Dynamic regression modeling of daily nitrate-nitrogen concentrations in a large agricultural watershed

Nitrate-nitrogen concentrations in rivers represent challenges for water supplies that use surface water sources. Nitrate concentrations are often modeled using time-series approaches, but previous efforts have typically relied on monthly time steps. In this study, we developed a dynamic regression model of daily nitrate concentrations in the Raccoon River, Iowa, that incorporated contemporaneous and lags of precipitation and discharge occurring at several locations around the basin. Results suggested that 95 % of the variation in daily nitrate concentrations measured at the outlet of a large agricultural watershed can be explained by time-series patterns of precipitation and discharge occurring in the basin. Discharge was found to be a more important regression variable than precipitation in our model but both regression parameters were strongly correlated with nitrate concentrations. The time-series model was consistent with known patterns of nitrate behavior in the watershed, successfully identifying contemporaneous dilution mechanisms from higher relief and urban areas of the basin while incorporating the delayed contribution of nitrate from tile-drained regions in a lagged response. The first difference of the model errors were modeled as an AR(16) process and suggest that daily nitrate concentration changes remain temporally correlated for more than 2 weeks although temporal correlation was stronger in the first few days before tapering off. Consequently, daily nitrate concentrations are non-stationary, i.e. of strong memory. Using time-series models to reliably forecast daily nitrate concentrations in a river based on patterns of precipitation and discharge occurring in its basin may be of great interest to water suppliers.     

Optimal strategies for best management practice placement in a synthetic watershed

It is significant to arrange suitable design and placement of best management practices (BMPs) for reaching the aim that can not only satisfy environmental quality standards, but also decrease the total cost of BMPs. This study applied WinVAST model to predict watershed responses. The objective of this work was to discuss both the economic costs and benefits of BMPs and the control efficiency of discharge and pollutant exports, and to create some suitable standards for the optimal BMPs placement strategies. It is significant to find an optimal number and location of BMPs. In the case study herein, the number of BMPs including a detention pond and a grassy swale would be better to be given by four. The number of BMPs should also be determined by the environmental standards. Moreover, the result shows that the optimal location of BMPs placement is in the downstream area near the outlet and on the mainstream of the catchment. When the BMPs are set in these regions, it cannot only reduce the peak flow and peak pollutant exports, but also have slow time to peak watershed responses.     

Fate of selenium in a small urban watershed

A field study was conducted on a small urban watershed (residential and golf course dominated) in southern Nevada to assess the concentration and speciation of selenium (Se) in a series of drain lines and monitoring wells and to quantify the mass discharge of Se from the drain system. Water samples were collected on a monthly basis and analyzed for total Se, selenate (SeO₄ ⁼) and selenite (SeO₃ ⁼). In addition, where possible, flow was assessed as was, temperature, redox potential, pH, dissolved oxygen (DO) along with all major cations and anions. The data were then modeled with PhreeqC to identify selenium speciation. Results revealed a SeO₄ ⁼ dominated system with SeO₄ ⁼ concentrations ranging from 13 to 62 ppb. In the monitoring wells, 66 % of the variation in the total Se concentration could be described based on depth to groundwater, temperature and sulfate concentrations (P?<?0.001). In particular, higher total Se concentrations were predicted for shallower depth to groundwater, suggesting the solubilization of Se evapo-concentrates near the surface could be reduced by lowering water tables. The highest of all correlations was found between SeO₄ ⁼ concentrations (↑) and the sodium (↑) and DO (↑) concentrations in the monitoring wells (R ²?=?0.77, P?<?0.001). An excellent curvilinear relationship was found between total Se and the electrical conductivity in the water (R ²?=?0.73, P?<?0.001). Based on the Se data and time line identified in this study, high concentrations of Se could be expected to drain from this area for many years to come, with salinity acting as a good proxy for Se concentration. In the drain lines, Se concentrations were found to be invariant to flow (P?>?0.05). Flow discharge from the main drain system to the Las Vegas Wash was estimated at 559 acre feet during the 1 year study period. This flow was estimated to carry 4,203 Mg of salts 6.71 Mg of nitrate-N and 27.1 kg of total Se.     

Spatial heterogeneity of stream environmental conditions and macroinvertebrates community in an agriculture dominated watershed and management implications for a large river (the Liao River,China) basin

Understanding the effects of watershed land uses (e.g., agriculture, urban industry) on stream ecological conditions is important for the management of large river basins. A total of 41 and 56 stream sites (from first to fourth order) that were under a gradient of watershed land uses were monitored in 2009 and 2010, respectively, in the Liao River Basin, Northeast China. The monitoring results showed that a total of 192 taxa belonging to four phyla, seven classes, 21 orders and 91 families were identified. The composition of macroinvertebrate community in the Liao River Basin was dominated by aquatic insect taxa (Ephemeroptera and Diptera), Oligochaeta and Molluscs. The functional feeding group GC (Gatherer/Collector) was dominant in the whole basin. Statistical results showed that sites with less watershed impacts (lower order sites) were characterized by higher current velocity and habitat score, more sensitive taxa (e.g., Ephemeroptera), and the substrate was dominated by high percentage of cobble and pebble. The sites with more impacts from agriculture and urban industry (higher order sites) were characterized by higher biochemical (BOD₅) and chemical oxygen demand (COD), more tolerant taxa (e.g., Chironominae), and the substrate was dominated by silt and sand. Agriculture and urban-industry activities have reduced habitat condition, increased organic pollutants, reduced macroinvertebrate abundance, diversity, and sensitive taxa in streams of the lower Liao River Basin. Restoration of degraded habitat condition and control of watershed organic pollutants could be potential management priorities for the Basin.     

Assessing the value of information for water quality management: a watershed perspective from China

To tackle China’s pervasive water pollution, tremendous efforts are needed to achieve more and better information. However, resources for information collection (e.g., water quality monitoring, field experiments, etc.) are very limited for large watersheds with significant nonpoint source pollution. Thus, it is crucial to identify the priority of information acquisition. Based on the theory of value of information (VOI), a stochastic optimization approach was developed in this study to evaluate the importance of information. The approach was applied to several key polluted water bodies in China (e.g., Lake Taihu, Lake Chaohu, and Lake Dianchi). The major findings include: (1) because of the severe pollution and large uncertainty, the VOI for the targeted water bodies is substantial; (2) when the uncertainty is significant, a stricter regulation would result in a higher VOI, and therefore provide more incentives for data collection; (3) due to the interaction among different information sources, collecting multiple types of information simultaneously could be more valuable than collecting one after another; and (4) the importance of a specific type of information could vary significantly across watersheds. The proposed approach can be readily extended to more complex models and more sophisticated watershed cases. It could effectively support watershed management in China, as well as in other countries.     

Evaluating performance of stormwater sampling approaches using a dynamic watershed model

Accurate quantification of stormwater pollutant levels is essential for estimating overall contaminant discharge to receiving waters. Numerous sampling approaches exist that attempt to balance accuracy against the costs associated with the sampling method. This study employs a novel and practical approach of evaluating the accuracy of different stormwater monitoring methodologies using stormflows and constituent concentrations produced by a fully validated continuous simulation watershed model. A major advantage of using a watershed model to simulate pollutant concentrations is that a large number of storms representing a broad range of conditions can be applied in testing the various sampling approaches. Seventy-eight distinct methodologies were evaluated by "virtual samplings" of 166 simulated storms of varying size, intensity and duration, representing 14 years of storms in Ballona Creek near Los Angeles, California. The 78 methods can be grouped into four general strategies: volume-paced compositing, time-paced compositing, pollutograph sampling, and microsampling. The performances of each sampling strategy was evaluated by comparing the (1) median relative error between the virtually sampled and the true modeled event mean concentration (EMC) of each storm (accuracy), (2) median absolute deviation about the median or "MAD" of the relative error or (precision), and (3) the percentage of storms where sampling methods were within 10% of the true EMC (combined measures of accuracy and precision). Finally, costs associated with site setup, sampling, and laboratory analysis were estimated for each method. Pollutograph sampling consistently outperformed the other three methods both in terms of accuracy and precision, but was the most costly method evaluated. Time-paced sampling consistently underestimated while volume-paced sampling over estimated the storm EMCs. Microsampling performance approached that of pollutograph sampling at a substantial cost savings. The most efficient method for routine stormwater monitoring in terms of a balance between performance and cost was volume-paced microsampling, with variable sample pacing to ensure that the entirety of the storm was captured. Pollutograph sampling is recommended if the data are to be used for detailed analysis of runoff dynamics.     

Long-term monitoring for environmental change in U.S. National Parks a watershed approach

The U.S. National Park Service (NPS) is faced with direct questions about the condition of Park natural resources. The watershed approach to long-term monitoring of natural, and remote areas within the National Parks has provided important data for detecting both spacial and temporal changes in environmental conditions. These data collections allow the partitioning of cause and effect relationships of ecological change within a given watershed. They also serve to meet both reference and early warning objectives. Success in advancing a number of acid precipitation goals has demonstrated the usefulness of these integrated watershed data for inter-ecosystem comparison and for analogy between watersheds. The watershed program owing to the NPS experience is proposed as a model for focusing the National Park Service's inventory and monitoring program inititiative. This approach provides to park researchers and resources managers the needed tools for dealing with today's complex local, regional and global natural resources issues.     

Spatial and temporal estimation of soil loss for the sustainable management of a wet semi-arid watershed cluster

The ungauged wet semi-arid watershed cluster, Seethagondi, lies in the Adilabad district of Telangana in India and is prone to severe erosion and water scarcity. The runoff and soil loss data at watershed, catchment, and field level are necessary for planning soil and water conservation interventions. In this study, an attempt was made to develop a spatial soil loss estimation model for Seethagondi cluster using RUSLE coupled with ARCGIS and was used to estimate the soil loss spatially and temporally. The daily rainfall data of Aphrodite for the period from 1951 to 2007 was used, and the annual rainfall varied from 508 to 1351 mm with a mean annual rainfall of 950 mm and a mean erosivity of 6789 MJ mm ha^?1 h^?1 year^?1. Considerable variation in land use land cover especially in crop land and fallow land was observed during normal and drought years, and corresponding variation in the erosivity, C factor, and soil loss was also noted. The mean value of C factor derived from NDVI for crop land was 0.42 and 0.22 in normal year and drought years, respectively. The topography is undulating and major portion of the cluster has slope less than 10°, and 85.3 % of the cluster has soil loss below 20 t ha^?1 year^?1. The soil loss from crop land varied from 2.9 to 3.6 t ha^?1 year^?1 in low rainfall years to 31.8 to 34.7 t ha^?1 year^?1 in high rainfall years with a mean annual soil loss of 12.2 t ha^?1 year^?1. The soil loss from crop land was higher in the month of August with an annual soil loss of 13.1 and 2.9 t ha^?1 year^?1 in normal and drought year, respectively. Based on the soil loss in a normal year, the interventions recommended for 85.3 % of area of the watershed includes agronomic measures such as contour cultivation, graded bunds, strip cropping, mixed cropping, crop rotations, mulching, summer plowing, vegetative bunds, agri-horticultural system, and management practices such as broad bed furrow, raised sunken beds, and harvesting available water using farm ponds and percolation tanks. This methodology can be adopted for estimating the soil loss from similar ungauged watersheds with deficient data and for planning suitable soil and water conservation interventions for the sustainable management of the watersheds.     

Structuring expert input for a knowledge-based approach to watershed condition assessment for the Northwest Forest Plan, USA

Assessments of watershed condition for aquatic and riparian species often have to rely on expert opinion because of the complexity of establishing statistical relationships among the many factors involved. Such expert-based assessments can be difficult to document and apply consistently over time and space. We describe and reflect on the process of developing a computer-based decision support application from expert judgments for assessing aquatic and riparian conditions over the 100,000?km² managed by the US federal government under the Northwest Forest Plan. The decision support system helped structure and document the assessment process and provided consistency and transparency to the evaluation methodology. However, many decisions and trade-offs were required in the expert engagement and model-building processes. Knowledge elicitation in an interactive group had a number of benefits over nominal group or Delphi processes, but efficient knowledge capture required considerable planning and expertise in the subject matter and modeling process. Communicating model results for validation was problematic and only effectively accomplished via in-person workshops. The choice to use different expert groups for each biophysical province provided more opportunities for participation and promoted greater ownership in the assessment, but it also led to increased variation among the resulting model structures. We propose three possible approaches for better managing the consistency of assessment models when multiple expert groups are involved.     

Determining storm sampling requirements for improving precision of annual load estimates of nutrients from a small forested watershed

This study sought to determine the lowest number of storm events required for adequate estimation of annual nutrient loads from a forested watershed using the regression equation between cumulative load (∑L) and cumulative stream discharge (∑Q). Hydrological surveys were conducted for 4 years, and stream water was sampled sequentially at 15-60-min intervals during 24 h in 20 events, as well as weekly in a small forested watershed. The bootstrap sampling technique was used to determine the regression (∑L-∑Q) equations of dissolved nitrogen (DN) and phosphorus (DP), particulate nitrogen (PN) and phosphorus (PP), dissolved inorganic nitrogen (DIN), and suspended solid (SS) for each dataset of ∑L and ∑Q. For dissolved nutrients (DN, DP, DIN), the coefficient of variance (CV) in 100 replicates of 4-year average annual load estimates was below 20% with datasets composed of five storm events. For particulate nutrients (PN, PP, SS), the CV exceeded 20%, even with datasets composed of more than ten storm events. The differences in the number of storm events required for precise load estimates between dissolved and particulate nutrients were attributed to the goodness of fit of the ∑L-∑Q equations. Bootstrap simulation based on flow-stratified sampling resulted in fewer storm events than the simulation based on random sampling and showed that only three storm events were required to give a CV below 20% for dissolved nutrients. These results indicate that a sampling design considering discharge levels reduces the frequency of laborious chemical analyses of water samples required throughout the year.     

Mercury proxies and mercury dynamics in a forested watershed of the US Northeast

Although many studies focus on mercury (Hg) and methylmercury (MeHg) dynamics in streams, challenges remain in identifying the relative importance of land cover and seasonality at regulating Hg and MeHg dynamics at the watershed scale. Developing robust proxies for Hg and/or MeHg determination also remains a challenge. Our study used Hg, MeHg, and dissolved organic carbon (DOC) concentration measurements and various DOC fluorescence indices to characterize Hg and DOC dynamics in a forested watershed of the US Northeast. Principal component analysis indicated that land cover/landscape position (i.e., headwater vs. wetland-influenced area vs. lake-influenced area) explained 44 % of the variance in Hg, MeHg, DOC concentrations, and DOC quality during the snow-free season, while seasonality (i.e., air temperature and discharge) explained only 21 % of the variance in the results. Furthermore, finding a good proxy for Hg that is valid across a range of landscape positions remains a challenge; however, regression analysis indicated that the fluorescence peak Humic C (excitation?=?350 nm; emission?=?max (420–480)), which corresponds to the presence of melanoidins in water, explained 21 % of the variability in MeHg concentrations across both space and time (p?=?0.001), and thus appears to be a possible proxy for MeHg determination in our study watershed. From a management perspective, land cover modifications (lake, reservoir, and wetland) are likely to play more important roles at regulating Hg, MeHg, and DOC exports at the watershed scale than long-term changes in the climate of this region.     

清河流域水质目标管理技术应用示范

针对清河流域三级水生态功能分区功能定位和水质保护目标,按照"分类、分区、分级、分期"理念,应用流域水质目标管理技术方法,研究清河流域控制单元污染负荷核定、水环境容量计算与分配、污染负荷削减、污染物总量控制等关键技术应用示范,完善流域水质目标管理技术,为清河流域水环境管理提供科技支撑。     

Derivation of unit and flood hydrographs using a gis

A geographic information system (GIS) supporting a flood hydrograph prediction software package is described. The hydrograph prediction method is based on the convolution of excess rainfall with a synthetic unit hydrograph, derived by the Soil Conservation Service runoff curve number and a regional dimensionless unit hydrograph method, respectively. The GIS uses a raster method to store the following data: land use and land cover, soil type, rainfall intensity-frequency-duration statistics, runoff curve numbers (CN), regional dimensionless unit hydrograph, and regional lag-time relationship. The GIS has also the capability of computing a number of watershed and hydrologic parameters required for predictions, such as a watershed average rainfall and CN value, area, centroid, stream length etc. Most of the data for such computations are input from a digitizer. Substantial time and cost savings are possible once the data base has been created. Application of the system is illustrated by an example predicting flood frequency curves for selected watersheds in Alberta's Rocky Mountain foothills, Canada.     

Using multiple-criteria decision-making techniques for eco-environmental vulnerability assessment: a case study on the Chi-Jia-Wan Stream watershed, Taiwan

The Chi-Jia-Wan Stream watershed, located in the area of the upstream Da-Chia River in central Taiwan, is famous for slopeland agriculture and the land-locked salmon. Improper agricultural activities have caused apparent ecosystem vulnerability and sensitivity. In this study, a system that combined three watershed-based environmental indicators with multiple-criteria decision-making techniques, the Analytical Hierarchy Process, and the Preference Ranking Organization METHod for Enrichment Evaluations was developed to assess eco-environmental vulnerability. The composite evaluation index system was set up including sediment, runoff, and nutrient factors. Supported by geographic information system and K-means clustering and taking the subwatershed as the evaluation unit, the vulnerability is classified into four levels: potential, low, moderate, and high. The evaluated results show that 8.82% of subwatersheds (six subwatersheds) are in the moderately and highly vulnerable zones. These subwatersheds represent vertical-belt distribution, mainly concentrated in the right side of the studied area and near the riparian zone along the Chi-Jia-Wan Stream. The exploited farmland in the moderately and highly vulnerable zones is about 142.21 ha, occupying 75.38% of the total farmland in the studied watershed. These seriously vulnerable zones that have caused degradation in the quality of the eco-environment should be treated with more best management practices for eco-environmental rehabilitation. Additionally, the proposed model can effectively evaluate the eco-environmental vulnerability grade for reference in policy planning and ecological restoration in this area.     

Antibiotic resistance analysis of fecal coliforms to determine fecal pollution sources in a mixed-use watershed

Antibiotic resistance analysis was performed on fecal coliform(FC) bacteria from a mixed-use watershed to determine thesource, human or nonhuman, of fecal coliform contamination. The study consisted of discriminant analysis of antibioticresistance patterns generated by exposure to fourconcentrations of six antibiotics (ampicillin, gentamicinsulfate, kanamycin, spectinomycin dihydrochloride,streptomycin sulfate, and tetracycline hydrochloride). Areference database was constructed from 1125 fecal coliformisolates from the following sources: humans, domestic animals(cats and dogs), agricultural animals (chickens, cattle, andhorses), and wild animals. Based on similar antibioticresistance patterns, cat and dog isolates were grouped asdomestic animals and horse and cattle isolates were grouped aslivestock. The resulting average rate of correctclassification (ARCC) for human and nonhuman isolates was94%. A total of 800 FC isolates taken from the watershedduring either a dry event or a wet event were classifiedaccording to source. Human sources contribute a majority(>50%) of the baseflow FC isolates found in the watershed inurbanized areas. Chicken and livestock sources areresponsible for the majority of the baseflow FC isolates foundin the rural reaches of the watershed. Stormwater introducesFC isolates from domestic (16%) and wild (21%) sourcesthroughout the watershed and varying amounts (up to 60%) fromchicken and livestock sources. These results suggest thatantibiotic resistance patterns of FC may be used to determinesources of fecal contamination and aid in the direction ofwater quality improvement.     

Characterization of Solang valley watershed in western Himalaya for bio-resource conservation using remote sensing techniques

The development activities in mountainous region though provide comfort to the human being and enhance the socioeconomic status of the people but create pressure on the bio-resources. In this paper, the current status of land use/landcover and the vegetation communities of the Solang valley watershed in Himachal Pradesh of Indian western Himalaya has been mapped and presented using remote sensing. This watershed area was dominated by alpine and sub-alpine pastures (30.34%) followed by scree slopes (22.34%) and forests (21.06%). Many tree, shrub, and herb species identified in the study area are among the prioritized species for conservation in the Indian Himalayan Region. Thus, scientific interventions and preparation of action plans based on ecological survey are required for conservation of the Solang valley watershed.