistency and Discrepancy of Global Surface Soil Moisture Changes From Multiple Model‐Based Data Sets Against Satellite Observations

Global characterization of hydrological and meteorological droughts under future climate change: The importance of timescales, vegetation‐CO2feedbacks and changes to distribution functions

Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network

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Global characterization of hydrological and meteorological droughts under future climate change: The importance of timescales, vegetation‐CO₂feedbacks and changes to distribution functions