Streamflow drought time series forecasting

Drought is considered to be an extreme climatic event causing significant damage both in the natural environment and in human lives. Due to the important role of drought forecasting in water resources planning and management and the stochastic behavior of drought, a multiplicative seasonal autoregressive integrated moving average (SARIMA) model is applied to the monthly streamflow forecasting of the Zayandehrud River in western Isfahan province, Iran. After forecasting 12 leading month streamflow, four drought thresholds including streamflow mean, monthly streamflow mean, 2-, 5-, 10- and 20-year return period monthly drought and standardized streamflow index were chosen. Both observed and forecasted streamflow showed a drought period with different severity in the lead-time. This study also demonstrates the usefulness of SARIMA models in forecasting, water resources planning and management.     

Dimensionality reduction in drought modelling

For monitoring hydrological events characterized by high spatial and temporal variability, the number and location of recording stations must be carefully selected to ensure that the necessary information is collected. Depending on the characteristics of each natural process, certain stations may be spurious or redundant, whereas others may provide most of the relevant data. With the objective of reducing the costs of the monitoring system and, at the same time, improving its operational effectiveness, three procedures were applied to identify the minimum network of rain gauge stations able to capture the characteristics of droughts in mainland Portugal. Drought severity is characterized by the standardized precipitation index applied to the timescales of 1, 3, 6 and 12 consecutive months. The three techniques used to reduce the dimensionality of the network of rain gauges were as follows: (i) artificial neural networks with sensitivity analysis, (ii) application of the mutual information criterion and (iii) K‐means cluster analysis using Euclidean distances. The results demonstrated that the best dimensionality reduction method was case dependent in the three regions of Portugal (northern, central and southern) previously identified by cluster analysis. All the reduction techniques lead to the selection of a subset of rain gauges capable of reproducing the original temporal patterns of drought. For specific severe drought events in Portugal in the past, the comparison between drought spatial patterns obtained with the original stations and the selected subset indicated that the subset produced statistically satisfactory results (correlation coefficients higher than 0.6 and efficiency coefficients higher than 0.5). Copyright © 2012 John Wiley & Sons, Ltd.     

Spatial modeling of drought events using max-stable processes

With their severe environmental and socioeconomic impact, drought events belong to the most far-reaching natural disasters. Effects are tremendous in rain-fed agricultural areas as in Africa. We analyzed and modeled the spatio-temporal statistical behavior of the Normalized Difference Vegetation Index as a risk indicator for drought, reflecting its stochastic effects on vegetation. The study used a data set for Rwanda obtained from multitemporal satellite remote sensor measurements during a 14-year period and divided into season-specific spatial random fields. Maximal deviations from average conditions were modeled with max-stable Brown–Resnick processes taking methodological and computational challenges into account. Those challenges are caused by the large spatial extent and the relatively short time span covered by the data. Extensive simulations enabled us to go beyond the observations and, thus, to estimate several important characteristics of extreme drought events, such as their expected return period.     

Adaptive Neuro-Fuzzy Inference System for drought forecasting

Drought causes huge losses in agriculture and has many negative influences on natural ecosystems. In this study, the applicability of Adaptive Neuro-Fuzzy Inference System (ANFIS) for drought forecasting and quantitative value of drought indices, the Standardized Precipitation Index (SPI), is investigated. For this aim, 10 rainfall gauging stations located in Central Anatolia, Turkey are selected as study area. Monthly mean rainfall and SPI values are used for constructing the ANFIS forecasting models. For all stations, data sets include a total of 516 data records measured between in 1964 and 2006 years and data sets are divided into two subsets, training and testing. Different ANFIS forecasting models for SPI at time scales 1–12 months were trained and tested. The results of ANFIS forecasting models and observed values are compared and performances of models were evaluated. Moreover, the best fit models have been also trained and tested by Feed Forward Neural Networks (FFNN). The results demonstrate that ANFIS can be successfully applied and provide high accuracy and reliability for drought forecasting.     

The survey of climatic drought trend in Iran

Drought is one of the most important natural hazards in Iran. Therefore, drought monitoring has become a point of concern for most of the researchers. In the present study, the changes and trend of drought was surveyed, under the current global climate changes, by non parametric Mann–Kendall statistical test for 42 synoptic stations at different places of Iran. Standardized Precipitation Index (SPI) was calculated to recognize the drought condition at different time scales (3, 6, 9, 12, 18 and 24 months’ time series) for analyzing the drought trend in the recent 30 years. The obtained results have indicated a significant negative trend of drought in many parts of Iran, especially the South-East, West and South-West regions of the country. According to the results, although some parts of Iran such as North (around the Caspian Sea) and Northeast show no significant trend but in other parts of country, the severity of drought has increased during the last 30 years.     

Spatio-temporal drought analysis in the Trakya region,Turkey

Abstract

Since droughts are natural phenomena, their occurrence cannot be predicted with certainty and thus it must be treated as a random variable. Once drought duration and magnitude have been found objectively, it is possible to plan for the transport of water in known quantities to drought-stricken areas either from alternative water resources or from water stored during wet periods. The summation of deficits over a particular period is referred to as the drought magnitude. Drought intensity is the ratio of drought magnitude to its duration. These drought properties at different truncation levels provide significant hydrological and hydrometeorological design quantities. In this study, the run analysis and z-score are used for determining drought properties of given hydrological series. In addition, kriging is used as a spatial drought analysis for mapping. This study is applied to precipitation records for Istanbul, Edirne, Tekirdag and Kirklareli in the Trakya region, Turkey and then the drought period, magnitude and standardized precipitation index (SPI) values are presented to depict the relationships between drought duration and magnitude.     

Improving drought predictability in Arkansas using the ensemble PDSI forecast technique

Drought prediction is important for improved water resources management and agriculture planning. Although Arkansas has suffered severe droughts and economic loss in recent years, no significant study has been done. This study proposes a local nonparametric autoregressive model with designed stochastic residual-resampling approach to produce ensemble drought forecasts with associated confidence. The proposed model utilizes historical climate records, including drought indices, temperature, and precipitation to improve the quality of the short-term forecast of drought indices. Monthly forecasts of Palmer Drought Severity Index (PDSI) in Arkansas climate divisions show remarkable skills with 2–3 month lead-time based on selected performance measure such as, Normalized Root Mean Square Error (NRMSE) and the Kuiper Skill Score (KSS). Rank histograms also show that the model captures the natural variability very well in the produced drought forecasts. The incorporation of categorical long-term precipitation prediction significantly enhances the performance of the monthly drought forecasts.     

Risk assessment and drought management in the Thames basin

Abstract

A risk assessment procedure is described for use in managing a system of pumped-storage reservoirs in the Thames basin during a drought. Historical daily rainfall sequences are used as equi-probable scenarios of future rainfall. These are transformed to flow, reservoir level, and demand restriction sequences through the use of rainfall-runoff and water resource system models. The risk assessment information required is then obtained through a statistical analysis of these sequences. A novel technique is presented for incorporating monthly rainfall forecasts, presented as probabilities of rainfall being above average, average, or below average, into the risk assessment scheme. Information on current hydrological conditions is incorporated in the procedure through the use of recently observed natural flows to adjust the internal state variables of a conceptual rainfall-runoff model to achieve agreement between observed and model flow. The overall procedure is accommodated within a decision support system for drought management which is implemented on a microcomputer and makes extensive use of interactive menus, forms and colour graphic displays. A key feature of the system is the maintenance of an up-to-date archive of hydrometric data which is achieved through a real-time communication link with a second computer dedicated to real-time data acquisition via telemetry. Monitoring the reliability of the water resource system during droughts is made a quick and easy task, and the effect of a change in the operating policy on system reliability can be readily assessed. The information obtained provides valuable support for tactical decision-making within the overall long-term operating strategy.     

Evaluation of multi-sensor satellite data for monitoring different drought impacts

Drought is a natural disaster that significantly affects human life; therefore, precise monitoring and prediction is necessary to minimize drought damage. Conventional drought monitoring is based predominantly on ground observation stations; however, satellite imagery can be used to overcome the disadvantages of existing monitoring methods and has the advantage of monitoring wide areas. In this research, we assess the applicability of drought monitoring based on satellite imagery, focusing on historic droughts in 2001 and 2014, which caused major agricultural and hydrological issues in South Korea. To assess the applicability and accuracy of the drought index, drought impact areas in the study years were investigated, and spatiotemporal comparative analyses between the calculated drought index and previously affected areas were conducted. For drought monitoring based on satellite imagery, we used hydro-meteorological factors such as precipitation, land surface temperature, vegetation, and evapotranspiration, and applied remote sensing data from various sensors. We verified the effectiveness of using precipitation data for meteorological drought monitoring, vegetation and land surface temperature data for agricultural drought monitoring, and evapotranspiration data for hydrological drought monitoring. Moreover, we confirmed that the Standard Precipitation Index (SPI) can be indirectly applied to agricultural or hydrological drought monitoring by determining the temporal correlation between SPI, calculated for various time scales, and satellite-based drought indices.     

Episodic stream water pH decline during autumn storms following a summer drought in northern Sweden

The sources of episodic pH decline in four streams from northern Sweden during the autumn of 1996 were quantified. The events, in which pH dropped by between 1·0 and 2·4 units, were preceded by an extensive summer drought. Total organic carbon, which increased 100% to 160% during peak flow, was the most important driving mechanism of the episodic pH decline. Sulphate, however, was relatively more important during these autumn events than during spring flood. In the sites where past and present anthropogenic deposition were believed to be the main source of sulphate in stream water, sulphate contributed less than 0·3 pH units to the pH decline. In catchments where natural sources of sulphate are known to be important, sulphate contributed up to 0·6 units of pH decline. The export of sulphate during the episodes was two to nine times higher than what was expected from deposition only. The drought preceding the study episodes resulted in some of the lowest ground water levels during the 1990s in that region. The large export of sulphate was probably due to oxidation of natural sulphate bearing minerals in the soil and/or previously deposited sulphate driven by the low ground water level preceding the episodes. Copyright © 2002 John Wiley & Sons, Ltd.     

Current drought and future hydroclimate projections in southeast Australia and implications for water resources management

Southeast Australia is currently in a prolonged drought. The ongoing drought has placed immense pressure on the limited water resources and a perception that this may be the start of a persistent change from historical conditions. Several studies have suggested that part of the current drought could be associated with global warming, and many global climate model projections for southeast Australia are for a drier future on average. However, it is difficult, if not impossible, to separate a global warming signal from the high natural variability observed over the last two centuries and revealed in palaeo-climate records.     

Validation of predicted meteorological drought in California using analogous orbital geometries

Recent studies have uncovered the predictability of hydrological conditions on Earth, based on an analysis of long‐term precipitation data in California and analogous orbital forcings during different years that are separated by Saros cycles. With similar orbital geometries of the Earth and Moon, water years 1976 and 1994, which are one and two Saros cycles apart from water year 2012, respectively, were both Critically Dry years in California, according to the California Department of Water Resources’ water year type classification. Further analysis of precipitation data at a cross section of gauging stations across the U.S. during water years 1976 and 1994 reinforces the previously observed association between analogous orbital forcings and hydrological conditions on earth. The current work is a validation of predicted meteorological drought in California during water year 2012 using: (1) real‐time precipitation data collected from a cross section of gauging stations across California, and (2) estimated full natural flows of eight major rivers in California. The data for these years were analyzed and found to have a high level of correlation. The results of the current work provide a significant mark in the validation exercises of the recent novel finding of the predictability of earth's meteorological and hydrological variability. Continued studies and mapping of observed hydrological conditions globally in the coming years using this approach will be highly valuable for sustainable water resources engineering and various other applications. Copyright © 2013 John Wiley & Sons, Ltd.     

A theoretical drought classification method for the multivariate drought index based on distribution properties of standardized drought indices

Drought indices have been commonly used to characterize different properties of drought and the need to combine multiple drought indices for accurate drought monitoring has been well recognized. Based on linear combinations of multiple drought indices, a variety of multivariate drought indices have recently been developed for comprehensive drought monitoring to integrate drought information from various sources. For operational drought management, it is generally required to determine thresholds of drought severity for drought classification to trigger a mitigation response during a drought event to aid stakeholders and policy makers in decision making. Though the classification of drought categories based on the univariate drought indices has been well studied, drought classification method for the multivariate drought index has been less explored mainly due to the lack of information about its distribution property. In this study, a theoretical drought classification method is proposed for the multivariate drought index, based on a linear combination of multiple indices. Based on the distribution property of the standardized drought index, a theoretical distribution of the linear combined index (LDI) is derived, which can be used for classifying drought with the percentile approach. Application of the proposed method for drought classification of LDI, based on standardized precipitation index (SPI), standardized soil moisture index (SSI), and standardized runoff index (SRI) is illustrated with climate division data from California, United States. Results from comparison with the empirical methods show a satisfactory performance of the proposed method for drought classification.     

Streamflow drought time series forecasting: a case study in a small watershed in North West Spain

Drought is a climatic event that can cause significant damage both in natural environment and in human lives. Drought forecasting is an important issue in water resource planning. Due to the stochastic behaviour of droughts, a multiplicative seasonal autoregressive integrated moving average model was applied to forecast monthly streamflow in a small watershed in Galicia (NW Spain). A better streamflow forecast obtained when the Martone index was included in the model as explanatory variable. After forecasting 12 leading month streamflow, three drought thresholds: streamflow mean, monthly streamflow mean and standardized streamflow index were chosen. Both observed and forecasted streamflow showed no drought evidence in this basin.     

A drought frequency formula

Abstract

The important elements of a drought phenomenon are the longest duration and the largest severity for a desired return period. These elements form a basis for designing water storage systems to cope with droughts. At times, a third element, drought intensity, is also used and is defined as the ratio of severity to duration. The commonly available statistics for the causative drought variables such as annual rainfall or runoff sequences are the mean, the coefficient of variation and the lag one serial correlation coefficient, and occasionally some indication of the probability distribution function (pdf) of the sequences. The extremal values of the duration and severity are modelled in the present paper using information on the aforesaid parameters at the truncation level equal to the mean of the drought sequence, which is generally taken as the truncation level in the analysis of droughts. The drought severity has been modelled as the product of the duration and intensity with the assumption of independence between them. An estimate of drought intensity has been realized from the concept of the truncated normal distribution of the standardized form of the drought sequences in the normalized domain. A formula in terms of the extremal severity and the T-year return period has been suggested similar to the flood frequency formulae, commonly cited in hydrological texts.     

Long-term periodic drought modeling

Drought modeling is essential to water resources management and planning. In this study, Fourier spectral analysis is used to examine the cyclic structure for drought patterns and develop a long-term periodic model. A case study for historical precipitation data, obtained from the arid region of Kuwait for the period spanning from January 1967 to December 2009, are converted to drought measurements following the Standardized Precipitation Index (SPI) criterion. The SPI calculations are performed for two time scales of 12 and 24 months. The periodogram technique used for both time scales reveals periodicities of 12, 14, 19, 26, 31, 43, 64, 103 and 258 months. It is advocated here that the 26- and 258-month periods present in the data are attributed, respectively, to a Quasi-Biennial Oscillation pattern and a solar cycle over which the magnetic polarity of the sun first reverses then reverts to its former state. The detected periods are manipulated in the SPI model to produce drought forecasts, which suggest that until the end of year 2024 the climate is considered normal to very wet. This finding may be implemented to assess policy requirements related to water resources management.     

Hydrological identification of drought

The problem of drought identification is considered from a primarily hydrological viewpoint. The problems related to the definition, identification and prediction of drought have not yet been solved. Although rainfall data are analysed as the main indicator and characteristic of drought, other characteristics resulting from a rainfall deficit are also important. A time unit of one month was selected as the most suitable for analysis. Special attention was paid to the selection of truncation levels with respect to their influence on the results of drought identification. Three methods for drought identification were applied to a series of monthly rainfall data in Osijek from January 1882 to December 1990: (1) run analysis; (2) a discrete Markov process; and (3) the percentile method. Although the results of these three methods are encouraging, different methods yielded similar results. Some drawbacks of the application of distribution curves are discussed. Drought events should be identified using a number of different procedures.     

A daily drought index based on evapotranspiration and its application in regional drought analyses

With climate warming, frequent drought events have occurred in recent decades, causing huge losses to industrial and agricultural production, and affecting people’s daily lives. The monitoring and forecasting of drought events has drawn increasing attention. However, compared with the various monthly drought indices and their wide application in drought research,daily drought indices, which would be much more suitable for drought monitoring and forecasting, are still scarce. The development of a daily drought index would improve the accuracy of drought monitoring and forecasting, and facilitate the evaluation of existing indices. In this study, we constructed a new daily drought index, the daily evapotranspiration deficit index(DEDI), based on actual and potential evapotranspiration data from the high-resolution ERA5 reanalysis dataset of the European Center for Medium-Range Weather Forecasts. This new index was then applied to analyze the spatial and temporal evolution characteristics of four drought events that occurred in southwest, north, northeast, and eastern northwest China in the spring and summer of 2019. Comparisons with the operationally used Meteorological Drought Composite Index and another commonly used index, the Standardized Precipitation Evapotranspiration Index, indicated that DEDI characterized the spatiotemporal evolution of the four drought events reasonably well and was superior in depicting the onset and cessation of the drought events,as well as multiple drought intensity peaks. Additionally, DEDI considers land surface conditions, such as vegetation coverage,which enables its potential application not only for meteorological purposes but also for agricultural drought warning and monitoring.     

Observed trends and relationships between ENSO and standardized hydrometeorological drought indices in central Chile

Droughts are natural phenomena that severely affect socio economic and ecological systems. In Chile, population and economic activities are highly concentrated in its central region (i.e. between latitudes 29°S and 40°S), which periodically suffers from severe droughts affecting agriculture, hydropower, and mining. Understanding the dynamics of droughts and large-scale atmospheric processes that influence the occurrence of dry spells is essential for forecasting and efficient early detection of drought events. Central Chile's climate is marked by a significant El Niño Southern Oscillation (ENSO) influence that might help to better characterize droughts as well as to identify the effects of ENSO on the spatial and temporal characteristics of meteorological and hydrological droughts in the region. We analysed the behaviour of the Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Streamflow Index (SSI) time series for 6-month accumulation periods over the austral winter and summer seasons. Multiple linear regression (MLR) and Generalized Linear Models (GLM) showed a significant ENSO influence on dry events for SPEI-6 and SSI-6 during winter and summer. We found that the magnitude of correlation between ENSO and SPEI-6 has changed over the last decades becoming weaker in winter periods and increasing in spring summer periods. Increasing trends in meteorological and hydrological drought events were also identified, along all latitudes, with significant trends during winter in the southern latitudes, and during summer in the semi-arid and Mediterranean zones. These results indicate that drought mitigation actions and policies are necessary to overcome their adverse effects. Given the monthly persistence of ENSO and its relationship to drought indices, there are opportunities for drought monitoring and seasonal forecasting that could become part of national drought management systems.