Standardized Precipitation Evaporation Index Research Articles

Drought intensification in Brazilian catchments: implications for water and land management

Droughts exert widespread impacts on both natural and social systems, and there is accumulating evidence that this situation may worsen in the context of global warming. Despite the importance of assessing changes in droughts to understand their potential future impacts on society, studies are unevenly distributed worldwide. In this study, utilizing bias-corrected CMIP6 simulations and a standard precipitation-evaporation index based approach, we quantified expected changes in future drought properties across 735 Brazilian catchments under SSP2-4.5 and SSP5-8.5 scenarios. Beyond evaluating the statistical properties of future droughts, we assessed their occurrence under both land use and water demand perspectives and propose a new framework to better understand their link with changes in long- and short-term conditions of precipitation ( P ) and potential evapotranspiration ( PET ). Our results indicate that drought events are projected to become more frequent and severe in the future, with high CMIP6 model agreement. According to the SSP5-8.5 scenario, at least half of Brazilian cropland and pasture areas will experience an increase of over 30% in drought properties by the end of the century. Furthermore, among the 85% of catchments expected to experience more severe droughts, nearly 90% are also projected to exhibit increased water demand, which will likely exacerbate future water scarcity. The investigation of the relationship between droughts changes and climate variables suggests that catchments with augmented droughts in the future will likely exhibit increased long-term average PET and P -variability, but not necessarily long-term average P . For instance, over 50% of evaluated Brazilian catchments are expected to experience an intensification of drought properties even with increases in Pmean . We believe this study may contribute (a) to improve Brazilian water resiliency by helping achieve the objectives of the National Water Security Plan and (b) to deepen our understanding of droughts in an uncertain future.

An enhanced drought forecasting in coastal arid regions using deep learning approach with evaporation index

Coastal arid regions are similar to deserts, where it receives significantly less rainfall, less than 10 cm. Perhaps the world's worst natural disaster, coastal area droughts, can only be detected using reliable monitoring systems. Creating a reliable drought forecast model and figuring out how well various models can analyze drought factors in coastal arid regions are two of the biggest obstacles in this field. Different time-series methods and machine-learning models have traditionally been utilized in forecasting strategies. Deep learning is promising when describing the complex interplay between coastal drought and its contributing variables. Considering the possibility of enhancing our understanding of drought features, applying deep learning approaches has yet to be tried widely. The current investigation employs a deep learning strategy. Coastal Drought indices are commonly used to comprehend the situation better; hence the Standard Precipitation Evaporation Index (SPEI) was used since it incorporates temperatures and precipitation into its computation. An integrated coastal drought monitoring model was presented and validated using convolutional long short-term memory with self-attention (SA-CLSTM). The Climatic Research Unit (CRU) dataset, which spans 1901–2018, was mined for the drought index and predictor data. To learn how LSTM forecasting could enhance drought forecasting, we analyzed the findings regarding numerous drought parameters (drought severity, drought category, or geographic variation). The model's ability to predict drought intensity was assessed using the Coefficient of Determination (R2), the Root Mean Square Error (RMSE), and the Mean Absolute Error (MAE). Both the SPEI 1 and SPEI 3 examples had R2 values more than 0.99 for the model. The range of predicted outcomes for each drought group was analyzed using a multi-class Receiver Operating Characteristic based Area under Curves (ROC-AUC) method. The research showed that the AUC for SPEI 1 was 0.99 and for SPEI 3, 0.99. The study's results indicate progress over machine learning models for one month in advance, accounting for various drought conditions. This work's findings may be used to mitigate drought, and additional improvement can be achieved by testing other models.

Significant relationships between drought indicators and impacts for the 2018–2019 drought in Germany

Despite the scientific progress in drought detection and forecasting, it remains challenging to accurately predict the corresponding impact of a drought event. This is due to the complex relationships between (multiple) drought indicators and adverse impacts across different places/hydroclimatic conditions, sectors, and spatiotemporal scales. In this study, we explored these relationships by analyzing the impacts of the severe 2018–2019 central European drought event in Germany. We first computed the standardized precipitation index (SPI), the standardized precipitation evaporation index (SPEI), the standardized soil moisture index (SSMI) and the standardized streamflow index (SSFI) over various accumulation periods, and then related these indicators to sectorial losses from the European drought impact report inventory (EDII) and media sources. To cope with the uncertainty associated with both drought indicators and impact data, we developed a fuzzy method to categorize them. Lastly, we applied the method at the region level (EU NUTS1) by correlating monthly time series. Our findings revealed strong and significant relationships between drought indicators and impacts over different accumulation periods, albeit in some cases region-specific and time-variant. Furthermore, our analysis established the interconnectedness between various sectors, which displayed systematically co-occurring impacts. As such, our work provides a new framework to explore drought indicators-impacts dependencies across space, time, sectors, and scales. In addition, it emphasizes the need to leverage available impact data to better forecast drought impacts.

Global high-resolution drought indices for 1981–2022

Abstract. Droughts are among the most complex and devastating natural hazards globally. High-resolution datasets of drought metrics are essential for monitoring and quantifying the severity, duration, frequency, and spatial extent of droughts at regional and particularly local scales. However, current global drought indices are available only at a coarser spatial resolution (>50 km). To fill this gap, we developed four high-resolution (5 km) gridded drought records based on the standardized precipitation evaporation index (SPEI) covering the period 1981–2022. These multi-scale (1–48 months) SPEI indices are computed based on monthly precipitation (P) from the Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS, version 2) and Multi-Source Weighted-Ensemble Precipitation (MSWEP, version 2.8), and potential evapotranspiration (PET) from the Global Land Evaporation Amsterdam Model (GLEAM, version 3.7a) and hourly Potential Evapotranspiration (hPET). We generated four SPEI records based on all possible combinations of P and PET datasets: CHIRPS_GLEAM, CHIRPS_hPET, MSWEP_GLEAM, and MSWEP_hPET. These drought records were evaluated globally and exhibited excellent agreement with observation-based estimates of SPEI, root zone soil moisture, and vegetation health indices. The newly developed high-resolution datasets provide more detailed local information and can be used to assess drought severity for particular periods and regions and to determine global, regional, and local trends, thereby supporting the development of site-specific adaptation measures. These datasets are publicly available at the Centre for Environmental Data Analysis (CEDA; https://doi.org/10.5285/ac43da11867243a1bb414e1637802dec) (Gebrechorkos et al., 2023).

Analyzing Temporal Patterns of Temperature, Precipitation, and Drought Incidents: A Comprehensive Study of Environmental Trends in the Upper Draa Basin, Morocco

Quantifying variation in precipitation and drought in the context of a changing climate is important to assess climate-induced changes and propose feasible mitigation strategies, particularly in agrarian economies. This study investigates the main characteristics and historical drought trend for the period 1980–2016 using the Standard Precipitation Index (SPI), Standard Precipitation Evaporation Index (SPEI), Run Theory and Mann–Kendall Trend Test at seven stations across the Upper Draa Basin. The results indicate that rainfall has the largest magnitude over the M’semrir and Agouim (>218 mm/pa) and the lowest in the Agouilal, Mansour Eddahbi Dam, and Assaka subregions (104 mm–134 mm/pa). The annual rainfall exhibited high variability with a coefficient of variation between 35−57% and was positively related to altitude with a correlation coefficient of 0.86. However, no significant annual rainfall trend was detected for all stations. The drought analysis results showed severe drought in 1981–1984, 2000–2001, and 2013–2014, with 2001 being the driest year during the study period and over 75% of both SPEI and SPI values returned drought. Conversely, wet years were experienced in 1988–1990 and 2007–2010, with 1989 being the wettest year. The drought frequency was low (<19%) across all the timescales considered for both SPI and SPEI, with Mansour Eddahbi Dam and Assaka recording the highest frequencies for SPI-3 and SPEI-3, respectively.

Comparing SPI and SPEI to detect different precipitation and temperature regimes in Chile throughout the last four decades

Droughts are one of the main environmental challenges facing the world this century. The latitudinal and orographic characteristics of continental Chile leads different areas within it to experience very different regimes of precipitation and temperature, resulting in a wide variation in the occurrence and severity of droughts. Using the CR2Met 5 × 5 km resolution gridded monthly dataset covering the years from 1979 to 2019, we calculated the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evaporation Index (SPEI) of March and September at 3-, 6-, 9-, 12- and 24-months to: 1) relate them with different climate modes, and 2) determine their temporal evolution. We found that the Pacific Decadal Oscillation shows low positive correlations with SPI but no significant correlations with SPEI. The Multivariate El Niño Southern Oscillation shows different correlations in northern Chile, as well as El Niño 1 + 2 and the Antarctic Oscillation, for both SPI and SPEI. Both SPI and SPEI show negative (drier) trends in the north and center of Chile, while positive (wetter) trends appear in the south. SPEI shows stronger and more significant negative trends, influenced by the overall warming of the country. Warming trends are lower on the coast, so SPI could be a good indicator for coastal areas, while SPEI could be a good indicator for inland areas. Climate modes are useful for monthly and annual predictions, and by being a good drought predictor, they can help inform key public policies. These results are expected to help Chilean decision makers dealing with the challenges facing water management in the immediate future.

Stronger Cumulative than Lagged Effects of Drought on Vegetation in Central Asia

In the context of global warming, the strength and frequency of drought events are projected to grow in the future, and the onset of drought can have dramatic effects on vegetation growth in terrestrial ecosystems. Central Asia is the largest non-territorial drought area in the world, and the response of vegetation to drought events is extremely sensitive in the area. However, few studies have quantified and compared the vegetation gross primary productivity (GPP) response to the lagged and cumulative effects of drought. In this research, the solar-induced chlorophyll fluorescence GPP and Standardized Precipitation Evaporation Index (SPEI) were used to analyze the time and space patterns of vegetation GPP and the SPEI in Central Asia and to quantify and compare the lagged and cumulative effects of drought on the GPP of various vegetation types. During the period from 2000 to 2018, the general trends of vegetation GPP showed a slight increase in Central Asia, with the ratio of variation being 1.35 g C m−2 y−1 and a spatially decreasing distribution from north to south. SPEI showed a trend of decreasing and then increasing over a period of 19 years, with a slight decreasing (drying) trend and a rate of change of −0.02 y−1, and the overall spatial pattern was drying out from north to south. In 13 months, 72.44% of regional droughts had lagged impacts on vegetation. The maximum correlation coefficients of vegetation and the lagged effectiveness of drought were concentrated in the range of 0.15–0.35, and the high correlation was distributed in southern and northwestern Kazakhstan, which are prairie regions. Of the regions in Central Asia, 75.86% showed cumulative drought effects concentrated at 9–12 months. The maximum correlation coefficients were concentrated in the range of 0.20–0.50, and the high correlation regions were primarily situated in south Kazakhstan and east Uzbekistan. Comparing the correlation coefficients of the lagged effect of vegetation GPP and SPEI with the cumulative effect shows that the cumulative rather than lagged impacts of drought on vegetation cover were found in 86.75% of the regions in Central Asia. This research enhances our comprehension of the influence of drought events on ecosystems in arid regions and has a certain reference value for helping arid region ecosystems to cope with global climate change.

Drought prediction: Insights from the fusion of LSTM and multi-source factors

In the context of current global climate change, accurate drought prediction is crucial for water resources management and agricultural production. Although traditional drought forecasting methods largely rely on historical climatic data, these methods cannot fully consider the long-term effects of factors, such as climate change, and the evaluation of prediction results is limited. Therefore, this study proposed a drought prediction and evaluation framework based on Long Short-Term Memory (LSTM), integrating multi-source factors to significantly enhance the accuracy and reliability of drought prediction models. This framework applied two distinct forecasting schemes. The first scheme utilized ten diverse factors, including precipitation, evaporation, bare soil percentage area coverage, percentage crop cover, leaf area index, runoff, surface runoff, soil moisture, temperature, and total vegetated percentage cover, to predict future precipitation and evaporation, which was then used to calculate the Standardized Precipitation-Evaporation Index (SPEI) to evaluate drought characteristics. The second scheme directly used these ten factors and historical SPEI to predict future SPEI, further assessing future drought characteristics. By comparing the drought prediction results of the two schemes in terms of data statistics, drought characteristics, and spatial patterns, it was found that the LSTM model significantly improved accuracy when handling high-dimensional complex data and predicting key factors such as precipitation, evaporation, temperature, and soil moisture. The first scheme was more accurate when predicting severe and extreme droughts, whereas the second scheme was more sensitive to predicting moderate and mild droughts and exhibited higher stability and regularity in predicting the spatial variability of drought. In summary, LSTM has made significant improvements in the accuracy, stability, and reliability of drought prediction, providing stronger support for practical applications, such as agriculture and water resources management, and offering a new research tool for further climate change research.

Suspended sediment yield under alternating dry/wet cycles in a Mediterranean river catchment: the case of the Ofanto River, southern Italy

ABSTRACT This paper investigates how hydrological drought affected suspended sediment yield (SSY) in the Mediterranean Ofanto River basin, southern Italy, and in its five sub-basins from 1951 to 1989. The Standardized Precipitation Evaporation Index over a scale of 12 months (SPEI12) has been used to compute the hydrological drought in the investigated span of time. SPEI12, mean and maximum monthly discharge (Qmean and Qmax), monthly rainfall erosivity, monthly simple daily intensity index and monthly SSY were used to assess the relationships between dry/wet cycles, streamflow and SSY through the Pearson correlation matrix. Qmean and Qmax are significantly correlated with SSY, while SPEI12 and rainfall intensity do not show good correlation with SSY. Furthermore, from the overall analyses it emerges that sediment yield estimations were higher mainly during the wet period following a period of drought or during a drought period.

Earlier Ecological Drought Detection by Involving the Interaction of Phenology and Eco‐Physiological Function

AbstractMeteorological drought indices like the Standardized Precipitation Evaporation Index (SPEI) are frequently used to diagnose “ecological drought,” despite the fact that they were not explicitly designed for this purpose. More recently developed indices like the Evaporative Stress Index (ESI), which is based on the degree of coupling between actual to potential evapotranspiration, may better capture dynamic plant response to moisture limitations. However, the skill of these indices at describing plant water stress is rarely evaluated at sub‐seasonal timescales over which drought evolves. Moreover, it remains unclear how variability in phenological timing impacts and complicates early drought detection. Here, we compared the ability of ESI and SPEI to reflect the dynamics of ecological drought in forests and grasslands, based on anomalies of Gross Primary Productivity (GPP), surface conductance (Gs, a proxy for stomatal conductance), soil moisture, and vapor pressure deficit. ESI performed better than SPEI in capturing the dynamics of GPP and Gs, but still missed early ecological drought signals due to biases linked to earlier onset of spring leaf development. Thus, we developed a modified variant of the ESI () that accounts for the complicating effects of phenological shifts in leaf area index (LAI). The detected drought onset up to 7–10 weeks earlier than SPEI and ESI. Additionally, drought onset dates determined from are close to (±2 weeks) the dates determined from LAI‐corrected anomalies of Gs, and GPP, as well as the onset dates of soil water deficit and atmospheric aridity.

Enhanced Linkage of Summer Drought in Southern China to the North Pacific Oscillation Since 2000

AbstractBased on the standardized precipitation evaporation index and reanalysis data, this article reveals that the relationship between the North Pacific Oscillation (NPO) and the drought over southern China in summer shifts from an insignificant correlation to a significant negative correlation after 2000. During the significant period (2001–2018), the positive NPO phase corresponds to an anomalous anticyclonic circulation dominating the mid‐low latitude North Pacific, which suppresses the moisture transport into southern China. Meanwhile, the easterly anomalies south of the anomalous anticyclonic circulation prevailing in the equatorial Pacific are coupled with a cooling of sea surface temperature in the central tropical Pacific, which enhances the Walker circulation and further induces an anomalous meridional circulation with the descending anomalies over southern China. Such moisture and dynamical conditions favor the occurrence of drought in southern China. During the former period (1983–2000), however, the NPO‐related atmospheric circulations over the North Pacific shift northward. In this case, the NPO shows insignificant relations with the moisture and dynamical conditions over southern China, thereby diluting the linkage of the NPO to the drought in southern China. The interdecadal strengthening in the relationship between the NPO and above atmospheric conditions is attributed to the westward (southward) extension of the NPO northern (southern) center. The larger interannual variability of spring sea ice in the Bering Sea and the southward movement of summer storm track over the North Pacific are hypothesized to account for the expansion of the NPO structure.

Productivity and water use efficiency of Pinus tabulaeformis responses to climate change in the temperate monsoon region

As one of the main afforestation species, Pinus tabulaeformis is an important carbon sink in China. We developed tree-ring chronologies of Pinus tabulaeformis at five plantation sites (Lingkong Mountain (LKM), Fujia (FJ), Heishui (HS), Caijiachuan (CJC) and Jiufeng (JF)), and measured the δ13C values of tree rings using the stable carbon isotope method to calculate intrinsic water use efficiency (iWUE). The responses of net primary productivity (NPP) and iWUE to climatic variables were explored. The annual variation in both NPP and iWUE had increasing trends, with lower iWUE increasing rate at LKM site in comparison with those at other 4 sites. iWUE of the young stand was higher than that of the old stand. Annual patterns of the δ13C values showed significant decreasing trends in LKM and CJC but a rising trend in HS. The growth of Pinus tabulaeformis was more sensitive to temperatures, and temperatures exerted positive effects on NPP and iWUE. At the old stands, both NPP and iWUE had closer relationships with temperatures. On the annual scale, precipitation had little effect on NPP and iWUE. Annual mean standardized precipitation evaporation index had a significant influence on iWUE at the young stand. Resistance was positively correlated with recovery, resilience and iWUE, but negatively correlated with NPP. Pinus tabulaeformis at CJC site had the stronger resistance to drought events in comparison with that at LKM and FJ sites. This study provides insight into the responses of Pinus tabulaeformis productivity and water use to climate change, and helps evaluate carbon sink and water-carbon trade-off of plantations.

In-Phase Variations of Spring and Summer Droughts over Northeast China and Their Relationship with the North Atlantic Oscillation

Abstract Analyses of the standardized precipitation evaporation index (SPEI), using the season-reliant empirical orthogonal function (S-EOF) method, indicate that the second leading mode of drought over Northeast China features an in-phase variation from spring to summer. Such an in-phase change is closely connected to the persistence of geopotential height anomalies around Lake Baikal. The positive height anomalies around Lake Baikal, with an equivalent barotropic structure in the troposphere, can decrease water vapor transport into Northeast China and induce anomalous descending motion over Northeast China during both seasons, favoring precipitation deficit and high temperature in situ and hence resulting in the synchronous variations of spring and summer droughts. Further investigation reveals that the spring North Atlantic Oscillation (NAO) plays a notable role in the in-phase change of spring–summer droughts over Northeast China. The positive phase of spring NAO could induce spring drought over Northeast China directly through its influence on the above atmospheric circulations via a zonal wave train emanating from the North Atlantic. Meanwhile, it can also increase the soil moisture in central Siberia by enhancing the local snow depth. The wetter soil moisture in the following summer, in turn, increases the meridional temperature gradient between the middle and high latitudes and then forces westerly anomalies around 60°N, consequently yielding positive height anomalies around Lake Baikal that favor the occurrence of summer drought over Northeast China. Therefore, the spring NAO is hypothesized to contribute to the in-phase variations of spring–summer droughts over Northeast China through the combined roles of zonal wave train and central Siberian soil moisture. Significance Statement Northeast China suffers from frequent droughts severely in recent decades. Thus, it is urgent to understand the physical mechanisms of drought in Northeast China. Using the season-reliant empirical orthogonal function (S-EOF) analysis, this study indicates that the second mode of S-EOF shows an in-phase change of drought from spring to summer over Northeast China, which is associated with the persistence of geopotential height anomalies around Lake Baikal. Our study further reveals that the spring NAO plays a pronounced role in the in-phase change of spring–summer droughts over Northeast China, through the combined roles of zonal wave train and the soil moisture in the central Siberian plateau. These findings are encouraging for better understanding of drought in Northeast China and are also important for disaster prevention and mitigation.

Drought timing and severity affect radial growth of Picea crassifolia at different elevations in the western Qilian Mountains.

In the context of continued global climate change, the intensity and frequency of droughts have increased to varying degrees in many places. Due to the complexity of drought events, the mechanisms by which trees respond to drought are not well understood. In this study, we analyzed the growth trends of Qinghai spruce (Picea crassifolia) at different elevations in the western part of Qilian Mountains and the dynamic response to climate change. We also compared the differences in radial growth of trees at different elevations in response to drought events in the growing and non-growing seasons based on resistance (Rt), recovery (Rc), and resilience (Rs). The results showed that (1) trees at all three elevations were limited by drought stress and the lower the elevation the more sensitive the trees were to drought. (2) The response of middle- and low-elevation trees to the standardized precipitation evaporation index in June of that year was stable. (3) Growing season drought limits radial growth of trees more than non-growing season drought, and Rt is smaller and Rc is larger at low elevations. With increasing drought severity, trees at all three elevations exhibited a trend of decreasing Rt and Rs and increasing Rc. (4) There were significant differences in the growth trends of trees at the three elevations. Therefore, we should continuously pay attention to the dynamics of the forest ecosystem in the western part of Qilian Mountains and take improved measures to cope with the adverse effects of drought on Qinghai spruce.

Temporal Variability of Temperature, Precipitation and Drought Indices in Hyper-Arid Region of Northwest China for the Past 60 Years

The temporal variability and abrupt change analysis of temperature and precipitation in Turpan was investigated and examined based on a monthly data set of temperature, precipitation and drought indices (1960–2019) from three meteorological stations over the study area. The Yamamoto method, Mann–Kendall test, Standardized Precipitation Index (SPI), Standardized Precipitation Evaporation Index (SPEI), and Reconnaissance Drought Index (RDI) were applied to reveal temperature, precipitation and drought indices trends in their annual volumes. The conclusions were as follows: (1) in the past 60 years, the annual average temperature in the Turpan region has increased at a rate of 0.33 °C·(10a)−1. Whereas the temperature has accelerated since the 1980s, the annual average minimum temperature has increased more than the annual average maximum temperature, and the temperature difference between winter and summer has increased since the 1990s. (2) The inter-annual, inter-decadal and normal value changes in precipitation in the Turpan region and its three meteorological stations indicated a decreasing trend during 1960–2019. Though the normal value of summer and autumn precipitation decreased and increased as a whole, the normal values of spring, summer, winter and annual precipitation in the Turpan region displayed downward trends. (3) Abrupt changes in temperature were observed in the mid-1990s, and abrupt changes in precipitation were not obvious. (4) The SPI and RDI responded quickly to precipitation and temperature, and the overall characteristics of dry and wet trend changes were consistent. When the SPEI considered the combined effect of temperature and precipitation, the SPI and SPEI are better correlated, and the SPI and RDI are better correlated than the SPEI and RDI. On the whole, the occurrence of drought has obvious regional and seasonal characteristics. These conclusions not only provide scientific data for sustainable development in Turpan but also offer scientific information to further understand the trends and periodicity of climate change and drought conditions in hyper-arid regions around the world.

Southwestern United States drought of the 21st century presages drier conditions into the future

Intense drought has occurred in the United States Southwest this century, causing unprecedented stress to water resources. Here we use paleoclimate and instrumental records to establish that the recent temperature rise is incompatible with random draws from past fluctuations, including the current period of warming. Consistent with and extending previous reconstructions, we find that the ongoing drought is the most intense at the 21-year scale back to 600 CE. Evaluation using standardized regression coefficients shows that recent warming damps the effect of moisture delivery on the Standardized Precipitation-Evaporation Index by approximately one-third. The probability of full recovery of the current moisture deficit is unlikely even by mid-century and about five percent in 10-15 years. Evaluation of future climate simulations indicates increasing regional temperature stress and soil moisture depletion, and coupled with long recovery periods for moisture delivery, very low chance for regional mega-reservoirs to regain full-capacity levels assuming current demand.

Spatio-Temporal Variation of Precipitation and Evaporation on the Tibetan Plateau and Their Influence on Regional Drought

The Tibetan Plateau (TP) is an important water source in Asia, and precipitation and evaporation patterns at different geographical and temporal scales play a significant role in managing water resource distribution. Based on quality control data from 87 meteorological stations, this study analyzed the spatial and temporal evolution patterns of precipitation and pan evaporation (Epan) on the TP in 1966–2016 using the Mann–Kendall test, the moving t-test, wavelet analysis, Sen’s slope method, and correlation analysis. The results revealed that the average mean temperature in the TP area increased by about 2.1 °C during the study period, and precipitation steadily increased at an average rate of 8.2 mm/10a, with summer and autumn precipitation making up about 80% of the year. In contrast, Epan showed an overall decreasing trend at a decline rate of 20.8 mm/10a, with spring and summer Epan values making up about 67% of the year. The time series of the precipitation and Epan within the TP region clearly exhibit nonstationary features. Precipitation is more concentrated in the southeast than in the northwest, while Epan is mostly concentrated in the southwest and northeast of the plateau around the Qaidam Basin. The “evaporation paradox” phenomenon was common in the TP region for about 40 years (1960s–1990s) and gradually faded in the 21st century. In addition, we introduced a standardized precipitation evaporation index (SPEI) to investigate the differences and relationships between precipitation and Epan time series over the past 50 years. The findings indicate that the southern Qinghai was dominated by an arid trend, while the central and southeast TP remained wet. Droughts and floods coexist in the eastern Qinghai and southern Tibet areas with high population concentrations, and the risk of both is rising as the inhomogeneity of precipitation distribution in the TP region will increase in the future. This study can be used as a reference for managing water resources and predicting regional drought and flood risk.

Observed Changes in Crop Yield Associated with Droughts Propagation via Natural and Human-Disturbed Agro-Ecological Zones of Pakistan

Pakistan’s agriculture and food production account for 27% of its overall gross domestic product (GDP). Despite ongoing advances in technology and crop varieties, an imbalance between water availability and demand, combined with robust shifts in drought propagation has negatively affected the agro-ecosystem and environmental conditions. In this study, we examined hydro-meteorological drought propagation and its associated impacts on crop yield across natural and human-disturbed agro-ecological zones (AEZs) in Pakistan. Multisource datasets (i.e., ground observations, reanalysis, and satellites) were used to characterize the most extensive, intense drought episodes from 1981 to 2018 based on the standardized precipitation evaporation index (SPEI), standardized streamflow index (SSFI), standardized surface water storage index (SSWSI), and standardized groundwater storage index (SGWI). The most common and intense drought episodes characterized by SPEI, SSFI, SSWSI, and SGWI were observed in years 1981–1983, 2000–2003, 2005, and 2018. SPEI yielded the maximum number of drought months (90) followed by SSFI (85), SSWSI (75), and SGWI (35). Droughts were frequently longer and had a slower termination rate in the human-disturbed AEZs (e.g., North Irrigated Plain and South Irrigated Plain) compared to natural zones (e.g., Wet Mountains and Northern Dry Mountains). The historical droughts are likely caused by the anomalous large-scale patterns of geopotential height, near-surface air temperature, total precipitation, and prevailing soil moisture conditions. The negative values (<−2) of standardized drought severity index (DSI) observed during the drought episodes (1988, 2000, and 2002) indicated a decline in vegetation growth and yield of major crops such as sugarcane, maize, wheat, cotton, and rice. A large number of low-yield years (SYRI ≤ −1.5) were recorded for sugarcane and maize (10 years), followed by rice (9 years), wheat (8 years), and cotton (6 years). Maximum crop yield reductions relative to the historic mean (1981–2017) were recorded in 1983 (38% for cotton), 1985 (51% for maize), 1999 (15% for wheat), 2000 (29% for cotton), 2001 (37% for rice), 2002 (21% for rice), and 2004 (32% for maize). The percentage yield losses associated with shifts in SSFI and SSWSI were greater than those in SPEI, likely due to longer drought termination duration and a slower termination rate in the human-disturbed AEZs. The study’s findings will assist policymakers to adopt sustainable agricultural and water management practices, and make climate change adaptation plans to mitigate drought impacts in the study region.

Development and application of high resolution SPEI drought dataset for Central Asia

Central Asia is a data scarce region, which makes it difficult to monitor and minimize the impacts of a drought. To address this challenge, in this study, a high-resolution (5 km) Standardized Precipitation Evaporation Index (SPEI-HR) drought dataset was developed for Central Asia with different time scales from 1981–2018, using Climate Hazards group InfraRed Precipitation with Station’s (CHIRPS) precipitation and Global Land Evaporation Amsterdam Model’s (GLEAM) potential evaporation (Ep) datasets. As indicated by the results, in general, over time and space, the SPEI-HR correlated well with SPEI values estimated from coarse-resolution Climate Research Unit (CRU) gridded time series dataset. The 6-month timescale SPEI-HR dataset displayed a good correlation of 0.66 with GLEAM root zone soil moisture (RSM) and a positive correlation of 0.26 with normalized difference vegetation index (NDVI) from Global Inventory Monitoring and Modelling System (GIMMS). After observing a clear agreement between SPEI-HR and drought indicators for the 2001 and 2008 drought events, an emerging hotspot analysis was conducted to identify drought prone districts and sub-basins.

Tree-Ring Stable Carbon Isotope as a Proxy for Hydroclimate Variations in Semi-Arid Regions of North-Central China

Carbon and oxygen isotope ratios (δ13C and δ18O) were measured in annual tree-ring cellulose samples dated from 1756 to 2015 CE. These samples were extracted from Chinese pine (Pinus tabulaeformis Carr.) trees located in a semi-arid region of north-central China. We found that tree-ring δ13C and δ18O values both recorded similar climatic signals (e.g., temperature and moisture changes), but found that tree-ring δ13C exhibited a stronger relationship with mean temperature, precipitation, average relative humidity, self-calibrating Palmer drought severity index (scPDSI), and standard precipitation evaporation index (SPEI) than δ18O during the period 1951–2015 CE. The strongest correlation observed was between tree-ring δ13C and scPDSI (previous June to current May), which explains ~43% of the variance. The resulting 130-year reconstruction reveals severe drought events in the 1920s and a sustained drying trend since the 1980s. This hydroclimate record based on tree-ring δ13C data also reveals similar dry and wet events to other proxy data (i.e., tree-ring width and historical documentation) that have allowed reconstructions to be made across the northern fringe of the Asian summer monsoon region. Our results suggest that both large-scale modes of climate variability (e.g., El Niño-Southern Oscillation, Pacific Decadal Oscillation, and North Atlantic Oscillation) and external forcing (e.g., solar variability) may have modulated moisture variability in this region. Our results imply that the relationship between tree-ring δ18O and local climate is less well-characterized when compared to δ13C and may be affected more strongly by the influences of these different atmospheric circulation patterns. In this semi-arid region, tree-ring δ13C appears to represent a better tool with which to investigate historical moisture changes (scPDSI).