Palmer Hydrological Drought Index Research Articles

Roadway floods and their associated weather-related conditions: New insights using CARS 511 data for state and federal highways in Nebraska, USA

This study examined roadway flooding event data provided by the Nebraska Department of Transportation via the Condition Acquisition Reporting System (CARS) 511 archive (2016–2021). With these data, a novel analysis was completed to further the understanding of where, when, and why (meteorologically) roadway flooding occurs on Nebraska state and federal highways. In the study period, 298 roadway floods occurred, with an annual median of 16 per year. There was a greater risk for roadway floods to transpire more frequently in eastern Nebraska, which was attributed to the spatial climatology of heavy rainfall and the higher density of roadways and rivers. Consequently, this increases the exposure and vulnerability of the highway system across the eastern portion of the state. Further, precursor soil moisture and river conditions proved to be critical initiators of roadway floods, as precipitation totals prior to each flood only averaged at 40 mm, while precursor Palmer Hydrological Drought Index values for 75% of events were above 4.0 and river levels were 200 cm above its daily medians. Overall, the complexities of these roadway flooding events are challenging for proactively forecasting as opposed to reacting to the situation. With the top roadway flooding locations identified along with the weather-related causes, further investigation of roadway flooding hotspots across the state needs to take place for mitigation and resiliency in a changing climate. The methods of this research can be used across other states and by other agencies to analyze and increase the resilience of their respective roadway networks.

The Combined Effect of Atmospheric and Solar Activity Forcings on the Hydroclimate in Southeastern Europe

The purpose of this study was to analyze the influence of solar activity described by the sunspot number (SSN) on certain terrestrial variables that might impact the Southeastern European climate at different spatio-temporal scales (the North Atlantic Oscillation Index, NAOI, and the Greenland–Balkan Oscillation Index, GBOI—on a large scale; the Palmer Hydrological Drought Index, PHDI—on a regional scale; the Danube discharge at the Orsova (lower basin), Q, representative of the Southeastern European climate—on a local scale). The investigations were carried out for the 20th century using the annual and seasonal averages. To find the connections between terrestrial (atmospheric and hydrological) parameters and SSN, the wavelet coherence were used both globally and in the time–frequency domain. The analyses were carried out for the time series and considered simultaneously (in the same year or season), as well as with lags from 1 to 5 years between the analyzed variables. For the annual values, the type of correlation (linear/non-linear) was also tested using elements from information theory. The results clearly revealed non-linear links between the SSN and the terrestrial variables, even for the annual average values. By applying the wavelet transform to test the solar influence on the terrestrial variables, it was shown that the connections depend on both the terrestrial variable, as well as on the considered lags. Since, in the present study, they were analyzed using wavelet coherence, but only the cases in which the coherence was significant for almost the entire analyzed time interval (1901–2000) and the terrestrial variables were in phase or antiphase with the SSN were considered. Relatively few results had a high level of significance. The analysis of seasonal averages revealed significant information, in addition to the analysis of annual averages. Thus, for the climatic indices, the GBOI and NAOI, a significant coherence (>95%) with the solar activity, associated with the 22-year (Hale) solar cycle, was found for the autumn season for lag = 0 and 1 year. The Hale solar cycle, in the case of the PHDI, was present in the annual and summer season averages, more clearly at lag = 0. For the Danube discharge at Orsova, the most significant SSN signature (~95%) was observed at periods of 33 years (Brüuckner cycle) in the autumn season for lags from 0 to 3 years. An analysis of the redundancy–synergy index was also carried out on the combination of the terrestrial variables with the solar variable in order to find the best synergistic combination for estimating the Danube discharge in the lower basin. The results differed depending on the timescale and the solar activity. For the average annual values, the most significant synergistic index was obtained for the combination of the GBOI, PHDI, and SSN, considered 3 years before Q.

Discriminant Analysis of the Solar Input on the Danube’s Discharge in the Lower Basin

This paper presents the extent to which the combination of extra-atmospheric and hydroclimatic factors can be deciphered to record their contribution to the evolution and forecasting of the Danube discharge (Q) in the lower basin. A combination of methods such as wavelet filtering and deep learning (DL) constitutes the basic method for discriminating the external factors (solar activity through Wolf numbers) that significantly contribute to the evolution and prediction of the lower Danube discharge. An ensemble of some of the most important factors, namely, those representing the atmospheric components, i.e., the Greenland-Balkan Oscillation Index (GBOI) and the North Atlantic Oscillation Index (NAOI); the hydroclimatic indicator, the Palmer Hydrological Drought Index (PHDI); and the extra-atmospheric factor, constitutes the set of predictors by means of which the predictand, Q, in the summer season, is estimated. The external factor has to be discriminated in the Schwabe and Hale spectra to make its convolutional contribution to the Q estimation in the lower Danube basin. An interesting finding is that adding two solar predictors (associated with the Schwabe and Hale cycles) to the terrestrial ones give a better estimation of the Danube discharge in summer, compared to using only terrestrial predictors. Based on the Nash–Sutcliffe (NS) index, a measure of performance given by the extreme learning machine (ELM), it is shown that, in association with certain terrestrial predictors, the contribution of the Hale cycle is more significant than the contribution of the Schwabe cycle to the estimation of the Danube discharge in the lower basin.

Developing Impacts-Based Drought Thresholds for Ohio

Abstract Drought monitoring is critical for managing agriculture and water resources and for triggering state emergency response plans and hazard mitigation activities. Fixed thresholds serve as guidelines for the U.S. Drought Monitor (USDM). However, fixed drought thresholds (i.e., using the same threshold in all seasons and climate regions) may not properly reflect local conditions and impacts. Therefore, this study develops impacts-based drought thresholds that are appropriate for drought monitoring in Ohio. We examined four drought indices that are currently used by the state of Ohio: standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), Palmer’s Z index, and Palmer hydrological drought index (PHDI). Streamflow and corn yield are used as indicators of hydrological and agricultural drought impacts, respectively. Our results show that fixed thresholds tend to indicate milder drought conditions in Ohio, while the proposed impacts-based drought thresholds are more sensitive to exceptional drought (D4) conditions. The area percentage of D4 based on impacts-based drought thresholds is more strongly correlated with corn yield and streamflow. This study provides a methodology for developing local impacts-based drought thresholds that can be applied to other regions where long-term drought impact records exist to provide regionally representative depictions of conditions and improve drought monitoring.

Drought affects sex ratio and growth of painted turtles in a long-term study in Nebraska

Climate forecasts suggest the Great Plains of North America have increased risk of droughts during global warming. Environmental factors have potential to influence turtle populations in aquatic habitats through temperature-dependent sex determination and influences on food availability. Long-term studies are critical to evaluate the influence of climatic variation on turtles. We used a 12-year set of mark-recapture data collected from painted turtles (Chrysemys picta, n = 162) in a pond in Keith County, Nebraska during 2005–2016 to assess variation in sex ratio and growth dynamics. Southwest Nebraska experienced two periods of drought during our study (Palmer Hydrologic Drought Index [PHDI] range: -4.5 to 6.7). Despite a relatively stable depth of water in our study pond, the proportion of males in the second size class (carapace length 95–130 mm) decreased when the PHDI during their incubation period indicated hotter, drier conditions. Discrete, mean annual growth (G) of females >30 mm below asymptotic carapace length was greater during wetter years (Gnon-drought = 15.0, Gdrought = 11.5), and a drought coefficient (D) in our modified von Bertalanffy model reflected reduced growth of both males (D = -0.0226) and females (D = -0.0393) during drought years. Our long-term research provides context to the complexity by which turtle species may respond to changes in long-term climate conditions.

Selection of Optimal Palmer Predictors for Increasing the Predictability of the Danube Discharge: New Findings Based on Information Theory and Partial Wavelet Coherence Analysis

The purpose of this study was to obtain synergistic information and details in the time–frequency domain of the relationships between the Palmer drought indices in the upper and middle Danube River basin and the discharge (Q) in the lower basin. Four indices were considered: the Palmer drought severity index (PDSI), Palmer hydrological drought index (PHDI), weighted PDSI (WPLM) and Palmer Z-index (ZIND). These indices were quantified through the first principal component (PC1) analysis of empirical orthogonal function (EOF) decomposition, which was obtained from hydro-meteorological parameters at 15 stations located along the Danube River basin. The influences of these indices on the Danube discharge were tested, both simultaneously and with certain lags, via linear and nonlinear methods applying the elements of information theory. Linear connections were generally obtained for synchronous links in the same season, and nonlinear ones for the predictors considered with certain lags (in advance) compared to the discharge predictand. The redundancy–synergy index was also considered to eliminate redundant predictors. Few cases were obtained in which all four predictors could be considered together to establish a significant information base for the discharge evolution. In the fall season, nonstationarity was tested through wavelet analysis applied for the multivariate case, using partial wavelet coherence (pwc). The results differed, depending on the predictor kept in pwc, and on those excluded.

Genetic and functional variation across regional and local scales is associated with climate in a foundational prairie grass.

Global change forecasts in ecosystems require knowledge of within-species diversity, particularly of dominant species within communities. We assessed site-level diversity and capacity for adaptation in Bouteloua gracilis, the dominant species in the Central US shortgrass steppe biome. We quantified genetic diversity from 17 sites across regional scales, north to south from New Mexico to South Dakota, and local scales in northern Colorado. We also quantified phenotype and plasticity within and among sites and determined the extent to which phenotypic diversity in B.gracilis was correlated with climate. Genome sequencing indicated pronounced population structure at the regional scale, and local differences indicated that gene flow and/or dispersal may also be limited. Within a common environment, we found evidence of genetic divergence in biomass-related phenotypes, plasticity, and phenotypic variance, indicating functional divergence and different adaptive potential. Phenotypes were differentiated according to climate, chiefly median Palmer Hydrological Drought Index and other aridity metrics. Our results indicate conclusive differences in genetic variation, phenotype, and plasticity in this species and suggest a mechanism explaining variation in shortgrass steppe community responses to global change. This analysis of B. gracilis intraspecific diversity across spatial scales will improve conservation and management of the shortgrass steppe ecosystem in the future.

Causal factors for pesticide trends in streams of the United States: Atrazine and deethylatrazine.

Pesticides are important for agriculture in the United States, and atrazine is one of the most widely used and widely detected pesticides in surface water. A better understanding of the mechanisms by which atrazine and its degradation product, deethylatrazine, increase and decrease in surface waters can help inform future decisions for water quality improvement. This study considers causal factors for trends in pesticide concentration in U.S. streams and models the causal factors, other than use, in structural equation models. The structural equation models use a concomitant trend in corn (Zea mays L.) and a latent variable model, indicating moisture supply and management. The moisture supply and management latent variable model incorporates long-term moisture conditions in the individual watersheds by using the Palmer hydrologic drought index, human influence on the hydrologic cycle through the percentage of the watershed drained by tile drains in 2012, and the base-flow contribution to streamflow, using the base-flow index. The structural equation models explain 77 and 38% of the variability in atrazine and deethylatrazine trends, respectively, across the conterminous United States. The models highlight future water quality challenges, particularly in tile-drained settings where fall precipitation and heavy precipitation are increasing.

Probabilistic long‐term hydrological drought forecast using Bayesian networks and drought propagation

AbstractEffective drought mitigation plans that can handle severe drought conditions require reliable drought forecasts. A probabilistic hydrological drought forecasting method was developed using Bayesian networks that incorporate dynamic model predictions and a drought propagation relationship. The resulting model, Bayesian networks based drought forecasting with drought propagation (BNDF_DP), was designed using current and forecast lead time drought conditions of a multi‐model ensemble. Hydrological drought conditions were represented by the Palmer Hydrological Drought Index. The ranked probability score (RPS) and receiver operating characteristic (ROC) curve analysis were employed to measure forecast proficiency. The BNDF_DP model showed good performance, with an RPS 4–50% higher than a climatological model. ROC analysis indicated that the BNDF_DP offered superior forecasting skills for long‐term drought, with a 2 and 3 month lead time, compared with a model that does not consider drought propagation. The overall results indicated that the BNDF_DP model was a promising tool for probabilistic drought forecasting that can provide water managers and decision‐makers with the flexibility to respond to undesirable drought risks, prepare drought mitigation action plans and regulate policies based on future uncertainties.

Dynamical delimitation of the Central American Dry Corridor (CADC) using drought indices and aridity values

The Central American Dry Corridor (CADC) is a sub-region in the isthmus that is relatively drier than the rest of the territory. Traditional delineations of the CADC’s boundaries start at the Pacific coast of southern Mexico, stretching south through Central America’s Pacific coast down to northwestern Costa Rica (Guanacaste province). Using drought indices (Standardized Precipitation Index, Modified Rainfall Anomaly Index, Palmer Drought Severity Index, Palmer Hydrological Drought Index, Palmer Drought Z-Index and the Reconnaissance Drought Index) along with a definition of aridity as the ratio of potential evapotranspiration (representing demand of water from the atmosphere) over precipitation (representing the supply of water), we proposed a CADC delineation that changes for normal, dry and wet years. The identification of areas that change their classification during extremely dry conditions is important because these areas may indicate the location of future expansion of aridity associated with climate change. In the same way, the delineation of the CADC during wet extremes allows the identification of locations that remain part of the CADC even during the wettest years and that may require special attention from the authorities.

The impact of drought on the productivity of two rainfed crops in Spain

Abstract. Drought events are of great importance in most Mediterranean climate regions because of the diverse and costly impacts they have in various economic sectors and on the environment. The effects of this natural hazard on rainfed crops are particularly evident. In this study the impacts of drought on two representative rainfed crops in Spain (wheat and barley) were assessed. As the agriculture sector is vulnerable to climate, it is especially important to identify the most appropriate tools for monitoring the impact of the weather on crops, and particularly the impact of drought. Drought indices are the most effective tool for that purpose. Various drought indices have been used to assess the influence of drought on crop yields in Spain, including the Standardized Precipitation Evapotranspiration Index (SPEI), the Standardized Precipitation Index (SPI), the Palmer drought indices (Palmer Drought Severity Index, PDSI; Palmer Z Index, Z Index; Palmer Hydrological Drought Index, PHDI; Palmer Modified Drought Index, PMDI), and the Standardized Palmer Drought Index (SPDI). Two sets of crop yield data at different spatial scales and temporal periods were used in the analysis. The results showed that drought indices calculated at different timescales (SPI, SPEI) most closely correlated with crop yield. The results also suggested that different patterns of yield response to drought occurred depending on the region, period of the year, and the drought timescale. The differing responses across the country were related to season and the magnitude of various climate variables.

The effects of multiyear and seasonal weather factors on incidence of Lyme disease and its vector in New York State

BackgroundMore frequent extreme weather and warmer weather due to climate change might change the spatiotemporal distributions of vector-borne diseases, including Lyme disease. However, limited studies have examined the associations of Lyme disease and its vectors with weather factors, especially multi-year and multi-weather factors related to vector life cycle. ObjectivesWe investigated the associations between multi-year, unique weather indicators (relevant to tick and host activities) and Lyme disease incidence or documented I. scapularis encounters in New York State (NYS). MethodsUsing a generalized estimating equation model, we linked Lyme disease and tick (I. scapularis) data, obtained from the NYS Department of Health (NYSDOH) Communicable Disease Surveillance and Tick Identification Service, with weather data. We used a season-specific exposure index by considering days in different seasons with certain temperature and precipitation ranges, summer Palmer Hydrological Drought Index, and fitted linear regression models using generalized estimating equations. ResultsLyme disease and I. scapularis encounters were modestly correlated (Spearman correlation = 0.60, p-value <0.001). The results indicate that summer Lyme disease cases and tick encounters may increase by 4–10%, per one day in spring with a minimum temperature range between 40 and 50 °F in the year of diagnosis and previous year. A day increase in summer with maximum temperature > 75 °F in the previous year was associated with 2% increase in summer disease counts. Mild winter days were associated with an increase in summer tick encounters. ConclusionsExtended spring and summer days and mild winter temperatures appear to increase Lyme disease cases and tick exposure risk in NYS.

The relationship between the Normalized Difference Vegetation Index and drought indices in the South Central United States

Drought indices are useful for quantifying drought severity and have shown mixed success as an indicator of drought damage and biophysical dryness. While spatial downscaling of drought indicators from the climate divisional level to the county level has been conducted successfully in previous work, little research to date has attempted to “upscale” remotely sensed biophysical indicators to match the downscaled drought indices. This upscaling is important because drought damage and indices are often reported at a coarser scale than the biophysical indicators provide. This research upscales National Oceanic and Atmospheric Administration’s Advanced Very High Resolution Radiometer sensor-acquired Normalized Difference Vegetation Index (NDVI) data to produce a county-level biophysical drought index, for a five-state region of the South Central United States. The county-level NDVI is then correlated with the downscaled drought indices for assessing the degree to which the biophysical data match well-documented drought indicators. Results suggest that the Palmer Drought Severity Index and Palmer Hydrologic Drought Index are effective indicators of biophysical drought in much of the arid western part of the study area and in larger swaths of the study area in summer. In nearly all cases except for autumn months, correlations are weakest in the ecotones, with significant negative correlations in the humid eastern part of the study area. Results generally corroborate the findings of recent research that correlations between drought indices and biophysical drought vary spatially. As long-lead climate forecasts continue to improve, these results can assist environmental planners in preparing for the impacts of drought.

Drought Sensitiveness on Forest Growth in Peninsular Spain and the Balearic Islands

Drought is one of the key natural hazards impacting net primary production and tree growth in forest ecosystems. Nonetheless, tree species show different responses to drought events, which make it difficult to adopt fixed tools for monitoring drought impacts under contrasting environmental and climatic conditions. In this study, we assess the response of forest growth and a satellite proxy of the net primary production (NPP) to drought in peninsular Spain and the Balearic Islands, a region characterized by complex climatological, topographical, and environmental characteristics. Herein, we employed three different indicators based on in situ measurements and satellite image-derived vegetation information (i.e., tree-ring width, maximum annual greenness, and an indicator of NPP). We used seven different climate drought indices to assess drought impacts on the tree variables analyzed. The selected drought indices include four versions of the Palmer Drought Severity Index (PDSI, Palmer Hydrological Drought Index (PHDI), Z-index, and Palmer Modified Drought Index (PMDI)) and three multi-scalar indices (Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI), and Standardized Precipitation Drought Index (SPDI)). Our results suggest that—irrespective of drought index and tree species—tree-ring width shows a stronger response to interannual variability of drought, compared to the greenness and the NPP. In comparison to other drought indices (e.g., PDSI), and our results demonstrate that multi-scalar drought indices (e.g., SPI, SPEI) are more advantageous in monitoring drought impacts on tree-ring growth, maximum greenness, and NPP. This finding suggests that multi-scalar indices are more appropriate for monitoring and modelling forest drought in peninsular Spain and the Balearic Islands.

Influence of climate on alpine stream chemistry and water sources

AbstractThe resilience of alpine/subalpine watersheds may be viewed as the resistance of streamflow or stream chemistry to change under varying climatic conditions, which is governed by the relative size (volume) and transit time of surface and subsurface water sources. Here, we use end‐member mixing analysis in Andrews Creek, an alpine stream in Rocky Mountain National Park, Colorado, from water year 1994 to 2015, to explore how the partitioning of water sources and associated hydrologic resilience change in response to climate. Our results indicate that four water sources are significant contributors to Andrews Creek, including snow, rain, soil water, and talus groundwater. Seasonal patterns in source‐water contributions reflected the seasonal hydrologic cycle, which is driven by the accumulation and melting of seasonal snowpack. Flushing of soil water had a large effect on stream chemistry during spring snowmelt, despite making only a small contribution to streamflow volume. Snow had a large influence on stream chemistry as well, contributing large amounts of water with low concentrations of weathering products. Interannual patterns in end‐member contributions reflected responses to drought and wet periods. Moderate and significant correlations exist between annual end‐member contributions and regional‐scale climate indices (the Palmer Drought Severity Index, the Palmer Hydrologic Drought Index, and the Modified Palmer Drought Severity Index). From water year 1994 to 2015, the percent contribution from the talus‐groundwater end member to Andrews Creek increased an average of 0.5% per year (p &lt; 0.0001), whereas the percent contributions from snow plus rain decreased by a similar amount (p = 0.001). Our results show how water and solute sources in alpine environments shift in response to climate variability and highlight the role of talus groundwater and soil water in providing hydrologic resilience to the system.

A Global Hydrological Drought Index Dataset Based on Gravity Recovery and Climate Experiment (GRACE) Data

The Gravity Recovery and Climate Experiment (GRACE) satellites have been used in drought/flood monitoring by observing terrestrial water storage (TWS) change. Meteorological drought indicators or other identified disaster information were usually adopted in association with GRACE-observed changes in TWS for the determination of the occurrence and severity of droughts/floods. Inter-comparisons of dry conditions based on TWS change on a global scale, however, were very difficult because TWS anomalies are not comparable for different hydro-climatic regions. In this paper, we established a global dataset of GRACE-based dimensionless drought index, the Total Storage Deficit Index (TSDI), which is spatially comparable and capable of independently examining the characteristics of dry/wet spells globally. The globally mapped GRACE-based TSDI was examined with some reported extreme hydrologic events, which suggested that the results were fairly consistent with documented drought/flood disaster information. Moreover, comparisons of the GRACE-based TSDI with other frequently used drought indicators, such as the Standardized Precipitation Index (SPI), the Palmer Drought Severity Index (PDSI), and the Palmer Hydrological Drought Index (PHDI), suggested that the TSDI was significantly correlated with the SPI at three different time scales, the PDSI, and the PHDI over most parts of the global surface. The longer the time scale of the selected SPI, the stronger the correlation tended to be with the TSDI. Moreover, the correlation of the TSDI with the PHDI was higher than that with the PDSI over almost the whole global surface. With regard to its performance, this study suggested that the TSDI derived from GRACE-based TWS could be a useful dimensionless index for global and regional hydrological drought monitoring, especially for areas where meteo-hydrological observations are insufficient or human activities are intensive.

Bayesian networks-based probabilistic forecasting of hydrological drought considering drought propagation

As the occurrence of drought is recently on the rise, the reliable drought forecasting is required for developing the drought mitigation and proactive management of water resources. This study developed a probabilistic hydrological drought forecasting method using the Bayesian Networks and drought propagation relationship to estimate future drought with the forecast uncertainty, named as the Propagated Bayesian Networks Drought Forecasting (PBNDF) model. The proposed PBNDF model was composed with 4 nodes of past, current, multi-model ensemble (MME) forecasted information and the drought propagation relationship. Using Palmer Hydrological Drought Index (PHDI), the PBNDF model was applied to forecast the hydrological drought condition at 10 gauging stations in Nakdong River basin. The receiver operating characteristics (ROC) curve analysis was applied to measure the forecast skill of the forecast mean values. The root mean squared error (RMSE) and skill score (SS) were employed to compare the forecast performance with previously developed forecast models (persistence forecast, Bayesian network drought forecast). We found that the forecast skill of PBNDF model showed better performance with low RMSE and high SS of 0.1~0.15. The overall results mean the PBNDF model had good potential in probabilistic drought forecasting.

Modeling drivers of phosphorus loads in Chesapeake Bay tributaries and inferences about long-term change

Causal attribution of changes in water quality often consists of correlation, qualitative reasoning, listing references to the work of others, or speculation. To better support statements of attribution for water-quality trends, structural equation modeling was used to model the causal factors of total phosphorus loads in the Chesapeake Bay watershed. By transforming, scaling, and standardizing variables, grouping similar sites, grouping some causal factors into latent variable models, and using methods that correct for assumption violations, we developed a structural equation model to show how causal factors interact to produce total phosphorus loads. Climate (in the form of annual total precipitation and the Palmer Hydrologic Drought Index) and anthropogenic inputs are the major drivers of total phosphorus load in the Chesapeake Bay watershed. Increasing runoff due to natural climate variability is offsetting purposeful management actions that are otherwise decreasing phosphorus loading; consequently, management actions may need to be reexamined to achieve target reductions in the face of climate variability.

Development of a Coastal Drought Index Using Salinity Data

Abstract A critical aspect of the uniqueness of coastal drought is the effects on the salinity dynamics of creeks, rivers, and estuaries. The location of the freshwater–saltwater interface along the coast is an important factor in the ecological and socioeconomic dynamics of coastal communities. Salinity is a critical response variable that integrates hydrologic and coastal dynamics including sea level, tides, winds, precipitation, streamflow, and tropical storms. The position of the interface determines the composition of freshwater and saltwater aquatic communities as well as the freshwater availability for water intakes. Many definitions of drought have been proposed, with most describing a decline in precipitation having negative impacts on the water supply. Indices have been developed incorporating data such as rainfall, streamflow, soil moisture, and groundwater levels. These water-availability drought indices were developed for upland areas and may not be ideal for characterizing coastal drought. The availability of real-time and historical salinity datasets provides an opportunity for the development of a salinity-based coastal drought index. An approach similar to the standardized precipitation index (SPI) was modified and applied to salinity data obtained from sites in South Carolina and Georgia. Using the SPI approach, the index becomes a coastal salinity index (CSI) that characterizes coastal salinity conditions with respect to drought periods of higher-saline conditions and wet periods of higher-freshwater conditions. Evaluation of the CSI indicates that it provides additional coastal response information as compared to the SPI and the Palmer hydrologic drought index, and the CSI can be used for different estuary types and for comparison of conditions along coastlines.

Examining the Relationship between Drought Indices and Groundwater Levels

Thorough characterization of the response of finite water resources to climatic factors is essential for water monitoring and management. In this study, groundwater level data from U.S. Geological Survey Ground-Water Climate Response Network wells were used to analyze the relationship between selected drought indices and groundwater level fluctuation. The drought episodes included in this study were selected using climate division level drought indices. Indices included the Palmer Drought Severity Index, Palmer Hydrological Drought Index, and Standardized Precipitation Index (SPI-6, 9, 12, 24). Precipitation and the average temperature were also used. SPI-24 was found to correlate best with groundwater levels during drought. For 17 out of 32 wells, SPI-24 showed the best correlation amongst all of the indices. For 12 out of 32 wells, SPI-24 showed correlation coefficients of −0.6 or stronger; and for other wells, reasonably good correlation was demonstrated. The statistical significance of SPI-24 in predicting groundwater level was also tested. The correlation of average monthly groundwater levels with SPI-24 does not change much throughout the timeframe, for all of the studied wells. The duration of drought also had a significant correlation with the decline of groundwater levels. This study illustrates how drought indices can be used for a rapid assessment of drought impact on groundwater level.