U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2019–5022

Prepared in cooperation with the New Mexico Department of Homeland Security and 

Emergency Management

Calibration of Precipitation-Runoff Modeling System (PRMS) 

to Simulate Prefire and Postfire Hydrologic Response in the 

Upper Rio Hondo Basin, New Mexico

Calibration of Precipitation-Runoff Modeling System (PRMS) 

to Simulate Prefire and Postfire Hydrologic Response in the 

Upper Rio Hondo Basin, New Mexico

By Kyle R. Douglas-Mankin and C. David Moeser

Prepared in cooperation with the New Mexico Department of Homeland Security 

and Emergency Management

Scientific Investigations Report 2019–5022

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

DAVID BERNHARDT, Secretary

U.S. Geological Survey

James F. Reilly II, Director

U.S. Geological Survey, Reston, Virginia: 2019

For more information on the USGS—the Federal source for science about the Earth, its natural and living 

resources, natural hazards, and the environment—visit https://www.usgs.gov or call 1–888–ASK–USGS.

For an overview of USGS information products, including maps, imagery, and publications, 

visit https://store.usgs.gov.

Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the 

U.S. Government.

Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials 

as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.

Suggested citation:

Douglas-Mankin, K.R., and Moeser, C.D., 2019, Calibration of Precipitation-Runoff Modeling System (PRMS) to 

simulate prefire and postfire hydrologic response in the upper Rio Hondo Basin, New Mexico: U.S. Geological Survey 

Scientific Investigations Report 2019–5022, 25 p., https://doi.org/10.3133/sir20195022.

ISSN 2328-0328 (online)
iii

Contents

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................1

Purpose and Scope ..............................................................................................................................2

Description of Study Area ...................................................................................................................2

Topography ...................................................................................................................................2

Climate, Vegetation, and Soils ...................................................................................................2

Wildfires in Rio Hondo Basin .....................................................................................................5

Hydrologic Effects of Fire ....................................................................................................................5

Precipitation-Runoff Modeling System......................................................................................................5

Sensitivity Analysis Methods.......................................................................................................................6

Purpose of Sensitivity Analysis ..........................................................................................................6

Fourier Amplitude Sensitivity Test......................................................................................................8

Fourier Amplitude Sensitivity Test Interface....................................................................................8

Model Calibration Methods..........................................................................................................................8

Purpose of Calibration .........................................................................................................................8

Let Us Calibrate Interface ...................................................................................................................8

Performance Measures.......................................................................................................................9

PRMS Model Sensitivity Analysis for Upper Rio Hondo Basin ..............................................................9

Sensitivity in Upper Rio Hondo Basin................................................................................................9

Sensitivity in the North Fork Eagle Creek Subwatershed ............................................................13

PRMS Model Calibration for the North Fork Eagle Creek Subwatershed..........................................13

Meteorological Data ..........................................................................................................................14

Baseline Prefire Parameter Adjustments.......................................................................................14

Prefire Model Calibration ..................................................................................................................15

Postfire Model Calibration ................................................................................................................15

Postfire Parameter Adjustments......................................................................................................15

Discussion and Application of Prefire and Postfire Models ................................................................19

Summary and Conclusions.........................................................................................................................22

References Cited..........................................................................................................................................23

Figures

1. Map showing the upper Rio Hondo Basin study area, Lincoln and Otero Counties, 

New Mexico ..................................................................................................................................3

2. Map showing the upper Rio Hondo Basin study area, precipitation-runoff 

modeling system hydrologic response unit boundaries, U.S. Geological Survey 

streamgage locations, and historical fire boundaries............................................................4

3. Maps showing hydrologic process with greatest sensitivity to each of three 

streamflow statistics by hydrologic response unit, upper Rio Hondo Basin, 

New Mexico ................................................................................................................................10

4. Maps showing normalized process sensitivity for three streamflow statistics 

by hydrologic response unit, upper Rio Hondo Basin, New Mexico .................................11

5. Maps showing normalized process sensitivity for three streamflow statistics 

by hydrologic response unit and model parameter, upper Rio Hondo Basin, 

New Mexico ................................................................................................................................12
iv

6. Graph showing daily simulated and observed streamflow and precipitation for 

the study period for the North Fork Eagle Creek subwatershed, New Mexico ...............13

7. Graphs showing observed and simulated streamflow and observed precipitation 

at the Cloudcroft raingage for prefire and postfire conditions, June through 

November, North Fork Eagle Creek subwatershed, New Mexico......................................20

8. Graphs showing observed streamflow and the calibrated Precipitation-Runoff 

Modeling System cumulative daily streamflow for prefire and postfire conditions, 

June through November, North Fork Eagle Creek subwatershed, New Mexico.............21

9. Graph showing annual total streamflow component contributions for prefire 

and postfire calibrated Precipitation-Runoff Modeling System models ...........................22

Tables

1. Precipitation-Runoff Modeling System calibration parameters...........................................7

2. U.S. Geological Survey streamgages in the upper Rio Hondo Basin and 

descriptions, including drainage area, elevation, and beginning and ending 

dates of the period of record ......................................................................................................9

3. Model baseline parameters adjusted manually prior to calibration and calibrated 

parameters adjusted using the Let Us Calibrate algorithm for prefire and postfire 

simulations in the North Fork Eagle Creek subwatershed...................................................16

4. User-specified Let Us Calibrate parameters for prefire calibration run in the 

North Fork Eagle Creek subwatershed, New Mexico..........................................................17

5. Model performance measures for initial parameters and calibrated parameters 

at the monthly time-step for prefire and postfire streamflow during summer–fall 

in the North Fork Eagle Creek subwatershed ........................................................................17

6. User-specified Let Us Calibrate parameters for postfire calibration run in the 

North Fork Eagle Creek subwatershed ...................................................................................18

7. Initial and assigned model parameter values to represent changes in forest 

cover after fire.............................................................................................................................18
v

Conversion Factors

U.S. customary units to International System of Units

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

acre 0.4047 hectare (ha)

acre 0.004047 square kilometer (km2

)

square mile (mi2

) 259.0 hectare (ha)

square mile (mi2

) 2.590 square kilometer (km2) 

Volume

cubic foot (ft3

) 0.02832 cubic meter (m3) 

acre-foot (acre-ft) 1,233 cubic meter (m3

)

Flow rate

cubic foot per second (ft3

/s) 0.02832 cubic meter per second (m3/s)

inch per year (in/yr) 25.4 millimeter per year (mm/yr)

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

°F = (1.8 × °C) + 32.

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows:

°C = (°F – 32) / 1.8.

Datum

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88) in figures 1 and 2 and to the National Geodetic Vertical Datum of 1929 (NGVD 29) in 

table 2.

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).
vi

Abbreviations

AR-1 Autoregressive lag-1 correlation coefficient

CV Coefficient of variation

ET Evapotranspiration

FAST Fourier amplitude sensitivity test

HRU Hydrologic response unit

HUC Hydrologic unit code

LUCA Let Us Calibrate

NHM National Hydrologic Model

NMDHSEM New Mexico Department of Homeland Security and Emergency Management 

NRMSE Normalized root-mean-square error 

NSE Nash-Sutcliffe model efficiency

PRMS Precipitation-Runoff Modeling System

R2 Coefficient of determination 

RMSE Root-mean-square error

RSR Ratio of RMSE to standard deviation 

USGS U.S. Geological Survey
Calibration of Precipitation-Runoff Modeling System 

(PRMS) to Simulate Prefire and Postfire Hydrologic 

Response in the Upper Rio Hondo Basin, New Mexico

By Kyle R. Douglas-Mankin and C. David Moeser

Abstract

The Precipitation-Runoff Modeling System (PRMS) is 

widely used to simulate the effects of climate, topography, 

land cover, and soils on landscape-level hydrologic responses 

and streamflow. The U.S. Geological Survey (USGS), in 

cooperation with the New Mexico Department of Homeland 

Security and Emergency Management, developed procedures 

to apply the PRMS model to simulate the effects of fire on 

hydrologic responses. 

A PRMS model was built of the upper Rio Hondo Basin 

from the headwaters to approximately 19 miles downstream 

from the USGS streamgage Rio Hondo above Chavez 

Canyon near Hondo, New Mexico, by using 24 hydrologic 

response units (HRUs), or hydrologically similar subareas, 

from the National Hydrologic Model. A quasi-graphical user 

interface was created to easily query and analyze published 

PRMS sensitivity-analysis data. Simulation of mean daily 

streamflow was most sensitive to parameters related to 

snowmelt or infiltration throughout the upper Rio Hondo 

Basin. In the basin’s eastern and northern HRUs, flashiness 

and timing of streamflow were most sensitive to interflow; in 

many western-basin HRUs (higher elevations), flashiness of 

streamflow was most sensitive to soil moisture parameters, 

and timing of streamflow was most sensitive to infiltration and 

evapotranspiration parameters. 

The PRMS model was calibrated for the fire-affected 

North Fork Eagle Creek subwatershed by comparing modeled 

to observed daily streamflow for the nonfrozen (May through 

October) period for a prefire and postfire time period. The 

prefire model was calibrated for the period 2007–12 before 

the 2012 fire, and the postfire model was calibrated for a 

2-year (2014–15) period after the fire. Model parameterization

combined manual adjustment of 8 parameters on the basis

of prior knowledge and automated adjustment of the most

sensitive parameters by using the Let Us Calibrate interface.

A gridded, daily precipitation dataset that captured the spatial

heterogeneity across the study watershed was used as the

precipitation input for calibration. Model performance was 

assessed as satisfactory by using standard statistical measures 

for prefire and postfire periods.

The calibrated model was run by using data from a 

single precipitation gage to better represent the effect of 

localized, extreme storms on postfire hydrologic response. The 

calibrated models for prefire and postfire conditions simulated 

streamflows with greater consistency than the uncalibrated 

model for the corresponding (prefire or postfire) period of 

hydrographic record. The effect of fire on streamflow was 

found to be primarily a shift from streamflow dominated by 

base flow prior to fire to streamflow dominated by surface 

runoff after fire.

Introduction

Wildfire is a natural watershed process that is important 

for ecological health and sustainability of land resources. 

A complex connection exists between humans and wildfire 

because wildfire can not only be altered by human activities 

but also have effects on human life and property (Moody and 

others, 2013). In addition to the often devastating impacts of 

the fire itself, postfire effects, including increased flooding and 

debris flows in addition to slow landscape recovery processes, 

can have important ramifications on communities dependent 

upon the fire-affected regions (Liu and others, 2010).

Postfire effects on watershed hydrologic processes and 

streamflow can vary greatly and are dependent on many 

watershed-specific and fire-specific factors (Konrad, 2004). 

Watershed models are widely used to simulate the effects 

of current and future climate, land characteristics, and 

management or disturbance on hydrologic responses and 

could be adapted to understand watershed-scale effects of 

fire. Results of postfire watershed model simulations can 

provide critical information for watershed, water-supply, and 

emergency planning and management decisions.
2 Calibration of Precipitation-Runoff Modeling System

A major fire occurred in June 2012 in the upper Rio 

Hondo Basin in south-central New Mexico following record 

drought conditions in 2011 and 2012 (Sherson and Rice, 

2015). The 2012 Little Bear Fire burned approximately 

69 square miles (mi2

) (44,300 acres) in the high-elevation 

forests of the upper Rio Bonito and Eagle Creek watersheds 

(Snyder and others, 2012). In response to this fire, the New 

Mexico Department of Homeland Security and Emergency 

Management (NMDHSEM) worked with the U.S. Geological 

Survey (USGS) to install precipitation gages as a part of an 

early warning system within fire-affected areas to help protect 

life and property. The precipitation gages provide real-time 

data on rainfall intensity that can be used to alert susceptible 

downstream populations about potential flooding. The USGS, 

in cooperation with NMDHSEM, also initiated a study to 

improve the understanding of the effects of fire on watershed 

processes and to provide relevant hydrologic information 

that can be used to guide the sustainable preservation and 

management of the affected water resources.

Purpose and Scope

The purpose of this report was to examine the extent to 

which the Precipitation-Runoff Modeling System (PRMS) 

can accurately represent the effects of fire on watershed-scale 

hydrologic response. This report describes the development, 

sensitivity analysis, calibration, and implementation of PRMS 

to simulate the prefire and postfire streamflow and surface 

hydrologic conditions of the fire-affected North Fork Eagle 

Creek subwatershed in the upper Rio Hondo Basin. Data 

associated with this report can be found in Moeser and others 

(2018).

Description of Study Area

The upper Rio Hondo Basin (fig. 1) has been described 

in detail by Tillery and Matherne (2013), Darr and others 

(2014), and Sherson and Rice (2015) and is briefly described 

in this section. 

The focus of this study was the fire-burned areas (fig. 2) 

of the North Fork Eagle Creek subwatershed area of the 

upper Rio Hondo Basin. The following general descriptions 

of the upper Rio Hondo Basin help provide context for the 

PRMS model sensitivity analysis, which was conducted at 

the basin scale. Additional details of the North Fork Eagle 

Creek subwatershed help provide context for the prefire and 

postfire PRMS model calibrations, which were conducted 

at the subwatershed scale. The North Fork Eagle Creek 

subwatershed has characteristics of the higher elevation 

forested areas of the Sacramento Mountains (fig. 1). 

Topography

The upper Rio Hondo Basin encompasses 931 mi2

(596,160 acres) in south-central New Mexico within Lincoln 

and Otero Counties, bounded on the north by the Capitan 

Mountains and on the west and south by the Sacramento 

Mountains (fig. 1). Basin elevation ranges from 11,981 feet 

(ft) in the Sierra Blanca range on the western edge of the 

study area to about 4,475 ft at the basin outlet, located about 

19 miles (mi) downstream from the USGS streamgage Rio 

Hondo above Chavez Canyon near Hondo, New Mexico 

(08390020). Surface water generally flows eastward. The 

primary source of streamflow in the study area is precipitation 

originating at high elevations in the Sacramento and Capitan 

Mountains (Darr and others, 2014). The basin includes two 

major rivers: Rio Bonito in the northern part of the basin is 

mostly perennial in the headwaters, becoming intermittent in 

the lower elevations, whereas Rio Ruidoso in the southern 

part of the basin is mostly perennial. The North Fork Eagle 

Creek subwatershed, above the town of Alto in the upper Rio 

Ruidoso watershed, is also perennial. The upper Eagle Creek 

subwatershed (8.17 mi2

) includes the North Fork (5.38 mi2) 

and South Fork (2.79 mi2

) Eagle Creek subwatersheds.

Climate, Vegetation, and Soils

The Köppen-Geiger climate classification uses three 

letters to characterize world climates based on vegetation 

group (first letter), precipitation (second letter), and air 

temperature (third letter) (Kottek and others, 2006). Climate 

in the lower elevation areas of the eastern upper Rio Hondo 

Basin is BSk, which is an arid steppe climate with cold 

subclassification (mean annual temperature < 64.4 degrees 

Fahrenheit [°F]) (using 1951–2000 data). In the higher 

elevation areas of the western basin, classification is either Cfb 

(using 1986–2010 data), which is a warm, temperate, fully 

humid climate with warm summers, or Csb (using 1951–2000 

data), which is a warm, temperate, dry summer climate with 

warm summers (Kottek and others, 2006; Rubel and others, 

2017). The BSk climate classification pervades much of the 

lower elevation regions of New Mexico. The Csb and Cfb 

classifications are in many of the mountainous regions of New 

Mexico that fall outside the classification of a snow climate 

(group D in the Köppen-Geiger climate classification).

Total annual precipitation varies spatially throughout the 

study area depending on elevation, ranging from 15 inches 

per year (in/yr) in the eastern lowland areas and valleys to 

40 in/yr at high elevations (Matherne and others, 2010; Darr 

and others, 2014). Mean monthly precipitation data indicate 

that a majority of the precipitation falls during the summer 

months (June–September) as monsoon storms (Sherson and 

Rice, 2015). Summer precipitation accounts for an average 

of 59 percent of the total annual precipitation for the period 

1942–2013 (Sherson and Rice, 2015). Snowpack in the study 

area typically reaches the maximum snow-water equivalent in 

February and March, followed by a rapid melt period in April 

and May (Sherson and Rice, 2015). 

Vegetation is dominated by mixed conifer forests 

interspersed with grasslands at higher elevations; Pinus 

edulis-Juniperus sp. (piñon pine-juniper) and Pinus ponderosa 
laf18-0872_fig 01

70

70

380

380

246

37

48

Capitan Mountains

Sacramento Mountains

SierraBlancas

North Fork Eagle

Creek subwatershed

South Fork Eagle

Creek subwatershed

Upper Rio Hondo Basin

LINCOLN COUNTY

OTERO COUNTY

MESCALERO APACHE INDIAN RESERVATION

GrindstoneLake

MescaleroLake

Bonito Lake

Rio Ruidoso

Rio Bonito

Rio Bonito

Rio Hondo

Little Creek

Eagle Creek

Capitan

Fort Stanton Lincoln

Hondo

Glencoe

Ruidoso Downs

Ruidoso

Alto

08387550

08387600

08387575

08390020

U.S. Geological Survey streamgage 08387600

6,951 to 7,550 Mescalero Apache Indian Reservation7,551 to 8,2008,201 to 9,2009,201 to 12,000

4,000 to 5,3005,301 to 5,8505,851 to 6,4006,401 to 6,950

Elevation, in feet above North American

Vertical Datum of 1988 (NAVD 88)

Cloudcroft precipitation gageNorth Fork Eagle Creek subwatershed

EXPLANATION

105°50' 105°40' 105°30' 105°20' 105°10' 

33°30'33°20'33°10'

Bases from U.S. Geological Survey, 2016, 10-meterDigital Elevation Model data and Bureau of Land Management digital data, 2016Universal Transverse Mercator projection, zone 13North American Vertical Datum of 1988 (NAVD 88)North American Datum of 1983 (NAD 83)

00 2 4 

4 6

6

2 8 KILOM

N

Upper Basin

Figure 1. Upper Rio Hondo Basin study area, Lincoln and Otero Counties, New Mexico.
laf18-0872_fig 02

0

20 4 8 MILES

4 6

6

2 8 KILOMETERS

U.S. Geological Survey streamgage

6,951 to 7,5507,551 to 8,2008,201 to 9,2009,201 to 12,000

4,000 to 5,3005,301 to 5,8505,851 to 6,4006,401 to 6,950

Elevation, in feet above North American

Vertical Datum of 1988 (NAVD 88)

Cloudcroft precipitation gageNorth Fork Eagle Creek subwatershed

EXPLANATION

Fire boundary and associated namePrecipitation-runoff modeling system

hydrologic response unit (PRMS HRU)

476

Bases from U.S. Geological Survey, 2016, 10-meterDigital Elevation Model data and Bureau of Land Management digital data, 2016Universal Transverse Mercator projection, zone 13North American Vertical Datum of 1988 (NAVD 88)North American Datum of 1983 (NAD 83)

105°50' 105°40' 105°30' 105°20' 105°10' 

33°30'33°20'33°10'

476

486

472

513

468

503

492

481 488

475 480

482 483

487

490

654

687

485

474

Homestead

White

Trap and Skeet

Musketball

Ski Run

Little Bear

Kokopelli

Rifle

Orio

479

491

501

516

520

Peppin

NE

Upper RBasin

Figure 2. Upper Rio Hondo Basin study area, precipitation-runoff modeling system (PRMS) hydrologic response unit (HRU) boundarie2014), U.S. Geological Survey streamgage locations, and historical fire boundaries (2000–14; U.S. Geological Survey, 2018a).
Precipitation-Runoff Modeling System 5

(ponderosa pine) forests at mid-elevations; and desert grasses, 

cacti, and shrubs at lower elevations (Sherson and Rice, 2015). 

Historically, the forested areas in the western part of the upper 

Rio Hondo Basin contained a tree density varying from 20 to 

70 trees per acre with grass-dominated understories. However, 

decades of fire suppression led to establishment of dense 

forests with 200 to 250 trees per acre with increased litter, 

little grass, and fewer openings (Wisner, 2004). 

Soil-zone data from the Soil Survey Geographic Database 

(U.S. Department of Agriculture, https://sdmdataaccess.

sc.egov.usda.gov, accessed June 25, 2017) were not available 

for a substantial part of the mountainous areas in the upper 

Rio Hondo Basin, including the North Fork Eagle Creek 

subwatershed, so a precise description of soils in the overall 

basin and the target subwatershed is not possible. Data are 

available for about two-thirds of the South Fork Eagle Creek 

subwatershed (Natural Resources Conservation Service, U.S. 

Department of Agriculture, 2012). Soils in the “Firo very 

stony loam” and “Firo-Stony land complex,” which represent 

a majority of the South Fork Eagle Creek subwatershed area, 

are well-drained soils in the “very high” runoff class, are 

classified as hydrologic soil group D, and typically have a 

very stony loam or stony loam surface soil layer (0–3 inches 

[in.]), a very cobbly loam underlying layer (3–15 in.), and a 

depth to restrictive layer of 7–20 in. A substantial portion of 

the remaining soils are Caballo loam, which are well-drained 

soils in the “high” runoff class, are classified as hydrologic 

soil group B, and typically have a loam surface soil layer 

(0–10 in.), an extremely cobbly clay loam underlying layer 

(10–42 in.), and a depth to restrictive layer of more than 80 in. 

In the middle part of the upper Rio Hondo Basin, roughly from 

Ruidoso to Capitan (fig. 1), soils are in the “well-drained” 

drainage class, have hydrologic soil groups of predominantly 

D (about 33 percent) and C (about 50 percent), and have 

depths to restrictive layer exceeding 6.5 ft (about 67 percent), 

with the remaining area evenly split between the 0–1.6-ft 

and 1.6–3.3-ft depths. In the lower part of the basin, roughly 

from Capitan to the basin outlet at the convergence of the Rio 

Bonito and Rio Ruidoso (fig. 1), soils are in the “well-drained” 

drainage class, have hydrologic soil groups of predominantly 

D (almost 90 percent) and C (about 10 percent), and have 

depths to restrictive layer of 1.6–3.3 ft (almost 80 percent), 

with most of the remaining area exceeding 6.6-ft depths.

Wildfires in Rio Hondo Basin

Fire has long been a part of ecological systems in 

forested basins of the western United States. The upper 

Rio Hondo Basin was affected by 11 recorded fires from 

2000 to 2014 (U.S. Geological Survey, 2018a). The Little 

Bear Fire (June 2012) was among the largest, having a 

total burned area of 44,300 acres, with 36,234 acres in 

the upper Rio Hondo Basin. Other fires in the upper Rio 

Hondo Basin include the following: Orio (2012, 827 acres), 

Donaldson (2011, 101,513 acres; not shown on figure 2, 

located approximately 6 mi south of Hondo, New Mexico), 

White (2011, 10,348 acres), Rifle (2011, 458 acres), Peppin 

(2004, 47,863 acres), Ski Run (2003, 265 acres), Kokepelli 

(2002, 1,000 acres), Trap and Skeet (2001, 463 acres), 

Homestead (2001, 200 acres), and Musketball (2001, 

152 acres). Two fires greatly affected the North Fork Eagle 

Creek subwatershed: Little Bear Fire (2012) burned the entire 

2,020-acre subwatershed, and the Ski Run Fire (2003) burned 

265 acres in the southern part of the subwatershed.

The Little Bear Fire originated in the high-elevation 

forests of the Sierra Blanca Range near the basin boundary 

of the North Fork Eagle Creek subwatershed and spread 

north and east into the Rio Hondo Basin, eventually burning 

55 mi2 (19 percent) of the Rio Bonito watershed and 14 mi2

(5 percent) of the Rio Ruidoso watershed. Burn severity was 

high or moderate on 53 percent of the burn area (Snyder 

and others, 2012). About 11 mi2

 of upper Eagle Creek 

subwatershed was burned, including the entire North Fork 

Eagle Creek subwatershed.

Hydrologic Effects of Fire

Wildfires alter landscapes by burning groundcover, 

understory vegetation, and canopies and by altering physical, 

chemical, and biological properties of soil (Doerr and Cerda, 

2005; Doerr and others, 2009; Terranova and others, 2009). 

Postfire landscapes have measurable hydrologic changes 

when compared to their prefire conditions, including reduced 

infiltration (Martin and Moody, 2001; Moody and Martin, 

2001; Terranova and others, 2009), reduced evapotranspiration 

(ET) (Doerr and others, 2009), increased surface runoff 

(Cerda and Doerr, 2005; Terranova and others, 2009; 

Vieira and others, 2015), and increased peak streamflows 

(Moody and Martin, 2001). As a result, postfire landscapes 

are more susceptible to floods and debris flows, which 

threaten property and life. Postfire effects that contribute to 

increased surface runoff include decreased canopy cover, 

which reduces canopy interception and storage; decreased 

ground cover, which enhances raindrop splash erosion, soil 

aggregate dispersion, and soil pore closure and, thus, reduces 

infiltration; and increased soil water repellency, which 

reduces infiltration rates. Depending on fire severity and 

vegetation and soil disruption, recovery from fire effects may 

be measurable within 2 to 6 years (MacDonald and Stednick, 

2003; MacDonald and Huffman, 2004; Cerda and Doerr, 

2005; Vieira and others, 2015), but some effects may still be 

measurable after 10 years or more (Cerda and Doerr, 2005).

Precipitation-Runoff Modeling System

The PRMS is a component of the National Hydrologic 

Model (NHM), developed by the USGS (Leavesley and 

others, 1983; Markstrom and others, 2015). PRMS is a 

deterministic, processed-based, distributed-parameter 

modeling system designed to analyze the effects of 
6 Calibration of Precipitation-Runoff Modeling System

precipitation, climate, and land use on streamflow and general 

basin hydrology on a daily time step. The national-scale 

framework of the NHM allows for subcatchment modeling 

by using predefined, spatially distributed hydrologic response 

units (HRUs). HRUs partition the model domain into areas 

with relatively uniform hydrologic response that are based 

on watershed topography, vegetation, soil, and climate. The 

model input data include precipitation, minimum temperature, 

and maximum temperature.

The PRMS model represents the hydrologic cycle as 

17 interconnected processes represented within the model as 

separate modules (Markstrom and others, 2015). Hydrologic 

processes implemented in PRMS include temperature 

distribution, precipitation distribution, combined climate 

distribution, solar radiation distribution, transpiration period, 

potential ET, canopy interception, snow, surface runoff, 

soil zone, groundwater, and streamflow, plus five model 

administrative processes (basin definition, cascading flow, 

solar table, time series data, and summary). 

Each module in PRMS operates by using parameters 

and variables to simulate hydrologic processes. Parameters 

are user-specified input values that are constant through time 

(year to year) and may vary spatially across the landscape 

(unique values per HRU), or may vary by month and HRU 

(giving unique values [HRUs × months]), but do not change 

during the simulation (for example, the area of each HRU). 

Variables are hydrologic states and fluxes that may vary with 

each time step during the simulation (Markstrom and others, 

2015). Variables may be user-input, time-series variables 

(for example, daily precipitation) or internal variables (for 

example, soil moisture for each HRU), which are calculated 

by the modules and may be used by other modules as input 

variables. 

PRMS, like most distributed hydrologic models, needs to 

be calibrated to be applicable to a specific study area. PRMS 

uses a total of 108 input parameters, but 74 of these parameters 

are not typically varied from their initial values, leaving 34 

as standard calibration parameters (table 1). Each calibration 

parameter is used within a single module, but because output 

variables from one module can be used as input variables to 

other modules, calibration parameters may influence several 

hydrologic process modules. PRMS has more than 200 output 

variables that represent specific hydrologic responses over 

time. As a result of model complexity and the extensive set of 

parameters, sensitivity analyses for PRMS are highly complex 

and essential for successful model application. During model 

sensitivity analysis, the influence of calibration parameters 

on output variables is assessed. During model calibration, 

calibration parameters are adjusted, and output variables 

are compared to observed hydrologic data to assess model 

performance. 

For this report, an uncalibrated PRMS model was 

built of the upper Rio Hondo Basin from the headwaters 

to approximately 19 mi downstream from the Rio Hondo 

above Chavez Canyon near Hondo streamgage that included 

24 HRUs from the NHM. This model was used for sensitivity 

analysis to identify the most sensitive calibration parameters 

for the upper Rio Hondo Basin for an 11-year evaluation 

period (1990–2000). Then, two calibrated PRMS models were 

developed for the North Fork Eagle Creek subwatershed: 

prefire (November 30, 2007, to April 30, 2012) and postfire 

(October 31, 2013, to December 31, 2015).

Sensitivity Analysis Methods

Purpose of Sensitivity Analysis

Selection of the most appropriate calibration parameters 

is critical to a successful calibration process (Hay and others, 

2006). Several important factors should be considered during 

parameter selection. First, it is important to understand which 

processes and parameters are most influential on hydrologic 

model results and how they vary within the watershed. 

Watershed hydrologic processes, such as infiltration, 

snowmelt, and overland flow, vary according to spatially 

and temporally varying combinations of land surface and 

climatological conditions (Markstrom and others, 2016). For 

example, streamflow from a cold mountainous watershed 

may demonstrate a high dependency on snowmelt processes, 

whereas streamflow from a warmer lowland watershed may 

demonstrate greater dependency on ET processes. As such, 

the most influential parameters may also be expected to 

vary depending on location within the watershed. Second, 

it is important to know if site-specific data are available 

to parameterize the model. Distributed hydrologic models 

typically have a large number of adjustable input parameters 

in order to allow accurate simulation of hydrologic response 

for a variety of conditions in different areas, but data sources 

are not normally available for all parameters (Beven, 2002; 

Kirchner, 2006). Lastly, it is important to understand how 

model parameters interact. Because of interactions among 

parameters, several different combinations of adjustable input 

parameters, some of which may be unrealistic (that is, “get 

the right result for the wrong reasons”), may produce similar 

model performance, a situation known as “equifinality.” 

An analysis conducted to determine the strength of 

input-parameter influence on model-output response is called a 

“sensitivity analysis.” Sensitivity analyses are conducted for a 

specific model and study area to focus model development and 

calibration efforts on the most influential parameters (Moriasi 

and others, 2015b; Yuan and others, 2015). Sensitivity 

analyses can focus model parameterization efforts on the most 

influential parameters, identify important interactions that may 

influence calibration strategies, and reduce the potential for 

unrealistic calibration results through equifinality.
Sensitivity Analysis Methods 7

Table 1. Precipitation-Runoff Modeling System (PRMS) calibration parameters (from Markstrom and others, 2016).

[ET, evapotranspiration]

Parameter name Description PRMS module Range

adjmix_rain Factor to adjust rain proportion in a mixed rain/snow event Climate 0.6–1.4

tmax_allrain Maximum air temperature above which precipitation is rain Climate −8.0–60.0

tmax_allsnow Maximum air temperature below which precipitation is snow Climate −10.0–40.0

dday_intcp Intercept in degree–day equation Solar radiation −60.0–10.0

dday_slope Slope in degree–day equation Solar radiation 0.2–0.9

ppt_rad_adj Solar radiation adjustment threshold for precipitation days Solar radiation 0.0–0.5

radj_sppt Solar radiation adjustment on summer precipitation days Solar radiation 0.0–1.0

radj_wppt Solar radiation adjustment on winter precipitation days Solar radiation 0.0–1.0

radmax Maximum solar radiation caused by atmospheric effects Solar radiation 0.1–1.0

tmax_index Temperature to determine precipitation adjustments to solar radiation Solar radiation −10.0–110.0

jh_coef Coefficient used in Jensen–Haise potential ET computations Potential ET 0.005–0.06

jh_coef_ hru Coefficient used in Jensen–Haise potential ET computations Potential ET 5.0–25.0

srain_intcp Summer rain interception storage capacity Interception 0.0–1.0

wrain_intcp Winter rain interception storage capacity Interception 0.0–1.0

cecn_coef Convection condensation energy coefficient Snow 2.0–10.0

emis_noppt Average emissivity of air on days without precipitation Snow 0.757–1.0

freeh2o_cap Free-water holding capacity of snowpack Snow 0.01–0.2

potet_sublim Snow sublimation fraction of potential ET Snow 0.1–0.75

carea_max Maximum area contributing to surface runoff Surface runoff 0.0–1.0

smidx_coef Nonlinear contributing area coefficient Surface runoff 0.001–0.06

smidx_exp Exponent in nonlinear contributing area coefficient Surface runoff 0.1–0.5

fastcoef_lin Linear coefficient in equation to route preferential flow Soil zone 0.001–0.8

fastcoef_sq Nonlinear coefficient in equation to route preferential flow Soil zone 0.001–1.0

pref_flow_den Fraction of the soil zone in which preferential flow occurs Soil zone 0.0–0.1

sat_threshold Water capacity between field capacity and total saturation Soil zone 1.0–999.0

slowcoef_lin Linear coefficient for interflow routing Soil zone 0.001–0.5

slowcoef_sq Nonlinear coefficient for interflow routing Soil zone 0.001–1.0

soil2gw_max Maximum soil-water excess that is routed directly to groundwater Soil zone 0.0–0.5

soil_moist_max Maximum available water-holding capacity of soil zone Soil zone 0.001–10.0

soil_rechr_max Maximum available water-holding capacity of recharge zone Soil zone 0.001–5.0

ssr2gw_exp Nonlinear coefficient in equation used to route soil-zone water to groundwater Soil zone 0.0–3.0

ssr2gw_rate Linear coefficient in equation used to route soil-zone water to groundwater Soil zone 0.05–0.8

transp_tmax Temperature that determines start of the transpiration period Soil zone 0.0–1000.0

gwflow_coef Linear groundwater discharge coefficient Groundwater 0.001–0.5
8 Calibration of Precipitation-Runoff Modeling System

Fourier Amplitude Sensitivity Test

A national-level sensitivity analysis was performed by 

Markstrom and others (2016) to better understand interactions 

between PRMS calibration parameters and process variables. 

They used a Fourier amplitude sensitivity test (FAST) 

procedure to analyze 110,000 subregions, or HRUs, of the 

coterminous United States for an 11-year evaluation period 

(1990–2000). The FAST analysis used seven fundamental 

daily streamflow statistics, where each statistic represented 

nonredundant information on hydrologic response (Archfield 

and others, 2014). Analysis in Markstrom and others (2016), 

as well as in this report, focuses on three of the seven 

statistics: mean daily flow (representative of total daily 

flow volume), coefficient of variation of daily flow (CV; 

representative of flow “flashiness”), and autoregressive lag-1 

correlation coefficient of daily flow (AR-1; representative of 

day-to-day timing of flow). 

The sensitivity analysis used by Markstrom and others 

(2016) focused on eight of the PRMS hydrologic processes: 

snowmelt, surface runoff, infiltration, soil moisture, ET, 

interflow, base flow, and streamflow. This sensitivity analysis 

allowed for a better understanding of key hydrologic processes 

for an HRU and the dominant parameters that affect each 

key process to improve runoff modeling within a study 

area (Markstrom and others, 2016). The FAST results have 

shown that on average (of 110,000 HRUs in the coterminous 

United States), 90 percent of the parameter sensitivity to 

key hydrologic processes in a given HRU comes from just 

2 to 9 parameters, which is a small subset of the 35 standard 

calibration parameters. Using the sensitivity analysis to 

selectively reduce the number of calibration parameters 

improves understanding of PRMS process-parameter 

interactions and reduces the potential for equifinality by 

reducing the number of parameters needed to be adjusted to 

make an accurate calibration.

Fourier Amplitude Sensitivity Test Interface

Because of the size of the FAST output for the 

coterminous United States, a large amount of data 

manipulation and analysis is needed to parse and analyze the 

data for a smaller study area. As part of this study, a platform 

was created to provide a quasi-graphical user interface to 

easily query the FAST data by region, HRU, groupings of 

HRUs, specific aspects of the flow regime, and groupings 

of flow-regime aspects. The interface also converted the 

dimensionless quantity outputs from FAST into percentages 

that represent (a) the amount of influence the calibration 

parameters have on the processes, and (b) the amount of 

influence the processes have within the modeling space. 

The parameters are then ordered in terms of total percent of 

influence for the study area(s). Similarly, the processes are 

ordered by influence. This procedure allows quick, sitespecific determination of the key parameters and processes 

and their associated influence on various aspects of the flow 

regime, which permits a more informed, quicker calibration 

process in which only key processes and affiliated parameters 

are optimized for an area.

Model Calibration Methods

Purpose of Calibration

Calibration is a process of adjusting model parameters 

to improve (optimize) simulation of real-world variables and 

processes, as indicated by comparison between simulated 

and observed data. All hydrologic models need to be 

calibrated to ensure that model results are reasonable and 

representative for the target study areas, conditions, and time 

periods. Despite having complex process representations and 

numerous parameters and variables, hydrologic models remain 

simplifications of reality. Hydrologic data used for comparison 

during calibration are limited and uncertain (Harmel and 

others, 2009); for example, each USGS streamflow discharge 

measurement is assigned an accuracy ranging from excellent 

(<2 percent error) to poor (>8 percent error) (Sauer and Meyer, 

1992). As a result, models are subject to equifinality “because 

no single (global) optimum parameter set exists when 

imperfect models are applied to incomplete data” (Zeckoski 

and others, 2015, p. 1624). The model calibration process 

should carefully apply multiple qualitative and quantitative 

assessments to ensure that the resulting set of calibrated 

parameters is adequately robust for the intended application 

(Harmel and others, 2014; Daggupati and others, 2015; 

Saraswat and others, 2015). 

Let Us Calibrate Interface

A graphical user interface, Let Us Calibrate (LUCA), has 

been developed for building and performing multiobjective, 

step-wise calibration of hydrologic models, including PRMS, 

by using a shuffled complex evolution global search algorithm 

(Hay and Umemoto, 2006; Hay and others, 2007). Model 

results are compared to observed comparison data for selected 

variables, such as solar radiation, potential ET, snow-water 

equivalent, soil moisture, or streamflow. For each comparison 

dataset, one or more model parameters are adjusted during 

calibration. Initial values for each parameter are user specified, 

and LUCA adjusts each parameter within user-specified 

bounds to optimize a set of user-specified objective functions 

(such as normalized root-mean-square error [NRMSE] by 

using daily values, monthly means, or annual means; NashSutcliffe efficiency [NSE]; and others). The individual 

objective functions are each assigned a weighting (0–1.0) that 

sum to 1.0 to define their relative contributions to an overall 

multiobjective function. 
PRMS Model Sensitivity Analysis for Upper Rio Hondo Basin 9

The calibration process includes a user-specified number 

of (a) steps, where each step simultaneously calibrates a 

set of parameters by using a specified comparison dataset, 

and parameters calibrated during each step are used as 

initial values for subsequent steps; and (b) rounds, where 

all predefined steps are performed for each round, and 

parameter values from one round serve as the initial values 

for subsequent rounds. During each step, the model is run 

many hundreds or thousands of times, each time with a 

different combination of values for the parameters selected 

to be calibrated in that step. Parameter values for the model 

run that has the best overall multiobjective function are 

selected and used as initial values for subsequent steps in 

that round. Different sets of parameters are adjusted and 

optimized in each step. After completing all the steps in a 

single round, the process is repeated in the next round with a 

new set of initial parameter values. Each round recalculates 

the optimal parameter values in each step, given a different 

set of initial parameter values from the previous round. In 

this way, interactions among parameters in different steps 

are considered. 

Performance Measures

Calibrated model performance was assessed by using 

graphical and statistical methods, as recommended by Harmel 

and others (2014, 2018) and Moriasi and others (2007, 2015a). 

Graphical methods included comparison of simulated and 

observed streamflow time-series hydrographs and cumulative 

streamflow distributions. Statistical performance measures 

were calculated by using means of daily values for the 

designated time step (annual, monthly, or daily). Statistical 

measures included mean and standard deviations of simulated 

and observed values, slope and coefficient of determination 

(R2

) for the simulated versus observed regression, mean bias 

of simulated versus observed values, square root of the mean 

of squared errors between simulated and observed values 

(RMSE), ratio of RMSE to standard deviation (RSR), and 

NSE relative to the observed mean (Moriasi and others, 2007). 

Additional model verification was performed to ensure that 

specific hydrologic processes were reasonable and could be 

explained on the basis of expected real-world processes.

PRMS Model Sensitivity Analysis for 

Upper Rio Hondo Basin

For this study, an uncalibrated PRMS model was 

built of the upper Rio Hondo Basin from the headwaters 

to approximately 19 mi downstream from the Rio Hondo 

above Chavez Canyon near Hondo streamgage (table 2). 

Twenty-four HRUs that covered the study area watershed were 

extracted from the NHM along with model input data from the 

NHM (Viger, 2014; Viger and Bock, 2014). FAST results from 

Markstrom and others (2016) for an 11-year evaluation period 

(1990–2000) were assessed for 24 individual HRUs in the 

upper Rio Hondo Basin.

Sensitivity in Upper Rio Hondo Basin

The FAST results were analyzed by HRU to determine 

which of the eight selected hydrologic processes exhibited 

the greatest influence in simulating three streamflow 

statistics: mean daily flow (representative of total daily 

flow volume), CV (representative of flow “flashiness”), and 

AR-1 (representative of day-to-day timing of flow) (fig. 3). 

Simulation of mean streamflow was most sensitive to 

parameters related to snowmelt or infiltration throughout the 

upper Rio Hondo Basin. Flashiness and timing statistics were 

most sensitive to interflow in most of the basin’s eastern and 

northern HRUs. In those HRUs of the extreme western basin 

or higher elevations in general, CV of streamflow was most 

sensitive to soil moisture parameters, and AR-1 of streamflow 

was most sensitive to infiltration and ET parameters.

Table 2. U.S. Geological Survey streamgages in the upper Rio Hondo Basin and descriptions, including drainage area, elevation, and 

beginning and ending dates of the period of record.

[Streamgage information from U.S. Geological Survey (2018b). mi2, square miles; ft, feet; NGVD 29, National Geodetic Vertical Datum of 1929; POR, period 

of record; NM, New Mexico; dates are presented in month/day/year format]

Station 

number Streamgage name Drainage area 

(mi2)

Elevation

(ft, NGVD 29) POR begin date POR end date

08387550 North Fork Eagle Creek near Alto, NM 3.16 7,900 9/6/2007 5/31/2017

08387575 South Fork Eagle Creek near Alto, NM 2.79 7,630 9/6/2007 5/31/2017

08387600 Eagle Creek below South Fork near Alto, NM 8.14 7,600 8/27/1969 5/31/2017

08390020 Rio Hondo above Chavez Canyon near Hondo, NM 588 5,160 8/21/2008 5/31/2017
10 Calibration of Precipitation-Runoff Modeling System

The hydrologic processes that exhibited the greatest 

overall sensitivity (72 percent) in the basin were interflow, 

soil moisture, ET, and infiltration (fig. 4). Sensitivity, 

normalized as a percentage of total influence contributed by 

each process on each streamflow statistic (total daily volume, 

flashiness, and timing), varied for each HRU for each of the 

three streamflow statistics. Overall sensitivity, calculated 

as an average of the three statistics, was 21 percent for 

interflow, 20 percent for soil moisture, 18 percent for ET, and 

13 percent for infiltration (fig. 4). Although snowmelt was a 

dominant process for total daily volume, it was not included 

because data were insufficient to define snowmelt’s effects on 

flashiness and timing. 

Contribution to streamflow-response sensitivity from 

the four dominant hydrologic processes varied widely across 

the basin, ranging from 10 percent influence to 25 percent 

influence among HRUs (fig. 4). Levels of sensitivity tended 

to exhibit similarity among clusters of adjacent HRUs within 

the basin, indicating similarity in local hydrologic response 

for adjacent HRUs with similar characteristics. However, 

sensitivity was not always consistent among streamflow 

statistics. Specific HRUs that were sensitive to the mean 

response of a hydrologic process often showed different 

levels of sensitivity for CV and AR-1 response of the same 

process. Several HRUs experienced a general trend of 

increasing sensitivity to interflow and soil moisture going 

from mean to CV to AR-1 statistics, indicating that for these 

HRUs, interflow and soil moisture were more influential 

for streamflow flashiness and timing than for streamflow 

volume. However, ET and infiltration did not demonstrate a 

similar general trend. Specific HRUs in the western basin that 

demonstrated higher sensitivity to infiltration demonstrated 

less sensitivity to ET and interflow. Overall, few generalities 

or trends were visually detected in sensitivity across HRUs, 

among processes, or among streamflow statistics.

The parameter (from those listed in table 1) with greatest 

influence on three streamflow statistics from each of four 

key hydrologic processes varied among HRUs (fig. 5). These 

results provided greater specificity about parameters that 

would be influential in model calibration. For example, the 

climate (temperature and precipitation distribution) module 

parameter tmax_allsnow was the most sensitive parameter for 

simulation of interflow mean volume and infiltration CV in 

most HRUs of the basin. Similarly, the potential ET-module 

parameter jh_coef was the most sensitive parameter for 

simulation of infiltration and soil moisture mean volumes 

in most of the basin and for simulation of ET and soil 

moisture CVs in all HRUs. The soil-zone module fastcoef_lin 

parameter had greatest influence on CV and AR-1 for interflow 

throughout most of the HRUs in the basin. Parameters that 

showed dominant sensitivity for large numbers of HRUs 

would be important to include in model calibration.

Figure 3. Hydrologic process with greatest sensitivity to each of three streamflow statistics (total daily flow volume 

[mean daily streamflow], flashiness [coefficient of variation of daily streamflow], and timing [autoregressive lag-1 

correlation coefficient of daily streamflow]) by hydrologic response unit (HRU), upper Rio Hondo Basin, New Mexico. 

Parameter and module descriptions are found in Markstrom and others (2015).

laf18-0872_fig 03

Longitude 

105° 41' 105° 30' 105° 19' 105° 08'

Latitude 

33° 39'

33° 33'

33° 27'

33° 21'

33° 15'

Total daily flow volume Flashiness Timing

Infiltration Soil moisture 

Interflow 

Snowmelt Evapotranspiration

Snow 

Module

Hortonian surface

runoff/infiltration/

impervious storage 

Soil zone recharge/

interflow/gravity drainage/

Dunnian surface runoff 

Potential evapotranspiration

distribution 
PRMS Model Sensitivity Analysis for Upper Rio Hondo Basin 11

Figure 4. Normalized process sensitivity for three streamflow statistics (total daily flow volume [mean daily 

streamflow], flashiness [coefficient of variation of daily streamflow], and timing [autoregressive lag-1 correlation 

coefficient of daily streamflow]) by hydrologic response unit, upper Rio Hondo Basin, New Mexico. Overall sensitivity, 

calculated as an average of the three statistics, is given in percentages on the y-axis. Contribution to streamflowresponse sensitivity from the four dominant hydrologic processes range from lower influences in darker shades to 

higher influences in lighter shades. laf18-0872_fig 04

Longitude 105° 41' 105° 30' 105° 19' 105° 08'

Latitude 33° 39' 33° 33' 33° 27' 33° 21' 33° 15'

Infiltration (13%) Evapotranspiration (18%) Soil moisture (20%) Interflow (21%)

Total daily flow volume Flashiness Timing

25% 

10% 

25% 

10% 

10% 

25% 

25% 

10% 
12 Calibration of Precipitation-Runoff Modeling System

Figure 5. Normalized process sensitivity for three streamflow statistics (total daily flow volume [mean 

daily streamflow], flashiness [coefficient of variation of daily streamflow], and timing [autoregressive lag-1 

correlation coefficient of daily streamflow]) by hydrologic response unit and model parameter, upper Rio 

Hondo Basin, New Mexico. Overall sensitivity, calculated as an average of the three statistics, is given in 

parentheses on the y-axis. Parameter and module descriptions are found in Markstrom and others (2015). laf18-0872_fig 05

Flashiness Timing

Longitude 

Latitude 

Infiltration (13%) Evapotranspiration (18%) Soil moisture (20%) Interflow (21%)

Total daily flow volume

105° 41' 105° 30' 105° 19' 105° 08'

33° 39' 33° 33' 33° 27' 33° 21' 33° 15'

carea_max

smidx_coef

smidx_exp

jh_coef

Module: Hortonian surface runoff/

infiltration/impervious storage 

Module: potential ET distribution 

fastcoef_lin

pref_flow_den

soil_moist_max

soil_rechr_max

Module: interception

srain_intcp

Module: soil zone recharge/interflow/

gravity drainage/Dunnian surface runoff 

Module: groundwater flow 

gwflow_coef

tmax_allsnow

Module: Climate, air temperature

and precipitation distribution 

EXPLANATION
PRMS Model Calibration for the North Fork Eagle Creek Subwatershed 13

Sensitivity in the North Fork Eagle Creek 

Subwatershed

The North Fork Eagle Creek subwatershed HRUs (HRUs 

487 and 490, fig. 2) showed greatest sensitivity to snowmelt 

for total volume, soil moisture for streamflow flashiness, and 

infiltration (HRU 487) and ET (HRU 490) for streamflow 

timing (figs. 3, 4). Simulation of total volume was most 

influenced by jh_coef (potential ET module) for the processes 

of interflow (both HRUs), infiltration (both HRUs), and ET 

(HRU 487), and by tmax_allsnow (climate [temperature 

and precipitation distribution] module) for the soil moisture 

process (fig. 5). Simulation of streamflow flashiness was most 

influenced by fastcoef_lin (soil-zone module) for the interflow 

process, jh_coef (potential ET module) for the soil moisture 

and ET processes, and tmax_allsnow (climate [temperature 

and precipitation] distribution module) for the infiltration 

process (fig. 5). Streamflow timing was most influenced by 

soil_moist_max (soil-zone module), jh_coef (potential ET 

module), srain_intcp (interception module), and smidx_coef 

and carea_max (surface runoff module) parameters (fig. 5). 

PRMS Model Calibration for the North 

Fork Eagle Creek Subwatershed

The PRMS model developed from the sensitivity analysis 

was adapted for use in calibration. The upper Rio Hondo 

Basin has a large number of reservoirs and irrigation canals, 

all of which alter natural streamflow in the basin. Bonito 

Lake (1,500-acre-foot [acre-ft] capacity) along Rio Bonito, 

Mescalero Lake (3,000-acre-ft capacity) on the Mescalero 

Apache Indian Reservation, and a reservoir commonly 

known as Grindstone Lake (1,520-acre-ft capacity) west of 

the town of Ruidoso and fed by surface water diverted from 

Rio Ruidoso are three of six constructed lakes in the basin. 

These reservoirs and diversions alter natural streamflows and 

complicate calibration of rainfall-runoff models. In addition, 

modeling efforts were focused on HRUs that were entirely 

within fire-affected areas and above existing streamflow 

gage(s) with period(s) of record before and after the 2012 

Little Bear Fire. 

Available USGS streamgages (fig. 1) in the fire-affected 

area (fig. 2) include North Fork (08387550) and South Fork 

(08387575) Eagle Creek near Alto, N. Mex., both with periods 

of record since September 2007, and Eagle Creek below South 

Fork near Alto, N. Mex. (08387600), with period of record 

since August 1969 (table 2). The North Fork Eagle Creek 

subwatershed was selected for calibration for several reasons: 

(1) the North Fork subwatershed is coincident with two HRUs 

(HRUs 490 and 487, fig. 2) in the NHM upper Rio Hondo 

Basin PRMS model, (2) both HRUs are entirely within the 

fire-affected area (fig. 2), (3) the South Fork subwatershed 

contains a reservoir, managed by the Apache Tribe, that 

can artificially affect streamflows, and (4) available USGS 

streamgages (table 2) allow for the construction of a daily 

timeseries of discharge for the North Fork subwatershed. The 

North Fork streamgage is located more than 1 mi upstream 

from the confluence of the North Fork and South Fork into 

Eagle Creek; however, the South Fork and Eagle Creek 

streamgages are within 300 ft of each other, just upstream 

and downstream from the confluence. This proximity allowed 

a daily timeseries of discharge for the North Fork to be 

constructed by subtraction of the South Fork streamgage data 

from the Eagle Creek streamgage data (fig. 6). 

Date

Precipitation, in inches

Streamflow, in cubic feet per second

1

2

3

4

2008 2009 2010 2011 2012 2013

0 

25 

50 

75 

100 

Observed precipitation

(Western Regional Climate Center, n.d.)

Streamflow

Simulated streamflow,

prefire calibrated model

Simulated streamflow,

postfire calibrated model

EXPLANATION

Figure 6. Daily simulated (prefire and postfire calibrated models) and observed streamflow and precipitation for the 

study period (2008–14) for the North Fork Eagle Creek subwatershed, New Mexico.
14 Calibration of Precipitation-Runoff Modeling System

In order to use the same PRMS model framework 

that was used for the sensitivity analysis directly, daily 

precipitation input data for all HRUs except the two North 

Fork HRUs were set to zero, which forced daily streamflow 

to represent only outflow from North Fork Eagle Creek. 

Initial input parameters for the North Fork Eagle Creek 

HRUs were adopted from the Geospatial Fabric for the NHM 

(Viger, 2014; Viger and Bock, 2014; Regan and others, 2018) 

except as modified by using specific baseline values or as 

adjusted during calibration. The NHM Geospatial Fabric soil 

parameters were derived from the Soil Survey Geographic 

database (Natural Resources Conservation Service, U.S. 

Department of Agriculture, 2012). Geospatial Fabric landcover parameters were derived from the 2001 National 

Land Cover Database (Homer and others, 2007). Geospatial 

Fabric subsurface flux parameters were derived from the map 

products of Gleeson and others (2011).

Calibration of PRMS was conducted only for the May 

through October period in this study for two reasons. The 

North Fork Eagle Creek and stream reaches at the Eagle 

Creek streamgages typically freeze each winter. Streamflow 

measurement during ice cover can be problematic because ice 

cover alters the relation between stream stage and discharge 

that is used to develop streamflow hydrographs at many USGS 

streamgages (Turnipseed and Sauer, 2010), including Eagle 

Creek. As a result, discharge often is not measured during 

part or all of the winter period, resulting in data gaps in daily 

discharge records. Second, a primary goal of this study was to 

characterize postfire streamflow conditions during higher flow 

conditions that may cause flooding and associated health and 

safety concerns important to the NMDHSEM. A majority of 

precipitation (59 percent of annual precipitation for the 1942–

2013 period of record) occurs in the summer months (June to 

September) from monsoon storms (Sherson and Rice, 2015). 

As a result, higher streamflow events that are a focus of this 

study typically occur because of these monsoonal convective 

storms during the summer to early fall period.

Meteorological Data 

Daily precipitation, minimum daily temperature, 

and maximum daily temperature data for the study period 

(2007–15) were obtained from the Daymet: Daily Surface 

Weather Data on a 0.62-mi Grid for North America, Version 

3 (Thornton and others, 2016). Daymet is a collection of 

0.62-mi gridded estimates of precipitation, temperature, and 

solar radiation derived by interpolation and extrapolation from 

observation data (Thornton and others, 2016) and a digital 

elevation model. These data were used for the calibration of 

the prefire and postfire models to the constructed, observed 

hydrograph for North Fork Eagle Creek. 

A gridded precipitation dataset that represents the spatial 

heterogeneity of precipitation over a landscape is superior 

to precipitation data from a singular point (for example, 

a weather station) for the purpose of simulating spatially 

distributed landscape processes by using a watershed model. 

However, it is possible that gridded data, which reflect 

integrated and interpolated meteorological data over large 

areas, may dampen signals of extreme events, including 

heterogeneous, localized monsoonal storms. Monsoonal-storm 

signal dampening may subsequently reduce the modeled 

hydrologic signal of fire-affected watersheds. As such, summer 

precipitation from one precipitation gage at a similar latitude 

and elevation band was used as meteorological forcing to the 

prefire and postfire calibrated models rather than Daymetbased precipitation. Although precipitation gage data from a 

site outside the subwatershed may not provide the best data 

for comparing simulated streamflow to the actual streamflow 

signal, it would be superior for comparing potential hydrologic 

effects of various summer precipitation regimes within the 

subwatershed for prefire and postfire conditions. 

Daily precipitation from the Cloudcroft, N. Mex., gage 

(site 291931) from the National Weather Service Cooperative 

Network (Western Regional Climate Center) was used as the 

precipitation input to the calibrated pre- and postfire models. 

The precipitation gage is located at an elevation of 8,660 ft 

(mean elevation of North Fork Eagle Creek is 8,500 ft), 

30.9 mi south of the centroid of the North Fork Eagle 

Creek subwatershed (fig. 1), and has a period of record of 

approximately 29 years (Western Regional Climate Center). 

Baseline Prefire Parameter Adjustments

Eight calibration parameters (covden_sum, covden_win, 

snow_intcp, srain_intcp, wrain_intcp, rad_trnc, soil_rechr_

max, and soil_moist_max) were adjusted to user-specified 

baseline values prior to calibration and were not included in 

the automated calibration (table 3). In the interception module, 

covden_sum and covden_win were based on the USGS 

LANDFIRE database (Ryan and Opperman, 2013) of existing 

vegetation cover for fire-affected Eagle Creek HRUs in 2010 

for prefire conditions and in 2014 for postfire conditions (U.S. 

Geological Survey, 2013). The vegetation density within the 

LANDFIRE database represents live canopy cover for a 98-ft 

(30-meter) grid cell by using the stem-map model from Toney 

and others (2009) and is informed from satellite imagery and 

extensive field-referenced data. Four parameters (srain_intcp, 

wrain_intcp, rad_trnc, and snow_intcp) are directly related 

to canopy density in PRMS and should also be manipulated 

if canopy density is manually changed. In order to establish 

reasonable values for these parameters, four basic linear 

regression equations (eqs. 1–4a) were created by using HRUbased parameter and canopy-density values initially derived 

from the NHM Geospatial Fabric: 

srain_intcp = 0.01293 [covden_xxx] + 0.039883, R2

=0.57 (1)

wrain_intcp = 0.012244 [covden_xxx] + 0.039883, R2

=0.54 (2)
PRMS Model Calibration for the North Fork Eagle Creek Subwatershed 15

rad_trnc = 1.0652 [covden_xxx] + 0.86557, R2=0.94 (3)

snow_intcp = 0.07 [covden_xxx] + 0.0417, R2

=0.93 (4a)

where covden_xxx is either covden_sum or covden_win, 

depending on the month. Despite a good fit (R2

 = 0.93), 

the adjusted values for snow_intcp were very low, with a 

maximum of less than 0.039 in., which is unrealistic. The 

snow_intcp equation slope was increased by an order of 

magnitude (eq. 4b) to give maximum capable wrain_intcp 

(within PRMS), which was still very low according to prior 

studies (Moeser and others, 2015a,b, 2016). 

snow_intcp = 0.7 [covden_xxx] + 0.0417 (4b)

Two parameters were manipulated in accordance with Konrad 

(2004): soil_rechr_max was changed postfire to 0.5, and soil_

moist_max was changed postfire to 1.5. 

Prefire Model Calibration

The PRMS prefire model was run for the entire period 

of Daymet gridded meteorological data record (October 1, 

2007, to December 31, 2015), and results from the prefire 

period (November 30, 2007, to April 30, 2012) were used 

for calibration. Because only discharge data from the May 

through October period each year were used for calibration, 

the 7-month precalibration period from October 1, 2007, to 

April 30, 2008, was used for model initialization. Calibration 

was conducted by using LUCA following a two-step process 

(and three rounds), with user-specified parameters as 

described in table 4. Prefire model statistical performance after 

calibration is summarized in table 5.

Overall, the prefire model was considered to be 

satisfactory. The calibrated model performance in simulating 

monthly prefire streamflow for the May through October 

period was assessed as satisfactory based on statistical 

measures of R2

 (0.74) and NSE (0.62), on the basis of 

watershed-scale model evaluation criteria proposed by 

Moriasi and others (2015a). RSR (0.61) was assessed as 

satisfactory by earlier criteria by Moriasi and others (2007). 

Although mean bias (0.35) was high and considered not 

satisfactory according to the criteria by Moriasi and others 

(2015a), slope of the observed versus simulated regression 

line was nearly 1:1 (1.07), which supports the conclusion of 

satisfactory performance. 

Prefire calibrated model results were comparable to other 

PRMS studies in watersheds that were largely unaffected by 

fire. Yazzie and Chang (2017) reported calibrated NSE of 0.73 

and bias of 3.5 percent in a PRMS study in a forested basin 

in Oregon. Hay and others (2006) reported calibrated-model 

NSE ranged from 0.6 to 0.9 in a mountainous 552-mi2

 basin 

in Colorado.

It is important to stress that the prefire (and postfire) 

model calibration was performed only for the May to October 

period and should be expected to capture watershed and 

rainfall-runoff characteristics only during that period. Results 

should capture the streamflow characteristics of monsoonal 

storms but may not adequately represent the characteristics of 

snowmelt runoff in the study area. 

Postfire Model Calibration

The PRMS postfire model was run for the entire period 

of Daymet gridded meteorological data record (October 1, 

2007, to December 31, 2015), and results from the postfire 

period (October 31, 2013, to December 31, 2015) were used 

for calibration. Because only discharge data from the May 

through October period each year were used for calibration, 

the 71⁄2-year precalibration period from October 1, 2007, to 

April 30, 2014, was used for model initialization. Calibration 

was conducted by using LUCA following a 2-step process, 

with user-specified parameters as described in table 6. Postfire 

model statistical performance after calibration is summarized 

in table 5. 

Overall, the postfire model was considered to be 

satisfactory to good. The calibrated model performance 

in simulating monthly postfire streamflow for the May 

through October period was assessed as satisfactory based 

on statistical measures of R2

 (0.62) and NSE (0.62) and good 

based on mean bias (0.09), according to watershed-scale 

model evaluation criteria proposed by Moriasi and others 

(2015a). RSR (0.6) was assessed as good by earlier criteria 

from Moriasi and others (2007). Slope of the observed versus 

simulated regression line (0.76) indicated an increasing 

underestimation bias as streamflow increased. No studies 

were found that calibrated the PRMS model for postfire 

watershed conditions.

Postfire Parameter Adjustments

Two studies used PRMS to simulate the hydrologic 

effects of fire but without calibration of the resulting 

parameters. Konrad (2004) and Yazzie and Chang (2017) 

determined that the following five parameters should be 

modified to represent the effects of fire on forest cover by 

using PRMS: covden_sum, covden_win, rad_trnc, soil_rechr_

max, and soil_moist_max (table 7). Konrad (2004) developed 

“plausible changes” to these values but did not document the 

source used to justify the changes or test the resulting postfire 

model performance after applying these changes. Yazzie 

and Chang (2017) adapted the method of Konrad (2004) to 

assign postfire PRMS parameters but also did not provide 

justification or verification of model parameters. 

In this study, selection of baseline values (postfire values 

in table 3) for each of the identified five parameters was 

supported by the literature (Konrad, 2004; Yazzie and Chang, 

2017). Although the individual values were not adjusted 

during calibration, the applicability of the values, together 

with the other calibration parameters (table 6), was confirmed 

by the postfire calibrated model performance (table 5).
16 Calibration of Precipitation-Runoff Modeling System

Table 3. Model baseline parameters adjusted manually prior to calibration (manual calibration values) and calibrated parameters 

adjusted using the Let Us Calibrate (LUCA) algorithm (automated calibration values) for prefire (November 30, 2007 to April 30, 2012) 

and postfire (October 31, 2013 to December 31, 2015) simulations in the North Fork Eagle Creek subwatershed. Values are presented as 

actual values for each of the two HRUs in the subwatershed (HRU487/HRU490) or a range of monthly values (minimum–maximum).

[PRMS, Precipitation-Runoff Modeling System; ET, evapotranspiration; in., inch; sub., sublimation; HRU, hydrologic response unit; coef., coefficient; precip., 

precipitation; temp., temperature; max., maximum; dist., distribution; °F, degrees Fahrenheit]

Parameter Description PRMS module Initial Prefire Postfire

Manual calibration values

covden_sum Canopy density in summer (fraction) Interception 0.74/0.73 0.75/0.75 0.25/0.25

covden_win Canopy density in winter (fraction) Interception 0.72/0.68 0.75/0.75 0.25/0.25

snow_intcp Snow interception storage capacity 

(in.)

Interception 0.098/0.093 0.90/0.90 0.33/0.33

srain_intcp Rain interception in summer (in.) Interception 0.05/0.05 0.059/0.059 0.042/0.042

wrain_intcp Rain interception in winter (in.) Interception 0.049/0.048 0.049/0.048 0.043/0.043

rad_trnc Transmission coef. for shortwave 

radiation through canopy

ET/sub. 0.132/0.135 0.129/0.569 0.569/0.569

soil_rechr_max Max. storage in soil recharge zone (in.) Soil-zone storage 1.89/1.84 1.89/1.84 0.5/0.5

soil_moist_max Max. available water-holding capacity 

of capillary reservoir (in.)

Soil-zone storage 3.11/2.99 3.19/3.07 1.5/1.5

Automated calibration values

potet_sublim Fraction of potential ET that is 

sublimated from snow in canopy

ET/sub. 0.5 0.43 0.56

gwflow_coef Linear coef. to compute groundwater 

discharge

Groundwater flow 0.0238/0.0250 0.264/0.265 0.277/0.279

smidx_coef Coef. in nonlinear contributing areas 

algorithm per HRU

Hortonian runoff 0.01/0.01 0.0048/0.0048 0.249/0.249

Jh_coef Monthly temp. coef. used in JensenHaise potential ET calculationPotential ET dist. 0.005–0.026 0.011–0.032 0.024–0.045

rain_cbh_adj Monthly adjustment factor for 

measured precip. /per HRU 

Precip. dist. 1–1 0.77–0.77 0.72–0.72

tmax_allrain Monthly max. air temp. when precip. 

is assumed to be liquid (°F)

Precip. dist. 32–32 35.34–35.34 48.20–48.20

tmax_allsnow Max. air temp. when precip. is 

assumed to be solid (°F)

Precip. dist. 32 30.3 33.1

adjmix_rain Monthly factor to adjust rain amount in 

a mixed rain/snow event

Precip. dist. 1–1 1.096–1.096 1.142–1.142

snow_cbh_adj Monthly adjustment factor for 

measured precip. /per HRU 

Precip. dist. 1–1 1.38–1.38 1.95–1.95

cecn_coef Monthly convection condensation 

energy coef.

Snow 5–5 4.374–4.374 9.595–9.595

emis_noppt Average emissivity of air on days 

without precip. (fraction)

Snow 0.757 0.815 0.921

freeh2o_cap Free-water holding capacity of snow Snow 0.05 0.09 0.156

fastcoef_lin Linear coef. to route preferential-flow 

storage

Soil zone storage 0.011/0.010 0.1966/0.1964 0.3779/0.3777

dday_intcp Monthly intercept in degree-day 

equation

Solar radiation −30– −10 −50.7– −41.07 −36.62– −19.92

radmax Max. fraction of solar radiation that 

can reach ground 

Solar radiation 0.8 0.47 0.5
PRMS Model Calibration for the North Fork Eagle Creek Subwatershed 17

Table 4. User-specified Let Us Calibrate (LUCA) parameters for prefire (November 30, 2007 to April 30, 2012) calibration run in the North 

Fork Eagle Creek subwatershed, New Mexico.

[NRMSE, normalized root-mean-square error; °F, degrees Fahrenheit; °C, degrees Celsius; >, greater than]

Dataset Objective 

function Parameter Parameter range Parameter units Calibration

Prefire calibration step 1: Streamflow volume (November 30, 2007–April 30, 2012)

Annual mean (weight: 0.333)

Mean monthly (weight: 0.333) 

Monthly mean (weight: 0.333)

NRMSE jh_coef 0.005–0.06 1/°F Mean

rain_cbh_adj 0.5–2.0 Decimal fraction Mean

snow_cbh_adj 0.5–2.0 Decimal fraction Mean

smidx_coef 0.0001–1 Decimal fraction Mean

tmax_allrain 32–50 F° Mean

tmax_allsnow 28–36 F° Mean

Prefire calibration step 2: Streamflow timing (November 30, 2007–April 30, 2012)

Daily (weight: 0.7)

Monthly mean (weight: 0.3)

NRMSE adjmix_rain 0.6–1.4 Decimal fraction Mean

cecn_coef 2.0–10.0 Calories per °C >0 Mean

dday_intcp −60–10 Degree day Mean

emis_noppt 0.757–1.0 Decimal fraction Mean

freeh2o_cap 0.01–0.20 Decimal fraction Mean

fastcoef_lin 0.0001–0.5 Decimal fraction Mean

gwflow_coef 0.01–0.3 Fraction/day Mean

potet_sublim 0.1–0.75 Decimal fraction Mean

radmax 0.1–1.0 Decimal fraction Mean

srain_intcp 0.01–0.6 Inches Mean

soil_moistmax 0.2–10 Inches Mean

Table 5. Model performance measures for initial (uncalibrated) parameters and calibrated parameters at the monthly time-step (mean 

of daily values) for prefire (November 30, 2007 to April 30, 2012) and postfire (October 31, 2013 to December 31, 2015) streamflow during 

summer–fall (May through October) in the North Fork Eagle Creek subwatershed.

[ft3/s, cubic feet per second; Stdev, standard deviation; R2, coefficient of determination; RMSE, root-mean-square error; RSR, ratio of RMSE to Stdev; NSE, 

Nash-Sutcliffe model efficiency]

Parameter

Prefire Postfire

Observed Initial Calibrated Observed Initial Calibrated

Mean (ft3

/s) 0.99 16.61 1.34 3.69 1.5 3.78

Stdev (ft3

/s) 2.28 73.4 2.7 4.54 1.57 3.33

Slope 1.41 1.07 0.29 0.76

R2 0.002 0.74 0.37 0.62

RMSE 73.76 1.39 4.27 2.71

RSR 32.3 0.61 0.94 0.6

Bias 15.62 0.35 −2.19 0.09

NSE −1,085.9 0.62 0.05 0.62
18 Calibration of Precipitation-Runoff Modeling System

Table 6. User-specified Let Us Calibrate (LUCA) parameters for postfire (October 31, 2013 to December 31, 2015) calibration run in the 

North Fork Eagle Creek subwatershed.

[NRMSE, normalized root-mean-square error; °F, degrees Fahrenheit; °C, degrees Celsius; >, greater than]

Dataset Objective 

function Parameter Parameter range Parameter units Calibration

Postfire calibration step 1: Streamflow volume (August 1, 2011–December 31, 2015)

Annual mean (weight: 0.333) 

Mean monthly (weight: 0.333) 

Monthly mean (weight: 0.333)

NRMSE jh_coef 0.005–0.06 1/F° Mean

rain_cbh_adj 0.5–2.0 Decimal fraction Mean

snow_cbh_adj 0.5–2.0 Decimal fraction Mean

smidx_coef 0.0001–1 Decimal fraction Mean

tmax_allrain 32–50 F° Mean

tmax_allsnow 28–36 F° Mean

Postfire calibration step 2: Streamflow timing (August 1, 2011–December 31, 2015)

Daily (weight: 0.7)

Monthly mean (weight: 0.3)

NRMSE adjmix_rain 0.6–1.4 Decimal fraction Mean

cecn_coef 2.0–10.0 Calories per C° >0 Mean

dday_intcp −60–10 Degree day Mean

emis_noppt 0.757–1.0 Decimal fraction Mean

freeh2o_cap 0.01–0.20 Decimal fraction Mean

fastcoef_lin 0.0001–0.5 Decimal fraction Mean

gwflow_coef 0.01–0.3 Fraction/day Mean

potet_sublim 0.1–0.75 Decimal fraction Mean

radmax 0.1–1.0 Decimal fraction Mean

srain_intcp 0.01–0.6 Inches Mean

soil_moistmax 0.2–10 Inches Mean

Table 7. Initial and assigned model parameter values to represent changes in forest cover after fire.

[HRU, hydrologic response unit]

Parameter Description This study Konrad (2004) Yazzie and Chang (2017)

Assigned Initial Assigned Initial Assigned

covden_sum Fraction of HRU area covered by 

dominant vegetation in summer

0.25 0.482–1.0 0.1 0.5 0.1

covden_win Fraction of HRU area covered by 

dominant vegetation in winter

0.25 0.349–1.0 0.1 0.5 0.1

rad_trnc Fraction of shortwave radiation 

transmitted through canopy

0.569 0.063–0.448 0.75 0.3 0.5

soil_rechr_max Upper soil storage capacity, in 

inches, over HRU area

0.5 1.69–1.75 0.5 1.643 0.55

soil_moist_max Total soil storage capacity, in 

inches, over HRU area

1.5 2.99–3.46 1.5 2.14–12.537 1.08
Discussion and Application of Prefire and Postfire Models 19

Discussion and Application of Prefire 

and Postfire Models

Statistical measures of watershed model performance 

indicated that both prefire and postfire PRMS models met 

satisfactory evaluation criteria. Assessment of graphical results 

provided further confirmation of model performance as well 

as insights into prefire and postfire hydrologic processes. 

For the application portion of this study, data from a single 

precipitation station (Cloudcroft, N. Mex.) were input into the 

prefire and postfire models instead of the gridded precipitation 

data that were used for calibration. Measured precipitation 

gage data included larger precipitation events, which tend 

to be attenuated in gridded interpolation methods. Inclusion 

of these extreme events (including monsoonal storm events 

that are normal in this area) allowed better representation 

of the prefire and postfire effects of extreme storm events, 

which were a particular concern for their effects on flood 

hazard during postfire conditions. For this reason, use of 

point precipitation data was expected to better represent the 

nonlinear hydrologic responses to extreme storm events that 

may be masked by using gridded precipitation data. 

Hydrographs for prefire and postfire calibrated models 

using the Cloudcroft precipitation gage are shown together 

with observed streamgage daily flows and daily hyetograph for 

a prefire (2010) year and a postfire (2014) year (fig. 7). Daily 

streamflow rates were consistently greater for the postfire 

model than for the prefire model for both 2010 and 2014 

climatic periods except for periods of sustained streamflow 

following extended rainfall periods (August 2010 and 

September 2014). The observed prefire streamflow hydrograph 

(2010) showed extended rising limbs and falling limbs in 

response to summer precipitation that were closely followed 

by the data from the prefire model hydrograph. Hydrographs 

from the observed streamflow and the postfire model (2014) 

were more flashy and responsive, with streamflow increasing 

almost immediately following most precipitation events. 

The observed postfire streamflow hydrograph showed sharp 

peaks in flow similar to the peaks simulated by the postfire 

model. Comparison of the distribution and magnitudes of 

prefire (2010) observed streamflow generally demonstrated a 

better match to prefire model results, and the postfire (2014) 

observed streamflow demonstrated a better match to postfire 

model results (fig. 7). 

Cumulative hydrographs for prefire and postfire 

calibrated models (fig. 8) show similar behavior. In response 

to climatic conditions of both years (2010, 2014), the prefire 

model demonstrated a sharp increase in streamflow during 

a fairly concentrated period of surface runoff, with periods 

of prolonged, gradual increases in flow before and after 

the sharp increases. The postfire model responded with a 

series of sharp, flashy increases in streamflow throughout 

the entire season. Comparing the patterns of the cumulative 

observed hydrograph to simulated hydrographs in each 

year demonstrated a better match with the prefire model 

hydrograph pattern in 2010 and postfire model hydrograph 

pattern in 2014 (fig. 8).

The prolonged periods of gradual increases in streamflow 

for the prefire model results (figs. 7, 8) appeared to be 

consistent with a greater contribution of base flow under 

prefire conditions. An assessment of annual contributions 

for surface runoff and two base-flow components, interflow 

and groundwater flow, confirm this interpretation (fig. 9). In 

PRMS, interflow originates from the shallow soil zone, and 

groundwater flow originates from the groundwater reservoir 

(Markstrom and others, 2015). The prefire model had large 

contributions from the base-flow component in both climatic 

years (2010, 2014), whereas the greatest contribution to 

streamflow in the postfire model was surface runoff. The 

prefire model displayed much more interflow relative to the 

postfire model in the postfire year (2014). 
laf18-0872_fig 07

Streamflow, in cubic feet per second 

0 

June 11 July 01 July 21 Aug. 10 Aug. 30 Sept. 19 Oct. 09 Oct. 29 

Date 

10 20 30 40 50 60 70 

Postfire: 2014 

10 20 30 40 50 60 70 

0 

Prefire: 2010 

StreamflowSimulated streamflo

prefire calibrate

Simulate streamflow

postfire calibrat

Observed precipitat

(Western Region

EXPLANATI

Figure 7. Observed and simulated streamflow and observed precipitation at the Cloudcroft raingage for prefire (2010) and postfire (2014) cJune through November, North Fork Eagle Creek subwatershed, New Mexico.
laf18-0872_fig 08

June 11 July 01 July 21 Aug. 10 Aug. 30 Sept. 19 Oct. 09 Oct. 29 

Streamflow, in cubic feet per second (cumulative) 

Date 

Prefire: 2010 Postfire: 2014 

100 200 300 400 500 600 700 

0 

800 900 100 200 300 400 500 600 700 

0 

800 900 

Streamflow Simulated streamflow,

prefire calibrated model 

Simulated streamflow,

postfire calibrated model 

EXPLANATION

Figure 8. Observed streamflow and the calibrated Precipitation-Runoff Modeling System cumulative daily streamflow for prefire (2010) aconditions, June through November, North Fork Eagle Creek subwatershed, New Mexico.
22 Calibration of Precipitation-Runoff Modeling System

Summary and Conclusions

The Precipitation-Runoff Modeling System (PRMS) is 

widely used to simulate the effects of climate, topography, 

land cover, and soils on landscape-level hydrologic responses 

and streamflow. The U.S. Geological Survey, in cooperation 

with the New Mexico Department of Homeland Security 

and Emergency Management, developed procedures to 

apply the PRMS model to simulate the effects of fire on 

hydrologic responses. 

A platform was created to provide a quasi-graphical user 

interface to easily query the Fourier amplitude sensitivity 

test (FAST) data by region and implemented to analyze 

national-scale sensitivity analysis (FAST) results at the 

local-basin scale. Results provided specific PRMS model 

calibration parameters that were most sensitive for simulating 

specific watershed processes at the hydrologic response 

unit (HRU) spatial scale. These results were applied toward 

selection of critical parameters that were used for subsequent 

model calibration.

Model calibration adjusted selected PRMS parameters 

from their default values by two methods. First, eight 

parameters were user defined on the basis of specific outside 

data and relations that were applied to the study area. Then, 

automated calibration using Let Us Calibrate (LUCA) 

adjusted the most sensitive parameters, as determined from the 

sensitivity analysis. Model calibration applied a gridded, daily 

precipitation dataset that captured the spatial heterogeneity 

across the study watershed. Model performance was assessed 

as satisfactory by using standard statistical measures for 

prefire (2007–12) and postfire (2014–15) periods.

A basic application of the calibrated model was 

demonstrated. The calibrated model was applied by using 

data from one precipitation gage to better represent effects of 

localized, extreme storms on postfire hydrologic response. The 

calibrated models for prefire and postfire conditions simulated 

streamflows with greater consistency for the corresponding 

(prefire or postfire) period of hydrographic record. Analysis of 

sources of streamflow contributions indicated that the effect 

of fire on streamflow was primarily due to the shift from 

streamflow dominated by base flow prior to fire to streamflow 

dominated by surface runoff after fire. 

laf18-0872_fig 09

2010 2014 Prefire model 

Prefire model 

Postfire model 

Postfire model 

1.76 

3.52 

5.28 

0 

7.04 

Annual total streamflow component contributions, in inches 

Year

Components to streamflow 

Surface runoff to stream 

Interflow to stream 

Groundwater to stream

EXPLANATION

Figure 9. Annual total streamflow component contributions for prefire (2010) 

and postfire (2014) calibrated Precipitation-Runoff Modeling System models 

(May through October).
References Cited 23

References Cited

Archfield, S.A., Kennen, J.G., Carlisle, D.M., and Wolock, 

D.M., 2014, An objective and parsimonious approach for

classifying nature flow regimes at a continental scale: River

Research Applications, v. 30, p. 1166–1183.

Beven, K.J., 2002, Towards a coherent philosophy for 

environmental modeling—Proceedings of the Royal Society 

A: Mathematical, Physical and Engineering Sciences, 

v. 458, no. 2026, p. 2465–2484. [Also available at https://

doi.org/10.1098/rspa.2002.0986.]

Cerda, A., and Doerr, S.H., 2005, The influence of vegetation 

recovery on soil hydrology and erodibility following 

fire—An eleven-year investigation: International Journal of 

Wildland Fire, v. 14, no. 4, p. 423–437.

Daggupati, P., Pai, N., Ale, S., Douglas-Mankin, K.R., 

Zeckoski, R.W., Jeong, J., Parajuli, P.B., Saraswat, D., 

and Youssef, M.A., 2015, A recommended calibration and 

validation strategy for hydrologic and water quality models: 

Transactions of the ASABE, v. 58, no. 6, p. 1705–1719. 

[Also available at https://doi.org/10.13031/trans.58.10712.]

Darr, M.J., Rattray, G.W., McCoy, K.J., and Durall, R.A., 

2014, Hydrogeology, water resources, and water budget of 

the upper Rio Hondo Basin, Lincoln County, New Mexico, 

2010: U.S. Geological Survey Scientific Investigations 

Report 2014–5153, 72 p.

Doerr, S.H., and Cerda, A., 2005, Fire effects on soil 

system functioning—New insights and future challenges: 

International Journal of Wildland Fire, v. 14, no. 4, 

p. 339–342.

Doerr, S.H., Shakesby, R.A., and MacDonald, L.H., 2009, 

Soil water repellency—A key factor in post-fire erosion, in

Cerda, A., and Robichaud, P.R., eds., Fire effects on soils 

and restoration strategies: Boca Raton, Fla., CRC Press. 

Gleeson, T., Smith, L., Moosdorf, N., Hartmann, J., Dürr, 

H.H., Manning, A.H., van Beek, L.P.H., and Jellinek,

A.M., 2011, Mapping permeability over the surface of the

Earth: Geophysical Research Letters, v. 38, L02401. [Also

available at https://doi.org/10.1029/2010GL045565.]

Harmel, R.D., Baffaut, C., and Douglas-Mankin, K.R., 2018, 

Review and development of ASABE Engineering Practice 

621: Guidelines for calibrating, validating, and evaluating 

hydrologic and water quality models: Transactions of the 

ASABE, v. 61, no. 4, p. 1393–1401. [Also available at 

https://doi.org/10.13031/trans.12806.] 

Harmel, R.D., Smith, D.R., King, K.W., and Slade, R.M., 

2009, Estimating storm discharge and water quality data 

uncertainty—A software tool for monitoring and modeling 

applications: Environmental Modelling and Software, v. 24, 

p. 832–842. [Also available at https://doi.org/10.1016/j.

envsoft.2008.12.006.]

Harmel, R.D., Smith, P.K., Migliaccio, K.W., Chaubey, 

I., Douglas-Mankin, K.R., Benham, B., Shukla, S., 

Muñoz-Carpena, R., and Robson, B.J., 2014, Evaluating, 

interpreting, and communicating performance of 

hydrologic/water quality models considering intended 

use—A review and recommendations: Environmental 

Modelling and Software, v. 57, p. 40–51. [Also available at 

https://doi.org/10.1016/j.envsoft.2014.02.013.] 

Hay, L.E., Clark, M.P., Pagowski, M., Leavesley, G.H., 

and Gutowski, W.J., Jr., 2006, One-way coupling of an 

atmospheric and a hydrologic model in Colorado: Journal 

of Hydrometeorology, v. 7, p. 569–589. [Also available at 

https://doi.org/10.1175/JHM512.1.]

Hay, L.E., Leavesley, G.H., Clark, M.P., Markstrom, S.L., 

Viger, R.J., and Umemoto, M., 2007, Step wise, multiple 

objective calibration of a hydrologic model for a snowmelt 

dominated basin: Journal of the American Water Resources 

Association, v. 42, no. 4, p. 877–890. [Also available at 

https://doi.org/10.1111/j.1752-1688.2006.tb04501.x.]

Hay, L.E., and Umemoto, M., 2006, Multiple-objective 

stepwise calibration using Luca: U.S. Geological Survey 

Open-File Report 2006–1323, 25 p. [Also available at 

https://pubs.er.usgs.gov/publication/ofr20061323.]

Homer, C., Dewitz, J., Fry, J., Coan, M., Hossain, N., 

Larson, C., Herold, N., McKerrow, A., VanDriel, J.N., 

and Wickham, J., 2007, Completion of the 2001 National 

Land Cover Database for the conterminous United States: 

Photogrammetric Engineering and Remote Sensing, v. 73, 

no. 4, p. 337–341.

Kirchner, J.W., 2006, Getting the right answers for the right 

reasons—Linking measurements, analyses, and models 

to advance the science of hydrology: Water Resources 

Research, v. 42, no. 3, p. 1–5. [Also available at https://doi.

org/10.1029/2005WR004362.]

Konrad, C.P., 2004, Simulated water-management alternatives 

using the Modular Modeling System for the Methow River 

Basin, Washington: U.S. Geological Survey Open-File 

Report 2004–1051, 12 p. 

Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, 

F., 2006, World map of the Köppen-Geiger climate 

classification updated: Meteorologische Zeitschrift, 

v. 15, no. 3, p. 259–263. [Also available at https://doi.

org/10.1127/0941-2948/2006/0130.]

Leavesley, G.H., Lichty, R.W., Troutman, B.M., and Saindon, 

L.G., 1983, Precipitation-Runoff Modeling System—

User’s manual: U.S. Geological Survey Water-Resources

Investigations Report 83–4238, 207 p. [Also available at

https://pubs.er.usgs.gov/publication/wri834238.]

Liu, Y., Stanturf, J., and Goodrick, S., 2010, Trends in global 

wildfire potential in a changing climate: Forest Ecology and 

Management, v. 259, no. 4, p. 685–697. [Also available at 

https://doi.org/10.1016/j.foreco.2009.09.002.]
24 Calibration of Precipitation-Runoff Modeling System

MacDonald, L., and Huffman, E., 2004, Post-fire soil water 

repellency—Persistence and soil moisture thresholds: Soil 

Science Society of America Journal, v. 68, p. 1729–1734. 

[Also available at https://doi.org/10.2136/sssaj2004.1729.]

MacDonald, L.H., and Stednick, J.D., 2003, Forests and 

water—A state-of-the-art review for Colorado: Fort Collins, 

Colorado Water Resources Research Institute, Colorado 

State University, CWRRI Completion Report No. 196.

Markstrom, S.L., Hay, L.E., and Clark, M.P., 2016, Towards 

simplification of hydrologic modeling—Identification of 

dominant processes: Hydrology and Earth System Sciences 

Discussions, (January), p. 1–33. [Also available at https://

doi.org/10.5194/hess-2015-508.]

Markstrom, S.L., Regan, S., Hay, L.E., Viger, R.J., Webb, 

R.M.T., Payn, R.A., and LaFontaine, J.H., 2015, PRMS-IV,

the Precipitation-Runoff Modeling System, version 4:

U.S. Geological Survey Techniques and Methods, book 6,

chap. B7, 158 p. [Also available at https://doi.org/10.3133/

tm6B7.]

Martin, D.A., and Moody, J.A., 2001, Comparison of soil 

infiltration rates in burned and unburned mountainous 

watersheds: Hydrological Processes, v. 15, p. 2893–2903. 

[Also available at https://doi.org/10.1002/hyp.380.]

Matherne, A.M., Myers, N.C., and McCoy, K.J., 2010, 

Hydrology of Eagle Creek Basin and effects of groundwater 

pumping on streamflow, 1969–2009: U.S. Geological 

Survey Scientific Investigations Report 2010–5205, 73 p. 

Moeser, C.D., Douglas-Mankin, K.R., Mitchell, A.C., and 

Chavarria, S.B., 2018, Model input and output for prefire 

and postfire hydrologic simulations in the upper Rio 

Hondo Basin, New Mexico using the Precipitation-Runoff 

Modeling System (PRMS): U.S. Geological Survey data 

release, https://doi.org/10.5066/F7KD1X7Q.

Moeser, D., Mazzotti, G., Helbig, N., and Jonas, T., 

2016, Representing spatial variability of forest snow—

Implementation of a new interception model: Water 

Resources Research, v. 52, no. 2, p. 1208–1226. [Also 

available at https://doi.org/10.1002/2015WR017961.]

Moeser, D., Morsdorf, F., and Jonas, T., 2015a, Novel forest 

structure metrics from airborne LiDAR data for improved 

snow interception estimation: Agricultural and Forest 

Meteorology, v. 208, p. 40–49. [Also available at https://doi.

org/10.1016/j.agrformet.2015.04.013.]

Moeser, D., Stähli, M., and Jonas, T., 2015b, Improved snow 

interception modeling using canopy parameters derived 

from airborne LiDAR data: Water Resources Research, 

v. 51, no. 7, p. 5041–5051. [Also available at https://doi.

org/10.1002/2014WR016724.]

Moody, J.A., and Martin, D.A., 2001, Hydrologic and 

sedimentologic response of two burned watersheds in 

Colorado: U.S. Geological Survey Water-Resources 

Investigations Report 01–4122, 142 p.

Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Binger, R.L., 

Harmel, R.D., and Veith, T.L., 2007, Model evaluation 

guidelines for systematic quantification of accuracy in 

watershed simulations: Transactions of the ASABE, v. 50, 

no. 3, p. 885–900. 

Moriasi, D.N., Gitau, M.W., Pai, N., and Daggupati, P., 

2015a, Hydrologic and water quality models—Performance 

measures and evaluation criteria: Transactions of the 

ASABE, v. 58, no. 6, p. 1763–1785. [Also available at 

https://doi.org/10.13031/trans.58.10715.]

Moriasi, D.N., Zeckoski, R.W., Arnold, J.G., Baffaut, C., 

Malone, R.W., Daggupati, P., Guzman, J.A., Saraswat, 

D., Yuan, Y., Wilson, B.N., Shirmohammadi, A., and 

Douglas-Mankin, K.R., 2015b, Hydrologic and water 

quality models—Key calibration and validation topics: 

Transactions of the ASABE, v. 58, no. 6, p. 1609–1618. 

[Also available at https://doi.org/10.13031/trans.58.11075.]

Natural Resources Conservation Service, U.S. Department 

of Agriculture, 2012, Soil Survey Geographic (SSURGO) 

Database: Natural Resources Conservation Service website, 

accessed July 10, 2013, at https://sdmdataaccess.sc.egov.

usda.gov.

Regan, R.S., Markstrom, S.L., Hay, L.E., Viger, R.J., 

Norton, P.A., Driscoll, J.M., and LaFontaine, J.H., 2018, 

Description of the National Hydrologic Model for use 

with the Precipitation-Runoff Modeling System (PRMS): 

U.S. Geological Survey Techniques and Methods, book 

6, chap. B9, 38 p., accessed June 10, 2016, at https://doi.

org/10.3133/tm6B9.

Rubel, F., Brugger, K., Haslinger, K., and Auer, I., 2017, The 

climate of the European Alps—Shift of very high resolution 

Köppen-Geiger climate zones 1800–2100: Meteorologische 

Zeitschrift, v. 26, no. 2, p. 115–125, accessed May 2017 at 

http://koeppen-geiger.vu-wien.ac.at/.

Ryan, K.C., and Opperman, T.S., 2013, LANDFIRE—A 

national vegetation/fuels data base for use in fuels 

treatment, restoration, and suppression planning: Forest 

Ecology and Management, v. 294, p. 208–216.

Saraswat, D., Frankenberger, J.R., Pai, N., Ale, S., Daggupati, 

P., Douglas-Mankin, K.R., and Youssef, M.A., 2015, 

Hydrologic and water quality models—Documentation and 

reporting procedures for calibration, validation, and use: 

Transactions of the ASABE, v. 58, no. 6, p. 1787–1797. 

[Also available at https://doi.org/10.13031/trans.58.10707.]
References Cited 25

Sauer, V.B., and Meyer, R.W., 1992, Determination of error in 

individual discharge measurements: U.S. Geological Survey 

Open-File Report 92–144, 21 p.

Sherson, L.R., and Rice, S.E., 2015, Water resources during 

drought conditions and postfire water quality in the upper 

Rio Hondo Basin, Lincoln County, New Mexico, 2010–13: 

U.S. Geological Survey Scientific Investigations Report 

2015–5086, 56 p., https://doi.org/10.3133/sir20155086. 

Snyder, J., Banks, A., Brown, P., and Lovely, C., 2012, 2012 

Little Bear Fire, BAER hydrology report: U.S. Department 

of Agriculture, Forest Service, Southwestern Region, 

Lincoln National Forest, 20 p.

Terranova, O., Antronico, L., Coscarelli, R., and Iaquinta, 

P., 2009, Soil erosion risk scenarios in the Mediterranean 

environment using RUSLE and GIS—An application model 

for Calabria (southern Italy): Geomorphology, v. 112, 

p. 228–245. [Also available at https://doi.org/10.1016/j.

geomorph.2009.06.009.]

Thornton, P.E., Thornton, M.M., Mayer, B.W., Wei, Y., 

Devarakonda, R., Vose, R.S., and Cook, R.B., 2016, 

Daymet: Daily surface weather data on a 1-km grid for 

North America, version 3: Oak Ridge, Tenn., ORNL 

DAAC accessed June 10, 2016, at https://doi.org/10.3334/

ORNLDAAC/1328.

Tillery, A.C., and Matherne, A.M., 2013, Postwildfire 

debris-flow hazard assessment of the area burned by the 

2012 Little Bear Fire, south-central New Mexico: U.S. 

Geological Survey Open-File Report 2013–1108, 15 p., 

3 pls., accessed June 10, 2016, at http://pubs.usgs.gov/

of/2013/1108/. 

Toney, C., Shaw, J.D., and Nelson, M.D., 2009, A stem-map 

model for predicting tree canopy cover of Forest Inventory 

and Analysis (FIA) plots, in McWilliams, W., Moisen, G., 

and Czaplewski, R., eds., Forest Inventory and Analysis 

(FIA) Symposium 2008: October 21–23, 2008, Park City, 

Utah: Fort Collins, Colo., U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station, Proc. 

RMRS-P-56CD, 19 p.

Turnipseed, D.P., and Sauer, V.B., 2010, Discharge 

measurements at gaging stations: U.S. Geological Survey 

Techniques and Methods, book 3, chap. A8, 87 p. (Also 

available at https://pubs.usgs.gov/tm/tm3-a8/.)

U.S. Geological Survey, 2013, LANDFIRE existing vegetation 

type layer: U.S. Geological Survey, accessed November 

2016 at https://landfire.cr.usgs.gov/distmeta/servlet/gov.

usgs.edc.MetaBuilder?TYPE=HTML&DATASET=FBE.

U.S. Geological Survey, 2018a, GeoMAC—Geospatial MultiAgency Coordination Wildland Fire Support: GeoMAC 

Wildland Fire Support and U.S. Geological Survey website, 

accessed January 2018 at https://rmgsc.cr.usgs.gov/

outgoing/GeoMAC/.

U.S. Geological Survey, 2018b, USGS water data for the 

Nation: U.S. Geological Survey National Water Information 

System database, accessed January 2018 at https://doi.

org/10.5066/F7P55KJN.

Vieira, D., Fernández, C., Vega, J., and Keizer, J., 2015, Does 

soil burn severity affect the post-fire runoff and interrill 

erosion response? A review based on meta-analysis of 

field rainfall simulation data: Journal of Hydrology, v. 523, 

p. 452–464. [Also available at https://doi.org/10.1016/j.

jhydrol.2015.01.071.]

Viger, R.J., 2014, Preliminary spatial parameters for PRMS 

based on the Geospatial Fabric, NLCD2001 and SSURGO: 

U.S. Geological Survey, accessed June 10, 2016, at http://

dx.doi.org/10.5066/F7WM1BF7.

Viger, R.J., and Bock, A., 2014, GIS features of the Geospatial 

Fabric for National Hydrologic Modeling: U.S. Geological 

Survey, accessed June 10, 2016, at http://dx.doi.org/

doi:10.5066/F7542KMD.

Western Regional Climate Center, [n.d.], Cloudcroft, New 

Mexico (291931) period of record monthly climate 

summary, September 1, 1987, to June 10, 2016, accessed 

June 10, 2017, at https://wrcc.dri.edu/cgi-bin/cliMAIN.

pl?nm1931. 

Wisner, R.H., 2004, Upper Rio Hondo Watershed Restoration 

Action Strategy, 2d ed.: The Upper Hondo Watershed 

Coalition, Ruidoso River Association, N. Mex., accessed 

June 10, 2016, at https://www.env.nm.gov/swqb/wps/

WRAS/Upper_Hondo_13060008_WRAS_Apr_2004.pdf.

Yazzie, K., and Chang, H., 2017, Watershed response to 

climate change and fire-burns in the Upper Umatilla River 

Basin, USA: Climate, v. 5, no. 1, p. 7. [Also available at 

https://doi.org/10.3390/cli5010007.]

Yuan, Y., Khare, Y., Wang, X., Parajuli, P.B., Kisekka, I., and 

Finsterle, S., 2015, Hydrologic and water quality models—

Sensitivity: Transactions of the ASABE, v. 58, no. 6, 

p. 1721–1744. [Also available at https://doi.org/10.13031/

trans.58.10611.]

Zeckoski, R.W., Smolen, M.D., Moriasi, D.N., Frankenberger, 

J.R., and Feyereisen, G.W., 2015, Hydrologic and water

quality terminology as applied to modeling: Transactions of

the ASABE, v. 58, no. 6, p. 1619–1635. [Also available at

https://doi.org/10.13031/trans.58.10713.]
For more information about this publication, contact 

Director, New Mexico Water Science Center

U.S. Geological Survey

6700 Edith Blvd NE 

Albuquerque, New Mexico 87113

For additional information, visit

https://www.usgs.gov/centers/nm-water

Publishing support provided by

Lafayette Publishing Service Center

Douglas-Mankin and Moeser—Calibration of Precipitation-Runoff Modeling System—Scientific Investigations Report 2019–5022

ISSN 2328-0328 (online)

https://doi.org/10.3133/sir20195022