A Two‐Decade Watershed Approach to Stream Restoration Log Jam Design and Stream Recovery Monitoring: Finney Creek,Washington

A federal, state, and private partnership leveraged resources and employed a long‐term, systematic approach to improve aquatic habitat degraded by decades of intensive forest management in Finney Creek, a tributary to the Skagit River of Northwest Washington State. After more than a decade of work to reduce sediment sources and the risk of landslides within the watershed, log jam installation commenced in 1999 and progressed downstream through 2010. Log jam design was adapted as experience was gained. A total of 181 log jams, including 60 floating log ballasted jams, were constructed along 12 km of channel. The goal was to alter hydraulic processes that affect aquatic habitat formation along 39 km of stream with emphasis on 18.5 km of lower Finney Creek. Aquatic habitat surveys over a five‐year period show an increase in the area of large pools and an accompanying increase in residual and maximum pool depth in the lower river reach. Channel cross sections show a generally deeper channel at the log jams, better channel definition in the gravel deposits at the head of the log jams, and improved riffle and thalweg development below the log jams. Stream temperature in the upper river decreased by 1.0°F in the first three years, and 1.1°F in the lowest treated reach over nine years. There is a trend of less stream heating over the restoration time period. Photo points show that riparian vegetation is recolonizing gravel bars.     

Investigating the Fate and Transport of Escherichia coli in the Charles River,Boston, Using High‐Resolution Observation and Modeling1

Abstract: The processes affecting the fate and transport of Escherichia coli in surface waters were investigated using high‐resolution observation and modeling. The concentration patterns in Boston’s Charles River were observed during four sampling events with a total of 757 samples, including two spatial surveys with two along‐river (1,500 m length) and three across‐river (600 m length) transects at approximately 25‐m intervals, and two temporal surveys at a fixed location (Community Boating) over seven days at hourly intervals. The data reveal significant spatial and temporal structure at scales not resolved by typical monitoring programs. A mechanistic, time‐variable, three‐dimensional coupled hydrodynamic and water quality model was developed using the ECOMSED and RCA modeling frameworks. The computational grid consists of 3,066 grid cells with average length dimension of 25 m. Forcing functions include upstream and downstream boundary conditions, Stony Brook, and Muddy River (major tributaries) combined sewer overflow (CSO) and non‐CSO discharge and wind. The model generally reproduces the observed spatial and temporal patterns. This includes the presence and absence of a plume in the study area under similar loading, but different hydrodynamic conditions caused by operation of the New Charles River Dam (downstream) and wind. The model also correctly predicts an episode of high concentrations at the time‐series station following seven days of no rainfall. The model has an overall root mean square error (RMSE) of 250 CFU/100 ml and an error rate (above or below the USEPA‐recommended single sample criteria value of 235 CFU/100 ml) of 9.4%. At the time series station, the model has an RMSE of 370 CFU/100 ml and an error rate of 15%.     

Assessing Satellite and Ground‐Based Potential Evapotranspiration for Hydrologic Applications in the Colorado River Basin

This study evaluates a remotely sensed and two ground‐based potential evapotranspiration (PET) products for hydrologic application in the Upper Colorado River Basin (UCRB). The remotely sensed Moderate Resolution Imaging Spectroradiometer product (MODIS‐PET) is a continuous, daily time series with 250 m resolution derived using the Priestley‐Taylor (P‐T) equation. The MODIS‐PET is evaluated against regional flux tower data as well as a synthetic pan product (Epan; 0.125°, daily) derived from the North American Land Data Assimilation System (NLDAS) and a Hargreaves PET derived from DAYMET variables (DAYMET‐PET; 1 km, daily). Compared to point‐scale PET computed using regional flux tower data, the MODIS‐PET had lower errors, with RMSE values ranging from 2.24 to 2.85 mm/day. Epan RMSE values ranged from 3.70 to 3.76 mm/day and DAYMET‐PET RMSE values ranged from 3.55 to 4.58 mm/day. Further investigation showed biases in temperature and radiation data contribute to uncertainty in the MODIS‐PET values, while bias in NLDAS temperature, downward shortwave (SW↓), and downward longwave (LW↓) propagate in the Epan estimates. Larger discrepancies between methods were observed in the warmer, drier regions of the UCRB, however, the MODIS‐PET was more responsive to landcover transitions and better captured basin heterogeneity. Results indicate the satellite‐based MODIS product can serve as a viable option for obtaining spatial PET values across the UCRB.     

Estimating the Frequency and Extent of Overbank Flow of a Baseflow‐Dominated Stream Flowing through a Wetland

River floodplains provide critical habitat for a wide range of animal and plant species and reduce phosphorus and nitrogen loads in streams. It has been observed that baseflow‐dominated streams flowing through wetlands are commonly at or near bankfull and overflow their banks much more frequently than other streams. However, there is very little published quantitative support for this observation. The study focuses on a 1‐km reach of Black Earth Creek, a stream in the Midwestern United States (U.S.). We used one‐dimensional hydraulic modeling to estimate bankfull discharge at evenly spaced stream cross sections, and two‐dimensional modeling to quantitate the extent of wetland inundation as a function of discharge. We then used historical streamflow data from two U.S. Geological Survey gaging stations to quantitate the frequency of wetland inundation. For the with‐sediment case, the frequency of overbank conditions at the 38 cross sections in the wetland ranged from 3 to 85 days per year and averaged 43 days per year. Ten percent of the wetland was inundated for an average of 35 days per year. For the without‐sediment case, the frequency of overbank conditions ranged from 2.6 to 48 days per year and averaged 14 days per year. Also, 10% of the wetland was inundated for an average of 25 days per year. These unusually high rates of floodplain inundation are likely due in part to the very low stream gradient and shallow depths of overbank flow.     

Simulating nitrogen dynamics in agricultural soils fertilized with pig slurry and urea

Within the framework of an interregional project in the Emilia Romagna region of northern Italy, the coupled MACRO-SOILN model was chosen to estimate soil protective capacity against pollutants. The aim of our study was to evaluate the model to better identify key parameters and processes that influence N losses in agricultural soils. Nitrate N content was monitored in soil under corn (Zea mays L.) fertilized with urea and/or pig slurry, in two field experiments performed on four different soils: a Fienili clay, a Barco-like silt, a Sant'Omobono silt loam, and a La Boaria silty clay soil. Measurements were compared with model predictions. For all soils, nitrate content was underestimated on average by 24 to 88% at lower N rates; it was overestimated by 1 to 104% at higher N rates. The root mean square error (RMSE) was equal to 81.1%. Simulation of crop N uptake and soil water flow, estimation of the ammonia losses at pig slurry spreading, and N transformation parameter setting were considered as possible error sources. The calibration of crop N uptake gave rise to good model efficiency index values. The RMSE for the simulation of soil water content varied between 9.8 and 20.2%. A more accurate setting of the ammonia losses and of the feces transformation parameter values could allow the RMSE for the simulation of soil nitrate content to be reduced by no more than 10 to 15%. It is possible for the model not to include the simulation of processes that could have relevant effects on the soil N dynamics.     

Assessing the Effects of Management Alternatives on Habitat Suitability in a Forested Landscape of Northeastern China

Forest management often has cumulative, long-lasting effects on wildlife habitat suitability and the effects may be impractical to evaluate using landscape-scale field experiments. To understand such effects, we linked a spatially explicit landscape disturbance and succession model (LANDIS) with habitat suitability index (HSI) models to assess the effects of management alternatives on habitat suitability in a forested landscape of northeastern China. LANDIS was applied to simulate future forest landscape changes under four management alternatives (no cutting, clearcutting, selective cutting I and II) over a 200-year horizon. The simulation outputs were linked with HSI models for three wildlife species, the red squirrel (Sciurus vulgaris), the red deer (Cervus elaphus) and the hazel grouse (Bonasa bonasia). These species are chosen because they represent numerous species that have distinct habitat requirements in our study area. We assessed their habitat suitability based on the mean HSI values, which is a measure of the average habitat quality. Our simulation results showed that no one management scenario was the best for all species and various forest management scenarios would lead to conflicting wildlife habitat outcomes. How to choose a scenario is dependent on the trade-off of economical, ecological and social goals. Our modeling effort could provide decision makers with relative comparisons among management scenarios from the perspective of biodiversity conservation. The general simulation results were expected based on our knowledge of forest management and habitat relationships of the species, which confirmed that the coupled modeling approach correctly simulated the assumed relationships between the wildlife, forest composition, age structure, and spatial configuration of habitat. However, several emergent results revealed the unexpected outcomes that a management scenario may lead to.     

Evaluation of Reference Evapotranspiration Methods in Arid,Semiarid, and Humid Regions

It is often necessary to find a simpler method in different climatic regions to calculate reference crop evapotranspiration (ET_o) since the application of the FAO‐56 Penman‐Monteith method is often restricted due to the unavailability of a comprehensive weather dataset. Seven ET_o methods, namely the standard FAO‐56 Penman‐Monteith, the FAO‐24 Radiation, FAO‐24 Blaney Criddle, 1985 Hargreaves, Priestley‐Taylor, 1957 Makkink, and 1961 Turc, were applied to calculate monthly averages of daily ET_o, total annual ET_o, and daily ET_o in an arid region at Aksu, China, in a semiarid region at Tongchuan, China, and in a humid region at Starkville, Mississippi, United States. Comparisons were made between the FAO‐56 method and the other six simple alternative methods, using the index of agreement D, modeling efficiency (EF), and root mean square error (RMSE). For the monthly averages of daily ET_o, the values of D, EF, and RMSE ranged from 0.82 to 0.98, 0.55 to 0.98, and 0.23 to 1.00 mm/day, respectively. For the total annual ET_o, the values of D, EF, and RMSE ranged from 0.21 to 0.91, ?43.08 to 0.82, and 24.80 to 234.08 mm/year, respectively. For the daily ET_o, the values of D, EF, and RMSE ranged from 0.58 to 0.97, 0.57 to 0.97, and 0.30 to 1.06 mm/day, respectively. The results showed that the Priestly‐Taylor and 1985 Hargreaves methods worked best in the arid and semiarid regions, while the 1957 Makkink worked best in the humid region.     

Applications of Explicitly Incorporated/Post‐Processing Measurement Uncertainty in Watershed Modeling

In the field of watershed modeling, the impact of measurement uncertainty (MU) on calibration results indicates the potential issue of inaccurate model predictions. It is important to note that MU refers to the uncertainty in measured data such as flow and nutrient values that are used to evaluate model outputs. The calculation of error statistics assuming measured data are deterministic may not be appropriate as has been frequently stated in literature. Although MU can affect model calibration results, it is rarely incorporated in modeling practice. MU can be incorporated in two schemes: explicitly incorporated (MU‐EI) during model calibration and post‐processed (MU‐PP) after calibration is completed. In this study, both schemes are implemented in a case study of the Arroyo Colorado Watershed, Texas. Unexpectedly, no substantial differences were observed between each scheme for flow predictions. Although MU did not cause dramatic differences in most sediment and NH₄‐N predictions, error statistics were affected in cases with MU greater than 50%, especially for sediment and NH₄‐N. Therefore, it is concluded that MU may not exert a significant impact on model predictions until certain threshold is reached. This study demonstrates that high levels of uncertainty in measured calibration/validation data significantly affect parameter estimation, especially in the auto‐calibration process.     

Models,Assumptions, and Stakeholders: Planning for Water Supply Variability in the Colorado River Basin1

Abstract: Declining reservoir storage has raised the specter of the first water shortage on the Lower Colorado River since the completion of Glen Canyon and Hoover Dams. This focusing event spurred modeling efforts to frame alternatives for managing the reservoir system during prolonged droughts. This paper addresses the management challenges that arise when using modeling tools to manage water scarcity under variable hydroclimatology, shifting use patterns, and institutional complexity. Assumptions specified in modeling simulations are an integral feature of public processes. The policymaking and management implications of assumptions are examined by analyzing four interacting sources of physical and institutional uncertainty: inflow (runoff), depletion (water use), operating rules, and initial reservoir conditions. A review of planning documents and model reports generated during two recent processes to plan for surplus and shortage in the Colorado River demonstrates that modeling tools become useful to stakeholders by clarifying the impacts of modeling assumptions at several temporal and spatial scales. A high reservoir storage‐to‐runoff ratio elevates the importance of assumptions regarding initial reservoir conditions over the three‐year outlook used to assess the likelihood of reaching surplus and shortage triggers. An ensemble of initial condition predictions can provide more robust initial conditions estimates. This paper concludes that water managers require model outputs that encompass a full range of future potential outcomes, including best and worst cases. Further research into methods of representing and communicating about hydrologic and institutional uncertainty in model outputs will help water managers and other stakeholders to assess tradeoffs when planning for water supply variability.     

IMPACT OF VARIED DATA RESOLUTION ON HYDRAULIC MODELING AND FLOODPLAIN DELINEATION1

ABSTRACT: Current data collection technologies such as light detection and ranging (LIDAR) produce dense digital terrain data that result in more accurate digital terrain models (DTMs) for engineering applications. However, such data are redundant and often cumbersome for hydrologic and hydraulic modeling purposes. Data filtering provides a means of eliminating redundant points and facilitates model preparation. This paper demonstrates the impact of varied data resolution on a case study completed for a 2.3 mi² area with mild slopes (about 001 ft/ft) along Leith Creek near Laurinburg, North Carolina. For the original data set and seven filtered data sets, filtering induced changes in elevation, area, and hydraulic radius were determined for 10 water depths at 23 cross sections. Water surface elevations resulting from HEC‐RAS (Hydrologic Engineering Center‐River Analysis System) models for each data set were then compared. A hydraulic model sensitivity analysis was also conducted to compare filtering error to error introduced by variation in flow rates and roughness values. Finally, automated floodplain delineation was performed for each filter level based on the computed hydraulic model results and the filtered LIDAR elevations. Data filtering results indicate that significant time savings are achieved throughout the modeling process and that filtering to four degrees can be performed without compromising cross‐sectional geometry, hydraulic model results, or floodplain delineation results.     

Modeling Streams and Hydrogeomorphic Attributes in Oregon From Digital and Field Data1

Abstract: Managers, regulators, and researchers of aquatic ecosystems are increasingly pressed to consider large areas. However, accurate stream maps with geo‐referenced attributes are uncommon over relevant spatial extents. Field inventories provide high‐quality data, particularly for habitat characteristics at fine spatial resolutions (e.g., large wood), but are costly and so cover relatively small areas. Recent availability of regional digital data and Geographic Information Systems software has advanced capabilities to delineate stream networks and estimate coarse‐resolution hydrogeomorphic attributes (e.g., gradient). A spatially comprehensive coverage results, but types of modeled outputs may be limited and their accuracy is typically unknown. Capitalizing on strengths in both field and regional digital data, we modeled a synthetic stream network and a variety of hydrogeomorphic attributes for the Oregon Coastal Province. The synthetic network, encompassing 96,000 km of stream, was derived from digital elevation data. We used high‐resolution but spatially restricted data from field inventories and streamflow gauges to evaluate, calibrate, and interpret hydrogeomorphic attributes modeled from digital elevation and precipitation data. The attributes we chose to model (drainage area, mean annual precipitation, mean annual flow, probability of perennial flow, channel gradient, active‐channel width and depth, valley‐floor width, valley‐width index, and valley constraint) have demonstrated value for stream research and management. For most of these attributes, field‐measured, and modeled values were highly correlated, yielding confidence in the modeled outputs. The modeled stream network and attributes have been used for a variety of purposes, including mapping riparian areas, identifying headwater streams likely to transport debris flows, and characterizing the potential of streams to provide high‐quality habitat for salmonids. Our framework and models can be adapted and applied to areas where the necessary field and digital data exist or can be obtained.     

Adequacy of Visually Classified Particle Count Statistics From Regional Stream Habitat Surveys1

Abstract: Streamlined sampling procedures must be used to achieve a sufficient sample size with limited resources in studies undertaken to evaluate habitat status and potential management‐related habitat degradation at a regional scale. At the same time, these sampling procedures must achieve sufficient precision to answer science and policy‐relevant questions with an acceptable and statistically quantifiable level of uncertainty. In this paper, we examine precision and sources of error in streambed substrate characterization using data from the Environmental Monitoring and Assessment Program (EMAP) of the U.S. Environmental Protection Agency, which uses a modified “pebble count” method in which particle sizes are visually estimated rather than measured. While the coarse (2?) size classes used in EMAP have little effect on the precision of estimated geometric mean (D_gm) or median (D₅₀) particle diameter, variable classification bias among observers can contribute as much as 0.3?, or about 15‐20%, to the root‐mean‐square error (RMSE) of D_gm or D₅₀ estimates. D_gm and D₅₀ estimates based on EMAP data are nearly equal when fine sediments (<2 mm) are excluded, but otherwise can differ by up to a factor of 2 or more, with D_gm < D₅₀ for gravel‐bed streams. The RMSE of reach‐scale particle size estimates based on visually classified particle count data from EMAP surveys, including variability associated with reoccupying unmarked sample reaches during revisits, is up to five to seven times higher than that reported for traditional measured pebble counts by multiple observers at a plot scale. Nonetheless, a variance partitioning analysis shows that the ratio of among site to revisit variance for several EMAP substrate metrics exceeds 8 for many potential regions of interest, suggesting that the data have adequate precision to be useful in regional assessments of channel morphology, habitat quality, or ecological condition.     

Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided DL and DA approach to make 7-day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1-day-ahead and 1.4 to 1.9°C for 7-day-ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process-guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision-making.     

Modeling of residential lighting load profile using adaptive neuro fuzzy inference system (ANFIS)

Most practices currently adopted in modeling the presence of people in residential houses do not reflect the complexity of the impact occupants have on lighting loads. Hence the needs to contribute a methodology, that will be able to look into such characteristics. This study entailed the use of adaptive Neuro Fuzzy inference system (ANFIS) for estimation and prediction of lighting load usage profile for energy and demand side management initiatives. Results obtained showed a better correlation analysis and root mean square error (RMSE) in contrast to the regression method model. This shows that ANFIS model has good prediction accuracy capability.     

Using SWAT to Model Streamflow in Two River Basins With Ground and Satellite Precipitation Data1

Abstract: Both ground rain gauge and remotely sensed precipitation (Next Generation Weather Radar – NEXRAD Stage III) data have been used to support spatially distributed hydrological modeling. This study is unique in that it utilizes and compares the performance of National Weather Service (NWS) rain gauge, NEXRAD Stage III, and Tropical Rainfall Measurement Mission (TRMM) 3B42 (Version 6) data for the hydrological modeling of the Middle Nueces River Watershed in South Texas and Middle Rio Grande Watershed in South Texas and northern Mexico. The hydrologic model chosen for this study is the Soil and Water Assessment Tool (SWAT), which is a comprehensive, physical‐based tool that models watershed hydrology and water quality within stream reaches. Minor adjustments to selected model parameters were applied to make parameter values more realistic based on results from previous studies. In both watersheds, NEXRAD Stage III data yields results with low mass balance error between simulated and actual streamflow (±13%) and high monthly Nash‐Sutcliffe efficiency coefficients (NS > 0.60) for both calibration (July 1, 2003 to December 31, 2006) and validation (2007) periods. In the Middle Rio Grande Watershed NEXRAD Stage III data also yield robust daily results (time averaged over a three‐day period) with NS values of (0.60‐0.88). TRMM 3B42 data generate simulations for the Middle Rio Grande Watershed of variable qualtiy (MBE = +13 to ?16%; NS = 0.38‐0.94; RMSE = 0.07‐0.65), but greatly overestimates streamflow during the calibration period in the Middle Nueces Watershed. During the calibration period use of NWS rain gauge data does not generate acceptable simulations in both watersheds. Significantly, our study is the first to successfully demonstrate the utility of satellite‐estimated precipitation (TRMM 3B42) in supporting hydrologic modeling with SWAT; thereby, potentially extending the realm (between 50°N and 50°S) where remotely sensed precipitation data can support hydrologic modeling outside of regions that have modern, ground‐based radar networks (i.e., much of the third world).     

Evaluation of Rio Grande Management Alternatives Using a Surface‐Water/Ground‐Water Model1

Abstract: Previous investigations observed significant seepage losses from the Rio Grande to the shallow aquifer between Socorro and San Antonio, New Mexico. High‐resolution telescopic modeling was used along a 10‐km reach of the Rio Grande and associated drains and canals to evaluate several management alternatives aimed at improving river conveyance efficiency. Observed data consisted of ground‐water and surface‐water elevations, seepage rates along the Rio Grande and associated canals and drains, and borehole geology. Model calibration was achieved by adjusting hydraulic conductivity and specific storage until the output matched observed data. Sensitivity analyses indicated that the system was responsive to changes in hydrogeologic properties, especially when such alterations increased vertical connectivity between layers. The calibrated model predicted that removal of the low flow conveyance channel, a major channel draining the valley, would not only decrease river seepage by 67%, but also decrease total flow through the reach by 75%. The decreased flow through the reach would result in increased water logging and an average increase in ground‐water elevations of 1.21 meter. Simulations of the system with reduced riparian evapotranspiration rates or a relocated river channel also predicted decreased river seepage, but to a much lesser degree.     

IMPROVED RAINFALL/RUNOFF ESTIMATES USING REMOTELY SENSED SOIL MOISTURE1

ABSTRACT: Remotely sensed soil moisture data measured during the Southern Great Plains 1997 (SGP97) experiment in Oklahoma were used to characterize antecedent soil moisture conditions for the Soil Conservation Service (SCS) curve number method. The precipitation‐adjusted curve number and the soil moisture were strongly related (r²= 0.70). Remotely sensed soil moisture fields were used to adjust the curve numbers and the runoff estimates for five watersheds, in the Little Washita watershed; the results ranged from 2.8 km² to 601.6 km². The soil moisture data were applied at two spatial scales, a finer one (800 m) measuring spatial resolution and a coarser one (28 km). The root mean square error (RMSE) and the mean absolute error (MAE) of the runoff estimated by the standard SCS method was reduced by nearly 50 percent when the 800 m soil moisture data were used to adjust the curve number. The coarser scale soil moisture data also significantly reduced the error in the runoff predictions with 41 percent and 28 percent reductions in MAE and RMSE, respectively. The results suggest that remote sensing of soil moisture, when combined with the SCS method, can improve rainfall runoff predictions at a range of spatial scales.     

Large Woody Debris and Land Management in California’s Hardwood-Dominated Watersheds

Although large woody debris (LWD) has been studied extensively in conifer-dominated watersheds, relatively little is known about LWD in hardwood-dominated watersheds. Field surveys of 32 hardwood-dominated stream reaches in northern coastal California revealed that levels of LWD varied with land ownership and that living trees strongly influenced debris jam formation. Almost half of the channel-spanning debris jams, which stored the most wood and were most likely to form a pool, were formed behind a key piece that was still living. These living key pieces might provide greater longevity and stability than would otherwise be expected from hardwood LWD. Compared to streams on private land, streams on public land had significantly greater LWD loading and debris-jam frequency. Land management practices that remove wood from streams might be contributing to the degradation of salmonid habitat in Californias hardwood-dominated watersheds.     

Karst Hydrological Processes and Grey System Model1

Hao, Yonghong, Jiaojuan Zhao, Huamin Li, Bibo Cao, Zhongtang Li, and Tian‐Chyi J. Yeh, 2012. Karst Hydrological Processes and Grey System Model. Journal of the American Water Resources Association (JAWRA) 48(4): 656‐666. DOI: 10.1111/j.1752‐1688.2012.00640.x Abstract: The karst hydrological processes are the response of karst groundwater system to precipitation. This study provided a concept model of karst hydrological processes. The hydraulic response time of spring discharge to precipitation includes the time that precipitation penetrates through the vadose zone, and the subsequent groundwater pressure wave propagates to a spring outlet. Due to heterogeneities in karst aquifers, the hydraulic response time is different in different areas. By using grey system theory, we proposed a karst hydrological model that offers a calculation of hydraulic response time, and a response model of spring discharge to precipitation. Then, we applied the models to the Liulin Springs Basin, China. In the south part of the Liulin Springs Basin, where large fields of carbonate rocks outcrop with intensive karstification, the hydraulic response time is one year. In the north, where the Ordovician karst aquifer is covered by Quaternary loess sediments, the response time is seven years. The grey system GM(1,3) response model of spring discharge to precipitation was applied in consideration of the hydraulic response time. The model calibration showed that the average error was 6.55%, and validation showed that the average error was 12.19%.     

Mapping Stream Habitats with a Global Positioning System: Accuracy,Precision, and Comparison with Traditional Methods

We tested the precision and accuracy of the Trimble GeoXT™ global positioning system (GPS) handheld receiver on point and area features and compared estimates of stream habitat dimensions (e.g., lengths and areas of riffles and pools) that were made in three different Oklahoma streams using the GPS receiver and a tape measure. The precision of differentially corrected GPS (DGPS) points was not affected by the number of GPS position fixes (i.e., geographic location estimates) averaged per DGPS point. Horizontal error of points ranged from 0.03 to 2.77 m and did not differ with the number of position fixes per point. The error of area measurements ranged from 0.1% to 110.1% but decreased as the area increased. Again, error was independent of the number of position fixes averaged per polygon corner. The estimates of habitat lengths, widths, and areas did not differ when measured using two methods of data collection (GPS and a tape measure), nor did the differences among methods change at three stream sites with contrasting morphologies. Measuring features with a GPS receiver was up to 3.3 times faster on average than using a tape measure, although signal interference from high streambanks or overhanging vegetation occasionally limited satellite signal availability and prolonged measurements with a GPS receiver. There were also no differences in precision of habitat dimensions when mapped using a continuous versus a position fix average GPS data collection method. Despite there being some disadvantages to using the GPS in stream habitat studies, measuring stream habitats with a GPS resulted in spatially referenced data that allowed the assessment of relative habitat position and changes in habitats over time, and was often faster than using a tape measure. For most spatial scales of interest, the precision and accuracy of DGPS data are adequate and have logistical advantages when compared to traditional methods of measurement.