Probability Of Drought Research Articles

Relationships between individual-tree mortality and water-balance variables indicate positive trends in water stress-induced tree mortality across North America.

Accounting for water stress-induced tree mortality in forest productivity models remains a challenge due to uncertainty in stress tolerance of tree populations. In this study, logistic regression models were developed to assess species-specific relationships between probability of mortality (Pm ) and drought, drawing on 8.1 million observations of change in vital status (m) of individual trees across North America. Drought was defined by standardized (relative) values of soil water content (Ws,z ) and reference evapotranspiration (ETr,z ) at each field plot. The models additionally tested for interactions between the water-balance variables, aridity class of the site (AC), and estimated tree height (h). Considering drought improved model performance in 95 (80) per cent of the 64 tested species during calibration (cross-validation). On average, sensitivity to relative drought increased with site AC (i.e. aridity). Interaction between water-balance variables and estimated tree height indicated that drought sensitivity commonly decreased during early height development and increased during late height development, which may reflect expansion of the root system and decreasing whole-plant, leaf-specific hydraulic conductance, respectively. Across North America, predictions suggested that changes in the water balance caused mortality to increase from 1.1%yr-1 in 1951 to 2.0% yr-1 in 2014 (a net change of 0.9±0.3% yr-1 ). Interannual variation in mortality also increased, driven by increasingly severe droughts in 1988, 1998, 2006, 2007 and 2012. With strong confidence, this study indicates that water stress is a common cause of tree mortality. With weak-to-moderate confidence, this study strengthens previous claims attributing positive trends in mortality to increasing levels of water stress. This 'learn-as-we-go' approach - defined by sampling rare drought events as they continue to intensify - will help to constrain the hydraulic limits of dominant tree species and the viability of boreal and temperate forest biomes under continued climate change.

Extreme hydrological changes in the southwestern US drive reductions in water supply to Southern California by mid century

The Southwestern United States has a greater vulnerability to climate change impacts on water security due to a reliance on snowmelt driven imported water. The State of California, which is the most populous and agriculturally productive in the United States, depends on an extensive artificial water storage and conveyance system primarily for irrigated agriculture, municipal and industrial supply and hydropower generation. Here we take an integrative high-resolution ensemble modeling approach to examine near term climate change impacts on all imported and local sources of water supply to Southern California. While annual precipitation is projected to remain the same or slightly increase, rising temperatures result in a shift towards more rainfall, reduced cold season snowpack and earlier snowmelt. Associated with these hydrological changes are substantial increases in the frequency and the intensity of both drier conditions and flooding events. The 50 year extreme daily maximum precipitation and runoff events are 1.5–6 times more likely to occur depending on the water supply basin. Simultaneously, a clear deficit in total annual runoff over mountainous snow generating regions like the Sierra Nevada is projected. On one hand, the greater probability of drought decreases imported water supply availability. On the other hand, earlier snowmelt and significantly stronger winter precipitation events pose increased flood risk requiring water releases from control reservoirs, which may potentially decrease water availability outside of the wet season. Lack of timely local water resource expansion coupled with projected climate changes and population increases may leave the area in extended periods of shortages.

Integrated assessment for hydrometeorological drought based on Markov chain model

Drought assessment based on a single index cannot comprehensively reflect the characteristics of a drought affected by multiple factors. Therefore, the main purpose of this study is to accurately assess the drought by constructing an integrated drought assessment method (PDSI-SDI) that can combine the meteorological drought and hydrological drought at the same time. To better evaluate and forecast the drought, the Markov chain model is employed in this study to calculate the expected residence time, return period and transition probabilities of the drought. Furthermore, the Mann–Kendall method is adopted to predict the trend of the drought. The Weihe River Basin is selected as the study area, and according to the distribution characteristics of the water system, it is divided into five districts in order to better assess the drought. Results indicate that: (1) spatially, drought probabilities increase from south to north and west to east. (2) Temporally, probabilities of spring droughts are the highest, followed by summer droughts and autumn droughts, winter droughts have the lowest probabilities, extreme droughts are more likely to occur in autumn. (3) Drought preferentially transfers within the same scenario, except scenario 4 (meteorological drought with no hydrological drought) in autumn is prone to shift to scenario 1 (no meteorological drought with no hydrological drought). (4) There is a significant drying trend of the drought in the Weihe River Basin at the significance level of 95 %. The integrated drought assessment method and other methods adopted in this study can be applied in other regions as well.

Prediction of climate change effects on the runoff regime of a forested catchment in northern Iran

ABSTRACTThe southern coast of the Caspian Sea in northern Iran is bordered by a mountain range with forested catchments which are susceptible to droughts and floods. This paper examines possible changes to runoff patterns from one of these catchments in response to climate change scenarios. The HEC-HMS rainfall–runoff model was used with downscaled future rainfall and temperature data from 13 global circulation models, and meteorological and hydrometrical data from the Casilian (or “Kassilian”) Catchment. Annual and seasonal predictions of runoff change for three future emissions scenarios were obtained, which suggest significantly higher spring rainfall with increased risk of flooding and significantly lower summer rainfall leading to a higher probability of drought. Flash floods arising from extreme rainfall may become more frequent, occurring at any time of year. These findings indicate a need for strategic planning of water resource management and mitigation measures for increasing flood hazards.EDITOR M.C. Acreman ASSOCIATE EDITOR not assigned

Defining drought in the context of stream health

Droughts affect many sectors, such as agriculture, economic, social, human health, and ecosystems. Many drought indices have been developed; yet, none of them quantifies the impacts of drought on stream health. The purpose of this study is to define a new drought index capable of assessing fish vulnerability. To accomplish this, a hydrological model, called the Soil and Water Assessment Tool (SWAT), and the Regional-scale Habitat Suitability model were integrated in order to understand the state of drought within 13,831 stream segments within the Saginaw Bay Watershed. The ReliefF algorithm was used as the variable selection method, and partial least squared regression was used to develop two sets of predictor models capable of determining current and future drought severities. Forty-seven different climate scenarios were used to investigate drought model predictability of future climate scenarios. The results indicated that the best drought model has a high capability for predicting future drought conditions with R2 values ranging from 0.86 to 0.89. In general, the majority of reaches (94%) will experience higher drought probability under future climate scenarios compared to current conditions. The procedure introduced in this study is transferable to other watersheds with regional standards for environmental flow to measure the impacts of drought on stream health.

Analysis of Long-Term Water Level Variation in Dongting Lake, China

The water level of Dongting Lake has changed because of the combined impact of climatic change and anthropogenic activities. A study of the long-term statistical properties of water level variations at Chenglingji station will help with the management of water resources in Dongting Lake. In this case, 54 years of water level data for Dongting Lake were analyzed with the non-parametric Mann–Kendall trend test, Sen’s slope test, and the Pettitt test. The results showed the following: (1) Trends in annual maximum lake water level (WLM), annual mean lake water level (WL), and annual minimum lake water level (WLm) increased from 1961 to 2014; however, the three variables showed different trends from 1981 to 2014; (2) The annual change trends in Dongting Lake between 1961–2014 and 1981–2014 were found to be from approximately 0.90 cm/year to −2.27 cm/year, 1.65 cm/year to −0.79 cm/year, and 4.58 cm/year to 2.56 cm/year for WLM, WL, and WLm, respectively; (3) A greater degree of increase in water level during the dry season (November–April) was found from 2003 to 2014 than from 1981 to 2002, but a smaller degree of increase, even to the point of decreasing, was found during the wet season (May–October); (4) The measured discharge data and numerical modeling results showed the operation of Three Gorge Reservoir (TGR) pushed to influence partly the recent inter-annual variation of water level in Dongting Lake region, especially in the flood and dry seasons. The analysis indicated that the water level of Dongting Lake has changed in the long term with decreasing of range between WLM and WLm, and may decrease the probability of future drought and flood events. These results can provide useful information for the management of Dongting Lake.

Probabilistic forecasting of drought: a hidden Markov model aggregated with the RCP 8.5 precipitation projection

The creeping characteristics of drought make it possible to mitigate drought’s effects with accurate forecasting models. Drought forecasts are inevitably plagued by uncertainties, making it necessary to derive forecasts in a probabilistic framework. In this study, we proposed a new probabilistic scheme to forecast droughts that used a discrete-time finite state-space hidden Markov model (HMM) aggregated with the Representative Concentration Pathway 8.5 (RCP) precipitation projection (HMM-RCP). The standardized precipitation index (SPI) with a 3-month time scale was employed to represent the drought status over the selected stations in South Korea. The new scheme used a reversible jump Markov chain Monte Carlo algorithm for inference on the model parameters and performed an RCP precipitation projection transformed SPI (RCP-SPI) weight-corrected post-processing for the HMM-based drought forecasting to perform a probabilistic forecast of SPI at the 3-month time scale that considered uncertainties. The point forecasts which were derived as the HMM-RCP forecast mean values, as measured by forecasting skill scores, were much more accurate than those from conventional models and a climatology reference model at various lead times. We also used probabilistic forecast verification and found that the HMM-RCP provided a probabilistic forecast with satisfactory evaluation for different drought categories, even at long lead times. In a drought event analysis, the HMM-RCP accurately predicted about 71.19 % of drought events during the validation period and forecasted the mean duration with an error of less than 1.8 months and a mean severity error of <0.57. The results showed that the HMM-RCP had good potential in probabilistic drought forecasting.

When every drop counts: Analysis of Droughts in Brazil for the 1901-2013 period

To provide information useful in policy formulation and management of drought impacts in Brazil, in this study, a sequence of drought events based on monthly rainfall of 1901-2013 on ~25 km x 25 km grid are derived at 4 timescales that include short-timescales (3-month and 6-month) and medium to long-timescales (12-month and 24-month). Subsequently, probability of drought occurrences, intensity, duration and areal-extent are calculated. The probabilities of occurrence of severe and extreme droughts at short-timescales are 1 in 12 and 1 in 66 years, respectively, all over the country. At medium to long-timescales, the probability of severe droughts is about 1 in 20 years in northern Brazil, and 1 in 10 years in the south. The probabilities of extreme droughts are 1 in 9 and 1 in 12 years over northern Brazil and in the south, respectively. In general, no evidence of significant (α =0.05) trend is detected in drought frequency, intensity, and duration over the last 11 decades (since 1901) at all the 4 timescales. The drought areal-extent show increasing trends of 3.4%/decade over Brazil for both 3-month and 6-month timescales. However, the trend increases for the 12-month and 24-month timescales are relatively smaller, i.e., 2.4%/decade and 0.5%/decade, respectively.

Future drought probabilities in the Greater Alpine Region based on COSMO-CLM experiments – spatial patterns and driving forces

Future drought probabilities in the Greater Alpine Region based on COSMO-CLM experiments – spatial patterns and driving forces

Joint probability of precipitation and reservoir storage for drought estimation in the headwater basin of the Huaihe River, China

Reservoir storage plays an important role in water supply during the dry season when precipitation is insufficient. In a watershed where the streams are controlled by reservoirs, drought occurrences depend on not only precipitation variations but also reservoir regulation. In this study, the joint dependence structure of the reservoir storage and its relevant variables of precipitation and/or upstream outflow were analyzed for two cascade reservoirs in a headwater basin of the Huaihe River, China. Correlation analysis indicates that the reservoir storage in October (the end of the wet season) depends highly on the regional precipitation at time scales of several months, e.g., 7 months for the upstream and 9 months for the downstream. Additionally, the downstream storage is correlated with outflow from the upstream reservoir at the 5-month timescale significantly. For estimation of the joint probability of pairs of the storage and its relevant variables, univariate marginal distributions and bivariate copula were appropriately selected in terms of statistical tests. The bivariate return period of \(T(X < x,Y < y)\) and \(T(X \le x,Y \ge y)\) and the conditional probability of \(P(Y \ge y|X \le x)\) were estimated by using the selected Clayton copula. The results from contour lines of the bivariate return period demonstrate that the probability of drought occurrences affected by both reservoir storage and precipitation/outflow is smaller than that by either of the variables. Meanwhile, the concurrent drought probability between precipitation and reservoir storage in the upstream is higher than that in the downstream. The estimated conditional probability offers useful information on how much the regular storage could be remained under some specified drought levels of precipitation/upstream outflow. Therefore, the results are helpful for improving the operation strategies of the cascade reservoirs for the adaptive management of drought under different climate variations.

Short-term droughts forecast using Markov chain model in Victoria, Australia

A comprehensive risk management strategy for dealing with drought should include both short-term and long-term planning. The objective of this paper is to present an early warning method to forecast drought using the Standardised Precipitation Index (SPI) and a non-homogeneous Markov chain model. A model such as this is useful for short-term planning. The developed method has been used to forecast droughts at a number of meteorological monitoring stations that have been regionalised into six (6) homogenous clusters with similar drought characteristics based on SPI. The non-homogeneous Markov chain model was used to estimate drought probabilities and drought predictions up to 3 months ahead. The drought severity classes defined using the SPI were computed at a 12-month time scale. The drought probabilities and the predictions were computed for six clusters that depict similar drought characteristics in Victoria, Australia. Overall, the drought severity class predicted was quite similar for all the clusters, with the non-drought class probabilities ranging from 49 to 57 %. For all clusters, the near normal class had a probability of occurrence varying from 27 to 38 %. For the more moderate and severe classes, the probabilities ranged from 2 to 13 % and 3 to 1 %, respectively. The developed model predicted drought situations 1 month ahead reasonably well. However, 2 and 3 months ahead predictions should be used with caution until the models are developed further.

Spring land temperature anomalies in northwestern US and the summer drought over Southern Plains and adjacent areas

Recurrent drought and associated heatwave episodes are important features of the US climate. Many studies have examined the connection between ocean surface temperature changes and conterminous US droughts. However, remote effects of large-scale land surface temperature variability, over shorter but still considerable distances, on US regional droughts have been largely ignored. The present study combines two types of evidence to address these effects: climate observations and model simulations. Our analysis of observational data shows that springtime land temperature in northwest US is significantly correlated with summer rainfall and surface temperature changes in the US Southern Plains and its adjacent areas. Our model simulations of the 2011 Southern Plains drought using a general circulation model and a regional climate model confirm the observed relationship between land temperature anomaly and drought, and suggest that the long-distance effect of land temperature changes in the northwest US on Southern Plains droughts is probably as large as the more familiar effects of ocean surface temperatures and atmospheric internal variability. We conclude that the cool 2011 springtime climate conditions in the northwest US increased the probability of summer drought and abnormal heat in the Southern Plains. The present study suggests a strong potential for more skillful intra-seasonal predictions of US Southern Plains droughts when such facts as ones presented here are considered.

Probabilistic Forecasting of Drought Events Using Markov Chain- and Bayesian Network-Based Models: A Case Study of an Andean Regulated River Basin

The scarcity of water resources in mountain areas can distort normal water application patterns with among other effects, a negative impact on water supply and river ecosystems. Knowing the probability of droughts might help to optimize a priori the planning and management of the water resources in general and of the Andean watersheds in particular. This study compares Markov chain- (MC) and Bayesian network- (BN) based models in drought forecasting using a recently developed drought index with respect to their capability to characterize different drought severity states. The copula functions were used to solve the BNs and the ranked probability skill score (RPSS) to evaluate the performance of the models. Monthly rainfall and streamflow data of the Chulco River basin, located in Southern Ecuador, were used to assess the performance of both approaches. Global evaluation results revealed that the MC-based models predict better wet and dry periods, and BN-based models generate slightly more accurately forecasts of the most severe droughts. However, evaluation of monthly results reveals that, for each month of the hydrological year, either the MC- or BN-based model provides better forecasts. The presented approach could be of assistance to water managers to ensure that timely decision-making on drought response is undertaken.

Transitional properties of droughts and related impacts of climate indices in the Pearl River basin, China

Summary Drought is the natural hazard poorly understood so far due to various mechanisms behind. Moreover, disastrous effects of drought on human society necessitate accurate forecasting of drought behaviors. In this case, to improve forecasting of drought in the Pearl River basin, a trivariate copula model has been developed and used to include the El Nino Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Indian Ocean Dipole (IOD) and Pacific Decadal Oscillation (PDO) into model structure of Markov chain. The Standardized Precipitation Evapotranspiration Index (SPEI) has been used to monitor the drought in this study. Comparison with the preliminary correlation analysis between each month climate index and SPEI series indicated that the trivariate copula performs satisfactorily well in evaluation of influences of climate indices on the transition probabilities of drought. It is considered that the region with the negative vertical velocity is dominated by more precipitation and vice versa. Moreover, field patterns of 500 hPa vertical velocity anomalies related to each climate index have further corroborated the influences of climate indices on the drought behaviors. Besides, the mean extreme drought durations under different conditions of each climate index have also been investigated in this study. Results indicated that the mean extreme drought duration tends to be longer in the western part of the Pearl River basin during positive phase of ENSO while tends to be shorter during the positive phase of NAO and vice versa; in the central part of the Pearl River basin, the mean extreme drought duration tends to be shorter during the positive phase of ENSO, NAO and PDO while tends to be longer during the positive episode of IOD, and vice versa; in the eastern part of the Pearl River basin, the mean extreme drought duration tends to be shorter during the positive episode of ENSO and PDO, and vice versa. This study sheds new light on transitional behaviors of droughts as a result of influences from climate indices.

Spatio-temporal variation of hydrological drought under climate change during the period 1960–2013 in the Hexi Corridor, China

In recent years, climate change has been aggravated in many regions of the world. The Hexi Corridor is located in the semiarid climate zone of Northwest China, which is particularly affected by climate change. Climate change has led to the spatial and temporal variations of temperature and precipitation, which may result in hydrological drought and water shortage. Thus, it is necessary to explore and assess the drought characteristics of river systems in this area. The patterns of hydrological drought in the Hexi Corridor were identified using the streamflow drought index (SDI) and standardized precipitation index at 12-month timescale (SPI12) from 1960 to 2013. The evolution of drought was obtained by the Mann–Kendall test and wavelet transform method. The results showed that both the mean annual SDI and SPI12 series in the Hexi Corridor exhibited an increasing trend during the study period. According to the results of wavelet analysis, we divided the study period into two segments, i.e. before and after 1990. Before 1990, the occurrence of droughts showing decreased SDI and SPI12 was concentrated in the northern part of the corridor and shifted to the eastern part of the corridor after 1990. The probability of drought after 1990 in Shule River basin decreased while increased in Shiyang River basin. The wavelet analysis results showed that Shiyang River basin will be the first area to go through the next drought period. Additionally, the relationships between drought pattern and climate indices were analyzed. The enhanced westerly winds and increased precipitation and glacier runoff were the main reasons of wet trend in the Hexi Corridor. However, the uneven spatial variations of precipitation, temperature and glacier runoff led to the difference of hydrological drought variations between the Shule, Heihe and Shiyang River basins.

A Bayesian Network-Based Probabilistic Framework for Drought Forecasting and Outlook

Reliable drought forecasting is necessary to develop mitigation plans to cope with severe drought. This study developed a probabilistic scheme for drought forecasting and outlook combined with quantification of the prediction uncertainties. The Bayesian network was mainly employed as a statistical scheme for probabilistic forecasting that can represent the cause-effect relationships between the variables. The structure of the Bayesian network-based drought forecasting (BNDF) model was designed using the past, current, and forecasted drought condition. In this study, the drought conditions were represented by the standardized precipitation index (SPI). The accuracy of forecasted SPIs was assessed by comparing the observed SPIs and confidence intervals (CIs), exhibiting the associated uncertainty. Then, this study suggested the drought outlook framework based on probabilistic drought forecasting results. The overall results provided sufficient agreement between the observed and forecasted drought conditions in the outlook framework.

Is worst-case scenario streamflow drought underestimated in British Columbia? A multi-century perspective for the south coast, derived from tree-rings

Summary Recent streamflow droughts in south coastal British Columbia have had major socioeconomic and ecological impacts. Increasing drought severity under projected climate change poses serious water management challenges, particularly in the small coastal watersheds that serve as primary water sources for most communities in the region. A 332-year dendrohydrological record of regionalized mean summer streamflow for four watersheds is analyzed to place recent drought magnitudes in a long-term perspective. We present a novel approach for optimizing tree-ring based reconstructions in small watersheds in temperate environments, combining winter snow depth and summer drought sensitive proxies as model predictors. The reconstruction model, estimated by regression of observed flows on Tsuga mertensiana ring-width variables and a tree-ring derived paleorecord of the Palmer Drought Severity Index, explains 64% of the regionalized streamflow variance. The model is particularly accurate at estimating lowest flow events, and provides the strongest annually resolved paleohydrological record in British Columbia. The extended record suggests that since 1658 sixteen natural droughts have occurred that were more extreme than any within the instrumental period. Flow-duration curves show more severe worst-case scenario droughts and a higher probability of those droughts in the long-term reconstruction than in the hydrometric data. Such curves also highlight the value of dendrohydrology for probabilistic drought assessment. Our results suggest current water management strategies based on worst-case scenarios from historical gauge data likely underestimate the potential magnitudes of natural droughts. If the low-flow magnitudes anticipated under climate change co-occur with lowest possible natural flows, streamflow drought severities in small watersheds in south coastal British Columbia could exceed any of those experienced in the past ∼350 years.

Annual Drought in California: Association with Monthly Precipitation and Climate Phases

AbstractAnnual precipitation in California is more variable than in any other state and is highly influenced by precipitation in winter months. A primary question among stakeholders is whether low precipitation in certain months is a harbinger of annual drought in California. Historical precipitation data from 1895 to 2013 are investigated to identify leading monthly indicators of annual drought in each of the seven climate divisions (CDs) as well as statewide. For this study, drought conditions are defined as monthly/annual (October–September) precipitation below the 20th/30th percentile, and a leading indicator is defined as a monthly drought preceding or during an annual drought that has the strongest association (i.e., joint probability of occurrence) with a statewide annual drought. Monthly precipitation variability and contributions to annual precipitation, along with joint probabilities of drought among the winter months, are first analyzed. Then the probabilities of annual drought and the variability in leading indicators are analyzed according to different climate phases and CDs. This study identified December within a water year as being the leading indicator that is most frequently associated with annual drought statewide (56%) and in most of the CDs (the highest was CD2 at 65%). Associated with its leading-indicator status, December drought was most frequently associated with drought in other winter months (joint probability &gt; 30%). Results from this study can help stakeholders to understand and assess the likelihood of annual drought events given monthly precipitation preceding or early in the water year.

Probabilistic forecasting of seasonal droughts in the Pearl River basin, China

Forecasting of droughts is essential for developing measures for mitigation of drought hazards and for reducing drought-induced loss. In this study, droughts were characterized by the standardized precipitation-evapotranspiration index with a time scale of 3 months. Copula-based probabilistic forecasting models were developed to predict drought occurrences. Results indicated higher probability of occurrence of seasonal droughts after the occurrence of more severe seasonal droughts, and extreme drought in winter tended to persist with higher probability till spring, whereas extreme drought in autumn might not probably last to winter. Furthermore, results indicated high probability of occurrence of droughts in southeast parts of the Pearl River basin during spring to winter. Thus, droughts in the Pearl River basin are subject to lengthening duration, particularly in the southeastern part of the basin. It should be noted here that the southeastern part is densely populated with a high degree of socioeconomic development. Thus, higher probability of droughts in the southeastern part should attract considerable concern. Higher drought risk was also identified in the western part of the basin. Results of this study provide a theoretical framework for water resources management and conservation of eco-environment in the Pearl River basin in a changing climate, and may serve as a reference for evaluation of drought risk in other regions of the world.

Investigation of the probability of concurrent drought events between the water source and destination regions of China's water diversion project

In this study, we investigate the concurrent drought probability between the water source and destination regions of the central route of China's South to North Water Diversion Project. We find that both regions have been drying from 1960 to 2013. The estimated return period of concurrent drought events in both regions is 11years. However, since 1997, these regions have experienced 5years of simultaneous drought. The projection results of global climate models show that the probability of concurrent drought events is highly likely to increase during 2020 to 2050. The increasing concurrent drought events will challenge the success of the water diversion project, which is a strategic attempt to resolve the water crisis of North China Plain. The data suggest great urgency in preparing adaptive measures to ensure the long-term sustainable operation of the water diversion project.