Self-calibrating Palmer Drought Severity Index Research Articles

Drought reconstruction since 1796 CE based on tree-ring widths in the upper Heilongjiang (Amur) River basin in Northeast Asia and its linkage to Pacific Ocean climate variability

Abstract. The economic and environmental impacts of persistent droughts in East Asia are of growing concern, and therefore it is important to study the cyclicity and causes of these regional droughts. The self-calibrating Palmer drought severity index (scPDSI) has been extensively employed to describe the severity of regional drought, and several scPDSI reconstructions based on tree rings have been produced. We compiled a tree-ring chronology for Hailar pine (Pinus sylvestris var. mongolica) from two sites in the Hailar region in the upper Heilongjiang (Amur) River basin. Analysis of the climate response revealed that scPDSI was the primary factor limiting tree ring growth from May to July. The mean May to July scPDSI in the Hailar region since 1796 was reconstructed from the tree-ring width chronology. The results of spatial correlation analysis revealed that the reconstructed scPDSI in this region responded significantly to climate change. Analysis of the synoptic climatology indicated that the drought in the upper Heilongjiang (Amur) River basin is closely related to El Niño–Southern Oscillation (ENSO) and the Silk Road teleconnection. The results of atmospheric water cycle analysis show that water vapor transport processes are the dominant factor in the development of drought in this region.

Drought Forecasting Using Integrated Variational Mode Decomposition and Extreme Gradient Boosting

The current study seeks to conduct time series forecasting of droughts by means of the state-of-the-art XGBoost algorithm. To explore the drought variability in one of the semi-arid regions of Turkey, i.e., Denizli, the self-calibrated Palmer Drought Severity Index (sc-PDSI) values were used and projections were made for different horizons, including short-term (1-month: t + 1), mid-term (3-months: t + 3 and 6-months: t + 6), and long-term (12-months: t + 12) periods. The original sc-PDSI time series was subjected to the partial autocorrelation function to identify the input configurations and, accordingly, one- (t − 1) and two-month (t − 2) lags were used to perform the forecast of the targeted outcomes. This research further incorporated the recently introduced variational mode decomposition (VMD) for signal processing into the predictive model to enhance the accuracy. The proposed model was not only benchmarked with the standalone XGBoost but also with the model generated by its hybridization with the discrete wavelet transform (DWT). The overall results revealed that the VMD-XGBoost model outperformed its counterparts in all lead-time forecasts with NSE values of 0.9778, 0.9405, 0.8476, and 0.6681 for t + 1, t + 3, t + 6, and t + 12, respectively. Transparency of the proposed hybrid model was further ensured by the Mann–Whitney U test, highlighting the results as statistically significant.

Evaluation of COSMIC-2 performance in detecting atmospheric boundary layer height and analysis of its variation

The atmospheric boundary layer (ABL) height (ABLH) is an essential index for climate and weather studies in the troposphere. Obtaining abundant and precise observations is crucial for modeling the top of the ABL. The radio occultation can provide observation data of Earth’s atmosphere with high vertical resolution and comprehensive spatial coverage, benefiting from its measurement mode. Thanks to the improved quality of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC)-2 lower atmosphere observations, its substantial amount and higher-quality atmospheric profile data are well suited for ABLH detection. Therefore, 1.4 million COSMIC-2 refractivity profiles from October 2019 to September 2020 were employed to determine the ABLH, and its retrieval performance is assessed using the official COSMIC-2 product (zbalmax) and radiosonde data. The retrieval results show that most of the effectively detected ABLH occultation events occur over the ocean. Comparing COSMIC-2 ABLH and zbalmax, consistent accuracy is found with a standard deviation of 0.22 km. Similar biases are observed each month. And the inversion accuracy in the Northern Hemisphere is higher than in the Southern Hemisphere. The ABLH derived from radiosonde data and COSMIC-2 has a high correlation coefficient of no less than 0.75. The better accuracy happened in the DJF (i.e. from December to February) for both comparisons with two datasets. Additionally, the distribution and seasonal variations were revealed by modeling verified COSMIC-2 ABLH. The fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5) surface temperature and self-calibrated Palmer Drought Severity Index are used to analyze the impact of temperature and drought on ABLH. The results indicate that higher surface temperatures lead to a smaller ABLH, whereas drought at the surface leads to a larger ABLH. Therefore, the higher precision ABLH can be derived from COSMIC-2 after verification, and we can reveal its seasonal variation and influencing factors on this basis.

Experiments of the efficacy of tree ring blue intensity as a climate proxy in central and western China

Abstract. To investigate the potential value of tree ring blue intensity as a robust climate proxy in central and western China, four species from five sites were assessed. As well as latewood inverted blue intensity, we also examined earlywood blue intensity. To explore the sensitivity of using different extraction parameter settings using the software CooRecorder, seven percentile (P) variant settings for earlywood blue intensity and latewood inverted blue intensity were used, namely P50 : 50, P60 : 40, P70 : 30, P80 : 20, P85 : 15, P90 : 10, and P95 : 5. Age-dependent spline was used for all, and the positive trends were not retained. Correlation analysis was applied between the tree ring parameter chronologies and monthly/seasonal mean temperature, precipitation, and self-calibrated Palmer drought severity index variables. Linear regression was also used to further highlight the potential of developing climate reconstructions using these species. Only subtle differences were found between the different percentile extraction variants. As has been shown for many other Northern Hemisphere studies, latewood inverted blue intensity expresses a strong positive relationship with growing-season temperatures (the two southern sites explain almost 56 % of the temperature variance when combined). However, the low latitude of these sites shows an exciting potential for regions south of 30∘ N that are traditionally not targeted for temperature reconstructions. Earlywood blue intensity also shows good potential to reconstruct hydroclimate parameters in some humid areas.

Increasing vapor pressure deficit accelerates land drying

Increase Vapor Pressure Deficit (VPD) imposes higher atmospheric demand on land evaporation in the warming climate. However, it remains uncertain that how the increasing evaporative stress may enhance the global pattern of land drying. Here, we have used self-calibrating Palmer Drought Severity Index (scPDSI) based on outputs from 20 CMIP6 models to study the contribution of VPD, precipitation and other climatic factors to scPDSI changes globally. Global land drying and wetting are found to be regionally dependent, with statistically significant enhancement of drying and wetting under SSP2-4.5, SSP3-7.0 and SSP5-8.5 pathways (2015–2100) compared to the historical period (1961–2014). Increasing precipitation is the main reason of land wetting, while the land drying is initially associated with precipitation deficit, it is partly dominated by the increasing VPD over 20 ∼ 27% of the global land, where the estimated VPD contribution is nearly 6 times of that of precipitation under high forcing pathways. Elevated CO2 concentration is suggested to partly mitigate land drying by altering vegetation physiology; however, this mitigation is quantified to be limited. Our findings highlight the key role of VPD in accelerating global pattern of land drying and its increasing dominance in the warming climate.

Analysis of tsagaan lake sediment, valley of the gobi lakes, Mongolia, to determine past environmental changes and the effect of ongoing global warming

The arid region of Mongolia is sensitive to climate change. Understanding of climate variations in the past plays an important role in predicting further climate changes. In this study, we aim to investigate the relationship between the climatic factor and sedimentation history in the late Holocene. We analyzed the 18TS1 core sample (136 cm in length) from the Tsagaan lake in the Valley of the Gobi Lakes, Mongolia. The sediment core mainly consists of clay and silt sediments, occasionally interrupted by the sand-dominated layers. 210Pb dating method is applied and indicates that the upper 24 cm sediment spans the sedimentation history of the last 87 years. Along-core variations in sediment characteristics such as organic matter, carbonate, and amorphous silica abundance are indicative of the drought -inducing period based on the significant correlations between sediment characteristics and Self-calibrated Palmer Drought Severity Index. Moreover, the enrichment of carbonates is a great indicator of shallow lake conditions prior to the lake disappearance during the dry period. The chemical composition of sand-dominated units in the sediment core well agrees with that of sand sheets and sand dunes in the catchment. Modern expansion and migration of sand sheets are identified by aerial photo imagery. The insufficient sediment chronology is limiting our interpretation, though; the comparison with the other paleoclimate records helps the establishment of environmental changes in the last millennium.

Drought characteristics and their impacts on vegetation net primary productivity in the subtropical China

Drought is one of the main factors limiting forest productivity, and thus greatly affects the carbon sink capacity of forests. Here we first chose two drought indices including standardized precipitation evapotranspiration index (SPEI) and self-calibrating Palmer drought severity index (scPDSI) to reflect and analyze the spatiotemporal patterns of drought in the subtropical China. Then, the validated CASA (Carnegie-Amer-Stanford Approach) model was applied to estimate forest net primary productivity (NPP) and further quantify the contributions of drought events and their characteristics on forest NPP. The results showed that drought events during 2000–2015 have resulted in a mean decline of forest NPP of 7.2%. Moderate or severe drought events reduced NPP more significantly than extremely severe drought events. In addition, there was 1–2 years of lagging in the NPP responses to drought, and the lagging time varied with forest types. Our study suggests that forest managers and local governments should pay more attention to the places with moderate and severe drought events, and take measures to avoid NPP decline within the 2 years after drought. Our study also provides data support for further identifying the contribution of drought to ecosystem carbon fluxes in the subtropical China.

Effect of Climate and Competition on Radial Growth of Pinus sylvestris var. mongolica Forest in Hulunbuir Sandy Land of Inner Mongolia, China

(1) Background: The forest of Pinus sylvestris var. mongolica is an important semi-arid ecosystem in Hulunbuir sandy land that plays a key role in the carbon cycle and wind erosion control. It is crucial to explore the main factors affecting the radial growth of trees of P. sylvestris var. mongolica. (2) Methods: The study established the tree-ring chronology of P. sylvestris var. mongolica and analyzed the relationships among the radial growth, competition index, and climate variables using correlation analysis and a linear mixed effect model to explore the influence of competition and climate on radial growth of P. sylvestris var. mongolica. (3) Results: The results indicated that tree growth is mainly affected by the maximum average temperature (Tmax) and precipitation in June and July of the current year and that tree growth significantly decreased with increasing competition pressure. Analysis of the linear mixed effect model showed that tree age, competition intensity, self-calibrating Palmer drought severity index (scPDSI) from May to July, and vapor pressure deficit (VPD) have a significant impact on radial growth. (4) Conclusions: The competition plays a dominant role in radial growth of P. sylvestris var. mongolica compared to climate factors. This study helps to understand the growth mechanism of P. sylvestris var. mongolica forests under climate change and provides a scientific basis for effective management of semi-arid forests.

Streamflow Reconstructions Using Tree-Ring-Based Paleo Proxies for the Sava River Basin (Slovenia)

The Sava River Basin (SRB) extends across six countries (Slovenia, Croatia, Bosnia and Herzegovina, Serbia, Albania, and Montenegro) and is a major tributary of the Danube River (DR). The Sava River (SR) originates in the alpine region of Slovenia, and, in support of a Slovenian government initiative to increase clean, sustainable energy, multiple hydropower facilities have been constructed within the past ~20 years. Given the importance of this river system for varying demands, including energy production, information about past (paleo) drought and pluvial periods would provide important information to water managers and planners. Seasonal (April–May–June–July–August–September—AMJJAS) streamflow data were obtained for two SRB gauges (Jesenice and Catez) in Slovenia. The Jesenice gauge is in the extreme headwaters of the SR, upstream of any major water control structures, and is considered an unimpaired (minimal anthropogenic influence) gauge. The Catez gauge is located on the SR near the Slovenia–Croatia border, thus providing an estimate of streamflow leaving Slovenia (entering Croatia). 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, this dataset was used as a proxy to reconstruct (for ~2000 years) seasonal streamflow. Prescreening methods included the correlation and temporal stability of seasonal streamflow and scPDSI cells. The retained scPDSI cells were then used as predictors (independent variables) to reconstruct streamflow (predictive and/or dependent variables) in regression-based models. This resulted in highly skillful reconstructions of SRB seasonal streamflow from 0 to 2012 AD. The reconstructions were evaluated, and both low flow (i.e., drought) and high flow (i.e., pluvial) periods were identified for various filters (5-year to 30-year). When evaluating the most recent ~20 years (2000 to present), multiple low-flow (drought) periods were identified. For various filters (5-year to 15-year), the 2003 end-year consistently ranked as one of the lowest periods, while the 21-year period ending in 2012 was the lowest flow period in the ~2000-year reconstructed-observed-historic period of record. The ~30-year period ending in 2020 was the lowest flow period since the early 6th century. A decrease in pluvial (wet) periods was identified in the observed-historic record when compared to the paleo record, again confirming an apparent decline in streamflow. Given the increased activities (construction of water control structures) impacting the Sava River, the results provide important information to water managers and planners.

Drought characterization using different indices, theory of run and trend analysis in bilate river watershed, rift valley of Ethiopia

Droughts have become more powerful and frequent, affecting more people for longer periods than any other natural disaster, particularly in eastern Africa. The unprecedented climate change has increased the severity, duration, and frequency of droughts. The objectives of this study were to evaluate performances of different drought indices for spatiotemporal drought characterization in the Bilate river watershed that represents part of the rift valley drylands in Ethiopia. Historical data for 39 years (1981–2019) from seven stations were used for drought analyses using the following indices: Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Reconnaissance Drought Index (RDI), enhanced Reconnaissance Drought Index (eRDI) with different time scale and Self-Calibrated Palmer Drought Severity Index (scPDSI). Among them, SPI, SPEI, RDI and eRDI with 6-month and 9-month time scales were found to be the best correlated drought indices to characterize the historical drought events. Then, using the selected drought indices, temporal drought analysis showed occurrence of major drought events in the years: 1984/85, 1999/2000, 2002/3, and 2009. Some of these years are well known as famine years in some parts of Ethiopia including the study area. The results revealed spatial variation the severity of drought with extreme droughts occurred in the southern part of the Bilate watershed. Application of the theory of run confirmed that the maximum severity and duration of drought were observed at the Bilate Tena station that is located in the southern part of the watershed; the most severe being observed on a 9-month scale during 1984/85. Hossana and Wulbareg stations showed the highest frequency of drought over the study period. The Mann-Kendal trend test statistics showed an increasing trend of drought conditions in the study watershed.

The impact of warming climate on Himalayan silver fir growth along an elevation gradient in the Mt. Everest region

The impacts of rapid climate change in the Himalayan region on growth and sensitivity of tree species across their entire elevation distribution range are not well understood. This study investigates the growth and climate sensitivity of the endemic species Himalayan silver fir (Abies spectabilis, D.Don) along an elevation gradient in the sub-alpine belt in the Mt. Everest region. We developed tree ring-width chronologies, spanning 98 to 310 years, from six sites along an elevation gradient (3400–4100 m a.s.l.). The growth of Himalayan silver fir is primarily limited by low temperatures during the growing season and moisture availability during spring. Significant positive correlations were found between tree growth and mean, minimum and maximum temperatures for most parts of the vegetation period except spring. In contrast, significant positive correlations are apparent for precipitation, self-calibrated Palmer drought severity index (scPDSI), and standardized precipitation-evapotranspiration index (SPEI) during the spring season (March–May) at most of the elevations except for the upper treeline site. Thus, low summer temperatures are the primary limiting factor at all investigated elevations, while moisture supply during spring triggers growth variations at low- to mid-elevations. The long-term growth trends of A. spectabilis have decreased at low- to mid-elevation sites but increased at the treeline during the period 1981–2011. Warming and intensified moisture stress during spring could have negative effects on tree growth, potentially resulting in growth decline and forest dieback at middle and low elevations. These findings emphasize the importance of understanding the vulnerability of forests at middle and low elevations to future climate change.

Himalayan fir reveals moist phase during Little Ice Age in the Kashmir region of the western Himalayas

The Kashmir region in the western Himalayas is located in a transition zone between areas dominated by the South Asian Summer Monsoon (SASM) and the North Atlantic Oscillation (NAO). Currently being primarily influenced by westerly disturbances (WDs), the area is important to decipher teleconnections between these two important circulation systems for the assessment of past climate variability. We evaluated climate-growth relationships of Abies pindrow (Royle ex D. Don) and reconstructed April to June (AMJ) self-calibrated Palmer drought severity index (scPDSI) for the south Kashmir region during the period 1643–2016 CE. Our reconstructed scPDSI revealed a long wet phase during 1650–1816 CE, indicating the impact of the Little Ice Age (LIA) over the region, followed by prominent drier post-LIA episodes. The mid 18th century (1730–1760 CE) was the wettest period in the past four centuries, whereas the period 1817 to 1865 CE marked the driest phase. These phases are consistent with other precipitation reconstructions from the WD-dominated western and Trans-Himalayan regions, but inconsistant with summer precipitation reconstructions from the SASM-dominated Himalayan regions. A significant positive correlation between our scPDSI reconstruction and the North Atlantic Oscillation (NAO) for the wet phase of the LIA suggests that the NAO remained dominant in modulating the winter and spring precipitation at the study region. During the 19th and 20th centuries, scPDSI was either weakly or negatively correlated with the NAO index, indicating the influence of other atmospheric circulation systems in driving the spring/summer precipitation in the study area. This study, augmented with other moisture records, contributes to analyze the temporal and spatial extent of moisture variability in a regional perspective.

Investigating Drought Events and Their Consequences in Wildfires: An Application in China

Understanding the impact of drought on fire dynamics is crucial for assessing the potential effects of climate change on wildfire activity in China. In this study, we present a series of multiple linear regression (MLR) models linking burned area (BA) during mainland China’s fire season from 2001 to 2019, across seven regions, to concurrent drought, antecedent drought, and time trend. We estimated burned area using Collection 6 Moderate Resolution Imaging Spectradiometer (MODIS) and drought indicators using either the Standardized Precipitation Evapotranspiration Index (SPEI) or the self-calibrated Palmer Drought Severity Index (sc-PDSI). Our findings indicate that the wildfire season displays a spatial variation pattern that increases with latitude, with the Northeast China (NEC), North China (NC), and Central China (CC) regions identified as the primary areas of wildfire occurrence. Concurrent and antecedent drought conditions were found to have varying effects across regions, with concurrent drought as the dominant predictor for NEC and Southeast China (SEC) regions and antecedent drought as the key predictor for most regions. We also found that the Northwest China (NWC) and CC regions exhibit a gradual decrease in burned area over time, while the NEC region showed a slight increase. Our multiple linear regression models exhibited a notable level of predictive power, as evidenced by the average correlation coefficient of 0.63 between the leave-one-out cross-validation predictions and observed values. In particular, the NEC, NWC, and CC regions demonstrated strong correlations of 0.88, 0.80, and 0.76, respectively. This indicates the potential of our models to contribute to the prediction of future wildfire occurrences and the development of effective wildfire management and prevention strategies. Nevertheless, the intricate relationship among fire, climate change, human activities, and vegetation distribution may limit the generalizability of these findings to other conditions. Consequently, future research should consider a broad range of factors to develop more comprehensive models.

The response and sensitivity of global vegetation to water stress: A comparison of different satellite-based NDVI products

Normalized Difference Vegetation Index (NDVI) is an effective proxy for vegetation activity and terrestrial productivity, and the widely used global NDVI products include the third generation Global Inventory Modelling and Mapping Studies Advanced Very High-Resolution Radiometer NDVI (NDVI3g), Satellite Pour l’Observation de la Terre VEGETATION NDVI (NDVIsp) and Terra Moderate Resolution Imaging Spectroradiometer NDVI (NDVItr). Here, for the first time, we investigated the differences between the three NDVIs in response and sensitivity to water stress represented by root zone soil moisture, vapor pressure deficit, self-calibrating Palmer Drought Severity Index and Standardized Precipitation Evapotranspiration Index across climate zones, humidity gradients, vegetation types and tree cover gradients. Over the temperate and arid climates region, the three NDVIs show higher consistent response fractions (CRFs) to water stress in the water-limited regions, with their sensitivity being in the order of NDVI3g < NDVIsp < NDVItr. The three NDVIs tend to exhibit inconsistent responses to water stress in the areas with higher humidity and tree cover, and their sensitivities to water stress fall as humidity and tree cover increase. In addition, the three NDVIs display higher CRFs and sensitivities to water stress over the non-forested areas. Our results also show that, in the cold climates region, the three NDVIs respond in the opposite way to water stress as that in water-limited areas, and NDVI3g has higher sensitivity to water stress than NDVIsp and NDVItr. This study highlights the differences between three NDVIs products for qualifying and quantifying their response and sensitivity to water stress.

Exploring the Relationship between Hydroclimate and Lake Area in Source Area of the Yellow River: Implications for the Paleoclimate Studies

The large tectonic lake is one of the most important water bodies in the source area of the Yellow River (SAYR), northeastern Qinghai-Tibet Plateau (QTP). It plays a key role in decelerating climatic change and regulating regional climate patterns. In this study, we used Landsat images (MSS, TM, ETM+ and OLI) of Lake Gyaring and Lake Ngoring (the Two Sisters Lakes), which are the two largest tectonic lakes in the SAYR, to determine annual lake area fluctuations from 1986 to 2020. The results show that lake area increases were generally consistent with a warming trend in the SAYR. The temperature signals were separated from the lake area changes by using a detrending analysis and found that the processed data are closely correlated with variations of precipitation and streamflow in the SAYR, and the previously reported paleoclimate records, which include the δ18O record from stalagmite, A/C (Artemisia/Chenopodiaceae) ratio from lake sediment and scPDSI (self-calibrating Palmer Drought Severity Index) from the tree ring on the northeastern margin of the QTP. The phase of relatively large lake areas typically coincides with a negative excursion in δ18O, a high A/C ratio, and elevated scPDSI values, while the opposite is true for smaller lake areas. It is suggested that the total area of the Two Sisters Lakes is closely associated with hydroclimatic conditions in the SAYR. Furthermore, an association of high TSI anomalies with the water area expansion of the Two Sisters Lakes is also observed, implying that solar activity is the key driving factor for the hydrologic variability in the SAYR on decadal timescales. The findings of our study highlight the validity of previous paleoclimate archives in the northeastern QTP and demonstrate the potential of using remote sensing techniques to investigate paleoclimate.

Detection of spatiotemporal changes in ecological quality in the Chinese mainland: Trends and attributes

Global climate change and revegetation programs have significantly changed the ecological quality (EQ) in the Chinese mainland after 1999. Monitoring and assessing the changes in the regional EQ and analyzing their drivers are crucial for ensuring ecological restoration and rehabilitation. However, it is challenging to carry out a long-term and large-scale quantitative assessment of the EQ of a region based on traditional field investigations and experiment methods alone; notably, in previous studies, the effects of carbon and water cycles and human activities on the variations in EQ have not been studied comprehensively. Therefore, in addition to remote sensing data and principal component analysis, we used the remote sensing-based ecological index (RSEI), to assess the EQ changes in the Chinese mainland during 2000–2021. Additionally, we also analyzed the impacts of carbon and water cycles and anthropological activities on the changes in the RSEI. The main conclusions of this study were: since the beginning of the 21st century, we observed a fluctuating upward trend in the EQ changes in the Chinese mainland and eight climatic regions. From 2000 to 2021, in terms of the EQ, North China (NN) portrayed the highest increase rate (2.02 × 10−3 year−1, P < 0.05). There was a breaking point in 2011, the EQ in the region experienced a change, from a downward trend to an upward one. Northwest China, Northeast China, and NN portrayed an overall significant increasing trend in the RSEI, whereas the southwest part of the Southwest Yungui Plateau (YG) and a part of the plain region of the Changjiang (Yangtze) River (CJ) river region portrayed a significant decreasing trend in the EQ. Overall, the carbon and water cycles and human activities played a pivotal role in determining the spatial patterns and trends of the EQ in the Chinese mainland. In particular, the self-calibrating Palmer Drought Severity Index, actual evapotranspiration (AET), gross primary productivity (GPP), and soil water content (Soil_w) were identified as the key drivers of the RSEI. In the central and western Qinghai–Tibetan Plateau (QZ) and the northwest region of NW, the changes in RSEI were dominated by AET; however, in central NN, southeastern QZ, northern YG, and central NE, the changes were driven by GPP, and in the southeast region of NW, south region of NE, northern region of NN, middle YG region, and a part of the middle CJ region, the changes were driven by Soil_w. The population-density-related change in the RSEI was positive in the northern regions (NN and NW) but negative in the southern regions (SE), whereas the RSEI change related to ecosystem services was positive in the NE, NW, QZ, and YG regions. These results are beneficial for the adaptive management and protection of the environment and the realization of green and sustainable developmental strategies in the Chinese mainland.

Drought in Shanxi Province Based on Remote Sensing Drought Index Analysis of Spatial and Temporal Variation Characteristics

Drought is a natural disaster with long duration and which causes great harm. Studying the characteristics of drought evolution in Shanxi Province can grasp the regularity of drought occurrence and provide a basis for drought prevention and resistance. This study utilizes MODIS products to analyze and quantify the extent of drought in a specific area. The study calculates several indices, including the Crop Water Stress Index (CWSI), Vegetation Supply Water Index (VSWI), and Temperature Vegetation Dryness Index (TVDI), using variables such as the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Evapotranspiration (ET), and Potential Evapotranspiration (PET). Additionally, three drought indices are analyzed for correlation with the self-calibrated Palmer Drought Severity Index (sc-PDSI), and the most suitable drought index is selected through validation with typical drought events. Finally, the selected indices are used to investigate the spatiotemporal characteristics of drought in the study area from 2001 to 2020. The results show: (1) CWSI and sc-PDSI have a strong correlation both in terms of time and spatial analysis. Furthermore, CWSI has been shown to be more effective in monitoring significant drought events. (2) The multi-year mean values of CWSI range from 0.71 to 0.85, with a significant degree of spatial heterogeneity. In the study area, the percentage of the area affected by different levels of drought is in the following order: moderate drought &gt; severe drought &gt; mild drought &gt; no drought. (3) The trend of CWSI changes shows that the drought situation in Shanxi Province has been alleviated from 2001 to 2020, and the overall spatial distribution indicates that the degree of drought alleviation in the southern region is greater than that in the northern region. The turning point from drought to wetness in the study area was in 2011, showing the overall characteristic of “dry in the north and wet in the south”.

Assessment of Meteorological and Agricultural Drought Indices under Climate Change Scenarios in the South Saskatchewan River Basin, Canada

Climate change has amplified the severity of droughts with potentially adverse impacts on agriculture in western Canada. This study assessed meteorological and agricultural drought in the Southern Saskatchewan River Basin (SSRB) using an array of drought indices, including the Standardized Precipitation Index (SPI), the Standardized Precipitation-Evapotranspiration Index (SPEI), the Self-Calibrated Palmer Drought Severity Index (scPDSI), the Soil Moisture Deficit Index (SMDI), and the Evapotranspiration Deficit Index (ETDI). These indices were evaluated using multiple regional climate model (RCM) projections assuming 1.5, 2.0, and 3.0 °C thresholds of global warming. A modified Soil and Water Assessment Tool (SWAT-M) was used to simulate the soil water content (SWC), actual evapotranspiration (AET), and potential evapotranspiration. The results of a sensitivity analysis using the SUFI-2 method in SWAT-CUP showed that the model performed well with BIAS lower than 10% and NSE and R higher than 0.7, and the range of SWC output closely matched the observed SWC. According to the RCM projections, the annual precipitation increases for all three global temperature thresholds while the annual mean temperature increases at a greater rate than the rise in global mean temperature. The projected PDSI and the SPEI suggest that drought duration and severity will exceed historical values while SPI will remain largely unchanged. Furthermore, severe drought conditions (SMDI &lt; 2.0) are more frequent under the 3.0 °C global temperature scenario. The mean ETDI was historically 0.58 while the projected value is 0.2, 0.1, and −0.2 for the first to third scenarios, respectively. Simulated values, spatial maps, and heat maps of SMDI and ETDI illustrated that Canesm2.CRCM5 projects the driest conditions among all the RCMs. Agricultural drought indices, which incorporate SWC data, show more significant effects than meteorological drought indices. The increasing dryness will potentially impact agricultural crop production, particularly under the third scenario (3 °C) in the SSRB.

Hydroclimate Variations across North-Central China during the Past 530 Years and Their Relationships with Atmospheric Oscillations

Detailed study of historical drought events in North-Central China (NCC) is important to understand current hydroclimate variability in the background of global warming. Here, we combined 12 published tree-ring chronologies and 12 dryness/wetness indices (DWI) to reconstruct dry and wet climate variability across NCC. These 24 proxy records showed similarly significant responses to warm season (May–June–July–August–September, MJJAS) moisture signals. A new 530-year-long reconstruction of self-calibrating Palmer Drought Severity Index (scPDSI) values for the warm season in NCC was determined using a nested principal component regression (PCR) approach. The new reconstruction shows significant correlations with the instrumental MJJAS scPDSI data across NCC during the period AD 1901–2012. The reconstructed MJJAS scPDSI revealed seven severe dry/wet events from AD 1470 to 2012. The periods AD 1701–1727 and AD 1985–2011 represent the longest dry periods, and the drought during the 1920s is identified as the most severe one over the past 530 years. Our reconstruction shows significant interannual spectral peaks at the frequency domain of 2–7 years, together with relatively weaker decadal frequencies of 16, 24, and 78 years. The results of superposed epoch analysis (SEA) show that extreme North Atlantic Oscillation (NAO) years may modulate drought variability in NCC.

Spatial Downscaling of GRACE Data Based on XGBoost Model for Improved Understanding of Hydrological Droughts in the Indus Basin Irrigation System (IBIS)

Climate change may cause severe hydrological droughts, leading to water shortages which will require to be assessed using high-resolution data. Gravity Recovery and Climate Experiment (GRACE) satellite Terrestrial Water Storage (TWSA) estimates offer a promising solution to monitor hydrological drought, but its coarse resolution (1°) limits its applications to small regions of the Indus Basin Irrigation System (IBIS). Here we employed machine learning models such as Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) to downscale GRACE TWSA from 1° to 0.25°. The findings revealed that the XGBoost model outperformed the ANN model with Nash Sutcliff Efficiency (NSE) (0.99), Pearson correlation (R) (0.99), Root Mean Square Error (RMSE) (5.22 mm), and Mean Absolute Error (MAE) (2.75 mm) between the predicted and GRACE-derived TWSA. Further, Water Storage Deficit Index (WSDI) and WSD (Water Storage Deficit) were used to determine the severity and episodes of droughts, respectively. The results of WSDI exhibited a strong agreement when compared with the Standardized Precipitation Evapotranspiration Index (SPEI) at different time scales (1-, 3-, and 6-months) and self-calibrated Palmer Drought Severity Index (sc-PDSI). Moreover, the IBIS had experienced increasing drought episodes, e.g., eight drought episodes were detected within the years 2010 and 2016 with WSDI of −1.20 and −1.28 and total WSD of −496.99 mm and −734.01 mm, respectively. The Partial Least Square Regression (PLSR) model between WSDI and climatic variables indicated that potential evaporation had the largest influence on drought after precipitation. The findings of this study will be helpful for drought-related decision-making in IBIS.