ABSTRACT: The Caloosahatchee River has two major sources of freshwater one from its watershed and the other via an artificial connection to Lake Okeechobee. The contribution of each source to the freshwater discharge reaching the downstream estuary varies and either may dominate. Routine monitoring data were analyzed to determine the effects of total river discharge and source of discharge (river basin, lake) on water quality in the downstream estuary. Parameters examined were: color, total suspended solids, light attenuation, chlorophyll a, and total and dissolved inorganic nitrogen and phosphorus. In general, the concentrations of color, and total and dissolved inorganic nitrogen increased, and total suspended solids decreased, as total discharge increased. When the river basin was the major source, the concentrations of nutrients (excepting ammonia) and color in the estuary were relatively higher than when the lake was the major source. Light attenuation was greater when the river basin dominated freshwater discharge to the estuary. The analysis indicates that water quality in the downstream estuary changes as a function of both total discharge and source of discharge. Relative to discharge from the river basin, releases from Lake Okeechobee do not detectably increase concentrations of nutrients, color, or TSS in the estuary. 相似文献
Rapid response vertical profiling instrumentation was used to document spatial variability and patterns in a small urban lake, Onondaga Lake, associated with multiple drivers. Paired profiles of temperature, specific conductance (SC), turbidity (Tn), fluorometric chlorophyll a (Chlf), and nitrate nitrogen (NO3?) were collected at >30 fixed locations (a “gridding”) weekly, over the spring to fall interval of several years. These gridding data are analyzed (1) to characterize phytoplankton (Chlf) patchiness in the lake's upper waters, (2) to establish the representativeness of a single long‐term site for monitoring lake‐wide conditions, and (3) to resolve spatial patterns of multiple tracers imparted by buoyancy effects of inflows. Multiple buoyancy signatures were resolved, including overflows from less dense inflows, and interflows to metalimnetic depths and underflows to the bottom from the plunging of more dense inputs. Three different metrics had utility as tracers in depicting the buoyancy signatures as follows: (1) SC, for salinity‐enriched tributaries and the more dilute river that receives the lake's outflow, (2) Tn, for the tributaries during runoff events, and (3) NO3?, for the effluent of a domestic waste treatment facility and from the addition of NO3? solution to control methyl mercury. The plunging inflow phenomenon, which frequently prevailed, has important management implications. 相似文献
The National Flood Interoperability Experiment (NFIE) was an undertaking that initiated a transformation in national hydrologic forecasting by providing streamflow forecasts at high spatial resolution over the whole country. This type of large‐scale, high‐resolution hydrologic modeling requires flexible and scalable tools to handle the resulting computational loads. While high‐throughput computing (HTC) and cloud computing provide an ideal resource for large‐scale modeling because they are cost‐effective and highly scalable, nevertheless, using these tools requires specialized training that is not always common for hydrologists and engineers. In an effort to facilitate the use of HTC resources the National Science Foundation (NSF) funded project, CI‐WATER, has developed a set of Python tools that can automate the tasks of provisioning and configuring an HTC environment in the cloud, and creating and submitting jobs to that environment. These tools are packaged into two Python libraries: CondorPy and TethysCluster. Together these libraries provide a comprehensive toolkit for accessing HTC to support hydrologic modeling. Two use cases are described to demonstrate the use of the toolkit, including a web app that was used to support the NFIE national‐scale modeling. 相似文献
Solar energy application in a large spectrum has the potential for high-efficiency energy conversion. Though, solar cells can only absorb photon energy of the solar spectrum near their band-gap energy, and the remaining energy will be converted into thermal energy. The use of the thermoelectric generator becomes a necessity for convert this thermal energy dissipated so as to increase efficiency conversion.
This paper analyses the feasibility of photovoltaic-thermoelectric hybrid system and reviews their performance in order to optimize harvested energy. Regarding the thermoelectric effect, a new method of the ambient energy harvesting is presented. This method combines thermoelectric generators and the effects of heat sensitive materials associated to photovoltaic cells in phase change for generating both energy day and night. Experimental measures have been conducted primarily in laboratory conditions for a greater understanding of hybridization phenomena under real conditions and to test the actual performance of devices made. Results show that the hybrid system can generate more power than the simple PV and TEG in environmental conditions. This hybrid technology will highlight the use of renewable energies in the service of the energy production. 相似文献
To assess historical loads of nitrogen (N), phosphorus (P), and suspended sediment (SS) from the nontidal Chesapeake Bay watershed (NTCBW), we analyzed decadal seasonal trends of flow‐normalized loads at the fall‐line of nine major rivers that account for >90% of NTCBW flow. Evaluations of loads by season revealed N, P, and SS load magnitudes have been highest in January‐March and lowest in July‐September, but the temporal trends have followed similar decadal‐scale patterns in all seasons, with notable exceptions. Generally, total N (TN) load has dropped since the late 1980s, but particulate nutrients and SS have risen since the mid‐1990s. The majority of these rises were from Susquehanna River and relate to diminished net trapping at the Conowingo Reservoir. Substantial rises in SS were also observed, however, in other rivers. Moreover, the summed rise in particulate P load from other rivers is of similar magnitude as from Susquehanna. Dissolved nutrient loads have dropped in the upland (Piedmont and above) rivers, but risen in two small rivers in the Coastal Plain affected by lagged groundwater input. In addition, analysis of fractional contributions revealed consistent N trends across the upland watersheds. Finally, total N:total P ratios have declined in most rivers, suggesting the potential for changes in nutrient limitation. Overall, this integrated study of historical data highlights the value of maintaining long‐term monitoring at multiple watershed locations. 相似文献
Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust. 相似文献
Watershed simulation models such as the Soil & Water Assessment Tool (SWAT) can be calibrated using “hard data” such as temporal streamflow observations; however, users may find upon examination of model outputs, that the calibrated models may not reflect actual watershed behavior. Thus, it is often advantageous to use “soft data” (i.e., qualitative knowledge such as expected denitrification rates that observed time series do not typically exist) to ensure that the calibrated model is representative of the real world. The primary objective of this study is to evaluate the efficacy of coupling SWAT‐Check (a post‐evaluation framework for SWAT outputs) and IPEAT‐SD (Integrated Parameter Estimation and Uncertainty Analysis Tool‐Soft & hard Data evaluation) to constrain the bounds of soft data during SWAT auto‐calibration. IPEAT‐SD integrates 59 soft data variables to ensure SWAT does not violate physical processes known to occur in watersheds. IPEAT‐SD was evaluated for two case studies where soft data such as denitrification rate, nitrate attributed from subsurface flow to total discharge ratio, and total sediment loading were used to conduct model calibration. Results indicated that SWAT model outputs may not satisfy reasonable soft data responses without providing pre‐defined bounds. IPEAT‐SD provides an efficient and rigorous framework for users to conduct future studies while considering both soft data and traditional hard information measures in watershed modeling. 相似文献
Accurate spatial representation of climatic patterns is often a challenge in modeling biophysical processes at the watershed scale, especially where the representation of a spatial gradient in rainfall is not sufficiently captured by the number of weather stations. The spatial rainfall generator (SRGEN) is developed as an extension of the “weather generator” (WXGEN), a component of the Agricultural Policy/Environmental eXtender (APEX) model. SRGEN generates spatially distributed daily rainfall using monthly weather statistics available at multiple locations in a watershed. The spatial rainfall generator as incorporated in APEX is tested on the Cowhouse watershed (1,178 km2) in central Texas. The watershed presented a significant spatial rainfall gradient of 2.9 mm/km in the lateral (north‐south) directions based on four rainfall gages. A comparative analysis between SRGEN and WXGEN indicates that SRGEN performs well (PBIAS = 2.40%). Good results were obtained from APEX for streamflow (NSE = 0.99, PBIAS = 8.34%) and NO3‐N and soluble P loads (PBIAS ≈ 6.00% for each, respectively). However, APEX underpredicted sediment yield and organic N and P loads (PBIAS: 24.75‐27.90%) with SRGEN, although its uncertainty in output was lower than WXGEN results (PBIAS: ?13.02 to ?46.13%). The overall improvement achieved in rainfall generation by SRGEN is demonstrated to be effective in the improving model performance on flow and water quality output. 相似文献
Abstract: Lakes are important water resources on the North Slope of Alaska. Freshwater is required for oilfield production as well as exploration, which occurs largely on ice roads and pads. Since most North Slope lakes are shallow, the quantity and quality of the water under ice at the end of winter are important environmental management issues. Currently, water‐use permits are a function of the presence of overwintering fish populations, and their sensitivity to low oxygen concentrations. Sampling of five North Slope lakes during the winter of 2004‐2005 shed some light on the winter chemistry of four lakes that were used as water supplies and one undisturbed lake. Field analysis was conducted for oxygen, conductivity, pH, and temperature throughout the lake depth, as well as ice thickness and water depth. Water samples were retrieved from the lakes and analyzed for Na, Ca, K, Mg, Fe, dissolved‐organic carbon, and alkalinity in the laboratory. Lake properties, rather than pumping, were the best predictors of oxygen depletion, with the highest dissolved‐oxygen levels maintained in the lake with the lowest concentration of constituents. Volume weighted mean dissolved‐oxygen concentrations ranged from 4 to 94% of saturation in March. Dissolved oxygen and specific conductance data suggested that the lakes began to refresh in May. 相似文献
In terrain analysis and hydrological modeling, surface depressions (or sinks) in a digital elevation model (DEM) are commonly treated as artifacts and thus filled and removed to create a depressionless DEM. Various algorithms have been developed to identify and fill depressions in DEMs during the past decades. However, few studies have attempted to delineate and quantify the nested hierarchy of actual depressions, which can provide crucial information for characterizing surface hydrologic connectivity and simulating the fill‐merge‐spill hydrological process. In this paper, we present an innovative and efficient algorithm for delineating and quantifying nested depressions in DEMs using the level‐set method based on graph theory. The proposed level‐set method emulates water level decreasing from the spill point along the depression boundary to the lowest point at the bottom of a depression. By tracing the dynamic topological changes (i.e., depression splitting/merging) within a compound depression, the level‐set method can construct topological graphs and derive geometric properties of the nested depressions. The experimental results of two fine‐resolution Light Detection and Ranging‐derived DEMs show that the raster‐based level‐set algorithm is much more efficient (~150 times faster) than the vector‐based contour tree method. The proposed level‐set algorithm has great potential for being applied to large‐scale ecohydrological analysis and watershed modeling. 相似文献