Drought forecasting using stochastic models

Abstract

Drought is a global phenomenon that occurs virtually in all landscapes causing significant damage both in natural environment and in human lives. Due to the random nature of contributing factors, occurrence and severity of droughts can be treated as stochastic in nature. Early indication of possible drought can help to set out drought mitigation strategies and measures in advance. Therefore drought forecasting plays an important role in the planning and management of water resource systems. In this study, linear stochastic models known as ARIMA and multiplicative Seasonal Autoregressive Integrated Moving Average (SARIMA) models were used to forecast droughts based on the procedure of model development. The models were applied to forecast droughts using standardized precipitation index (SPI) series in the Kansabati river basin in India, which lies in the Purulia district of West Bengal state in eastern India. The predicted results using the best models were compared with the observed data. The predicted results show reasonably good agreement with the actual data, 1-2 months ahead. The predicted value decreases with increase in lead-time. So the models can be used to forecast droughts up to 2 months of lead-time with reasonably accuracy.

Monthly Prediction of Drought Classes Using Log-Linear Models under the Influence of NAO for Early-Warning of Drought and Water Management

“…Among the techniques used for drought forecasting, statistical models are chosen many times, since they are simple to implement, do not have a high computational burden, and produce useful predictions [58]. There are a variety of statistical methodologies available which can be applied for the intended purpose, namely autoregressive integrated moving average (ARIMA)-type approaches [59,60], artificial neural network (ANN) models [61,62] or even other types of stochastic and probability models, such as Markov chains [63], log-linear models [64,65], and others [66,67]. A thorough discussion on various methodologies used for drought modeling and prediction showing the limitations and advantages of each modeling/technique was done by Mishra and Singh [58].…”

Historical trends and variability of meteorological droughts in Taiwan / Tendances historiques et variabilité des sécheresses météorologiques à Taiwan

“…Vicente-Serrano (2006) performed spatial and temporal analysis of droughts using the SPI on the Iberian Peninsula for 1910-2000, and identified the principal drought episodes; the most intense droughts were in the 1940s, 1950s, 1980s and 1990s. Moreover, the SPI has been used to investigate meteorological droughts in many countries and regions, for example: Canada (Quiring & Papakryiakou, 2003); China (Wu et al, 2001); East Africa (Ntale & Gan, 2003); Greece (Tsakiris & Vangelis, 2004); India (Mishra & Desai, 2005); Italy (Bonaccorso et al, 2003;Piccarreta et al, 2004); Spain (Lana et al, 2001); Taiwan (Shiau, 2006); and the USA (Edwards & McKee, 1997;Ji & Peters, 2003).…”

The effect of climate on timescales of drought propagation in an ensemble of global hydrological models

“…Drought forecasting potential is expected to be higher in regions with relatively slow drought propagation, such as the dry and continental climates in this study, as drought forecasting for longer SPI-n tends to be more 15 accurate than for shorter SPI-n (Mishra and Desai, 2005). Additional research using lagged SPI-n could assess the potential for forecasting different types of drought based on meteorological data.…”