Stochastic Prediction of Drought Class Transitions

This paper aims at the stochastic characterization of droughts applying Markov chains modeling to drought class transitions derived from SPI time series. Several sites in Southern Portugal having updated data on precipitation available were considered. The drought class probabilities, the expected residence time in each class of severity, the expected time for the transition between drought classes and the drought severity class predictions 1, 2, or 3 months ahead have been obtained. Those predictions are then compared with observed drought classes for the recent drought periods of 2003–2006. In addition, the estimation of the cumulated precipitation deficits, amount of monthly precipitation needed to decrease drought severity, and foreseen SPI values depending on different precipitation scenarios are also presented as complementing the prediction of drought class transitions.     

Hydrological Drought Class Transition Using SPI and SRI Time Series by Loglinear Regression

Loglinear models for three-dimensional contingency tables was used with data from 21 rainfall stations and 7 hydrometric stations in the Luanhe river basin, northeast China, for short term prediction of drought severity class. Loglinear models were fitted to drought class transitions derived from standardized precipitation index (SPI) and standardized runoff index (SRI) time series to find which series was more suitable for hydrological drought class prediction 1 and 2 months ahead, respectively. Expected frequencies for two consecutive transitions between drought classes were first calculated, and based on this the predicted drought classes 1 and 2 months ahead were obtained. The results showed that despite the contingency tables of drought class transitions presented the maintenance of the precedent drought class, results of three-dimensional loglinear modeling presented good results when comparing predicted and observed drought classes. Only for a few cases predictions did not fully match the observed drought class, mainly for 2-month lead and when the SRI values are near the limit of the severity class predicted by SRI time series. Based on the correlation analysis of SPI and SRI, we presented the well-known method of hydrological drought class prediction by SPI time series. It was found that, using loglinear regression method, the accuracy of predictions for 2-month lead predicted by SPI time series was higher than those predicted by SRI time series. When we divided the SPI and SRI time series into 2 sub-periods (pre- and post-1980 where land cover changed), we got the same drought class prediction as that predicted by the entire SPI and SRI time series, which illustrated that changes in land use did not affect predictions of hydrological drought classes in the Luanhe river basin. It could be concluded that loglinear prediction of drought class transitions is a useful tool for short term hydrological drought warning, and the results could provide significant information for water resources managers and policy makers to mitigate drought effects.     

Drought Forecasting Using Neural Networks,Wavelet Neural Networks,and Stochastic Models: Case of the Algerois Basin in North Algeria

Drought forecasting is a major component of a drought preparedness and mitigation plan. This paper focuses on an investigation of artificial neural networks (ANN) models for drought forecasting in the algerois basin in Algeria in comparison with traditional stochastic models (ARIMA and SARIMA models). A wavelet pre-processing of input data (wavelet neural networks WANN) was used to improve the accuracy of ANN models for drought forecasting. The standard precipitation index (SPI), at three time scales (SPI-3, SPI-6 and SPI-12), was used as drought quantifying parameter for its multiple advantages. A number of different ANN and WANN models for all SPI have been tested. Moreover, the performance of WANN models was investigated using several mother wavelets including Haar wavelet (db1) and 16 daubechies wavelets (dbn, n varying between 2 and 17). The forecast results of all models were compared using three performance measures (NSE, RMSE and MAE). A comparison has been done between observed data and predictions, the results of this study indicate that the coupled wavelet neural network (WANN) models were the best models for drought forecasting for all SPI time series and over lead times varying between 1 and 6 months. The structure of the model was simplified in the WANN models, which makes them very convenient and parsimonious. The final forecasting models can be utilized for drought early warning.

     

Drought Forecasting using Markov Chain Model and Artificial Neural Networks

Water resources management is a complex task. It requires accurate prediction of inflow to reservoirs for the optimal management of surface resources, especially in arid and semi-arid regions. It is in particular complicated by droughts. Markov chain models have provided valuable information on drought or moisture conditions. A complementary method, however, is required that can both evaluate the accuracy of the Markov chain models for predicted drought conditions, and forecast the values for ensuing months. To that end, this study draws on Artificial Neural Networks (ANNs) as a data-driven model. The employed ANNs were trained and tested by means of a statistically-based input selection procedure to accurately predict reservoir inflow and consequently drought conditions. Thirty three years’ data of inflow volume on a monthly time resolution were selected to enable calculation of the standardized streamflow index (SSI) for the Markov chain model. Availability of hydro-climatic data from the Doroodzan reservoir in the Fars province, Iran, allowed us to develop a reservoir specific ANN model. Results demonstrated that both models accurately predicted drought conditions, by employing a randomization procedure that facilitated the selection of the required data for the ANN to forecast reservoir inflow close to the observed values over a validation period. The results confirmed that combining the two models improved short-term prediction reliability. This was in contrast to single model applications that resulted into substantial uncertainty. This research emphasized the importance of the correct selection of data or data mining, prior to entering a specific modeling routine.     

Simple Short-Term Probabilistic Drought Prediction Using Mediterranean Teleconnection Information

Timely forecasts of the onset or possible evolution of droughts is an important contribution to mitigate their manifold negative effects; therefore, in this paper, we propose a mathematically-simple drought forecasting framework gaining Mediterranean Sea temperature information (SST-M) to predict droughts. Agro-metrological drought index addressing seasonality and autocorrelation (AMDI-SA) was used in a Markov model in Urmia lake basin, North West of Iran. Markov chain is adopted to model drought for joint occurrence of different classes of drought severity and sea surface temperature of Mediterranean Sea, which is called 2D Markov chain model. The proposed model, which benefits suitability of Markov chain models for modeling droughts, showed improvement results in prediction scores relative to classic Markov chain model not including SST-M information, additionally.     

Comparability Analyses of the <Emphasis Type="Italic">SPI</Emphasis> and <Emphasis Type="Italic">RDI</Emphasis> Meteorological Drought Indices in Different Climatic Zones

Comparability analyses are performed to investigate similarities/differences of the standard precipitation index (SPI) and the reconnaissance drought index (RDI), respectively, utilizing precipitation and ratio of precipitation over potential evapotranspiration (ET ₀). Data are from stations with different climatic conditions in Iran. Drought characteristics of the 3-month, 6-month and annual SPI and RDI time series are developed and Markov chain order dependencies are investigated by the Log-likelihood, AIC and BIC tests. Steady state probabilities and Markov chain characteristics, i.e., expected residence time in different drought classes and time to reach “Near Normal” class are investigated. According to results, both indices exhibit an overall similar behaviour; particularly, they follow the first order Markov chain dependency. However, climatic variability may produce some differences. In several cases, the “Extremely Dry” class has received a more critical value by RDI. Furthermore, the expected residence time of “Near Normal” class and expected time to reach “Near Normal” class are quite different in a number of cases. The results show that the RDI by utilizing the ET ₀ can be very sensitive to climatic variability. This is rather important, since if the drought analyses are for agricultural applications, utilization of the RDI would seem to serve a better purpose.     

Assessment of Hydrological Drought Revisited

A variety of indices for characterising hydrological drought have been devised which, in general, are data demanding and computationally intensive. On the contrary, for meteorological droughts very simple and effective indices such as the Standardised Precipitation Index (SPI) have been used. A methodology for characterising the severity of hydrological droughts is proposed which uses an index analogous to SPI, the Streamflow Drought Index (SDI). Cumulative streamflow is used for overlapping periods of 3, 6, 9 and 12 months within each hydrological year. Drought states are defined which form a non-stationary Markov chain. Prediction of hydrological drought based on precipitation is also investigated. The methodology is validated using reliable data from the Evinos river basin (Greece). It can be easily applied within a Drought Watch System in river basins with significant storage works and can cope with the lack of streamflow data.     

Water Quality Prediction of Gangapur Reservoir (India) Using LS‐SVM and Genetic Programming

Water quality analysis involves analysis of physio‐chemical, biological and microbiological parameters that reflect the abiotic and biotic status of ecosystems. This assessment facilitates planning for the utilization, antipollution and conservation strategies for sustainable use of aquatic ecosystem. Many mathematical models are available for predicting water quality. They have complex structures and require detailed information about sources and receptors, which are difficult and non‐economical. Difficulties in applying mathematical models promote the application of alternative approaches for data‐driven techniques for analysis of the results. The present study focuses on water quality predictions for the Gangapur Reservoir for a 30 days in advance scenario, using genetic programming (GP) and least square support vector machines (LS‐SVMs). A data period of 11 years (2000–2011) of Gangapur Reservoir temporal water quality was evaluated. The data were taken from a single sampling point representing climatological, hydrological and surface water quality measurements. One of the most important steps in application of data‐driven technique is selection of significant input parameters. Genetic programming equations were used for selecting significant input parameters. These significant input parameters are used for 30 days advance predictions of faecal coliform. A performance analysis of GP and LS‐SVM models was carried out with the help of coefficient of determination, root‐mean‐square error and correlation coefficient. In the absence of availability of data, a typical situation for Indian case studies, the model runs were conducted with the use of available parameters. The developed models, along with their performance indicators, also are discussed.     

Integrated Methodological Framework for Assessing the Risk of Failure in Water Supply Incorporating Drought Forecasts. Case Study: Andean Regulated River Basin

Hydroclimatic drought conditions can affect the hydrological services offered by mountain river basins causing severe impacts on the population, becoming a challenge for water resource managers in Andean river basins. This study proposes an integrated methodological framework for assessing the risk of failure in water supply, incorporating probabilistic drought forecasts, which assists in making decisions regarding the satisfaction of consumptive, non-consumptive and environmental requirements under water scarcity conditions. Monte Carlo simulation was used to assess the risk of failure in multiple stochastic scenarios, which incorporate probabilistic forecasts of drought events based on a Markov chains (MC) model using a recently developed drought index (DI). This methodology was tested in the Machángara river basin located in the south of Ecuador. Results were grouped in integrated satisfaction indexes of the system (DSI_G). They demonstrated that the incorporation of probabilistic drought forecasts could better target the projections of simulation scenarios, with a view of obtaining realistic situations instead of optimistic projections that would lead to riskier decisions. Moreover, they contribute to more effective results in order to propose multiple alternatives for prevention and/or mitigation under drought conditions.     

Evaluation of Drought Condition in Arid and Semi- Arid Regions,Using RDI Index

Investigation of drought event has a great importance in the natural resources management and planning water resources management. One strategy to manage drought is to predict drought conditions by probabilistic tools. In this study climate data of 11 synoptic stations in south of Iran during 1980–2014 were used to estimate of seasonal drought based on RDI index. To prediction of drought (from 2015 to 2020) and analysis of changes trend of it, time series model, first-order Markov Chain model and parametric and non- parametric statistical methods were used. Results showed that MA (5), MA (10), AR (12) and AR (15) were the best time series models that fitted in data of all stations. According to results of prediction of drought classes, classes with normal and moderate dry condition had allocated the most frequency of seasonal drought classes from 2015 to 2020 based on time series model and Markov Chain method. Analysis of changes trend of drought classes showed that based on observed data (1980–2014) and predicted data (1980–2020) changes trend of drought classes in all stations had increasing trend based on parametric and non- parametric statistical methods but increasing trend in about 27% of stations include: Bandar Abbas, Bandar Lengeh, Jask and Shiraz had significant level of 5%. Finally result showed that the study area in 2020 compared to 2014 will be drier.     

Operational Prediction of Groundwater Fluctuation in South Florida using Sequence Based Markovian Stochastic Model

The ecosystem of South Florida is characterized by a vast wetland system, karst surficial hydrogeology, and extended coastal boundary. The ecosystem is poised under risks of: ecological failure due to increased fragmentation by urbanization; groundwater flow disruption because of sinkhole formation; and intrusion of oceanic water with decreasing water table head because of drought or over pumping. It was found important to synthesize the spatiotemporal state of the groundwater hydrology and also develop a forecasting model to support the intensive management and monitoring in place. In this study, an objective was set to develop a stochastic sequence model capable of forecasting groundwater levels on a monthly span at a daily time scale. The groundwater level simulation was conceptualized as a sequence of daily fluctuating states of magnitudes and patterns that has a defined probability of occurrence. The model setup involved representation of daily fluctuation magnitudes in ten states and pattern changes in three states. The sequential occurrence of states of magnitudes and patterns at each time step was used for estimation of the transitional probabilities and employed in a hidden Markov model frame work for ensemble generation and estimation of posterior probabilities. A realization was chosen based on the highest maximum likelihood ratio of 90% and smallest root mean square error of 0.05–0.12 m against the historical data. A monthly forecasting at daily time step was done dynamically incorporating observed data at each time step and revising prior and posterior probability estimation in the hidden Markov model formulation. A case study was conducted at three well sites, which are situated at three different hydrogeologic settings. The model not only reproduced annual groundwater fluctuation patterns but also forecasted preceding monthly fluctuations at maximum likelihood ratio above 90% and root mean square error below 0.15 m. A further study was recommended first to analyze break point parametric estimation for seasonal analysis, and secondly to integrate the approach in other hydrological models for the purpose of synthetic groundwater fluctuation generation.     

Integrated Reservoir Management System for Adaptation to Climate Change: The Nakdong River Basin in Korea

This study begins with the premise that current reservoir management systems do not take into account the potential effects of climate change on optimal performance. This study suggests an approach in which multi-purpose reservoirs can adapt to climate change using optimal rule curves developed by an integrated water resources management system. The system has three modules: the Weather Generator model, the Hydrological Model, and the Differential Evolution Optimization Model. Two general circulation models (GCMs) are selected as examples of both dry and wet conditions to generate future climate scenarios. This study is using the Nakdong River basin in Korea as a case study, where water supply is provided from the reservoir system. Three different climate change conditions (historic, wet and dry) are investigated through the compilation of six 60 years long scenarios. The optimal rule curves for three multi-purpose reservoirs in the basin are developed for each scenario. The results indicate that although the rule curve for large-size reservoir is less sensitive to climate change, medium or small-size reservoirs are very sensitive to those changes. We further conclude that the large reservoir should be used to release more water, while small or medium-size reservoirs should store inflow to mitigate severe drought damages in the basin.     

Recent Climate Trends and Drought Behavioral Assessment Based on Precipitation and Temperature Data Series in the Songhua River Basin of China

Precise analysis of spatiotemporal trends of temperature, precipitation and meteorological droughts plays a key role in the sustainable management of water resources in the given region. This study first aims to detect the long-term climate (monthly/seasonally and annually) trends from the historical temperature and precipitation data series by applying Spearmen’s Rho and Mann-Kendall test at 5 % significant level. The measurements of both climate variables for a total period of 49 years (1965–2013) were collected from the 11 different meteorological stations located in the Songhua River basin of China. Secondly, the two well-known meteorological drought indices including the Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) were applied on normalize data to detect the drought hazards at 3, 6, 9 and 12 month time scale in the study area. The analysis of monthly precipitation showed significant (p < 0.05) increasing trends during the winter (November and December months) season. Similarly, the results of seasonal and annual air temperature showed a significant increase from 1 °C to 1.5 °C for the past 49 years in the basin. According to the Sen’s slope estimator, the rate of increment in seasonal temperature slope (0.26 °C/season) and precipitation (9.02 mm/season) were greater than annual air temperature (0.04 °C/year) and precipitation (1.36 mm/year). By comparing the results of SPI and RDI indices showed good performance at 9 (r = 0.96, p < 0.01) and 12 (r = 0.99, p < 0.01) month drought analysis. However, the yearly drought analysis at over all stations indicated that a 20 years were under dry conditions in entire study area during 49 years. We found the extreme dry and wet conditions in the study region were prevailing during the years of 2001 and 2007, and 1994 and 2013, respectively. Overall, the analysis and quantifications of this study provides a mechanism for the policy makers to mitigate the impact of extreme climate and drought conditions in order to improve local water resources management in the region.

     

Drought Monitoring by Reconnaissance Drought Index (RDI) in Iran

Drought is one of the most important natural hazards in Iran and frequently affects a large number of people, causing tremendous economic losses, environmental damages and social hardships. Especially, drought has a strong impact on water resources in Iran. This situation has made more considerations toward the study and management of drought. The present study is focused on two important indices; SPI and RDI, for 3, 6, 9, 12, 18 and 24 months time scales in 40 meteorological synoptic stations in Iran. In the case of RDI computation, potential evapotranspiration was an important factor toward drought monitoring. So, evapotranspiration was calculated by Penman-Monteith equation. The correlation of RDI and SPI was also surveyed. Drought severity maps for SPI and RDI were also presented in the driest year (1999–2000). The present results have shown that the correlation of SPI and RDI was more considerable in the 3, 6 and 9 months than longer time scales. Furthermore, drought severity maps have shown that during 1999–2000, the central, eastern and south-eastern parts of Iran faced extremely dry conditions. While, according to SPI and RDI trends, other parts of the country suffered from severe drought. The SPI and RDI methods showed approximately similar results for the effect of drought on different regions of Iran. Since, RDI resolved more climatic parameters, such as evapotranspiration, into account which had an important role in water resource losses in the Iranian basins, it was worthwhile to consider RDI in drought monitoring in Iran, too.     

Critical Flow for Water Management in a Shallow Tidal River Based on Estuarine Residence Time

To support the development of protective water resources management strategies, a 3D hydrodynamic model was applied to the Little Manatee River (LMR) to evaluate the effects of reducing river flow and drought on the Estuarine Residence Time (ERT). ERT is an important indicator for estuarine environmental quality. The Little Manatee River is a small tidal river estuary with a yearly mean gaged freshwater inflow of 4.8 m³/s. The hydrodynamic model was calibrated and verified by using two continuous data sets for a six month period. Model simulations were conducted for 17 river inflow scenarios. Among the flow scenarios, 13 scenarios were within a flow range from 0.26 m³/s to 10 m³/s total freshwater inflow. A regression equation (R ² = 0.98) fitted by a power-law function was derived from analysis of the hydrodynamic modeling results to correlate model predicted ERT to total river inflow, though ERT can be predicted from gaged freshwater inflow as well. The study indicates that the estuarine residence time reaches 53.3 days under an extreme drought condition of 0.26 m³/s total inflow. When river inflow falls below the critical flow (4 m³/s or less), further flow reductions can cause the substantial increases of ERT by a factor of 2 to 10 times. This suggests that the management of flow reductions is particularly critical when total river flows are 4 m³/s or less if adverse impacts to the water quality and ecological characteristics of the Little Manatee River are to be avoided.     

Analysis of Drought from Humid,Semi-Arid and Arid Regions of India Using DrinC Model with Different Drought Indices

The study aims at evaluating the various drought indices for the humid, semi-arid and arid regions of India using conventional indices, such as rainfall anomaly index, departure analysis of rainfall and other indices such as Standard Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) that were analyzed using the DrinC software. In SPI, arid region has seven drought years, whereas humid and semi-arid regions have four. In case of RDI, the humid and semi-arid regions have 11 drought years, whereas arid regions have 10 years. The difference in SPI and RDI was due to the fact that RDI considered potential evapotranspiration, and hence, correlation with plants would be better in case of RDI. Humid region showed a decreasing trend in initial value of RDI during the drought as compared to semiarid and arid regions and indicated possible climate change impact in these regions. Among all the indices, RDI was considered as an effective indicator because of implicit severity and high prediction matches with the actual drought years. SPI and RDI were found to be well correlated with respect to 3 months rainfall data and SPI values led to prediction of annual RDI. The results of our study established that this correlation could be used for developing disaster management plan well in advance to combat the drought consequences.

     

标准化径流指数在阿克苏河水文干旱特征识别中的应用

阿克苏河位于丝绸之路经济带重要通道,是典型的中亚跨境河流,阿克苏丰富的水资源成为沿岸各国争夺的焦点。针对阿克苏河未来水资源评估和管理的迫切需要,以阿克苏河干流月径流为数据基础,结合径流距平百分比,采用标准化径流指数(Standardized Runoff Index,SRI)辨识阿克苏河水文干旱事件,并验证了水文干旱识别的合理性。结果表明:SRI丰枯等级划分临界值在阿克苏河能够有效地识别水文干旱事件及其干旱等级,SRI趋势分析发现未来阿克苏河春、夏季洪水和冬旱现象的可能性在逐渐增大,水资源季节差异将越来越明显,这将是未来水资源合理利用和管理的一个新挑战。     

New Insights Into The Natural Variability of Water Resources in The Lower Jordan River Basin

Water availability is naturally low in the Lower Jordan River Basin (LJRB) extending from Lake Tiberias to the Dead Sea, whereas water demand is high. Still, no basin-wide overview of naturally available surface water resources exists up to now. The aim of this study is to estimate these water resources through application of the TRAIN-ZIN model. This hydrological model combines physically-based and conceptual approaches to incorporate dominant processes of (semi-)arid areas in adequate temporal and spatial scale. An adequate space-time resolution is achieved by using rainfall radar data as model input. Three rainfall seasons are simulated: a drought, an average season and a wet extreme. Simulation results emphasize the non-linear behaviour of (semi-)arid systems and resulting impacts on the spatial and temporal variability of water resources. Basin averages of seasonal water balance components ranged between 65 and 489 mm (rainfall), 53 and 270 mm (evapotranspiration), 7 and 87 mm (overland flow), 4 and 129 mm (percolation). However, all values comprise enormous spatial variability. It is concluded that space-time variability must be considered for water resources assessment in the LJRB in order to make accurate predictions of future water availability.     

Droughts Prediction: a Methodology Based on Climate Seasonal Forecasts

This study proposes a methodology for the drought assessment based on the seasonal forecasts. These are climate predictions of atmospheric variables, such as precipitation, temperature, wind speed, for upcoming season, up to 7 months. In regions particularly vulnerable to droughts and to changes in climate, such as the Mediterranean areas, predictions of precipitation with months in advance are crucial for understanding the possible shifts, for example, in water resource availability. Over Europe, practical applications of seasonal forecasts are still rare, because of the uncertainties of their skills; however, the predictability varies depending on the season and area of application. In this study, we describe a methodology which integrates, through a statistical approach, seasonal forecast and reanalysis data to assess the climate state, i.e. drought or not, of a region for predefined periods in the next future, at monthly scale. Additionally, the skill of the forecasts and the reliability of the released climate state assessment are estimated in terms of the false rate, i.e. the probability of missing alerts or false alarms. The methodology has been first built for a case study in Zakynthos (Greece) and then validated for a case study in Sicily (Italy). The selected locations represent two areas of the Mediterranean region often suffering from drought and water shortage situations. Results showed promising findings, with satisfying matching between predictions and observations, and false rates ranging from 1 to 50%, depending on the selected forecast period.

     

Multi-Model Approach to Assess the Dynamics of Hydrologic Components in a Tropical Ecosystem

Estimation of terrestrial water budget at global and regional scales are essential for efficient agricultural water management, flood predictions, and, hydrological modeling. In hydrological modeling, it is a challenging task to quantify the major hydrological components like runoff, evapotranspiration (ET), and total water storage (TWS) due to improper and limited availability of detailed meteorological datasets. Furthermore, there has been no consensus to answer a-decade-long critical question that a less data-intensive models can be an alternate to robust data-intensive models in data scarce conditions. This study aims at multi-model approach over the single models usage for representing the hydrological behaviour in the Kangsabati River Basin (KRB), India. It is done by applying the standard model selection criteria over various hydrological models. Two hydrological models are selected, a semi- distributed model, Variable Infiltration Capacity (VIC-3 L), and a conceptually lumped model, Identification of unit Hydrograph and Component flows from Rainfall, Evapotranspiration and Streamflow (IHACRES). Both models were calibrated against the observed daily discharge at the KRB outlet for the period of 2001–2006 and validated for 2008–2010. The results show that both VIC-3 L and IHACRES produce reasonable runoff estimates at daily and monthly time scale in the KRB. The ET estimates show that VIC-3 L and IHACRES captured the seasonal variations with the percent change of 0.4% and 6.6% respectively. As IHACRES is simpler, parsimonious, fewer parameters, and better performances, it can be useful for hydrological modeling in data-scarce regions.