Hydrological Simulation Program-Fortran Research Articles

Application and Evaluation of Stage–Storage–Discharge Methodology in Hydrological Study of the Southern Phosphate Mining Model Domain in Southwest Florida

This hydrological study investigated a combined rating methodology tested on a 14,090 km2 area in Southwest Florida. The approach applied the Hydrological Simulation Program-Fortran (HSPF) over a 23-year period and was validated by 28 stream gauging stations. The regional hydrological complexity includes natural and agricultural areas, as well as extensive phosphate mining and urbanizing areas. This application is a novel and efficient methodology for generating stage–storage–discharge relationships using a geographic information system (GIS), empirical equations, and spreadsheets for 148,000 isolated and connected alluvial wetlands within the model domain. The validation metrics used to evaluate the applied methodology for populating the stage–storage–discharge relationship demonstrated the model effectiveness in simulating a range of hydrological events across various regions. For discharge prediction, the Nash–Sutcliffe efficiency values surpassed 0.7 at most stations, with an average of 0.67, and the average R squared was 0.74. This methodology, when applied, achieved a root-mean-square error of 4 m3/s for discharge prediction and 0.47 m for stage prediction. However, limitations emerged in simulating baseflow (low flows), highlighting the need for integrated modeling approaches to accurately capture groundwater–surface water interactions. The research provides an improved means for modeling regional water resources and lays the groundwork for enhanced hydrological modeling in watersheds with complex alluvial and isolated wetland systems.

Impacts of climate change on streamflow of Qinglong River, China

Abstract Climate change significantly influences water resources and flood hazards in global watersheds. This study focuses on predicting the impact of climate change on the streamflow of the Qinglong River situated in northern China. The streamflow of the Qinglong River (2021-2100) under two climate change scenarios (RCP 4.5 and RCP 8.5) was mainly synthesized over multiple timescales. Meteorological data from 31 Global Climate Models (GCMs) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) served as inputs for the Hydrological Simulation Program-Fortran (HSPF) to conduct hydrological simulations. Results show that: (1) The peak flood flow and average daily streamflow for the RCP4.5 scenario are at least 101.15% and 110.14% of the historical phase, and at least 108.89% and 121.88% of the historical phase for the RCP8.5 scenario. (2) Under both scenarios, the proportion of summer streamflow to the annual total is expected to increase from 61.46% (historical phase) to over 85%, while the proportion of winter streamflow to the annual total is expected to decrease from 8.84% (historical phase) to below 0.5%. (3) Compared to the historical period, the maximum increase in future multi-year average annual streamflow for the RCP4.5 and RCP8.5 scenarios is 30.34%, 31.48%, respectively.

Sustainable land-use/Land-cover for stormwater management in three coastal watersheds in central Chile

ABSTRACT Central Chile’s Concon City urbanization trend has severely affected local hydrological processes. This research hypothesizes that switching to sustainable land-use/land-cover (LULC) in three Concon watersheds reduces the severity of their corresponding hydrological responses. High sustainability LULC zones would decrease runoff and enhance interception and infiltration, generating hydrograph curves with extended lag time and moderate peak flows. The Hydrological Simulation Program FORTRAN (HSPF) is used for simulating watershed processes. Decreases in peak flows of 31.1%, 19.8%, and 54.5%, were estimated. Therefore, sustainable LULC reduces the severity of the hydrological responses, benefiting storm water management. Real-world applications in Europe and the USA achieved comparable results. Urban master plans in Chile have favored the alteration of natural landscapes (anthropization). Per national regulations, changes to urban plans can be made every ten years. The results of this research will be valuable in future revisions of Concon’s urban plan.

Application of hydrological models in climate change framework for a river basin in India

Abstract Soil Water Assessment Tool (SWAT), Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS), and Hydrologic Simulation Program Fortran (HSPF) are explored for streamflow simulation of Lower Godavari Basin, India. The simulating ability of models is evaluated using four indicators. SWAT has shown exceptional simulating ability in calibration and validation compared to the other two. Accordingly, SWAT is used in the climate change framework using an ensemble of 13 Global Climate Models and four Shared Socioeconomic Pathways (SSPs). Three time segments, near-future (2021–2046), mid-future (2047–2072), and far-future (2073–2099), are considered for analysis. Four SSPs show a substantial increase in streamflow compared to the historical period (1982–2020). These deviations range from 17.14 (in SSP245) to 28.35% (in SSP126) (near-future), 31.32 (SSP370) to 43.28% (SSP585) (mid-future), and 30.41 (SSP126) to 70.8% (SSP585) (far-future). Across all timescales covering 948 months, the highest projected streamflows observed in SSP126, SSP245, SSP370, and SSP585 were 4962.36, 6,108, 6,821, and 6,845 m3/s, respectively. Efforts are also made to appraise the influence of multi-model combinations on streamflow. The present study is expected to provide a platform for holistic decision-making, which helps develop efficient basin planning and management alternatives.

Climate Change Impacts on the Côa Basin (Portugal) and Potential Impacts on Agricultural Irrigation

The increasing gap between water demands and availability is a significant challenge for sustainable water management, particularly in the context of growing irrigation needs driven by climate change. In the Côa region (inner-north Portugal), agriculture plays a vital role in the local economy, ensuring food security and contributing to the conservation of natural resources, though also threatened by climate change. The present study assesses how streamflow in the Côa River can be affected by climate change. The HSPF (Hydrological Simulation Program-FORTRAN) hydrological model was coupled with three global–regional climate model chains to simulate historical monthly and annual streamflow (1986–2015), and to predict future (2040–2099) streamflow under RCP8.5. Irrigation scenarios were subsequently developed considering a potential future increase from 10% to 50% per decade. The evaluation of HSPF performance during the historical period revealed good agreement (R2 > 0.79) between simulated and observed flows. A general decrease in streamflow is found in the future, particularly in 2070–2099, with annual mean streamflow projected to decrease by −30% until 2099. Interannual variability is also expected to increase. Generally, the simulations indicated higher future flows in winter/early spring, whilst they are expected to decrease over the rest of the year, suggesting drought intensification. An increase in water demands for irrigation, potentially rising from 46 hm3·yr−1 (baseline scenario) up to 184 hm3·yr−1 (50% increase per decade) may lead to unsustainable irrigation. Managing these opposite trends poses significant challenges, requiring a comprehensive and integrated approach from stakeholders and policymakers. Strategies should focus on both demand-side and supply-side measures to optimize water use, improve water efficiency, and preserve water availability.

Long-term effects of land-use change on water resources in urbanizing watersheds

The changes in energy balance resulting from land-use change may significantly affect the amount and timing of water loss to the atmosphere as evapotranspiration (ET). Also, these will impact water fluxes in the watershed system, influencing runoff rate, flow volume, intensity, and frequency of floods. During the past century, land-use change in the SuAsCo (Sudbury-Assabet and Concord) watershed has altered basin hydrology, sediment, and nutrient load that is detrimental to water resources in SuAsCo. This study uses an integrated physically-based model Hydrological Simulation Program-FORTRAN (HSPF), along with Land Transformation Model (LTM), to assess predicted temporal and spatial changes in water, nutrient, and sediment yields for future land-use scenarios of 2035, 2065, and 2100. Results showed that a 75% increase in effective impervious area and a 50% decrease in forest area in 2100 (from 2005 baseline levels) are projected to cause a 3% increase in annual streamflow and a 69% increase in total yearly mean surface runoff. The average annual total suspended solid (TSS) yield at the watershed outlet is estimated to increase by 54% in 2100. An increase of 12% and 13% concentrations of average annual total phosphorus (TP) and total nitrogen (TN) are predicted by 2100 due to urban expansion and increased runoff volume. This integrated modeling approach will inform watershed managers and landowners about critical areas of the SuAsCo watershed to apply best management practices (BMPs) to mitigate the effects of land-use land cover (LULC) change.

Multi-parameter approaches for improved ensemble prediction accuracy in hydrology and water quality modeling

Ensemble approaches can be a quick way to improve hydrological prediction accuracy by considering multiple plausible modeling representations of a system. Previous studies showed that the use of multiple types of models could increase prediction accuracy, but model selection subjectivity and parameter equifinality issues remain unsolved. This study proposes to consider multiple parameter sets identified in the model calibration process and information on their performance for improved accuracy of ensemble hydrological prediction. In this study, we investigate whether multi-parameter ensemble (MP) compared to multi-model ensemble (MM) approaches can improve the accuracy of hydrology and water quality prediction. A variety of ensemble methods, including simple averaging, median filtering, weighted averaging, Bayesian model averaging (BMA), and their variants, were tested in watershed modeling implemented to predict daily water discharge and total phosphorus (TP) loads of a study watershed. The parameter spaces of two watershed loading models, Soil and Water Assessment Tool (SWAT) and Hydrological Simulation Program-FORTRAN (HSPF), were sampled using a multi-objective heuristic optimization algorithm, namely A Multi-Algorithm Genetically Adaptive Multiobjective (AMALGAM). The modeling experiment showed that the MP approach more effectively improved modeling accuracy statistics compared to the MM of both models as well as single-model and single-parameter approaches. The ensemble approaches did not always improve modeling accuracy, and their efficiency depended on ways to determine weights for ensemble members. The BMA method outperformed the other weighting schemes tested in this study, and its performance was responsive to the number and diversity (or variance) of ensemble member candidates and the computational cost to create the candidates. The findings demonstrate how the information of parameter sets sampled in model calibration and their performance statistics could help improve hydrological prediction accuracy.

Evaluation of Non-Point Source Pollution Load Characteristics and Identifying Management Sub-Basin Area and Best Management Practices Using HSPF Model in Yongdam Dam Watershed

Objectives : To identify and estimate the non-point source (NPS) pollution loads in the Yongdam watershed, and to take an appropriate NPS management plan suitable for the environmental conditions of the sub-basin of Yongdam watershed.Methods : The NPS pollutant loads of the Yongdam watershed was investigated to establish the hydrological simulation program-fortran (HSPF) model for the study area so that the characteristics of non-point runoff were simulated. Furthermore, the detailed catchment area subject to non-point pollution management was selected and the effect of reducing non-point pollution was examined based on the various simulation results.Results and Discussion : The three sub-basins, including specific component of catchments, of Yongdam watershed was selected according to the load intensity and distribution of NPS pollution. The result of analyzing the load flowing into the downstream by rainfall intensity revealed that the pollutant load accounted for 11.8~19.9% in the dry season, and for Jinan-cheon sub-basin the load was high in the range of 10~30 mm while Janggye-cheon sub-basin and downstream of Janggye-cheon junction sub-basin showed a high load in the range 30~60 mm. In addition, the load duration curve (LDC) showed the excess frequency of BOD load was high when the flow rate increased at the end of stream for each sub-basin, whereas the load excess frequency was high in T-P in the entire flow range. LDC analysis led to us that the water quality of stream was affected by point and non-point pollutants in a complex way. When the ground cover in a farm were installed by selecting 10 detailed catchments with high need for NPS pollution management, the reduction of T-P load was estimated to be reduced by 325.256~995.912 g/d in Jinan-cheon sub-basin, 1279.813~3327.513 g/d in Janggye-cheon sub-basin, and 1316.831~3555.443 g/d in downstream of Janggye-cheon junction sub-basin.Conclusion : Through various simulation experiments using the HSPF model, it was feasible to select an catchment subject to NPS pollution control in detail and evaluate the effect of reducing NPS pollution.

Systematic tracing of nitrate sources in a complex river catchment: An integrated approach using stable isotopes and hydrological models

Quantitative estimation for tracking the transport of various nitrate sources is required to effectively manage nitrate loading in complex river systems. In this study, we validated an integrated framework using field isotopic data (δ15NNO3 and δ18ONO3) of nitrates and hydrological modeling (hydrological simulation program FORTRAN; HSPF) to determine anthropogenic nitrate flux among different land-use types within a watershed. Nitrate isotopic compositions showed different ranges among four land-use types (4.9 to 15.5‰ for δ15NNO3, -4.9 to 12.1‰ for δ18ONO3), reflecting the different nitrate sources (sewage, synthetic fertilizer, effluent and soil) within watersheds. Based on the integration of HSPF modeling, we also found that total nitrate loads might be partially controlled by hydrological conditions such as water discharge (12,040.3–22,793.2 L/s) from upstream to downstream. Among the nitrate sources, the sewage transport showed unique enhancement near urban boundaries, along with an increase in total nitrate load (>193.5 NO3-N g/s km2) in downstream areas. In addition, the isotopic- and model-based nitrate fluxes showed good correlation for urban sources (R2=0.73, p < 0.05) but poor correlations for agriculture-dominated land use (R2=0.13, p > 0.05), reflecting the potential influence of surface runoff and ground infiltration into the watershed. Consequently, this research provided useful information to establish nitrogen management policy controlling point and non-point nitrate source loads in various land-use types for the restoration of water quality and aquatic ecosystem in the complex river system. Considering the recent increase in human activities near aquatic environments, this framework would be effective for individually estimating the quantitative contributions of anthropogenic nitrate sources transported along river-coastal systems.

Analysis of the effects of low impact development practices on hydrological components using HSPF

The purposes of this study are to propose the new approach for modeling the effectiveness of low impact development (LID) practices using Hydrological Simulation Program-Fortran (HSPF)'s Surface-FTABLE (function table) and to evaluate the impacts of LID application on hydrological components and water balance. LID was simulated using Surface-FTABLE, and changes in hydrological components and water balance were analyzed. These results were compared with results simulating LID using the HSPF LID Controls Tool built in the HSPF model. Embedded within the HSPF model, the HSPF LID Controls Tool is used to design and simulate infiltration-based best management practices. Surface runoff decreased similarly for both methods using Surface-FTABLE and LID Controls Tool. For Surface-FTABLE, the infiltration in the facility was reflected in the model, so interflow, outflow and baseflow outflow increased. As a result of the water balance analysis, the results of Surface-FTABLE showed a similar bias to those of the model without LID. In contrast, the results of the LID Controls Tool showed a large bias due to uninvolved infiltration. This study showed that HSPF Surface-FTABLE is applicable to LID simulation and that it is possible to simulate the change of each element of hydrologic components reasonably.

Assessing the Performance of a Long Short-Term Memory Algorithm in the Dataset with Missing Values

This study was conducted to assess the performance of a long short-term memory algorithm (LSTM), which was suitable for time series prediction, in the multivariate dataset with missing values. The full dataset for the adopted LSTM model was prepared by running a popular watershed model Hydrological Simulation Program-Fortran (HSPF) in the upper Nam River Basin for 3 years from 2016 to 2018, excluding a one-year warm-up period, on a daily time step. The accuracy of prediction for the LSTM model was evaluated in response to various interpolation methods as well as changes in the number of missing values (for dependent variables) and independent variables (containing a fixed number of missing values for either single or multiple variables). Note that the entire dataset is divided into training and test datasets at a ratio of 7:3. Results showed that different interpolation methods resulted in a considerable variation in performance of the LSTM model. Out of them, StructTS and RPART were selected as the best imputation methods recovering missing values for discharge and total phosphorus, respectively. The prediction error of the LSTM model increased gradually with increasing the number of missing values from 300 to 700. The LSTM model, however, appeared to maintain its performance fairly well even in data sets with a large amount of missing values as long as adequate interpolation methods were adopted for each dependent variable. The performance of the LSTM model degraded further as the number of independent variables containing the fixed number of missing values increased from 1 to 7. We believe that the proposed methodology can be used not only to reconstruct missing values in a real-time monitoring dataset with excellent performance, but also to improve the accuracy of prediction for (time series) deep learning models.

Adaptation measures under the impacts of climate and land-use/land-cover changes using HSPF model simulation: Application to Gongola river basin, Nigeria

Recently, there is an upsurge in flood emergencies in Nigeria, in which their frequencies and impacts are expected to exacerbate in the future due to land-use/land cover (LULC) and climate change stressors. The separate and combined forces of these stressors on the Gongola river basin is feebly understood and the probable future impacts are not clear. Accordingly, this study uses a process-based watershed modelling approach – the Hydrological Simulation Program FORTRAN (HSPF) (i) to understand the basin's current and future hydrological fluxes and (ii) to quantify the effectiveness of five management options as adaptation measures for the impacts of the stressors. The ensemble means of the three models derived from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are employed for generating future climate scenarios, considering three distinct radiative forcing peculiar to the study area. Also, the historical and future LULC (developed from the hybrid of Cellular Automata and Markov Chain model) are used to produce the LULC scenarios for the basin. The effective calibration, uncertainty and sensitivity analyses are used for optimising the parameters of the model and the validated result implies a plausible model with efficiency of up to 75 %. Consequently, the results of individual impacts of the stressors yield amplification of the peak flows, with more profound impacts from climate stressor than the LULC. Therefore, the climate impact may trigger a marked peak discharge that is 48 % higher as compared to the historical peak flows which are equivalent to 10,000-year flood event. Whilst the combine impacts may further amplify this value by 27 % depending on the scenario. The proposed management interventions such as planned reforestation and reservoir at Dindima should attenuate the disastrous peak discharges by almost 36 %. Furthermore, the land management option should promote the carbon-sequestering project of the Paris agreement ratified by Nigeria. While the reservoir would serve secondary functions of energy production; employment opportunities, aside other social aspects. These measures are therefore expected to mitigate feasibly the negative impacts anticipated from the stressors and the approach can be employed in other river basins in Africa confronted with similar challenges.

Prediction of Water Quantity and Quality Variables Using Deep Learning Algorithms Adopted in Time Series Domain

We developed sequential deep learning models to predict time-series water quantity and quality data. The data set consisting of 10 input variables was prepared from a watershed model Hydrological Simulation Program - FORTRAN (HSPF) fine-tuned to existing conditions in the Nam River Basin on a daily time step for the simulation period 2019-2021. The whole data set was partitioned into training and test sets in the ratio 7 to 3. The predictive accuracy of four deep learning models, created differently in terms of the types of algorithms as well as the number of layers, was tested with respect to mean squared error (MSE). We found that changes in input time steps from 1 to 2 days led to a sharp reduction in prediction errors of all applied models for two target variables during the training and test phases, except for a few cases. In addition, at least 3 important variables were enough to maintain the predictive accuracy of the original deep learning model with 10 variables. The performance of the deep learning model was sensitive to output time steps rather than input time steps. In all test conditions, the MSE values were extremely low, reaching as high as 0.0056. Therefore, sequential deep learning models, regardless of their types and architectures, are most suitable for predictive modeling of time series data compiled on a daily basis or less such as remote sensing data in hydrology and agriculture.

A Study on Identifying Priority Management Areas and Implementing Best Management Practice for Effective Management of Nonpoint Source Pollution in a Rural Watershed, Korea

It is difficult to accurately identify and manage the paths of nonpoint source (NPS) pollution in rural watersheds because their discharge patterns vary depending on season, region, and agricultural characteristics. In this study, flow and water quality during rainfall events were monitored in Songya watershed, an impaired, rural area in South Korea. A method of identifying priority management areas was proposed through scientific objectification and quantification of key factors controlling NPS, such as land use, agricultural type, and load. For the load calculation, a watershed model was developed using Hydrological Simulation Program Fortran (HSPF). Three priority management areas—Mulhan Stream, Osan Stream, and the upstream area of Songya Stream—were selected. Using the developed model, constructed wetlands with the capacity of 1000 m3 were applied at the lower reach of each priority management subbasin and the impacts on NPS pollution reduction were tested. The simulated results showed that BOD and TP concentrations at the outlet of Songya watershed were lowered by 9.2% and 6.0%, respectively. It is expected that the method proposed in this study for identifying priority management areas and implementing best management practice in agricultural watersheds can be applied to similar areas which struggled with NPS pollution.

Applying a coupled hydrologic-economic modeling framework: Evaluating conjunctive use strategies for alleviating seasonal watershed impacts caused by agricultural intensification

Economic models and watershed models provide useful results, but when seeking to integrate these systems, the temporal units typically utilized by these models must be reconciled. A hydrologic-economic modeling framework is built to couple the Hydrological Simulation Program-Fortran (HSPF), representing the watershed system, with the Rectangular Choice-of-Technology (RCOT) model, an extension of the basic input-output (I-O) model. This framework is implemented at different sub-annual timesteps to gain insight in selecting temporal units best suited for addressing questions of interest to both economists and hydrologists. Scenarios are designed to examine seasonal increases in nitrogen concentration that occur because of agricultural intensification in Cedar Run Watershed, located in Fauquier County, northern Virginia. These scenarios also evaluate the selection among surface water, groundwater, or a mix of (conjunctive use) practices for irrigation within the crop farming sector in response to these seasonal impacts. When agricultural intensification occurs in Cedar Run Watershed, implementing conjunctive use in irrigation reduces the seasonal increases in nitrogen concentration to specified limits. The most efficient of the conjunctive use strategies explicitly considered varies depending on which timestep is utilized in the scenario: a bi-annual timestep (wet and dry season) vs. a seasonal timestep. This modeling framework captures the interactions between watershed and economic systems at a temporal resolution that expands the range of questions one can address beyond those that can be analyzed using the individual models linked in this framework.

Developing a quantitative framework to track the fate and transport of estrogens on a watershed scale

• The first study to develop an HSPF modeling framework to simulate three estrogens with interconversion. • The modeling framework can be used to characterize the temporal and spatial variation of estrogens in streams. • The modeling framework can help to investigate the factors leading to high estrogen levels, evaluate the potential health risks to aquatic animals, and determine the best management practices for estrogens. The risks associated with estrogens in detectable concentrations in the aquatic environment underscore the need to best manage the release of estrogenic compounds. Modeling work can effectively help to understand the variation of estrogens in the natural water bodies at low costs. This study developed a modeling framework based on the Hydrological Simulation Program – FORTRAN (HSPF) code to simulate the fate and transport of three dominant estrogens, estrone (E1), 17β-estradiol (E2β), and 17α -estradiol (E2α) in surface water by integrating their excretion, transport, interconversion, and attenuation processes. This work focused on estrogens from sewage systems, failing septic systems, grazing animals, and manure land application. The developed modeling framework was applied to the Redwood River Watershed in Minnesota to model the in-stream estradiol equivalents (EEQs). The modeled EEQs were comparable to the values measured at three sampling sites along the Redwood River in 2007. The modeling results indicate that wastewater treatment plant discharges elevate estrogen levels on dry days, and surface runoff caused by storms or snow melting after the manure land application and/or cattle grazing can drastically spike EEQs levels, suggesting the application of buffer stripes and manure storage/composting for estrogen control. The case study shows that the modeling framework can be used to characterize the temporal and spatial variation of estrogens in streams, evaluate their risk to aquatic animals, and determine the best management practices for estrogen control.

Development of a simulation method for paddy fields based on surface FTABLE of hydrological simulation program–FORTRAN

The Hydrological Simulation Program–FORTRAN (HSPF) is a widely used watershed-scale model; however, it does not comprise any functions that can be used to simulate rice paddy fields, thereby hindering its applicability in many Asian countries. To address this limitation, a paddy field simulation method was developed by considering the water detention effect and seasonal changes in the outlet height of paddy fields; this was achieved using the existing functions of HSPF. Surface FTABLE, a function of the relationship between surface water storage and outflow, was applied to represent the physical structure and inundated condition of paddy fields. Surface FTABLE was temporally changed by Special Actions, an HSPF module, to reflect the seasonal variation in drainage outlet height. By employing these methods, HSPF was applied to paddy fields for evaluating the model performance. On comparing the simulated and observed values of ponding depth and surface outflow, the model showed good performance with a coefficient of determination that ranged from 0.71 to 0.89 and an index of agreement that ranged from 0.92 to 0.97. Comparing the results of the simulation performed with or without Surface FTABLE, the simulation with Surface FTABLE exhibited a reduced surface outflow (−40.0%) due to the blocking effect of the paddy field, a decreased infiltration (−17.3%) due to the saturation of the upper soil zone, and a higher level of soil water in all subsurface layers. Based on these differences in each component, it is necessary to apply Surface FTABLE when considering the hydrological processes of paddy fields for HSPF modeling. The simulation method developed in this study only uses the existing functions of HSPF without performing any external modifications; therefore, it can be used conveniently to improve the applicability of HSPF in rice-growing regions.

Contributions of Sediment From the Slate Canyon Alluvial Fan to the Formation and Morphogenesis of the Keeler Dunes, CA

The Keeler Dunes Complex is an active dunefield located adjacent to Owens (dry) Lake, California. The source of sediment to the Keeler Dunes area is often assumed to be from the Owens Lake playa; however, the dunes lie at the toe of the Slate Canyon alluvial fan (the Fan). Here hydrologic and hydraulic modeling was conducted for the Fan to assess the contribution of fan sediment to the Keeler Dunes. Assessment of the potential for sediment deposition was conducted for two scenarios based on the relocation of State Highway 136 from the Owens Lake playa upgradient on the Fan and the subsequent construction of flow diversion berms. The berm construction (1954 and 1967) coincided with observations of the destabilization and migration of the Keeler Dunes. Runoff from Slate Canyon watershed was estimated using a Hydrological Simulation Program–Fortran (HSPF) model based on hourly precipitation records. The resulting hydrology output served as inputs to FLO-2D models of the Fan. With the model of hydraulic output, it was estimated that approximately one million tons of sediment were moved from the Fan hydrographic apex toward the Keeler Dunes area during the peak streamflow event of record. This represents a significant volume with respect to the total volume of the Keeler Dunes. Our modeling of the peak flow event indicates the construction of the highway diversion berms resulted in the partial redirection of fan flows and therefore sediment deposition in relation to the Keeler Dunes. This localized change in sediment availability and spatial distribution is a likely factor in the subsequent morphogenesis of the dunes.

Applying a Coupled Hydrologic-Economic Modeling Framework: Evaluating Alternative Options for Reducing Impacts for Downstream Locations in Response to Upstream Development

Economic input-output and watershed models provide useful results, but these kinds of models do not use the same spatial units, which typically limits their integration. A modular hydrologic-economic modeling framework is designed to couple the Rectangular Choice-of-Technology (RCOT) model, a physically constrained, input-output (I-O) model, with the Hydrological Simulation Program-Fortran (HSPF). Integrating these two models can address questions relevant to both economists and hydrologists, beyond addressing only administrative or watershed concerns. This framework is utilized to evaluate alternative future development prospects within Fauquier County, northern Virginia, specifically residential build-up, and agricultural intensification in the upstream location of the local watershed. Scenarios are designed to evaluate the downstream impacts on watershed health caused by upstream development and changes made within the economic sectors in response to these impacts. In the first case, an alternative residential water technology is more efficient than the standard for ensuring adequate water supply downstream. For scenarios involving upstream agricultural intensification, a crop shift from grains to fruits and vegetables is the most efficient of the alternatives considered. This framework captures two-way feedback between watershed and economic systems that expands the types of questions one can address beyond those that can be analyzed using these models individually.

Analysis of Water Balance Changes and Parameterization Reflecting Soil Characteristics in a Hydrological Simulation Program—FORTRAN Model

Efficient water resource management requires accurate analyses of hydrological components and water balance. The Hydrological Simulation Program—FORTRAN (HSPF) model serves this purpose at the watershed scale. It has limited accuracy in calculating runoff and infiltration because the model simulates hydrological processes using one representative parameter for each land use in the watershed. Accuracy requires field-scale analysis of hydrological components. We calculated the lower zone storage nominal parameter, which markedly affects runoff in HSPF, from effective moisture content and depth of each soil layer. Analysis of hydrological components suggested re-calculating the parameters reflecting soil characteristics. We investigated two scenarios through simulations: Scenario 1 used the existing method. Scenario 2 used parameters that reflected soil properties. Total flows for each sub-catchment were identical, but proportions of direct and intermediate runoff were larger in Scenario 1. Ratios of baseflow, evapotranspiration, and infiltration were larger in Scenario 2, reflecting soil characteristics. Comparing the baseflow ratio to total flow, Scenario 2 values were similar to observed values. Comparisons of R2 and Nash–Sutcliffe Efficiency (NSE) at the end of the watershed were well matched (R2 and NSE are higher than 0.9) in both scenarios, but proportions of each hydrological component differed. It is important to consider soil characteristics when applying water quantity and quality analyses in an HSPF simulation.