Climate Change Effect on Sediment Yield at King Talal Dam (Jordan)

With a yearly precipitation of 200 mm in most of the country, Jordan is considered one of the least water-endowed regions in the world. Water scarcity in Jordan is exacerbated by growing demands driven by population and industrial growth and rising living standards. Major urban and industrial centers in Jordan including the Capital Amman are concentrated in the northern highlands, mostly contained within the boundaries of the Zarqa River Watershed (ZRW). The ZRW is the third most productive basin in the greater Jordan River System. King Talal Dam was built few kilometers upstream of the Zarqa-Jordan confluence to regulate its input mostly for the benefit of agricultural activities in the Jordan Valley. King Talal Dam (KTD) is the most important one in Jordan. It lies at the outlet of Zarqa River watershed (ZRW). This dam has a capacity of 86×106 m3, which serves irrigation purposes in the Jordan Valley. However, the dam suffers from accelerated annual sedimentation. Concerns regarding the sensitivity of the ZRW to potential climate change have prompted the authors to carry out the current study. The methodology adopted is based on simulating the hydrological response of the basin under alternative climate change scenarios. The Soil and Water Assessment Tool (SWAT) is a powerful time-variable hydrologic model that has rarely been applied in arid environments. In this paper, the performance of SWAT in the semi-arid Zarqa River watershed (ZRW) was assessed. The SWAT model was calibrated for Zarqa basin using records spanning from 1980 through 1994. The model was validated against an independent data record extending from 1995 through 2002. Calibration and verification results were assessed based on linear regression fitting of monthly and daily flows. Monthly calibration and verifications produced good fit with regression coefficient r values equal to 0.928 and 0.923, respectively.  Annual volume predictions correlated well with measured flow in both the calibration (r = 0.94) and validation (r = 0.93) periods.  For model validation the simulation results were compared to the measured values over 12 years period.  Good agreement was obtained in some of the years. Utilizing the SWAT modeling environment, scenarios representing climate conditions with ±20% change in rainfall, and 1oC , 2oC and 3.5oC increases in average temperature were simulated and assessed. Unique relationship between the percent change in precipitation scenarios and the parallel change in sediment yield has been studied at different change in temperature to evaluate the degree of sediment sensitivity with temperature and precipitation. The study shows that climate warming can dramatically impact runoffs, groundwater recharge and sediment yield in the basin. However the impact of warming can be greatly influenced by significant changes in rainfall volume. Keywords: Zarqa River, Climate change, Sediment, SWA

Application of Automated Geospatial Watershed Assessment (AGWA) Tool to Evaluate the Sediment Yield in a Semi-arid Region: Case Study, Kufranja Basin-Jordan

Prediction of sediment yield from catchments is essential in the investigation of reservoir sedimentation and other hydrological and geological studies. Many methods have been used in the prediction of sediment yield. Soil and Water Assessment Tool (SWAT) is a newly developed model that can be applied to rural watershed. SWAT model has used Modified Universal Soil Loss Equation (MUSLE) in sediment calculation. The Automated Geospatial Watershed Assessment Tool (AGWA) is a GIS based watershed modeling tool. This paper improved a hydrological modeling using modeling environment AGWA and SWAT model to evaluate the sediment yield in Kufranja basin in Jordan. The sediment yield has been calculated at three proposed dam sites in the basin. The calibration process depended on the most sensitive parameters in SWAT model. Long term rainfall series were used in the modeling process. AGWA studies the change in the most sensitive parameter in the SWAT model. The change in this parameter can be considered as different scenarios in Kufranja basin

The Impact of Climate Change on Water Availability and Recharge of Aquifers in the Jordan River Basin

Climate change can seriously affect the Middle East region by reduced and erratic rainfall. Formulating appropriate coping policies should account for local effects and changing flows interconnecting spatial units. We apply statistical downscaling techniques of coarse global circulation models to predict future rainfall patterns in the Yarmouk Basin, using a linear regression to extrapolate these results to the entire Jordan River Basin (JRB). Using a detailed water economy model for the JRB we predict rainfall patterns to evaluate the impact of climate change on agriculture and groundwater recharge. For the JRB, rainfall in 2050 will be around 10% lower than present precipitation, but with substantial spatial spreading. An overall reduction of net revenue from crop cultivation is estimated at 150 million USD, with major losses in Israel, Jordan, and the West Bank; Syrian revenues will slightly increase. The recharge of groundwater is affected negatively, and outflow to the Dead Sea is substantially lower, leading to further increases in salinization

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Determination of Markov Chain Transition Probabilities for Daily Rainfall Data in Jordan

This study aims to determine Markov chain transition probabilities for daily rainfall data of 39 meteorological stations across Jordan. Two states were imposed to the chains, namely dry and wet, and first order was used as the dependence structure. This leads to four transition probabilities for each station in each month, namely dry-to-dry (pdd), dry-to-wet (pdw), wet-to-dry (pwd), and wet-to-wet (pww). In the end of the study, it is concluded that pdd > pdw for all stations in all months, and pww ? pwd in only 15.1% of the times, which are concentrated in the middle of the rainy season (i.e., December–March) at North of Jordan. Also, all months tend to be dry in the long term, especially October, November, April, and May. Most of the expected dry spell lengths range from 5 to 100 days, while the expected wet spell lengths range mostly from 1 to 2 days, which indicates the tendency of the Jordanian weather to be dry across the country

Generation of Synthetic Daily Rainfall Data in Jordan

This study aims to generate synthetic daily rainfall data for 39 meteorological stations in Jordan by estimating the distributional parameters of daily rainfall occurrence and amounts. Daily rainfall occurrence was modeled by the use of rainfall interarrival times, which were fitted to the one-parameter exponential distribution, except zero values which were represented using the ratio of the number of zero interarrival times to the total number of times, which was called zero ratio. Daily rainfall amounts were fitted to the two-parameter gamma distribution. Goodness-of-fit for one of the stations was tested using chi-square test. This test was performed using Microsoft Office Excel. Distributional parameters were calculated for both occurrence and amounts models, and 100 sequences of synthetic daily rainfall data were then generated, of which every sequence included 1000 non-zero daily rainfall data points (1000 wet days) and 1000 interarrival times (1000 dry spells of which some have a length of zero). These sequences were generated using the embedded random number generators in Python, for one-parameter exponential distribution, two-parameter gamma distribution, and uniform distribution. Percent errors were then calculated and found all to be less than 10%, which was considered acceptable