Severe Drought Events Research Articles

Decline in net primary productivity caused by severe droughts: evidence from the Pearl River basin in China

AbstractUnderstanding the spatiotemporal characteristics of drought events and their impacts on terrestrial net primary productivity (NPP) is crucial for drought mitigation and environmental protection. This study, by taking the Pearl River basin as the case region, investigated drought duration, severity, intensity, affected area, and centroids during 1960–2015 based on the Standardized Evapotranspiration Deficit Index and three-dimensional clustering algorithm and then revealed how these drought characteristics have affected NPP. Results showed that there were altogether 32 severe drought events lasting at least 3 months in the basin, with half lasting longer than 6 months. The total NPP loss significantly correlated with drought severity and intensity. Most drought events caused a reduction in NPP across more than half of the drought-affected area; specifically, the February–December drought in 2011 has cut NPP by 31.85 Tg C, accounting for 11.7% of the regional annual mean NPP, while the September 2009–September 2010 drought caused a decrease of 20.26 Tg C in NPP. Our research improves the insight into the relationship between NPP and drought, which helps decision-makers manage droughts and provides guidance for drought-related studies across other regions.

A maximum entropy copula-based frequency analysis method for assessing bivariate drought risk: a case study of the Kaidu River Basin

Abstract In this study, a maximum entropy copula-based frequency analysis (MECFA) method is developed through integrating maximum entropy, copulas and frequency analysis into a general framework. The advantages of MECFA are that the marginal modeling requires no assumption and joint distribution preserves the dependence structure of drought variables. MECFA is applied to assessing bivariate drought frequency in the Kaidu River Basin, China. Results indicate that the Kaidu River Basin experienced 28 drought events during 1958–2011, and drought inter-arrival time is 10.8 months. The average duration is 6.2 months (severity 4.6), and the most severe drought event lasts for 35 months (severity 41.2) that occurred from June 1977 to March 1980. Results also disclose that hydrological drought index (HDI) 1 is suitable for drought frequency analysis in the target year of return periods of 5 and 10, HDI 3, HDI 6 and HDI 12 are fit for the target year of return periods of 20, 50 and 100. The joint return period can be used as the upper bound of the target return period, and the joint return period that either duration or severity reaches the drought threshold can be used as the lower bound of the target return period.

Exploring the past of Mavrovouni forest in the\xa0Pindus Mountain range (Greece) using tree rings of Bosnian pines

Key messageLong Bosnian pine chronologies from different mountains are shaped by different climatic parameters and can help identify past drought events and reconstruct landscape histories.We developed a 735-year-long Pinus heldreichii chronology from the southern distribution limit of the species, expanding the available database of long Bosnian pine chronologies. Tree-ring growth was mainly positively correlated with growing degree days (GDD: r1950–2018 = 0.476) while higher temperatures during both winter and growing season also enhanced growth (TWT: r1950–2018 = 0.361 and TGS: 0.289, respectively). Annual precipitation, during both calendar and water years, had a negative but weaker impact on annual tree growth. The newly developed chronology correlates well with chronologies developed from the neighboring mountains. The years with ring width index (RWI) lower than the average were found to correspond to cool years with dry summers. Still, the newly developed chronology was able to capture severe drought events, such as those in 1660, 1687, and 1725. Several old living trees had internal scars presumably caused by fires. Therefore, old mature trees could be used for fire history reconstruction in addition to climate reconstruction. Although the presence of lightning scars indicates an important natural agent of fire ignition, human activities associated with animal grazing could also be an underlying reason for fires in the region.

Global exposure of population and land‐use to meteorological droughts under different warming levels and SSPs: A CORDEX‐based study

AbstractGlobal warming is likely to cause a progressive drought increase in some regions, but how population and natural resources will be affected is still underexplored. This study focuses on global population, forests, croplands and pastures exposure to meteorological drought hazard in the 21st century, expressed as frequency and severity of drought events. As input, we use a large ensemble of climate simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX), population projections from the NASA‐SEDAC dataset and land‐use projections from the Land‐Use Harmonization 2 project for 1981–2100. The exposure to drought hazard is presented for five Shared Socioeconomic Pathways (SSP1‐SSP5) at four Global Warming Levels (GWLs: 1.5°C to 4°C). Results show that considering only Standardized Precipitation Index (SPI; based on precipitation), the SSP3 at GWL4 projects the largest fraction of the global population (14%) to experience an increase in drought frequency and severity (versus 1981–2010), with this value increasing to 60% if temperature is considered (indirectly included in the Standardized Precipitation‐Evapotranspiration Index, SPEI). With SPEI, considering the highest GWL for each SSP, 8 (for SSP2, SSP4, SSP5) and 11 (SSP3) billion people, that is, more than 90%, will be affected by at least one unprecedented drought. For SSP5 at GWL4, approximately 2 × 106 km2 of forests and croplands (respectively, 6% and 11%) and 1.5 × 106 km2 of pastures (19%) will be exposed to increased drought frequency and severity according to SPI, but for SPEI this extent will rise to 17 × 106 km2 of forests (49%), 6 × 106 km2 of pastures (78%) and 12 × 106 km2 of croplands (67%), being mid‐latitudes the most affected. The projected likely increase of drought frequency and severity significantly increases population and land‐use exposure to drought, even at low GWLs, thus extensive mitigation and adaptation efforts are needed to avoid the most severe impacts of climate change.

Agricultural Drought Prediction Based on Conditional Distributions of Vine Copulas

AbstractMonitoring and prediction of agricultural drought are paramount to food security at the global and regional scales, particularly under the influence of climate change and anthropogenic activities. Soil moisture is an effective indicator for monitoring and characterizing agricultural drought. Soil moisture (agricultural drought) is mainly affected by precipitation (meteorological drought) and temperature (hot conditions). Owing to the flexibility of vine copulas in handling multidimensional variables by decomposing them into pair copula constructions (PCCs), we propose a novel drought prediction method using three predictors, namely antecedent meteorological drought, previous hot conditions, and persistent agricultural drought, based on the conditional distributions of C‐vine copulas in a four‐dimensional scenario. The proposed model was applied to agricultural drought (characterized by the standardized soil moisture index (SSI)) prediction with 1–2‐months lead time for the summer season (i.e., August at a 6‐months timescale) in China. Taking two severe agricultural drought events that occurred in many regions across China in August of 2006 and 2014 as validation cases, the SSI predictions with 1–2‐months lead time using the conditional C‐vine copulas model were found to be generally consistent with the corresponding historical SSI observations in most parts of China. Performance evaluation using the Nash‐Sutcliffe efficiency (NSE), coefficient of determination (R2), and F1 score (F1S) for different climate regions also indicated that this model provided a reliable prediction of agricultural drought for most areas of China. The outcome of this study can serve as a guidance for drought prediction, early warning, and drought mitigation.

Intensified Soil Moisture Extremes Decrease Soil Organic Carbon Decomposition: A Mechanistic Modeling Analysis

AbstractEarth system models have predicted that there will be more frequent and severe precipitation and drought events in terrestrial ecosystems. Microbially mediated decomposition of soil organic carbon (SOC) tends to increase as soils wet and decrease as soils dry. However, the long‐term SOC change under intensified moisture extremes remains poorly known as it depends on the frequency and intensity of soil drying and wetting. In this study, we explored long‐term SOC dynamics under scenarios of alternating drying‐wetting cycles using the Microbial‐ENzyme Decomposition model, a mechanistic microbial model. The model was parameterized with 11 years of observations from a temperate deciduous broadleaf forest site, showing satisfactory model performance in both model calibration (R2 = 0.67) and validation (R2 = 0.69) against heterotrophic respiration. We then used the model to simulate the long‐term SOC dynamics under five scenarios of alternating drying‐wetting cycles with different frequencies and severities over a period of 100 years. Results showed that the changes in active microbial biomass C and the corresponding turnover rates of SOC pools were more sensitive to soil drying than soil wetting. As a result, the cumulative soil carbon emission from microbial respiration decreased by 433.7 g C m−2 after the 100‐year simulation in the highest frequency and intensity moisture scenario, but was not significantly affected by the lowest frequency and intensity scenario. This study emphasizes the nonlinear response of SOC decomposition to soil moisture changes, which causes decreased decomposition by microbes under drying that is, not compensated by increased decomposition under wetting conditions.

Spatial multicriteria approach to support water resources management with multiple sources in semi-arid areas in Brazil

Semi-arid regions often face severe drought events that reduce agricultural and livestock production. In recent years, some international studies have used multicriteria decision analysis (MCDA) approaches combined with geographic information systems (GIS-MCDA) to support decision-makers in assessing the suitability of agricultural land for irrigation in semi-arid regions. Unlike previous studies, which have only considered a single source of water for crop irrigation, this study proposes a GIS-MCDA approach that considers all potentially available local water sources (e.g., groundwater, surface water, and wastewater) as possible alternatives for better multisource water resource management (MWRM) in regions facing water shortages. The geospatial multicriteria evaluation implemented in this study considers a series of technical, environmental, and agricultural productivity criteria using the analytical hierarchy process (AHP) method. Three independent baseline maps were generated, showing the spatial distribution of suitable areas for crop irrigation for each considered water source in the studied area. Surface water, groundwater, and wastewater offered suitable crop irrigation for 83%, 70%, and 26% of the study area, respectively. Overlapping these areas produced a final map showing all the feasible areas for each crop irrigation alternative at the same time. The MWRM approach considering all water sources increased the coverage of suitable areas to be irrigated in the study area by 2.2%, 20.4%, and more than 225% compared to considering surface water, groundwater, and wastewater, respectively, independently. The GIS-MCDA framework proposed in this study provides better support for decision-makers and stakeholders, favouring a reduction in possible conflicts over water scarcity, the diversification of irrigated crops, and an improvement in the quality-quantitative management of water resources in semi-arid regions.

Forest Resistance and Resilience to 2002 Drought in Northern China

Drought can weaken forest activity and even lead to forest mortality, and the response of different forest types to drought can be diverse. Deciduous broadleaf forest (DBF) and deciduous needleleaf forest (DNF) are two of the majority forest types in northern China. In this region, a severe drought event happened in 2002. However, due to the lack of data, the spatio-temporal characteristics of the ecosystem stability of different forest types here remain unclear. In this study, we used a machine learning approach (model tree ensemble, MTE) to drive fluxsite gross primary productivity (GPP), combined with remote sensing-based GPP and a vegetation index data (EVI), to analyze the spatial and temporal characteristics of resistance and resilience of DNF and DBF in northern China during and after the 2002 drought. The results showed that the DBFs were more acclimatized to the drought, while the resistance and resilience of DNF and DBF were diverse under different consecutive drought events. These results could be suggestive for forest conservation and vegetation modeling.

Testing standard growth substrates for establishing native dry sandy grassland species on extensive green roofs in Northern Germany

Green roofs are known to mitigate the negative effects of urban consolidation by offering diverse ecosystem functions compared to non-vegetated roofs. However, the support for native biodiversity might be improved by using native plant species. In a mesocosm experiment, we studied the suitability of three commercial green-roof growth substrates for the establishment of 27 native plant species from dry sandy grasslands of northwestern Germany over the course of four years. The substrates were mineral-based, but differed in the layering of organic matter. Total establishment rates reached 44–59% in Year 4, indicating the general suitability of the substrates. During the first weeks after seeding, with light irrigation, the vascular plant cover was greater in the similar substrates Zincolit® Plus (Z) and Zincolit® Plus-Leicht (ZL) with their compost-based organic mulch layers than in the substrate Sedumteppich (ST) with its organic matter evenly admixed with the mineral aggregates. In Years 2 and 3, however, the vascular plant cover was greater in the ST substrate, likely due to the better availability of water and nutrients from the organic matter compared to the dry surface-mulch layer variants Z and ZL. After severe drought events, the decline in plant cover was more pronounced in the ST substrate, likely representing a trade-off between lush growth and a susceptibility to drought. An indicator-species analysis revealed differences in species composition between the ST and Z/ZL substrates. Annual plant species were indicators of the ST substrate. Perennials, such as Thymus pulegioides and Achillea millefolium, were typical of the Z and ZL substrates. In addition to the general suitability of the tested standard substrates for target species establishment, the study indicated that a combination of different layers of substrate components resulted in different vegetation patterns that may have a positive effect on green-roof biodiversity.

Fusion-based framework for meteorological drought modeling using remotely sensed datasets under climate change scenarios: Resilience, vulnerability, and frequency analysis

Severe drought events in recent decades and their catastrophic effects have called for drought prediction and monitoring needed for developing drought readiness plans and mitigation measures. This study used a fusion-based framework for meteorological drought modeling for the historical (1983–2016) and future (2020–2050) periods using remotely sensed datasets versus ground-based observations and climate change scenarios. To this aim, high-resolution remotely sensed precipitation datasets, including PERSIANN-CDR and CHIRPS (multi-source products), ERA5 (reanalysis datasets), and GPCC (gauge-interpolated datasets), were employed to estimate non-parametric SPI (nSPI) as a meteorological drought index against local observations. For more accurate drought evaluation, all stations were classified into different clusters using the K-means clustering algorithm based on ground-based nSPI. Then, four Individual Artificial Intelligence (IAI) models, including Adaptive Neuro-Fuzzy Inference System (ANFIS), Group Method of Data Handling (GMDH), Multi-Layer Perceptron (MLP), and General Regression Neural Network (GRNN), were developed for drought modeling within each cluster. Finally, two advanced fusion-based methods, including Multi-Model Super Ensemble (MMSE) as a linear weighted model and a nonlinear model called machine learning Random Forest (RF), combined results by IAI models using different remotely sensed datasets. The proposed framework was implemented to simulate each remotely sensed precipitation data for the future based on CORDEX regional climate models (RCMs) under RCP4.5 and RCP8.5 scenarios for drought projection. The efficiency of IAI and fusion models was evaluated using statistical error metrics, including the coefficient of determination (R2), Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The proposed methodology was employed in the Gavkhooni basin of Iran, and results showed that the RF model with the lowest estimation error (RMSE of 0.391 and R2 of 0.810) had performed well compared to all other models. Finally, the resilience, vulnerability, and frequency of probability metrics indicated that the 12-month time scale of drought affected the basin more severely than other time scales.

MODIS PRI performance to track Light Use Efficiency of a Mediterranean coniferous forest: Determinants, restrictions and the role of LUE range

The relationship between the Photochemical Reflectance Index (PRI) and Light Use Efficiency (LUE) is well established at leaf and small canopy scales, but upscaling to ecosystem level is still a challenge. Only few studies have applied satellite-derived PRI to estimate LUE, mostly using MODIS, and although the results are promising, many external factors have been identified affecting PRI performance. The present study investigates determinants and restrictions of MODIS-derived PRI potential to follow the LUE variability of a Mediterranean coniferous forest.Daily and half-hour LUE values were calculated from eddy covariance measurements, dividing GPP by either Photosynthetically Active Radiation (PAR) or the absorbed fraction of PAR (APAR). Also, various PRI datasets were created based on different sensor (Terra, Aqua, Both), reference band (1, 12, 13) and observation/illumination angles. Overall, PRI correlated better with LUE calculated using PAR instead of APAR and Aqua PRI yielded better results than Terra. Restricting acquisitions according to observation/illumination angles improves the PRI:LUE relationship (maximum R2 = 0.512), with backscatter observations yielding the best correlations. Our findings suggest that MODIS-derived PRI is more sensitive to relatively large seasonal LUE changes, but is unable to closely follow severe drought events. Among the tested reference bands, the best results were derived using band 12 (546 - 556 nm), although the optimum reference band seems to depend on viewing conditions. The PRI:LUE relationship was further improved when half-hour LUE of the satellite overpass was used instead of daily LUE. However, it was found that the PRI:LUE relationships for the different datasets were strongly affected by the range of LUE values corresponding to each PRI group, with lower LUE variability resulting to weaker PRI:LUE correlations. LUE range effect should be accounted for in future studies, when different PRI datasets are compared and might explain the contradicting findings in the existing literature.

Meteorological Drought Events and Their Evolution from 1960 to 2015 Using the Daily SWAP Index in Chongqing, China

Meteorological droughts are natural disasters that have been linked to economic losses and casualties. Decision-makers need to understand the temporal and spatial variation of meteorological drought events at a daily to weekly scale to develop a more elaborate framework for drought risk management. The present study used the standardized weighted average of precipitation index (SWAP) as an indicator of meteorological droughts, computed from the daily precipitation dataset (1960–2015) of 34 meteorological stations in Chongqing, China. The multi-threshold run theory was applied to identify drought events. Variation of drought characteristics was estimated by the modified Sen’s trend test. The results suggested the following findings: (1) the onset, duration, and severity of drought events identified by the SWAP index are in good agreement with the real local records; (2) there was no significant linear trend and abrupt change in annual duration and severity of drought events, but the decadal variation was obvious. From a decadal perspective, the annual frequency, duration, and severity of drought events showed a steady decreasing trend before the 1990s, and then fluctuated upward; (3) the spatial variation of the duration and severity of ordinary drought events was quite inconsistent at different periods. The annual drought days and severity increased from 1960 to 2015 but decreased after 1990. From 1960 to 2015, the duration days and severity of persistent, long persistent, severe, and extreme drought events declined insignificantly in most parts of the middle and southeast regions but increased in the western and northeast regions. The drought situation in Chongqing shows a large range of variation and obvious spatial heterogeneity. The SWAP index is an effective tool to identify the evolution of daily scale meteorological drought events.

Physical storylines of future European drought events like 2018 based on ensemble climate modelling

In the aftermath of observed extreme weather events, questions arise on the role of climate change in such events and what future events might look like. We present a method for the development of physical storylines of future events comparable to a chosen observed event, to answer some of these questions. A storyline approach, focusing on physical processes and plausibility rather than probability, improves risk awareness through its relation with our memory of the observed event and contributes to decision making processes through their user focus. The method is showcased by means of a proof-of-concept for the 2018 drought in western Europe. We create analogues of the observed event based on large ensemble climate model simulations representing 2 °C and 3 °C global warming scenarios, and discuss how event severity, event drivers and physical processes are influenced by climate change. We show that future Rhine basin meteorological summer droughts like 2018 will be more severe. Decreased precipitation and increased potential evapotranspiration, caused by higher temperatures and increased incoming solar radiation, lead to higher precipitation deficits and lower plant available soil moisture. Possibly, changes in atmospheric circulation contribute to increased spring drought, amplifying the most severe summer drought events. The spatial extent of the most severe drought impacts increases substantially. The noted changes can partly be explained by changes in mean climate, but for many variables, changes in the relative event severity on top of these mean changes contribute as well.

GRACE and land surface models reveal severe drought in eastern China in 2019

An extreme drought on record occurred in eastern China in 2019 and caused severe terrestrial water storage (TWS) deficits. It is important to understand this drought process and assess its impacts. We combine the Gravity Recovery and Climate Experiment (GRACE) observations and land surface model (LSM) outputs to thoroughly investigate this drought event. To consolidate the quantitative analysis, we fuse five sets of GRACE-derived TWS data and four sets of LSM-based soil moisture storage (SMS) data, respectively, using the generalized three-cornered hat method and the least-square method, and thereby generate robust TWS and SMS results. Both the GRACE and LSM results suggest that this drought event started in July in northeastern and northern China and then spread south to southern China in August and lasted until December, influencing a vast area of ∼2.0 × 106 km2 in eastern China. The drought center first appeared in North China, then moved southward to the middle and lower reaches of the Yangtze River basin, and finally stayed in South China. TWS results from GRACE show that the largest regional TWS deficit was 94.1 ± 17.7 mm and happened in September in the middle and lower reaches of the Yangtze River basin. SMS results from LSMs exhibit similar spatiotemporal characteristics to the GRACE results but with smaller magnitudes. Meteorological Drought Composite Index from the China Meteorological Administration agrees well with the GRACE-derived TWS deficits and LSM-based SMS deficits in terms of their spatiotemporal distributions. These three independent indicators together identify and assess this severe drought event, providing us a quantitative understanding of it. The water budget analysis demonstrates that the precipitation deficit in the second half of 2019 is the main reason for this drought event. The precipitation deficit is mainly due to the northerly wind anomaly and sinking anomaly over eastern China, which are closely associated with the anomalous cyclonic circulation over the western Pacific.

Past and future drought trends, duration, and frequency in the semi‐arid Urmia Lake Basin under a changing climate

AbstractAlthough the Urmia Lake has undergone remarkable drought conditions within the past decades mainly due to climate change, drought studies covering the entire Urmia Lake Basin and all drought aspects are lacking. The present study investigated the spatial and temporal drought conditions in the Urmia Lake Basin for the past (1988–2017) and future (2021–2050 and 2051–2080) periods using five general circulation models (GCMs) under the IPCC (Intergovernmental Panel on Climate Change) scenarios RCP 2.6, RCP 4.5, and RCP 8.5. The standardized precipitation index (SPI) and standardized precipitation and evapotranspiration index (SPEI) were compared. The SPEI predicted more drought events than the SPI, and it seemed to be a more suitable drought index than the SPI for the basin. In general, the future periods would encounter less drought conditions in terms of significant drought trends and duration than the observed period under all scenarios, but the frequency of quarterly severe drought events in the future periods would be higher than in the observed period. Furthermore, the stations Urmia (western bank) and Tabriz and Maragheh (eastern banks) would face the highest frequency of different types of quarterly drought events in the future periods compared with the observed period. The predicted high frequencies of drought events for the future periods can intensify the current low water level situation of the Urmia Lake, which seriously threatens all types of ecosystems in the basin. Therefore, serious actions need to be taken into account for efficient ecosystem and water resources management in the basin.

Intra-annual tree growth responds to micrometeorological variability in the central Amazon

Intra-annual distribution of precipitation in central Amazonia leads to a short mild dry season, which is associated with an increase in irradiance and temperature and a decline in relative humidity; however, the independent effect of each individual climatic variable on tree growth is still under investigation. The objective of this study was to determine how tree growth (inferred from radial stem increment) responds to monthly variations of micrometeorological variables in the central Amazon. During five years (2013-2017) we measured tree growth in 51 trees from nine species and, above the forest canopy, collected environmental data, such as photosynthetically active radiation (PAR), air temperature (T), precipitation, air relative humidity (RH), air vapor pressure deficit (VPD), reference evapotranspiration (ETo), and soil water content (SWC). We used principal component regression to evaluate the effect of micrometeorological variability on tree growth. Mean tree growth across species was responsive to variations in almost all the micrometeorological variables examined, with the exception of mean and minimum temperature, maximum RH, and minimum VPD. Mean tree growth across species increased with increasing precipitation, RHmean, RHmin and SWC, while it decreased with increasing PAR, Tmax, and ETo. It was also shown that an increase in VPDmean and VPDmax has a negative effect on tree growth. These results contribute to improve our understanding of effect of climate variability on tree growth, and shed light on the potential effect of severe droughts in the central Amazon.

Using spectral indices as early warning signals of forest dieback: The case of drought-prone Pinus pinaster forests

Forest dieback processes linked to drought are expected to increase due to climate warming. Remotely sensed data offer several advantages over common field monitoring methods such as the ability to observe large areas on a systematic basis and monitoring their changes, making them increasingly used to assess changes in forest health. Here we aim to use a combined approximation of fieldwork and remote sensing to explore possible links between forest dieback and land surface phenological and trend variables derived from long Landsat time series. Forest dieback was evaluated in the field over 31 plots in a Mediterranean, xeric Pinus pinaster forest. Landsat 31-year time series of three greenness (EVI, NDVI, SAVI) and two wetness spectral indices (NMDI and TCW) were derived covering the period 1990–2020. Spectral indices from time series were decomposed into trend and seasonality using a Bayesian estimator while the relationships of the phenological and trend variables among levels of damage were assessed using linear and additive mixed models. We have not found any statistical pieces of evidence of extension or shortening patterns for the length of the phenological season over the examined 31-year period. Our results indicate that the dieback process was mainly related to the trend component of the spectral indices series whereas the phenological metrics were not related to forest dieback. We also found that plots with more dying or damaged trees displayed lower spectral indices trends after a severe drought event in the middle of the 1990s, which confirms the Landsat-derived spectral indices as indicators of early-warning signals. Drops in trends occurred earlier for wetness indices rather than for greenness indices which suggests that the former could be more appropriate for dieback detection, i.e. they could be used as early warning signals of impending loss of tree vigor.

GRACE-based high-resolution propagation threshold from meteorological to groundwater drought

Groundwater drought could cause tremendous damage to the social-economy via land subsidence, seawater intrusion and permanent loss of aquifer storage capacity, and often show strong association with meteorological drought. To date, the threshold for meteorological drought triggering groundwater drought and its dominant factors have been not clarified, which inhibits the effective groundwater drought risk management based on preceding meteorological drought information. In this study, we used the Standardized precipitation index (SPI) and the drought severity index of groundwater storage anomalies (GWSA-DSI) to characterize meteorological and groundwater droughts in the Xijiang River Basin (XRB) of China, respectively. A probabilistic framework is proposed to identify the high-resolution propagation thresholds from meteorological to groundwater drought on 0.25° grid. Results show that GWSA-DSI can reliably identify groundwater drought events, and the propagation time from meteorological to groundwater drought ranges from 8 to 42 months. Although the XRB is located in a humid region with abundant precipitation, the probability of groundwater drought occurrence reached 43.8%, 54.8%, 61.2%, and 64.2% under a light, moderate, severe and extreme meteorological drought event, respectively. The propagation threshold triggering light groundwater drought is mainly dominated by moderate and severe meteorological droughts, which showed an increasing trend from central to southeast of XRB. Soil evaporation and watershed elevation are the main influencing factors on the propagation threshold. It is worth noting that anthropogenic overexploitation of groundwater not only destroy the dynamic balance of regional groundwater system, but also interfere with the propagation processes of meteorological to groundwater drought. The results have great implications for more reliably monitoring and predicting the dynamics of groundwater systems under drought stress, and our proposed framework can also be extended to other regions.

Development and Verification of the Available Number of Water Intake Days in Ungauged Local Water Source Using the SWAT Model and Flow Recession Curves

Climate change significantly affects water supply availability due to changes in the magnitude and seasonality of runoff and severe drought events. In the case of Korea, despite a high water supply ratio, more populations have continued to suffer from restricted regional water supplies. Though Korea enacted the Long-Term Comprehensive Water Resources Plan, a field survey revealed that the regional government organizations limitedly utilized their drought-related data. These limitations present a need for a system that provides a more intuitive drought review, enabling a more prompt response. Thus, this study presents a rating curve for the available number of water intake days per flow, and reviews and calibrates the Soil and Water Assessment Tool (SWAT) model mediators, and found that the coefficient of determination, Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS) from 2007 to 2011 were at 0.92%, 0.84%, and 7.2%, respectively, which were “very good” levels. The flow recession curve was proposed after calculating the daily long-term flow and extracted the flow recession trends during days without precipitation. In addition, the SWAT model’s flow data enables the quantitative evaluations of the number of available water intake days without precipitation because of the high hit rate when comparing the available number of water intake days with the limited water supply period near the study watershed. Thus, this study can improve drought response and water resource management plans.

Impacts of regional climate change on hydrological drought characteristics in headwaters of the Ore Mountains

AbstractChanges in the frequency, magnitude, and seasonality of hydrological extremes are one of the expected consequences of climate change. Recent years suggested an increasing risk of drought occurrence and restricted water supply even in usually humid mountain regions. Our research is focused on the evaluation of regional climate change and its effect on hydrological drought characteristics in headwater areas in the Ore Mountains along the Czech/German border. The main aim was to evaluate and compare streamflow regime changes and trends in long‐term hydrological and climatic time series within selected sub‐catchments of the Svatava (Zwota) River, the Rolava River, and the Načetínský Brook (Natzschung). A comparative statistical analysis for the 1967–2018 period was performed using various homogeneity and trend tests. Longer runoff time series with earlier beginnings were examined for two catchments as well. The results pointed to significant, continuous warming in the region which is accompanied by decreasing trends in snow cover depths in the colder half of the year. Drought indices based on runoff data were derived in the Indicators of Hydrologic Alterations (IHA 7.1) software environment. The seasonality of hydrological drought occurrence changed markedly after 1992, when most severe drought events started to concentrate between August and October in the study area. Increasing trends in deficit volumes were found in two catchments characterized by lower elevation. Furthermore, there was a significant decrease in average and minimum flows in spring and summer months in all catchments observed between 1967 and 2018 despite relatively stable amounts of precipitation and land cover.