Standardized Precipitation Evapotranspiration Index Research Articles

Spatio-temporal heterogeneous graph using multivariate earth observation time series: Application for drought forecasting

Accurate forecasting is required for the effective risk management of drought disasters. Many machine learning- and deep learning-based models have been proposed for drought forecasting, however, they cannot handle the temporal and/or spatial dependencies in the input data, causing unexpected forecasting results. In order to solve the challenging issue, in this paper we proposed the Heterogeneous Spatio-Temporal Graph (HetSPGraph), for drought forecasting. It includes three major layers: spatial aggregations including inter and intra aggregations, temporal aggregation, and a forecasting network. The main function of HetSPGraph is to learn the dynamic spatiotemporal correlations between the regions and to further predict the drought in different regions, based on which accurate drought forecasting can be achieved. Experimental forecasting results of the Standardized Precipitation Evapotranspiration Index (SPEI) in China indicated that the HetSPGraph model outperformed the traditional baseline methods including the Long Short-Term Memory model (LSTM), Convolutional Neural Network-LSTM (CNN-LSTM), Gated Recurrent Unit (GRU), Spatio-Temporal Graph Convolutional Networks (STGCN) and Geographic-Semantic-Temporal Hypergraph Convolutional Network (GST-HCN). Even for long-term forecasting (12 months), more accurate forecasting results, with the coefficient of determination R2 higher than 0.89, can also be obtained by HetSPGraph compared to the other three models. The proposed HetSPGraph model has the potential for wider use in forecasting drought and other natural disasters.

CMIP6‐projected changes in drought over Xinjiang, Northwest China

AbstractXinjiang, located in Northwest China, is a typical arid and semiarid region. In this region, frequent droughts have caused great losses in agricultural production and social economy. Therefore, changes in drought over Xinjiang have attracted considerable attention. This study projects the drought changes over Xinjiang during the 21st century, based on the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations under three Shared Socioeconomic Pathways (SSPs). The Standardized Precipitation Evapotranspiration Index (SPEI) is applied to measure drought variation. After quantitatively evaluating the performance of 25 CMIP6 models in annual and seasonal precipitation and evapotranspiration in Xinjiang during 1995–2014, we select 13 preferred models for projection. The projection results show significant decreasing trends in annual SPEI during 2021–2100 in Xinjiang, with the trends of −0.1/10a, −0.1/10a and −0.2/10a under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5, respectively. For seasons, the projected decreasing trends of SPEI in summer, autumn and spring are greater than that in winter. Relative to 1995–2014, during 2081–2100, moderate and severe droughts are projected to occupy a major increase in drought areas in Xinjiang under the three scenarios. In addition, drought tends to become more frequent and intense and last longer especially under higher scenarios. Under SSP5‐8.5, the projected regionally averaged drought frequency, intensity and duration increase by 15%, 8% and 1 month at the end of the 21st century. On the whole, the intermodel uncertainties of the projected changes in drought over Xinjiang increase with time in each emission. The findings of this study can provide information for decision makers to develop drought adaptation measures.

Standardised indices to monitor energy droughts

To mitigate the effects of climate change, energy systems are becoming increasingly reliant on renewable energy sources. Since these energy sources are typically dependent on the prevailing weather, renewable energy systems are susceptible to shortages during certain weather conditions. As renewable sources become larger contributors to the energy mix, the risks associated with these shortages, referred to as energy droughts, increase. Techniques are therefore required that can help policymakers to understand and mitigate the impacts associated with energy droughts. In this paper, two standardised indices are introduced to monitor droughts in renewable energy systems. The indices incorporate energy demand and renewable energy production, and constitute analogues to the standardised precipitation index (SPI) and standardised precipitation evapotranspiration index (SPEI), two indices regularly employed operationally to monitor meteorological droughts. The indices are straightforward to construct, can be defined on any timescale, and facilitate comparisons between regions with different climates and installed capacities. We demonstrate how the standardised energy indices proposed herein can be used to define renewable energy droughts, and illustrate the practical utility of these indices in an application to reconstructed time series of electricity demand and wind and solar power generation across Europe.

Drought characteristics and dominant factors across China: Insights from high-resolution daily SPEI dataset between 1979 and 2018

Drought, a complex phenomenon exacerbated by climate change, is influenced by various climate factors. The escalating global temperatures associated with climate change, impact precipitation patterns and water cycle processes, consequently intensifying the occurrence and severity of droughts. To effectively address and adapt to these challenges, it is crucial to identify the dominant climate factors driving drought events. In this study, we utilized the 1979–2018 Chinese meteorological forcing dataset to calculate the daily Standardized Precipitation Evapotranspiration Index (SPEI). The Theil-Sen and Mann-Kendall (M-K) tests were employed to analyze the spatial and temporal trends of drought severity and duration. Additionally, partial correlation analysis was conducted to examine the relationship between climate factors (precipitation and potential evapotranspiration (PET)) and drought characteristic (drought severity and duration). Through this comprehensive analysis, we aimed to identify the primary factors influencing drought severity and duration. The findings revealed the following key results: (1) Over the 40-year period from 1979 to 2018, drought trends in China and its seven climate divisions exhibited an increasing pattern. (2) During drought periods, most regions exhibited a positive correlation between PET and drought severity and duration, while precipitation demonstrated a negative correlation. However, certain areas experiencing severe drought displayed a negative correlation between PET and drought severity and duration, precipitation demonstrated a positive correlation with drought severity and duration. (3) PET emerged as the dominant climatic factor for meteorological drought in the majority of China. These findings contribute valuable insights for policymakers in the development of climate change adaptation and mitigation strategies. By understanding the dominant climate factors driving drought events, policymakers can implement effective measures to mitigate the adverse socioeconomic and environmental impacts associated with climate change.

Multi‐index characterization of compound dry hot events in India

AbstractCompound dry hot events (CDHEs), where hot events and droughts coexist, have received a lot of attention lately due to their catastrophic effects on the economy, environment and human health. In this study, we use two CDHE indices, the Standardized Compound Event Indicator (SCEI) and the Standardized Dry and Hot Index (SDHI), to assess changes in CDHE characteristics (severity, frequency, spatial extent) over the historical past and future CMIP6 simulations across the Indian subcontinent. To understand the role of the drought index selected on CDHE characterization two drought indices namely the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) are employed in the calculation of the CDHE indices. Further, the role of climatic oscillations such as El Nino Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Equatorial Indian Ocean Monsoon Oscillation and Indian Ocean Dipole in modulating CDHE characteristics have also been explored. Results show that SPI and SPEI based CDHE indices exhibit contrasting trends in northern India, while similar trends are observed in southern India in the historical past. Also, more frequent, extended, and severe CDHEs are reported by CDHE indices calculated using SPEI than by SPI. Temperature is found to be the dominant factor contributing to increases in CDHEs in the recent past and ENSO phases significantly modulate the severity and frequency of CDHE events in India. CMIP6 simulations generally report an increase in CDHE events for a 3°C global warming scenario. Overall, our findings show that the choice of the drought index has a greater impact on CDHE characterization than the choice of the CDHE index itself. Results from this study provide useful information towards understanding the risk of CDHEs in India under global warming and urge for the development and implementation of adaptation and mitigation measures.

Ensemble learning based on remote sensing data for monitoring agricultural drought in major winter wheat-producing areas of China

Drought is mainly triggered by the lack of precipitation, which can lead to insufficient water supply for crops thus affecting their growth and development. Reliable drought monitoring is crucial to understanding drought risk and avoiding drought-induced crop yield losses. Based on the Stacking regression method and multiple remotely-sensed drought factors from 2001 to 2017, this study developed an ensemble learning framework for monitoring agricultural drought in major winter wheat-producing areas in China. Stacking used five machine learning algorithms, namely, extreme gradient boosting, support vector regression, extra trees, and multi-layer perceptron, as the base learners to model the relationship between remote sensing drought factors and 1-, 3-, and 6-month standardized precipitation evapotranspiration index (SPEI). In this study, county-level winter wheat yield records and drought maps provided by the Global SPEI database (SPEIbase) were adopted to assess the suitability of Stacking-predicted SPEI drought maps in agricultural drought monitoring. The results show that Stacking outperformed other machine learning algorithms in terms of estimation accuracy, with the highest R2 value of 0.77 and the lowest root mean square error (RMSE) of 0.47. The longer the time scale of model-predicted SPEI, the higher its correlation with detrended winter wheat yields. The comparison with the drought maps of SPEIbase shows that the Stacking-predicted drought maps successfully captured the spatial pattern and intensity change of drought events. The approach presented in the study has good applicability for agricultural drought monitoring and could be extended to the rest of the areas.

Drought Severity and Trends in a Mediterranean Oak Forest

Drought is a significant natural hazard with widespread socioeconomic and environmental impacts. This study investigated the long-term drought characteristics in a Mediterranean oak forest ecosystem using the Standardized Precipitation Evapotranspiration Index (SPEI) at various time scales and seasons. The analysis was based on a long-term time series dataset obtained from a meteorological station located at the University Forest of Taxiarchis in Greece. The dataset encompassed a substantial time span of 47 years of continuous monitoring, from 1974 to 2020. To accomplish the goals of the current research, the SPEI was calculated for 3, 6, 12, and 24-month periods, and drought events were identified. The Mann-Kendall (M-K) test was used to analyze the trends in drought severity and evaluate the trends significance. The results showed that shorter time scales (SPEI3 and SPEI6) were more efficient for identifying short-term droughts, while longer time scales (SPEI12 and SPEI24) were better for identifying less frequent but longer-lasting drought episodes. The analysis consistently revealed positive trends across all seasons and time scales, indicating an overall transition towards wetter conditions. Nearly all the data series for SPEI12 and SPEI24 exhibited statistically significant upward trends (wetter conditions) at a 95% confidence level. However, more intense events were detected during the recent decade using the seasonal analysis. Additionally, as the time scale expanded, the magnitude of these trends increased. The findings contributed to a better understanding of drought dynamics in Mediterranean oak forests and provided valuable information for forest management and climate change adaptation planning.

Asymmetric response of global drought and pluvial detection to the length of climate epoch

Standardized indices (SIs) are routinely used to assess hydroclimate extreme events. However, to what extent such assessment is robust considering the choice of climate epoch (that is, a specific time period with explicit data length) is still not clear especially when developing new SIs. In this study, we use multiple SIs to systematically investigate the sensitivities of global drought and pluvial detection to the length of climate epoch by separating the parameter fitting step from the entire SI calculation process. We find that the sensitivity of event classifications to data length is more similar between Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Moisture Anomaly Index (SZI) at the six-month time scale, compared to that of Standardized Precipitation Index (SPI). Moreover, despite the symmetric definition of droughts and pluvials, the choice of climate epoch has asymmetric impacts on event detection. Detection of SZI-based droughts is more sensitive to data length than pluvials and generally requires longer series (additional 10 years compared to a 30-year climatology) to accurately fit the tailed distribution, especially over a few hot spot regions, including South Africa, South Asia and Oceania.

Soil properties zoning of agricultural fields based on a climate-driven spatial clustering of remote sensing time series data

The identification of zones within an agricultural field that respond differently to environmental factors and agronomic management is a key requirement for the adoption of more precise and sustainable agricultural practices. Several approaches based on spatial clustering methods applied to different data sources, e.g. yield maps, proximal sensors and soil surveys, have been proposed in the last decades. The current availability of a huge amount of free remote sensing data allows to apply these approaches to agricultural areas where ground or proximal data are not available. However, in order to provide useful agronomic management information, it is essential that the zoning obtained by clustering is linked to the underlying spatial variability of soil properties. In this work we explore the hypothesis that the response of crop vigor to temporal climate variability, assessed by remote sensing data time series, selected to correspond to specific growth phases and seasonal climate patterns, provides indications on the variability of soil properties within agricultural fields, for both herbaceous and tree crops. NDVI time-series for 38 years (1984–2021) were obtained for fourteen non-irrigated herbaceous and tree crop fields in Central Italy, from multispectral satellites data (Landsat 5/7/8, Sentinel 2). The Standardized Precipitation-Evapotranspiration Index (SPEI) was used to classify time series into three climatic classes (dry/normal/wet) for five different periods of the growth season, covering the main phenological phases. K-means clustering was used to identify patterns of crop growth from climatically classified image sets, as well as for all the bulked images for comparison (bulk clustering). Clustering results were compared with soil maps obtained from spatialized ground data, for soil texture (clay, silt and sand), soil organic matter and available soil water (ASW). The agreement between the different clustering results and soil maps was assessed by the Adjusted Rand Index. Agreement with soil maps varied depending on the field, the phenological phase considered and the soil property considered. Climate driven clustering from long, late growth season periods best matched soil properties, both for herbaceous and tree crops, despite being based on a limited number of images. The clustering from images spanning a longer growth period for dry years systematically outpaced the bulk clustering for silt, sand and ASW, while the clustering for normal climatic conditions was the best for organic matter. The performance of the matching between clustering and soil maps increased with soil variability significantly more (P < 0.05) than in the bulk clustering (mean slopes respectively 0.468 ± 0.167; 0.113 ± 0.270). The integration of the SPEI climatic index into the clustering procedure systematically improved the identification of zones with homogeneous soil properties, highlighting that a greater attention should be posed to the climate-crop-field interactions when using remotely sensed images.

Spatio-Temporal Drought Assessment Using Standardized Precipitation Evapotranspiration Index (SPEI) over Mantaro Valley, Peru

Peru has several studies based on the Peruvian Tropical Andes (PTA) and its effects due to climate change. It has been shown that due to orography characteristics, different kinds of climate conditions are seen. In that sense, the PTA has a very complex climate system, which causes significant variability mostly related to an increase in temperature and a decrease in precipitation patterns. The Mantaro Valley is located in the PTA, Junin region. Where vulnerable farmers are already affected by poverty, practice agriculture based on rain-fed cropping systems. Hence, climate variability causes agricultural vulnerability and water resource scarcity. This study aims to elaborate the Spatio-temporal drought assessment over Mantaro Valley to provide information on local climate change events. In order to do so, the Standardized Precipitation Evapotranspiration Index (SPEI), recognized by the World Meteorological Organization (WMO), was used. The results have shown that almost every station has clear evidence of warming over the years and only a precipitation decrease in two out of six stations. Furthermore, the Spatio-temporal analysis shows around 30% of drought events from the total data, considering the frequency, severity, and duration. In conclusion, it has been demonstrated that all analyzed stations within Mantaro Valley have the same deportment regarding drought characteristics with different highest frequency per time scale. Finally, it is recommended to keep tracking stations with the lowest available data, realize comparison analysis with different drought assessment methods, and study the correlation between drought and ENSO events and other climate events such as floods.

Comparative analysis of the influence of temperature and precipitation on drought assessment in the Marmara region of Turkey: an examination of SPI and SPEI indices

Abstract Drought is one of the most serious problems for human societies and ecosystems. Climate change has begun to affect the whole world, but regional climate change can have different effects. Precipitation is frequently used to determine drought; besides, temperature has started to be used in recent years. The effect of temperature on the occurrence of drought was investigated for the Marmara region, located in the northeast of Turkey. The spatiotemporal variability of drought in the Marmara region was analyzed by using the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) for 3-, 6-, and 12-month series. Results show that SPEI droughts occur with longer duration and increased magnitude, which can be attributed to the rise in potential evapotranspiration rates with an increase in temperature. The correlation between SPI and SPEI is strong for the same time series, and the number of extreme drought values for SPI is higher than SPEI, while the number of severe and moderate drought values is lower compared to SPEI. The years 1989, 1990, 2001, 2007, and 2014 were characterized as the highest drought years throughout the region. The study concludes that temperature should be considered in future studies.

Tree rings in Tsuga dumosa reveal increasing drought variability in subtropical southwest China over the past two centuries

High-resolution palaeoclimate proxy records have been lacking for the subtropical region of southwest China. To gain insights into hydroclimate variability in central Yunnan Province, we constructed a tree ring-width chronology of Tsuga dumosa from the Wuliang Mountain area. Correlation analysis shows that moisture availability is predominantly the limiting factor on radial growth, particularly during the spring-early summer season. Using a well-calibrated regression model (R2 = 0.36, p < 0.001), we reconstructed the March–June Standardized Precipitation-Evapotranspiration Index (SPEI) for the period from 1826 to 2020 CE. Eleven extreme drought years (1860, 1897, 1958, 1960, 1979, 1984, 1987, 2006, 2010, 2013, and 2019) were identified, with most of them confirmed by historical evidence. The reconstruction series shows inter-annual to inter-decadal variations and an increasing occurrence of severe dry or wet events in the recent past, particularly during the last 50 years. Our drought reconstruction series shows high frequency cycle of 2–8 years. Spatial correlations indicate that our drought reconstruction captures hydrological signals over a larger area. In line with this, the reconstructed dry periods exhibit broad consistency with other tree-ring based drought reconstructions conducted in nearby regions. Our findings demonstrate the potential of tree-ring proxies for hydroclimate reconstruction in the subtropical areas of southwest China and provide crucial information for evaluating long-term climate change in this region.

Magnitude and direction of green-up date in response to drought depend on background climate over Mongolian grassland

Increasing drought is one major consequence of ongoing global climate change and is expected to cause significant changes in vegetation phenology, especially for naturally vulnerable ecosystems such as grassland. However, the linkage between the response characteristic of green-up date (GUD) to drought and background climate remains largely unknown. Here, we focused on how the GUD of Mongolian grassland responds to extreme drought events (EDE). We first extracted the GUD from the MODIS Enhanced Vegetation Index data during 2001–2020 and identified the preseason EDE using the standardized precipitation evapotranspiration index data. Subsequently, we quantified the response of GUD to preseason EDE (DGUD) in each pixel as the difference in GUD between drought and normal years. The effect of 12 factors on DGUD was analyzed using the random forest algorithm. The results showed that the GUD under EDE may delay or advance by > 20 days compared to normal years. For the regions with mean annual temperature > −2 °C, the GUD was delayed under EDE due to the dominant role of water restriction on GUD, while the GUD was advanced under EDE in colder areas due to the warmer temperature during drought. However, the magnitude of delay in GUD under drought was greater in regions with less precipitation and more severe droughts. Our results could help to develop appropriate management strategies to mitigate the impacts of drought on grasslands.

Assessment of drought trends in the Upper Ewaso Ng'iro River Basin using the SPI and SPEI

Abstract Drought assessment is necessary for creating adaptation and resilience measures for the livelihoods of the affected communities. This study assessed drought trends in Kenya's Upper Ewaso Ng'iro River Basin (UENB) from 1981 to 2020. A Standardized Precipitation Index (SPI), a precipitation-based index, and a Standardized Precipitation Evapotranspiration Index (SPEI), a multivariate index that considers the difference between precipitation and potential evapotranspiration (PET), were used to evaluate drought severity and frequency over varying timescales. Monthly rainfall and temperature data for 10 stations within the basin were analyzed to calculate the SPI and SPEI time series values for 3, 6, and 12 months. The results demonstrate an alarming increase in the severity and frequency of drought events in the UENB since 1999. Additionally, the study reveals that the SPI and SPEI indices differ in identifying temporal and spatial drought characteristics, with longer timescales showing improved accuracy. Notably, the SPEI identifies more extensive and severe drought periods in the region compared to the SPI. The research findings are crucial in guiding policy decisions related to SDGs as they provide valuable information on drought trends necessary for implementing effective drought adaptation and resilience measures and promoting sustainable development in the UENB.

Drought Hazard Analysis in the Jilin Province Based on a Three-Dimensional Copula Method

Based on the precipitation and potential evapotranspiration at 23 stations in the Jilin Province, the standardized precipitation evapotranspiration index (SPEI) was calculated. Based on run theory and combined with previous research in Jilin Province, we redefine the drought threshold, with three characteristics, that is, drought duration, intensity, and severity, were identified and extracted. Two- and three-dimensional copula functions were selected to fit and combine drought variables to calculate the joint recurrence period. In order to make the alternative copula model more complete, 25 representative copula functions are selected for the first time to construct the Joint probability distribution function. The Mann–Kendall test was used to examine abrupt changes in Meihekou County. SPEI trends and the joint cumulative probability of drought variables in the Changbai area were analyzed. The results show that: (1) the SPEI in the Meihekou area was unstable around 2000 and 2010, with abrupt change points; (2) the trend of the SPEI in the Changbai Mountains area indicates that the drought occurrence is similar at various timescales; the drought range and duration increase with increasing timescale; and (3) the correlation between the three drought variables is strong and the fit is good. The results of two- and three-dimensional joint recurrence period calculations are similar, with a high probability of recurrence within three years.

Dry and Wet Period Analysis using Meteorological Drought Indices in Sabarkantha district Gujarat, India

Drought assessment is very important to manage water resources in lean period. In the present study, identification of drought years and extent of deficit of annual rainfall is accomplished by use of three meteorological based drought indices like the Standardized Precipitation Evapotranspiration index (SPEI), China-Z index (CZI) and Modified China-Z index (MCZI) on 1, 3, 6, 9 and 12 month (short term and long term) timescales using monthly precipitation and temperature data from 1901 to 2002 at Sabarkantha to specify the drought conditions in present study area. The analysis of multiple time steps in drought indices make it harder to decide the best time step to show the drought conditions. The months of January, March, November and December have been identified 76, 79, 77 and 80 times as drought months respectively in the 20th century, indicating that these months must be provided with assured Irrigation. It is observed that 1904, 1911, 1915, 1923, 1974, 1987 and 2002 affected by severe drought condition in SPEI-12. The China Z Index and Modified China Z index shows that’s 1904, 1911, 1915, 1918, 1923, 1948, 1951, 1965, 1968, 1969, 1972, 1974, 1985, 1987, 1991, 1995 and 2002 affected by extreme drought. The study also reveals that only 1% year obtain as extreme wet conditions. From result it shows that 23% years are categorised into moderate to extreme drought years, which includes 15% Moderate dry, 8% severe dry years.

Quantitative analysis of input data uncertainty for SPI and SPEI in Peninsular Malaysia based on the bootstrap method

ABSTRACT Drought assessment has attracted attention in the research community, especially regarding the accuracy of drought indices due to input data uncertainty. This study addressed the impacts of input data uncertainty on the estimation of the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) for drought assessment in Peninsular Malaysia. The Kolmogorov-Smirnov test recommended the gamma distribution function for the SPI and the log-logistic distribution function for the SPEI. The bootstrap method was used to estimate SPIU and SPEIU, which account for input data uncertainty, and provided estimates for SPI and SPEI values. However, the standard deviation indicated significant input data uncertainty, with values ranging from 0.1038 to 0.1378. The two drought indices exhibited similar classifications of drought categories, but SPIU showed greater uncertainty for very dry and extremely dry events. The findings emphasize the importance of input data uncertainty, especially when dealing with extreme drought events.

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.

Drought Characterization and Potential of Nature-Based Solutions for Drought Risk Mitigation in Eastern Ethiopia

Drought is one of the challenges in Ethiopia that affects the agricultural production. Ecosystems can mitigate the effects of climate-related hazards including drought. For this reason, nature-based solutions (NbS) are becoming more prominent to manage climate-related impacts in developing countries; however, there is still limited empirical evidence, which would inform NbS policy and practices. Thus, the aim of this study was to characterize droughts in Eastern Ethiopia (the case of Erer Sub-basin) and assess the effectiveness of various NbS for drought. The temporal changes in soil moisture deficit index (SMDI) (agricultural drought) and standard precipitation evapotranspiration index (SPEI) (meteorological drought) at various timescales (1, 3, and 6 months) between 1981 and 2020 were analyzed. Qualitative analysis was used to categorize and evaluate the effectiveness of NbS to mitigate drought risk by adopting a Living Labs approach. Overall, the study revealed that droughts showed an increasing trend with more frequency, longevity, and severity. The drought analysis results showed that SPEI’s variants were less reliable than SMDI0–5 and SMDI5–100. With an increase in rainfall, SPEI showed stronger relationships with SMDI0–5 at one and two-month delays between May and July. SPEI and SMDI5–100 performed better in terms of capturing actual drought occurrences than SMDI0–5. The results of focus group discussions (FGD) suggested NbS such as habitat restoration, structural restoration, reforestation, rehabilitation, revegetation, land enclosures, conservancies, and locally managed areas have been practiced as a strategy to manage drought risks. Overall, the study indicated that a new, comprehensive approach through nature-based solutions to reduce the risk of drought is urgently needed.

Vegetation Dynamics and Their Response Patterns to Drought in Shaanxi Province, China

Effective water and vegetation management requires a better understanding of vegetation dynamics, and their response patterns to drought. Here, based on the normalized difference vegetation index (NDVI) and the standardized precipitation evapotranspiration index (SPEI), we investigate the vegetation response patterns to drought in Shaanxi Province (SAX), using Spearman’s correlation analysis. The results show that the NDVI increased significantly (p &lt; 0.01) from 2000 to 2019, with a trend of 0.054/10 yr. The maximum correlation coefficient between the NDVI and the SPEI (Rmax) showed a significantly positive correlation (p &lt; 0.05) in most areas (84.5%) of SAX. The Rmax of Northern Shaanxi (NS, median = 0.55) was higher than that of Central Shaanxi (CS, median = 0.53) and Southern Shaanxi (SS, median = 0.52). The corresponding timescale of Rmax was longer in CS (median = 7 months) than NS (median = 4 months) and SS (median = 3 months). The occurrence date of Rmax in NS (median = July) lagged that in CS and SS (median = May). The vegetation response patterns to drought varied with elevation; the Rmax decreased significantly (p &lt; 0.01) with increasing elevation, while the corresponding timescale exhibited fluctuations. Additionally, Hurst exponent analysis indicated that 78.6% of the areas in SAX will exhibit a humidification trend in the future, and that vegetation growth in 74.7% of the areas in the region will be promoted.