Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index

Xu, Lei; Abbaszadeh, Peyman; Moradkhani, Hamid; Chen, Nengcheng; Zhang, Xiang

doi:10.1016/j.rse.2020.112028

Cited by 113 publications

(69 citation statements)

References 71 publications

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“…It is known that soil moisture has remarkable impacts on the exchanges of water, energy and carbon fluxes between land surface, vegetation, and atmosphere (Western et al, 2004;Fischer et al, 2007;Trenberth et al, 2007;Qiu et al, 2016;Gu et al, 2019a;Hu et al, 2021). Moreover, it is used to quantify the irrigation water consumption (Jalilvand et al, 2019) and to monitor the drounght variation (Dai et al, 2004;Li et al, 2017;Hu et al, 2019a;Xu et al, 2020). Therefore, the accurate representation and estimation of soil moisture in hydrological models will control the performances of simulations and predications of the hydrological cycle, such as the estimation of groundwater (Scanlon et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Variations of Soil Moisture Over Xinjiang Based on Multiple GLDAS Datasets

Chen

et al. 2021

Front. Earth Sci.

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Under the global warming, as the typical arid region of Central Asia, the Xinjiang Uygur Autonomous Region (Xinjiang) has been experienced the remarkable warming and increased precipitation based on large previous studies. The arid and semiarid ecosystem of Xinjiang is very sensitive and vulnerable to climate change and water resource variations. However, the sparse and highly unevenly distributed in-situ stations in this region provide limited data for understanding of the soil moisture variations. In this study, the spatial and temporal changes and variations of soil moisture were explored at annual and seasonal time scales during the period of 2000–2017. The soil moisture data are from the Global Land Data Assimilation System (GLDAS) models, including four GLDAS 1 models: CLM, Mosaic, VIC and Noah 2.7 and one GLDAS 2.1 model: Noah 3.3. Major results show that 1) Noah 3.3 and VIC have the significant positive trends of annual soil moisture with the values of 2.64°mm/a and 0.98°mm/a. The trend of CLM is significant negative. The other two models Mosaic and Noah 2.7 have the weak positive trends. The temporal variations of seasonal soil moisutre are similar the annual soil moisture for each of the model. 2) For the spatial characteristics of the soil mositure variations, CLM displays the negative trends over large part of Xinjiang. Mosaic and VIC have the similar spatial characteristics of the linear trends. Noah 3.3 has the significant positive trends over almost Xinjiang which is different with Noah 2.7. All the five models have the positive trends over KLM. Our results have a better understanding of the soil moisture variations across Xinjiang, and they also enhance the reconginzing of the complex hydrological circulation in the arid regions.

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Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Variations of Soil Moisture Over Xinjiang Based on Multiple GLDAS Datasets

Chen

et al. 2021

Front. Earth Sci.

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“…The SPI, SPEI and RDI Drought indices are important and effective elements for providing a comprehensive picture of drought to plan, monitor, and quantify drought and its risk management [45,46]. Multiscale drought meteorological drought indices are commonly used as proxies for characterising soil moisture at different layers [47,48]. However, due to drought complexity, using a single index is not sufficient for precisely identifying the drought dimensions [49], and the combined use of several different drought indices would give an actual and clearer picture of drought conditions [50].…”

Section: Selection Of Drought Indicatorsmentioning

confidence: 99%

Mediterranean-Scale Drought: Regional Datasets for Exceptional Meteorological Drought Events during 1975–2019

et al. 2021

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Drought is one of the most complex climate-related phenomena and is expected to progressively affect our lives by causing very serious environmental and socioeconomic damage by the end of the 21st century. In this study, we have extracted a dataset of exceptional meteorological drought events between 1975 and 2019 at the country and subregional scales. Each drought event was described by its start and end date, intensity, severity, duration, areal extent, peak month and peak area. To define such drought events and their characteristics, separate analyses based on three drought indices were performed at 12-month timescale: the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Reconnaissance Drought Index (RDI). A multivariate combined drought index (DXI) was developed by merging the previous three indices for more understanding of droughts’ features at the country and subregional levels. Principal component analysis (PCA) was used to identify five different drought subregions based on DXI-12 values for 312 Mediterranean stations and a new special score was defined to classify the multi-subregional exceptional drought events across the Mediterranean Basin (MED). The results indicated that extensive drought events occurred more frequently since the late 1990s, showing several drought hotspots in the last decades in the southeastern Mediterranean and northwest Africa. In addition, the results showed that the most severe events were more detected when more than single drought index was used. The highest percentage area under drought was also observed through combining the variations of three drought indices. Furthermore, the drought area in both dry and humid areas in the MED has also experienced a remarkable increase since the late 1990s. Based on a comparison of the drought events during the two periods—1975–1996 and 1997–2019—we find that the current dry conditions in the MED are more severe, intense, and frequent than the earlier period; moreover, the strongest dry conditions occurred in last two decades. The SPEI-12 and RDI-12 have a higher capacity in providing a more comprehensive description of the dry conditions because of the inclusion of temperature or atmospheric evaporative demand in their scheme. A complex range of atmospheric circulation patterns, particularly the Western Mediterranean Oscillation (WeMO) and East Atlantic/West Russia (EATL/WRUS), appear to play an important role in severe, intense and region-wide droughts, including the two most severe droughts, 1999–2001 and 2007–2012, with lesser influence of the NAO, ULMO and SCAND.

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“…Equations (10)(11)(12)(13)(14) summarize the ET formulation in the proposed model. Figure 4 shows an example the three-stage ET coefficient (C i ) for a given soil layer and residual soil moisture.…”

Section: B Richard's Equation Implementation In Hlm (Hlmr)mentioning

confidence: 99%

“…Satellite soil moisture missions provide an unprecedented opportunity to study the global water cycle (e.g., [7]- [9]). They offer information on soil moisture that improves flood and drought predictions (e.g., [3], [10], [11]). Two current Lband soil moisture missions are SMOS (Soil Moisture Ocean Salinity) and SMAP (Soil Moisture Active Passive), which have been in operation from 2010 and 2015, respectively [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Potential of SMAP Soil Moisture for Improving Real-time Streamflow Prediction in the U.S. Corn Belt

Jadidoleslam¹,

Hornbuckle²,

Krajewski³

et al. 2021

Preprint

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L-band microwave satellite missions provide soil moisture information potentially useful for streamflow and hence flood predictions. However, these observations are also sensitive to the presence of vegetation that makes satellite soil moisture estimations prone to errors. In this study, the authors evaluate satellite soil moisture estimations from SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity), and two distributed hydrologic models with measurements from in~situ sensors in the Corn Belt state of Iowa, a region dominated by annual row crops of corn and soybean. First, the authors compare model and satellite soil moisture products across Iowa using in~situ data for more than 30 stations. Then, they compare satellite soil moisture products with state-wide model-based fields to identify regions of low and high agreement. Finally, the authors analyze and explain the resulting spatial patterns with MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation indices and SMAP vegetation optical depth. The results indicate that satellite soil moisture estimations are drier than those provided by the hydrologic model and the spatial bias depends on the intensity of row-crop agriculture. The work highlights the importance of developing a revised SMAP algorithm for regions of intensive row-crop agriculture to increase SMAP utility in the real-time streamflow predictions.

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Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index

Cited by 113 publications

References 71 publications

Temporal and Spatial Variations of Soil Moisture Over Xinjiang Based on Multiple GLDAS Datasets

Temporal and Spatial Variations of Soil Moisture Over Xinjiang Based on Multiple GLDAS Datasets

Mediterranean-Scale Drought: Regional Datasets for Exceptional Meteorological Drought Events during 1975–2019

Exploring the Potential of SMAP Soil Moisture for Improving Real-time Streamflow Prediction in the U.S. Corn Belt

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