Temporal Patterns Of Drought Research Articles

Developing a Combined Drought Index to Monitor Agricultural Drought in Sri Lanka

Developing an agricultural drought monitoring index through integrating multiple input variables into a single index is vital to facilitate the decision-making process. This study aims to develop an agricultural drought index (agCDI) to monitor and characterize the spatial and temporal patterns of drought in Sri Lanka. Long-term (1982 to 2020) remote sensing and model-based agroclimatic input parameters—normalized difference vegetation index (NDVI), land surface temperature (LST), 3-month precipitation z-score (stdPCP), and evaporative demand drought index (EDDI)—were used to develop agCDI. The principal component analysis (PCA) approach was employed to qualitatively determine the grid-based percentage contribution of each input parameter. The agCDI was apparently evaluated using an independent dataset, including the crop yield for the major crop growing districts and observed streamflow-based surface runoff index (SRI) for the two main crop growing seasons locally, called Yala (April to September) and Maha (October to March), using 20-years of data (from 2000 to 2020). The results illustrate the good performance of agCDI, in terms of predominantly capturing and characterizing the historic drought conditions in the main agricultural producing districts both during the Yala and Maha seasons. There is a relatively higher chance of the occurrence of moderate to extreme droughts in the Yala season, compared to the Maha season. The result further depicts that relatively good correlation coefficient values (> 0.6) were obtained when agCDI was evaluated using a rice crop yield in the selected districts. Although the agCDI correlated well with SRI in some of the stations (>0.6), its performance was somehow underestimated in some of the stations, perhaps due to the time lag of the streamflow response to drought. In general, agCDI showed its good performance in capturing the spatial and temporal patterns of the historic drought and, hence, the model can be used to develop agricultural drought monitoring and an early warning system to mitigate the adverse impacts of drought in Sri Lanka.

Drought Monitoring over Yellow River Basin from 2003–2019 Using Reconstructed MODIS Land Surface Temperature in Google Earth Engine

Drought is one of the most complex and least-understood environmental disasters that can trigger environmental, societal, and economic problems. To accurately assess the drought conditions in the Yellow River Basin, this study reconstructed the Land Surface Temperature (LST) using the Annual Temperature Cycle (ATC) model and the Normalized Difference Vegetation Index (NDVI). The Temperature Condition Index (TCI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Temperature-Vegetation Drought Index (TVDI), which are four typical remote sensing drought indices, were calculated. Then, the air temperature, precipitation, and soil moisture data were used to evaluate the applicability of each drought index to different land types. Finally, this study characterized the spatial and temporal patterns of drought in the Yellow River Basin from 2003 to 2019. The results show that: (1) Using the LST reconstructed by the ATC model to calculate the drought index can effectively improve the accuracy of drought monitoring. In most areas, the reconstructed TCI, VHI, and TVDI are more reliable for monitoring drought conditions than the unreconstructed VCI. (2) The four drought indices (TCI, VCI, VH, TVDI) represent the same temporal and spatial patterns throughout the study area. However, in some small areas, the temporal and spatial patterns represented by different drought indices are different. (3) In the Yellow River Basin, the drought level is highest in the northwest and lowest in the southwest and southeast. The dry conditions in the Yellow River Basin were stable from 2003 to 2019. The results in this paper provide a basis for better understanding and evaluating the drought conditions in the Yellow River Basin and can guide water resources management, agricultural production, and ecological protection of this area.

Assessment of agricultural drought in Morocco based on a composite of the Vegetation Health Index (VHI) and Standardized Precipitation Evapotranspiration Index (SPEI)

According to IPCC, Morocco is a highly vulnerable country to extreme climate events, especially droughts; this will affect different socioeconomic sectors, mainly the agriculture sector. Droughts are controlled by the variability of precipitation and evapotranspiration but also not neglecting the effect of land surface conditions such as land surface temperature. In this present study, the remote sense observations MODIS Normalized Difference Vegetation Index (NDVI) and CMSAF Land Surface Temperature (LST) were used for calculating the Vegetation Health Index (VHI). The main advantage of remote sensing products is that they are reasonably efficient in terms of temporal and spatial coverage, and they are useful for the monitoring and assessment of drought in the near real-time. Furthermore, ERA5 Reanalysis-based SPEI is calculated. The goal of this study is to assess the spatial and temporal patterns of drought, this study offers the composite of SPEI and VHI drought monitoring obtained by plotting maps and graphs to show the monthly and annual variability of drought for the period 2000–2015 over the whole of Morocco. This monitoring can be used as a near real-time warning system in a changing climate.

Model-Based Estimation of Amazonian Forests Recovery Time after Drought and Fire Events

In recent decades, droughts, deforestation and wildfires have become recurring phenomena that have heavily affected both human activities and natural ecosystems in Amazonia. The time needed for an ecosystem to recover from carbon losses is a crucial metric to evaluate disturbance impacts on forests. However, little is known about the impacts of these disturbances, alone and synergistically, on forest recovery time and the resulting spatiotemporal patterns at the regional scale. In this study, we combined the 3-PG forest growth model, remote sensing and field derived equations, to map the Amazonia-wide (3 km of spatial resolution) impact and recovery time of aboveground biomass (AGB) after drought, fire and a combination of logging and fire. Our results indicate that AGB decreases by 4%, 19% and 46% in forests affected by drought, fire and logging + fire, respectively, with an average AGB recovery time of 27 years for drought, 44 years for burned and 63 years for logged + burned areas and with maximum values reaching 184 years in areas of high fire intensity. Our findings provide two major insights in the spatial and temporal patterns of drought and wildfire in the Amazon: (1) the recovery time of the forests takes longer in the southeastern part of the basin, and, (2) as droughts and wildfires become more frequent—since the intervals between the disturbances are getting shorter than the rate of forest regeneration—the long lasting damage they cause potentially results in a permanent and increasing carbon losses from these fragile ecosystems.

Using multiple indexes to analyze temporal and spatial patterns of precipitation and drought in Xinjiang, China

There is an urgent need to understand the characteristics of both precipitation and drought in arid regions of China. Different indexes may provide differing results in terms of the detection and estimation of the temporal and spatial extent of droughts. In this study, the temporal and spatial characteristics of rainfall and drought at 53 meteorological stations from 1960 to 2016 across the Xinjiang region of China were analyzed. Four precipitation-related indexes (precipitation concentration degree (PCD), precipitation concentration period (PCP), standardized precipitation index (SPI), and long-cycle drought–flood abrupt alternation index (LDFAI)) were used to investigate the spatial and temporal patterns of drought and flood. In addition, the Bayesian method and Pettitt test were used as detection tools to identify changing points in precipitation time series. The study indicated the following: (1) a wetting tendency was evident in recent years and was supported by the upward trend in SPI and inter-decadal variation in summer precipitation; (2) the spatial patterns and tendencies of PCD, PCP, and SPI were markedly different in southern and northern Xinjiang; (3) compared with the Pettitt test, use of the Bayesian method provided data closer to the real drought events; (4) it is suggested that multiple indices be used for detecting precipitation and drought characteristics as use of the SPI may lead to other indexes related to flood and drought risks being ignored.

Spatial and temporal patterns of drought in Zambia

Drought acutely affects economic sectors, natural habitats and communities. Understanding the past spatial and temporal patterns of drought is crucial because it facilitates the forecasting of future drought occurrences and informs decision-making processes for possible adaptive measures. This is especially important in view of a changing climate. This study employed the World Meteorological Organization (WMO)-recommended standardized precipitation index (SPI) to investigate the spatial and temporal patterns of drought in Zambia from 1960 to 2016. The relationship between the occurrence of consecutive dry days (CDD; consecutive days with less than 1 mm of precipitation) and SPI was also investigated. Horizontal wind vectors at 850 hPa during the core of the rainy season (December–February) were examined to ascertain the patterns of flow during years of extreme and severe drought; and these were contrasted with the patterns of flow in 2007, which was a generally wet year. Pressure vertical velocity was also investigated. Based on the gamma distribution, SPI successfully categorized extremely dry (with a SPI value less than or equal to –2.0) years over Zambia as 1992 and 2015, a severely dry (–1.9 to–1.5) year as 1995, moderately dry (–1.4 to –1.0) years as 1972, 1980, 1987, 1999 and 2005, and 26 near normal years (–0.9 to 0.9). The occurrence of CDD was found to be strongly negatively correlated with SPI with a coefficient of –0.6. Further results suggest that, during wet years, Zambia is influenced by a clockwise circulating low-pressure zone over the south-eastern Angola, a second such zone over the northern and eastern parts, and a third over the Indian Ocean. In stark contrast, years of drought were characterized by an anti-clockwise circulating high-pressure zone over the south-western parts of Zambia, constraining precipitation activities over the country. Further, wet years were characterized by negative pressure vertical velocity anomalies, signifying ascending motion; while drought years were dominated by positive anomalies, signifying descending motion, which suppresses precipitation. These patterns can be used to forecast drought over Zambia and aid in strategic planning to limit the potential damage of drought.

Spatial and temporal patterns of drought in Oklahoma (1901–2014)

This study uses the Standardized Precipitation Evapotranspiration Index (SPEI) at a 6‐month timescale to document the spatial and temporal patterns of drought in Oklahoma from 1901 to 2014. A principal component analysis (PCA) with a VARIMAX rotation was used to divide Oklahoma into four spatially contiguous regions with similar drought patterns that are located in northwestern, northeastern, southwestern and southeastern regions of the state. Droughts tend to occur more frequently in northwestern Oklahoma, but all four regions show that droughts in Oklahoma have substantial variability at decadal timescales. A Mann–Kendall trend test demonstrates that there has been a statistically significant increase in the SPEI in Oklahoma since 1901. This indicates that conditions are generally getting wetter in Oklahoma. However, there is substantial variability in moisture conditions that is evident at shorter timescales. Prolonged periods of drier than normal conditions occurred in the 1910s, 1930s, 1950s, 1970s and 1980s, while conditions from the 1980s to the 2000s have been wetter than normal. Recently, the SPEI has decreased, which suggests that Oklahoma may be at the beginning of another dry cycle. In addition, while most droughts during the study period were caused by decreases in precipitation, recent droughts, such as the severe drought in 2011, were caused by both decreases in precipitation and increases in potential evapotranspiration. This may be indicative of future drought conditions in Oklahoma.

Characterization of drought in the Kerio Valley Basin, Kenya using the Standardized Precipitation Evapotranspiration Index: 1960−2016

The Kerio Valley basin in Kenya has undergone several periods of drought, yet drought patterns in the region are not well understood due to limited climatic data. Drought events in the region have resulted in crop failure and livestock deaths, exacerbating food shortages. In this study, the Standardized Precipitation Evapotranspiration Index (SPEI), a multi‐scalar drought index was used to examine the onset, duration, severity, intensity, and frequency of agricultural and hydrological drought in the region. The gridded 0.5° × 0.5° climatic datasets from Climatic Research Unit for the period 1960−2016 was used for analysis. Temporal evolutions of SPEI at 6‐ and 12‐month lags were subsequently used to evaluate agricultural and hydrological drought, respectively. Additionally, the Mann‐Kendall trend test was used to test for trends in the time series. Results from the analysis show that: 1) droughts are becoming more frequent in the region, 2) drought intensities in the arid and semi‐arid lands have weakened, 3) regions west of the Kerio River have recently recorded a wetting trend, and 4) the southern and central regions of the basin are drought‐prone. Understanding the spatial and temporal patterns of drought in the basin can assist in drought preparation and mitigation planning.

Evaluation of Satellite-Based Rainfall Estimates and Application to Monitor Meteorological Drought for the Upper Blue Nile Basin, Ethiopia

Drought is a recurring phenomenon in Ethiopia that significantly impacts the socioeconomic sector and various components of the environment. The overarching goal of this study is to assess the spatial and temporal patterns of meteorological drought using a satellite-derived rainfall product for the Upper Blue Nile Basin (UBN). The satellite rainfall product used in this study was selected through evaluation of five high-resolution products (Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) v2.0, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), African Rainfall Climatology and Time-series (TARCAT) v2.0, Tropical Rainfall Measuring Mission (TRMM) and Africa Rainfall Estimate Climatology version 2 [ARC 2.0]). The statistical performance measuring techniques (i.e., Pearson correlation coefficient (r), mean error (ME), root mean square error (RMSE), and Bias) were used to evaluate the satellite rainfall products with the corresponding ground observation data at ten independent weather stations. The evaluation was carried out for 1998–2015 at dekadal, monthly, and seasonal time scales. The evaluation results of these satellite-derived rainfall products show there is a good agreement (r > 0.7) of CHIRPS and TARCAT rainfall products with ground observations in majority of the weather stations for all time steps. TARCAT showed a greater correlation coefficient (r > 0.70) in seven weather stations at a dekadal time scale whereas CHIRPS showed a greater correlation coefficient (r > 0.84) in nine weather stations at a monthly time scale. An excellent score of Bias (close to one) and mean error was observed in CHIRPS at dekadal, monthly and seasonal time scales in a majority of the stations. TARCAT performed well next to CHIRPS whereas PERSSIAN presented a weak performance under all the criteria. Thus, the CHIRPS rainfall product was selected and used to assess the spatial and temporal variability of meteorological drought in this study. The 3-month Z-Score values were calculated for each grid and used to assess the spatial and temporal patterns of drought. The result shows that the known historic drought years (2014–2015, 2009–2010, 1994–1995 and 1983–1984) were successfully indicated. Moreover, severe drought conditions were observed in the drought prone parts of the basin (i.e., central, eastern and southeastern). Hence, the CHIRPS rainfall product can be used as an alternative source of information in developing the grid-based drought monitoring tools for the basin that could help in developing early warning systems.

Spatial and temporal patterns of drought in the Continental U.S. during the past century

AbstractThis study investigates the spatial and temporal patterns of multiple drought characteristics (duration, severity, and intensity) under different return periods during 1900–2012 in the Continental U.S. (CONUS). We find two significant patterns: Pattern I shows persistent droughts in western and eastern U.S. and the Great Plains, which experienced large variations in the drought characteristics over long time; Pattern II shows transient droughts in the interior of CONUS, which experienced short‐term variations in drought characteristics. Trend analysis shows that duration, severity, and intensity of droughts under the various return periods are increasing in most of the Pattern I regions. Moreover, spatial distributions of duration, severity, and intensity of more frequent and less severe drought events are found to be different from those of less frequent and more severe droughts in the same time period; trends in these drought characteristics at long and short return periods are different at some locations, showing the different trends of extreme and mild droughts.

Research on spatial and temporal characteristics of drought based on GIS using Remote Sensing Big Data

Drought is a stochastic natural hazard that is caused by intense and persistent shortage of precipitation. Spatial and temporal patterns of drought have been analyzed by several methods, ranging fr...

Spatiotemporal variability of drought on a short–medium time scale in the Calabria Region (Southern Italy)

The study analyses spatial and temporal patterns of drought in an area with a wide range of precipitation characteristics (the Calabria region in southern Italy) during the period 1921–2007. The short-time (2, 3 and 6 months) Standardised Precipitation Index (SPI) was estimated to analyse drought especially from the agricultural point of view. Principal component analysis (PCA) was applied to the SPI to assess the spatial variability of drought. During the period of observation, moderate to severe drought occurred at a frequency of almost 13 % in wet seasons (autumn and winter). Almost half of the region was affected by drought in the years 1981–1990 when the area experienced its most severe drought. Although the spatial patterns of drought estimated by PCA were logical and consistent with precipitation distribution, very complex patterns were observed for all the time scales looked at. The high fragmentation of the maps obtained makes them of limited value, and caution is recommended in classifying the region into homogeneous areas.

Amazon drought and its implications for forest flammability and tree growth: a basin‐wide analysis

AbstractSevere drought in moist tropical forests provokes large carbon emissions by increasing forest flammability and tree mortality, and by suppressing tree growth. The frequency and severity of drought in the tropics may increase through stronger El Niño Southern Oscillation (ENSO) episodes, global warming, and rainfall inhibition by land use change. However, little is known about the spatial and temporal patterns of drought in moist tropical forests, and the complex relationships between patterns of drought and forest fire regimes, tree mortality, and productivity. We present a simple geographic information system soil water balance model, called RisQue (Risco de Queimada – Fire Risk) for the Amazon basin that we use to conduct an analysis of these patterns for 1996–2001. RisQue features a map of maximum plant‐available soil water (PAWmax) developed using 1565 soil texture profiles and empirical relationships between soil texture and critical soil water parameters. PAW is depleted by monthly evapotranspiration (ET) fields estimated using the Penman–Monteith equation and satellite‐derived radiation inputs and recharged by monthly rain fields estimated from 266 meteorological stations. Modeled PAW to 10 m depth (PAW10 m) was similar to field measurements made in two Amazon forests. During the severe drought of 2001, PAW10 m fell to below 25% of PAWmax in 31% of the region's forests and fell below 50% PAWmax in half of the forests. Field measurements and experimental forest fires indicate that soil moisture depletion below 25% PAWmax corresponds to a reduction in leaf area index of approximately 25%, increasing forest flammability. Hence, approximately one‐third of Amazon forests became susceptible to fire during the 2001 ENSO period. Field measurements also suggest that the ENSO drought of 2001 reduced carbon storage by approximately 0.2 Pg relative to years without severe soil moisture deficits. RisQue is sensitive to spin‐up time, rooting depth, and errors in ET estimates. Improvements in our ability to accurately model soil moisture content of Amazon forests will depend upon better understanding of forest rooting depths, which can extend to beyond 15 m. RisQue provides a tool for early detection of forest fire risk.