ESTIMATION OF STREAMFLOW,BASE FLOW,AND NITRATE‐NITROGEN LOADS IN IOWA USING MULTIPLE LINEAR REGRESSION MODELS1

Nineteen variables, including precipitation, soils and geology, land use, and basin morphologic characteristics, were evaluated to develop Iowa regression models to predict total streamflow (Q), base flow (Qb), storm flow (Qs) and base flow percentage (%Qb) in gauged and ungauged watersheds in the state. Discharge records from a set of 33 watersheds across the state for the 1980 to 2000 period were separated into Qb and Qs. Multiple linear regression found that 75.5 percent of long term average Q was explained by rainfall, sand content, and row crop percentage variables, whereas 88.5 percent of Qb was explained by these three variables plus permeability and floodplain area variables. Qs was explained by average rainfall and %Qb was a function of row crop percentage, permeability, and basin slope variables. Regional regression models developed for long term average Q and Qb were adapted to annual rainfall and showed good correlation between measured and predicted values. Combining the regression model for Q with an estimate of mean annual nitrate concentration, a map of potential nitrate loads in the state was produced. Results from this study have important implications for understanding geomorphic and land use controls on streamflow and base flow in Iowa watersheds and similar agriculture dominated watersheds in the glaciated Midwest.     

Effects of Urbanization on Flow Duration and Stream Flashiness: A Case Study of Puget Sound Streams,Western Washington,USA

The overall influence of urbanization on how flows of different frequency might change over time, while important in hydrologic design, remains imprecisely known. In this study, we investigate the effects of urbanization on flow duration curves (FDCs) and flow variability through a case study of eight watersheds that underwent different amounts of growth, in the Puget Sound region in Western Washington State, United States. We computed annual FDCs from flow records spanning 1960‐2010 and, after accounting for the effects of precipitation, we conducted statistical trend analyses on flow metrics to quantify how key FDC percentiles changed with time in response to urbanization. In the urban watersheds, the entire FDC tended to increase in magnitude of flow, especially the 95th‐99th percentile of the daily mean flow series, which increased by an average of 43%. Stream flashiness in urban watersheds was found to increase by an average of 70%. The increases in FDC magnitude and flashiness in urbanizing watersheds are most likely a result of increasing watershed imperviousness and altered hydrologic routing. Rural watersheds were found to have decreasing FDC magnitude over the same time period, which is possibly due to anthropogenic extractions of groundwater, and increasing stream flashiness, which is likely a result of reductions in base flow and increasing precipitation intensity and variability.     

Characterizing Uncertainty in Daily Streamflow Estimates at Ungauged Locations for the Massachusetts Sustainable Yield Estimator

Hydrologic characterization at ungauged locations is one of the quintessential challenges of hydrology. Beyond simulation of historical streamflows, it is similarly important to characterize the level of uncertainty in hydrologic estimates. In tandem with updates to Massachusetts Sustainable Yield Estimator, this work explores the application of global uncertainty estimates to daily streamflow simulations. Expanding on a method developed for deterministic modeling, this approach produces confidence intervals on daily streamflow developed through nonlinear spatial interpolation of daily streamflow using flow duration curves; the 95% confidence is examined. Archived cross‐validations of daily streamflows from 66 watersheds in and around Massachusetts are used to evaluate an approach to uncertainty characterization. Neighboring sites are treated as ungauged, producing relative errors that can be resampled and applied to target sites. The method, with some modification, is found to provide appropriately narrow confidence intervals that contain 95% of the observed streamflows in cross‐validation. Further characterizing uncertainty, multiday means of daily streamflow are evaluated. Working through cross‐validation in Massachusetts, two‐ to three‐month averages of daily streamflow show the best performance. These two approaches to uncertainty characterization inform how streamflow simulation produced for prediction in ungauged basins can be used for water resources management.     

Estimation of Daily Streamflow of Southeastern Coastal Plain Watersheds by Combining Estimated Magnitude and Sequence

Commonly used methods to predict streamflow at ungauged watersheds implicitly predict streamflow magnitude and temporal sequence concurrently. An alternative approach that has not been fully explored is the conceptualization of streamflow as a composite of two separable components of magnitude and sequence, where each component is estimated separately and then combined. Magnitude is modeled using the flow duration curve (FDC), whereas sequence is modeled by transferring streamflow sequence of gauged watershed(s). This study tests the applicability of the approach on watersheds ranging in size from about 25‐7,226 km² in Southeastern Coastal Plain (U.S.) with substantial surface storage of wetlands. A 19‐point regionalized FDC is developed to estimate streamflow magnitude using the three most selected variables (drainage area, hydrologic soil index, and maximum 24‐h precipitation with a recurrence interval of 100 years) by a greedy‐heuristic search process. The results of validation on four watersheds (Trent River, North Carolina: 02092500; Satilla River, Georgia: 02226500; Black River, South Carolina: 02136000; and Coosawhatchie River, South Carolina: 02176500) yielded Nash‐Sutcliffe efficiency values of 0.86‐0.98 for the predicted magnitude and 0.09‐0.84 for the predicted daily streamflow over a simulation period of 1960‐2010. The prediction accuracy of the method on two headwater watersheds at Santee Experimental Forest in coastal South Carolina was weak, but comparable to simulations by MIKE‐SHE.     

Evaluation of regression methodology with low-frequency water quality sampling to estimate constituent loads for ephemeral watersheds in Texas

Water quality regulation and litigation have elevated the awareness and need for quantifying water quality and source contributions in watersheds across the USA. In the present study, the regression method, which is typically applied to large (perennial) rivers, was evaluated in its ability to estimate constituent loads (NO(3)-N, total N, PO(4)-P, total P, sediment) on three small (ephemeral) watersheds with different land uses in Texas. Specifically, regression methodology was applied with daily flow data collected with bubbler stage recorders in hydraulic structures and with water quality data collected with four low-frequency sampling strategies: random, rise and fall, peak, and single stage. Estimated loads were compared with measured loads determined in 2001-2004 with an autosampler and high-frequency sampling strategies. Although annual rainfall and runoff volumes were relatively consistent within watersheds during the study period, measured annual nutrient and sediment concentrations and loads varied considerably for the cultivated and mixed watersheds but not for the pasture watershed. Likewise, estimated loads were much better for the pasture watershed than the cultivated and mixed landuse watersheds because of more consistent land management and vegetation type in the pasture watershed, which produced stronger correlations between constituent loads and mean daily flow rates. Load estimates for PO(4)-P were better than for other constituents possibly because PO(4)-P concentrations were less variable within storm events. Correlations between constituent concentrations and mean daily flow rate were poor and not significant for all watersheds, which is different than typically observed in large rivers. The regression method was quite variable in its ability to accurately estimate annual nutrient loads from the study watersheds; however, constituent load estimates were much more accurate for the combined 3-yr period. Thus, it is suggested that for small watersheds, regression-based annual load estimates should be used with caution, whereas long-term estimates can be much more accurate when multiple years of concentration data are available. The predictive ability of the regression method was similar for all of the low-frequency sampling strategies studied; therefore, single-stage or random strategies are recommended for low-frequency storm sampling on small watersheds because of their simplicity.     

GIS‐Based Stream Classification in a Mountain Watershed for Jurisdictional Evaluation

This study analyzed stream characteristics in a mountain watershed in southwestern Colorado and developed a three‐level hierarchical classification scheme using national datasets to demonstrate jurisdictional evaluation as “waters of the United States (U.S.)” under U.S. Clean Water Act Section 404 at the watershed scale. The National Hydrography Dataset and USGS StreamStats were used with field observations to classify streams in the 53 km² Cement Creek Watershed based on flow duration (Level 1), stream order (Level 2), and other biophysical metrics (Level 3). Kruskal‐Wallis tests and discriminant analysis showed significant differences among Level 2 classes. Level 3 classification used cluster analysis for stream length, distance to the downstream traditional navigable water (TNW), and the ratio of mean annual flow from the source stream to the TNW. Results showed all perennial and intermittent streams are jurisdictional relatively permanent waters (RPWs), which include over a third of all streams, 64% are intermittent or ephemeral, and almost half are ephemeral first order. All ephemeral reaches are non‐RPWs requiring significant nexus evaluation to determine jurisdiction. These ephemeral first‐order streams can contribute 5% of the annual flow to the TNW at the confluence, while the Cement Creek main stem contributes 21% of the TNW flow. The study demonstrated that the classification provides key biophysical and regulatory information to aid jurisdictional evaluations in mountain watersheds.     

Probabilistic Streamflow Generation Model for Data Sparse Arid Watersheds1

Abstract: The authors present a model that generates streamflow for ephemeral arid streams. The model consists of a stochastic hourly precipitation point process model and a conceptual model that transforms precipitation into flow. It was applied to the Santa Cruz River at the border crossing from Mexico into Southern Arizona. The model was constructed for four different seasons and three categories of inter‐annual variability for the wet seasons of summer and winter. The drainage area is ungauged and precipitation information was inferred from a precipitation gauge downstream. The precipitation gauge record was evaluated against simulated precipitation from a mesoscale numerical weather prediction model, and was found to be the representative of the regional precipitation variability. The flow generation was found to reproduce the variability in the observed record at the daily, seasonal and annual time scales, and it is suitable for use in planning studies for the study site.     

Use of Streamflow Data to Estimate Base Flow/Ground‐Water Recharge For Wisconsin1

Abstract: The average annual base flow/recharge was determined for streamflow‐gaging stations throughout Wisconsin by base‐flow separation. A map of the State was prepared that shows the average annual base flow for the period 1970‐99 for watersheds at 118 gaging stations. Trend analysis was performed on 22 of the 118 streamflow‐gaging stations that had long‐term records, unregulated flow, and provided aerial coverage of the State. The analysis found that a statistically significant increasing trend was occurring for watersheds where the primary land use was agriculture. Most gaging stations where the land cover was forest had no significant trend. A method to estimate the average annual base flow at ungaged sites was developed by multiple‐regression analysis using basin characteristics. The equation with the lowest standard error of estimate, 9.5%, has drainage area, soil infiltration and base flow factor as independent variables. To determine the average annual base flow for smaller watersheds, estimates were made at low‐flow partial‐record stations in 3 of the 12 major river basins in Wisconsin. Regression equations were developed for each of the three major river basins using basin characteristics. Drainage area, soil infiltration, basin storage and base‐flow factor were the independent variables in the regression equations with the lowest standard error of estimate. The standard error of estimate ranged from 17% to 52% for the three river basins.     

Modeling Nutrient Export From Coastal California Watersheds

Nitrate and phosphate export coefficient models were developed for coastal watersheds along the Santa Barbara Channel in central California. One approach was based on measurements of nutrient fluxes in streams from specific land use classes and included a watershed response function that scaled export up or down depending on antecedent moisture conditions. The second approach for nutrient export coefficient modeling used anthropogenic nutrient loading for land use classes and atmospheric nutrient deposition to model export. In an application of the first approach to one watershed, the nitrate and phosphate models were within 20% of measured values for most storms. When applied to another year, both nitrate and phosphate models generally performed adequately with annual, storm‐flow, and base‐flow values within 20% of measured nutrient loadings. Less satisfactory results were found when applied to neighboring watersheds with difference percentages of land use and hydrologic conditions. Application of the second approach was less successful than the first approach.     

DETERMINING CRITICAL WATER QUALITY CONDITIONS FOR INORGANIC NITROGEN IN DRY,SEMI‐URBANIZED WATERSHEDS1

ABSTRACT: Traditional approaches to establishing critical water quality conditions, based on statistical analysis of low flow conditions and expressed as a recurrence interval for low flow conditions (e.g., 7Q10), may be inappropriate for drier watersheds. The use of 7Q10 as a standard design flow assumes year‐round flow, but in these watersheds, 7Q10 is zero or very small. In addition, the increasing use of multiple year dynamic water quality models at daily time steps can supercede the use of steady state approaches. Many of these watersheds are also under increasing urbanization pressure, which accentuates the flashiness of runoff and the episodic nature of critical water quality conditions. To illustrate, the conditions in the Santa Clara River, California, are considered. A statistical analysis indicates that higher inorganic nitrogen concentrations correlate strongly with low flow. However, peaks in concentrations can occur during the first storms, particularly where nonpoint source contribution is significant. Critical conditions can thus occur at different flow regimes depending on the relative magnitude of flow and pollutant contributions from various sources. The use of steady state models for these dry semi‐urbanized watersheds based on 7Q10 flows is thus unlikely to accurately simulate the potential for exceeding water quality objectives. Dynamic simulation of water quality is necessary, and as the recent intense storm event sampling data indicate, the models should be formulated to consider even smaller time steps. This places increasing demand on computational resources and datasets to accurately calibrate the models at this temporal resolution.     

Development and Comparison of Multiple Regression Models to Predict Bankfull Channel Dimensions for Use in Hydrologic Models

Channel dimensions are important input variables for many hydrologic models. As measurements of channel geometry are not available in most watersheds, they are often predicted using bankfull hydraulic geometry relationships. This study aims at improving existing equations that relate bankfull width, depth, and cross‐sectional area to drainage area (DA) without limiting their use to well‐gauged watersheds. We included seven additional variables in the equations that can be derived from data that are generally required by hydrologic models anyway and conducted several multiple regression analyses to identify the ideal combination of additional variables for nationwide and regional models for each Physiographic Division of the United States (U.S.). Results indicate that including the additional variables in the regression equations generally improves predictions considerably. The selection of relevant variables varies by Physiographic Division, but average annual precipitation (PCP) and temperature (TMP) were generally found to improve the models the most. Therefore, we recommend using regression equations with three independent variables (DA, PCP, and TMP) to predict bankfull channel dimensions for hydrologic models. Furthermore, we recommend using the regional equations for watersheds within regions from which data were used for model development, whereas in all other parts of the U.S. and the rest of the world, the nationwide equations should be given preference.     

EVALUATION OF NINE MODELS FOR UNGAUGED URBAN BASINS IN LOUISIANA1

ABSTRACT: Nine flood-estimation models used for ungauged urban watersheds in Louisiana were evaluated. Flood-quantile predictions from simple regression models calibrated by local data were found to be more reliable than those more complicated models or models with many parameters that may not be accurately estimated. Flood prediction from models developed by using regionalization techniques were found to be reasonably good. Finally, application of a model outside of its limitations or domain may lead to substantial prediction error.     

Utility of Gridded Rainfall for IHACRES Daily River Flow Predictions in Southern California Watersheds1

Abstract: Studies to regionalize conceptual hydrologic models generally require rainfall and river flow data from multiple watersheds. Besides the considerable time (cost) to assemble and process rainfall data for many watersheds, investigators often need to choose from a number of candidate gauges, subjectively weighing the relative importance of proximity and elevation to select a representative rainfall dataset. The Unified Raingauge Dataset (URD) is a gridded daily rainfall dataset that covers the conterminous United States at 0.25 × 0.25 degrees spatial resolution and is available from 1948 to present. The objective of this study was to determine whether uncertainty in daily river flow predictions using the conceptual hydrologic model IHACRES in small to moderate size watersheds (50‐400 km²) in southern California would increase if URD gridded rainfall data were used in place of single rain gauge data to calibrate the model. Rain gauge data were obtained from the gauge nearest the watershed centroid and the gauge closest in elevation to the watershed mean elevation. Results from 20 randomly selected watersheds indicated that IHACRES calibration performance was similar using rainfall data from the URD grids and rain gauge data. There was some evidence of greater uncertainties associated with the URD calibrations in areas where topography may affect rainfall amounts. In contrast to the URD data, monthly gridded data produced by the Parameter‐Elevation Regressions on Independent Slopes Model (PRISM) includes adjustments for elevation and produces gridded values at a finer spatial resolution (4 km²). A limited test on two watersheds demonstrated that scaling the URD daily rainfall estimates to match the PRISM monthly values may improve rainfall estimates and model simulation performance.     

RECHARGE ESTIMATES USING A GEOMORPHIC/DISTRIBUTED-PARAMETER SIMULATION APPROACH,AMARGOSA RWER BASIN1

ABSTRACT: Average-annual volumes of runoff, evapotranspiration, channel loss, upland (interchannel) recharge, and total recharge were estimated for watersheds of 53 channel sites in the Amargosa River basin above Shoshone, California. Estimates were based on a water-balance approach combining field techniques for determining streamflow with distributed-parameter simulation models to calculate transmission losses of ephemeral streamflow and upland recharge resulting from high-magnitude, low-frequency precipitation events. Application of the water-balance models to the Amargosa River basin, Nevada and California, including part of the Nevada Test Site, suggests that about 20.5 million cubic meters of water recharges the ground-water reservoir above Shoshone annually. About 1.6 percent of precipitation becomes recharge basinwide. About 90 percent of the recharge is by transmission loss in channels, and the remainder occurs when infrequent storms yield sufficient precipitation that soil water percolates beyond the rooting zone and reaches the zone of saturation from interchannel areas. Highest rates of recharge are in headwaters of the Amargosa River and Fortymile Wash; the least recharge occurs in areas of relatively low precipitation in the lowermost Amargosa River watershed.     

Impact of Temporal Resolution of Flow‐Duration Curve on Sediment Load Estimation1

Chen, Li, Rina Schumer, Anna Knust, and William Forsee, 2011. Impact of Temporal Resolution of Flow‐Duration Curve on Sediment Load Estimation. Journal of the American Water Resources Association (JAWRA) 48(1): 145‐155. DOI: 10.1111/j.1752‐1688.2011.00602.x Abstract: Estimates of a channel’s annual sediment transport capacity typically incorporate annualized flow‐duration curves. Average daily flow data, commonly used to develop flow‐duration curves, may not adequately describe sediment‐transporting flows in arid and semiarid ephemeral streams. In this study, we examined impacts of varied temporal resolution flow data on annual sediment load estimation. We derived flow‐duration curves for eight sites in the Southwestern United States based on both 15‐min and daily‐averaged flow data. We then estimated sediment loads for both flow‐duration curves using the Sediment Impact Analysis Method, implemented in HEC‐RAS. When average daily flow is used to generate flow‐duration curves, sediment load estimation is lower by up to an order of magnitude. This trend is generally unaffected by uncertainty associated with sediment particle size or hydraulic roughness. The ratio of sediment loads estimated by 15‐min versus daily‐averaged flow‐duration curves is strongly correlated with channel slope, being greater on steep‐slope channels. Sediment loads estimated by the two types of flow‐duration curves are closely correlated, suggesting possible relationships for improving predictions when high‐temporal resolution data are unavailable. Results also suggest that the largest flow contributes significantly to total sediment load, and thus will greatly impact ephemeral stream geomorphology in arid and semiarid regions.     

MODELING THE FOREST HYDROLOGY OF WETLAND-UPLAND ECOSYSTEMS IN FLORIDA1

ABSTRACT: Few hydrological models are applicable to pine flat-woods which are a mosaic of pine plantations and cypress swamps. Unique features of this system include ephemeral sheet flow, shallow dynamic ground water table, high rainfall and evapotranspiration, and high infiltration rates. A FLATWOODS model has been developed specifically for the cypress wetland-pine upland landscape by integrating a 2-D ground water model, a Variable-Source-Area (VAS)-based surface flow model, an evapotranspiration (ET) model, and an unsaturated water flow model. The FLATWOODS model utilizes a distributed approach by dividing the entire simulation domain into regular cells. It has the capability to continuously simulate the daily values of ground water table depth, ET, and soil moisture content distributions in a watershed. The model has been calibrated and validated with a 15-year runoff and a four-year ground water table data set from two different pine flat woods research watersheds in northern Florida. This model may be used for predicting hydrologic impacts of different forest management practices in the coastal regions.     

Using GIS to Delineate Headwater Stream Origins in the Appalachian Coalfields of Kentucky

Headwater streams have a significant nexus or physical, chemical, and/or biological connection to downstream reaches. Generally, defined as 1st‐3rd order with ephemeral, intermittent, or perennial flow regimes, these streams account for a substantial portion of the total stream network particularly in mountainous terrain. Due to their often remote locations, small size, and large numbers, conducting field inventories of headwater streams is challenging. A means of estimating headwater stream location and extent according to flow regime type using publicly available spatial data is needed to simplify this complex process. Using field‐collected headwater point of origin data from three control watersheds, streams were characterized according to a set of spatial parameters related to topography, geology, and soils. These parameters were (1) compared to field‐collected point of origin data listed in three nearby Jurisdictional Determinations, (2) used to develop a geographic information system (GIS)‐based stream network for identifying ephemeral, intermittent, and perennial streams, and (3) applied to a larger watershed and compared to values obtained using the high‐resolution National Hydrography Dataset (NHD). The parameters drainage area and local valley slope were the most reliable predictors of flow regime type. Results showed the high‐resolution NHD identified no ephemeral streams and 9 and 65% fewer intermittent and perennial streams, respectively, than the GIS model.     

Simulation of Daily Flow Pathways,Tile‐Drain Nitrate Concentrations,and Soil‐Nitrogen Dynamics Using SWAT

Tile drainage significantly alters flow and nutrient pathways and reliable simulation at this scale is needed for effective planning of nutrient reduction strategies. The Soil and Water Assessment Tool (SWAT) has been widely utilized for prediction of flow and nutrient loads, but few applications have evaluated the model's ability to simulate pathway‐specific flow components or nitrate‐nitrogen (NO₃‐N) concentrations in tile‐drained watersheds at the daily time step. The objectives of this study were to develop and calibrate SWAT models for small, tile‐drained watersheds, evaluate model performance for simulation of flow components and NO₃‐N concentration at daily intervals, and evaluate simulated soil‐nitrogen dynamics. Model evaluation revealed that it is possible to meet accepted performance criteria for simulation of monthly total flow, subsurface flow (SSF), and NO₃‐N loads while obtaining daily surface runoff (SURQ), SSF, and NO₃‐N concentrations that are not satisfactory. This limits model utility for simulating best management practices (BMPs) and compliance with water quality standards. Although SWAT simulates the soil N‐cycle and most predicted fluxes were within ranges reported in agronomic studies, improvements to algorithms for soil‐N processes are needed. Variability in N fluxes is extreme and better parameterization and constraint, through use of more detailed agronomic data, would also improve NO₃‐N simulation in SWAT. 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.     

Comparison of Two Parametric Methods to Estimate Pesticide Mass Loads in California’s Central Valley1

Saleh, Dina K., David L. Lorenz, and Joseph L. Domagalski, 2010. Comparison of Two Parametric Methods to Estimate Pesticide Mass Loads in California’s Central Valley. Journal of the American Water Resources Association (JAWRA) 00(0):1‐11. DOI: 10.1111/j.1752‐1688.2010.00506.x Abstract: Mass loadings were calculated for four pesticides in two watersheds with different land uses in the Central Valley, California, by using two parametric models: (1) the Seasonal Wave model (SeaWave), in which a pulse signal is used to describe the annual cycle of pesticide occurrence in a stream, and (2) the Sine Wave model, in which first‐order Fourier series sine and cosine terms are used to simulate seasonal mass loading patterns. The models were applied to data collected during water years 1997 through 2005. The pesticides modeled were carbaryl, diazinon, metolachlor, and molinate. Results from the two models show that the ability to capture seasonal variations in pesticide concentrations was affected by pesticide use patterns and the methods by which pesticides are transported to streams. Estimated seasonal loads compared well with results from previous studies for both models. Loads estimated by the two models did not differ significantly from each other, with the exceptions of carbaryl and molinate during the precipitation season, where loads were affected by application patterns and rainfall. However, in watersheds with variable and intermittent pesticide applications, the SeaWave model is more suitable for use on the basis of its robust capability of describing seasonal variation of pesticide concentrations.     

Predicting Debris Yield From Burned Watersheds: Comparison of Statistical and Artificial Neural Network Models1

Abstract: Alluvial fans in southern California are continuously being developed for residential, industrial, commercial, and agricultural purposes. Development and alteration of alluvial fans often require consideration of mud and debris flows from burned mountain watersheds. Accurate prediction of sediment (hyper‐concentrated sediment or debris) yield is essential for the design, operation, and maintenance of debris basins to safeguard properly the general population. This paper presents results based on a statistical model and Artificial Neural Network (ANN) models. The models predict sediment yield caused by storms following wildfire events in burned mountainous watersheds. Both sediment yield prediction models have been developed for use in relatively small watersheds (50‐800 ha) in the greater Los Angeles area. The statistical model was developed using multiple regression analysis on sediment yield data collected from 1938 to 1983. Following the multiple regression analysis, a method for multi‐sequence sediment yield prediction under burned watershed conditions was developed. The statistical model was then calibrated based on 17 years of sediment yield, fire, and precipitation data collected between 1984 and 2000. The present study also evaluated ANN models created to predict the sediment yields. The training of the ANN models utilized single storm event data generated for the 17‐year period between 1984 and 2000 as the training input data. Training patterns and neural network architectures were varied to further study the ANN performance. Results from these models were compared with the available field data obtained from several debris basins within Los Angeles County. Both predictive models were then applied for hind‐casting the sediment prediction of several post 2000 events. Both the statistical and ANN models yield remarkably consistent results when compared with the measured field data. The results show that these models are very useful tools for predicting sediment yield sequences. The results can be used for scheduling cleanout operation of debris basins. It can be of great help in the planning of emergency response for burned areas to minimize the damage to properties and lives.