Depressional runoff cascade networks of the Des Moines Lobe of Iowa

In this study, we develop a general mathematical framework and algorithm for routing cumulative precipitation excess through depressional fill–spill cascade networks in a landscape using only information about depression morphology, local contributing areas, and potential overland flow pathways. The framework also allows for the classification of depressions according to their landscape position within a network, and calculation of precipitation- and non-precipitation-dependent network properties, including measures of network complexity and runoff connectivity. To demonstrate its use, we applied our framework to the 167,287 drained depressions of the Des Moines Lobe of Iowa, a sub-region of the Prairie Pothole Region of North America, over a large range of historically observed precipitation amounts for scenarios both neglecting and incorporating infiltration in runoff generation. Our results show that 85.3% of depressions in this region form 18,851 unique depressional runoff cascade networks, with the remainder being disjunct features. Most of the properties of the region's networks appear to conform to either a truncated power-law or lognormal distribution. For a given rainfall amount, surface runoff connectivity between depressions within networks, and between networks and off-network areas, is controlled primarily by available aggregate depressional volumetric storage and contributing area, and to a lesser degree, network complexity.     

Forecasting the Probability of Future Groundwater Levels Declining Below Specified Low Thresholds in the Conterminous U.S.

We present a logistic regression approach for forecasting the probability of future groundwater levels declining or maintaining below specific groundwater‐level thresholds. We tested our approach on 102 groundwater wells in different climatic regions and aquifers of the United States that are part of the U.S. Geological Survey Groundwater Climate Response Network. We evaluated the importance of current groundwater levels, precipitation, streamflow, seasonal variability, Palmer Drought Severity Index, and atmosphere/ocean indices for developing the logistic regression equations. Several diagnostics of model fit were used to evaluate the regression equations, including testing of autocorrelation of residuals, goodness‐of‐fit metrics, and bootstrap validation testing. The probabilistic predictions were most successful at wells with high persistence (low month‐to‐month variability) in their groundwater records and at wells where the groundwater level remained below the defined low threshold for sustained periods (generally three months or longer). The model fit was weakest at wells with strong seasonal variability in levels and with shorter duration low‐threshold events. We identified challenges in deriving probabilistic‐forecasting models and possible approaches for addressing those challenges.     

Modeling Changes to Streamflow,Sediment, and Nutrient Loading from Land Use Changes Due to Potential Natural Gas Development

Natural gas development using hydraulic fracturing has many potential environmental impacts, but among the most certain is the land disturbance required to build the well pads and other infrastructure required to drill and extract the gas. We used the Soil and Water Assessment Tool (SWAT) model to investigate how natural gas development could impact streamflow and sediment, total nitrogen (TN), and total phosphorous (TP) loadings in the upper Delaware River Basin (DRB), a relatively undeveloped watershed of 7,950 km² that lies above the Marcellus Shale formation. If gas development was permitted, our projections show the DRB could experience development of over 600 well pads to extract natural gas at build out, which, with supporting infrastructure (roads, gathering pipelines), could convert over 5,000 ha from existing land uses in the study area. In subbasins with development activity we found sediment, TN, and TP yields could increase by an average of 15, 0.08, and 0.03 kg/ha/yr, respectively (an increase of 2, 3, and 15%, respectively) for each one percent of subbasin land area converted into natural gas infrastructure. At the study area outlet on the Delaware River at Port Jervis, New York, we found increases in the annual average streamflow and sediment, nitrogen, and phosphorus loads of up to 0.01, 0.2, 0.2, and 1%, respectively, for a rapid development year, and 0.08, 1.3, 2.0, and 11%, respectively, for the full development scenario. Editor's note : This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

On the Role of Spatial,Temporal, and Climatic Forces on Stream Sediment Loading from Rural and Urban Ecosystems

In this study, we characterize the greatest sediment loading events by their sediment delivery behavior; dominant climate, watershed, and antecedent conditions; and their seasonal distribution for rural and urban land uses. The study area is Paradise Creek Watershed, a mixed land use watershed in northern Idaho dominated by saturation excess processes in the upstream rural area and infiltration excess in the downstream urban area. We analyzed 12 years of continuous streamflow, precipitation, and watershed data at two monitoring stations. We identified 137 sediment loading events in the upstream rural section of the watershed and 191 events in the downstream urban section. During the majority of these events conditions were transport limited and the sediment flush occurred early in the event, generally in the first 20% of elapsed event time. Statistical analysis including two dozen explanatory variables showed peak discharge, event duration, and antecedent baseflow explained most of the variation in event sediment load at both stations and for the watershed as a whole (R² = 0.73‐0.78). In the rural area, saturated soils combined with spring snowmelt in March led to the greatest loading events. The urban area load contribution peaked in January, which could be a re‐suspension of streambed sediments from the previous water year. Throughout the study period, one event contributed, on average, 33% of the annual sediment load but only accounted for 2% of the time in a year.     

Estimating Root Zone Soil Moisture at Continental Scale Using Neural Networks

This study investigates the feasibility of artificial neural networks (ANNs) to retrieve root zone soil moisture (RZSM) at the depths of 20 cm (SM20) and 50 cm (SM50) at a continental scale, using surface information. To train the ANNs to capture interactions between land surface and various climatic patterns, data of 557 stations over the continental United States were collected. A sensitivity analysis revealed that the ANNs were able to identify input variables that directly affect the water and energy balance in root zone. The data important for RZSM retrieval in a large area included soil texture, surface soil moisture, and the cumulative values of air temperature, surface soil temperature, rainfall, and snowfall. The results showed that the ANNs had high skill in retrieving SM20 with a correlation coefficient above 0.7 in most cases, but were less effective at estimating SM50. The comparison of the ANNs showed that using soil texture data improved the model performance, especially for the estimation of SM50. It was demonstrated that the ANNs had high flexibility for applications in different climatic regions. The method was used to generate RZSM in North America using Soil Moisture and Ocean Salinity (SMOS) soil moisture data, and achieved a spatial soil moisture pattern comparable to that of Global Land Data Assimilation System Noah model with comparable performance to the SMOS surface soil moisture retrievals. The models can be efficient alternatives to assimilate remote sensing soil moisture data for shallow RZSM retrieval.     

Evaluating Flow Metric‐Based Stream Classification Systems to Support the Determination of Ecological Flows in North Carolina

Hydroecological classification systems are typically based on an assemblage of streamflow metrics and seek to divide streams into ecologically relevant classes. Assignment of streams to classes is suggested as an initial step in the process of establishing ecological flow standards. We used two distinct hydroecological river classification systems available within North Carolina to evaluate the ability of a hydrologic model to assign the same classes as those determined by observed streamflows and to assess the transferability of such systems to ungaged streams. Class assignments were examined by rate of overall matches, rate of class matches, spatial variability in matches, and time period used in class assignment. The findings of this study indicate assignments of stream class: (1) are inconsistent among different classification systems; (2) differ between observed and modeled data; and (3) are sensitive to the period of record within observed data. One clear source of inconsistency/sensitivity in class assignments lies with the use of threshold values for metrics that distinguish stream classes, such that even small changes in metric values can result in different class assignments. Because these two hydroecological classification systems are representative of other classification systems that rely on quantitative decision thresholds, it can be surmised that the use of such systems based on stream flow metrics is not a reliable approach for guiding ecological flow determinations.     

Continental‐Scale River Flow Modeling of the Mississippi River Basin Using High‐Resolution NHDPlus Dataset

As a key component of the National Flood Interoperability Experiment (NFIE), this article presents the continental scale river flow modeling of the Mississippi River Basin (MRB), using high‐resolution river data from NHDPlus. The Routing Application for Parallel computatIon of Discharge (RAPID) was applied to the MRB with more than 1.2 million river reaches for a 10‐year study (2005‐2014). Runoff data from the Variable Infiltration Capacity (VIC) model was used as input to RAPID. This article investigates the effect of topography on RAPID performance, the differences between the VIC‐RAPID streamflow simulations in the HUC‐2 regions of the MRB, and the impact of major dams on the streamflow simulations. The model performance improved when initial parameter values, especially the Muskingum K parameter, were estimated by taking topography into account. The statistical summary indicates the RAPID model performs better in the Ohio and Tennessee Regions and the Upper and Lower Mississippi River Regions in comparison to the western part of the MRB, due to the better performance of the VIC model. The model accuracy also increases when lakes and reservoirs are considered in the modeling framework. In general, results show the VIC‐RAPID streamflow simulation is satisfactory at the continental scale of the MRB.     

Spatiotemporal Variability of Snow Depletion Curves Derived from SNODAS for the Conterminous United States, 2004‐2013

Assessment of water resources at a national scale is critical for understanding their vulnerability to future change in policy and climate. Representation of the spatiotemporal variability in snowmelt processes in continental‐scale hydrologic models is critical for assessment of water resource response to continued climate change. Continental‐extent hydrologic models such as the U.S. Geological Survey National Hydrologic Model (NHM) represent snowmelt processes through the application of snow depletion curves (SDCs). SDCs relate normalized snow water equivalent (SWE) to normalized snow covered area (SCA) over a snowmelt season for a given modeling unit. SDCs were derived using output from the operational Snow Data Assimilation System (SNODAS) snow model as daily 1‐km gridded SWE over the conterminous United States. Daily SNODAS output were aggregated to a predefined watershed‐scale geospatial fabric and used to also calculate SCA from October 1, 2004 to September 30, 2013. The spatiotemporal variability in SNODAS output at the watershed scale was evaluated through the spatial distribution of the median and standard deviation for the time period. Representative SDCs for each watershed‐scale modeling unit over the conterminous United States (n = 54,104) were selected using a consistent methodology and used to create categories of snowmelt based on SDC shape. The relation of SDC categories to the topographic and climatic variables allow for national‐scale categorization of snowmelt processes.     

Association between plant species diversity and edaphic factors in the lower reaches of the Heihe River,northwestern China

The lower reaches of the Heihe River, in northwestern China, is characterised by unique local edaphic conditions that have influenced the development of local desert riparian forests. This study examines the variations in spatial variation patterns to reveal the relationships between plant species diversity and soil moisture/salinity/texture gradients at different soil depths, providing insights into the management and restoration of vegetation in ecosystems in the study area. The species–environmental relationships are investigated by redundancy analysis based on the plant species diversity matrix and the edaphic gradient matrix. A survey of 61 sampling plots identified 37 plant species in the study area. The distribution pattern of the plant species diversity are mainly affected by soil moisture, soil salinity, and soil texture at different soil depths. These edaphic factors are able to explain 98.47% of the total variation in the analysed vegetation dataset. Soil moisture, salinity, and texture content vary in terms of both the soil depths and the vegetation types in the study area. The plant community Class IV, xeric shrub, has the lowest soil water content among different vegetation types. The surface soil salinity differs for different plants and follows the order: Sophora alopecurides?>?Tamarix chinensis?>?Populus euphratica.     

Changes in desert steppe soil culturable bacteria from northwestern China and correlation with physicochemical parameters

Seasonal variations effects on desert steppe soil culturable bacteria in northwestern China and physicochemical parameters were investigated. Soil temperature ranged from 5.37 to 23.73 °C. Moisture content varied from 3.22% to 7.62%. The pH was between 7.98 and 8.72. Soil nutrients were altered in different seasons. Total potassium (K) remained the same, but available K levels ranged from 101.29 to 227.55 mg/kg. The culturable bacterial counts in January, April, July and October were 0.13 × 10⁷, 4.09 × 10⁷, 5.33 × 10⁷ and 1.8 × 10⁷ colony forming units (cfu)/g, respectively. Bacterial populations were most abundant in surface layers and declined with increase of soil depth. Seventy-two bacterial strains were isolated and classified according to 27 genera of 5 phyla groups: Firmicutes, Actinobacteria, Alphaproteobacteria, Gammaproteobacteria and Bacteroidates. The bacterial diversities varied according to seasons. In spring, only Firmicutes and Actinobacteria were isolated. In summer, Firmicutes, Actinobacteria and Bacteroidetes were detected with Firmicutes the dominant species. In autumn and winter, Firmicutes, Actinobacteria, α-Proteobacteria and γ-Proteobacteria predominated. Our results indicated that climatic variations, vegetation coverage and soil physicochemical parameters are critical factors for maintaining culturable bacterial populations and diversity in the desert steppe. Soil moisture content and temperature exerted marked influence on bacterial quantities and diversities.