Spatio-Temporal Analysis of Drought Variability in Myanmar Based on the Standardized Precipitation Evapotranspiration Index (SPEI) and Its Impact on Crop Production

Drought research is an important aspect of drought disaster mitigation and adaptation. For this purpose, we used the Standardized Precipitation Evapotranspiration Index (SPEI) to investigate the spatial-temporal pattern of drought and its impact on crop production. Using monthly precipitation (Precip) and temperature (Temp) data from 1986–2015 for 39 weather stations, the drought index was obtained for the time scale of 3, 6, and 12 months. The Mann–Kendall test was used to determine trends and rates of change. Precip and Temp anomalies were investigated using the regression analysis and compared with the drought index. The link between drought with large-scale atmospheric circulation anomalies using the Pearson correlation coefficient (R) was explored. Results showed a non-uniform spatial pattern of dryness and wetness which varied across Myanmar agro-ecological zones and under different time scales. Generally, results showed an increasing trend for the SPEI in the three-time scales, signifying a high tendency of decreased drought from 1986–2015. The fluctuations in dryness/wetness might linked to reduction crop production between 1986–1999 and 2005, 2008, 2010, 2013 cropping years. Results show relationship between main crops production and climate (teleconnection) factors. However, the low correlation values (i.e., <0.49) indicate the extent of the relationship within the natural variability. However, readers are urged to interpret this result cautiously as reductions in crop production may also be affected by other factors. We have demonstrated droughts evolution and trends using weather stations, thus providing useful information to aid policymakers in developing spatially relevant climate change adaptation and mitigation management plans for Myanmar.

Download Full-text

Modeling a Drought Index Using a Nonparametric Approach

Global NEST Journal ◽

10.30955/gnj.001959 ◽

2017 ◽

Vol 19 (1) ◽

pp. 58-68 ◽

Cited By ~ 1

Keyword(s):

Time Scales ◽

Gamma Distribution ◽

Drought Index ◽

Standardized Precipitation Index ◽

Kernel Density ◽

Density Estimator ◽

Monthly Precipitation ◽

Nonparametric Approach ◽

The Standardized Precipitation Index ◽

Drought Classes

Alternatively, to other studies that used parametric distributions (e.g. Gamma) in the estimation of the Standardized Precipitation Index (SPI), this study aims to apply a nonparametric method based on Kernel Density Estimator (KDE) for calculating the SPI. Results of the proposed method were compared with the ones from the most widely used parametric distribution, using a long dataset of monthly precipitation of four meteorological stations in Iran (including Bushehr, Mashhad, Tehran and Esfahan) over a period of 107 water years (1895-2002). The capability of KDE-based SPI was compared with the Gamma-based SPI at four-time scales of 3, 6, 9 and 12 months. The frequencies of the drought classes of SPI were calculated and compared with corresponding expected frequencies. The results revealed that the KDE is more consistent with the expected values of the SPI drought/wet classes frequencies (especially in the extreme classes) at all stations as well as at the four-time scales, compared to the Gamma distribution. The greatest deviation from the expected frequencies for KDE and Gamma distribution were about 10% and 150%, respectively. This study proposes a new analytical approach in modeling SPI that provides more accurate results pertaining frequency of occurrences of extreme drought events. The output of the study can be used in many fields (e.g. tourism, agriculture, insurance, etc.) that are influenced by severe droughts.

Download Full-text

Evaluating the Spatiotemporal Characteristics of Agricultural Drought in Bangladesh Using Effective Drought Index

Water ◽

10.3390/w11122437 ◽

2019 ◽

Vol 11 (12) ◽

pp. 2437 ◽

Cited By ~ 1

Author(s):

Mohammad Kamruzzaman ◽

Syewoon Hwang ◽

Jaepil Cho ◽

Min-Won Jang ◽

Hanseok Jeong

Keyword(s):

Central Region ◽

Drought Index ◽

Regional Analysis ◽

Mitigation Strategies ◽

Agricultural Drought ◽

Monthly Precipitation ◽

Effective Drought Index ◽

Spatiotemporal Characteristics ◽

North Western

This study aims to assess the spatiotemporal characteristics of agricultural droughts in Bangladesh during 1981–2015 using the Effective Drought Index (EDI). Monthly precipitation data for 36 years (1980–2015) obtained from 27 metrological stations, were used in this study. The EDI performance was evaluated for four sub-regions over the country through comparisons with historical drought records identified by regional analysis. Analysis at a regional level showed that EDI could reasonably detect the drought years/events during the study period. The study also presented that the overall drought severity had increased during the past 35 years. The characteristics (severity and duration) of drought were also analyzed in terms of the spatiotemporal evolution of the frequency of drought events. It was found that the western and central regions of the country are comparatively more vulnerable to drought. Moreover, the southwestern region is more prone to extreme drought, whereas the central region is more prone to severe droughts. Besides, the central region was more prone to extra-long-term droughts, while the coastal areas in the southwestern as well as in the central and north-western regions were more prone to long-term droughts. The frequency of droughts in all categories significantly increased during the last quinquennial period (2011 to 2015). The seasonal analysis showed that the north-western areas were prone to extreme droughts during the Kharif (wet) and Rabi (dry) seasons. The central and northern regions were affected by recurring severe droughts in all cropping seasons. Further, the most significant increasing trend of the drought-affected area was observed within the central region, especially during the pre-monsoon (March–May) season. The results of this study can aid policymakers in the development of drought mitigation strategies in the future.

Download Full-text

The Evaporative Demand Drought Index. Part II: CONUS-Wide Assessment against Common Drought Indicators

Journal of Hydrometeorology ◽

10.1175/jhm-d-15-0122.1 ◽

2016 ◽

Vol 17 (6) ◽

pp. 1763-1779 ◽

Cited By ~ 48

Author(s):

Daniel J. McEvoy ◽

Justin L. Huntington ◽

Michael T. Hobbins ◽

Andrew Wood ◽

Charles Morton ◽

...

Keyword(s):

United States ◽

Soil Moisture ◽

Time Scales ◽

Drought Index ◽

Standardized Precipitation Index ◽

Stress Index ◽

Multiple Time Scales ◽

Multiple Time ◽

Evaporative Demand ◽

Hydrologic Drought

Abstract Precipitation, soil moisture, and air temperature are the most commonly used climate variables to monitor drought; however, other climatic factors such as solar radiation, wind speed, and humidity can be important drivers in the depletion of soil moisture and evolution and persistence of drought. This work assesses the Evaporative Demand Drought Index (EDDI) at multiple time scales for several hydroclimates as the second part of a two-part study. EDDI and individual evaporative demand components were examined as they relate to the dynamic evolution of flash drought over the central United States, characterization of hydrologic drought over the western United States, and comparison to commonly used drought metrics of the U.S. Drought Monitor (USDM), Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI), and the evaporative stress index (ESI). Two main advantages of EDDI over other drought indices are that it is independent of precipitation (similar to ESI) and it can be decomposed to identify the role individual evaporative drivers have on drought onset and persistence. At short time scales, spatial distributions and time series results illustrate that EDDI often indicates drought onset well in advance of the USDM, SPI, and SSI. Results illustrate the benefits of physically based evaporative demand estimates and demonstrate EDDI’s utility and effectiveness in an easy-to-implement agricultural early warning and long-term hydrologic drought–monitoring tool with potential applications in seasonal forecasting and fire-weather monitoring.

Download Full-text

Near-real time mapping of Keetch-Byram drought index in the south-eastern United States

International Journal of Wildland Fire ◽

10.1071/wf02013 ◽

2002 ◽

Vol 11 (4) ◽

pp. 281 ◽

Cited By ~ 17

Author(s):

Michael J. Janis ◽

Michael B. Johnson ◽

Gloria Forthun

Keyword(s):

United States ◽

Drought Index ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Mean Annual Precipitation ◽

Eastern United States ◽

The South ◽

South Eastern ◽

Input Parameters ◽

Weather Stations

High spatial resolution maps of daily Keetch-Byram Drought Index (KBDI) are constructed for the south-eastern United States. KBDI is a cumulative algorithm for estimating fire potential from meteorological information, including daily maximum temperature, daily total precipitation, and mean annual precipitation. With few input parameters, the KBDI is attractive for providing estimates of fire potential at a large number of locations. The Southeast Regional Climate Center (SERCC) applies the original algorithms over daily time steps to maximize the response time in the event of rapidly increasing fire potential. Algorithms are applied to a network of 261 weather stations across the south-eastern United States to provide regional contour maps of KBDI as well as maps of week-to-week KBDI difference. Though uniformity and spatial density of weather stations and the consistency of input parameters are potential hurdles, it is shown that careful compilation of meteorological databases makes KBDI a tractable and valuable monitoring tool for automated fire-potential monitoring.

Download Full-text

Impact of Atmospheric Moisture Storage on Precipitation Recycling

Journal of Climate ◽

10.1175/jcli3691.1 ◽

2006 ◽

Vol 19 (8) ◽

pp. 1513-1530 ◽

Cited By ~ 134

Author(s):

Francina Dominguez ◽

Praveen Kumar ◽

Xin-Zhong Liang ◽

Mingfang Ting

Keyword(s):

United States ◽

Time Scales ◽

Analytical Models ◽

Spatiotemporal Variability ◽

Spatial And Temporal Variability ◽

Monthly Precipitation ◽

Computationally Efficient ◽

Precipitation Recycling ◽

Moisture Storage ◽

Storage Term

Abstract Computations of precipitation recycling using analytical models are generally performed under the assumption of negligible change in moisture storage in the atmospheric column. Because the moisture storage term is nonnegligible at smaller time scales, most recycling studies using analytical models are done at monthly or longer time scales. A dynamic precipitation recycling model, which incorporates the change in moisture storage, is developed. It is derived formally from the conservation of mass equation and is presented in a simple and computationally efficient form. This model allows for recycling analysis at a range of temporal scales, from daily to monthly and longer. In comparison to the traditional models that do not include the storage term, the new model presents almost identical spatial and temporal variability, but predicts recycling ratios that are 12%–33% larger at a monthly level. The dynamic model is used to study the variability of monthly precipitation recycling over the conterminous United States using Reanalysis-II data from 1979 to 2000. On average, the southeastern and southwestern parts of the country exhibit high summer recycling ratios, contrasting with the low values in the northeastern and northwestern United States. The Colorado region also presents high recycling ratios. Dominant modes of spatiotemporal variability in recycling are identified using EOF analysis. The first mode captures strong recycling ratios over the western United States during the summers of 1986, 1992, and 1998. The second mode captures anomalous high recycling ratios during 1988 and 1989 over the central part of the country, and anomalous low ratios during 1980 and 1993.

Download Full-text

A novel index for ecological drought monitoring based on ecological water deficit: a case study of Northwestern China

10.5194/egusphere-egu21-5439 ◽

2021 ◽

Author(s):

Tianliang Jiang ◽

Xiaoling Su

Keyword(s):

Water Deficit ◽

Time Scales ◽

Drought Index ◽

Vegetation Index ◽

Standardized Precipitation Index ◽

Northwestern China ◽

Drought Monitoring ◽

Drought Indices ◽

Drought Severity ◽

Different Time Scales

Although the concept of ecological drought was first defined by the Science for Nature and People Partnership (SNAPP) in 2016, there remains no widely accepted drought index for monitoring ecological drought. Therefore, this study constructed a new ecological drought monitoring index, the standardized ecological water deficit index (SEWDI). The SEWDI is based on the difference between ecological water requirements and consumption, referred to as the standardized precipitation index (SPI) method, which was used to monitor ecological drought in Northwestern China (NWRC). The performances of the SEWDI and four widely-used drought indices [standardized root soil moisture index (SSI), self-calibrated Palmer drought index (scPDSI), standardized precipitation-evaporation drought index (SPEI), and SPI) in monitoring ecological drought were evaluated through comparing the Pearson correlations between these indices and the standardized normalized difference vegetation index (SNDVI) under different time scales, wetness, and water use efficiencies (WUEs) of vegetation. Finally, the rotational empirical orthogonal function (REOF) was used to decompose the SEWDI at a 12-month scale in the NWRC during 1982&#8211;2015 to obtain five ecological drought regions. The characteristics of ecological drought in the NWRC, including intensity, duration, and frequency, were extracted using run theory. The results showed that the performance of the SEWDI in monitoring ecological drought was highest among the commonly-used drought indices evaluated under different time scales [average correlation coefficient values (r) between SNDVI and drought indices: SEWDI= 0.34, SSI= 0.24, scPDSI= 0.23, SPI= 0.20, SPEI= 0.18), and the 12-month-scale SEWDI was largely unaffected by wetness and WUE. In addition, the results of the monitoring indicated that serious ecological droughts in the NWRC mainly occurred in 1982&#8211;1986, 1990&#8211;1996, and 2005&#8211;2010, primarily in regions I, II, and V, regions II, and IV, and in region III, IV, and V, respectively. This study provides a robust approach for quantifying ecological drought severity across natural vegetation areas and scientific evidence for governmental decision makers.

Download Full-text

Exploring the Evolution of Drought Characteristics in Balochistan, Pakistan

Applied Sciences ◽

10.3390/app10030913 ◽

2020 ◽

Vol 10 (3) ◽

pp. 913 ◽

Cited By ~ 1

Author(s):

Shoaib Jamro ◽

Falak Naz Channa ◽

Ghulam Hussain Dars ◽

Kamran Ansari ◽

Nir Y. Krakauer

Keyword(s):

Climate Change ◽

Drought Index ◽

Risk Index ◽

Trend Test ◽

Standardized Precipitation Evapotranspiration Index ◽

Long Duration ◽

Tree Edge ◽

Edge Removal ◽

The 1960S

In the wake of a rapidly changing climate, droughts have intensified, in both duration and severity, across the globe. The Germanwatch long-term Climate Risk Index ranks Pakistan among the top 10 countries most affected by the adverse effects of climate change. Within Pakistan, the province of Balochistan is among the most vulnerable regions due to recurring prolonged droughts, erratic precipitation patterns, and dependence on agriculture and livestock for survival. This study aims to explore how the characteristics of droughts have evolved in the region from 1902–2015 using 3-month and 12-month timescales of a popular drought index, the Standardized Precipitation Evapotranspiration Index (SPEI). The region was divided into six zones using Spatial “K”luster Analysis using Tree Edge Removal (SKATER) method, and run theory was applied to characterize droughts in terms of duration, severity, intensity, and peak. The results of the non-parametric Mann–Kendall trend test applied to SPEI indicate prevailing significant negative trends (dryer conditions) in all the zones. Balochistan experienced its most severe droughts in the 1960s and around 2000. The effects of climate change are also evident in the fact that all the long duration droughts occurred after 1960. Moreover, the number of droughts identified by 3-month SPEI showed a significant increase after 1960 for all six zones. The same trend was found in the 12-month SPEI but for only three zones.

Download Full-text

Drought and climate change assessment using Standardized Precipitation Index (SPI) for Sarawak River Basin

Journal of Water and Climate Change ◽

10.2166/wcc.2019.036 ◽

2019 ◽

Vol 11 (4) ◽

pp. 956-965 ◽

Cited By ~ 3

Author(s):

C. H. J. Bong ◽

J. Richard

Keyword(s):

Climate Change ◽

River Basin ◽

Drought Index ◽

Standardized Precipitation Index ◽

Recent Decade ◽

Kendall Test ◽

Precipitation Index ◽

Irrigated Agriculture ◽

Drought Event ◽

Monthly Precipitation

Abstract Severe droughts in the year 1998 and 2014 in Sarawak due to the strong El Niño has impacted the water supply and irrigated agriculture. In this study, the Standardized Precipitation Index (SPI) was used for drought identification and monitoring in Sarawak River Basin. Using monthly precipitation data between the year 1975 and 2016 for 15 rainfall stations in the basin, the drought index values were obtained for the time scale of three, six and nine months. Rainfall trend for the years in study was also assessed using the Mann–Kendall test and Sen's slope estimator and compared with the drought index. Findings showed that generally there was a decreasing trend for the SPI values for the three time scales, indicating a higher tendency of increased drought event throughout the basin. Furthermore, it was observed that there was an increase in the numbers of dry months in the recent decade for most of the rainfall stations as compared to the previous 30 to 40 years, which could be due to climate change. Findings from this study are valuable for the planning and formulating of drought strategies to reduce and mitigate the adverse effects of drought.

Download Full-text

Disaster mitigation of climate change effects on small islands (case of Harapan and Kelapa Islands)

E3S Web of Conferences ◽

10.1051/e3sconf/20187412004 ◽

2018 ◽

Vol 74 ◽

pp. 12004

Author(s):

Ade Panca Zulriskan ◽

Raldi Hendrotoro Koestoer ◽

Adil Faisal Alwini

Keyword(s):

Climate Change ◽

Social Economic ◽

Spatial Dynamics ◽

Disaster Mitigation ◽

Small Island ◽

Small Islands ◽

Population Number ◽

Climate Change Effects ◽

Mitigation And Adaptation ◽

Key Indicators

The vulnerability is the sequence of condition that determines whether a hazard will cause disaster or not. The small islands are easily affected by the climate change effects. The small islands are particularly vulnerable to the projected of climate change effects. Harapan and Kelapa Islands is a small island in the Thousand Islands Region, DKI Jakarta Province. This study aims to analyze of climate change effects on social, economic, and environmental on small islands. This study using key indicators consist of social, economic, environment, and climate change. The methods applied in this research is GIS and system dynamics or called by spatial dynamics. The result showed that the area of study categorized as the vulnerable area to the climate change effects, this is due to the increasing sea level rise and rainfall, the growing population number and density, and the expanding built area. Therefore, the need for disaster mitigation and adaptation efforts on the climate change effects.

Download Full-text

Daily monitoring of retrogressive thaw slumps in the Fosheim Peninsula, Ellesmere Island, Canada

10.5194/egusphere-egu2020-360 ◽

2020 ◽

Author(s):

Melissa Ward Jones ◽

Benjamin Jones ◽

Wayne Pollard

Keyword(s):

Time Scales ◽

Air Temperature ◽

Polynomial Regression ◽

Pearson Correlation ◽

Daily Basis ◽

The Arctic ◽

Regression Equations ◽

Daily Monitoring ◽

Sediment Redistribution ◽

Short Time

Retrogressive thaw slumps (RTS) occur from the mass wasting of ice-rich permafrost. These horseshoe-shaped features have an ablating or retreating ice-rich headwall with fluidized sediment that is transported along the RTS floor. RTS can remain active for up to decades and enlarge as the headwall retreats. With observed increases in RTS number, rates and sizes in recent decades, there is a need to understand these highly dynamic landforms, however there is a general lack of detailed field observations of RTSs. We monitored 3 RTS for over half of the 2017 thaw period by setting up and tracking survey transects on a near daily basis. We correlated mean daily and cumulative retreat to mean daily air temperature (MDAT), total daily precipitation (TDP) and thawing degree days (TDD) using various polynomial regressions and Pearson correlation techniques. Our results show that July retreat was highly variable and periods of increased RTS retreat did not always align with periods of increased air temperature. Also, multiple periods of increased retreat could occur within a single period of increased air temperature. These retreat trends were observed to be largely driven by sediment redistribution in the RTS floor. Retreat rates decreased suddenly in early August, indicating a threshold of either air temperature, solar radiation or a combination of both must be reached for increased retreat rates. There was a statistically significant correlation between daily mean and mean cumulative retreat with MDAT (p < 0.001) and TDD (p < 0.001 and < 0.0001) but not with TDP. Correlating mean cumulative retreat and cumulative TDD using polynomial regression (quadratic and cubic) generated R2 values greater than 0.99 for all 3 sites as these variables account for past and current conditions within the monitoring period, as well as lag responses of retreat. This suggests the potential of accurately modelling RTS retreat with minimal field data (air temperature and headwall position), however this is currently restricted to individual RTSs and only within short time scales. We tested this idea by modelling 2 weeks of cumulative retreat in 2018 for 2 of our sites we monitored using the 2017 regression equations. Percent prediction error was 8% at one site and 16% at the other. Monitoring RTS on a daily scale allows RTS behaviour and trends to be identified that may be obscured at annual time scales. With the widespread increased numbers of RTSs being observed around the Arctic, understanding their dynamics is critical as these landforms impact surrounding ecosystems and infrastructure which will be exacerbated with climate change. &#160;

Download Full-text