1. Introduction
Soil moisture (SM) refers to the wetness of the soil and is typically associated with water in the unsaturated soil zone. It is an important component of terrestrial hydrology and ecosystems, influencing the exchange of energy, water, and carbon within the soil–vegetation–atmosphere system. Soil moisture feedback may alter the atmospheric water vapor and heat states via regulation of evapotranspiration, and hence it provides a source for climate prediction (Koster et al. 2010). In ecosystem life cycle, SM significantly influences the plant phenology, and SM shortage will lead to agricultural drought and then result in crop failure. Reliable SM is crucial for many aspects of hydrometeorology, including drought reconstruction and identification (Wang et al. 2011; Wang and Kong 2021), climate variability (Yang et al. 2016), and climate prediction (Xue et al. 2017). The World Climate Research Program (WCRP) International phase 6 of the Coupled Model Intercomparison Project (CMIP6) has launched a subproject, the Land Surface, Snow, and Soil Moisture Model Intercomparison Project (LS3MIP), in which SM is one of the key surface elements (van den Hurk et al. 2016).
Globally, the importance of SM in the climate system has been recognized for decades, and SM measurement at point sites has been conducted since a very early era. Several research organizations have collected SM station measurements from different countries to construct the global databases (Robock et al. 2000; Dorigo et al. 2011). Land surface models driven by observation-based atmospheric forcing datasets have been extensively applied to reproduce SM datasets. The atmospheric reanalysis SM data have been evaluated and applied in numerous research studies. In addition, remote sensing–based and artificial intelligent (AI)-powered SM products have also substantially developed in recent years (Dorigo et al. 2017; Vance et al. 2024). These SM datasets have been extensively used in numerous studies on agriculture, hydrology, and climate.
China is a large country with a wide range of climates and notable geographical heterogeneity. Since the Chinese mainland’s environment varies from wet in the southeast to arid in the northwest, SM shows great geographical variance across the continent (Wang et al. 2019). China is also a big agricultural country, with more than 500 million square kilometers of agricultural area. Chinese scientists have recognized the importance of SM to agriculture for quite some time. For example, in the early 1950s, Chinese researchers already started to observe the specific plant growth under different soil water levels to advise crop planting (Chen 1952). After that, crop management has gradually investigated how SM influences single crop species, individual trees, and crop yields in small areas (Zhuang 1989). Until the early 1980s, China began measuring SM at a few locations in its primary agricultural regions (Robock et al. 2000). Over the last few decades, an in situ SM observation network has been established substantially, and currently more than 2000 stations provide routine measurements (Xue and Ye 2013). Furthermore, a large number of high-resolution SM products have been produced based on satellite remote sensing retrieval, numerical simulation, as well as the innovative artificial intelligence technology. Particularly, the advanced methods have greatly improved the accuracy of SM on a fine spatiotemporal scale over large areas.
In the past decades, Chinese scientists have made significant contributions to SM data reproduction and research applications. This paper aims to review those developments regarding the state of SM and their hydrometeorological applications in China. The data sources, variability, and several major applications are discussed and summarized. We attempt to provide a comprehensive review of various viewpoints on SM from recent decades in China, which will greatly benefit for future development of high-quality SM products as well as their application in various research fields. The remaining contexts are arranged as follows. Section 2 describes different SM terminologies and forms. Section 3 overviews major SM data sources in China along with their advantages and disadvantages. Section 4 discusses three SM hydrometeorological applications in China. Section 5 presents a summary and also provides some recommendations for future SM data development and their applications in China.
2. Terminology and definitions
Soil moisture is a conventional name for representative moisture in the vadose zone. The soil column contains pores, which are mainly occupied by water and air. The fraction of those pores per unit volume of soil is defined as soil porosity (SP), which is the upper limit of soil water. It can be calculated by the bulk density (BD) and particle density (PD): SP = 1 − BD/PD, where BD is the mass of solid particles (Wd) divided by the total volume of solids and pores (Vv) and its values usually varying 1.0–1.6 g cm−3 in China mainland (Shangguan et al. 2014; Wang et al. 2019), PD is usually assumed as 2.65 g cm3 (Maidment 1993), and then SP is generally below 0.55 cm3 cm−3 for typical soils.
Soil moisture can be expressed in different forms for specific purposes. The commonly used SM expressions for research in hydrometeorology are briefly summarized below.
- 1)Plant-available moisture (/water) refers to the amount of water in the soil that can be used for plant growth, with values ranging from the permanent wilting point (PWP) to field capacity (FC). PWP denotes the point at which plants are no longer able to uptake water from soil for transpiration, and it is the minimum soil water requirement for vegetation growth. FC is the maximum water content that the soil can hold and is also the threshold of the occurrence of gravity drainage (Robock et al. 2000).
- 2)Absolute soil water content (Wm) is the entire amount of water, including liquid and ice in the soil column and is commonly used to calculate the water budgets in the atmosphere–vegetation–soil system. The Wm is also employed in the numerical model procedures to ensure water conservation during model time integration. Its unit is normally kilograms per square meter, but it can also be as centimeters or millimeters when the density of liquid water (ρw) is taken to 1.0 × 103 kg m−3.
- 3)The relative wetness of soil (R) is the percentage ratio of total soil water volume (Wm / Wd) to FC (i.e., R = Wm / Wd / FC × 100%). It ranges from 0% (drying up) to 100% (saturated). It usually adopts to record the manually measured water contents of soil samples in the laboratory, e.g., the measured SM by the gravimetric method. It is common practice to manually assess the water content of soil samples by gravimetrically weighed SM measurement in the early period.
- 4)The volumetric SM (θυ; cm3 cm−3 or kg m−3) is the most common SM expression, indicating the volumetric water content at a specific soil depth. The θυ can refer to either liquid, solid, or total water in soil. Unless otherwise noted, the expression of SM is referred to as θυ in the following sections. According to the mass–volume law (Maidment 1993), there is an equation to describe the relationships of the definitions of soil moisture as follows:
This SM expression is also a standard output for most land or climate models.
3. Soil moisture data sources in China
a. In situ measurement.
The ground-based measurement is a contact-based method, and SM records can be collected directly, while SM from the contact-free methods rely on specific relationships derived from physical or empirical constraints (Robinson et al. 2008). A more recent study on observation-based SM datasets highlighted the spatial-scale issues, particularly for the satellite-based SM datasets from retrieval methods, validation, and analysis (Babaeian et al. 2019). All of those approaches, when combined, may yield cross-scale SM datasets that can serve for a variety of applications.
The gravimetrically weighed technique is conventionally used in SM measurement at agrometeorology stations around the world since its inception (Susha Lekshmi et al. 2014). Soil samples are dug at certain underground depths, then weighed, oven dried, and weighed again. The absolute soil water content Wm is obtained by comparing the weights of raw and oven-dried samples. Traditionally, in situ measured SM was primarily for agricultural applications; thus, the gravimetrically weighed technique was only undertaken during the growing season at the agrometeorological stations. In China, measurements were usually taken on the 8, 18, and 28 from April to September at each 10-cm depth from the ground surface to 1 m since 1980s. Robock et al. (2000) collected SM observations over 600 stations worldwide and produced the Global Soil Moisture Data Bank (GSMDB), which includes in situ SM data at 43 Chinese stations from 1981 to 1991. The GSMDB was lately incorporated into the International Soil Moisture Network (ISMN; https://ismn.earth/en/; Dorigo et al. 2011), which now collects SM from over 3000 stations across 81 networks in various countries, including 393 stations from nine Chinese networks (Dorigo et al. 2011, accessed on 14 September 2024). Those SM data are obtained through a variety of techniques, including the gravimetric method, neutron probes, electromagnetic techniques, and other indirect techniques. Among these techniques, the gravimetric one is inefficient and is not suitable for conducting over large areas. With the advent of modern technology, it has been gradually replaced by the automatic sensors that can measure SM over large areas with high spatial and temporal resolution SM measurements (Robinson et al. 2008). Since 2011, the China Meteorological Administration (CMA) has begun to install automatic SM sensors (Wang et al. 2014), with 2776 sites operationally running by 2017 (Chen and Yuan 2020).
All of the approaches described above were employed to measure SM at point sites (Fig. 1). Based on gravimetric-technique-measured soil relative wetness and observed soil characteristics, Wang and Shi (2019) collected and derived a set of SM datasets in five soil layers (10, 20, 50, 70, and 100 cm) at 732 stations from 1992 to 2013 in China (Fig. 2). Besides, there exist several regional observation networks, including ChinaFlux (Ma et al. 2008), Tibetan Plateau observatory (Su et al. 2011), a multiscale Soil Moisture and Temperature Monitoring Network on the central Tibetan Plateau (TP) (CTP-SMTMN; Yang et al. 2013), Heihe Watershed Allied Telemetry Experimental Research (HiWATER; X. Li et al. 2017), and a soil observation network in typical ecosystems in China (SONTE-China; Wang et al. 2023). These networks offer significant data support for scientific research, and most of them can be obtained from the website of the Tibetan Plateau Data Center (https://data.tpdc.ac.cn/).
Distribution of SM observation stations in China. The 48 TP stations are from the Tibetan Plateau Observation Network. WS2019 is referred from Wang and Shi (2019), and black triangles indicate the locations of automatic observation stations from CMA [modified from Ma and Wang (2022)].
Citation: Bulletin of the American Meteorological Society 106, 6; 10.1175/BAMS-D-24-0197.1
The mean volumetric soil water content (cm3 cm−3) averaged from April to September during 1992–2013. The number of stations for (a) 10-cm (732 stations), (b) 20-cm (732 stations), (c) 50-cm (730 stations), (d) 70-cm (183 stations), and (e) 100-cm (203 stations) soil depth. The data were measured based on the gravimetric method [derived from Wang and Shi (2019)].
Citation: Bulletin of the American Meteorological Society 106, 6; 10.1175/BAMS-D-24-0197.1
b. Remote sensing measurement.
Microwave signals can penetrate clouds and are sensitive to water in the soil, and satellite observations at the microwave band (L-band, approximately 1–2 GHz) are usually used to retrieve SM datasets (Wagner et al. 2007). During the past 40 years, remote sensing technology have been greatly developed, and a lot of remote sensing–based SM datasets have been produced using observations from passive and active microwave satellites, and their detailed information was thoroughly studied and intercompared in Babaeian et al. (2019). For example, the European Space Agency (ESA) merged surface SM products retrieved from multiple microwave satellites to achieve a global surface SM dataset (Dorigo et al. 2017). China has been developing meteorology satellites since the 1960s (Zhang et al. 2009; Yang et al. 2012), and a series of satellites known as “Fengyun” (abbreviated as FY) have been launched to date (Zhang et al. 2019).
Compared to in situ observation, remote sensing–based SM products have many advantages, such as wide spatial coverage, temporal continuity, and fine resolution. They can specifically offer SM data for regions that are difficult to measure with traditional in situ methods. Combining remote sensing–based and station-based SM products, Chinese scientists have developed a number of global or regional SM datasets for use in hydrometeorology studies (e.g., Yao et al. 2023; Zheng et al. 2023). For example, Yao et al. (2023) developed a new global surface SM product at 36-km resolution based on FY-3B microwave SM data from 2010 to 2019 and revealed that it had a substantially enhanced accuracy compared to the original FY-3B product. However, remote sensing–based SM typically represents moisture conditions within a 2–5-cm soil column because microwave sensors can only penetrate the superficial soil layer. This characteristic restricts its vertical representation and its usage. The shallow-layer SM is sensitive to atmospheric and environmental conditions, resulting in more spatiotemporal variability. With the help of land surface models (LSMs), some remote sensing–based products also provide root zone SM (e.g., Dorigo et al. 2017). However, the retrieval algorithm, land surface condition, and timing and frequency of satellite overpassing all affect the accuracy of remotely sensed SM (Peng et al. 2017). As a result, the satellite SM may contain larger biases in some places, and it must be calibrated and validated before being utilized in practice (Zhao et al. 2006; Xing et al. 2021).
c. Numerical model simulations.
1) Offline LSM simulation.
LSMs describe the terrestrial hydrological, biogeophysical, and biogeochemical processes based on physical theory and/or empirical functions (Dai 2020). Near-surface meteorological datasets are widely utilized to offline-driven LSMs to simulate land surface water and heat fluxes due to their relative accuracy and ease of availability (Wang et al. 2016; Wang and Kong 2021). Offline LSM simulations can be carried out on single points, regional, or global land with very fine spatial and temporal resolutions.
Change in the total column of soil water content is governed by the water balance equation (dSM = Pr − ET − Ro − SW), in which the temporal change in SM is balanced by input precipitation Pr, evapotranspiration (ET), runoff Ro, and other water storage terms (SW). In LSMs, the representation of the total soil column varies from a few centimeters to tenths of meters, and the soil hydrological and thermal properties are vertically heterogeneous (Shangguan et al. 2014). Therefore, the soil column is usually divided into several layers. At each soil layer, the change in water content is calculated using the water budget components such as Pr, ET, and Ro. In general, variations in the soil water content are explicitly parameterized using two typical equations in LSMs (Wang et al. 2008). One is the Richard’s equation, in which the soil water content change over the course of two successive time steps is equal to the rate of water fluxes vertically into minus out of this soil layer without the consideration of the lateral flow (Richards 1931). This approach is adopted by a number of LSMs, such as Common Land Model (CoLM) (Dai et al. 2019), CLM (Lawrence et al. 2019), and Noah-MP (Niu et al. 2011; Yang et al. 2011; He et al. 2023). The other models are straightforward and explicitly compute the soil water content changes from the balance between the input water from the upper soil layer due to gravity force and drainage out due to transpiration and subsurface runoff, e.g., the VIC model (Liang et al. 1994).
The offline LSM simulation, by its nature, represents the response of the land surface to near-surface atmospheric processes, as manifested by the variations in water, heat, and other land surface components. The accuracy of SM from offline LSM simulations is dependent on atmospheric forcing quality, parameterization representations, and input parameters (Wang et al. 2016; Dai 2020). In recent decades, efforts have been made to reduce the uncertainties of all these aspects in LSM simulations, resulting in substantial improvement of SM data both qualitatively and quantitatively. For instance, Shangguan et al. (2014) developed a soil property parameter dataset from 8979 soil profiles in China for land surface models. Dai et al. (2019) established a global high-resolution dataset of soil hydraulic and thermal properties for use in LSMs. Xie et al. (2018) developed a LSM of the Chinese Academy of Sciences (CAS-LSM) that improved the reproduction of ecohydrological processes by incorporating human-related phenomena into CLM. Li et al. (2011) built a 0.5° × 0.5° observation-based atmospheric forcing dataset covering the entire area of China during 1950–2008. Driven with this forcing, it was found that SM simulations perform better than those driven with reanalysis atmospheric forcings (Li and Ma 2010; Li et al. 2011). Lately, He et al. (2020) developed a 0.1° × 0.1° atmospheric forcing dataset covering China mainland from 1979 to 2018 by fusing remote sensing products, reanalysis datasets, and in situ station data. Using this dataset, offline CLM5 produced high-quality land surface water and heat fluxes (Ma and Wang 2022). These observation-based atmospheric forcing datasets enhance the accuracy of atmospheric inputs for land models to simulate SM over China for different time periods.
2) Reanalysis product.
A reanalysis product synthesizes the simulations of state-of-the-art global climate models and observations to provide an important supply of SM datasets. Since the first reanalysis system was constructed by the NCEP–NCAR in the 1990s (Kalnay et al. 1996), various reanalysis systems have been developed by different institutions, e.g., ERA-Interim (Dee et al. 2011) and ERA5 from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis, the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA; Rienecker et al. 2011; and MERRA2; Gelaro et al. 2017), the NCEP Climate Forest System Reanalysis (CFSR; Saha et al. 2010), the Japanese 55-year Reanalysis (JRA-55; Kobayashi et al. 2015), and the Japanese Reanalysis for three quarters of a century (JRA-3Q; Kosaka et al. 2024). The CMA also released a 40-yr global Chinese reanalysis (CRA-40) dataset (Z. Liu et al. 2023). Table 1 summarizes the general information of those reanalysis systems, including their LSMs and soil-layer divisions. The products tilted with “land” are essentially offline LSM simulations, e.g., ERA-Interim-land, ERA5-land, MERRA-land, MERRA2-land, and CRA-Interim/land, in which the LSM is driven by the reanalysis-based surface meteorological variables after some extent of bias corrections. For instance, in the reproduction of ERA-Interim-land, the precipitation from the ERA-Interim is bias corrected by global observation-based datasets, and the land surface schemes [Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL)] are updated with improved parameterization schemes (Balsamo et al. 2015). These land products have been proved to increase the accuracy of land surface state variables as well as better represent the land surface hydrology cycle, especially over high-latitude regions and in winter. For example, when compared with in situ observations in China, CRA-Interim/land SM is comparable to or better than that of the GLDAS and CFSR datasets for the 0–10-cm soil layer, and it shows higher correlations and slightly lower root-mean-square errors for the SM at the 10–40-cm soil layer (Liang et al. 2020). As a result, such land SM products may be more appropriate than raw reanalysis estimates for land surface products utilized in hydrometeorology research.
A summary of SM data information in the global and regional reanalysis and LDAS. Abbreviations: CRA-40 = China’s first-generation global atmospheric and land reanalysis products for a 40-yr (1979–2018) period; CLSM = Catchment based Land Surface Scheme; TESSEL = Tiled ECMWF Scheme for Surface Exchanges over Land; HTESSEL = Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land; JMA = Japanese Meteorological Agency; SiB = Simple Biosphere Model; VIC = Variable Infiltration Capacity; CLDAS = CMA LDAS; FLDAS = Famine Early Warning Systems Network LDAS-global model; HydrGlobe.
Typically, the more recent versions of reanalysis products from the same institution usually have higher resolution and better representation of atmospheric and land surface states, benefited from improved model physical processes, advanced data assimilation schemes, and newer observation inputs. For instance, compared to ERA-40, SM in ERA-Interim is more consistent with the observations in terms of spatial and temporal characteristics (Albergel et al. 2012). MERRA does not assimilate land observation directly, and its land surface products are the simulations from its land surface model (Reichle et al. 2011), while MERRA2 assimilates more state-of-the-art land observations, and its estimates display improvement in many aspects compared to its predecessor (Gelaro et al. 2017).
The above reanalysis SM products have been used in a variety of studies. However, a reanalysis system’s forecast model, data assimilation scheme, and land surface schemes are rarely changed over its service period (about 5–10 years). Thus, the innovations and improvements in the individual components listed above are not instantly implemented into the reanalysis system. A long-term period of a reanalysis product, on the other hand, is usually generated through several time segments (or streams), which may result in the discontinuity of some state variables between two adjacent streams, particularly land surface state variables. For the reasons stated above, postprocessing reanalysis products are needed to provide better land surface estimates.
3) Land Data Assimilation System (LDAS).
The LDAS integrates the multiple sources of ground and/or remote sensing observations with offline LSM simulations, and it is also an important source of land surface data. In LDAS, LSMs are driven by observation-based atmospheric forcing data, along with a novel data assimilation (DA) technique that, to some extent, adjusts the simulations to approach the observations. The first global LDAS was developed in the early 2000s (Rodell et al. 2004). Since then, a diverse range of global and regional LDASs and their products have been developed for research or operational purposes. A recent review article by Xia et al. (2019) provided a comprehensive summary of global and regional LDASs in terms of their innovations, challenges, and prospects. Soil moisture from LDAS has proven to have better representation than the reanalysis products in China (Chen and Yuan 2020).
The first Chinese LDAS was developed as a research project in the late 2000s (Li et al. 2007). The CMA LDAS (CLDAS) began releasing SM products in July 2013 after applying DA and establishing a dense observation network (Shi et al. 2014). Since 2017, the CLDAS (version 2.0) has provided 0.0625° × 0.0625° hourly land surface products covering all Asian land areas (0°–65°N, 60°–160°E), including gridded volumetric SM in five soil levels (i.e., 0–5, 0–10, 10–40, 40–100, and 100–200 cm). Table 1 highlights some LDAS SM data utilized in Chinese hydrometeorology study, and more detailed information is available in Xia et al. (2019).
d. AI-powered soil moisture datasets.
In recent decades, the rapid growth of AI technique has contributed to a significant increase in the production of SM data. One of the game-changing benefits is its ability to develop functional links among ever-larger variables by learning from massive amounts of data (Sun et al. 2024). Furthermore, the improvements in ground-based and remote-sensed observations, as well as finer-resolution numerical models, have resulted in an explosion in the volume of Earth science data (Vance et al. 2024). With the combination of AI technology and vast amounts of data, AI-based SM datasets have demonstrated encouraging growth in terms of accuracy and spatial coverage at fine resolution (Lei et al. 2022; Han et al. 2023). Specifically, the advancements in machine learning (ML)/deep learning (DL) algorithms leverage extensive Earth science data to promote the quality of SM datasets (Zhao et al. 2024). The AI-powered SM datasets in China have made remarkable progress in the following aspects.
The AI-based method is used to fill the spatial gaps of remote sensing–based SM dataset in China. For example, the random forest method was used to fit the ESA-based SM data due to the relationship between SM and biophysical variables (Sun and Xu 2021). Similar ML methods have also used to generate the spatially continuous SM data based on the Chinese Fengyun-3C satellite-retrieved products (L. Wang et al. 2022). To understand the hydrological and ecological processes associated with SM in a limited region of China, the estimation of SM from the ML method of support vector regression model, based on radar imagery, effectively fulfill the stringent data requirements for continuity, accuracy, and timeliness (J. Wang et al. 2022).
From the perspective of finer resolution, a 1-km resolution SM dataset was produced through the ML method of random forest model trained by the in situ measurements in China (Q. Li et al. 2022). Subsequently, the gap-free global-coverage 1-km resolution daily surface SM datasets for 2000–20 have been generated with the random forest ML algorithm (Zheng et al. 2023) and the extreme gradient boosting—XGBoost ensemble learning model (Zhang et al. 2023) through disaggregating the coarse-resolution data onto 1-km grid cells with the help of in situ observations, remote sensing datasets, and other existing datasets. Those SM datasets have high spatial resolution and facilitate hydrological and ecological studies. For similar purposes, SM data on the site and farmland scales were also generated by ML methods with multisource input of Earth science data (Liu et al. 2021; Q. Liu et al. 2023).
Currently, DL as a subset of ML has demonstrated unique advantages in SM data production owing to its utilization of complex neural networks with multiple layers to reveal complex relationships associated with SM (Li et al. 2024). For example, a spatiotemporally seamless SM dataset over eastern China during 2015–20 has been derived using DL methods of multiperceptron and convolution neural networks to combine the advantages of ground-based and remote-sensed data; and the comparison with observations demonstrates high data quality (Zhou et al. 2023). Nevertheless, there is currently a shortage of long-term multilayer SM datasets created by DL approaches at Chinese or global scales.
e. Intercomparison among different soil moisture datasets in China.
Soil moisture datasets may contain diverse uncertainties, and comprehensive data intercomparisons will reveal the strengths and weaknesses of each dataset in terms of their respective features. Using the in situ SM measurements as the “ground truth,” data evaluation and intercomparison have been conducted at many temporal scales throughout various locations in China (Li and Ma 2010; A. Wang et al. 2022; Ma et al. 2024). Conclusions of those studies provide the important references for selecting appropriate SM products for practical applications. Validations are also critical for LSM development and satellite retrieval technology improvement. In early time, Chinese SM stations were quite rare, and measurements were only undertaken during crop-growing months, with SM validation being carried out at the individual stations. For example, Li et al. (2005) validated SM from ERA-40, NCEP/NCAR, and NCEP/DOE reanalysis products based on in situ measurements at 40 Chinese stations from 1981 to 1999, and they found that ERA-40 has an overall better performance on the interannual variability, while NCEP/DOE has the highest temporal correlation with observations. With the emergence of new SM products, validations are also carried out in rather large areas. Compared with in situ SM data at various global sites, ERA5 shows the highest skill among five reanalysis products and a significant improvement over its predecessor, while the CFSR performs better with respect to long-term trends. Notably, long-term trends of reanalysis SM are substantially weaker than the observed ones, particularly over the high latitudes during cold seasons (Li et al. 2020).
Ground-based SM within the upper 0–10-cm soil layer has fewer missing values than deep soil layers; hence, the majority of data intercomparisons were performed at this surface level. Using the 0–10-cm SM values from 225 Chinese stations for 1993–2008 as a reference, Liu et al. (2014) reported that ERA-Interim has the best interannual variability and MERRA has the best climatology of the five reanalysis products. Chen and Yuan (2020) examined nine global SM products from reanalyses and LDAS in terms of 0–10-cm SM measurements at 2437 stations for 2010–17 and found that the CLDAS performed best on daily to annual time scales. Comparing model-simulated SM with ground-based measurements can help to find model deficiencies and then further improve the model representations. In spite of significant differences between models, A. Wang et al. (2022) reported that multimodel mean simulations are generally better than individual model simulations in terms of spatial variability, seasonal to interannual variation, and long-term trend. Additionally, they also revealed that SM in CMIP6 models from the same institution likely manifests similar performances because those coupled models have incorporated the same land surface model. This means that the features of SM (extension to other land surface products) derived from climate or Earth system models are heavily influenced by land surface process parameterization techniques, which require more attention.
4. Soil moisture data in hydrometeorology research in China
Soil moisture data have been widely used in hydrometeorology studies in China. The most common applications of SM dataset include the drought identification, land–atmosphere interaction, and climate prediction.
a. Drought identification.
Water in the soil is essential to the life cycle of crops, including sowing, sprouting, growing, and yielding. Significant SM deficits may increase drought stress and plant mortality. Under the global warming context, combined SM and temperature anomalies explain 10.9% of the observed leaf area index changes in China, which is 4 times larger than that directly explained by precipitation and surface air temperature (2.7%) (Li et al. 2022a). Thus, SM is generally used as an indicator of agriculture drought, and its anomaly or derived index is widely employed (Wang et al. 2009, 2011; Li and Ma 2015; Wang et al. 2015; Liu et al. 2019; Wang and Kong 2021). In China, the importance of SM for crop growth and yield has been recognized for quite a long time. Due to the lack of reliable SM datasets in China, SM has only been employed in drought studies in recent decades. Wang et al. (2011) reconstructed SM drought events and explored their characteristics using the SM ensemble of four offline LSM models in China from 1950 to 2006, and their findings revealed that SM droughts have gotten more severe and extended, particularly in northeastern and central China (Fig. 3).
Annual trends in (a) SM percentile, (b) drought severity, (c) drought duration, and (d) drought frequency for 1950–2006. The trends were computed using the seasonal Mann–Kendall algorithm, and drought was reconstructed from the ensemble SM of four offline LSM simulations (derived from Wang et al. 2011).
Citation: Bulletin of the American Meteorological Society 106, 6; 10.1175/BAMS-D-24-0197.1
Lately, Wang and Kong (2021) examined China’s century-long SM drought using a regional climate model–simulated SM products and found that drought characteristics differed by location, with less than 40% of the land displaying a major SM drought trend. Furthermore, some studies have attempted to examine drought indices that were calculated directly from meteorological variables or in conjunction with other sources of variables. Qin et al. (2015) constructed drought indices based on observed precipitation and an LSM-simulated SM in the Haihe River basin of North China from 1960 to 2010, and they found that both indices could accurately reproduce the drought conditions represented by the remote sensing–based vegetation index. Wang et al. (2015) reported that the relationship between SM anomaly and several commonly used meteorological drought indices in China varied with soil depths, regions, and soil properties.
On various time scales, Li and Ma (2015) reported that monthly SM droughts affect 54.3% and 8.4% of the total area of China in winter and summer, respectively. Droughts lasting 3 months or longer are more likely to occur in semiarid and semihumid regions, with a likelihood of more than 51.7%, and even >77.6%, whereas droughts lasting 6 and 12 months or longer are more common in arid and semiarid regions from 1951 to 2008. After reviewing recent SM drought research progress in China, Wang and Ma (2023) suggested that future research should focus on cross-regional SM drought identification on multiple time scales, as well as the development of an SM drought prediction system that uses climate prediction systems, land surface models, and multiple-source SM datasets. With the development of LSM and the increased availability of SM datasets, it is expected that SM will be utilized more frequently in drought monitoring and prediction in the future.
b. Soil moisture–climate interaction.
Soil moisture–climate interaction and its impact on climate variability have been extensively investigated in both regional and global land areas. In general, wet/dry soil will increase/decrease ET and then cool/warm the lower atmosphere. In turn, rainfall provides a water source for soil, and snowmelt water can also penetrate the ground to increase soil water storage. This land–atmosphere coupling affects not only the local weather and climate but also the climate in the remote areas. Accordingly, SM–climate interaction, to varying extents, regulates local and nonlocal water and energy cycles between the land and atmosphere, hence contributing to the formation of global climate characteristics.
1) Effects on local climate.
(i) Variation with climate regimes. The characteristics of SM–climate interactions differed depending on the land regimes, either water-limited or energy-limited regimes (Seneviratne et al. 2010). In China, the migration of the northernmost margin of the East Asian summer monsoon (EASM) has a major effect on the distribution of climate regimes (Wu et al. 2021). Along the EASM’s edge, northwest China features arid climate with low SM, but southeast China is dominated by EASM, which brings considerably precipitation and then enhances moisture in soil. In general, ET is heavily dependent on available energy in monsoon-affected areas, where SM provides enough evaporative water. In contrast, the arid Northwest China, which is not affected by the monsoon, is a typical water-limited region, with ET mostly governed by available SM. In the regions within the swing range of the northernmost boundary of EASM, which is also the arid–humid transition zone, SM and climate are strongly coupled due to the synthetical effects of both SM and radiative energy on ET.
Nevertheless, using multisource datasets, delving deeply into land–atmospheric coupling across China points out complicated and multifaceted variations in these coupling processes on various spatiotemporal scales (M. Li et al. 2017). Spatially, northern China features strong land–atmosphere coupling, especially in the transition zone between arid and humid climates. Temporally, land–atmosphere coupling is of marked seasonality. Water-limited coupling is stronger in the summer, as is energy-related coupling in dry areas, while stronger coupling occurs in humid areas in the winter. Furthermore, water-limited coupling is prevalent mainly in northern arid, semiarid, and subhumid areas, while energy-limited coupling prevails at high altitudes and in humid areas. The coupling strength between SM and summer precipitation over China is closely related to SM variability, and SM anomaly in the previous winter may have a significant impact on precipitation in the subsequent summer (Liu et al. 2017).
(ii) Effects on local climate extremes.
Observation evidence has suggested that the SM deficiency is closely related to extreme temperature in eastern China (Meng and Shen 2014; Zhang et al. 2015). The SM condition in spring was found to be closely linked to summer hot days and heat waves over North China, accounting for 19%–34% of the total variances (Wu and Zhang 2015). It was discovered that the SM anomaly and hot temperature extreme relationship enhanced over the transition zone because the proportion of latent heat flux increased during 1961–2010 in China (Zhang et al. 2015). The SM–air temperature coupling enhances the concurrent development of heat waves and drought. Under low SM conditions, large amounts of surface net radiation are partitioned to sensible heat instead of latent heat, and the SM–temperature coupling pattern shifts from energy-limited to water-limited regimes, leading to intensified hot extremes (Ni et al. 2024), which is especially true during the 2022 extreme hot summer in East China (Chen and Wang 2024). The SM–atmosphere coupling also affects the frequency of heat waves in China, with changes in SM accounting for 30%–70% of their occurrence in eastern and southwestern China (Zhang and Wu 2011).
The interannual range of EASM has a significant impact on the land surface water and heat processes, as well as on the land–atmosphere coupling strength. Future intensification of EASM activities is expected to accelerate the land–atmosphere energy and hydrological cycles in the transition zone (Li et al. 2021). Soil moisture feedback on summer surface air temperature has a dry–wet asymmetry feature because surface heat and the atmospheric boundary layer respond differently to dry and wet SM anomalies (Zhang and Dong 2010; Li et al. 2019). However, in the future, with a warmer climate throughout China, SM, temperature, and SM–temperature feedback are projected to vary significantly in space and time (Li et al. 2022b).
Regarding the physical mechanisms underlying land–atmospheric coupling in China (Fig. 4), a lack of precipitation for the water-limited coupling causes reduced SM and ET, which lead to rising surface air temperature, and vice versa. For the energy-led coupling, air temperature decreases are predominantly caused by atmospheric processes such as increased cloud cover fraction and humidity, which result in decreased ET, and vice versa. The diverse response of the land surface to precipitation in different coupling regimes highlights the complicacy of the land–atmosphere coupling processes. However, given changes in prevailing atmospheric circulation and ocean–atmosphere interactions, land–atmosphere coupling processes may shift back and forth in space and time.
Feedbacks between P–C–Rnet–SM–ET–T processes in water-dominated and energy-dominated coupling regimes. The P denotes precipitation, C denotes cloud fraction, Rnet denotes net radiation, SM denotes soil moisture, ET denotes evapotranspiration, and T denotes temperature. Upward arrows indicate increasing processes, and downward arrows indicate decreasing processes. The θCRIT is a specific critical SM, below which SM controls ET (water-limited regime) and above which ET is not sensitive to SM (energy-limited regime) [derived from Fig. 11 by M. Li et al. (2017)].
Citation: Bulletin of the American Meteorological Society 106, 6; 10.1175/BAMS-D-24-0197.1
2) Effects on nonlocal climate.
Soil moisture anomalies have an impact on the climate in remote areas because they influence the atmospheric water vapor and heat components in low to midtroposphere. These signals are then transmitted to downwind areas along with the atmospheric wave train, ultimately influencing climate in downstream regions (Koster et al. 2016).
(i) Effects on nonlocal precipitation.
Research has found that summer precipitation in the Yangtze River basin is significantly correlated with SM anomalies in the Indo-China Peninsula, and the abnormally lower SM in the upper basin could enhance the local temperature anomaly until the summer, which may influence the westward extension of the western Pacific subtropical high and moisture transport to the Yangtze River basin (Gao et al. 2020). High SM in the Yellow River Valley–North China region can connect the spring atmospheric heat source over the Tibetan Plateau with the summer precipitation in Northeast China via the memory of SM (Han et al. 2024). In addition, SM meridional oscillation between North and South China correlates significantly with summer precipitation, and SM forcing may explain about 40% of the interannual variances of precipitation (Ullah et al. 2023). Research indicated that May SM had a significantly positive (negative) correlation with summer precipitation in eastern (southwest) China during the 1980s–1990s due to strong SM persistence and precipitation autocorrelation (Meng et al. 2014).
While summer monsoon circulation has a determined influence on precipitation in eastern China, the SM anomalies impact monsoon circulation by regulating surface thermodynamic conditions. The excessively wet soil may enhance surface latent heat flux but inhibit surface sensible heat flux, resulting in a wetter and colder land surface and reducing temperature gradients between the land and sea, hence leading to a weaker EASM circulation (Zhang and Zuo 2011). Numerical experiments have indicated that the effects of SM anomalies on the EASM are comparable to or greater than those of sea surface temperature (SST; Zhou et al. 2020). The future intensification of EASM activity is expected to accelerate the land–atmosphere energy and water cycle in the transition zone.
(ii) Effects on nonlocal surface air temperature.
Soil moisture anomalies play a vital influence in regulating surface air temperatures over China. Soil moisture–temperature coupling is strongest in the transitional climate zones, especially in spring over arid areas where land–atmosphere coupling regimes are mainly water limited. Analysis of observation and reanalysis datasets shows that positive summer SM anomalies may delay ground cooling in early winter in eastern China by altering thermal capacity, and the warmer ground could cause a rise in surface air temperature via direct land–air interactions and indirectly alter the East Asian winter monsoon (EAWM; Liu et al. 2012).
Numerical experiments have also indicated that SM variability has a significant impact on air temperature simulations over East China, with the strong negative SM–temperature feedback appearing over the dry–wet climate transition zones in northern China and Mongolia, but slightly positive feedback over some areas of Northeast Asia (Zhang and Dong 2010). In particular, winter SM on the Tibetan Plateau also exhibits a significant positive connection with subsequent summer-mean surface air temperature (Lin et al. 2023). Significant negative SM–temperature feedback has been also observed in some parts of the highlands in southwestern China, and SM-induced negative feedback may explain 10%–50% of the total surface air temperature variance via regulating the Bowen ratio (Zhao et al. 2020).
On the decadal scale, SM covariation with surface air temperature is observed in many regions in China, and nonlinear interactions between SM and temperature partly accounts for the observed decadal covariabilities (Su and Wang 2007). In addition, SM feedback has a substantial effect on the diurnal temperature range in China associated with seasonal change, and the negative feedback is found in spring and summer over northern China and in fall over the northeastern transition zone (Wu and Zhang 2013).
c. Effects on climate prediction.
Soil moisture anomaly in a certain soil depth may persist for a period of time because they are less impacted by atmospheric processes. Soil moisture has a memory ranging from weeks to months (Wang and Shi 2019), typically 1 or 2 months for 10 cm and up to 5 months for 50 cm soil depth (Zhao et al. 2021). In arid regions, deep-layer SM anomalies can even last longer than a year (Song et al. 2019). This crucial feature enables SM application in climate prediction. For example, spring SM anomalies may persist into the summer, affecting land surface heating states, and thus having significant effects on summer hot extremes (Chen and Wang 2024). Due to the rapid change of the atmosphere, climate prediction relies on slow-varying external variables such as SST, SM, and snow cover. As a critical precursor, the influence of SST anomalies on climate prediction has been extensively studied and verified in China. However, SST tends not to be the dominant external variable for midlatitude summer climate predictions (Trenberth et al. 1998). Other than SST, numerical experiments using coupled models show that initial anomalies in SM are crucial for summer climate predictions at midlatitudes (Koster et al. 2010). The potential forecasting significance of SM anomalies in subseasonal to seasonal (S2S) scale climate prediction has been acknowledged and exploited in recent decades (Merryfield et al. 2020). Spring SM over the Indo-China Peninsula is negatively correlated with the following summer precipitation over the Yangtze River basin, and this relationship has obvious decadal variations (Gao et al. 2020). A great number of forecast studies have confirmed that the preceding SM anomaly (including the previous autumn to spring) is an important predictor of summer precipitation in China. For example, Shi et al. (2021) found that SM anomalies linked with a weakly coupled dipole mode with wetter north and drier south had a substantial impact on atmospheric circulation and precipitation. Because the distribution pattern of preceding SM anomalies has a significant impact on climate variability, the SM anomaly is an essential precursor signal for summer climate prediction in China (Chen et al. 2023).
Aside from applications in hydrometeorological research, SM data have also been applied in agricultural ecosystems, flooding forecasting, forest fire prediction, water supply management, soil quality, urban planning, etc. For instance, accurate SM information helps improve the understanding of the hydrological cycle, enabling better water resource management and flood and drought prediction. Farmers may use SM information to determine the irrigation timing and volume monitor, ensuring crop growth and increasing yields. The latter one is of particular importance in China due to vast irrigated crop areas. Discussions of these SM applications are beyond the scope of this article, and each of them requires a specific review.
5. Summary and future perspectives
Soil moisture, as a fundamental land surface variable, is of great importance in the Earth system. It has been measured, simulated, and then used in hydrometeorology for quite a long time in China. In recent decades, a vast number of SM datasets have been developed and used in variety of studies over China. This article overviews SM data in China, covering its definition, data sources, and applications. This section presents a summary and offers potential future perspectives on SM data and their applications.
a. Development of soil moisture datasets.
Basically, an SM dataset can be obtained from observation (in situ or satellite remotely sensed measurements) and numerical simulations (land surface/hydrology modeling, atmospheric reanalysis system, and land data assimilation system). Each of them has its strengths and weaknesses. In situ observation has high accuracy but limited spatial coverage and time continuity. Satellite remotely sensed SM data sources are particularly valuable over areas without ground stations, but their accuracy is greatly affected by the retrieval methods, cloud coverage, and vegetation parameters. Furthermore, remotely sensed SM typically represents moisture condition at a few centimeters of the soil. Numerical models, particularly land surface models, can simulate high-resolution, long-term, and spatially continuous SM data, but they are biased by uncertainties in atmospheric forcing, model schemes, and parameters (Ma et al. 2024). Chinese scientists have been developing LSMs since the 1990s (Ji and Hu 1989; Dai et al. 2003), and model performance has improved substantially in recent years in China (Dai 2020). Due to the complex physical and chemical properties of the soil, diverse vegetation coverage, and variable climate conditions, it is difficult for models to accurately simulate the actual situation of soil moisture. For instance, in some mountainous areas (e.g., the Tibetan Plateau) and complex vegetation-covered regions (e.g., Yangtze River basin), the terrain and vegetation have a significant impact on SM, but these factors are difficult to accurately describe in the model, resulting in large errors between the simulation and the actual condition (e.g., Gao et al. 2015; Peng et al. 2022; Wang and Kong 2021).
Land surface models (as an independent model or as a land component in coupled models) are essential tools for generating SM data, the accuracy of which depends on the representations of model parameterizations, specifically SM-related schemes (land surface infiltration, soil water transport scheme, soil hydrological conductivity, soil evaporation and root transpiration scheme) and parameters (soil texture, vegetation distribution, land-cover type, root distribution, etc.). Moreover, the accuracy of land surface atmospheric forcing has a significant impact on the quality of offline LSM simulations (Gao et al. 2015; Wang et al. 2016). Globally, several reconstructed atmospheric forcing datasets have been extensively used in LSM simulations. The majority of those datasets are derived from atmospheric reanalysis products after being bias corrected by a small number of in situ/remotely sensed observations. However, these datasets do not accurately reflect the climate in China, particularly over regions with complex environmental conditions (Wang and Zeng 2011). As such, it is also needed to develop the atmosphere forcing datasets with more Chinese observations incorporated (e.g., He et al. 2020).
In recent years, artificial intelligent (AI) technology has begun to prevail in data fusion. Chinese scientists have employed machine learning (ML) schemes to construct regional and global SM datasets from modeling simulation and in situ/remotely sensed observations in China (Q. Li et al. 2022; Han et al. 2024; Han et al. 2023; Liu et al. 2021). Undoubtedly, ML or other AI techniques will be more extensively used in the near future. Because SM is a strong physical constraint variable, which varies between wilting point and field capacity and may have phase shifts near the freezing point, data fusion or AI-based construction should account for these characteristics. Furthermore, the absolute SM magnitude depends on the climate regimes, and its variability is also closely linked with other variables (e.g., precipitation, evaporation). Thus, the previously mentioned issues should be carefully examined when validating or using AI-generated SM data.
Prior to applying those SM datasets, a systematic assessment is needed to determine their degree of accuracy in comparison to in situ observations (A. Wang et al. 2022). This requires using high-quality ground-based measurements as the reference. To achieve this, it is necessary to enhance in situ SM observation capability by establishing an observation network, undertaking extensive observations, and inventing advanced sensors. So far, stations for SM measurements are much less than those for meteorological variables (i.e., precipitation and temperature) in China. Furthermore, accessible in situ SM datasets are limited to a short period at a few stations (Wang and Shi 2019). In recent years, CMA has offered daily based multilayer SM measurements and these have been employed in some studies (Chen and Yuan 2020). Those datasets are limited within 50-cm soil layers which is much shallower than the root zone of tall vegetation (e.g., shrub and trees). As a result, it is suggested that the observation agencies consider providing SM measurements from near the soil surface to deeper soil layers (e.g., 2 m) while simultaneously enhancing accessibility for research purposes. Besides, given that SM measurements are carried out by multiple sectors in China, the data products may lack consistency. One of the challenges is integrating and sharing these data to conduct in-depth research. It is critical to construct a national universal SM data management platform that integrates data from diverse departments and develops unified standards to facilitate data inquiry, analysis, and utilization.
b. Applications in future hydrometeorology research.
Soil moisture denotes the wet condition in the soil. It has been applied in various fields, including agriculture, water resource management, and climate change. Drought identification and climate variability are two of the most important applications in hydrometeorology study. In this review, we focus on those in China and highlight the research progress achieved in recent decades.
Soil moisture has a direct impact on plant phenology and production, and its deficit is commonly regarded as an indication of agricultural droughts. In recent decades, a variety of drought detection research projects have been undertaken in China using some retrospectively constructed SM datasets (Wang et al. 2009, 2011; Wang and Kong 2021). Although these studies have reproduced the major SM drought events and their characteristics (i.e., spatial and temporal extent, relative intensity, occurrence frequency, and time evolution), understanding of their causes and physical mechanisms remains limited in China.
The annual cycle and interannual range of the EASM have significant impacts on the land surface hydrology and heat processes, as well as the land–atmosphere coupling. In particular, the swing of the northwestern boundary of the EASM enhances the complexity of climate variability in impacted areas, which also happen to be the primary northern agricultural zone, making SM drought prediction particularly challenging. Furthermore, SM is greatly influenced by human activities (i.e., irrigation and groundwater extraction), and it is difficult to discern from in situ and remotely sensed measurements (Zeng et al. 2017). Recent findings have revealed that human extraction of groundwater has had a significant impact on the local climate in Northwest China through land–atmosphere interaction (Wang et al. 2019). In the future, human activities and other water demands will be intensified as China’s economy grows. This highlights the need to improve numerical models by accounting for soil water uptake and usage for human activities, which is often overlooked or simplified in current models (Zeng et al. 2017; Xie et al. 2018).
In regard to SM’s influence on climate variability, the interaction between SM and atmospheric factors is essential. Evidence from observations, reanalysis, and model simulations has proved that SM anomalies can have a direct impact on both local and nonlocal climate. Processes involving SM–climate interaction are usually mixed with other factors such as SST, sea ice, and even upper-layer atmospheric signals. Recent studies have used a circulation-analogy-based dynamic adjustment approach to isolate the effects of large-scale circulation and local SM anomalies on the precipitation (Chen et al. 2023) and heat extremes in China (Chen and Wang 2023). Using land–atmosphere coupled model, the sensitivity modeling experiments have been conducted by inhibiting SM feedback in the coupled model, and alternatively replacing it with the prescribed SM climatology, and then through intercomparing the simulated results to isolate the effects of local SM (Zhang et al. 2011). This method may only exhibit the impacts qualitatively, but it cannot quantify their influence. The reason for this is that the inactive SM feedback may cause a water imbalance between the land and the atmosphere exchange, as well as inconsistency in SM-related land components. To separate the effects of SM on climate variability from other causes, future study in this area needs to be strengthened. Moreover, it is also equally essential to consider the impacts of SM on other atmospheric components, apart from precipitation and air temperature. Multimodel evaluation research using coupling metrics between SM (land state) and lifting condensation level (atmospheric state) has demonstrated that models tend to overestimate the interaction between SM and surface fluxes, yet underestimate the coupling strength between these fluxes and the lifting condensation level (Dirmeyer et al. 2018).
In summary, recent studies on SM in China have achieved great progress, but further study is needed to strengthen SM data reproduction and integration, understand its effects on climate variability and climate change, as well as enhance its application in other research fields. In this study, we concentrated on looking at the three major applications of SM in climate sciences in China, although there are many other usages such as wildfire ignition, dust emission, agricultural development, urban planning, environmental protection, and disaster prevention and mitigation. These applications might call for transdisciplinary research to fully improve the usage of SM in practice and provide comprehensive decision-making support. In addition, China has a diverse ecosystem ranging from natural vegetation cover to human-induced ones which are heavily influenced by climate change. The interactions and feedbacks between various ecosystems and SM are complex in diverse climate regions. As a result, the current review is also applicable to guiding relevant research in other countries or regions, particularly those with similar climate characteristics. Finally, in addition to its applications in hydrometeorology research, SM data can be applied in other sectors to strengthen decision-making. This is critical for translating research findings into practical advantages for social and economic development.
Acknowledgments.
This work is jointly supported by the National Natural Science Funds for Distinguished Young Scholars (Grant 41925021) and for Innovation Research Groups of the National Natural Science Foundation of China (Grant 42221004) and the National Key Technologies R&D Program of China (Grant 2022YFC3002803). We are also thankful to the three anonymous reviewers for their constructive comments. This work does not have a conflict of interest.
Data availability statement.
No datasets were generated or analyzed during the current study. Software (other than for typesetting) was not used for this research.
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