Forecasting the Future Drought Indices Due to the Effects of Climate Change in Al Najaf City, Iraq.

Drought is a natural disaster associated with a shortage of water availability for specified region within a specific time period. The impacts of drought are significant and extend to damage many important life aspects such as environmental, economic, and social activities. The forecasting of the drought events is an essential element for planning this disaster, reducing its effectiveness and response. The three characteristic frequency, intensity, and time period are the key parts for forecasting and assessment of droughts. Here, two drought indices (The Reconnaissance Drought Index (RDI), standardized precipitation index (SPI)) were used for forecasting of the future drought within Al Najaf city, Iraq. Thirty years meteorological data (average monthly precipitation and temperature) were used for the period (2021–2050) downloaded from the site of the Centre for Environmental Data Analysis (CEDA) for five grid points to cover overall study area. The computation of these indices conducted at a 12-month time scale and included the calculation of potential evapotranspiration by Thorthwaite method. The temporal drought intensity as well as drought frequency configurations were calculated and analyzed for each drought index. The results showed that the general average drought level expected will mildly dry while the maximum drought level expected will extremely dry. The more severe seasons of drought were forecasted in the years 2038, 2034 and 2021, respectively. Also, the prevailing event will be a one year drought and the maximum drought interval occurred within the study period will four consecutive years, with a 3.33% exceedance probability.

Patterns of Past and Future Droughts in Permanent Lowland Rivers

The problem of droughts is acute due to climate change. The study aims to assess the temporal and spatial drought patterns in Lithuanian lowland rivers in the past and to project these phenomena according to climate scenarios and models. Drought analysis was based on Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI) and Streamflow Drought Index (SDI). To evaluate the past patterns, the hydrometeorological data of 17 rivers were used from 1961–2020. Future drought changes were analyzed in 2021–2100 according to the selected RCPs (Representative Concentration Pathways) using the hydrological model HBV. There were different patterns of droughts in three hydrological regions of Lithuania (Western, Central and Southeastern). The Southeastern region was more prone to extreme summer hydrological droughts, and they had a shorter accumulation period compared to the other two regions. SPI and RDI indices showed that the number of dry months and the minimum value of the index increased, extending the accumulation period. The highest correlation was recorded between RDI-12/SPI-12 and SDI-12. The amplitude between extremely wet and dry values of river runoff will increase according to RCP8.5. The projections indicated that hydrological drought intensity in the Central region is expected to increase under both analyzed RCPs.

Comparison of Long-Term Changes in Non-Linear Aggregated Drought Index Calibrated by MERRA–2 and NDII Soil Moisture Proxies

This study aimed at evaluating Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA–2) and Normalized Difference Infrared Index (NDII) soil moisture proxies in calibrating a comprehensive Non-linear Aggregated Drought Index (NADI). Soil moisture plays a critical role in temperature variability and controlling the partitioning of water into evaporative fluxes as well as ensuring effective plant growth. Long-term variability and change in climatic variables such as precipitation, temperatures, and the possible acceleration of the water cycle increase the uncertainty in soil moisture variability. Streamflow, temperature, rainfall, reservoir storage, MERRA–2, and NDII soil moisture proxies’ data from 1986 to 2016 were used to formulate the NADI. The trend analysis was performed using the Mann Kendall, SQ-MK was used to determine the point of trend direction change while Theil-Sen trend estimator method was used to determine the magnitude of the detected trend. The seasonal correlation between the NADI-NDII and NADI-MERRA–2 was higher in spring and autumn with an R2 of 0.9 and 0.86, respectively. A positive trend was observed over the 30 years period of study, NADI-NDII trend magnitude was found to be 2.94 units per year while that of NADI-MERRA–2 was 1.21 units. Wavelet analysis showed an in-phase relationship with negligible lagging between the NDII and MERRA–2 calibrated NADI. Although a robust comparison is recommended between soil moisture proxies and observed soil moisture, the soil moisture proxies in this study were found to be useful in monitoring long-term changes in soil moisture.

A global-scale relationship between crop yield anomaly and multiscalar drought index based on multiple precipitation data

Drought impact on crop production is well known as crop yield is strongly controlled by climate variation. Previous studies assessed the drought impact using a drought index based on a single input data set, while the variability of the drought index to the input data choice is notable. In this study, a drought index based on the Standardized Precipitation Index with multiple timescales using several global precipitation datasets was compared with the detrended anomaly based on the global dataset of historical yield for major crops over 1981-2016. Results show that the drought index based on the ensemble precipitation dataset correlates better with the crop yield anomaly than a single dataset. Based on the drought index using ensemble datasets, global crop areas significantly affected by drought during the study period were around 23, 8, 30, and 29% for maize, rice, soybean, and wheat, respectively, induced mainly by medium to longer drought timescale (5 – 12-months). This study indicates that most crops cultivated in dry regions were affected by droughts worldwide, while rice shows less correlation to drought as it is generally irrigated and cultivated in humid regions with less drought exposure. This study provides a valuable framework for data choices in drought index development and a better knowledge of the drought impact on agriculture using different timescales on a global scale towards understanding crop vulnerability to climate disruptions.

Vegetation Drought Vulnerability Mapping Using a Copula Model of Vegetation Index and Meteorological Drought Index

Since vegetation is closely related to a variety of hydrological factors, the vegetation condition during a drought is greatly affected by moisture supply or moisture demand from the atmosphere. However, since feedback between vegetation and climate in the event of drought is very complex, it is necessary to construct a joint probability distribution that can describe and investigate the interrelationships between them. In other words, it is required to understand the interaction between vegetation and climate in terms of joint probability. In this study, the possibility of drought stress experienced by vegetation under various conditions occurring during drought was investigated by dividing drought into two aspects (atmospheric moisture supply and moisture demand). Meteorological drought indices that explain different aspects of drought and vegetation-related drought indexes that describe the state of vegetation were estimated using data remotely sensed by satellites in parts of Far East Asia centered on South Korea. Bivariate joint probability distribution modeling was performed from vegetation drought index and meteorological drought index using Copula. It was found that the relationship between the vegetation drought index and the meteorological drought index has regional characteristics and there is also a seasonal change. From the copula-based model, it was possible to quantify the conditional probability distribution for the drought stress of vegetation under meteorological drought scenarios that occur from different causes. Through this, by mapping the vulnerability of vegetation to meteorological drought in the study area, it was possible to spatially check how the vegetation responds differently depending on the season and meteorological causes. The probabilistic mapping of vegetation vulnerability to various aspects of meteorological drought may provide useful information for establishing mitigation strategies for ecological drought.

Comparative evaluation of precipitation-temperature based drought indices (DIs): A case study of Moroccan Lower Sebou basin

Due to the lack of studies on drought in the Lower Sebou basin (LSB), the complexity of drought event and the difference in climate conditions. The identification of the most appropriate drought indices (DIs) to assess drought conditions has become a priority. Therefore, assessing the performance of different drought indices was considered in order to identify the universal drought indices that are well adapted to the LSB. Based on data availability, five DIs were used: Standardized Precipitation Index (SPI), Standardized Precipitation and Evapotranspiration Index (SPEI), Reconnaissance Drought Index (RDI), self-calibrated Palmer Drought Severity Index (sc-PDSI) and Streamflow Drought Index (SDI). The DIs were calculated on an annual scale using monthly time series of precipitation, temperature and river flow from 1984-2016. Thornthwaite's method was used to calculate potential evapotranspiration (PET). Pearson's correlation (r) were analyzed. Furthermore, five decision criteria namely robustness, traceability, transparency, sophistication and scalability were used to evaluate the performance of these indices. The results proved the fact that SPI is suitable to detect the drought duration and intensity compared to other indices with high correlation coefficients especially in sub humid regions, knowing that it tends to give more results that are humid in stations with semi-arid climates. SPI, SPEI and RDI follow the same trend during the period studied. However, sc-PDSI appears to be the most sensitive to temperature and precipitation by overestimating the drought conditions. Eventually, the results of the performance evaluation criteria revealed that SPEI classified first (total score = 137) among other meteorological drought indices, followed by SPI, RDI and sc-PDSI.