Deriving optimal operational policies for off-stream man-made reservoir considering conjunctive use of surface- and groundwater at the Bar dam reservoir (Iran)

Study region: The off-stream artificial Bar lake, built in 2015 to store the flood flows of the Bar river for domestic and industrial needs and with the objective to intentionally recharge the aquifer, is situated in the Razavi Khorasan province (Iran). Study focus: We present a methodology, based on the combination of a MODFLOW groundwater flow model for estimating seepage rates, and an optimization model, for the management and operation of an artificial reservoir considering surface/groundwater interactions for satisfying 12 Mm3/year of water demand. We simulated the reliable amount of water that can be supplied from the reservoir, considering reservoir seepage, maximizing water supply yields subject to the water supply reliability requirements, and the additional intentional volume of groundwater recharge. New hydrological insights for the region: Our results demonstrate the reliability of conjunctive use of surface-and ground-water in water scarce areas by exploiting reservoir infrastructures with relevant leakage losses, also for creating additional aquifer storage. In such systems, man-induced changes of lake stages can significantly affect the volume of water that seeps through the lakebed. The aquifer, under managed aquifer recharge operations, may then provide the resource not satisfied by the reservoir release, fulfilling 100 % reliability of water supply. The conjunctive use of surface- and ground-water, by improving water security, may open new sustainability views for leaking reservoirs, even if they were not initially designed for increasing aquifer recharge, in many areas worldwide

Uniform fractional part: a simple fast method for generating continuous random variates

A known theorem in probability is adopted and through a probabilistic approach, it is generalized to develop a method for generating random deviates from the distribution of any continuous random variable. This method, which may be considered as an approximate version of the Inverse Transform algorithm, takes two random numbers to generate a random deviate, while maintaining all the other advantages of the Inverse Transform method, such as the possibility of generating ordered as well as correlated deviates and being applicable to all density functions, regardless of their parameter value

A comparison of gap-filling algorithms for eddy covariance fluxes and their drivers

The errors and uncertainties associated with gap-filling algorithms of water, carbon, and energy fluxes data have always been one of the main challenges of the global network of microclimatological tower sites that use the eddy covariance (EC) technique. To address these concerns and find more efficient gap-filling algorithms, we reviewed eight algorithms to estimate missing values of environmental drivers and nine algorithms for the three major fluxes typically found in EC time series. We then examined the algorithms' performance for different gap-filling scenarios utilising the data from five EC towers during 2013. This research's objectives were (a) to evaluate the impact of the gap lengths on the performance of each algorithm and (b) to compare the performance of traditional and new gap-filling techniques for the EC data, for fluxes, and separately for their corresponding meteorological drivers. The algorithms' performance was evaluated by generating nine gap windows with different lengths, ranging from a day to 365gd. In each scenario, a gap period was chosen randomly, and the data were removed from the dataset accordingly. After running each scenario, a variety of statistical metrics were used to evaluate the algorithms' performance. The algorithms showed different levels of sensitivity to the gap lengths; the Prophet Forecast Model (FBP) revealed the most sensitivity, whilst the performance of artificial neural networks (ANNs), for instance, did not vary as much by changing the gap length. The algorithms' performance generally decreased with increasing the gap length, yet the differences were not significant for windows smaller than 30gd. No significant differences between the algorithms were recognised for the meteorological and environmental drivers. However, the linear algorithms showed slight superiority over those of machine learning (ML), except the random forest (RF) algorithm estimating the ground heat flux (root mean square errors - RMSEs - of 28.91 and 33.92 for RF and classic linear regression - CLR, respectively). However, for the major fluxes, ML algorithms and the MDS showed superiority over the other algorithms. Even though ANNs, random forest (RF), and eXtreme Gradient Boost (XGB) showed comparable performance in gap-filling of the major fluxes, RF provided more consistent results with slightly less bias against the other ML algorithms. The results indicated no single algorithm that outperforms in all situations, but the RF is a potential alternative for the MDS and ANNs as regards flux gap-filling

Optimal Water Allocation from Subsurface Dams: A Risk-Based Optimization Approach

Subsurface dams, strongly advocated in the 1992 United Nations Agenda-21, have been widely studied to increase groundwater storage capacity. However, an optimal allocation of augmented water with the construction of the subsurface dams to compensate for the water shortage during dry periods has not so far been investigated. This study, therefore, presents a risk-based simulation–optimization framework to determine optimal water allocation with subsurface dams, which minimizes the risk of water shortage in different climatic conditions. The developed framework was evaluated in Al-Aswad falaj, an ancient water supply system in which a gently sloping underground channel was dug to convey water from an aquifer via the gravity force to the surface for irrigation of downstream agricultural zones. The groundwater dynamics were modeled using MODFLOW UnStructured-Grid. The data of boreholes were used to generate a three-dimensional stratigraphic model, which was used to define materials and elevations of five-layer grid cells. The validated groundwater model was employed to assess the effects of the subsurface dam on the discharge of the falaj. A Conditional Value-at-Risk optimization model was also developed to minimize the risk of water shortage for the augmented discharge on downstream agricultural zones. Results show that discharge of the falaj is significantly augmented with a long-term average increase of 46.51%. Moreover, it was found that the developed framework decreases the water shortage percentage in 5% of the worst cases from 87%, 75%, and 32% to 53%, 32%, and 0% under the current and augmented discharge in dry, normal, and wet periods, respectively

Prevalence and molecular characterization of Glucose-6-Phosphate dehydrogenase deficient variants among the Kurdish population of Northern Iraq

<p>Abstract</p> <p>Background</p> <p>Glucose-6-Phosphate dehydrogenase (G6PD) is a key enzyme of the pentose monophosphate pathway, and its deficiency is the most common inherited enzymopathy worldwide. G6PD deficiency is common among Iraqis, including those of the Kurdish ethnic group, however no study of significance has ever addressed the molecular basis of this disorder in this population. The aim of this study is to determine the prevalence of this enzymopathy and its molecular basis among Iraqi Kurds.</p> <p>Methods</p> <p>A total of 580 healthy male Kurdish Iraqis randomly selected from a main regional premarital screening center in Northern Iraq were screened for G6PD deficiency using methemoglobin reduction test. The results were confirmed by quantitative enzyme assay for the cases that showed G6PD deficiency. DNA analysis was performed on 115 G6PD deficient subjects, 50 from the premarital screening group and 65 unrelated Kurdish male patients with documented acute hemolytic episodes due to G6PD deficiency. Analysis was performed using polymerase chain reaction/restriction fragment length polymorphism for five deficient molecular variants, namely G6PD Mediterranean (563 C→T), G6PD Chatham (1003 G→A), G6PD A- (202 G→A), G6PD Aures (143 T→C) and G6PD Cosenza (1376 G→C), as well as the silent 1311 (C→T) mutation.</p> <p>Results</p> <p>Among 580 random Iraqi male Kurds, 63 (10.9%) had documented G6PD deficiency. Molecular studies performed on a total of 115 G6PD deficient males revealed that 101 (87.8%) had the G6PD Mediterranean variant and 10 (8.7%) had the G6PD Chatham variant. No cases of G6PD A-, G6PD Aures or G6PD Cosenza were identified, leaving 4 cases (3.5%) uncharacterized. Further molecular screening revealed that the silent mutation 1311 was present in 93/95 of the Mediterranean and 1/10 of the Chatham cases.</p> <p>Conclusions</p> <p>The current study revealed a high prevalence of G6PD deficiency among Iraqi Kurdish population of Northern Iraq with most cases being due to the G6PD Mediterranean and Chatham variants. These results are similar to those reported from neighboring Iran and Turkey and to lesser extent other Mediterranean countries.</p

Modern Recharge in a Transboundary Groundwater Basin Deduced from Hydrochemical and Isotopic Investigations: Al Buraimi, Oman

Groundwater samples (54) collected from different geological units (alluvium, Tertiary, ophiolite, and Hawasina) located in the transboundary groundwater basin in north Oman at the United Arab Emirates (UAE) borders were analyzed for general hydrochemistry and water isotopes, and subsets thereof were analyzed for 14C and 3H and 87Sr/86Sr. The chemical composition, percentage of modern carbon (pmc), δ2H, δ18O, and 87Sr/86Sr of the groundwater in the study area progressively change from the recharge zone in the elevated area of the North Oman Mountains (NOM) to the flat plains at the UAE borders. While the water-rock interaction is the dominant process controlling the groundwater chemistry, evaporation and groundwater mixing affect the hydrochemistry at the UAE borders. Therefore, groundwater evolves from carbonate-dominant in the NOM into sodium chloride-dominant close to the UAE borders. It is also evident that groundwater lateral recharge from the ophiolites into the alluvium retains the chemical affinity of the ophiolites. Groundwater dating (high pmc), homogeneous 87Sr/86Sr ratios, and enriched δ2H and δ18O demonstrate the presence of modern recharge in the shallow zones of the ophiolites and alluvium. However, deep zones and areas at the UAE border contain older groundwater form during cooler and wetter climatic conditions as supported by the depleted δ2H and δ18O and lower 87Sr/86Sr ratios and pmc. Furthermore, the data clearly showed that modern groundwater mixes with older groundwater along the flow path from the NOM into the UAE border. Modern recharge occurs as lateral recharge from NOM and direct recharge in the plain area. The current findings support future development of aflaj system along NOM slopes and shallow wells in the plain areas

Daily soil temperature modeling using ‘panel-data’ concept

<p>The purpose of this research was to predict soil temperature profile using ‘panel-data’ models. Panel-data analysis endows regression analysis with both spatial and temporal dimensions. The spatial dimension pertains to a set of cross-sectional units of observation. The temporal dimension pertains to periodic observations of a set of variables characterizing these cross-sectional units over a particular time-span. This study was conducted in <i>Khorasan-Razavi</i> Province, Iran. Daily mean soil temperatures for 9 years (2001–2009), in 6 different depths (5, 10, 20, 30, 50 and 100 cm) under bare soil surface at 10 meteorological stations were used. The data were divided into two sub-sets for training (parameter training) over the period of 2001–2008, and validation over the period of the year 2009. The panel-data models were developed using the average air temperature and rainfall of the day before (<math><mrow><msub><mi>T</mi><mrow><mi>d</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></math> and <math><mrow><msub><mi>R</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></math>, respectively) and the average air temperature of the past 7 days (<i>T</i>_w) as inputs in order to predict the average soil temperature of the next day. The results showed that the two-way fixed effects models were superior. The performance indicators (<i>R</i>² <i>=</i> 0.94 to 0.99, RMSE = 0.46 to 1.29 and MBE = −0.83 and 0.74) revealed the effectiveness of this model. In addition, these results were compared with the results of classic linear regression models using <i>t</i>-test, which showed the superiority of the panel-data models.</p