Streamflow Values Research Articles

Hydrologic regionalization to assess anthropogenic changes

Within the past few decades, Minnesota’s land use change has responded rapidly to prevailing economic growth conditions, resulting in hydrologic characteristic alterations of the landscape and shifting the hydrologic balance of its watersheds. Regionalization using mean annual and mean monthly streamflow values was used to delineate hydrologic regimes with distinct temporal flow variations. By identifying hydrologic relationships between watersheds through an initial regionalization of mean annual streamflow time-series data, hydrologic regimes, each composed of watersheds with common hydrologic controlling variables, were identified. This paper summarizes how by applying factor analysis techniques to complete a statewide regionalization for Minnesota, hydrologic regimes were identified, each with a specific hydrologic signature; varying between three and four runoff periods of different durations. A geographic information system database was established to display the results of the regionalization and to identify hydrologic regime changes between the 1936–2008, 1936–1980, and 1950–2008 analysis intervals. Results delineated five hydrologic regimes for each of the three analysis periods. By focusing on each specific regime, further analyses were completed to identify significant increasing and decreasing trend characteristics. Review of the temporal variation for each regime using Kendall Tau trend analyses suggests that although variation in annual precipitation has an important influence on hydrologic variability, land cover and management proved to be a more direct controlling agent. Understanding the consequences of anthropogenic land use change on hydrologic processes within each defined regime should be the focus of future analyses.

Systematic evaluation of autoregressive error models as post-processors for a probabilistic streamflow forecast system

Summary In this study, different versions of autoregressive error models are evaluated as post-processors for probabilistic streamflow forecasts. The post-processors account for hydrologic uncertainties that are introduced by the precipitation–runoff model. The post-processors are evaluated with the discrete ranked probability score (DRPS), and a non-parametric bootstrap is applied to investigate the significance of differences in model performance. The results show that differences in performance between most model versions are significant. For these cases it is found that (1) error models with state dependent parameters perform better than those with constant parameters, (2) error models with an empirical distribution for the description of the standardized residuals perform better than those with a normal distribution, and (3) procedures that use a logarithmic transformation of the original streamflow values perform better than those that use a square root transformation.

Impacts of conservation buffers and grasslands on total phosphorus loads using hydrological modeling and remote sensing techniques

Abstract To better assess the impacts of conservation buffers and grasslands on water quality at large spatial scales, development and integration of novel approaches are crucial to ensure that these land management practices are functioning properly and meeting their original goals. Recent developments in remote sensing technology have greatly enriched the availability of geospatial data that can be used in hydrological modeling to assess the potential hydrological response of conservation practices over larger areas. A methodology was developed using the object-based image analysis approach with Landsat-5 TM imagery of the year 2005 and thematic layers of streams to quantify conservation buffers and grasslands (OBIA-2005). The OBIA-2005 land cover data was used in the Soil and Water Assessment Tool hydrologic model to assess the impacts of vegetative conservation practices on total phosphorus (TP) loads. The model was calibrated and validated for discharge and TP loads in the Cedar Creek Watershed (CCW) in northeast Indiana. In general model efficiency for streamflow values was within acceptable statistical ranges. While calibration of TP loads was satisfactory for the total contributing area of two nested catchments within the upper CCW. Vegetative buffers of 30.5 m and 61 m combined with conservation grasslands generated from the OBIA-2005 resulted in a large reduction of TP loads as compared to no practices. The results also showed that including conservation grassland alone reduced TP loads by less than 2%. However, the combination of these practices with the width of edge-of-field buffer strips module of the SWAT model achieved the largest TP loads reduction. These findings demonstrate that improved representation of vegetative conservation practices in geospatial land cover data sets are more effective in assessing the impacts of conservation buffers and grasslands on water quality through hydrologic modeling.

Causes of systematic over‐ or underestimation of low streamflows by use of index‐streamgage approaches in the United States

AbstractLow‐flow characteristics can be estimated by multiple linear regressions or the index‐streamgage approach. The latter transfers streamflow information from a hydrologically similar, continuously gaged basin (‘index streamgage’) to one with a very limited streamflow record, but often results in biased estimates. The application of the index‐streamgage approach can be generalized into three steps: (1) selection of streamflow information of interest, (2) definition of hydrologic similarity and selection of index streamgage, and (3) application of an information‐transfer approach. Here, we explore the effects of (1) the range of streamflow values, (2) the areal density of streamgages, and (3) index‐streamgage selection criteria on the bias of three information‐transfer approaches on estimates of the 7‐day, 10‐year minimum streamflow (Q7, 10). The three information‐transfer approaches considered are maintenance of variance extension, base‐flow correlation, and ratio of measured to concurrent gaged streamflow (Q‐ratio invariance). Our results for 1120 streamgages throughout the United States suggest that only a small portion of the total bias in estimated streamflow values is explained by the areal density of the streamgages and the hydrologic similarity between the two basins. However, restricting the range of streamflow values used in the index‐streamgage approach reduces the bias of estimated Q7, 10 values substantially. Importantly, estimated Q7, 10 values are heavily biased when the observed Q7, 10 values are near zero. Results of the analysis also showed that Q7, 10 estimates from two of the three index‐streamgage approaches have lower root‐mean‐square error values than estimates derived from multiple regressions for the large regions considered in this study. Published in 2011 by John Wiley & Sons, Ltd.

Assessing sensitivity of hydrologic responses to climate change from forested watershed in Mississippi

AbstractThe climatic processes such as changes in precipitation, temperature and atmospheric CO2 concentration can intensify the effects on water resources. An assessment of the effects of long‐term climate change on water resources is essential to the development of water quality improvement programs. This study was conducted in the Upper Pearl River Watershed (UPRW) in east‐central Mississippi to assess the effects of long‐term potential future climate change on average mean monthly stream flow from the five spatially distributed U. S. Geological Survey (USGS) gage stations in the UPRW using the Soil and Water Assessment Tool. The model was calibrated (January 1981 to December 1994) and validated (January 1995 to September 2008) using monthly measured stream flow data. The calibrated and validated model determined good to very good performance for stream flow prediction (R2 and E from 0·60 to 0·86) between measured and predicted stream flow values. The root mean square error values (from 14 to 37 m3 s−1) were estimated at similar levels of errors during model calibration and validation. The results showed that long‐term (50 years) average monthly stream flow sensitivity due to climate change effects was found the greatest as a result of percentage change in the precipitation followed by carbon dioxide (CO2) concentration and temperature. The long‐term model simulation scenarios as compared with the base scenario for all five spatially distributed USGS gage stations in the UPRW estimated an average monthly stream flow decrease (from 54 to 67%) and average monthly stream flow increase (from 67 to 79%) depending on the spatial characteristics of the USGS gage stations. Overall, the results indicate that the UPRW hydrology is very sensitive to potential future climate changes and that these changes could stimulate increased streamflow generation from the watershed. Copyright © 2010 John Wiley & Sons, Ltd.

Potential impact of climate change on flooding in the Upper Great Miami River Watershed, Ohio, USA: a simulation-based approach

This study aims to assess the potential impact of climate change on flood risk for the city of Dayton, which lies at the outlet of the Upper Great Miami River Watershed, Ohio, USA. First the probability mapping method was used to downscale annual precipitation output from 14 global climate models (GCMs). We then built a statistical model based on regression and frequency analysis of random variables to simulate annual mean and peak streamflow from precipitation input. The model performed well in simulating quantile values for annual mean and peak streamflow for the 20th century. The correlation coefficients between simulated and observed quantile values for these variables exceed 0.99. Applying this model with the downscaled precipitation output from 14 GCMs, we project that the future 100-year flood for the study area is most likely to increase by 10–20%, with a mean increase of 13% from all 14 models. 79% of the models project increase in annual peak flow. Citation Wu, S.-Y. (2010) Potential impact of climate change on flooding in the Upper Great Miami River Watershed, Ohio, USA: a simulation-based approach. Hydrol. Sci. J. 55(8), 1251–1263.

Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model

Summary Streamflow forecasting is an important issue in hydrologic engineering, as it determines the reservoir inflow as well as the flooding events, in spite of several other applications in water resources engineering. In the present study, the application of hybrid wavelet-neuro-fuzzy model has been investigated to model daily, monthly and yearly streamflows. Streamflow data of Derecikviran Station on the Filyos River in the Western Black Sea region of Turkey were used in the study. The data sample consisted of 31 years of streamflow records. In the first part of the study, single neuro-fuzzy (NF) and wavelet-neuro-fuzzy (WNF) models were established based on the previously recorded streamflow values and compared with each other. It was found that the WNF model increase the accuracy of the single NF models especially in forecasting yearly streamflows. In the second part of the study, the single NF and WNF models were compared with each other by adding periodicity component into the their inputs. The comparison results indicated that adding periodicity component generally increased the models’ accuracy.

Solute dynamics during bank storage flows and implications for chemical base flow separation

Chemical base flow separation is a widely applied technique in which contributions of groundwater and surface runoff to streamflow are estimated based on the chemical composition of stream water and the two end‐members. This method relies on the assumption that the groundwater end‐member can be accurately defined and remains constant. We simulate solute transport within the aquifer during and after single and multiple river flow events, to show that (1) water adjacent to the river will have a concentration intermediate between that of the river and that of regional groundwater and (2) the concentration of groundwater discharge will approach that of regional groundwater after a flow event but may take many months or years before it reaches it. In applying chemical base flow separation, if the concentration in the river prior to a flow event is used to represent the pre‐event or groundwater end‐member, then the groundwater contribution to streamflow will be overestimated. Alternatively, if the concentration of regional groundwater a sufficient distance from the river is used, then the pre‐event contribution to streamflow will be underestimated. Changes in concentration of groundwater discharge following changes in river stage predicted by a simple model of stream‐aquifer flows show remarkable similarity to changes in river chemistry measured over a 9 month period in the Cockburn River, southeast Australia. If the regional groundwater value was used as the groundwater end‐member, chemical base flow separation techniques would attribute 8% of streamflow to groundwater, as opposed to 25% if the maximum stream flow value was used.

A combined watershed–water quality modeling analysis of the Lake Waco reservoir: I. Calibration and confirmation of predicted water quality

A coupled watershed–reservoir modeling system was applied to the Lake Waco reservoir and watershed to test possible sources of seasonal excess nutrient concentrations in Lake Waco. The Soil Water Assessment Tool (SWAT) was used for modeling watershed production of water and nutrients. This model included small lakes and dairies in the watershed, located spatially using geographic data. For the reservoir, the 2-dimensional, hydrodynamic model CE-Qual-W2 was used with inputs derived from the SWAT simulations. Calibration of the models was based on observed hydrographic information and stream and reservoir nutrient data. The relationship between predicted and observed stream flow values for the North Bosque River, the major tributary to the reservoir, were highly correlated (r 2= 0.86) for the calibration period 1997–1998. Predicted daily nutrient values near the inflow of the North Bosque into Lake Waco reservoir were variable but similar to previously estimated annual loading values. Comparison of predicted water quality characteristics from the CE-Qual-W2 model with observed values showed acceptably reliable correspondence seasonally. Chlorophyll-a was used as the main measure for calibration accuracy due to its importance for reservoir management and was predicted to within 6.6% based on the mean percent error or an absolute root mean square error of 9.76 μg/L of observed values. The prediction of stream values from the SWAT model and reservoir nutrient concentrations were sensitive and robust for the next phase of the Comprehensive Lake Waco Study, which includes evaluation of multiple watershed and reservoir management options.

Hydrologic and Morphologic Variability of Streams With Different Cranberry Agriculture Histories, Southern New Jersey, United States1

Procopio, Nicholas A., 2010. Hydrologic and Morphologic Variability of Streams With Different Cranberry Agriculture Histories, Southern New Jersey, United States. Journal of the American Water Resources Association (JAWRA) 46(3):527‐540. DOI: 10.1111/j.1752‐1688.2010.00432.xAbstract: The creation of reservoirs and the modification of stream channels are common practices used to facilitate the efficient production of cranberries. The potential impacts to hydrologic and geomorphic aspects of streamflow and channel structure have not been adequately assessed. In this study, the streamflow regime of 12 streams and the channel morphologies of 11 streams were compared for study sites in the Pinelands region of New Jersey with upstream active‐cranberry bogs, upstream abandoned‐cranberry bogs, and basins with no apparent agricultural history. Flow regime metrics included measures of low‐flow, median‐flow, and bankfull discharge, two measures of streamflow variability (spread and a modified Richards‐Baker Flashiness index), and the frequency of overbank flooding. Stream‐channel morphology metrics included average bank slope, average bankfull width, average bankfull depth, average bankfull width‐to‐depth ratio, and average bankfull area. No significant differences between stream types were apparent for any of the metrics. Basin‐area normalized streamflow values of all 12 study sites were highly correlated to each other. Significant relationships existed between some of the flow‐regime and channel‐morphology metrics. Due to the lack of significant differences between stream types, it appears that neither historic nor current cranberry agricultural practices considerably influence flow regimes or the channel morphology of streams in the New Jersey Pinelands.

NEXRAD Stage III Precipitation Local Bias Adjustment for Streamflow Prediction

Prediction accuracy of physically based hydrologic models largely depends on how well the models and input data represent the real world in terms of precipitation, topography, soils, land use/land cover, and land management. Precipitation is, by far, the largest source of uncertainty; misestimating it by a small percentage leads to inadequate soil-moisture accounting and subsequent inaccuracy in runoff and streamflow estimation. The objectives of this study were (1) to determine the optimum local bias adjustment factor (BAF) threshold limits (TLs) by using SWAT-modeled streamflow values and frequency distributions with NEXRAD or raingauge precipitation inputs, and (2) to assess the impact of BAF TLs derived from 40 raingauges in and around a large watershed in west central Kansas on hydrologic response of the SWAT model. NEXRAD overestimated precipitation depths (45% to 184%) in warm months including April to September and underestimated depths (33% to 64%) in cold months including December to February; the remaining months had fairly close estimates (within 20%). Assessment of bias-adjusted NEXRAD data based on daily raingauge comparisons found the best model efficiency (Ef) using 0.15 lower TL, whereas the lowest bias was for 0.33 lower TL, both with 2.0 upper TL. However, assessment of NEXRAD data based on predicted versus observed daily streamflow found the best Ef using 0.15 lower TL and the lowest bias using 0.25 lower TL. NEXRAD data bias-adjusted using TLs of 2.0 (upper) combined with 0.15 (lower) produced satisfactory streamflow results, similar to the default nearest-raingauge method used in SWAT. This result is encouraging considering the substantial seasonal bias evident in the Stage III NEXRAD data and it indicates the potential for future improvements in streamflow simulation accuracy as NEXRAD accuracy increases. This study demonstrated that TLs are needed in bias-adjustment of NEXRAD data for reasonable hydrologic simulation, and that better streamflow simulation accuracy is obtained if the optimum TLs are based on streamflow assessment rather than assessment of the precipitation data.

Classification of hydro-meteorological conditions and multiple artificial neural networks for streamflow forecasting

Abstract. This paper presents the application of a modular approach for real-time streamflow forecasting that uses different system-theoretic rainfall-runoff models according to the situation characterising the forecast instant. For each forecast instant, a specific model is applied, parameterised on the basis of the data of the similar hydrological and meteorological conditions observed in the past. In particular, the hydro-meteorological conditions are here classified with a clustering technique based on Self-Organising Maps (SOM) and, in correspondence of each specific case, different feed-forward artificial neural networks issue the streamflow forecasts one to six hours ahead, for a mid-sized case study watershed. The SOM method allows a consistent identification of the different parts of the hydrograph, representing current and near-future hydrological conditions, on the basis of the most relevant information available in the forecast instant, that is, the last values of streamflow and areal-averaged rainfall. The results show that an adequate distinction of the hydro-meteorological conditions characterising the basin, hence including additional knowledge on the forthcoming dominant hydrological processes, may considerably improve the rainfall-runoff modelling performance.

Augmentation of seasonal low stream flows by artificial recharge in the Spokane Valley-Rathdrum Prairie aquifer of Idaho and Washington, USA

Conjunctive management of surface water and groundwater requires sophisticated spatial and temporal analysis. In situations involving multiple jurisdictions such as state boundaries, management problems are magnified due to often conflicting regulations and policies. A transient MODFLOW model of the Spokane Valley-Rathdrum Prairie (SVRP) aquifer/river system mutually accepted by both the states of Idaho and Washington, USA, was used to evaluate regional solutions to potential water shortages through the use of strategically placed infiltration basins or injection wells. Artificial recharge of the SVRP aquifer was simulated using diversions from Lake Pend Oreille during winter periods when flows are high and excess water is available. Alternative locations for potential wells and detention basins were examined. Lag times for the water to impact stream/groundwater interaction areas along the Spokane River were evaluated to assess the potential for augmenting stream flows from July through September. Results indicated that the aquifer could be used to improve low-flow season streamflow values utilizing both infiltration basins and injection wells with winter surface water diversions. Depending on the location, as much as 30% of the winter diversion rate could be lagged to improve summer flows at the Spokane gage. Thus, a regional mitigation strategy is scientifically feasible.

Comparison of fuzzy inference systems for streamflow prediction

A comparison of the Mamdani and the Takagi-Sugeno (TS) fuzzy inference systems is presented for predicting streamflow values. The TS fuzzy rule base uses linear functions of inputs to predict the output, whereas the Mamdani version of inference determines outputs through fuzzy sub-sets. A genetic algorithm-trained Mamdani system is applied for streamflow forecasting. All the uncertainties and model complications are treated in linguistic expressions in the form of IF—THEN statements. Fuzzy membership functions, rules and the type of defuzzification method are adjusted until the best correlation between measured and predicted values is reached. The two methods are then applied to flow predictions on the Euphrates River in Turkey, without employing exogenous variables such as rainfall. The advantages and disadvantages of the models are discussed using the case study. It is shown that the Mamdani type of fuzzy inference modelling outperforms the Takagi-Sugeno approach in terms of error criteria comparisons, but neither of the two outperforms a standard ARMA(2, 2) model.

Modeling water balance for the small-forested watershed in Korea

In this paper, the water balance of the 15-ha forested watershed in the southern part of Korea was evaluated to determine the model performance and the fractions of precipitation that become streamflow, evapotranspiration, and ground water flow. The BROOK90 model, a lumped hydrologic simulation model, was calibrated and used for the water balance analysis. Results showed that the model efficiency performance of r2 and Nash-Sutcliffe were fitted quite well over the observed and simulated streamflow values. The water balance investigation showed that about 46 percent of the annual precipitation released as evapotranspiration, 39 percent as streamflow, and 15 percent for the seepage loss. The BROOK90 model was significantly correlated as compared to the PART and WHAT system programs in terms of ground water flow simulation. In particular, it can be asserted that the partitioned amount of water varied from one component to another as affected by seasonal variations, canopy, soil, and drainage flow characteristics.

Prediction of daily flow duration curves and streamflow for ungauged catchments using regional flow duration curves

A method is presented to predict flow duration curves (FDCs) and streamflow for ungauged catchments in the Mid-Atlantic Region, USA. Twenty-nine catchments were selected from the Appalachian Plateau, Ridge and Valley, and Piedmont physiographic provinces to develop and test the proposed method. Using a step-wise multiple regression analysis, the dominant landscape and climate descriptors were identified and regional FDC models were developed for each province. Predictive performance of the proposed method was estimated using data from three evaluation sites that were not included in parameter estimation for the regional FDC models. The results of this study show that climate and geomorphological descriptors strongly influence the hydrology of the Appalachian Plateau and some Ridge and Valley catchments, whereas soil and geomorphological descriptors strongly influence the hydrology of the Piedmont and some Ridge and Valley catchments. Streamflow values calculated by the drainage area ratio method and those reconstructed by the FDC method were compared with the observed streamflow values. The results indicate that the FDC-based method shows great promise for predicting streamflow in ungauged basins.

Drought Mitigation through Long-Term Operation of Reservoirs: Case Study

Dealing with climate variability in a river basin presents many challenges in managing a water resources system. Occurrence of severe and persistent droughts deplete reservoirs storage to critical levels, which may lead to future water supply disaster. This paper illustrates certain benefits of using long-lead streamflow forecasts as well as restriction rules for reservoir operation to help manage the water resources system in the Zayandeh-rud River Basin in Iran. An approach is developed for activating restrictions on allocating water to agricultural demands during a drought and predicting low flow regimes using long-lead forecasts. The long-lead forecasts could utilize valuable hydroclimatic information such as the El-Nino southern Oscillation and northern Atlantic Oscillation to predict seasonal streamflow values. Hedging rules for optimal water supply releases is developed based on the benefit functions of release and carryover storage at each agricultural season. Hedging rules are triggered by different levels of drought indices determined by the predicted water availability at the beginning of each agricultural season. The method is used on an historical data set of hydroclimatic variables of the system to simulate the real time operation of the Zayandeh-rud Reservoir. The utility of the method is demonstrated for operating the Zayandeh-rud Reservoir from the drought mitigation point of view. Furthermore, the proposed model is compared to a stochastic dynamic programming model by investigating different indices such as drought duration, drought severity, drought loss, and reliability of agricultural water demands allocation. The results indicate that the use of the proposed approach can significantly reduce the vulnerability of the system during hydrological droughts and increases the long-term benefits of agricultural water demand allocation.

Modeling GCM and scenario uncertainty using a possibilistic approach: Application to the Mahanadi River, India

Climate change impact assessment on water resources with downscaled General Circulation Model (GCM) simulation output is characterized by uncertainty due to incomplete knowledge about the underlying geophysical processes of global change (GCM uncertainties) and due to uncertain future scenarios (scenario uncertainties). Disagreement between different GCMs and scenarios in regional climate change impact studies indicates that overreliance on a single GCM with a scenario could lead to inappropriate planning and adaptation responses. This paper focuses on modeling GCM and scenario uncertainty using possibility theory in projecting streamflow of Mahanadi river, at Hirakud, India. A downscaling method based on fuzzy clustering and Relevance Vector Machine (RVM) is applied to project monsoon streamflow from three GCMs with two green house emission scenarios. Possibilities are assigned to all the GCMs with scenarios based on their performance in modeling the streamflow of the recent past (1991–2005), when there are signals of climate forcing. The possibilities associated with different GCMs and scenarios are used as weights in computing the possibilistic mean of the CDFs projected for three standard time slices 2020s, 2050s, and 2080s. The result shows that the value of streamflow at which the CDF reaches 1 reduces with time, which shows the reduction in probability of occurrence of extreme high flow events in future. Historic record of monsoon streamflow of Mahanadi river also shows similar decreasing trend, which may be due to the effect of high surface warming. Reduction in Mahandai streamflow is likely to pose a major challenge for water resources engineers in meeting water demands in future.

Quantifying the Predictability of Winter River Flow in Iberia. Part II: Seasonal Predictability

Abstract The role of the Atlantic summer and autumn SSTs on the predictability of the winter Iberian Peninsula river flows is analyzed during the period 1923–2004. The analysis is based on the results of an interannual predictability experiment, using autoregressive-moving-average (ARMA) models, carried out in the first part of this work. A standard principal component analysis (PCA) was applied to the summer and autumn SST fields for the entire Atlantic Ocean. Then, the association between the resulting principal component (PC) series and the Iberian Peninsula streamflow series was analyzed, in order to use the PC series as additional predictor variables in a seasonal forecasting regression model. Results proved, first, that during autumn, the SST variability in the so-called North Atlantic horseshoe pattern has a statistically significant linear influence in the following winter streamflow values. In particular, the use of this SST information considerably improves the skill of the linear forecast (improvements against climatology range from 61% to 90%) compared to the ARMA-alone model (51%–53%). These improvements are mostly related to the ability of the SST information to provide better estimates of extreme streamflow values. Additionally, results showed that the summer tropical Atlantic and the autumn southwestern Atlantic SST variability have a significant nonlinear influence on the following winter streamflow values. In particular, there is a tendency for negative streamflow anomalies following tropical Atlantic summer negative SST anomalies and following southwestern Atlantic autumn SST positive anomalies. It is finally concluded that the linear interannual predictability of the Iberian Peninsula winter streamflow is greater (two-thirds of the total predictability) than the predictability associated with the previous season autumn SSTs (one-third).

Geospatial Analysis of Climatic and Geomorphic Interactions Influencing Stream Discharge, Appalachian Mountains, USA

Streamflow values are commonly synthesized for locations where flow measurement stations are lacking or where only intermittent measurements are available. In an Appalachian Mountains dataset comprised of 29 watersheds, the most appropriate among geomorphic, geologic, and hydrogeologic datasets were selected for use in prediction of streamflow at watershed scale. A statistical model was developed using principal components analysis (PCA) and cluster analysis (CA) for. Using CA on variables derived from the PCA, an optimum set of variables was derived for predicting streamflow. Results indicate there are two categories of watersheds in the study area. The first is strongly correlated with climatic variables (precipitation, temperature, elevation, and groundwater recharge). The second is strongly correlated with two geomorphic variables (watershed slope and percentage of forested area). The spatial distribution of cluster classifications shows that watersheds dominated by the climatic component are located along the Allegheny Front while watersheds dominated by the geomorphic component are located in the Allegheny Plateau and Valley and Ridge physiographic provinces. These variations between the Allegheny Plateau and Valley and Ridge physiographic provinces suggest that, to accurately model streamflow, modeling needs be done based on natural physiographic boundaries rather than political boundaries. In this physiographic setting, elevation seems to be a major control.