Lower Susquehanna River Basin Research Articles

Balancing exploration, uncertainty and computational demands in many objective reservoir optimization

Abstract Reservoir operations are central to our ability to manage river basin systems serving conflicting multi-sectoral demands under increasingly uncertain futures. These challenges motivate the need for new solution strategies capable of effectively and efficiently discovering the multi-sectoral tradeoffs that are inherent to alternative reservoir operation policies. Evolutionary many-objective direct policy search (EMODPS) is gaining importance in this context due to its capability of addressing multiple objectives and its flexibility in incorporating multiple sources of uncertainties. This simulation-optimization framework has high potential for addressing the complexities of water resources management, and it can benefit from current advances in parallel computing and meta-heuristics. This study contributes a diagnostic assessment of state-of-the-art parallel strategies for the auto-adaptive Borg Multi Objective Evolutionary Algorithm (MOEA) to support EMODPS. Our analysis focuses on the Lower Susquehanna River Basin (LSRB) system where multiple sectoral demands from hydropower production, urban water supply, recreation and environmental flows need to be balanced. Using EMODPS with different parallel configurations of the Borg MOEA, we optimize operating policies over different size ensembles of synthetic streamflows and evaporation rates. As we increase the ensemble size, we increase the statistical fidelity of our objective function evaluations at the cost of higher computational demands. This study demonstrates how to overcome the mathematical and computational barriers associated with capturing uncertainties in stochastic multiobjective reservoir control optimization, where parallel algorithmic search serves to reduce the wall-clock time in discovering high quality representations of key operational tradeoffs. Our results show that emerging self-adaptive parallelization schemes exploiting cooperative search populations are crucial. Such strategies provide a promising new set of tools for effectively balancing exploration, uncertainty, and computational demands when using EMODPS.

A diagnostic assessment of evolutionary algorithms for multi-objective surface water reservoir control

Globally, the pressures of expanding populations, climate change, and increased energy demands are motivating significant investments in re-operationalizing existing reservoirs or designing operating policies for new ones. These challenges require an understanding of the tradeoffs that emerge across the complex suite of multi-sector demands in river basin systems. This study benchmarks our current capabilities to use Evolutionary Multi-Objective Direct Policy Search (EMODPS), a decision analytic framework in which reservoirs’ candidate operating policies are represented using parameterized global approximators (e.g., radial basis functions) then those parameterized functions are optimized using multi-objective evolutionary algorithms to discover the Pareto approximate operating policies. We contribute a comprehensive diagnostic assessment of modern MOEAs’ abilities to support EMODPS using the Conowingo reservoir in the Lower Susquehanna River Basin, Pennsylvania, USA. Our diagnostic results highlight that EMODPS can be very challenging for some modern MOEAs and that epsilon dominance, time-continuation, and auto-adaptive search are helpful for attaining high levels of performance. The ϵ-MOEA, the auto-adaptive Borg MOEA, and ϵ-NSGAII all yielded superior results for the six-objective Lower Susquehanna benchmarking test case. The top algorithms show low sensitivity to different MOEA parameterization choices and high algorithmic reliability in attaining consistent results for different random MOEA trials. Overall, EMODPS poses a promising method for discovering key reservoir management tradeoffs; however algorithmic choice remains a key concern for problems of increasing complexity.

Rescued from Synonymy: A Redescription of Percina bimaculata Haldeman and a Molecular Phylogenetic Analysis of Logperch Darters (Percidae: Etheostomatinae)

Percina nebulosa and Percina bimaculata have been considered synonyms of P. caprodes for over 130 years. The taxonomic history of P. nebulosa is complicated by the fact that the name is preoccupied by Perca nebulosa Rafinesque. Percina bimaculata Haldeman is the available and appropriate name for this species, and the Chesapeake Logperch is the proposed common name. Recent phylogenetic analyses of mitochondrial DNA gene sequences support the hypothesis that P. bimaculata is a distinct species; however, a morphological comparison between P. bimaculata and other logperch darter species has never been published. An examination of morphological characters and a new molecular phylogeny of both mitochondrial and nuclear gene sequence data support the recognition of P. bimaculata as a distinct species most closely related to P. kathae and P. austroperca. Historically, P. bimaculata was distributed in the lower Susquehanna River Basin of Pennsylvania and Maryland and the middle to lower Potomac River Basin of Maryland, Virginia and the District of Columbia, USA. The species has not been recorded from the Potomac Basin since the 1930s and is currently found only in the lower Susquehanna River Basin. The restricted geographic distribution and extirpation from the Potomac Basin indicates that P. bimaculata is an imperiled species and may warrant protection under the Endangered Species Act of 1973. The molecular phylogeny also indicates that populations recognized as P. caprodes sampled from the upper Mississippi River Basin in Illinois, Wisconsin and Minnesota are not closely related to P. caprodes populations sampled from the Ohio, Tennessee, White and Hudson river drainages.

FACTORS AFFECTING HERBICIDE YIELDS IN THE CHESAPEAKE BAY WATERSHED, JUNE 19941

ABSTRACT: Median concentrations and instantaneous yields of alachlor, metolachlor, atrazine, cyanazine, and simazine were generally highest at sites in the Lower Susquehanna River Basin and in agricultural subbasins. Instantaneous herbicide yields are related to land use, hydrogeologic setting, streamflow yield, and agricultural row cropping practices. The significance of these relations may be affected by the interdependence of the factors. The percentage of basin area planted in corn is the most influential factor in the prediction of herbicide yield. Instantaneous yields of all five herbicides measured in June 1994 related poorly to averaged 1990–94 herbicide use. Annually averaged herbicide‐use data are too general to use as a predictor for short‐term herbicide yields. An evaluation of factors affecting herbicide yields could be refined with more‐current land use and land cover information and a more accurate estimate of the percentage of basin area planted in corn. Factors related to herbicide yields can be used to predict herbicide yields in other basins within the Chesapeake Bay watershed and to develop an estimate of herbicide loads to Chesapeake Bay.

A Model of Regional Ground‐Water Flow in Secondary‐Permeability Terrane

ABSTRACTThe ground‐water flow system in the Lower Susquehanna River Basin in Pennsylvania and Maryland can be considered as one complex unconfined aquifer in which secondary porosity and permeability are the dominant influences on the occurrence and flow of ground water. The degree of development of secondary porosity and permeability in the various lithologies of the lower basin determines the aquifer characteristics of each lithology. Based on qualitative evidence, the use of a porous‐media model was assumed to be appropriate on a regional scale and a finite‐difference ground‐water flow model was constructed for the lower basin.The conceptual model of ground‐water flow in the lower basin incorporates the major features of the flow system. Through the use of two layers, 21 hydrogeologic units, and five topographic settings, the conceptual model was systematically reduced to arrive at a simplified conceptual model. Further reduction produced a numerical model representation of the conceptual model, in which the essential features of the lower‐basin flow system were quantified for input into the numerical model.The model was calibrated under both steady‐state and transient conditions, and was used to evaluate the water‐supply potential of the 21 hydrogeologic units. The carbonate units have the greatest potential for ground‐water development and the Triassic sedimentary and crystalline units have the least potential. A total ground‐water yield potential of about 900 million gallons per day could be obtained from the lower basin with a consequent 50‐percent reduction of base flow in streams.