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.
The Jordan Valley is the prime irrigated agricultural area in Jordan which suffers shortage of water putting severe limitation on water allocation to farmers. To alleviate the problem, deficit irrigation was proposed for some vegetables such as bell pepper. Two field experiments in two growing seasons were conducted using bell pepper (Capsicum Annuum L.) to assess the effect of deficit irrigation on yield, water use efficiency (WUE), and water productivity (WP). The treatments were three irrigation levels: 100% (T1), 80% (T2), and 60% (T3) of the calculated crop evapotranspiration (ETc) using class A pan method. A cost–benefit analysis was carried out to determine the best economically suitable season for crop growth. The yields in both seasons were higher under T1, but there was no difference in WUE and WP between T1 and T2. The yield, WUE, and WP for T3 were significantly lower than for T1 and T2. Therefore, it is recommended to irrigate at 80% of ET. The best results were obtained for the total gross margin and the net present value in the winter season. Using deficit irrigation reduces water usage without significant yield loss, meanwhile maintaining relatively high WUE and supporting the sustainability of agriculture in the Jordan Valley.
The great demand for water resources from the Zarqa River Basin (ZRB) has resulted in a base-flow reduction of the River from 5 m 3 /s to less than 1 m 3 /s. This paper aims to predict Curve Numbers (CNs) as a baseline scenario and propose restoration scenarios for the ZRB. The method includes classifying the soil type and land use, predicting CNs, and proposing CN restoration scenarios. The prediction of existing CNs will be in parallel with the runoff prediction by using the US Army Corps of Engineers HEC-1 Model, and the Rainfall-Runoff Model (RRM). The models have been set up at the land use distribution of 0.3% water body, 9.3% forest and orchard, 71% mixture of grass, weeds, and desert shrubs, 7.0% crops, 4.0% urban areas, and 8.4% bare soil. The results show that CNs are 59, 78 and 89 under dry, normal and wet conditions, respectively. During the vegetation period, CNs are 52, 72 and 86 for dry, normal and wet conditions respectively. The restoration scenarios include how CNs decrease the runoff and increase the soil moisture when using the contours, terraces and crop residues. Analyzing the results of CN scenarios will be a fundamental tool in achieving watershed restoration targets.
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