Hydrologic Modeling of Three Sub-Basins in the Kenai River Watershed, Alaska, USA

water

Article Hydrologic Modeling of Three Sub-Basins in the Kenai River Watershed, Alaska, USA

Brett Wells 1,* and Horacio Toniolo 2 ID

1 Alaska Department of Transportation and Public Facilities, 6860 Glacier Highway, Juneau, AK 99801, USA 2 Department of Civil and Environmental Engineering, University of Alaska Fairbanks, 505 N. Chandalar Dr., Fairbanks, AK 99775, USA; [email protected] * Correspondence: [email protected] or [email protected]; Tel.: +1-(907)-465-1607

Received: 3 March 2018; Accepted: 23 May 2018; Published: 25 May 2018

Abstract: Streams in the Kenai River watershed are characterized by a fish-rich environment. The commercial fishing industry and recreational users compete for the fish in these streams, and resource managers strive to balance both groups’ needs while maintaining the sustainability of the resource. The ability to estimate future river conditions could help preserve the resource and a strong economy on the Kenai Peninsula. This research used the U.S. Army Corps of Engineers Hydrological Modeling System, which transforms rainfall to river discharge. The main goal was to define a set of parameters that were calibrated using an event-based condition of concurrent rainfall and discharge data. The model was calibrated and validated in three sub-basins located in different environmental settings (i.e., lowlands, mid, and high elevation).

Keywords: hydrologic modeling; event based; discharge

1. Introduction Residents of the Kenai Peninsula rely on rivers for both recreation and economic opportunities. Czarneski and Yaeger [1] describe it best in their Kenai River Center publication: “Healthy rivers, lakes and oceans are vital to healthy economies and good local standards of living, particularly on the Kenai Peninsula where so much of the economy and culture is centered on the water”. The importance of the health of the water systems is recognized both by government agencies, and local industry. On its website, the Kenai River Sportfishing Association [2] discusses its goal to maintain the health of Alaskan fisheries. This goal can be achieved through the sustainable harvesting of resources, and protection of the habitat that the resources need to survive. An understanding of the watershed could provide a cornerstone for supporting the resources that exist in the area, and the economy which is served by those resources. Though many agencies in the area study the basin, most of them focus on the water quality of anadromous streams in the watershed. The quality of the water in these streams is important, as are estimates of water temperature, low flows, and peak discharge values. However, the watershed is characterized by a scarcity of hydro-meteorological data. The goals of this research were to implement a hydrological model during the warm season (due to the lack of winter data), and to provide a set of calibrated parameters that can be used by other researchers to help answer questions related to the current and future hydrology of the Kenai River watershed, such as possible variability in discharge rates as the precipitation frequency and landscape change. Three sub-basins in the Kenai River watershed were selected for hydrological modeling. These sub-basins represent the three hydrologic scenarios present in the watershed, i.e., low, middle, and steep gradients. Beaver Creek, located near Kenai, Alaska, is representative of a lowland, low-gradient wetlands stream. Russian River is a middle-gradient valley stream, and Ptarmigan

Water 2018, 10, 691; doi:10.3390/w10060691 www.mdpi.com/journal/water Water 2018, 10, 691 2 of 13 Water 2018, 10, x FOR PEER REVIEW 2 of 14

Creeka steep is‐gradient a steep-gradient mountain mountain stream. stream. The Kenai The KenaiRiver watershed River watershed and sub and‐basin sub-basin areas, areas, along along with with the theselected selected sub sub-basins,‐basins, are are shown shown in inFigure Figure 1.1 The. The figures ﬁgures and and tables tables in in this this article article were adapted fromfrom Event Based Modeling Studying Three Sub-BasinsSub‐Basins inin thethe KenaiKenai RiverRiver WatershedWatershed [ [3]3]..

Figure 1. Kenai River Watershed with outlined sub-basinssub‐basins and selected sub-basinssub‐basins highlighted.highlighted.

Communities on the Kenai Peninsula have survived thanks to the resources available in their Communities on the Kenai Peninsula have survived thanks to the resources available in their region. Commercial and recreational fishing accounts for a large percentage of employment. A study region. Commercial and recreational fishing accounts for a large percentage of employment. A study of of the local economy and of economic prospects on the Kenai Peninsula shows that 62% of gross the local economy and of economic prospects on the Kenai Peninsula shows that 62% of gross earnings earnings are from commercial fishing; about $72 million came solely from the harvest of salmon [4]. are from commercial fishing; about $72 million came solely from the harvest of salmon [4]. Though Though many regulations are currently in place to ensure the future of sport and commercial many regulations are currently in place to ensure the future of sport and commercial fisheries, most of fisheries, most of the regulations are in place to protect the environmental quality of the water. the regulations are in place to protect the environmental quality of the water. Factors that were not previously considered in the management of the watershed are becoming Factors that were not previously considered in the management of the watershed are becoming a concern—changes in precipitation patterns, for example. The Kenai Peninsula Borough states that a concern—changes in precipitation patterns, for example. The Kenai Peninsula Borough states that the average total precipitation in the Kenai area is 48.9 cm [5]. Studies on the drying of the wetlands the average total precipitation in the Kenai area is 48.9 cm [5]. Studies on the drying of the wetlands in in the Kenai Peninsula Lowland and throughout south‐central Alaska have been published [6]. Berg the Kenai Peninsula Lowland and throughout south-central Alaska have been published [6]. Berg et al. et al. reported that, since 1968, available water from precipitation has declined by 55% due to changes reported that, since 1968, available water from precipitation has declined by 55% due to changes in in evapotranspiration rates and precipitation amounts. All these potential changes could lead to evapotranspiration rates and precipitation amounts. All these potential changes could lead to changes changes in the availability and quality of salmon rearing habitat. With a heavy economic dependence in the availability and quality of salmon rearing habitat. With a heavy economic dependence on on fisheries in the Kenai River watershed, a modeling tool is needed to help predict the effects of fisheries in the Kenai River watershed, a modeling tool is needed to help predict the effects of future future precipitation, development, and landscape changes on the watershed. precipitation, development, and landscape changes on the watershed.

2. Research Methods The U.S.U.S. Army Army Corps Corps of Engineersof Engineers Hydrologic Hydrologic Engineering Engineering Center Center Hydrological Hydrological Modeling Modeling System (HEC-HMS)System (HEC was‐HMS) used was as used the modeling as the modeling platform platform to accomplish to accomplish the objective the objective of this study. of this Because study. Because only sparse current and coinciding precipitation and discharge data were available, the

Water 2018, 10, 691 3 of 13 only sparse current and coinciding precipitation and discharge data were available, the model was calibrated to rainfall events that had discharge measurements. This method was applied to each of the three sub-basins.

2.1. Precipitation Where available, historic precipitation data was used for the calibration of the model. Data was obtained from the National Climactic Data Center (NCDC) historical archives of the National Oceanic and Atmospheric Administration (NOAA). Ptarmigan Creek and the Russian River precipitation was collected as part of the ﬁeld work for this research. ONSET HOBO RG-3 data-logging tipping bucket precipitation gauges were used. This type of precipitation gauge is accurate to within 0.254 mm (0.01 in) of precipitation, and is capable of recording up to 406.4 cm (160 in) of precipitation at a rate of up to 12.7 cm/h (5 in/h) [7]. The gauges, which were calibrated to 0.254 mm (0.01 in) prior to placement in the ﬁeld, were set to record precipitation occurrences at 15-min intervals. The Ptarmigan Creek precipitation gauge data was supplemented with data from the next nearest gauge, Grandview, during periods of malfunction. The location and collection dates of these monitoring sites can be viewed in Table1.

Table 1. Precipitation Gauges.

Gauge Name Station ID Latitude Longitude Coverage (%) Period of Record Kenai Municipal Airport USW00026523 60.58 −151.24 70% 01/05/1899–Present Ptarmigan Creek N/A 60.41 −149.3 N/A 10/08/2015–18/09/2015 Russian River N/A 60.36 −149.89 N/A 15/05/2015–19/09/2015

2.2. Stream Flow Current and historical stream ﬂow data are available through the United States Geological Survey (USGS) surface water database for varying periods at each site. Sites used in this work are shown in Table2. To supplement this data set, pressure transducers were installed in the Russian River, Beaver Creek, and Ptarmigan Creek, to provide current data on these streams.

Table 2. Streamﬂow Gauge Site Information.

Stream Name Gauge ID Number Latitude Longitude Period of Record Ptarmigan Creek (Historic) USGS1524400 60.41 −149.36 01/05/1947–13/09/1958 Russian River (Historic) USGS15264000 60.45 −149.98 01/05/1947–13/09/1958 Beaver Creek (Historic) USGS15266500 60.56 −151.12 01/10/1967–13/09/1978

The monitoring, maintenance, and data collection at these locations were performed by the Kenai River Watershed Forum. Water stage height was recorded at 15-min intervals, at or near the USGS gauge locations in Table2, using OTT Orpheus Mini pressure transducers. These devices monitored the water stage at 15-min intervals. Manual stream ﬂow measurements were taken at the three locations along with stream stage measurements to develop rating curves to convert the pressure transducer data into discharge information.

2.3. Baseflow Direct baseflow information for the sub-basins that compose the Kenai River watershed is not available. To obtain the baseflow input for the HEC-HMS, available discharge information was analyzed to determine the minimum, average, and maximum of the minimum monthly flows. Assuming that the minimum flow is representative of the baseflow, the average baseflow was determined, along with the bounds of the baseflow. The results were then plotted to visualize the bounds of the baseflow. An example of these bounds is shown in Figure2. Water 2018, 10, 691 4 of 13 Water 2018, 10, x FOR PEER REVIEW 4 of 14

FigureFigure 2. 2.Baseﬂow Baseflow of of the the Kenai Kenai RiverRiver as observed at Soldotna, Soldotna, Alaska Alaska discharge discharge measurement measurement station. station.

2.5. Impervious Surface 2.4. Impervious Surface The impervious surface quantification for the Kenai River watershed and sub‐basins was determinedThe impervious using the surface ESRI ArcGIS quantiﬁcation (GIS) software. for the Two Kenai datasets River were watershed used in GIS and for quantification: sub-basins was determinedthe National using Hydrography the ESRI ArcGIS Database (GIS) (NDH) software. water Two surface datasets data, were and used the inNational GIS for Land quantiﬁcation: Cover theDatabase National Hydrography(NLCD) 2011 Databaseimpervious (NDH) development water surface dataset. data, andThe the impervious National Land surface, Cover due Database to (NLCD)development 2011 impervious and waterbody development impervious dataset. areas, was The combined impervious to surface,create the due sub to‐basin development impervious and waterbodyarea, which impervious was then compared areas, was with combined the sub to‐basin create area the to sub-basin determine impervious the percentage area, of whichthe sub was‐basin then comparedarea that with is impervious. the sub-basin area to determine the percentage of the sub-basin area that is impervious.

2.5.2.6. Surface Surface Storage Storage DataData on on surface surface storage storage for grasses,for grasses, shrubbery, shrubbery, and ground and ground covering covering in the Kenaiin the River Kenai watershed River arewatershed not available are not in published available in works. published All values works. used All invalues the modelingused in the efforts modeling were estimatedefforts were and calibrated.estimated Starting and calibrated. values used Starting in the values estimation used in iterationsthe estimation were iterations based on were information based on found information on water retentionfound on by water grass retention [8]. by grass [8].

2.6.2.7. Canopy Canopy Storage Storage DataData on on canopy canopy storage storage in in the the KenaiKenai RiverRiver watershed are are not not directly directly available. available. To To determine determine thethe canopy canopy storage, storage, initial initial values values were were chosen chosen based on foliage foliage types types in in the the sub sub-basins.‐basins. These These foliage foliage typestypes were were then then referenced referenced to to the the British British ColumbiaColumbia forest hydrology hydrology guide, guide, which which has has published published precipitationprecipitation interception interception values values for for various various forestforest typestypes [9]. [9].

2.7.2.8. Stream Stream Function Function TheThe stream stream transform transform for for each each sub-basinsub‐basin is a calibrated time time lag lag parameter parameter in in the the HEC HEC-HMS‐HMS model.model. The The calibration calibration for for this this parameter parameter waswas performed by by comparing comparing the the model’s model’s discharge discharge peak peak timetime with with the the observed observed discharge discharge peak peak time.time. The lagging lagging of of the the peak peak was was then then applied applied to tothe the model, model, soso that that the the modeled modeled peak peak discharge discharge time time coincided coincided with the observed observed discharge discharge peak peak time. time.

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2.8. Soil Inﬁltration Soil inﬁltration and loss estimates were calculated with the Soil Conservation Service (SCS) method and the impervious surface as found, as described above. The soil type was determined by referring to the United States Department of Agriculture (USDA) published soil survey [10]. Most soil data for the Kenai River watershed is available through published studies starting in 1918, with subsequent updates.

2.9. Lake Storage Lake storage and elevation information are available through the Alaska Department of Fish and Game (ADF&G). Lakes used for ﬁsh spawning, or those that are stocked by ADF&G, have been surveyed, and bathymetric maps have been created [11]. To determine the volume of storage at a given lake depth, the bathymetric maps were georeferenced and overlaid on a mapping software. Elevation storage relationships were determined by tracing the depth contour lines, and multiplying them by the depth increments [12].

2.10. Evapotranspiration Evapotranspiration (ET) for the Kenai River watershed was not analyzed in this modeling effort due to the lack of direct measurements of ET, prevailing low air temperatures, and the model being calibrated to single storm events, with reduced modeled time [13]. Thus, the ET was assumed to be negligible.

3. Modeling

3.1. Beaver Creek Sub-Basin The Beaver Creek sub-basin, located near Kenai, Alaska, consists of several small low-gradient drainages. The sub-basin was separated into four separate drainages that feed into the main channel. These drainages are Timberlost Lake, Ootka Lake, Beaver Lake, and the Beaver Creek stream basin area. The HEC-HMS model was organized based on the layout determined from the topographic map delineation. Figure3B shows the layout used in the HEC-HMS model for the Beaver Creek sub-basin. The input precipitation data for all sub-drainages within the Beaver Creek sub-basin were acquired from the Kenai Municipal Airport (Table1). The modeled storms were approximately one week in duration and occurred in the early 1970’s, as seen the Figures4–6. This time period was chosen based on coinciding precipitation and discharge data at the site location. This setup was used in the creation of three sets of parameters that distinguish the difference in the basin hydrology during the spring, summer, and fall hydrologic regimes. Final model parameters used during spring, summer, and fall are available in the Supplementary Materials.

3.2. Russian River Sub-Basin The Russian River sub-basin is split into two sub-drainages: the Upper Russian, that accounts for all the land upstream of the outlet of Lower Russian Lake, and the Lower Russian sub-drainage, that accounts for the area downstream of the Lower Russian Lake. With a single discharge gauge in the basin located at the outlet of Lower Russian Lake, the storage capacity of both the Upper and Lower Russian Lakes is combined to create a composite lake. The Upper Russian sub-drainage is routed through the stream length between the Upper and Lower Russian Lakes. The Upper Russian sub-drainage has a total area of 499.76 km2. The Lower Russian sub-drainage has an area of 166.59 km2. The Upper Russian Lake precipitation gauge data were used for modeling both the Upper Russian and Lower Russian sub-drainages. The conﬁguration of the Russian River sub-basin in HEC-HMS is shown in Figure3C. Final model parameters used for this sub-basin during spring, summer, and fall are available in Supplementary Materials. Water 2018, 10, 691 6 of 13 Water 2018, 10, x FOR PEER REVIEW 6 of 14

(A) Ptarmigan Creek sub‐basin model set‐up.

(B) Beaver Creek sub‐basin model set‐up.

Figure 3. Sub-basin model set-up. Figure 3. Sub‐basin model set‐up.

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3.3. Russian Lakes The Russian Lakes reservoir uses the outflow curve [14] and the elevation-storage-discharge methods. These methods relate the elevation of the lake depth to its corresponding lake storage. The lake storage is then correlated to discharge. The primary relationship setting is storage-discharge, and the initial conditions are set so that inflow is equal to outflow. Due to the absence of a discharge gauge at the outlet of Upper Russian Lake, both Upper Russian Lake and Lower Russian Lake were combined to create a composite lake. The composite lake combined the depth and volume of both lakes in the sub-basin.

3.4. Ptarmigan Creek Sub-Basin The Ptarmigan Creek sub-basin was split into two sub-drainages for modeling. Ptarmigan Creek Upper accounts for the area of Ptarmigan Lake and upstream, and Ptarmigan Creek Lower accounts for the area downstream of the lake. The area of Ptarmigan Creek Upper is 262.13 km2. Ptarmigan Creek Lower has an area of 87.39 km2. The model was arranged based on this information. Ptarmigan Creek sub-basin used the precipitation data from the Ptarmigan Lake precipitation gauge; the Grandview precipitation gauge was used for dates on which the Ptarmigan Lake gauge was inoperable. The conﬁguration used in the HEC-HMS model for Ptarmigan Creek is shown in Figure3A. Final model parameters used during spring, summer, and fall are available in Supplementary Materials.

3.5. Ptarmigan Lake For Ptarmigan Lake, the model used the elevation-storage-discharge storage method. This method uses the outflow curve and the primary storage-discharge; the initial conditions were set so that inflow is equal to outflow conditions.

4. Results

4.1. Spring Beaver Creek After calibration, the spring Beaver Creek modeled ﬂows were very close to observed values. The period used to perform the calibration was 27 May 1972 through 9 June 1972. A discussion on the validity of calibrating this model to historic data from the 1970s is included later in the paper. During the calibration event, almost 2.5 cm (1 in) of rain fell across the basin. The maximum standard error of the daily modeled discharge over this period of study is approximately 8%. The averaged discharge standard error for the calibration of the model is 2.8%, and the standard error in the volume of throughput during the period is 1.6%, with a root mean square error of the discharge (RMSE-Q) of 0.048 cm. The observed versus modeled hydrograph can be seen in Figure4.

4.2. Summer Beaver Creek The summer modeling of Beaver Creek proved to be successful. The summer modeling calibration window used was 11 July 1971 through 18 July 1971. The summer calibration trial supports the spring calibration, since the hydrologic parameters remained constant except for those accounting for summer growth of vegetation. On the second and third days of this period, more than a 1.2 cm (0.5 in) of precipitation was measured at the nearest precipitation gauge to the sub-basin. With this precipitation applied to all sub-drainages within the sub-basin, the modeled outﬂow and observed discharge trended in the same pattern. The largest standard error on the daily discharge values for the modeling period is 7.4%. The averaged discharge standard errors are 3.9%, and the standard error in the volume is 1.4%, with an RMSE-Q of 0.031 cm. The observed versus modeled hydrograph can be seen in Figure5. Water 2018, 10, x FOR PEER REVIEW 8 of 14

1.80 2.00

1.70 1.80 1.60 1.60 1.40 (cm)

1.50 Water 2018, 10, 691 1.20 8 of 13 Water 2018, 10, x FOR PEER REVIEW1.40 1.00 8 of 14 1.30 0.80 Discharge (cms)

1.80 0.602.00Precipitaiton 1.20 0.40 1.70 1.80 1.10 0.20 1.60 1.601.00 0.00 1.40 (cm) 1.50 1.20 1.40 1.00 Observed Modeled Rainfall 0.80 1.30 Discharge (cms)

0.60 Precipitaiton Figure 4. Observed1.20 and calibrated flows in Beaver Creek for the period 27 May 1972 through 9 June 1972. 0.40 4.2. Summer Beaver1.10 Creek 0.20 The summer1.00 modeling of Beaver Creek proved to be successful. The0.00 summer modeling calibration window used was 11 July 1971 through 18 July 1971. The summer calibration trial supports the spring calibration, since the hydrologic parameters remained constant except for those accounting for summer growth of vegetation. On the second and third days of this period, more than a 1.2 cm (0.5 in) of precipitation was measured at the nearest precipitation gauge to the sub‐basin. Observed Modeled Rainfall With this precipitation applied to all sub‐drainages within the sub‐basin, the modeled outflow and

observed discharge trended in the same pattern. The largest standard error on the daily discharge Figurevalues 4.for Observed the modeling and calibrated period isflows 7.4%. in BeaverThe averaged Creek for discharge the period standard 27 May 1972errors through are 3.9%, 9 June and 1972. the Figure 4. Observed and calibrated ﬂows in Beaver Creek for the period 27 May 1972 through standard error in the volume is 1.4%, with an RMSE‐Q of 0.031 cm. The observed versus modeled 9 June 1972. 4.2. Summerhydrograph Beaver can Creek be seen in Figure 5. The summer modeling of Beaver Creek proved to be successful. The summer modeling calibration window0.95 used was 11 July 1971 through 18 July 1971. The summer1.40 calibration trial supports the spring0.90 calibration, since the hydrologic parameters remained constant except for those 1.20 accounting for summer0.85 growth of vegetation. On the second and third days of this period, more than 1.00 a 1.2 cm (0.5 in) of0.80 precipitation was measured at the nearest precipitation gauge to the sub‐basin. (cm)

With this precipitation(cms) applied to all sub‐drainages within the sub‐basin, the modeled outflow and 0.75 0.80 observed discharge trended in the same pattern. The largest standard error on the daily discharge 0.70 values for the modeling period is 7.4%. The averaged discharge standard errors0.60 are 3.9%, and the 0.65 standard error inDischarge the volume is 1.4%, with an RMSE‐Q of 0.031 cm. The observed versus modeled 0.40 Precipitation hydrograph can be0.60 seen in Figure 5. 0.20 0.55 0.50 0.00 0.95 1.40 0.90 1.20 0.85 Date 1.00 0.80 Observed Modeled Rainfall (cm)

(cms) 0.75 0.80 Figure 5. Observed and calibrated flows in Beaver Creek for the period 11 July 1971 through 18 July Figure 5. 1971. 0.70Observed and calibrated ﬂows in Beaver Creek for the period 11 July0.60 1971 through 18 July 1971. 0.65 Discharge

0.40 Precipitation 4.3. Fall Beaver0.60 Creek 0.20 0.55 The fall calibration effort was the least successful of the Beaver Creek modeling work. The adjustments0.50 made to the parameters between seasonal modeling were limited0.00 mainly to adjustments to the surface and canopy storage. The fall modeling event supports the summer calibration trial. The modeling period used for the fall study extends from 28 August 1970 through 21 September 1970. The length of the modeling period is extended due to the length of a storm in Date the area. Storms of similar length to the spring and summer calibration periods were not available Observed Modeled Rainfall for times of coinciding precipitation and discharge measurements. The maximum daily standard Figure 5. Observed and calibrated flows in Beaver Creek for the period 11 July 1971 through 18 July 1971.

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4.3. Fall Beaver Creek The fall calibration effort was the least successful of the Beaver Creek modeling work. The adjustments made to the parameters between seasonal modeling were limited mainly to adjustments to the surface and canopy storage. The fall modeling event supports the summer calibration trial. The modelingWater 2018, 10 period, 691 used for the fall study extends from 28 August 1970 through 21 September 1970.9 of 13 The length of the modeling period is extended due to the length of a storm in the area. Storms of similar length to the spring and summer calibration periods were not available for times of coinciding precipitationerror for the falland modeling discharge period measurements. is 24%, the The average maximum daily daily standard standard error error is 9.4%, for the and fall the modeling standard perioderror in is volume 24%, the is 6.6%,average with daily an RMSE-Qstandard oferror 0.091 is 9.4%, cm. The and precipitation the standard over error this in timevolume occurred is 6.6%, in withtwo peaks; an RMSE total‐Q precipitation of 0.091 cm. was The 6.8 cmprecipitation (2.69 in). The over observed this time versus occurred modeled in hydrograph two peaks; can total be precipitationseen in Figure was6. 6.8 cm (2.69 in). The observed versus modeled hydrograph can be seen in Figure 6.

1.20 2.50 1.10 2.00

1.00 (cm)

(cms) 0.90 1.50 0.80 1.00 0.70 Discharge

0.50 Precipitation 0.60 0.50 0.00

Date Observed Modeled Rainfall

Figure 6. Observed and calibrated flows in Beaver Creek for the period 30 August 1970 through 17 Figure 6. Observed and calibrated ﬂows in Beaver Creek for the period 30 August 1970 through 17 September 1970. September 1970. 4.4. Russian River Sub‐Basin 4.4. Russian River Sub-Basin The Russian River calibration proved very successful for the modeling period. The period used The Russian River calibration proved very successful for the modeling period. The period used was 15 August 2015 through 20 August 2015. During this time, over 2.54 cm (1 in) of precipitation fell was 15 August 2015 through 20 August 2015. During this time, over 2.54 cm (1 in) of precipitation fell on the sub‐basin. The maximum daily standard error in the modeled discharge is 0.7%, and the on the sub-basin. The maximum daily standard error in the modeled discharge is 0.7%, and the average average daily standard error is 0.3%, with a standard error in the volume of 0.2% over the modeling daily standard error is 0.3%, with a standard error in the volume of 0.2% over the modeling period, period, with an RMSE‐Q of 0.013 cm. The observed versus modeled hydrograph can be seen in Figure with an RMSE-Q of 0.013 cm. The observed versus modeled hydrograph can be seen in Figure7. 7.Water 2018, 10, x FOR PEER REVIEW 10 of 14 To validate the Russian River calibration, the model was applied to a second storm. The period modeled for the validation is 15 July 2015 to 25 July 2015. All parameters remained constant, and the average standard error in the discharge increased to 10%; the error in the volume increased to 10.1% with an RMSE‐Q of 0.459 cm. The observed versus modeled hydrograph can be seen in Figure 8.

Figure 7. Observed and calibrated flows in Russian River for the period 15 August 2015 through 20 Figure 7. Observed and calibrated ﬂows in Russian River for the period 15 August 2015 through 20 August 2015. August 2015.

Figure 8. Observed and calibrated flows in Russian River for the period 15 July 2015 through 25 July 2015.

4.5. Ptarmigan Creek Sub‐Basin The Ptarmigan Creek calibration trends close to the observed data, with minor fluctuations in flow. The period modeled in the calibration is 16 August 2015 through 20 August 2015. During this time, almost 2.54 cm (1 in) of rainfall occurred. The maximum modeled daily discharge standard error is 1.3%, and the average daily discharge standard error is 0.6%. The standard error in the volume is 0.1% with an RMSE‐Q of 0.031 cm. The observed versus modeled hydrograph can be seen in Figure 9.

Water 2018, 10, x FOR PEER REVIEW 10 of 14

Water 2018, 10, 691 10 of 13

To validate the Russian River calibration, the model was applied to a second storm. The period modeled for the validation is 15 July 2015 to 25 July 2015. All parameters remained constant, and the averageFigure standard 7. Observed error in and the calibrated discharge flows increased in Russian to River 10%; for the the error period in 15 the August volume 2015 increased through 20 to 10.1% with anAugust RMSE-Q 2015. of 0.459 cm. The observed versus modeled hydrograph can be seen in Figure8.

FigureFigure 8. 8. ObservedObserved and and calibrated calibrated flows ﬂows in Russian in Russian River River for the for period the period15 July 2015 15 July through 2015 25 through July 2015. 25 July 2015.

4.5. Ptarmigan Creek Sub‐Basin 4.5. Ptarmigan Creek Sub-Basin The Ptarmigan Creek calibration trends close to the observed data, with minor fluctuations in flow.The The Ptarmigan period modeled Creek calibration in the calibration trends is close 16 August to the 2015 observed through data, 20 withAugust minor 2015. ﬂuctuations During this in ﬂow.time, The almost period 2.54 modeled cm (1 in) in of the rainfall calibration occurred. is 16 The August maximum 2015 through modeled 20 daily August discharge 2015. Duringstandard this time,error almost is 1.3%, 2.54 and cm the (1 average in) of rainfall daily discharge occurred. standard The maximum error is 0.6%. modeled The dailystandard discharge error in standard the volume error is 1.3%,is 0.1% and with the an average RMSE‐Q daily of 0.031 discharge cm. The standardobserved versus error is modeled 0.6%. The hydrograph standard can error be inseen the in volume Figure is 0.1%9.Water with 2018 an, 10, RMSE-Q x FOR PEER of REVIEW 0.031 cm. The observed versus modeled hydrograph can be seen in11 Figure of 14 9.

Figure 9. Observed and calibrated flows in Ptarmigan Creek for the period 16 August 2015 through Figure 9. Observed and calibrated ﬂows in Ptarmigan Creek for the period 16 August 2015 through 20 20 August 2015. August 2015. The validation of the Ptarmigan Creek calibration occurred from 15 July 2015 to 25 July 2015. Since the period was outside the available data window for the Ptarmigan Lake precipitation gauge, data from the Grandview precipitation gauge were used for input. There were increases in both the average discharge error and the volume error. The average discharge standard error increased to 7.5%, and the volume standard error increased to 7.4% with an RMSE‐Q of 0.71 cm. The observed versus modeled hydrograph can be seen in Figure 10.

Figure 10. Observed and calibrated flows in Ptarmigan Creek for the period 15 July 2015 through 25 July 2015.

5. Discussion The Beaver Creek sub‐basin modeled scenarios perform well under standard open‐water seasons of late spring and summer, with errors of less than 4%. The model output follows the fluctuations in discharge over multiple peaks and low water levels. It was found that during the shoulder seasons of early spring and late fall, the model does not perform well, as the average error and volume error increased to 9.4% and 6.6%, respectively. The spring errors may be attributed to not accounting for snowmelt in the basin. Though the modeling windows were selected when the

Water 2018, 10, x FOR PEER REVIEW 11 of 14

Water Figure2018, 10 ,9. 691 Observed and calibrated flows in Ptarmigan Creek for the period 16 August 2015 through11 of 13 20 August 2015.

The validation of the Ptarmigan Creek calibration calibration occurred occurred from from 15 15 July July 2015 2015 to to 25 25 July July 2015. 2015. Since the period was outside the available data window for the Ptarmigan Lake Lake precipitation precipitation gauge, gauge, data from the Grandview precipitation gauge were used for input. There were increases in both the average dischargedischarge error error and and the the volume volume error. error. The The average average discharge discharge standard standard error error increased increased to 7.5%, to 7.5%,and the and volume the volume standard standard error increased error increased to 7.4% to with 7.4% an with RMSE-Q an RMSE of 0.71‐Q of cm. 0.71 The cm. observed The observed versus versusmodeled modeled hydrograph hydrograph can be seencan be in seen Figure in 10Figure. 10.

Figure 10. Observed and calibrated flows ﬂows in Ptarmigan Creek for the period 15 July 2015 through 25 July 2015.

5. Discussion 5. Discussion The Beaver Creek sub‐basin modeled scenarios perform well under standard open‐water The Beaver Creek sub-basin modeled scenarios perform well under standard open-water seasons seasons of late spring and summer, with errors of less than 4%. The model output follows the of late spring and summer, with errors of less than 4%. The model output follows the fluctuations in fluctuations in discharge over multiple peaks and low water levels. It was found that during the discharge over multiple peaks and low water levels. It was found that during the shoulder seasons shoulder seasons of early spring and late fall, the model does not perform well, as the average error of early spring and late fall, the model does not perform well, as the average error and volume and volume error increased to 9.4% and 6.6%, respectively. The spring errors may be attributed to error increased to 9.4% and 6.6%, respectively. The spring errors may be attributed to not accounting not accounting for snowmelt in the basin. Though the modeling windows were selected when the for snowmelt in the basin. Though the modeling windows were selected when the channels were ice-free, high-altitude snowmelt may have contributed to the overall observed discharge. The fall errors may be caused by errors in the estimation of storage in the wetlands, which comprise much of the sub-basin, or spatially differing precipitation to what was observed at the Kenai Municipal Airport. The adjustments made between the spring, summer, and fall modeling parameters account for the differing water storage and interception of the canopy and ground surface during these seasons, as well as differing system response time to precipitation events. The greatest variations are seen in the transition from spring to summer. The large changes to the parameters are speculated to be attributed to drying of the soils after spring snowmelt, and the growth of vegetation in and around the stream and basin. With calibration of the model using data from the 1970s, a question could arise as to the validity of the use of the calibration for current and future modeling scenarios. As Bauret and Stuefer found, the precipitation trend has decreased since the mid-1960s [15]. Changes in the impervious surface due to increased land development were found to be 0.51% by interpolating the visual quantification of development from aerial photography [16]. The impervious surface in 1950 due to human development in the Kenai River watershed was 0.05%. This figure increased to 1.04% by the 1980s. The impervious surface in 2013 due to human development was found to be 1.28%, assuming a linear development of infrastructure between the 1950s and 1980s, and interpolated human development impervious of Water 2018, 10, 691 12 of 13

0.71% for the 1970s. Thus, one could argue that the errors associated with increasing runoff due to increased development and the observed reductions in precipitation tend to balance out. The Russian River sub-basin model performs well under the open-water season. The period of late spring to early fall produced acceptable results under the conditions present in the calibration year of 2015. Since flow data for lake discharge are limited, extreme precipitation events, whether high or low, have the potential to introduce errors in outflow calculations from the combined Russian Lakes. Ptarmigan Creek sub-basin has reliable modeled output under average precipitation events. The narrow, steep-gradient basin has a large lake. Due to the positioning of the lake, and the runoff below the lake, the hydrograph increases steeply and then gradually decreases as the runoff drains from the lake. During the open-water season, the model reproduces peak flow due to lake outflow, and the modeled rate of decrease in the discharge follows the observed discharge closely. To achieve the steep rise in the hydrograph, the model required that the lower basin have a high level of flashiness. Thus, under heavy rainfall events, artificial peaks appear in the rise of the modeled hydrograph. This can be seen in the validation run of Ptarmigan Creek, where average errors increase to 7.6%, and volume errors increase to 7.4%, from the less than the 0.6% errors seen in the calibration event.

6. Conclusions Sub-basin models have a variety of applications. With the use of modeled precipitation or predicted precipitation, stream flows could be estimated for use in fisheries studies. These studies could have an impact on economic planning for communities in the Kenai River watershed. As another application, the model could be used for exploring the effects of changing urbanization in the watershed area. As towns in the watershed increase in size, the percentage of impervious surface will increase with development, which will cause increased runoff during precipitation events, resulting in increased stream flow. With the landscape changes seen on the Kenai Peninsula, such as the wetland drying discussed by Berg et al., the effects of landscape changes on stream flow can be examined [17]. The sub-basin modeling effort successfully fulfilled the scope of the research task. However, to obtain a watershed-scale model that produces reasonable results, more information about the watershed must be collected. To address the gaps in information, a study of glacial melt and baseflow contributions to the hydrologic system is needed.

Supplementary Materials: The following are available online at http://www.mdpi.com/2073-4441/10/6/691/s1, Table S1: Beaver Creek Spring Parameters [3], Table S2: Beaver Creek Summer Parameters [3], Table S3: Beaver Creek Fall Parameters [3], Table S4: Russian River Parameters [3], Table S5: Ptarmigan Creek Parameters [3], Figure S1: Upper Russian Lake bathymetry [11], Figure S2: Lower Russian Lake bathymetry [11], Figure S3: Ptarmigan Lake bathymetry [11], Figure S4: Ptarmigan Lake storage-discharge relationship [3], Figure S5: Beaver Creek spring observed and modeled hydrographs [3], Figure S6: Beaver Creek summer observed and modeled hydrographs [3], Figure S7: Beaver Creek fall observed and modeled hydrographs [3], Figure S8: Russian River calibration observed and modeled hydrographs [3], Figure S9: Russian River validation observed and modeled hydrographs [3], Figure S10: Ptarmigan Creek calibration observed and modeled hydrographs [3], Figure S11: Ptarmigan Creek calibration observed and modeled hydrographs [3]. Author Contributions: Conceptualization, T.H.; Methodology, W.B.; Validation, W.B.; Formal Analysis, W.B.; Investigation, W.B; Resources, T.H.; Data Curation, W.B.; Writing-Original Draft Preparation, W.B.; Writing-Review & Editing, T.H.; Supervision, T.H.; Project Administration, T.H.; Funding Acquisition, T.H. Funding: This research was partially funded by the National Science Foundation grant number 1208927 to the University of Alaska. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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