Reynolds Creek Experimental Watershed Research Articles

Seasonality and evaporation of water resources in Reynolds Creek Experimental Watershed and Critical Zone Observatory, Southwestern Idaho, USA

AbstractThe Reynolds Creek Experimental Watershed (RCEW) and Critical Zone Observatory (CZO), located south of the western Snake River Plain in the Intermountain West of the United States, is the site of over 60 years of research aimed at understanding integrated earth processes in a semi‐arid climate to aid sustainable use of environmental resources. Meteoric water lines (MWLs) are used to interpret hydrologic processes, though equilibrium and nonequilibrium processes affect the linear function and can reveal seasonal and climatological effects, necessitating the development of local meteoric water lines (LMWLs). At RCEW‐CZO, an RCEW LMWL was developed using non‐volume‐weighted, orthogonal regression with assumed error in both predictor and response variables from several years of precipitation (2015, 2017, 2019, 2020, and 2021) primarily at three different elevations (1203, 1585, and 2043 m). As most precipitation is evaporated or intercepted by vegetation in the driest months, an RCEW LMWL for groundwater recharge (RCEW LMWL‐GWR) was also developed using precipitation from the wettest months (November through April). The RCEW LMWL (δ2H = 7.41 × δ18O – 3.09) is different from the RCEW LMWL‐GWR (δ2H = 8.21 × δ18O + 9.95) and compares favorably to other LMWLs developed for the region and climate. Comparative surface, spring, and subsurface water datasets within the RCEW‐CZO are more similar to precipitation during the wettest months than dry months, illustrating that some semi‐arid hydrologic systems may most appropriately be compared to MWLs developed from precipitation only from the wettest season.

Controls on the presence and storage of soil inorganic carbon in a semi-arid watershed

Soil inorganic carbon (SIC) constitutes ∼40–50% of the terrestrial soil carbon and is an integral part of the global carbon cycle. Rainfall is a primary factor controlling SIC accumulation; however, the distribution and hierarchy of controls on SIC development in arid and semi-arid regions is poorly understood. The Reynolds Creek Experimental Watershed (RCEW) in southwestern Idaho is an ideal location to study factors influencing SIC because it spans a wide mean annual precipitation range (235 mm to 900 mm) along a 1,425 to 2,111 m elevation gradient and has soils derived from a wide variety of parent materials (granite, basalt, dust, and alluvium). We collected soil samples along this elevational gradient to understand local controls on SIC distributions. SIC content was quantified at 71 soil pits and/or augered cores collected between approximately 0–1 m depth or until refusal. Consistent with previous studies, we found variations in precipitation governed the presence or absence of SIC; field measurements of the top 1 m of soils confirm little or no SIC in soils receiving > 500 mm in mean annual precipitation. Below this 500 mm threshold, SIC pools varied substantially and significantly between sites. Results showed that 90% of sites (64 sites) contained less than 10 kg m−2 SIC, 7% (5 sites) contained 10–20 kg m−2, and 3% (2 sites) contain between 24 and 29 kg m−2 SIC. The total SIC within RCEW was estimated at ∼5.17 × 105 Mg. After precipitation, slope consistently ranked as the second most important predictor of SIC accumulation in random forest analysis. Wind-blown dust likely contributed to SIC accumulation; prior work indicates an average dust flux rate in RCEW of about 11 ± 4.9 g m−2 year−1. This study provides an initial model predicting SIC distribution and accumulation in a shrub-dominated dryland watershed.

Understanding the effect of fire on vegetation composition and gross primary production in a semi-arid shrubland ecosystem using the Ecosystem Demography (EDv2.2) model

Abstract. Wildfires in sagebrush (Artemisia spp.)-dominated semi-arid ecosystems in the western United States have increased dramatically in frequency and severity in the last few decades. Severe wildfires often lead to the loss of native sagebrush communities and change the biogeochemical conditions which make it difficult for sagebrush to regenerate. Invasion of cheatgrass (Bromus tectorum) accentuates the problem by making the ecosystem more susceptible to frequent burns. Managers have implemented several techniques to cope with the cheatgrass–fire cycle, ranging from controlling undesirable fire effects by removing fuel loads either mechanically or via prescribed burns to seeding the fire-affected areas with shrubs and native perennial forbs. There have been a number of studies at local scales to understand the direct impacts of wildfire on vegetation; however there is a larger gap in understanding these impacts at broad spatial and temporal scales. This need highlights the importance of dynamic global vegetation models (DGVMs) and remote sensing. In this study, we explored the influence of fire on vegetation composition and gross primary production (GPP) in the sagebrush ecosystem using the Ecosystem Demography (EDv2.2) model, a dynamic global vegetation model. We selected the Reynolds Creek Experimental Watershed (RCEW) to run our simulation study, an intensively monitored sagebrush-dominated ecosystem in the northern Great Basin. We ran point-based simulations at four existing flux tower sites in the study area for a total of 150 years after turning on the fire module in the 25th year. Results suggest dominance of shrubs in a non-fire scenario; however under the fire scenario we observed contrasting phases of high and low shrub density and C3 grass growth. Regional model simulations showed a gradual decline in GPP for fire-introduced areas through the initial couple of years instead of killing all the vegetation in the affected area in the first year itself. We also compared the results from EDv2.2 with satellite-derived GPP estimates for the areas in the RCEW burned by a wildfire in 2015 (Soda Fire). We observed moderate pixel-level correlations between maps of post-fire recovery EDv2.2 GPP and MODIS-derived GPP. This study contributes to understanding the application of ecosystem models to investigate temporal dynamics of vegetation under alternative fire regimes and post-fire ecosystem restoration.

Examining Interactions Between and Among Predictors of Net Ecosystem Exchange: A Machine Learning Approach in a Semi-arid Landscape

Net ecosystem exchange (NEE) is an essential climate indicator of the direction and magnitude of carbon dioxide (CO2) transfer between land surfaces and the atmosphere. Improved estimates of NEE can serve to better constrain spatiotemporal characteristics of terrestrial carbon fluxes, improve verification of land models, and advance monitoring of Earth’s terrestrial ecosystems. Spatiotemporal NEE information developed by combining ground-based flux tower observations and spatiotemporal remote sensing datasets are of potential value in benchmarking land models. We apply a machine learning approach (Random Forest (RF)) to develop spatiotemporally varying NEE estimates using observations from a flux tower and several variables that can potentially be retrieved from satellite data and are related to ecosystem dynamics. Specific variables in model development include a mixture of remotely sensed (fraction of photosynthetically active radiation (fPAR), Leaf Area Index (LAI)) and ground-based data (soil moisture, downward solar radiation, precipitation and mean air temperature) in a complex landscape of the Reynolds Creek Experimental Watershed (RCEW) in southwest Idaho, USA. Predicted results show good agreement with the observed data for the NEE (r2 = 0.87). We then validate the temporal pattern of the NEE generated by the RF model for two independent years at the two sites not used in the development of the model. The model development process revealed that the most important predictors include LAI, downward solar radiation, and soil moisture. This work provides a demonstration of the potential power of machine learning methods for combining a variety of observational datasets to create spatiotemporally extensive datasets for land model verification and benchmarking.

Reynolds Creek Experimental Watershed and Critical Zone Observatory

Core Ideas Long‐term watersheds may provide a coherent description of climate change impacts. Observatories provide a stable experimental platform for process model development. Local environmental gradients support high spatial resolution hydrological modeling. Long‐term hydrologic data support large scale biogeochemical process integration. Large‐scale, high spatial resolution operational hydrologic modeling is feasible. The Reynolds Creek Experimental Watershed (RCEW) was established in 1960 as an “outdoor hydrological laboratory” to investigate hydrological processes of interest in the interior northwestern part of the United States. Initial emphasis was on installing and testing instrumentation and data collection and dissemination. The initial instrumentation network sampled the climatic gradient within the 239‐km2 watershed and focused on specific subwatersheds for intensive instrumentation. This network has expanded and supported ad hoc research and provides a stable platform for the development of long‐term programs supporting research and model development in snow hydrology, climate change, water and energy balance, land management, carbon cycling, and critical zone hydrology. Recently, the challenge taken up at the RCEW is to integrate different processes over space for applications to larger areas outside the watershed. The presence of steep local environmental gradients associated with topography in addition to more gradual, elevational gradients requires high‐resolution modeling. The snow hydrology program has demonstrated the potential for high‐resolution, process‐based modeling across large landscapes. The direct linkage of biogeochemical processes with hydrological processes ultimately requires a multidisciplinary approach that has been adopted at the RCEW since inclusion in the Critical Zone Observatory program. We think that coupling of these processes will lead to a better understanding and management of natural resources on the landscape.

Soil Temperature Variability in Complex Terrain Measured Using Fiber‐Optic Distributed Temperature Sensing

Core Ideas Soil temperature variability within land surface units is low, with a SD of <1.5°C. Land surface effects are greater than a 911‐m elevation change. Fiber‐optic DTS provides a unique look at soil temperature variability. Soil temperature (Ts) exerts critical controls on hydrologic and biogeochemical processes, but the magnitude and nature of Ts variability in a landscape setting are rarely documented. Fiber‐optic distributed temperature sensing (DTS) systems potentially measure Ts at high density across a large extent. A fiber‐optic cable 771 m long was installed at a depth of 10 cm in contrasting landscape units (LUs) defined by vegetative cover at Upper Sheep Creek in the Reynolds Creek Experimental Watershed (RCEW) and Critical Zone Observatory in Idaho. The purpose was to evaluate the applicability of DTS in remote settings and to characterize Ts variability in complex terrain. Measurement accuracy was similar to other field instruments (±0.4°C), and Ts changes of approximately 0.05°C at a monitoring spatial scale of 1 m were resolved with occasional calibration and an ambient temperature range of 50°C. Differences in solar inputs among LUs were strongly modified by surface conditions. During spatially continuous snow cover, Ts was practically homogeneous across LUs. In the absence of snow cover, daily average Ts was highly variable among LUs due to variations in vegetative cover, with a standard deviation (SD) greater than 5°C, and relatively uniform (SD < 1.5°C) within LUs. Mean annual soil temperature differences among LUs of 5.2°C was greater than those of 4.4°C associated with a 910‐m elevation difference within the RCEW. In this environment, effective Ts simulation requires representation of relatively small‐scale (<20 m) LUs due to the deterministic spatial variability of Ts.

An evaluation of methods for determining during-storm precipitation phase and the rain/snow transition elevation at the surface in a mountain basin

Determining surface precipitation phase is required to properly correct precipitation gage data for wind effects, to determine the hydrologic response to a precipitation event, and for hydrologic modeling when rain will be treated differently from snow. In this paper we present a comparison of several methods for determining precipitation phase using 12years of hourly precipitation, weather and snow data from a long-term measurement site at Reynolds Mountain East (RME), a headwater catchment within the Reynolds Creek Experimental Watershed (RCEW), in the Owyhee Mountains of Idaho, USA. Methods are based on thresholds of (1) air temperature (Ta) at 0°C, (2) dual Ta threshold, −1 to 3°C, (3) dewpoint temperature (Td) at 0°C, and (4) wet bulb temperature (Tw) at 0°C. The comparison shows that at the RME Grove site, the dual threshold approach predicts too much snow, while Ta, Td and Tw are generally similar predicting equivalent snow volumes over the 12year-period indicating that during storms the cloud level is at or close to the surface at this location. To scale up the evaluation of these methods we evaluate them across a 380m elevation range in RCEW during a large mixed-phase storm event. The event began as snow at all elevations and over the course of 4h transitioned to rain at the lowest through highest elevations. Using 15-minute measurements of precipitation, changes in snow depth (zs), Ta, Td and Tw, at seven sites through this elevation range, we found precipitation phase linked to the during-storm surface humidity. By measuring humidity along an elevation gradient during the storm we are able to track changes in Td to reliably estimate precipitation phase and effectively track the elevation of the rain/snow transition during the event.

Implications of climate-driven variability and trends for the hydrologic assessment of the Reynolds Creek Experimental Watershed, Idaho

Summary The Soil and Water Assessment Tool (SWAT) model was used to assess the implications of long-term climate trends for the hydroclimatology of the Reynolds Creek Experimental Watershed (RCEW) in the Owyhee Mountains, Idaho of the Intermountain West over a 40-year period (1967–2006). Calibration and validation of the macroscale hydrology model in this highly monitored watershed is key to address the watershed processes that are vulnerable to both natural climate variability and climate change. The model was calibrated using the streamflow data collected between 1997 and 2006 from the three nested weirs, the Reynolds Mountain East (RME), Tollgate and Outlet. For assessing the performance of the calibrated model, this study used 30 years of streamflow data for the period between 1966 and 1996. This investigation suggested that the model predicted streamflow was best at RME, and inadequate at Outlet. Simulated soil moisture was also verified using the data available from five soil moisture measurement sites. The model was able to capture the seasonal patterns of changes in soil water storage considering the differences in the spatial extent of the observed and predicted soil water storage (point measurements against the spatially averaged values for the HRU) and uncertainty associated with the soil moisture measurements due to instrument effects. Water budget partitioning during a wet (1984) water year and a dry (1987) water year were also analyzed to characterize the differences in hydrologic cycles during the extreme hydrologic conditions. Our analysis showed that in the dry water year, vegetation at the higher elevation were under water stress by the end of the water year. Contrastingly, in the wet water year only the vegetation at low and mid-elevations were under water stress whereas vegetation at the higher elevations derived substantial soil moisture for ET processes even towards the end of the growing season. To understand the effect of climate change on the hydrologic cycle, the observed and simulated streamflow were analyzed for trends in Center of Timing (CT). Earlier CT timings for the simulated and observed streamflow at RME weir was obvious thus manifesting global warming signals at the watershed scale level in the Intermountain west region. Observed streamflow at the Tollgate and Outlet weirs, where streamflow is partially affected by the agricultural diversions, showed later CT timings and these results appeared to suggest that climate impact assessment studies need to carefully distinguish the system behavior that is altered by both natural and human-induced changes.

Characterizing Times Between Storms in Mountainous Areas

Storm simulation models are sometimes used to provide input to watershed models where there are no precipitation data. However, a basic understanding of the seasonal and spatial characteristics of parameters that comprise the inputs to the models is needed, and then methods for estimating the parameters must be developed. This study is an investigation of the behavior and estimation of two parameters for storm-occurrence modeling, average time between storms (ATBS) and minimum time between storms (MDPD), and how they vary with average annual precipitation (Pann), average monthly precipitation (Pmo), and elevation (E) in mountainous terrain where snow is a major component of precipitation. Data used came from the dual-rain-gauge network in the USDA-ARS Reynolds Creek Experimental Watershed (RCEW) in southwest Idaho. Median ATBS and MDPD data show consistent trends over the RCEW with narrow variability. The exponential distribution adequately models times between storms in snowfall-predominant mountainous areas except for very long dry periods. The power equation form generally fitted the data best for regressions where there was a statistically significant regression compared with a linear equation. For four of the regressions found in this article (Pmo vs. E, ATBS vs. Pmo, ATBS vs. E, and MDPD vs. ATBS), parameters were correlated, suggesting parameter mapping may be useful in their estimation. ATBS can be estimated by using the power equation for ATBS vs. Pmo and ATBS vs. E (both inversely correlated). MDPD vs. ATBS correlations were positive, showing that as the average time between storms increases, so does the minimum time between storms. Guidance is given for estimating ATBS and MDPD for a specific location. Magnitudes of often-used fixed values of MDPD (e.g., 6 h) are too small to be representative of times between storms, and are not representative of the seasonal variability of times between storms. The results in this article for characterizing ATBS and MDPD in the mountainous 239-km2 RCEW area follow closely those found for the 225,000-km2 Colorado plains area in another study. Thus, equation forms identified in this article can suggest those that might be expected in general and may be used as guidance for estimating ATBS and MDPD in ungauged areas, including areas with snowfall as a major component of precipitation.

Long‐Term Snow Database, Reynolds Creek Experimental Watershed, Idaho, United States

Snow is the dominant form of precipitation in the Reynolds Creek Experimental Watershed (RCEW). Water from snowmelt is critical to the ecosystems and resources in RCEW because the water stored in the seasonal snow cover is the primary source of spring and summer soil moisture and streamflow. Snow water equivalent (SWE) has been measured at eight locations in RCEW every 2 weeks throughout the snow season (December 1 to June 1) for 35 water years (1962–1996). SWE was continuously monitored at one reference site for 14 water years (1983–1996). The measurement sites are described, the methods used are presented and discussed, these data are summarized, and examples of how they have been used are presented.

Long‐Term Soil Temperature Database, Reynolds Creek Experimental Watershed, Idaho, United States

We describe long‐term soil temperature data collected at the Reynolds Creek Experimental Watershed (RCEW). These data were collected for over 15 years at five locations representing different climatic regimes and soils in the RCEW. Measurements were made at several depths at each site to a depth of 180 cm in most cases. Each site is located in close proximity to a climate station. Descriptive soil profile information is also available for each site. We illustrate two aspects of the data set, the impact of snow depth on soil temperature and the interannual variability of soil temperature at a site. These data are available to the public via the U.S. Department of Agriculture, Agricultural Research Service, Northwest Watershed Research Center anonymous ftp site ftp.nwrc.ars.usda.gov.

Long‐Term Soil Water Content Database, Reynolds Creek Experimental Watershed, Idaho, United States

We describe long‐term soil water data collected at the Reynolds Creek Experimental Watershed (RCEW). Data were collected for 10–25 years at 18 sites representing different climatic regimes and soils in the RCEW. Soil profile data are also available. High correlation between neutron probe and lysimeter measurements are the basis for assessing the accuracy of neutron probe–measured changes in soil water content. These data are available to the public via the U.S. Department of Agriculture, Agricultural Research Service, Northwest Watershed Research Center anonymous ftp site ftp.nwrc.ars.usda.gov.

Geographic Database, Reynolds Creek Experimental Watershed, Idaho, United States

The Reynolds Creek Experimental Watershed (RCEW) exhibits spatial variability typical of the intermountain region. We provide a geographic database to provide continuous spatial coverage of landscape properties that may be useful for distributed hydrological modeling or other kinds of spatial analyses and to provide a spatial context for point measurements that have been part of the long‐term monitoring described in companion papers. All data are available as separate geographic information system (GIS) layers which can be selected independently according to need. The base map for all the RCEW GIS layers is a 30 m resolution digital elevation model. Data are available in either vector or raster format where appropriate via the U.S. Department of Agriculture, Agricultural Research Service, Northwest Watershed Research Center anonymous ftp site ftp.nwrc.ars.usda.gov.

Comparison of Snow Deposition, the Snow Cover Energy Balance, and Snowmelt at Two Sites in a Semiarid Mountain Basin

Abstract Significant differences in snow deposition, development of the seasonal snow cover, and the timing of melt can occur over small spatial distances because of differences in topographically controlled wind exposure and canopy cover. To capture important intrabasin hydrological processes related to heterogeneous snow cover and energy inputs, models must explicitly account for these differences. The “SNOBAL” point snow cover energy and mass balance model is used to evaluate differences in snow cover energy and mass balance at two sites in a small headwater drainage of the Reynolds Creek Experimental Watershed (RCEW) in the Owyhee Mountains of southwestern Idaho. Though these sites are separated by only 350 m, they are located in distinctly different snow cover regimes. The “ridge” site (elevation 2097 m) is located on a broad shelf on the southern ridge of RCEW, and the “grove” site (elevation 2061 m) is sheltered by topography and forest canopy in a grove of aspen and fir trees just in the lee of th...

Intermountain Plant Community Classification Using Landsat TM and SPOT HRV Data

Rangeland plant communities of the Intermountain West differ in their ecology and management requirements. Successful management of extensive areas at plant community-level resolution first requires an efficient, cost-effective means of plant community classification and mapping. We evaluated the influence of image acquisition date and satellite imaging system on the accuracy of plant community maps created from multispectral satellite imagery of Reynolds Creek Experimental Watershed (RCEW) (234 km2) in southwestern Idaho. Maps delineating 6 native and 2 non-native Intermountain plant communities were created from Landsat 5 TM and SPOT 3 HRV data using a maximum likelihood classification procedure. Map accuracy was assessed using ground reference points. Maps created from satellite data acquired during dry-down (early August) had higher overall accuracy (average = 70.5%) than from data acquired during peak growth (early June) (average = 54.4%). Overall accuracy of maps generated by Landsat (average = 60.1%) and SPOT (average = 65.5%) were statistically similar. Given their broad spatial coverages (3,600 to 31,450 km2 scene(-1), respectively), moderate resolutions (20 to 30 m pixels, respectively), and potential to provide high classification accuracies, the SPOT 3 HRV and Landsat 5 TM satellite systems were well-suited for classifying plant communities in the Reynolds Creek Watershed and similar areas of the Intermountain West. Practical procedures for plant community classification and map accuracy assessment are presented for use by natural resource managers. DOI:10.2458/azu_jrm_v54i2_clark

Intermountain plant community classification using Landsat TM and SPOT HRV data

Rangeland plant communities of the Intermountain West differ in their ecology and management requirements. Successful management of extensive areas at plant community-level resolution first requires an efficient, cost-effective means of plant community classification and mapping. We evaluated the influence of image acquisition date and satellite imaging system on the accuracy of plant community maps created from multispectral satellite imagery of Reynolds Creek Experimental Watershed (RCEW) (234 km2) in southwestern Idaho. Maps delineating 6 native and 2 non-native Intermountain plant communities were created from Landsat 5 TM and SPOT 3 HRV data using a maximum likelihood classification procedure. Map accuracy was assessed using ground reference points. Maps created from satellite data acquired during dry-down (early August) had higher overall accuracy (average = 70.5%) than from data acquired during peak growth (early June) (average = 54.4%). Overall accuracy of maps generated by Landsat (average = 60.1%) and SPOT (average = 65.5%) were statistically similar. Given their broad spatial coverages (3,600 to 31,450 km2 scene(-1), respectively), moderate resolutions (20 to 30 m pixels, respectively), and potential to provide high classification accuracies, the SPOT 3 HRV and Landsat 5 TM satellite systems were well-suited for classifying plant communities in the Reynolds Creek Watershed and similar areas of the Intermountain West. Practical procedures for plant community classification and map accuracy assessment are presented for use by natural resource managers.

Correction of Surface Roughness and Topographic Effects on Airborne SAR in Mountainous Rangeland Areas

Synthetic aperture radar (SAR) has potential use as a means of measuring soil-water content from space or high altitude airborne platforms. However, the signal from other landscape features, such as topography, vegetation, and surface roughness, may overwhelm and confound the soil moisture signal. Previous researchers tried to correct for topographic effects using incident angle (θ) as the most significant landscape variable. Those corrections were not effective because of the poor correlation between the SAR backscatter coefficient (σ 0 ) and θ. This may be attributed to surface roughness, vegetation effects, or low resolution digital elevation data. In this article, we determined the effects of vegetation, surface roughness, and topography on σ 0 . By comparing the normalized difference vegetation index (NDVI) and the SAR-based vegetation index (SBVI), it was found that the sparse vegetation cover of sagebrush in the rangeland areas of the Reynolds Creek Experimental Watershed (RCEW) does not affect L band HH polarization SAR σ 0 . Root mean square (RMS) height of surface (h), determined using L band copolarized SAR σ 0 in an empirical algorithm (Dubois et al., 1995), helped in stratifying the study area into low and high surface roughness areas. The high surface roughness areas, determined using the algorithm, were found to match with very rocky and extremely rocky areas, classified using detailed soil type information in a Geographic Information System (GIS). The performance of a θ-based correction function improved significantly after incorporating h into the model.