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Reservoirs are fundamental human-built infrastructures that collect, store, and deliver fresh surface water in a timely manner for many purposes. Efficient reservoir operation requires policy makers and operators to understand how reservoir inflows are changing under different hydrological and climatic conditions to enable forecast-informed operations. Over the last decade, the uses of Artificial Intelligence and Data Mining [AI & DM] techniques in assisting reservoir streamflow subseasonal to seasonal forecasts have been increasing. In this study, Random Forest [RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR) are employed and compared with respect to their capabilities for predicting 1 month-ahead reservoir inflows for two headwater reservoirs in USA and China. Both current and lagged hydrological information and 17 known climate phenomenon indices, i.e., PDO and ENSO, etc., are selected as predictors for simulating reservoir inflows. Results show (1) three methods are capable of providing monthly reservoir inflows with satisfactory statistics; (2) the results obtained by Random Forest have the best statistical performances compared with the other two methods; (3) another advantage of Random Forest algorithm is its capability of interpreting raw model inputs; (4) climate phenomenon indices are useful in assisting monthly or seasonal forecasts of reservoir inflow; and (5) different climate conditions are autocorrelated with up to several months, and the climatic information and their lags are cross correlated with local hydrological conditions in our case studies. [ABSTRACT FROM AUTHOR]

Background and Aim: Non-alcoholic steatohepatitis (NASH) is one of the most common causes of chronic liver disease and is considered to be a causative factor of cryptogenic cirrhosis and hepatocellular carcinoma. The aim of this work was to investigate whether treatment with geraniol (a monoterpene) attenuated NASH induced by methionine-choline-deficient (MCD) diet in rats. Methods: Rats were fed with MCD diet to induce NASH and treated with geraniol (200 mg/kg/day) for 10 weeks. Results: Treatment with geraniol reduced histological scores, fibrosis, and apoptosis in livers, lowered activities of alanine aminotransferase and aspartate aminotransferase in serum, and attenuated hepatic fat accumulation in rats fed with MCD diet. Treatment with geraniol preserved hepatic mitochondrial function, evidenced by reduced mitochondrial reactive oxygen species formation, enhanced adenosine triphosphate formation and membrane integrity, restored mitochondrial electron transport chain enzyme activity, and increased mitochondrial DNA content in rats fed with MCD diet. Treatment with geraniol reduced uncoupling protein 2 protein expression, and enhanced protein expression of prohibitin, mRNA expression of peroxisome proliferator-activated receptor α, and activity of mitochondrial carnitine palmitoyl transferase-I in livers of rats fed with MCD diet. Treatment with geraniol abated oxidative stress, evidenced by reduced malondialdehyde and 3-nitrotyrosine formation, enhanced activity of glutathione S-epoxide transferase, and down-regulated expression of inducible nitric oxide synthase and cytochrome P450 2E1 in livers of rats fed with MCD diet. Treatment with geraniol reduced myeloperoxidase activity and protein expression of tumor necrosis factor alpha and IL-6 in livers of rats fed with MCD diet. Conclusion: Treatment with geraniol attenuated MCD-induced NASH in rats. [ABSTRACT FROM AUTHOR]

The controlled outflows from a reservoir or dam are highly dependent on the decisions made by the reservoir operators, instead of a natural hydrological process. Difference exists between the natural upstream inflows to reservoirs and the controlled outflows from reservoirs that supply the downstream users. With the decision maker's awareness of changing climate, reservoir management requires adaptable means to incorporate more information into decision making, such as water delivery requirement, environmental constraints, dry/wet conditions, etc. In this paper, a robust reservoir outflow simulation model is presented, which incorporates one of the well-developed data-mining models (Classification and Regression Tree) to predict the complicated human-controlled reservoir outflows and extract the reservoir operation patterns. A shuffled cross-validation approach is further implemented to improve CART's predictive performance. An application study of nine major reservoirs in California is carried out. Results produced by the enhanced CART, original CART, and random forest are compared with observation. The statistical measurements show that the enhanced CART and random forest overperform the CART control run in general, and the enhanced CART algorithm gives a better predictive performance over random forest in simulating the peak flows. The results also show that the proposed model is able to consistently and reasonably predict the expert release decisions. Experiments indicate that the release operation in the Oroville Lake is significantly dominated by SWP allocation amount and reservoirs with low elevation are more sensitive to inflow amount than others. [ABSTRACT FROM AUTHOR]

The authors discuss the study that illustrates the improved performance of the Markov Chain Monte Carlo (MCMC) algorithm, dubbed MT-DREAM. They raise concerns about the ability of a disrect search method, SP-UCI, with complex and multimodal searching spaces. They praise the consistent performances for the test examples provided in the MT-DREAM(ZS) and DREAM(ZS) packages. They confuse about the effectiveness of the algorithm in assessing complex posterior distributions.

An innovative algorithm, shuffled complexes with principal components analysis (SP-UCI), is developed to overcome a critical deficiency of the shuffled complex evolution scheme: population degeneration. Population degeneration means that, during the evolutionary search process, the population of search particles may degenerate into a subspace of the full parameter space, thereby missing the capacity of fully exploring the parameter space. Being confined in a subspace may even lead the particle population to converge to nonstationary points, which is a fatal malfunction. To overcome this problem, SP-UCI employs the principal components analysis to detect the occurrence of population degeneration and remedy the adverse effects. The ensemble of calibrations of the Sacramento soil moisture accounting model with the SP-UCI method over the Leaf River basin, Mississippi, retrieves the optimal parameter values with the lowest recorded root-mean-squared error of simulated daily runoff against the observation. Moreover, the result also provides consistent (narrow ranges) model parameter distribution, which results in a better understanding of the model's behavior, given the watershed's hydrologic features. [ABSTRACT FROM AUTHOR]

Snow anomalies in the western USA have a significant impact on water availability, and hence have been widely investigated by many researchers. This study focuses on how anomalous atmospheric circulation affects snowpack accumulation in the western USA. Our results indicate that the mid‐latitude atmospheric circulation anomalies induced by the El Niño–Southern Oscillation (ENSO) tend to drive winter precipitation shifts, leading to an anomalous snowpack distribution in the western USA. The warm phase of ENSO produces increased snowpack in the Southwest, whereas the cold phase generates increased snowpack in the Northwest. The results also indicate that temperature has a secondary impact on the anomalous snowpack distribution during ENSO episodes. Additionally, the atmospheric internal variability‐related Pacific–North American (PNA) pattern is found to affect snow anomalies strongly in the western USA independent from ENSO. The positive phase of the PNA pattern produces colder temperature and stronger precipitation due to the lower pressure in the region, leading to an above‐average snowpack. Conversely, the negative phase of the PNA pattern generates warmer temperature and weaker precipitation resulting from the higher pressure, producing below‐average snowpack in the western USA. The significant findings that result from this study strengthen our understanding of climate and water supply variability in the western USA. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Accurate measurement of rainfall distribution at various spatial and temporal scales is crucial for hydrological modeling and water resources management. In the literature of satellite rainfall estimation, many efforts have been made to calibrate a statistical relationship (including threshold, linear, or nonlinear) between cloud infrared (IR) brightness temperatures and surface rain rates (RR). In this study, an automated neural network for cloud patch-based rainfall estimation, entitled self-organizing nonlinear output (SONO) model, is developed to account for the high variability of cloud-rainfall processes at geostationary scales (i.e., 4 km and every 30 min). Instead of calibrating only one IR-RR function for all clouds the SONO classifies varied cloud patches into different clusters and then searches a nonlinear IR-RR mapping function for each cluster. This designed feature enables SONO to generate various rain rates at a given brightness temperature and variable rain/no-rain IR thresholds for different cloud types, which overcomes the one-to-one mapping limitation of a single statistical IR-RR function for the full spectrum of cloud-rainfall conditions. In addition, the computational and modeling strengths of neural network enable SONO to cope with the nonlinearity of cloud-rainfall relationships by fusing multisource data sets. Evaluated at various temporal and spatial scales, SONO shows improvements of estimation accuracy, both in rain intensity and in detection of rain/no-rain pixels. Further examination of the SONO adaptability demonstrates its potentiality as an operational satellite rainfall estimation system that uses the passive microwave rainfall observations from low-orbiting satellites to adjust the IR-based rainfall estimates at the resolution of geostationary satellites. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) can be useful in the prediction of hydrologic variables, such as streamflow, particularly when the underlying processes have complex nonlinear interrelationships. However, conventional ANN structures suffer from network training issues that significantly limit their widespread application. This paper presents a multivariate ANN procedure entitled self-organizing linear output map (SOLO), whose structure has been designed for rapid, precise, and inexpensive estimation of network structure/parameters and system outputs. More important, SOLO provides features that facilitate insight into the underlying processes, thereby extending its usefulness beyond forecast applications as a tool for scientific investigations. These characteristics are demonstrated using a classic rainfall-runoff forecasting problem. Various aspects of model performance are evaluated in comparison with other commonly used modeling approaches, including multilayer feedforward ANNs, linear time series modeling, and conceptual rainfall-runoff modeling. [ABSTRACT FROM AUTHOR]

The importance of high-resolution rainfall data to understanding the intricacies of the dynamics of hydrological processes and describing them in a sophisticated and accurate way has been increasingly realized. The last decade has witnessed a number of studies and numerous approaches to the possibility of transformation of rainfall data from one scale to another, nearly unanimously pointing to such a possibility. However, an important limitation of such approaches is that they treat the rainfall process as a realization of a stochastic process, and therefore there seems to be a lack of connection between the structure of the models and the underlying physics of the rainfall process. The present study introduces a new framework based on the notion of deterministic chaos to investigate the behavior of the dynamics of rainfall transformation between different temporal scales aimed toward establishing this connection. Rainfall data of successively doubled resolutions (i.e., 6, 12, 24, 48, 96, and 192 hours) observed at Leaf River basin, in the state of Mississippi, United States of America, are studied. The correlation dimension method is employed to investigate the presence of chaos in the rainfall transformation. The finite and low correlation dimensions obtained for the distributions of weights between rainfall data of different scales indicate the existence of chaos in the rainfall transformation, suggesting the applicability of a chaotic model. The formulation of a simple chaotic disaggregation model and its application to the Leaf River rainfall data provides encouraging results with practical potential. The disaggregation model results themselves indicate the presence of chaos in the dynamics of rainfall transformation, providing support for the results obtained using the correlation dimension method. [ABSTRACT FROM AUTHOR]

We developed and evaluated a three-layer snow model for application in general circulation models. This one-dimensional snow model has many features of the detailed physically based model SNTHERM, yet is computationally much simpler. We have also extended the point model to vegetated areas using the parameterization concepts of the Biosphere-Atmosphere Transfer Scheme (BATS). Results of model applications for two types of vegetated fields - a short grassland in the French Alps and an old aspen forest in the southern study area of BOREAS - were presented. The results, on one hand, indicate the suitability of the model structure and parameter setting; on the other hand, the results explore the limitation of using point field observations to evaluate an area model. [ABSTRACT FROM AUTHOR]