Hydrological Drought Research Articles

A grid-wise approach for accurate computation of Standardized Runoff Index (SRI)

The Standardized Runoff Index (SRI) is a major indicator for evaluating hydrological drought conditions, accomplished by comparing the current runoff data with retrospective runoff conditions of an area for the same period. This hydrological drought indicator facilitates the characterisation of runoff variations across diverse regions. This study introduces a refined methodology for accurate computation of SRI by employing a grid-wise approach. Distinct probability distributions were fitted to each grid within the study area, diverging from the conventional practice of using a single probability distribution for the entire basin or sub-basin. The research endeavours to assess the efficacy of the grid-wise approach in improving the representation of drought characteristics when compared to the traditional areal approach. A comparative analysis between the performances of SRI computed through grid-wise fitting (where the probability distribution dynamically adapts to each grid) and the areal fitting approach (employing a uniform distribution across all grids) was conducted within the Godavari Basin, India. The findings in this study underscore that the misrepresentation of extreme events is inevitable for large heterogeneous basins like Godavari when the traditional areal approach was employed for SRI computation. Consequently, the grid-wise fitting emerges as a more accurate method for computing the SRI, particularly in characterising extreme dry or wet events.

GNSS Geodesy Quantifies Water‐Storage Gains and Drought Improvements in California Spurred by Atmospheric Rivers

AbstractAtmospheric rivers (ARs) deliver significant and essential precipitation to the western United States (US) with consequential interannual variability. The intensity and frequency of ARs strongly influence reservoir levels, mountain snowpack, and groundwater recharge, which are key drivers of water‐resource availability and natural hazards. Between October 2022 and April 2023, western states experienced exceptionally heavy precipitation from several families of powerful ARs. Using observations of surface‐loading deformation from Global Navigation Satellite Systems, we find that terrestrial water‐storage gains exceeded 100% of normal within vital California watersheds. Independent water‐storage solutions derived from different data‐analysis and inversion methods provide an important measure of precision. The sustained storage increases, which we show are closely associated with ARs at daily‐to‐weekly timescales, alleviated both meteorological and hydrological drought conditions in the region, with a lag in hydrological‐drought improvements. Quantifying water‐storage recovery associated with extreme precipitation after drought advances understanding of an increasingly variable hydrologic cycle.

Formative drought rate to quantify propagation from meteorological to hydrological drought

AbstractIn this study, we propose a probabilistic metric, the formative drought rate (FDR), to quantify drought propagation. It is the probability that a meteorological drought in precipitation forms a hydrological drought in streamflow. Drought events were identified based on the standardized precipitation index and streamflow drought index, respectively, at 1‐, 3‐, 6‐ and 12‐month timescales. The method was tested in three river basins in Turkey (Kucuk Menderes, Gediz and Ergene). In each river basin, meteorological stations were coupled with streamflow gauging stations to form pairs of stations depending on their distance from each other and the length of their common record periods. The FDR was calculated across all timescales for each pair of stations. It was found capable to describe the river basin‐specific spatial and temporal variability of drought propagation. As the FDR is defined in the form of probability, it is expected to be a useful metric for quantifying propagation from meteorological to hydrological drought. Thus, it carries a potential for scientific research and practice in water resources management.

A spatial downscaling method for SMAP soil moisture considering vegetation memory and spatiotemporal fusion

ABSTRACT Soil moisture (SM) plays an essential role in the hydrological cycle, drought monitoring, and water resources management. However, passive microwave remote sensing products offer coarse spatial resolutions (approximately 25–40 km), greatly limiting their applications. In this study, we considered vegetation memory and increased the amount of vegetation data by spatiotemporal fusion, integrated lagged vegetation index and fusion data as model inputs, and downscaled the spatial resolution of SMAP SM using the Random Forest (RF) model as well as multi-source remote sensing data. The downscaled SM was validated with 30 in situ SM measurements and daily precipitation data and explored the contributions of NDVIlagged and NDVIfused in downscaling. The results show that the downscaled SM is highly consistent with the in situ SM measurements. The correlation ranges from 0.628 to 0.849 with an average of 0.689. The root mean square error (RMSE) ranges from 0.024 to 0.094 m3/m3 with an average of 0.057 m3/m3. The proposed method leverages the strengths of the RF model, fusion vegetation data, and lagged vegetation data to improve high-resolution SM accuracy.

Soil Moisture Information Improves Drought Risk Protection Provided by the USDA Livestock Forage Disaster Program

Abstract The USDA Livestock Forage Disaster Program (LFP) offers financial assistance to farmers and ranchers with grazed forage losses caused by fire or drought. Payments for drought losses are based on the U.S. Drought Monitor (USDM), which is designed to integrate meteorological, agricultural, hydrological, ecological, and socioeconomic droughts. Because soil moisture deficit is a more specific measure of agricultural drought, we hypothesized that basing LFP payments on soil moisture observations could better reduce producers’ risk. Therefore, our objectives were to 1) quantify relationships of forage yield with USDM-based LFP payment multipliers and with in situ soil moisture, 2) develop an alternative LFP payment multiplier structure based on in situ soil moisture, and 3) quantify risk reduction using the current and alternative payment structures. We focused on Oklahoma, United States, which has led the nation in LFP payments received and has >25 years of in situ soil moisture observations statewide. Using non-alfalfa hay yield as a surrogate for forage production, we found that LFP payment multiplier values and soil moisture anomaly were each related to yield, and soil moisture anomaly explained 54% of yield variability. However, the USDM-based LFP payment structure sometimes resulted in payments for above-average yield, and higher payments did not always correspond with greater yield losses. We developed an alternative soil-moisture-based payment structure that reduced financial risk by >20% compared with the current USDM-based structure. Our study identifies an improved LFP payment structure for Oklahoma that can be evaluated and refined in other states or nationwide using other soil moisture data sources.

Prediction of hydrological drought by the Standardized Precipitation Evapotranspiration Index in Chihuahua, Mexico, using machine learning algorithms

Despite being very common in the territory of Chihuahua, Chihuahua, Mexico, to experience drought, its consequences continue to severely impact the population without prior warning. Machine learning has proven to have a significant capacity for predicting time series, and the Standardized Precipitation Evapotranspiration Index (SPEI) is emerging as the most accurate drought indicator. In this study, predictive models were developed using Artificial Neural Networks (ANN), Long-Short Term Memory (LSTM), and Support Vector Regression (SVR) for estimating SPEI. Temporal scales of 12 months (SPEI 12) and 24 months (SPEI 24) for the period 1901-2020 in the mentioned territory were considered. This was done in order to simulate the behavior of drought cycles and enhance the ability to anticipate consequences. The accuracy indices used to evaluate the models were the mean squared error (MSE), mean absolute error (MAE), mean bias error (MBE), coefficient of determination (R2), and Kendall coefficient. In total, 956 experiments were conducted using the three methods, varying parameters such as the number of neurons, kernel, and polynomial degree. The two best models for each method were selected, and the average results revealed MSE = 0.0051, MAE = 0.0537, MBE = 0.0218, R2 = 0.8495, and Kendall coefficient = 0.7592 for SPEI 12; and MSE = 0.0024, MAE = 0.0375, MBE = 0.0162, R2 = 0.9218, and Kendall coefficient = 0.8558 for SPEI 24.

Clash of Drought Narratives: A Study on the Role of Small Reservoirs in the Emergence of Drought Impacts

AbstractIn regions characterized by a high concentration of small reservoirs, there is often public debate about the effectiveness of these structures in locally adapting to and mitigating drought impacts, bearing in mind their potential to modify or induce drought events in downstream areas. In this study, we investigated the influence of a Dense Network of Small Reservoirs (DNR) on the emergence and intensification of drought impacts at catchment scale, as well as their local social benefits. This analysis was based on the Socio‐Hydrological‐Agricultural‐Reservoir (SHARE) model, specially developed for this purpose, with a medium‐sized catchment in the semi‐arid region of Brazil as a case study. We identified that, while a DNR can prolong the effects of a hydrological drought on storage in a large strategic reservoir at the catchment outlet by obstructing surface‐runoff connectivity, it plays a crucial role in mitigating drought impacts at a local level. Specifically, the presence of small reservoirs has the potential to boost local agricultural production by up to 5 times compared to scenarios without these structures. In addition, our simulation results suggest there is a notable reduction in the need for emergency water distribution by water trucks in the presence of a DNR. This study highlights the need for a balanced approach to implementing public policies, weighing the local benefits of small reservoirs against the possible downstream impacts on large reservoirs.

Drought and wetness events encounter and cascade effect in the Yangtze River and Yellow River Basin

Under the background of global change, the multivariate attributes, multi-temporal and multi-watershed regional characteristics of extreme drought and flood disasters are significant. It is very important to identify the drought and wetness encounter law and spatial cascade effect in the source region-upper-middle-lower reaches of the basin. In this paper, the Yangtze River and Yellow River basins are taken as the research objects. Based on the standardized precipitation index (SPI) characterizing meteorological drought and wetness and the standardized runoff index (SRI) characterizing hydrological drought and wetness, the evolution law of extreme drought and flood events in the source region-upper-middle-lower reaches of the two basins is analyzed. On this basis, the correlation analysis and transfer function analysis are used to explore the response relationship and lag effect between the two in different partitions. A two-dimensional joint distribution model is constructed by Copula function to quantitatively analyze the joint probability of drought and wetness encounters of 9 combined events, and to deeply analyze the impact of different drought and wetness encounter scenarios. The results show that: from 1957 to 2020, the two basins showed a trend from drought to wet. The SPI-12 in the Yellow River Basin ranged from-1.57 to 1.32, and that in the Yangtze River Basin ranged from-1.24 to 1.11. The severity of drought and wetness in the Yellow River Basin was higher than that in the Yangtze River Basin; The correlation coefficients between SPI and SRI in the source-upper-middle-lower reaches of the Yellow River (Yangtze River) were 0.78 (0.76), 0.23 (0.73), 0.44 (0.55) and 0.23 (0.63), respectively. The correlation degree of meteorological-hydrological drought and wetness in the Yangtze River Basin was significantly higher than that in the Yellow River Basin. The average probability of synchronous combination events of drought and wetness in the two basins is greater than 51.43 %. The probability of drought in the Yellow River Basin is greater than that of flood, while the Yangtze River Basin is the opposite. In the same drought and wetness scenario, an average of 54.1% of the event pairs were aggravated by disasters, and more than 52 % of the events in the wetness-drought scenario had obvious drought mitigation effects. The research results can provide important support for improving the early warning and risk management of river basin disasters.

Local Responses to Climate Change Impact on Intangible Cultural Heritage in Poland

Tradition and intangible cultural heritage help local communities cope with the uncertainty through familiar and repetitive actions. What if it all depends on variable and unstable factors? This paper presents an anthropological approach to the impact of climate change on local practices towards intangible cultural heritage based on human-environment interactions. The author presents early results of an ongoing research project. By drawing on ethnographic data, this article explores the implications of declining snow resources, alterations in the vegetation cycle and hydrological drought on the traditions of winter horse-drawn carriage races, Corpus Christi flower carpets and wickerwork. Based on the notions of ethnoclimatology and anthropology of weather, the text draws attention to local perceptions of climate change and potential methods of safeguarding tradition in times of uncertainty. It serves as an outline of a possible way of thinking about the relationship between climate change and intangible cultural heritage.

Characterization of Drought Propagation in the Han River Basin Based on Chaos Theory and Nonlinear Dynamic System

This study characterized drought propagation in the Han River basin based on chaos theory and nonlinear dynamic systems. The nonlinear complexity of drought propagation, specifically between meteorological and hydrological droughts, was identified using convergent cross-mapping and phase-space reconstruction techniques. The analysis results on the causality of drought propagation between meteorological and hydrological droughts revealed that among the 24 middle-sized watersheds of the Han River basin, 11 watersheds had bidirectional causality, and the other 13 watersheds had one-directional causality. This study also confirmed that there were large correlations between the two drought types when 12 of the 13 watersheds with one-directional causality had a lag time of 4 months and the remaining watershed had a delay time of 7 months. The complexity of the interaction between different droughts was confirmed through adjustments of key parameters, such as the embedding dimension, time delay, correlation dimension, and Lyapunov exponent. The approach proposed in this study made it possible to analyze the nonlinear dynamic characteristics of drought propagation, which have not been analyzed in previous studies.

Hydrological Drought Risk Assessment and Its Spatial Transmission Based on the Three-Dimensional Copula Function in the Yellow River Basin

Administrative strategies to cope with drought are steadily changing, from emergency procedures to day-to-day monitoring. More consideration must be paid to long-term and preventive drought control measures in the future. This paper discusses the risk of hydrological drought in the Yellow River Basin. The standardized runoff index (SRI) was used to characterize hydrological drought, and the run theory was used to identify drought states and quantify drought characteristic variables. Based on the drought severity and duration, a drought development plan was proposed and a three-dimensional copula function was constructed to obtain the joint distribution function of three-dimensional drought characteristic variables. A drought risk assessment system based on the loss × probability risk theory was constructed to explore the spatial and temporal characteristics of hydrological drought risk in the Yellow River Basin. Finally, according to the risk assessment results, the risk level was divided into low, medium and high risk, and a Bayesian network was used to explore the probability of hydrological drought. The main results are as follows: (1) From 1960 to 2018, the severity of hydrological drought in the Yellow River Basin increased, the duration lengthened, and the development speed accelerated. (2) The hydrological drought risk in the Yellow River Basin showed an overall upward trend, with the fastest increase in the HJ region of 0.041/10a. The highest annual average drought risk in the TDG region is 0.598. (3) The spatial transmission of hydrological drought risk is divided into three types: constant, enhanced and mitigation types, of which the constant type is the most common. The transmission probabilities of low, medium and high risk of hydrological drought from the HYK region to the low, medium and high risk of hydrological drought in the LJ region are 0.68, 0.66 and 0.78, respectively.

Comparative evaluation of meteorological and hydrological drought using stationary and non-stationary indices in a semi-arid river basin in India

Comparative evaluation of meteorological and hydrological drought using stationary and non-stationary indices in a semi-arid river basin in India

Features of underground flow formation to the Golovesnya river (Desna basin) during the dry period 2007-2021

Purpose. To analyze the peculiarities of the dynamics of underground flow on the catchment of the Golovesnya River, a right-bank tributary of the Desna River, during the dry period of 2007-2021 in comparison with the period of 1960-1985 - before the beginning of noticeable climatic changes. Methods. The hydrodynamic finite-difference method of calculating the specific consumption of groundwater based on the data of routine observations of the levels of groundwater and surface water, the hydrodynamic method of calculating the components of the groundwater balance. Results. The specific flow of groundwater to the Golovesnya River was calculated based on the data of observations for 2007-2021; the share of groundwater flow in the total flow of the river was determined, changes in the dynamics of infiltration nutrition and the inflow of groundwater to the river were revealed, which can be associated with the sign of the charge of the static electric field of the surface layer of the atmosphere, an increase in air temperature, and an increase in the volume of moisture retention in the aeration zone after reducing GWT. Underground flow to the river largely compensates for the losses and stabilizes the river flow in low-water years and during prolonged hydrological drought. Important factors in the regulation of the groundwater regime in low-water periods (in addition to precipitation) are the level and flow of water in the river, as well as the loss of moisture due to the saturation of the aeration zone, and therefore, the lithological and granulometric composition of this zone. Losses in the aeration zone are also an important regulatory factor of balance and river flow in the scale of the Desna River catchment. The largest volumes of underground runoff to the Golovesnya River for 2007-2021 were set for the multi-water year 2016, the smallest - for 2021. By 2016, the chronological graphs show clearer seasonal patterns: repeatability of underground flow fluctuations in the spring and autumn seasons; opposite flow fluctuations in winter and spring and a relatively stable summer flow; typical for autumn are maximum, and for summer always minimum values ​​of reserves and infiltration recharge of groundwater. In the abundant water year of 2016, infiltration recharge and groundwater reserves during autumn and winter sharply decrease and acquire negative values ​​due to a significant decrease in GWT in the previous period, an unfavorable course of temperature and precipitation distribution, and increased outflow to the river. In 2017-2021, the seasonal differences in the recharge of the river with groundwater are almost equalized: the role of recharge increases during the summer period, and decreases during the spring and autumn seasons. The role of the condensation mechanism of moisture transfer in the aeration zone is increasing. During the summer, the lateral inflow of groundwater to the river was the highest in 2016-2018. Conclusions. Calculations and analysis of the dynamics of infiltration feeding and groundwater flow to the Golovesnya River have been performed. Changes in groundwater supply and discharge, especially in the period after 2015, have been identified, which can be interpreted as the consequences of adverse weather and climate conditions in 2014-2015. In 2018-2021, there was a decrease in groundwater and surface water resources, which corresponds to signs of hydrological drought.

Evaluation of the impact of anthropogenic storage on the hydrological drought propagation in two contrasting semi-arid river basins

ABSTRACT This study quantitatively investigated reservoir-effects on drought propagation in two semi-arid river basins: India’s Tapi basin with the Ukai reservoir and Uzbekistan’s Chirchik basin with the Charvak reservoir. Meteorological drought (MD) is analyzed using the Standardized Precipitation Index (SPI) and hydrological drought (HD) using the Standardized Streamflow Index (SSI) for the duration from 1980 to 2004. Both the river basins, especially in upstream reservoir areas, exhibited a notable correlation between HD and MD. Reservoir operation was observed to reduce the downstream MD–HD correlation at shorter SPI timescales. Hit-score-based evaluations indicated that reservoir operation has induced changes in the drought propagation patterns for both river basins. Due to the contrasting characteristics, the river basins showed a significant variation in the drought propagation time (DPT), with distinct influences from monsoon (Tapi) and snow-melting (Chirchik). The average DPT (average DPT over 12 months) for the reservoir-influenced part of the Tapi (∼ six months) and Chirchik (∼ nine months) basins is higher than that of the natural parts of both basins (Tapi: ∼ four months; Chirchik: ∼ six months) as a result of the natural and anthropogenic storage influence.

Interactive Changes in Climatic and Hydrological Droughts, Water Quality, and Land Use/Cover of Tajan Watershed, Northern Iran

In response to novel and complex uncertainties, the present research is conducted to characterize the most significant indicators of watershed health including drought, water quality, and vegetation for the Tajan watershed, Mazandaran, Iran. The Standardized Precipitation Index (SPI) and Streamflow Drought Index (SDI) are, respectively, used to quantify the meteorological and hydrological droughts in the present (1993–2020) and future (2023–2050) employing optimistic RCP2.6 and pessimistic RCP8.5 scenarios. To concoct discharge data for the future, IHACRES v1.0 software is used with a Nash–Sutcliffe coefficient (NSE) of 0.48 and a coefficient of determination (R2) of 0.58. Maps of land use and Normalized Difference Vegetation Index (NDVI) are also prepared using Landsat images. Subsequently, the surface water quality is assessed using AqQA v1.1.0 software. The results show the difference in the severity of future meteorological droughts in different stations. In addition, the predominance of non-drought (SDI ≥ 0) or mild drought (−1 ≤ SDI < 0) is indicated for future hydrology. The land use changes show a decrease in rangeland (−5.47%) and an increase in residential land (9.17%). The water quality analysis also indicates an increase in carbonate ions in the watershed outlet. Communicating the relationships between study indicators, which is a big gap in the current watershed management approach, avoids future failures and catastrophes.

Streamflow response to drought in a managed coast redwood catchment

AbstractA 60‐year precipitation and streamflow record from the Caspar Creek Experimental Watersheds in northern California was used to explore the propagation of meteorological drought to hydrological drought. Standardized precipitation and runoff indices were calculated for the two forested catchments using integration periods of 12, 24, and 36 months. The resulting time series were used to define three severe drought events (1976–1977, 2013–2014, and 2020–2022). The earliest drought followed the 1971–1973 harvest of the 417 ha South Fork (SF) watershed, a second followed the 1989–1992 harvest of the 479 ha North Fork watershed, and a third followed the 2017–2019 reentry harvest of the SF. From these time series, we calculated drought metrics and anomalies to model differences in catchment responses in the context of climate and management. The meteorological drought in the 1977 event was more severe and extreme than the streamflow response. Both of the 21st Century droughts were hydrologically more severe than the 1977 drought. Timber harvest initially shortened and reduced streamflow drought (1977 and 2021) but prolonged and intensified the 2014 streamflow drought. Declining fall precipitation has reduced streamflows, thereby impeding salmonid migration and exacerbating impacts on native fish. Our results provide new insights into the role of climate variation, particularly long‐term and seasonal drought dynamics, in managed forests along the North American Pacific coast.

Impacts of hydrometeorological regime shifts on drought Propagation: The meteorological to hydrological perspective

Exploring drought propagation relationships and thresholds is crucial for drought control and mitigation. However, previous studies have overlooked the impacts of regime shifts in trends of hydrometeorological variables over time on drought propagation and its thresholds. This study proposes an integrated framework focusing on drought propagation from meteorological to hydrological drought. The regime shift periods, both increasing and decreasing trends, were identified using the cumulative difference curve-rank test on precipitation and streamflow at an annual time scale. The Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) were used as meteorological and hydrological drought indices, respectively. Additionally, the propagation relationships and thresholds from meteorological drought to hydrological drought were identified and compared across various regime shift periods. The integrated framework’s applicability was verified by applying it to the Three-Rivers Headwaters region of China, which encompasses the Yellow River, Yangtze River, and Lancang River. This region is strategically important for China and has relatively little human activity. The framework revealed the variability in drought propagation and thresholds during different regime shift periods. The response sensitivity of SSI to SPI and the probabilities and thresholds for propagation from meteorological to hydrological drought differed between periods of increasing and decreasing trends in hydrometeorological variables. When regime shift characteristics were considered, the thresholds for meteorological drought propagating to hydrological drought were 2.81 %–16.90 % higher. These results indicated that, when examining drought propagation relationships and thresholds, accounting for regime shifts in hydrometeorological variables can improve early drought warning and enhance drought control and mitigation efforts.

Compound droughts under climate change in Switzerland

Abstract. The co-occurrence of meteorological, agricultural, and hydrological droughts (multivariate compound droughts) in Switzerland during growing season is problematic due to limitations in water abstractions from rivers during low-flow periods, while at the same time the need for irrigation is high. We analyse compound droughts for 52 catchments in Switzerland during the extended summer season (May–October) using the transient climate and hydrological scenarios for Switzerland (CH2018 and Hydro-CH2018) for both a scenario with mitigation (representative concentration pathway 2.6 (RCP2.6), 8 model chains) and a scenario without mitigation (RCP8.5, 20 model chains). In the RCP8.5 scenario the number of compound drought days is projected to significantly increase by mid-century across all greater regions of Switzerland. The increased frequency is mainly a result of more frequent events (significant) rather than longer event durations (non-significant). Models generally agree on the sign of change. By 2085, compound drought events are projected to occur in median once per catchment per extended summer season north of the Alps and every 1–2 years south of the Alps. Further, the increases in compound drought days mainly occur between May–October, leading to a shift in the main agricultural production season and a more pronounced seasonality with the highest occurrence probabilities between mid-July and the beginning of October. Coupled to the increase in days and events, significantly more catchments are projected to be affected by compound droughts at the same time. In the RCP2.6 (mitigation) scenario, the increase in the number of compound drought days and events is not significant by the end of the 21st century. In comparison with RCP8.5, the number of compound drought days is reduced by 50 %–55 % north of the Alps and by up to 75 % south of the Alps by the end of the century. This emphasizes the need for coordinated adaptation in combination with mitigation measures taken at an early stage.

Comprehensive evaluation of extreme hydrometeorological events coincidence and their interrelationships in the Hanjiang River Basin, China

Extreme hydrometeorological events are often highly destructive and can cause huge damage to the natural environment and social economy. As climate change and human activities intensify, the frequency and intensity of such extreme events continue to increase. This phenomenon is also shown in the Hanjiang River Basin (HRB) in recent decades. In this study, we focus on the coincidence occurrence patterns of extreme hydrometeorological events. First, we used the standardized index method to extract the annual maximum and minimum values of runoff, precipitation, maximum and minimum temperatures from 1961 to 2019 in the control area of the Baihe (BH) hydrological station, with time scales from 1 month to 12 months. Then through the Copula and nested structure Copula functions, the joint occurrence probability of bivariate and multivariate were analyzed. The results show that too high a maximum temperature will inhibit runoff, while an increase in minimum temperature will promote an increase in runoff to a certain extent. Both the maximum and minimum temperatures in the basin shown a significant increasing trend, and the intensity of hydrological drought also shows an increasing trend. An increase in maximum temperature will aggravate hydrological drought, as the combined joint occurrence probability of them is the greatest. Analysis through the nested structure Copula functions show that when extreme hydrometeorological events both occurred at the median of their intervals, their coincidence occurrence probability is the maximum, and when the time scale is eight months, their coincidence occurrence probability is the highest. This study can provide a reference for joint probability analysis of extreme hydrometeorological events.

A novel GNSS and precipitation-based integrated drought characterization framework incorporating both meteorological and hydrological indicators

The Global Navigation Satellite System (GNSS) has become instrumental in developing drought indices, particularly meteorological drought indicators derived from atmospheric precipitable water vapor and hydrological drought indicators based on inverted terrestrial water storage changes. However, these indices traditionally focus on individual aspects of droughts, either meteorological or hydrological droughts, and do not fully capture the integrated nature of drought phenomena. Addressing this gap, this study proposes a novel integrated drought characterization framework using the Gringorten plotting position to derive joint probabilities for GNSS-derived meteorological and hydrological drought indicators. This leads to the creation of a comprehensive multivariate drought severity index (GNSS-MDSI). The analysis across the western United States indicates significant spatial variability in multiyear average precipitation efficiency, ranging from 7.51% to 28.1%. This variability corresponds with marked differences in seasonal terrestrial water storage changes, which oscillate between 25 and 123 mm. Applying this framework in eight states, 9–13 comprehensive drought events from January 2006 to December 2021, with durations spanning from 3 to 54 months, were identified. The GNSS-MDSI not only captured these comprehensive drought periods across various temporal and spatial scales but also aligned closely with drought classifications provided by the US Drought Monitor. These results underscore the utility of this framework in providing a more nuanced and multifaceted perspective on drought conditions, surpassing the capabilities of single-indicator systems. Overall, this study presents an innovative framework for drought monitoring by integrating two GNSS-derived drought indicators, enabling precise and comprehensive delineation of drought characteristics, and offering a geodesy-based solution for integrated global and regional drought monitoring.