Skill assessment of a seasonal forecast model to predict drought events for water resource systems (original) (raw)

Drought forecasting using the standardized precipitation index

Water resources …, 2007

Unlike other natural disasters, drought events evolve slowly in time and their impacts generally span a long period of time. Such features do make possible a more effective drought mitigation of the most adverse effects, provided a timely monitoring of an ...

Global meteorological drought – Part 2: Seasonal forecasts

Hydrology and Earth System Sciences, 2014

Global seasonal forecasts of meteorological drought using the standardized precipitation index (SPI) are produced using two data sets as initial conditions: the Global Precipitation Climatology Centre (GPCC) and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis (ERAI); and two seasonal forecasts of precipitation, the most recent ECMWF seasonal forecast system and climatologically based ensemble forecasts. The forecast evaluation focuses on the periods where precipitation deficits are likely to have higher drought impacts, and the results were summarized over different regions in the world. The verification of the forecasts with lead time indicated that generally for all regions the least reduction on skill was found for (i) long lead times using ERAI or GPCC for monitoring and (ii) short lead times using ECMWF or climatological seasonal forecasts. The memory effect of initial conditions was found to be 1 month of lead time for the SPI-3, 4 months for the SPI-6 and 6 (or more) months for the SPI-12. Results show that dynamical forecasts of precipitation provide added value with skills at least equal to and often above that of climatological forecasts. Furthermore, it is very difficult to improve on the use of climatological forecasts for long lead times. Our results also support recent questions of whether seasonal forecasting of global drought onset was essentially a stochastic forecasting problem. Results are presented regionally and globally, and our results point to several regions in the world where drought onset forecasting is feasible and skilful.

Prospects for Dynamical Prediction of Meteorological Drought

Journal of Applied Meteorology and Climatology, 2012

The prospects for U.S. seasonal drought prediction are assessed by diagnosing simulation and hindcast skill of drought indicators during 1982–2008. The 6-month standardized precipitation index is used as the primary drought indicator. The skill of unconditioned, persistence forecasts serves as the baseline against which the performance of dynamical methods is evaluated. Predictions conditioned on the state of global sea surface temperatures (SST) are assessed using atmospheric climate simulations conducted in which observed SSTs are specified. Predictions conditioned on the initial states of atmosphere, land surfaces, and oceans are next analyzed using coupled climate-model experiments. The persistence of the drought indicator yields considerable seasonal skill, with a region’s annual cycle of precipitation driving a strong seasonality in baseline skill. The unconditioned forecast skill for drought is greatest during a region’s climatological dry season and is least during a wet sea...

Application of Climate Information and Predictions in Water Sector: Capabilities

Climate variability and climate change have a large impact on water resources since fundamental drivers of the hydrological cycle get affected. It is beneficial to understand the processes driving these changes, the sequences of the changes and their manifestation at different spatial and temporal scales. The purpose of this paper is to explore strategies to improve water management by tracking, anticipating and responding to seasonal to interannual climate variability and climate change. Sound water management is built upon long-term hydrological and meteorological monitoring networks that provide robust, accurate, timely and consistent data that can be used to develop and access tools needed to quantify uncertainty, forecast change and create the multi-phase, multi-level climate scenarios providing reasonable and relevant management of water resources. Several water management options might be considered in consultation with hydro-climatic and social scientists and stakeholders (decision-makers) to facilitate adaptation under climate variability and/or climate change and these are illustrated with suitable examples.

Forecasting of Drought: A Case Study of Water-Stressed Region of Pakistan

Atmosphere, 2021

Demand for water resources has increased dramatically due to the global increase in consumption of water, which has resulted in water depletion. Additionally, global climate change has further resulted as an impediment to human survival. Moreover, Pakistan is among the countries that have already crossed the water scarcity line, experiencing drought in the water-stressed Thar desert. Drought mitigation actions can be effectively achieved by forecasting techniques. This research describes the application of a linear stochastic model, i.e., Autoregressive Integrated Moving Average (ARIMA), to predict the drought pattern. The Standardized Precipitation Evapotranspiration Index (SPEI) is calculated to develop ARIMA models to forecast drought in a hyper-arid environment. In this study, drought forecast is demonstrated by results achieved from ARIMA models for various time periods. Result shows that the values of p, d, and q (non-seasonal model parameter) and P, D, and Q (seasonal model p...

Drought Forecasting: A Review and Assessment of the Hybrid Techniques and Data Pre-Processing

Hydrology

Drought is a prolonged period of low precipitation that negatively impacts agriculture, animals, and people. Over the last decades, gradual changes in drought indices have been observed. Therefore, understanding and forecasting drought is essential to avoid its economic impacts and appropriate water resource planning and management. This paper presents a recent literature review, including a brief description of data pre-processing, data-driven modelling strategies (i.e., univariate or multivariate), machine learning algorithms (i.e., advantages and disadvantages), hybrid models, and performance metrics. Combining various prediction methods to create efficient hybrid models has become the most popular use in recent years. Accordingly, hybrid models have been increasingly used for predicting drought. As such, these models will be extensively reviewed, including preprocessing-based hybrid models, parameter optimisation-based hybrid models, and hybridisation of components combination-b...

Drought Risk Analysis, Forecasting and Assessment under Climate Change

Water, 2020

Climate change is undoubtedly one of the world’s biggest challenges in the 21st century. Drought risk analysis, forecasting and assessment are facing rapid expansion, not only from theoretical but also practical points of view. Accurate monitoring, forecasting and comprehensive assessments are of the utmost importance for reliable drought-related decision-making. The framework of drought risk analysis provides a unified and coherent approach to solving inference and decision-making problems under uncertainty due to climate change, such as hydro-meteorological modeling, drought frequency estimation, hybrid models of forecasting and water resource management. This Special Issue will provide researchers with a summary of the latest drought research developments in order to identify and understand the profound impacts of climate change on drought risks and water resources. The ten peer-reviewed articles collected in this Special Issue present novel drought monitoring and forecasting app...

Baseline Probabilities for the Seasonal Prediction of Meteorological Drought

Journal of Applied Meteorology and Climatology, 2012

The inherent persistence characteristics of various drought indicators are quantified to extract predictive information that can improve drought early warning. Predictive skill is evaluated as a function of the seasonal cycle for regions within North America. The study serves to establish a set of baseline probabilities for drought across multiple indicators amenable to direct comparison with drought indicator forecast probabilities obtained when incorporating dynamical climate model forecasts. The emphasis is on the standardized precipitation index (SPI), but the method can easily be applied to any other meteorological drought indicator, and some additional examples are provided. Monte Carlo resampling of observational data generates two sets of synthetic time series of monthly precipitation that include, and exclude, the annual cycle while removing serial correlation. For the case of no seasonality, the autocorrelation (AC) of the SPI (and seasonal precipitation percentiles, movin...

Drought Forecasting Using Stochastic Models in a Hyper-Arid Climate

Drought forecasting plays a crucial role in drought mitigation actions. Thus, this research deals with linear stochastic models (autoregressive integrated moving average (ARIMA)) as a suitable tool to forecast drought. Several ARIMA models are developed for drought forecasting using the Standardized Precipitation Evapotranspiration Index (SPEI) in a hyper-arid climate. The results reveal that all developed ARIMA models demonstrate the potential ability to forecast drought over different time scales. In these models, the p, d, q, P, D and Q values are quite similar for the same SPEI time scale. This is in correspondence with autoregressive (AR) and moving average (MA) parameter estimate values, which are also similar. Therefore, the ARIMA model (1, 1, 0) (2, 0, 1) could be considered as a general model for the Al Qassim region. Meanwhile, the ARIMA model (1, 0, 3) (0, 0, 0) at 3-SPEI and the ARIMA model (1, 1, 1) (2, 0, 1) at 24-SPEI could be generalized for the Hail region. The ARIMA models at the 24-SPEI time scale is the best forecasting models with high R2 (more than 0.9) and lower values of RMSE and MAE, while they are the least forecasting at the 3-SPEI time scale. Accordingly, this study recommends that ARIMA models can be very useful tools for drought forecasting that can help water resource managers and planners to take precautions considering the severity of drought in advance. OPEN ACCESS