Soil Moisture Drought in China, 1950–2006

Abstract Four physically based land surface hydrology models driven by a common observation-based 3-hourly meteorological dataset were used to simulate soil moisture over China for the period 1950–2006. Monthly values of total column soil moisture from the simulations were converted to percentiles and an ensemble method was applied to combine all model simulations into a multimodel ensemble from which agricultural drought severities and durations were estimated. A cluster analysis method and severity–area–duration (SAD) algorithm were applied to the soil moisture data to characterize drought spatial and temporal variability. For drought areas greater than 150 000 km2 and durations longer than 3 months, a total of 76 droughts were identified during the 1950–2006 period. The duration of 50 of these droughts was less than 6 months. The five most prominent droughts, in terms of spatial extent and then duration, were identified. Of these, the drought of 1997–2003 was the most severe, accounting for the majority of the severity–area–duration envelope of events with areas smaller than 5 million km2. The 1997–2003 drought was also pervasive in terms of both severity and spatial extent. It was also found that soil moisture in north central and northeastern China had significant downward trends, whereas most of Xinjiang, the Tibetan Plateau, and small areas of Yunnan province had significant upward trends. Regions with downward trends were larger than those with upward trends (37% versus 26% of the land area), implying that over the period of analysis, the country has become slightly drier in terms of soil moisture. Trends in drought severity, duration, and frequency suggest that soil moisture droughts have become more severe, prolonged, and frequent during the past 57 yr, especially for northeastern and central China, suggesting an increasing susceptibility to agricultural drought.

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Reconstruction of droughts in India using multiple land-surface models (1951–2015)

Hydrology and Earth System Sciences ◽

10.5194/hess-22-2269-2018 ◽

2018 ◽

Vol 22 (4) ◽

pp. 2269-2284 ◽

Cited By ~ 26

Author(s):

Vimal Mishra ◽

Reepal Shah ◽

Syed Azhar ◽

Harsh Shah ◽

Parth Modi ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Crop Yields ◽

Soil Depth ◽

Monsoon Season ◽

Agricultural Drought ◽

Drought Severity ◽

Rabi Season ◽

Community Land Model ◽

Spatial Coverage

Abstract. India has witnessed some of the most severe historical droughts in the current decade, and severity, frequency, and areal extent of droughts have been increasing. As a large part of the population of India is dependent on agriculture, soil moisture drought affecting agricultural activities (crop yields) has significant impacts on socio-economic conditions. Due to limited observations, soil moisture is generally simulated using land-surface hydrological models (LSMs); however, these LSM outputs have uncertainty due to many factors, including errors in forcing data and model parameterization. Here we reconstruct agricultural drought events over India during the period of 1951–2015 based on simulated soil moisture from three LSMs, the Variable Infiltration Capacity (VIC), the Noah, and the Community Land Model (CLM). Based on simulations from the three LSMs, we find that major drought events occurred in 1987, 2002, and 2015 during the monsoon season (June through September). During the Rabi season (November through February), major soil moisture droughts occurred in 1966, 1973, 2001, and 2003. Soil moisture droughts estimated from the three LSMs are comparable in terms of their spatial coverage; however, differences are found in drought severity. Moreover, we find a higher uncertainty in simulated drought characteristics over a large part of India during the major crop-growing season (Rabi season, November to February: NDJF) compared to those of the monsoon season (June to September: JJAS). Furthermore, uncertainty in drought estimates is higher for severe and localized droughts. Higher uncertainty in the soil moisture droughts is largely due to the difference in model parameterizations (especially soil depth), resulting in different persistence of soil moisture simulated by the three LSMs. Our study highlights the importance of accounting for the LSMs' uncertainty and consideration of the multi-model ensemble system for the real-time monitoring and prediction of drought over India.

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On the utility of land surface models for agricultural drought monitoring

Hydrology and Earth System Sciences ◽

10.5194/hess-16-3451-2012 ◽

2012 ◽

Vol 16 (9) ◽

pp. 3451-3460 ◽

Cited By ~ 42

Author(s):

W. T. Crow ◽

S. V. Kumar ◽

J. D. Bolten

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Cross Correlation ◽

Root Zone ◽

Vegetation Indices ◽

Agricultural Drought ◽

Drought Monitoring ◽

Global Evaluation ◽

Land Surface Models ◽

Surface Models

Abstract. The lagged rank cross-correlation between model-derived root-zone soil moisture estimates and remotely sensed vegetation indices (VI) is examined between January 2000 and December 2010 to quantify the skill of various soil moisture models for agricultural drought monitoring. Examined modeling strategies range from a simple antecedent precipitation index to the application of modern land surface models (LSMs) based on complex water and energy balance formulations. A quasi-global evaluation of lagged VI/soil moisture cross-correlation suggests, when globally averaged across the entire annual cycle, soil moisture estimates obtained from complex LSMs provide little added skill (< 5% in relative terms) in anticipating variations in vegetation condition relative to a simplified water accounting procedure based solely on observed precipitation. However, larger amounts of added skill (5–15% in relative terms) can be identified when focusing exclusively on the extra-tropical growing season and/or utilizing soil moisture values acquired by averaging across a multi-model ensemble.

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Characterising recent drought events using current reanalysis and remote-sensing soil moisture products within a common framework

10.5194/egusphere-egu21-4483 ◽

2021 ◽

Author(s):

Martin Hirschi ◽

Bas Crezee ◽

Sonia I. Seneviratne

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Land Surface ◽

Root Zone ◽

Microwave Remote Sensing ◽

Agricultural Drought ◽

Climate Data ◽

Data Store ◽

Common Framework ◽

Long Term Trends

Drought events cause multiple impacts on the environment, the society and the economy. Here, we analyse recent major drought events with different metrics using a common framework. The analysis is based on current reanalysis (ERA5, ERA5-Land, MERRA-2) and merged remote-sensing products (ESA-CCI soil moisture, gridded satellite soil moisture from the Copernicus Climate Data Store), focusing on soil moisture (or agricultural) drought. The events are characterised by their severity, magnitude, duration and spatial extent, which are calculated from standardised daily anomalies of surface and root-zone soil moisture. We investigate the ability of the different products to represent the droughts and set the different events in context to each other. The considered products also offer opportunities for drought monitoring since they are available in near-real time.All investigated products are able to represent the investigated drought events. Overall, ERA5 and ERA5-Land often show the strongest, and the remote-sensing products often weaker responses based on surface soil moisture. The weaker severities of the events in the remote-sensing products are both related to shorter event durations as well as less pronounced average negative standardised soil moisture anomalies, while the magnitudes (i.e., the minimum of the standardised anomalies over time) are comparable to the reanalysis products. Differing global distributions of long-term trends may explain some differences in the drought responses of the products. Also, the lower penetration depth of microwave remote sensing compared to the top layer of the involved land surface models could explain the partly weaker negative standardized soil moisture anomalies in the remote-sensing products during the investigated events. In the root zone (based on the reanalysis products), the drought events often show prolonged durations, but weaker magnitudes and smaller spatial extents.

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A Soil Moisture Spatial and Temporal Resolution Improving Algorithm Based on Multi-Source Remote Sensing Data and GRNN Model

Remote Sensing ◽

10.3390/rs12030455 ◽

2020 ◽

Vol 12 (3) ◽

pp. 455 ◽

Cited By ~ 5

Author(s):

Yaokui Cui ◽

Xi Chen ◽

Wentao Xiong ◽

Lian He ◽

Feng Lv ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Temporal Resolution ◽

Land Surface ◽

Regional Scale ◽

Remote Sensing Data ◽

The Tibetan Plateau ◽

Sensing Data ◽

Spatio Temporal ◽

Input Variables

Surface soil moisture (SM) plays an essential role in the water and energy balance between the land surface and the atmosphere. Low spatio-temporal resolution, about 25–40 km and 2–3 days, of the commonly used global microwave SM products limits their application at regional scales. In this study, we developed an algorithm to improve the SM spatio-temporal resolution using multi-source remote sensing data and a machine-learning model named the General Regression Neural Network (GRNN). First, six high spatial resolution input variables, including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), albedo, Digital Elevation Model (DEM), Longitude (Lon) and Latitude (Lat), were selected and gap-filled to obtain high spatio-temporal resolution inputs. Then, the GRNN was trained at a low spatio-temporal resolution to obtain the relationship between SM and input variables. Finally, the trained GRNN was driven by the high spatio-temporal resolution input variables to obtain high spatio-temporal resolution SM. We used the Fengyun-3B (FY-3B) SM over the Tibetan Plateau (TP) to test the algorithm. The results show that the algorithm could successfully improve the spatio-temporal resolution of FY-3B SM from 0.25° and 2–3 days to 0.05° and 1-day over the TP. The improved SM is consistent with the original product in terms of both spatial distribution and temporal variation. The high spatio-temporal resolution SM allows a better understanding of the diurnal and seasonal variations of SM at the regional scale, consequently enhancing ecological and hydrological applications, especially under climate change.

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Commonly Used Drought Indices as Indicators of Soil Moisture in China

Journal of Hydrometeorology ◽

10.1175/jhm-d-14-0076.1 ◽

2015 ◽

Vol 16 (3) ◽

pp. 1397-1408 ◽

Cited By ~ 43

Author(s):

Hongshuo Wang ◽

Jeffrey C. Rogers ◽

Darla K. Munroe

Keyword(s):

Soil Moisture ◽

Time Scales ◽

Standardized Precipitation Index ◽

Meteorological Drought ◽

Severity Index ◽

Agricultural Drought ◽

Weather Data ◽

Drought Indices ◽

Drought Severity ◽

Better Than

Abstract Soil moisture shortages adversely affecting agriculture are significantly associated with meteorological drought. Because of limited soil moisture observations with which to monitor agricultural drought, characterizing soil moisture using drought indices is of great significance. The relationship between commonly used drought indices and soil moisture is examined here using Chinese surface weather data and calculated station-based drought indices. Outside of northeastern China, surface soil moisture is more affected by drought indices having shorter time scales while deep-layer soil moisture is more related on longer index time scales. Multiscalar drought indices work better than drought indices from two-layer bucket models. The standardized precipitation evapotranspiration index (SPEI) works similarly or better than the standardized precipitation index (SPI) in characterizing soil moisture at different soil layers. In most stations in China, the Z index has a higher correlation with soil moisture at 0–5 cm than the Palmer drought severity index (PDSI), which in turn has a higher correlation with soil moisture at 90–100-cm depth than the Z index. Soil bulk density and soil organic carbon density are the two main soil properties affecting the spatial variations of the soil moisture–drought indices relationship. The study may facilitate agriculture drought monitoring with commonly used drought indices calculated from weather station data.

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A Spatial and Temporal Continuum Remotely Sensed Soil Moisture Dataset of the Tibet Plateau From 2002 to 2015

10.5194/egusphere-egu2020-20846 ◽

2020 ◽

Author(s):

Yaokui Cui ◽

Chao Zeng ◽

Jie Zhou ◽

Xi Chen

Keyword(s):

Soil Moisture ◽

Tibetan Plateau ◽

Land Surface ◽

European Space Agency ◽

Remotely Sensed ◽

General Regression Neural Network ◽

Tibet Plateau ◽

The Tibetan Plateau ◽

Space Agency ◽

Spatio Temporal

Abstract:Surface soil moisture plays an important role in the exchange of water and energy between the land surface and the atmosphere, and critical to climate change study. The Tibetan Plateau (TP), known as &#8220;The third pole of the world&#8221; and &#8220;Asia&#8217;s water towers&#8221;, exerts huge influences on and sensitive to global climates. Long time series of and spatio-temporal continuum soil moisture is helpful to understand the role of TP in this situation. In this study, a dataset of 14-year (2002&#8211;2015) Spatio-temporal continuum remotely sensed soil moisture of the TP at 0.25&#176; resolution is obtained, combining MODIS optical products and ESA (European Space Agency) ECV (Essential Climate Variable) combined soil moisture products based on General Regression Neural Network (GRNN). The validation of the dataset shows that the soil moisture is well reconstructed with R2 larger than 0.65, and RMSE less than 0.08 cm3 cm-3 and Bias less than 0.07 cm3 cm-3 at 0.25&#176; and 1&#176; spatial scale, compared with the in-situ measurements in the central of TP. And then, spatial&#160;and&#160;temporal&#160;characteristics and trend of SM over TP were analyzed based on this dataset.Keywords: Soil moisture; Remote Sensing; Dataset; GRNN; ECV; Tibetan Plateau

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Comparing soil moisture anomalies from multiple independent sources over different regions across the globe

10.5194/hess-2017-196 ◽

2017 ◽

Cited By ~ 2

Author(s):

Carmelo Cammalleri ◽

Jürgen V. Vogt ◽

Bernard Bisselink ◽

Ad de Roo

Keyword(s):

Soil Moisture ◽

North America ◽

Southern Africa ◽

Land Surface ◽

Root Zone ◽

Agricultural Drought ◽

Drought Monitoring ◽

Distributed Hydrological Model ◽

Ground Station ◽

Insight Into

Abstract. Agricultural drought events can affect large regions across the World, implying the urge for a suitable global tool for an accurate monitoring of this phenomenon. Soil moisture anomalies are considered a good metric to capture the occurrence of agricultural drought events, and they have become an important component of several operational drought monitoring systems. In the framework of the JRC Global Drought Observatory (GDO, http://edo.jrc.ec.europa.eu/gdo/) the suitability of modelled and/or satellite-derived proxy of soil moisture anomalies was investigated. In this study, three datasets have been evaluated as possible proxies of root zone soil moisture anomalies: (1) soil moisture from the Lisflood distributed hydrological model (LIS), (2) remotely sensed land surface temperature data from the MODIS satellite (LST), and (3) the combined passive/active microwave skin soil moisture dataset developed by ESA (CCI). Due to the independency of these three datasets, the Triple Collocation (TC) technique has been applied, aiming at quantifying the likely error associated to each dataset in comparison to the unknown true status of the system. TC analysis was performed on five macro-regions (namely North America, Europe, India, Southern Africa and Australia) detected as suitable for the experiment, providing insight into the mutual relationship between these datasets as well as assessment of the accuracy of each method. A clear outcome of the TC analysis is the good performance of remote sensing datasets, especially CCI, over dry regions such as Australia and Southern Africa, whereas the outputs of LIS seem to be more reliable over areas that are well monitored through meteorological ground station networks, such as North America and Europe. In a global drought monitoring system, these results can be used to design an ensemble system that exploits the advantages of each dataset.

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LDAS-Monde Sequential Assimilation of Satellite Derived Observations Applied to the CONtiguous US: An ERA-5 Driven Reanalysis of the Land Surface Variables

10.20944/preprints201809.0105.v1 ◽

2018 ◽

Author(s):

Clément Albergel ◽

Simon Munier ◽

Aymeric Bocher ◽

Bertrand Bonan ◽

Yongjun Zheng ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Initial Conditions ◽

The United States ◽

Agricultural Drought ◽

Reference Network ◽

Surface Model ◽

Area Index ◽

Medium Range Weather Forecast ◽

The Impact

LDAS-Monde, an offline land data assimilation system with global capacity, is applied over the CONtiguous US (CONUS) domain to enhance monitoring accuracy for water and energy states and fluxes. LDAS-Monde ingests satellite-derived Surface Soil Moisture (SSM) and Leaf Area Index (LAI) estimates to constrain the Interactions between Soil, Biosphere, and Atmosphere (ISBA) Land Surface Model (LSM) coupled with the CNRM (Centre National de Recherches Météorologiques) version of the Total Runoff Integrating Pathways (CTRIP) continental hydrological system (ISBA-CTRIP). LDAS-Monde is forced by the ERA-5 atmospheric reanalysis from the European Center For Medium Range Weather Forecast (ECMWF) from 2010 to 2016 leading to a 7-yr, quarter degree spatial resolution offline reanalysis of Land Surface Variables (LSVs) over CONUS. The impact of assimilating LAI and SSM into LDAS-Monde is assessed over North America, by comparison to satellite-driven model estimates of land evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM) project, and upscaled ground-based observations of gross primary productivity from the FLUXCOM project. Also, taking advantage of the relatively dense data networks over CONUS, we also evaluate the impact of the assimilation against in-situ measurements of soil moisture from the USCRN network (US Climate Reference Network) are used in the evaluation, together with river discharges from the United States Geophysical Survey (USGS) and the Global Runoff Data Centre (GRDC). Those data sets highlight the added value of assimilating satellite derived observations compared to an open-loop simulation (i.e. no assimilation). It is shown that LDAS-Monde has the ability not only to monitor land surface variables but also to forecast them, by providing improved initial conditions which impacts persist through time. LDAS-Monde reanalysis has a potential to be used to monitor extreme events like agricultural drought, also. Finally, limitations related to LDAS-Monde and current satellite-derived observations are exposed as well as several insights on how to use alternative datasets to analyze soil moisture and vegetation state.

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Development of a Distributed Hydrologic Model for a Region with Fragipan Soils to Study Impacts of Climate on Soil Moisture: A Case Study on the Obion River Watershed in West Tennessee

Geosciences ◽

10.3390/geosciences8100364 ◽

2018 ◽

Vol 8 (10) ◽

pp. 364

Author(s):

Seyed Ghaneeizad ◽

Athanasios Papanicolaou ◽

Benjamin Abban ◽

Christopher Wilson ◽

Christos Giannopoulos ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Model Performance ◽

Hydrologic Model ◽

Infiltration Capacity ◽

River Watershed ◽

Saturation Zone ◽

Variable Infiltration Capacity Model ◽

Capacity Model ◽

Physically Based

Previous land surface modeling efforts to predict and understand water budgets in the U.S. Southeast for soil water management have struggled to characterize parts of the region due to an extensive presence of fragipan soils for which current calibration approaches are not adept at handling. This study presents a physically based approach for calibrating fragipan-dominated regions based on the “effective” soil moisture capacity concept, which accounts for the dynamic perched saturation zone effects created by the low hydraulic capacities of the fragipan layers. The approach is applied to the Variable Infiltration Capacity model to develop a hydrologic model of the Obion River Watershed (ORW), TN, which has extensive fragipan coverage. Model calibration was performed using observed streamflow data, as well as evapotranspiration and soil moisture data, to ensure correct partitioning of surface and subsurface fluxes. Estimated Nash-Sutcliffe coefficients for the various sub-drainage areas within ORW were all greater than 0.65, indicating good model performance. The model results suggest that ORW has a high responsivity and high resilience. Despite forecasted temperature increases, the simulation results suggest that water budget trends in the ORW are unlikely to change significantly in the near future up to 2050 due to sufficient precipitation amounts.

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Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau

Remote Sensing ◽

10.3390/rs12030509 ◽

2020 ◽

Vol 12 (3) ◽

pp. 509 ◽

Cited By ~ 6

Author(s):

Ruodan Zhuang ◽

Yijian Zeng ◽

Salvatore Manfreda ◽

Zhongbo Su

Keyword(s):

Soil Moisture ◽

Tibetan Plateau ◽

Land Surface ◽

Root Zone ◽

Cumulative Distribution ◽

Analytical Relationship ◽

The Tibetan Plateau ◽

Climate Systems

It is crucial to monitor the dynamics of soil moisture over the Tibetan Plateau, while considering its important role in understanding the land-atmosphere interactions and their influences on climate systems (e.g., Eastern Asian Summer Monsoon). However, it is very challenging to have both the surface and root zone soil moisture (SSM and RZSM) over this area, especially the study of feedbacks between soil moisture and climate systems requires long-term (e.g., decadal) datasets. In this study, the SSM data from different sources (satellites, land data assimilation, and in-situ measurements) were blended while using triple collocation and least squares method with the constraint of in-situ data climatology. A depth scaling was performed based on the blended SSM product, using Cumulative Distribution Function (CDF) matching approach and simulation with Soil Moisture Analytical Relationship (SMAR) model, to estimate the RZSM. The final product is a set of long-term (~10 yr) consistent SSM and RZSM product. The inter-comparison with other existing SSM and RZSM products demonstrates the credibility of the data blending procedure used in this study and the reliability of the CDF matching method and SMAR model in deriving the RZSM.

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