Assessment of Probabilistic Multi-Index Drought Using a Dynamic Naive Bayesian Classifier

The proper consideration of all plausible feature spaces of the hydrological cycle and inherent uncertainty in preceding developed drought indices is inevitable for comprehensive drought assessment. Therefore, this study employed the Dynamic Naive Bayesian Classifier (DNBC) for multi-index probabilistic drought assessment by integrating various drought indices (i.e., Standardized Precipitation Index (SPI), Streamflow Drought Index (SDI), and Normalized Vegetation Supply Water Index (NVSWI)) as indicators of different feature spaces (i.e., meteorological, hydrological, and agricultural) contributing to drought occurrence. The overall results showed that the proposed model was able to account for various physical forms of drought in probabilistic drought assessment, to accurately detect a drought event better than (or occasionally equal to) any single drought index, to provide useful information for assessing potential drought risk, and to precisely capture drought persistence in terms of drought state transition probability in drought monitoring. This easily produced an alternative method for comprehensive drought assessment with combined use of different drought indices.     

Prediction of SPI Drought Class Transitions Using Markov Chains

Using the SPI relative to 67 years data sets, a Markov chains approach has been utilized for several locations in Alentejo, southern Portugal, to characterize the stochasticity of droughts, which allowed predicting the transition from a class of severity to another up to 3 months ahead. Markov models were applied using both the homogeneous and non-homogeneous formulations. The results of the application of the Markov models are presented and discussed, showing in particular the usefulness of adopting a non-homogeneous formulation, which allows to differentiate predictions in relation to the initial month considered, thus understanding the probable evolution of a drought as influenced by the climate and, in particular, the seasonality of rainfall. However, these results, which are promising in view of drought management, require further developments and to be associated with other predictive tools of stochastic or physical nature. Possible approaches on using predictions of drought class transitions in view of drought risk management are also discussed.     

Application of Heuristic Approaches for Prediction of Hydrological Drought Using Multi-scalar Streamflow Drought Index

Water Resources Management - Quantification and prediction of drought events are important for planning and management of water resources in coping with climate change scenarios at global and local...     

Large Scale Probabilistic Drought Characterization Over Europe

A reliable assessment of drought return periods is essential to help decision makers in setting effective drought preparedness and mitigation measures. However, often an inferential approach is unsuitable to model the marginal or joint probability distributions of drought characteristics, such as drought duration and accumulated deficit, due to the relatively limited number of drought events that can be observed in the historical records of the hydrological variables of interest. As an alternative, the marginal and multivariate probability cdf’s of drought characteristics can be derived as functions of the parameters of the cdf of the underlying variable (e.g. precipitation), whose sample series is usually long enough to obtain trustworthy estimates in a statistical sense. In this study, the latter methodology is applied to investigate space-time variability of drought occurrences over Europe by using the CRU TS3.10.01 precipitation dataset for the period 1901–2009. In particular, a methodology able to take into account autocorrelation in the underlying precipitation series is adopted. First, a spatial analysis of historical droughts at European level is carried out. Then, the joint probability distributions of drought duration and accumulated deficit are derived for each cell, with reference to both historical and design drought events. Finally, the corresponding bivariate drought return periods are computed, as the expected values of the interarrival time between consecutive critical droughts.Results show that several heavy drought episodes have widely affected the continent. Among the most recent events, drought occurred during the period 1985–1995 was the worst in terms of extent of the regions characterized by return periods greater than 250 years. Besides Euro-Mediterranean regions, North Western and Central Eastern regions appear more drought prone than the rest of Europe, in terms of low values of return periods.     

An Approach for Probabilistic Hydrological Drought Forecasting

This paper proposes an approach to monitor and forecast hydrological drought in a probabilistic manner. The proposed approach deals with the supply and demand variables and the role of carryover in a system to estimate the probability of drought severity at different hydroclimatlogical conditions as well as different storage volume levels. This approach might be of significance when the supply and demand variables of a water resources system change considerably by climate variation. Major probability values and their mutual use in the proposed drought forecasting method are discussed. The presented approach is applied for the hydrological drought forecasting of Zayandeh-rud river basin in Iran. This probabilistic view of drought monitoring and forecasting is useful for risk-based decisions in water resources planning and management. The proposed index could be used to overcome the lack thereof in the existing surface water supply index.     

Probabilistic Characteristics of Drought Propagation from Meteorological to Hydrological Drought in South Korea

Water Resources Management - A reliable understanding of propagation from meteorological to hydrological drought is necessary for accurate forecasting of hydrological droughts. Our current...     

Gene-Expression Programming for Short-Term Forecasting of Daily Reference Evapotranspiration Using Public Weather Forecast Information

This study aimed to forecast the daily reference evapotranspiration (ETo) using a gene-expression programming (GEP) algorithm with limited public weather forecast information over Gaoyou station, located in Jiangsu province, China. To calibrate and validate the gene-expression code, important meteorological data and weather forecast information were collected from the local meteorological station and public weather media, respectively. The GEP algebraic formulation was successfully constructed based only on daily minimum and maximum air temperature using the true FAO56 Penman-Monteith (PM) set as reference values. The performance of the models was then assessed using the correlation coefficient (R), root mean squared error (RMSE), root relative squared error (RRSE) and mean absolute error (MAE). The study demonstrated that GEP is able to calibrate ETo (all errors ≤0.990 mm/day, R = 0.832–0.866) and forecast the daily ETo with good accuracy (RMSE = 1.207 mm/day, MAE = 0.902 mm/day, RRSE = 0.629 mm/day, R = 0.777). The model accuracies slightly decreased over a 7-day forecast lead-time. These results suggest that the GEP algorithm can be considered as a deployable tool for ETo forecast to anticipate decision on short-term irrigation schedule in the study zone.     

A Non-stationary and Probabilistic Approach for Drought Characterization Using Trivariate and Pairwise Copula Construction (PCC) Model

Under variable climatic conditions, the conventional Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) are inadequate for predicting extreme drought characteristics. Non-stationary Standardized Precipitation Index (NSPI) and Non-stationary Reconnaissance Drought Index (NRDI) are, therefore, developed by fitting non-stationary distributions. The Generalized Additive Model in Location, Scale and Shape (GAMLSS) framework, with time varying location parameters considering the external covariates, is used to fit the non-stationary distributions. Multivariate ENSO Index (MEI), Southern Oscillation Index (SOI), Sea Surface Temperature (SST), and Indian Ocean Dipole (IOD) are considered as external covariates for the non-stationary drought assessment. The performances of stationary and non-stationary models are compared. The study also concentrated on the trivariate and the Pairwise Copula Construction (PCC) models to estimate the drought return periods. The comparison of two copula models revealed that the PCC model performed better than the trivariate Student’s t copula model. The recurrence intervals arrived at for the drought events are different for trivariate copula model and PCC model. This study showed that non-stationary drought indices will be helpful in the accurate estimate of the drought characteristics under the changing climatic scenario.

     

Short-Term Urban Water Demand Prediction Considering Weather Factors

Accurate and reliable forecasting plays a key role in the planning and designing of municipal water supply infrastructures. Recent studies related to water demand prediction have shown that water demand is driven by weather variables, but the results do not clearly show to what extent. The principal aim of this research was to better understand the effects of weather variables on water demand. Additionally, it aimed to offer an appropriate and reliable technique to predict municipal water demand by using the Gravitational Search Algorithm (GSA) and Backtracking Search Algorithm (BSA) with Artificial Neural Network (ANN). Moreover, eight weather factors were adopted to evaluate their impact on the water demand. The principal findings of this research are that the hybrid GSA-ANN (Agent?=?40) model is superior in terms of fitness function (based on RMSE) for yearly and seasonal phases. In addition, it is evidently clear from the findings that the GSA-ANN model has the ability to simulate both seasonal and yearly patterns for daily data water consumption.     

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.     

Daily Streamflow Prediction and Uncertainty Using a Long Short-Term Memory (LSTM) Network Coupled with Bootstrap

Water Resources Management - Long short-term memory (LSTM) models with excellent data mining ability have great potential in streamflow prediction. The parameters and structure of the LSTM model,...     

Short-Term Streamflow Forecasting Using the Feature-Enhanced Regression Model

Reservoir inflow forecasting is extremely important for the management of a reservoir. In practice, accurate forecasting depends on the feature learning performance. To better address this issue, this paper proposed a feature-enhanced regression model (FER), which combined stack autoencoder (SAE) with long short-term memory (LSTM). This model had two constituents: (1) The SAE was constructed to learn a representation as close as possible to the original inputs. Through deep learning, the enhanced feature could be captured sufficiently. (2) The LSTM was established to simulate the mapping between the enhanced features and the outputs. Under recursive modeling, the patterns of correlation in the short term and dependence in the long term were considered comprehensively. To estimate the performance of the FER model, two historical daily discharge series were investigated, i.e., the Yangtze River in China and the Sava Dolinka River in Slovenia. The proposed model was compared with other machine-learning methods (i.e., the LSTM, SAE-based neural network, and traditional neural network). The results demonstrated that the proposed FER model yields the best forecasting performance in terms of six evaluation criteria. The proposed model integrates the deep learning and recursive modeling, and thus being beneficial to exploring complex features in the reservoir inflow forecasting. Moreover, for smaller catchments with significant torrential characteristics, more data are needed (e.g., at least 20 years) to effectively train the model and to obtain accurate flood-forecasting results.

     

灰色系统理论在干旱预测中的应用研究

干旱灾害频繁发生,导致了农业生产的恶性循环,造成环境的持续恶化和污染的加剧。开展干旱评估、预测预警等研究,已成为抵御干旱灾害亟待解决的问题。结合华县的年降水量资料,将干旱视为一个灰色系统,根据灰色GM(1,1)预测模型对未来干旱年份进行预测。经检验,模型结果符合实际,具有一定的可信度,能为华县地区抗旱和供水提供必要的预测信息。应用进行预测表明,华县2012年～2013年、2019年～2020年期间将发生干旱。     

基于灰色灾变及拓扑预测的干旱预测模型

采用1953—2006年河南省气象局历年降水资料,建立了灰色灾变预测模型和拓扑预测模型,对河南省未来的干旱情况进行了预测。灰色灾变模型预测结果表明,河南省将在2016年发生干旱。拓扑预测模型预测结果表明,河南省将在2017年和2025年发生干旱。据此,河南省在2016年、2017年、2025年发生干旱的可能性较大,应做好防旱抗旱的准备工作。     

Rainfall-Runoff Prediction Using Dynamic Typhoon Information and Surface Weather Characteristic Considering Monsoon Effects

Water Resources Management - In meteorology and engineering, the prediction of quantitative precipitation and streamflow during typhoon events is a vital research topic. In Southern Taiwan,...     

Probabilistic Event Based Rainfall-Runoff Modeling Using Copula Functions

Multivariate probability analysis of hydrological elements using copula functions can significantly improve the modeling of complex phenomena by considering several dependent variables simultaneously. The main objectives of this study were to: (i) develop a stand-alone and event-based rainfall-runoff (RR) model using the common bivariate copula functions (i.e. the BCRR model); (ii) improve the structure of the developed copula-based RR model by using a trivariate version of fully-nested Archimedean copulas (i.e. the FCRR model); and (iii) compare the performance of the developed copula-based RR models in an Iranian watershed. Results showed that both of the developed models had acceptable performance. However, the FCRR model outperformed the BCRR model and provided more reliable estimations, especially for lower joint probabilities. For example, when joint probabilities were increased from 0.5 to 0.8 for the peak discharge (q_p) variable, the reliability criteria value increased from 0.0039 to 0.8000 in the FCRR model, but only from 0.0010 to 0.6400 in the BCRR model. This is likely because the FCRR considers more than one rainfall predictor, while the BCRR considers only one.

     

Performance of Deterministic and Probabilistic Hydrological Forecasts for the Short-Term Optimization of a Tropical Hydropower Reservoir

Hydropower is the most important source of electricity in Brazil. It is subject to the natural variability of water yield. One building block of the proper management of hydropower assets is the short-term forecast of reservoir inflows as input for an online, event-based optimization of its release strategy. While deterministic forecasts and optimization schemes are the established techniques for short-term reservoir management, the use of probabilistic ensemble forecasts and multi-stage stochastic optimization techniques is receiving growing attention. The present work introduces a novel, mass conservative scenario tree reduction in combination with a detailed hindcasting and closed-loop control experiments for a multi-purpose hydropower reservoir in a tropical region in Brazil. The case study is the hydropower project Três Marias, which is operated with two main objectives: (i) hydroelectricity generation and (ii) flood control downstream. In the experiments, precipitation forecasts based on observed data, deterministic and probabilistic forecasts are used to generate streamflow forecasts in a hydrological model over a period of 2 years. Results for a perfect forecast show the potential benefit of the online optimization and indicate a desired forecast lead time of 30 days. In comparison, the use of actual forecasts of up to 15 days shows the practical benefit of operational forecasts, where stochastic optimization (15 days lead time) outperforms the deterministic version (10 days lead time) significantly. The range of the energy production rate between the different approaches is relatively small, between 78% and 80%, suggesting that the use of stochastic optimization combined with ensemble forecasts leads to a significantly higher level of flood protection without compromising the energy production.     

Identifying the Drought Impact Factors and Developing Drought Scenarios Using the DSD Model

Water Resources Management - Droughts have become increasingly severe and frequent due to climate change. Droughts cause water scarcity and various socio-economic issues. Therefore, it is necessary...     

Fuzzy Stress-based Modeling for Probabilistic Irrigation Planning Using Copula-NSPSO

Hydrological uncertainties are the main components of a sustainable framework in agricultural water management. Prediction of drought as a meteorological phenomenon should be considered to define the groundwater exploitation strategies. This study was conducted to develop a multiobjective-bivariate structure for reducing the soil moisture deficit and groundwater withdrawal in the Qazvin Irrigation District, Qazvin province, Iran. Therefore, non-dominated sorting theory, self-organizing particle swarm optimization and bivariate copula functions were incorporated under fuzzy uncertainty analysis. The results showed that the generalized extreme values and log-normal distribution functions had the best fitness on the drought peak and severity with Kolmogorov Smirnov amounts of 0.08 and 0.17, respectively. Furthermore, the goodness-of-fit tests were indicated the Joe joint function (MLE = 11) is the appropriate function for estimating the probabilistic values of drought characteristics. Proposed plans were to increase the water use efficiency for improving the expected yield production by an average of 20%. Furthermore, the standardized groundwater index was decreased from 1.1 to –4.3 for winter crops.