Traditional Drought Monitoring Tools

In situ-based observations of meteorological (e.g., precipitation and temperature) and hydrologic (e.g., soil moisture, streamflow, groundwater, and reservoir levels) parameters have provided the basis for most traditional indicators used for drought monitoring. Prime examples are the climate-based Palmer Drought Severity Index (PDSI, Palmer 1968) and Standardized Precipitation Index (SPI) (McKee et al. 1995), as well as anomalies of streamflows and reservoir levels from gauging stations and soil moisture from probes in the soil. For most of these indicators, an extended record of historical observations is used to calculate an "anomaly" measure to identify how the current conditions compare with the historical average conditions for drought detection and assessment of severity. In situ observations are point based and represent a measurement of conditions at a discrete geographic location.

To describe conditions between measurement locations, traditional spatial interpolation techniques have been applied to in situ-based data and derived drought indicators, or alternatively all in situ data within a specified geographic unit (e.g., county) may be areally averaged into a single value to represent the entire spatial unit. In either case, the ability to resolve detailed variations in drought conditions is limited by the number and spatial distribution of observing locations, which can vary considerably among regions and countries, as well as different environmental observations (e.g., rainfall vs. soil moisture measurements). In the United States, for example, the number and spatial density of National Weather Service (NWS) automated weather station locations measuring precipitation and temperature varies considerably, with a higher density of stations in the eastern United States compared to parts of the western United States. For hydrologic variables such as soil moisture, ground- based measurements in the United States are even more limited than are meteorological observations, and many countries around the world have few to no soil moisture observations.

The temporal length of in situ data records can also present a challenge, given that drought monitoring requires an extended record of observations to calculate historically meaningful anomalies that can be used to detect and measure drought events. The periods of record can vary among stations measuring a specific environmental parameter (e.g., temperature), and these varying lengths of data records must be considered and reconciled before the data can be transformed into a drought indicator. The length of record can also vary considerably among environmental parameters, as many weather stations measuring precipitation and temperature have data records spanning many decades while soil moisture probe locations often have data records no longer than 10-15 years.

Data quality and consistency are additional factors that can affect the applicability of in situ data for drought monitoring. Datasets with long histories of observations such as precipitation commonly have periods of missing observations that can range from few random days to longer blocks of weeks or months within the data record. In such cases, temporal interpolation methods have to be employed to fill data gaps, and may result in estimates that may or may not be representative of conditions during that period. Data consistency can also be an issue across sites that make in situ measurements. Data can be collected using different types of instrumentation and/or methods, as well as different data protocols, which can lead to data inconsistencies when observations are combined for different networks. For example, soil moisture observations available across the United States are collected by a series of networks at the national and state/regional scales, such as the US Department of Agriculture (USDA) Soil Climate Analysis Network (SCAN) and various state mesonets. Although these various soil moisture networks provide valuable measurements for drought monitoring, their data collection methods (e.g., type of soil moisture probe and different soil depths) and formats are often not consistent, leading to disparity in measurement quality and vertical support (Diamond et al. 2013).

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