Implementation of meteorological design droughts in ecosystem rehabilitation plans: applicability of meteorological drought indices

2017 ◽  
Author(s):  
Devanmini Halwatura
Keyword(s):  
Meteorological Drought ◽  
Drought Indices ◽  
Ecosystem Rehabilitation

scholarly journals Evaluation of Drought Indices Based on Thermal Remote Sensing of Evapotranspiration over the Continental United States

2011 ◽  
Vol 24 (8) ◽  
pp. 2025-2044 ◽  
Author(s):  
Martha C. Anderson ◽  
Christopher Hain ◽  
Brian Wardlow ◽  
Agustin Pimstein ◽  
John R. Mecikalski ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Drought Index ◽  
Temporal Correlation ◽  
Meteorological Drought ◽  
Rainfall Data ◽  
Added Value ◽  
Stress Index ◽  
Drought Indices ◽  
Shallow Water Table

Abstract The reliability of standard meteorological drought indices based on measurements of precipitation is limited by the spatial distribution and quality of currently available rainfall data. Furthermore, they reflect only one component of the surface hydrologic cycle, and they cannot readily capture nonprecipitation-based moisture inputs to the land surface system (e.g., irrigation) that may temper drought impacts or variable rates of water consumption across a landscape. This study assesses the value of a new drought index based on remote sensing of evapotranspiration (ET). The evaporative stress index (ESI) quantifies anomalies in the ratio of actual to potential ET (PET), mapped using thermal band imagery from geostationary satellites. The study investigates the behavior and response time scales of the ESI through a retrospective comparison with the standardized precipitation indices and Palmer drought index suite, and with drought classifications recorded in the U.S. Drought Monitor for the 2000–09 growing seasons. Spatial and temporal correlation analyses suggest that the ESI performs similarly to short-term (up to 6 months) precipitation-based indices but can be produced at higher spatial resolution and without requiring any precipitation data. Unique behavior is observed in the ESI in regions where the evaporative flux is enhanced by moisture sources decoupled from local rainfall: for example, in areas of intense irrigation or shallow water table. Normalization by PET serves to isolate the ET signal component responding to soil moisture variability from variations due to the radiation load. This study suggests that the ESI is a useful complement to the current suite of drought indicators, with particular added value in parts of the world where rainfall data are sparse or unreliable.

scholarly journals Seasonal drought prediction for semiarid northeastern Brazil: verification of six hydro-meteorological forecast products

2018 ◽  
Vol 22 (9) ◽  
pp. 5041-5056 ◽  
Author(s):  
José Miguel Delgado ◽  
Sebastian Voss ◽  
Gerd Bürger ◽  
Klaus Vormoor ◽  
Aline Murawski ◽  
...  
Keyword(s):  
Rainy Season ◽  
Meteorological Drought ◽  
Skill Score ◽  
Hydrological Drought ◽  
Northeastern Brazil ◽  
Drought Indices ◽  
Monthly Precipitation ◽  
Seasonal Forecasts ◽  
Seasonal Drought ◽  
Drought Prediction

Abstract. A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeastern Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Ceará's research foundation for meteorology) and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches (empirical quantile mapping, extended downscaling and weather pattern classification) were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a multivariate linear regression to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation, (b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE), the Brier skill score (BSS) and the relative operating characteristic skill score (ROCSS). The tested forecasting products showed similar performance in the analyzed metrics. Forecasts of monthly precipitation had little or no skill considering RMSE and mostly no skill with BSS. A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and BSS and significant skill when discriminating hit rate and false alarm rate given by the ROCSS (forecasting drought events of, e.g., SPEI1 showed a ROCSS of around 0.5). Regarding the temporal variation of the forecast skill of the meteorological indices, it was greatest for April, when compared to the remaining months of the rainy season, while the skill of reservoir volume forecasts decreased with lead time. This work showed that a multi-model ensemble can forecast drought events of timescales relevant to water managers in northeastern Brazil with skill. But no or little skill could be found in the forecasts of monthly precipitation or drought indices of lower scales, like SPI1. Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeastern Brazil.

Meteorological Drought Indices: Definitions

2017 ◽  
pp. 27-44
Author(s):  
Nicolas R. Dalezios ◽  
Zoltan Dunkel ◽  
Saeid Eslamian
Keyword(s):  
Meteorological Drought ◽  
Drought Indices

scholarly journals Análisis comparativo de índices de sequía en Andalucía para el periodo 1901-2012

2016 ◽  
Vol 42 (1) ◽  
pp. 67 ◽  
Author(s):  
M. Peña-Gallardo ◽  
S. R. Gámiz-Fortís ◽  
Y. Castro-Diez ◽  
M. J. Esteban-Parra
Keyword(s):  
Time Windows ◽  
Initial Period ◽  
Climatic Variability ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Precipitation Index ◽  
Drought Indices ◽  
Global Evaluation ◽  
Temporal Behaviour ◽  
The Standardized Precipitation Index

The aim of this paper is the analysis of the detection and evolution of droughts occurred in Andalusia for the period 1901-2012, by applying three different drought indices: the Standardized Precipitation Index (SPI), the Standardized Precipitation and Evapotranspiration Index (SPEI) and the Standardized Drought-Precipitation Index (IESP), computed for three time windows from the initial period 1901-2012. This analysis has been carried out after a preliminary study of precipitation trends with the intention of understanding the precipitation behaviour, because this climatic variable is one of the most important in the study of extreme events. The specific objectives of this study are: (1) to investigate and characterize the meteorological drought events, mainly the most important episodes in Andalusia; (2) to provide a global evaluation of the capacities of the three different considered indices in order to characterize the drought in a heterogeneous climatically territory; and (3) to describe the temporal behaviour of precipitation and drought indices series in order to establish the general characteristics of their evolution in Andalusia. The results have shown that not all the indices respond similarly identifying the intensity and duration of dry periods in this kind of region where geographical and climatic variability is one of the main elements to be considered.

scholarly journals Elucidating Diverse Drought Characteristics from Two Meteorological Drought Indices (SPI and SPEI) in China

2020 ◽  
Vol 21 (7) ◽  
pp. 1513-1530 ◽  
Author(s):  
Lingcheng Li ◽  
Dunxian She ◽  
Hui Zheng ◽  
Peirong Lin ◽  
Zong-Liang Yang
Keyword(s):  
Clustering Algorithm ◽  
Potential Evapotranspiration ◽  
Standardized Precipitation Index ◽  
Three Dimensional ◽  
Meteorological Drought ◽  
Drought Indices ◽  
Drought Severity ◽  
Severe Drought ◽  
Small Areas ◽  
Drought Characteristics

AbstractThis study elucidates drought characteristics in China during 1980–2015 using two commonly used meteorological drought indices: standardized precipitation index (SPI) and standardized precipitation–evapotranspiration index (SPEI). The results show that SPEI characterizes an overall increase in drought severity, area, and frequency during 1998–2015 compared with those during 1980–97, mainly due to the increasing potential evapotranspiration. By contrast, SPI does not reveal this phenomenon since precipitation does not exhibit a significant change overall. We further identify individual drought events using the three-dimensional (i.e., longitude, latitude, and time) clustering algorithm and apply the severity–area–duration (SAD) method to examine the drought spatiotemporal dynamics. Compared to SPI, SPEI identifies a lower drought frequency but with larger total drought areas overall. Additionally, SPEI identifies a greater number of severe drought events but a smaller number of slight drought events than the SPI. Approximately 30% of SPI-detected drought grids are not identified as drought by SPEI, and 40% of SPEI-detected drought grids are not recognized as drought by SPI. Both indices can roughly capture the major drought events, but SPEI-detected drought events are overall more severe than SPI. From the SAD analysis, SPI tends to identify drought as more severe over small areas within 1 million km2 and short durations less than 2 months, whereas SPEI tends to delineate drought as more severe across expansive areas larger than 3 million km2 and periods longer than 3 months. Given the fact that potential evapotranspiration increases in a warming climate, this study suggests SPEI may be more suitable than SPI in monitoring droughts under climate change.

scholarly journals Development of a Multiple Linear Regression Model for Meteorological Drought Index Estimation Based on Landsat Satellite Imagery

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3393
Author(s):  
Seon Woo Kim ◽  
Donghwi Jung ◽  
Yun-Jae Choung
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Drought Index ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Meteorological Drought ◽  
Drought Indices ◽  
Index Estimation ◽  
Cumulative Precipitation

Climate polarization due to global warming has increased the intensity of drought in some regions, and the need for drought estimation studies to help minimize damage is increasing. In this study, we constructed remote sensing and climate data for Boryeong, Chungcheongnam-do, Korea, and developed a model for drought index estimation by classifying data characteristics and applying multiple linear regression analysis. The drought indices estimated in this study include four types of standardized precipitation indices (SPI1, SPI3, SPI6, and SPI9) used as meteorological drought indices and calculated through cumulative precipitation. We then applied statistical analysis to the developed model and assessed its ability as a drought index estimation tool using remote sensing data. Our results showed that its adj.R2 value, achieved using cumulative precipitation for one month, was very low (approximately 0.003), while for the SPI3, SPI6, and SPI9 models, the adj.R2 values were significantly higher than the other models at 0.67, 0.64, and 0.56, respectively, when the same data were used.

scholarly journals Monitoring Droughts in the Greater Changbai Mountains Using Multiple Remote Sensing-Based Drought Indices

2020 ◽  
Vol 12 (3) ◽  
pp. 530 ◽  
Author(s):  
Yang Han ◽  
Ziying Li ◽  
Chang Huang ◽  
Yuyu Zhou ◽  
Shengwei Zong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Lag ◽  
Remote Sensing Data ◽  
Meteorological Drought ◽  
Condition Index ◽  
Temporal Variations ◽  
Changbai Mountains ◽  
Drought Indices ◽  
Drought Condition ◽  
Meteorological Observations

Various drought indices have been developed to monitor drought conditions. Each index has typical characteristics that make it applicable to a specific environment. In this study, six popular drought indices, namely, precipitation condition index (PCI), temperature condition index (TCI), vegetation condition index (VCI), vegetation health index (VHI), scaled drought condition index (SDCI), and temperature–vegetation dryness index (TVDI), have been used to monitor droughts in the Greater Changbai Mountains(GCM) in recent years. The spatial pattern and temporal trend of droughts in this area in the period 2001–2018 were explored by calculating these indices from multi-source remote sensing data. Significant spatial–temporal variations were identified. The results of a slope analysis along with the F-statistic test showed that up to 20% of the study area showed a significant increasing or decreasing trend in drought. It was found that some drought indices cannot be explained by meteorological observations because of the time lag between meteorological drought and vegetation response. The drought condition and its changing pattern differ from various land cover types and indices, but the relative drought situation of different landforms is consistent among all indices. This work provides a basic reference for reasonably choosing drought indices for monitoring drought in the GCM to gain a better understanding of the ecosystem conditions and environment.

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