Читать книгу Global Drought and Flood - Группа авторов - Страница 18

Agricultural drought is a result of precipitation deficit plus accumulated evapotranspiration over a prolonged period of time that eventually leads to extended periods of low soil moisture that affect crop yields and livestock production (Cunha et al., 2015). Agricultural drought disrupts the chain of supply and demand of agricultural products and contributes to socioeconomic drought (Wilhite & Glantz, 1985). Soil moisture is a key component of agricultural drought and defines the readily available water that plants can access from the soil through their root system. Soil moisture regulates the water and energy exchange between the land surface and the atmosphere. It also influences the partitioning of nonintercepted precipitation into surface runoff and infiltrations and influences the partitioning of net radiation into sensible, latent, and ground heat fluxes that are essential climate variables (WMO, 2006). Soil moisture condition directly reflects ecosystem functionality and agricultural productivity, therefore an agricultural drought influences the economy at local to global scales (IPCC, 2007; Ryu et al., 2014).

Warm surface temperature and rapidly decreasing soil moisture due to a lack of precipitation and hot temperatures are associated with rapidly developing drought conditions that are often known as “flash droughts” (M. C. Anderson et al., 2013; Otkin et al., 2016). Ford et al. (2015) demonstrated that measurements of soil moisture in situ would drastically enhance the identification of flash droughts. Therefore, identification and quantification of drought at different timescales with high‐resolution satellite imagery is crucial for decision making and developing drought mitigation strategies (D’Odorico et al., 2010). Several drought indices have been proposed to address deficiency in soil moisture, including the Crop Moisture Index (CMI; Palmer, 1965), Keetch–Byram Drought Index (KBDI; Keetch & Byram, 1968), Soil Moisture Percentile (Sheffield et al., 2004), Soil Moisture Deficit Index (SMDI; Narasimhan & Srinivasan, 2005), Scaled Drought Condition Index (SDCI) that uses multisensor data (Rhee et al., 2010), Microwave Integrated Drought Index (MIDI) that integrates precipitation, soil moisture, and land surface temperature derived from microwave sensors such as TRMM and AMSR‐E (Zhang & Jia, 2013), Soil Moisture Drought Index (SODI; Sohrabi et al., 2015), and Standardized Soil Moisture Index (SSI; Hao & Aghakouchak, 2013; Figure 1.3).

Figure 1.3 Near real‐time drought monitoring and prediction system by the Global Integrated Drought Monitoring and Prediction System (GIDMaPS) using the Standardized Soil Moisture Index (SSI) for February 2016 based on the Modern‐Era Retrospective analysis for Research and Applications (MERRA) data set. D0 indicates abnormally dry; D1 moderate drought; D2 severe drought; D3 extreme drought; D4 exceptional drought; and the same applies to wetness (W) scale.

What is required for agricultural drought and land surface models is the water content of the plant root zone in soil. This requires observatories in situ that are able to measure soil‐water content at deeper layers of soil and provide more accurate estimations of soil moisture for purposes of drought monitoring as well as validation of satellite estimations of soil moisture. The cosmic‐ray soil moisture observing system (COSMOS; Zreda et al., 2012) and the German terrestrial environmental observatories (TERENO; Zacharias et al., 2011) are two examples of such in situ measurement networks. Moreover, the International Soil Moisture Network (ISMN) (http://www.ipf.tuwien.ac.at/insitu) provides a long record of global in situ soil moisture data, however, these measurements are typically available at point scales and contain significant spatial and temporal gaps. While point‐based measurements are time consuming and costly, passive and active microwave sensor data retrieved from satellites readily provide spatiotemporally consistent observations of soil moisture from the top 5 cm of soil (Entekhabi et al., 2010; L. Wang & Qu, 2009). Given that agricultural drought monitoring requires information about soil moisture content of the entire soil column (i.e., surface and root zone), remotely sensed soil moisture data alone are not adequate for drought monitoring and complementary information about root zone soil moisture needs to be provided using modeling and data assimilation (e.g., Mladenova et al. 2019). Surface soil moisture data are derived mainly from passive or active microwave satellites (De Jeu et al., 2008; Njoku et al., 2003; Takada et al., 2009; Wagner et al., 1999). Currently, the Soil Moisture Active Passive (SMAP; Figure 1.4; Entekhabi et al., 2010) and the Soil Moisture Ocean Salinity (SMOS; Kerr et al., 2010) missions are the main sources of the remote‐sensing‐based soil moisture estimates. These data sets have been used extensively for drought monitoring (e.g., Mishra et al., 2017; Sadri et al., 2018; Sánchez et al., 2016). Soil moisture also can be inferred from other microwave sensors (Entekhabi et al., 2010; Martínez‐Fernández et al., 2016; Moradkhani, 2008; Scaini et al., 2015) such as: the Scanning Multichannel Microwave Radiometer (SMMR), the SSM/I, the European Remote Sensing (ERS) scatterometer, the TRMM microwave imager, the Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer2 (AMSR2). Long‐term soil moisture data appropriate for monitoring drought can be obtained through certain databases such as the Water Cycle Multimission Observation Strategy (WACMOS), which is derived from multiple satellites (Ambaw, 2013). Similarly, the European Space Agency's Climate Change Initiative (ESA CCI) offers a soil‐moisture data set with a record of over 30 years that is particularly suitable for monitoring agricultural drought. The ESA CCI merges soil moisture retrievals of a number of different satellites and provides three types of product: active microwave, passive microwave, and combined active–passive microwave (Gruber et al., 2019). The ESA CCI soil‐moisture data set, however, has large gaps over densely vegetated areas. Martínez‐Fernández et al. (2016) show the reliability of the CCI soil‐moisture data set for purposes of modeling agricultural drought.

Figure 1.4 Soil moisture observation by NASA’s Soil Moisture Active Passive (SMAP) satellite. (a) Soil moisture observation of the United States. (b) Global view.

(Courtesy of NASA’s earth observatory: https://earthobservatory.nasa.gov/images).

Monitoring agricultural drought requires high‐resolution data to reveal detailed variations of soil moisture. To improve the spatial resolution of soil moisture data, several downscaling methods have been used, such as machine learning frameworks (Im et al., 2016; Park et al., 2017), DISaggregation based on Physical And Theoretical scale CHange (DISPATCH) which uses shortwave and thermal data from Moderate‐Resolution Imaging Spectroradiometer (MODIS) to downscale SMOS data (Merlin et al., 2015), and Smoothing Filter‐based Intensity Modulation (SFIM) which integrates microwave data from SMAP, Sentinel‐1, and AMSR2 to downscale soil moisture data to an enhanced resolution of 0.1° × 0.1° (Santi et al. 2018).