Monte Carlo simulation of streamflow of the Salt River, Arizona

EXTRACT (SEE PDF FOR FULL ABSTRACT): Evaluations of the impact of climate change (such as a greenhouse effect) upon water resources should represent both the expected change and the uncertainty in that expectation. Since water resources such as streamflow and reservoir levels depend on a variety of factors, each of which is subject to significant uncertainty, it is desirable to formulate methods of representing that uncertainty in the forcing factors and from this determine the uncertainty in the response variables of interest. We report here progress in the representation of the uncertainty in climate upon the uncertainty in the estimated hydrologic response.

Survey of exist specifications morphometric, meristic and inter species molecular genetic on Bream (Abramis brava orientalis) in Anzali wetland, southern coast of Caspian Sea, Aras Dam Lake and Azerbijan Republic

Bream (Abramis brava orientalis) is one of Cyprindae the Caspian Sea and its basin which has a special ecological, biological and economical role. Stock of this fish in the Caspian Sea has reduced during several years for different reason the over fishing, different industrial, agriculture, urban pollution and destroy of the spawning habitat. So that fishery company decided to recover the stock of this fish by the way of artificial reproduction of a Bream couple hunted from south coast of the Caspian Sea (Iran) and setting the fingerling to the rivers and inflow wetlands of the Caspian Sea.This activity has due to 20 tons Bream annual fishing in the Iranian South coast of the Caspian Sea (Gilan province coast and Anzali wetland), The artificial reproduction has decreased Bream population diversity of Caspian sea and Anzali wetland.So it has been declined to improve Braem population diversity by the entrance of Azerbijan republic Bream and encounter to the Caspian sea Bream. Meanwhile there is Bream in the Aras Dam Lake which had been forgotten by the Fishery Company of Iran .For this reason specifications morphometric, meristic and inter species Molecular Genetic have been surveyed in Anzali wetland,Southern coast of Caspian Sea ,Aras Darn Lake and Azerbijan republic during 2003-2005. According to the research on specifications of Morphometric and Meristic of Anzali wetland(120 species),Southern coast of Caspian Sea(90 species), Aras Dam Lake(110 species) and Azerbijan Republic(125 species)has Morphometric and Meristic differences. So that average weight and total length of Anzali wetland Bream respectively was 167 g and 23/76 cm, 102 g and 27/62 cm in Caspian Sea , 461 g and 3 5/38 cm in Aras Darn Lake and 3 4189 g and 15/21 cm in Azerbijan republic (We forced to use 1 year Bream of artificial reproduction in Iran). Also variation coefficient average Morphometric, Morphometric specification Ration and meristic in Anzali wetland Bream was 17/45, 21/56 and 4/63, in Caspian Sea bream 22/58, 15/27 and 3124, in Aras Dam lake Lake 17145. 1.5/27 and 3/57 and Azerbaijan republic Bream 22/29, 19/66 and 4/22. Also Bream of these four regions in general status had Morphometric significant differences based on One Way ANOVA Analysis. Meanwhile Anzali wetland Bream with Caspian Sea Bream from 41 Morphometric surveyed factors in 33 factors, with Aras Darn Lake Bream in 41 factors, with Azerbkjan republic Bream in 41 factors,Caspian Sea Bream with Aras Darn Lake Bream in 36 factors,with Azerbijan republic B ream in 40 factors and A ras Dam L ake Bream with Azerbijan republic Bream in 38 factors had significant statistical differences. These four regions Bream had differences according to the Morphomertric specification ration based on One Way ANOVA Analysis. Also Anzali wetland Bream was surveyed with Caspian Sea Bream from 37 factors i n 27 factors, Anzali wetland Bream with Aras Dam 1ake in 37 factors Anzali wetland Bream with Azerbijan republic Bream in 32 factors,Caspian sea bream with Arsa Dam Lake Bream in 26 factors, Caspian Sea Bream with Azerbijan republic Bream in 29 factors and Aras Dam Lake Bream with Azerbijan republic Bream in 34 factor had significant statistical differences. Based on Meristic factor of four regions bream in 16 surveyed factors in 10 factors had meaningful differences according to the One Way ANOVA Analysis. While Anzali wetland Bream was surveyed with Caspian Sea Bream from in 3 factors,Anzali wetland Bream with Aras Dam lake in 8 factors,Anzali wetland Bream with Azerbijan republic B ream in 6 factors,Caspian Sea bream with Arsa Dam Lake Bream in 6 factors,Caspian sea Bream with Azerbijan republic Bream in 3 factors and Aras Dam Lake Bream with Azerijan republic Bream in 8 factor had significant statistical differences.Meanwihle based on Factor Analysis and Discriminant Breams had differences. Also according to the resrarchs Anzali wetland Bream in 0+ age group till 5+ (6 age groups),Caspian Sea bream in 1+ - 5+(5 age groups),Aras Darn Lake Bream in 1+ - 7+ (7 age groups) and Azerbijan republic Bream for Morphometric and Meristic studies in 1+age group and for molecular Genetic reaserch were in 8+and 9+ age groups. According to the research 4 ecosystems Bream in status of same age, Aras lake Bream were bigger according to weight and length.Also in this research genetic diversity between four population was researched by PCR-RFLP technic on a piece of mitochondrion genome with the length of 3500bp contain of tRNA-leu,tRNA-glu,ND5/6,Cytb. Between 17 used enzyme. 4 enzyme, Dral, Bc11, Haefll and Banff showed diversity in totally 6 composite haplotype was detected. Maximum nucleotide diversity by the value% 0/58 in Azerbijan republic Bream by all haplotype. Aras darn Lake Bream had 2 haplotype and nucleotide diversity of %0/35.Anzali wetland and Caspian Sea Bream had no diversity. Statistical analysis by the usage of Monte Carlo with 1000 repeat showed significant differences between Azerbaijan Bream and other Bream(P<0/0001) but there was no significant difference between 3 regions Bream(P>0/5).

A Bayesian method for identification of stock mixtures from molecular marker data

Molecular markers have been demonstrated to be useful for the estimation of stock mixture proportions where the origin of individuals is determined from baseline samples. Bayesian statistical methods are widely recognized as providing a preferable strategy for such analyses. In general, Bayesian estimation is based on standard latent class models using data augmentation through Markov chain Monte Carlo techniques. In this study, we introduce a novel approach based on recent developments in the estimation of genetic population structure. Our strategy combines analytical integration with stochastic optimization to identify stock mixtures. An important enhancement over previous methods is the possibility of appropriately handling data where only partial baseline sample information is available. We address the potential use of nonmolecular, auxiliary biological information in our Bayesian model.

Abundance of Belugas, Delphinapterus leucas, in Cook Inlet, Alaska, 1994–2000

Annual abundance estimates of belugas, Delphinapterus leucas, in Cook Inlet were calculated from counts made by aerial observers and aerial video recordings. Whale group-size estimates were corrected for subsurface whales (availability bias) and whales that were at the surface but were missed (detection bias). Logistic regression was used to estimate the probability that entire groups were missed during the systematic surveys, and the results were used to calculate a correction to account for the whales in these missed groups (1.015, CV = 0.03 in 1994–98; 1.021, CV = 0.01 in 1999– 2000). Calculated abundances were 653 (CV = 0.43) in 1994, 491 (CV = 0.44) in 1995, 594 (CV = 0.28) in 1996, 440 (CV = 0.14) in 1997, 347 (CV = 0.29) in 1998, 367 (CV = 0.14) in 1999, and 435 (CV = 0.23, 95% CI=279–679) in 2000. For management purposes the current Nbest = 435 and Nmin = 360. These estimates replace preliminary estimates of 749 for 1994 and 357 for 1999. Monte Carlo simulations indicate a 47% probability that from June 1994 to June 1998 abundance of the Cook Inlet stock of belugas was depleted by 50%. The decline appears to have stopped in 1998.

A stochastic model of temporal variations in monthly temperature, precipitation, snowfall, and resulting snowpack

We report a Monte Carlo representation of the long-term inter-annual variability of monthly snowfall on a detailed (1 km) grid of points throughout the southwest. An extension of the local climate model of the southwestern United States (Stamm and Craig 1992) provides spatially based estimates of mean and variance of monthly temperature and precipitation. The mean is the expected value from a canonical regression using independent variables that represent controls on climate in this area, including orography. Variance is computed as the standard error of the prediction and provides site-specific measures of (1) natural sources of variation and (2) errors due to limitations of the data and poor distribution of climate stations. Simulation of monthly temperature and precipitation over a sequence of years is achieved by drawing from a bivariate normal distribution. The conditional expectation of precipitation. given temperature in each month, is the basis of a numerical integration of the normal probability distribution of log precipitation below a threshold temperature (3°C) to determine snowfall as a percent of total precipitation. Snowfall predictions are tested at stations for which long-term records are available. At Donner Memorial State Park (elevation 1811 meters) a 34-year simulation - matching the length of instrumental record - is within 15 percent of observed for mean annual snowfall. We also compute resulting snowpack using a variation of the model of Martinec et al. (1983). This allows additional tests by examining spatial patterns of predicted snowfall and snowpack and their hydrologic implications.