The Siberian pine growth dynamics in Altai Mountains, China

Climatic factors play an essential role in the growth of tree ring width. In this study, we aimed to evaluate the correlation between climatic variables and tree-ring growth characteristics of Pinus sibirica in Altai mountains, northwestern China. This study being is first of its kind on climate growth analysis of Pinus sibirica in northwestern China. The study showed great potential to understand the species growing under the specific climatic conditions. Total of 70 tree cores collected from three sites in the sampling area, out of which 63 tree cores considered for this study. The effect of climatic variables which was studied include precipitation, temperature and PDSI. Our results showed that Tree Ring Width chronology has a significantly positive correlation with the late winter (March) temperature and significant negative correlation with the July temperatures. A significant correlation was observed with the late summer precipitation whereas no significant relation found with the Palmer Drought Severity Index. These significant correlations with temperature and precipitation suggested that this tree species had the potential for the reconstruction of the past climate in the area.

Spatial and Temporal Variation of Droughts in the Mongolian Plateau during 1959–2018 Based on the Gridded Self-Calibrating Palmer Drought Severity Index

Drought monitoring is challenging, but it is required for improving agricultural production, protecting the ecological environment, and reducing economic losses in drought-prone regions such as the Mongolian Plateau (MP). This study is a systematic analysis of the spatiotemporal changes in the characteristics of drought events (drought duration, severity, intensity, frequency, peak, and starting season) at the sub-regional scale between 1959 and 2018 based on the run theory and using the gridded self-calibrating Palmer Drought Severity Index (scPDSI) dataset. Principal component analysis and Varimax rotation and the Mann–Kendall trend and Sen’s slope were used for the sub-regional division and drought trend analysis, respectively. In addition, wavelet analysis was employed to analyze drought periodicity and determine the influence of large-scale climate indices on regional drought variation. The study results indicate clear differences in the spatial patterns of drought characteristics in the MP. The northern part suffered from droughts with longer duration and higher severity, whereas more drought events with shorter duration and less severity occurred in the southern part. Most of the MP experienced a relatively wet trend in 1996–2018 compared to the period of 1959–1995. The frequency of spring drought events showed an increasing trend in 1996–2018, unlike in 1959–1995. Some drought events simultaneously affected two or several sub-regions. The wavelet analysis results indicated that the drought periodicity in the MP was 10–64 months. The Arctic Oscillation (Pacific Decadal Oscillation) was significantly correlated with drought in the southern (northern) part.

Drought Estimation-and-Projection Using Standardized Supply-Demand-Water Index and Artificial Neural Networks for Upper Tana River Basin in Kenya

Drought occurrence, frequency and severity in the Upper Tana River basin (UTaRB) have critically affected water resource systems. To minimize the undesirable effects of drought, there is a need to quantify and project the drought trend. In this research, the drought was estimated and projected using Standardized Supply-Demand-Water Index (SSDI) and an Artificial Neural Network (ANN). Field meteorological data was used in which interpolated was conducted using kriging interpolation technique within ArcGIS environment. The results indicate those moderate, severe and extreme droughts at varying magnitudes as detected by the SSDI during 1972-2010 at different meteorological stations, with SSDI values equal or less than -2.0. In a spatial domain, the areas in south-eastern parts of the UTaRB exhibit the highest drought severity. Time-series forecasts and projection show that the best networks for SSDI exhibit respective ANNs architecture. The projected extreme droughts (values less than -2.00) and abundant water availability (SSDI values ³ 2.00) were estimated using Recursive Multi-Step Neural Networks (RMSNN). The findings can be integrated into planning the drought-mitigation-adaptation and early-warning systems in the UTaRB.

Concurrent and lagged relationships among various drought indices in the United states

The purpose of this study is to examine the nature of the concurrent and lagged relationships among various drought type-specific measures of drought severity. Monthly values of average temperature (TEMPZ), total precipitation (PREZ), the Palmer moisture anomaly index (ZINX) the Palmer drought severity index (PDSl), and the Palmer hydrologic drought seventy Index (PHDI) were examined from a sample of climatic divisions, in the United States for the period,1931-1985. The relationships are examined at two levels through the use of simple correlations. Level one utilizes data from the entire study period. Data from selected drought events are employed in level two. The results show that the strongest relationships are between drought indices with similar rates of response to changes in moisture supply and demand. The correlations also show that lagged values of fast-response drought indices (ZINX, PREZ) arc more strongly correlated with the slow-response PHD1 than concurrent values. Intersite differences between correlated pairs of indices are generally small and follow consistent trends; cross the flow pattern sample for both level one and level two analyses. Intra-site differences are large for some pairs of correlated indices indicating that characteristics of individual droughts can deviate substantially from average or normal conditions

Meteorological Drought Analysis Using Global Climate Model and Drought Indicators in West of Iran

Climate change and global warming impact the frequency of droughts and supply systems. Therefore, it is necessary to conduct appropriate studies to evaluate the impact of climate change on weather patterns and drought. For this purpose, data from 6 synoptic stations located in the wet and temperate areas in the Zagros region in western Iran were used to construct four general atmospheric models including BCC-CSM1, CANESM2, HADGEM2-ES, NORESM1-M under representative concentration pathways (RCPs) 2.6, 4.5, and 8.5, for three future periods (2010-2039), (2040-2069) and (2070-2099). Then, spatio-temporal variations of drought severity and frequency were studied in the study area using SPI and SPEI indices in different periods up to 2100. The results showed the spatial extent of areas classified as extremely dry will increase by 47.9% in the first period compared to the base period. In the second and third periods, however, the severely dry class covers more area. Analysis of SPEI showed that drought will be more severe in all future periods. According to SPEI, drought frequency will increase by 2% according to the first period (2010-2039) relative to the base period (1984-2013), and by 0.3% in the second and third periods by 2099. The results of this study indicate that the severity, frequency, and impacts of drought will increase in the study area until the end of the century. Therefore, appropriate measures should be taken to control and reduce its potential effects in the future.

Streamflow Reconstructions Using Tree-Ring Based Paleo Proxies for the Upper Adige River Basin (Italy)

The Adige River Basin (ARB) provides a vital water supply source for varying demands including agriculture (wine production), energy (hydropower) and municipal water supply. Given the importance of this river system, information about past (paleo) drought and pluvial (wet) periods would quantity risk to water managers and planners. Annual streamflow data were obtained for four gauges that were spatially located within the upper ARB. The Old World Drought Atlas (OWDA) provides an annual June–July–August (JJA) self-calibrating Palmer Drought Severity Index (scPDSI) derived from 106 tree-ring chronologies for 5414 grid points across Europe from 0 to 2012 AD. In lieu of tree-ring chronologies, the OWDA dataset was used as a proxy to reconstruct both individual gauge and ARB regional streamflow from 0 to 2012. Principal component analysis (PCA) was applied to the four ARB streamflow gauges to generate one representative vector of regional streamflow. This regional streamflow vector was highly correlated with the four individual gauges, as coefficient of determination (R2) values ranged from 85% to 96%. Prescreening methods included correlating annual streamflow and scPDSI cells (within a 450 km radius) in which significant (p ≤ 0.01 or 99% significance) scPDSI cells were identified. The significant scPDSI cells were then evaluated for temporal stability to ensure practical and reliable reconstructions. Statistically significant and temporally stable scPDSI cells were used as predictors (independent variables) to reconstruct streamflow (predictand or dependent variable) for both individual gauges and at the regional scale. This resulted in highly skillful reconstructions of upper ARB streamflow from 0 to 2012 AD. Multiple drought and pluvial periods were identified in the paleo record that exceed those observed in the recent, historic record. Moreover, this study concurred with streamflow reconstructions in nearby European watersheds.

Drought Spatiotemporal Characteristics Based on a Vegetation Condition Index in Erbil, Kurdistan Region, Iraq

Drought is a complex phenomenon that has severe impacts on the environment. Vegetation and its conditions are very sensitive to drought effects. This study aimed to monitor and assess the drought severity and its relationships to some ecological variables in ten districts of Erbil Governorate (Kurdistan Region), Iraq, throughout 20 years (1998-2017). The results revealed that droughts frequently hit Erbil throughout the study period. The Landsat time-series- based on Vegetation Condition Index (VCI) significantly correlated with precipitation, Digital Elevation Model (DEM), and latitude. Extreme VCI-based drought area percentages were recorded in 1999, 2000, 2008, and 2011 by 43.4%, 67.9%, 43.3%, and 40.0%, respectively. The highest crop yield reduction in the study area occurred mainly in 2000, 2008, and 2012 due to low precipitation rates. These results reveal the capability of the VCI for drought characteristics and highlighting relationships with some ecological variables, which provide vital information to the decision-makers, environmental, and economic sectors.

Time Series Trend Analysis of Rainfall and Temperature over Kolkata and Surrounding Region

Study of long term variability of temperature and rainfall in the context of climate change is of much importance particularly in the region where rainfed agriculture is predominant. Long term trends of temperature and rainfall have been investigated over Kolkata, India, a tropical region using gridded monthly precipitation and temperature data obtained from Global Precipitation and Climate Centre (GPCC V7) with 0.5° X 0.5° resolution for the period 1901 to 2014. Precipitation concentration index, coefficient of variation, rainfall anomaly have been calculated and Palmer drought severity index data have been analyzed. Furthermore, Mann-Kendall test and sen’s slope estimator have been used to detect time series trend. Annual temperature and rainfall have been increased with a rate of 0.0082°C/ year and 0.03 mm/ year respectively. Statistically significant increasing trend has been observed for most of the months for temperature and rainfall. Winter and monsoon period shows highest and lowest inter-annual variability respectively. Rainfall with high precipitation concentration index (16-20) has been observed for the period 1951-1975 and 1976-2000. It has been observed that the number of years with dry conditions have been increased. However, the intensity of dryness is very near to zero. The information from this study will be helpful for the farmers to plan for resilient farming.

Threshold Response to Extreme Drought Shifts Inter-Tree Growth Dominance in Pinus sylvestris

Many studies quantify short-term drought impact on tree growth relative to pre-drought growth averages. However, fewer studies examine the extent to which droughts of differing severity differentially impact tree growth or shape stand dynamics. Focusing on three droughts in high and low density stands of Pinus sylvestris in Scotland, we calculated pre-drought growth averages using climatically standardized antecedent growth years to assess tree level drought and post-drought growth performance as percentage growth change (PGC). We then used mixed-effects models to understand how droughts of differing severity impact tree growth and calculated indices of growth dominance (Gd), size inequality (Si), and size asymmetry (Sa) to detect changes in stand structure. Mixed-effects model results indicate that the magnitude and duration of the growth reduction during and following the more extreme drought was significantly larger compared to less severe droughts, for which we found limited evidence of drought impact. While no changes in Si or Sa were noted following any drought, we found evidence of a difference in Gd after the most extreme drought in both stand densities indicative of a threshold response, with smaller trees contributing proportionally more to stand growth relative to their size. Under less severe droughts, inter-tree variability may have partially buffered against stand-level growth change, however, a small increase in drought severity was associated with a significant reduction in average tree growth, an increase in the number of trees growing at >2SD below pre-drought levels and a shift in Gd toward smaller trees, indicating that a drought severity threshold in P. sylvestris may have been exceeded.

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.