Drought early warning based on optimal risk forecasts in regulated river systems: Application to the Jucar River Basin (Spain)

Abstract

Droughts are a major threat to water resources systems management. Timely anticipation results crucial to defining strategies and measures to minimise their effects. Water managers make use of monitoring systems in order to characterise and assess drought risk by means of indices and indicators. However, there are few systems currently in operation that are capable of providing early warning with regard to the occurrence of a drought episode. This paper proposes a novel methodology to support and complement drought monitoring and early warning in regulated water resources systems. It is based in the combined use of two models, a water resources optimization model and a stochastic streamflow generation model, to generate a series of results that allow evaluating the future state of the system. The results for the period 1998-2009 in the Jucar River Basin (Spain) show that accounting for scenario change risk can be beneficial for basin managers by providing them with information on the current and future drought situation at any given moment. Our results show that the combination of scenario change probabilities with the current drought monitoring system can represent a major advance towards improved drought management in the future, and add a significant value to the existing national State Index (SI) approach for early warning purposes. Highlights • Modelling the past to anticipate future drought is an ineffective and risky approach • A new method for continuous drought monitoring and early warning in regulated catchments is proposed • Reservoir storage probability is a reliable indicator for drought status in regulated catchments

Automatic design of basin-specific drought indexes for highly regulated water systems

“…The weights are established according to the demand class associated with the indicator, ranging from class A (demand > 100 hm 3 year −1 ) to D (demand < 10 hm 3 year −1 ). The Jucar river basin represents a Mediterranean droughtprone and highly regulated basin, featuring one of the most innovative and effective drought management systems, relying on the formulation of an empirically constructed basinspecific drought index (Andreu et al, 2009;Haro et al, 2014b;Haro-Monteagudo et al, 2017;Carmona et al, 2017). As a consequence, it represents the state of the art for basin-customized operational drought indexes employed for drought-restraining purposes, and a remarkable benchmark to test and validate the proposed FRIDA methodology.…”

“…In the computation of operational drought indicators, the available water is often represented by the streamflow, or a fraction of it, and the water need is usually quantified by a standard per capita or by a fixed nominal demand (Falkenmark et al, 1989;Raskin et al, 1997). Depending on the application scope, operational drought indicators are either river-basin-specific (Garrote et al, 2007;Haro-Monteagudo et al, 2017) or used in studies covering continental or global areas with an annual time resolution (Yang et al, 2003;Oki and Kanae, 2006;Alcamo et al, 2007;Kummu et al, 2010).…”

“…The potential of the proposed framework is demonstrated by the highly regulated Mediterranean basin of the Jucar river, in eastern Spain, where the state-index-based drought management system provides an ideal benchmark for testing the FRIDA index (Andreu et al, 2009;Haro et al, 2014b;Pedro-Monzonís et al, 2014;Macian-Sorribes and PulidoVelazquez, 2017;Haro-Monteagudo et al, 2017;Carmona et al, 2017). The Jucar state index provides guidelines for the application of FRIDA.…”

Assessment Analysis and Forecasting for Security Early Warning of Energy Consumption Carbon Emissions in Hebei Province, China

“…At present, energy consumption carbon emissions (ECCE) are a practical issue which runs through the political, economy, social and other fields more than a scientific problem. In this context, carrying out systematic studies on ECCE has become a priority in ecological environment, natural disaster and so on, such as river drought and pollution [14,15], air quality [16], earthquake [17] and floods [18], mainly include the following methods: Markov model [19], Gray model (GM) [20], support vector machine [21], neural networks [22] and so forth. Among these methods, the back propagation neural network (BPNN) which was proposed based on a neural network put forward by Rumelhart and McClelland [22], has better performance in forecasting with its strong non-linear mapping ability, high self-learning and self-adaptability [23,24].…”

Automatic design of basin-specific drought indexes for highly regulated water systems

“…In the computation of 80 operational drought indicators, the available water is often represented by the streamflow, or a fraction of it, and the water need is usually quantified by a standard per capita or by a fixed nominal demand (Falkenmark et al, 1989;Raskin et al, 1997). Depending on the application scope, operational drought indicators are either river basin specific (Garrote et al, 2007;Haro-Monteagudo et al, 2017) or used in studies covering continental or global areas with an annual time resolution (Yang 85 et al, 2003;Oki and Kanae, 2006;Alcamo et al, 2007;Kummu et al, 2010).…”