Characterizing, Monitoring and Forecasting of Drought in Jordan River Basin

Abstract

Jordan is very vulnerable to drought because of its location in the arid to semi-arid part of the Middle East. Droughts coupled with water scarcity are becoming a serious threat to the economic growth, social cohesion and political stability. Rainfall time series from four rain stations covering the Jordan River Basin were analyzed for drought characterization and forecasting using standardized precipitation index (SPI), Markov chain and autoregressive integrated moving average (ARIMA) model. The 7-year moving average of Amman data showed a decreasing trend while data from the other three stations were stable or showed an increasing trend. The frequency analysis indicated 2-year return period for near zero SPI values while the return period for moderate drought was 7 years. Successive droughts had occurred at least three times during the past 40 years. Severe droughts are expected once every 20 -25 year period at all rain stations. The extreme droughts were rare events with return periods between 80 and 115 years. There are equal occurrence probabilities for drought and wet conditions in any given year, irrespective, of the condition in the previous year. The results showed that ARIMA model was successful in predicting the overall statistics with a given period at annual scales. The overall number of predicted/observed droughts during the validation periods were 2/2 severe droughts for Amman station and, 0/1, 1/1, 0/1 extreme droughts for Amman, Irbid and Mafraq stations, respectively. In addition, the ARIMA model also predicted 3 out of 4 actual moderate droughts for Amman and Mafraq stations. It was concluded that early warning of developing droughts can be deduced form the monthly Markov transitional probabilities. ARIMA models can be used as a forecasting tool of the future drought trends. Using the first and second order Markov probabilities can complement the ARIMA predictions.

Linking drought characteristics to impacts on a spatial and temporal scale

“…A precise, unambiguous definition of drought remains elusive (Yevjevich et al, 1983;Karavitis, 1992Karavitis, , 1999aGrigg, 1996;Wilhite, 1997;Cancelliere et al, 2005;Karavitis et al, 2012a, b;Shatanawi et al, 2013). One source of confusion in devising an objective definition may be that drought implies a variety of things to various professionals according to the specialized field of study (meteorology, hydrology, water resources, agriculture, etc.).…”

Measuring and restructuring the risk in forecasting drought classes: an application of weighted Markov chain based model for standardised precipitation evapotranspiration index (SPEI) at one-month time scale

“…Each discrete Markov chain is characterised by a transition probability matrix that represents the probability of transition from one state to another. Shatanawi et al (2013) reported that the exact prediction of Drought Index (DI) values is impossible with ARIMA models. However, early warning of drought can be detected from monthly Markov transition probabilities.…”

Management of Climatic Extremes with Focus on Floods and Droughts in Agriculture

“…Precipitation‐based indices have been developed over time in order to quantify a drought as a departure of precipitation from the normal . Widely used indices, such as the Palmer drought severity index (PDSI), the National Oceanic and Atmospheric Administration (NOAA) drought index (NDI), and the standardized precipitation index (SPI), use precipitation either singly or in combination with other elements (Shatanawi et al, ).…”