Streamflow Estimates Research Articles

Variation of Rainfall with Space and Time in Duhok Governorate

Rainfall analysis is important in different domains such as: agricultural planning, water resources plannings, runoff prediction, climatological studies, environmental studies, stream flow estimation and human life activities. Variation of rainfall with space and time were studied in Duhok Governorate, Mean monthly, seasonal and annual values of rainfall were found in different weather stations (Duhok, Zakho, Semel, Zawaite, Sersing, Amedy and Akre station). Winter months represent about 45-55% of the total annual rainfall. The annual variability of rainfall in all these stations are high. Isoheytal method was used to estimate the mean monthly values and mean annual values of rainfall over Duhok Governorate. Simple and Multiple Regression Equation were found between rainfall and different meteorological elements.

Seasonal streamflow estimation in a woodlot subcatchment

In the present study, an attempt was made to evaluate the total seasonal stream flow, in terms of both surface runoff and baseflow contributions in a 25.5 ha woodlot subcatchment of New-Afaka, Kaduna in the Northern Guinea Savanna ecological zone of Nigeria. The seasonal flow could be impounded and harnessed for dry season irrigation of crops in 20.3 to 79.2 ha out of about 125 ha of arable land which exists downstream the drainage basin. The total seasonal flow, measured as change in volumetric storage of an earthdam at the watershed outlet, and by means of a weir installed in an outflow channel that drained the dam spillage was 435,606m3. The direct runoff and base flow contribution was in the ratio of 1:24, the seasonal flow being described as a groundwater-sustained ephemeral stream.Key Words: seasonal flow, runoff, baseflow, catchment, irrigation, drainage

A novel approach to infer streamflow signals for ungauged basins

In this paper, we present a novel paradigm for inference of streamflow for ungauged basins. Our innovative procedure fuses concepts from both kernel methods and data assimilation. Based on the modularity and flexibility of kernel techniques and the strengths of the variational Bayesian Kalman filter and smoother, we can infer streamflow for ungauged basins whose hydrological and system properties and/or behavior are non-linear and non-Gaussian. We apply the proposed approach to two watersheds, one in California and one in West Virginia. The inferred streamflow signals for the two watersheds appear promising. These preliminary and encouraging validations demonstrate that our new paradigm is capable of providing accurate conditional estimates of streamflow for ungauged basins with unknown and non-linear dynamics.

NEXRAD Stage III Precipitation Local Bias Adjustment for Streamflow Prediction

Prediction accuracy of physically based hydrologic models largely depends on how well the models and input data represent the real world in terms of precipitation, topography, soils, land use/land cover, and land management. Precipitation is, by far, the largest source of uncertainty; misestimating it by a small percentage leads to inadequate soil-moisture accounting and subsequent inaccuracy in runoff and streamflow estimation. The objectives of this study were (1) to determine the optimum local bias adjustment factor (BAF) threshold limits (TLs) by using SWAT-modeled streamflow values and frequency distributions with NEXRAD or raingauge precipitation inputs, and (2) to assess the impact of BAF TLs derived from 40 raingauges in and around a large watershed in west central Kansas on hydrologic response of the SWAT model. NEXRAD overestimated precipitation depths (45% to 184%) in warm months including April to September and underestimated depths (33% to 64%) in cold months including December to February; the remaining months had fairly close estimates (within 20%). Assessment of bias-adjusted NEXRAD data based on daily raingauge comparisons found the best model efficiency (Ef) using 0.15 lower TL, whereas the lowest bias was for 0.33 lower TL, both with 2.0 upper TL. However, assessment of NEXRAD data based on predicted versus observed daily streamflow found the best Ef using 0.15 lower TL and the lowest bias using 0.25 lower TL. NEXRAD data bias-adjusted using TLs of 2.0 (upper) combined with 0.15 (lower) produced satisfactory streamflow results, similar to the default nearest-raingauge method used in SWAT. This result is encouraging considering the substantial seasonal bias evident in the Stage III NEXRAD data and it indicates the potential for future improvements in streamflow simulation accuracy as NEXRAD accuracy increases. This study demonstrated that TLs are needed in bias-adjustment of NEXRAD data for reasonable hydrologic simulation, and that better streamflow simulation accuracy is obtained if the optimum TLs are based on streamflow assessment rather than assessment of the precipitation data.

Using NEXRAD and Rain Gauge Precipitation Data for Hydrologic Calibration of SWAT in a Northeastern Watershed

The value of watershed-scale, hydrologic and water quality models to ecosystem management is increasingly evident as more programs adopt these tools to evaluate the effectiveness of different management scenarios and their impact on the environment. Quality of precipitation data is critical for appropriate application of watershed models. In small watersheds, where no dense rain gauge network is available, modelers are faced with a dilemma to choose between different data sets. In this study, we used the German Branch (GB) watershed (~50 km2), which is included in the USDA Conservation Effects Assessment Project (CEAP), to examine the implications of using surface rain gauge and next-generation radar (NEXRAD) precipitation data sets on the performance of the Soil and Water Assessment Tool (SWAT). The GB watershed is located in the Coastal Plain of Maryland on the eastern shore of Chesapeake Bay. Stream flow estimation results using surface rain gauge data seem to indicate the importance of using rain gauges within the same direction as the storm pattern with respect to the watershed. In the absence of a spatially representative network of rain gauges within the watershed, NEXRAD data produced good estimates of stream flow at the outlet of the watershed. Three NEXRAD datasets, including (1) non-corrected (NC), (2) bias-corrected (BC), and (3) inverse distance weighted (IDW) corrected NEXRAD data, were produced. Nash-Sutcliffe efficiency coefficients for daily stream flow simulation using these three NEXRAD data ranged from 0.46 to 0.58 during calibration and from 0.68 to 0.76 during validation. Overall, correcting NEXRAD with rain gauge data is promising to produce better hydrologic modeling results. Given the multiple precipitation datasets and corresponding simulations, we explored the combination of the multiple simulations using Bayesian model averaging. The results show that this Bayesian scheme can produce better deterministic prediction than any single simulation and can provide reasonable uncertainty estimation. The optimal water balance obtained in this study is an essential precursor to acquiring realistic estimates of sediment and nutrient loads in future GB modeling efforts. The results presented in this study are expected to provide insights into selecting precipitation data for watershed modeling in small Coastal Plain catchments.

Modeling the Spatially Varying Water Balance Processes in a Semiarid Mountainous Watershed of Idaho1

Stratton, Benjamin T., Venakataramana Sridhar, Molly M. Gribb, James P. McNamara, and Balaji Narasimhan, 2009. Modeling the Spatially Varying Water Balance Processes in a Semiarid Mountainous Watershed of Idaho. Journal of the American Water Resources Association (JAWRA) 45(6):1390‐1408.Abstract: The distributed Soil Water Assessment Tool (SWAT) hydrologic model was applied to a research watershed, the Dry Creek Experimental Watershed, near Boise Idaho to investigate its water balance components both temporally and spatially. Calibrating and validating SWAT is necessary to enable our understanding of the water balance components in this semiarid watershed. Daily streamflow data from four streamflow gages were used for calibration and validation of the model. Monthly estimates of streamflow during the calibration phase by SWAT produced satisfactory results with a Nash Sutcliffe coefficient of model efficiency 0.79. Since it is a continuous simulation model, as opposed to an event‐based model, it demonstrated the limited ability in capturing both streamflow and soil moisture for selected rain‐on‐snow (ROS) events during the validation period between 2005 and 2007. Especially, soil moisture was generally underestimated compared with observations from two monitoring pits. However, our implementation of SWAT showed that seasonal and annual water balance partitioning of precipitation into evapotranspiration, streamflow, soil moisture, and drainage was not only possible but closely followed the trends of a typical semiarid watershed in the intermountain west. This study highlights the necessity for better techniques to precisely identify and drive the model with commonly observed climatic inversion‐related snowmelt or ROS weather events. Estimation of key parameters pertaining to soil (e.g., available water content and saturated hydraulic conductivity), snow (e.g., lapse rates, melting), and vegetation (e.g., leaf area index and maximum canopy index) using additional field observations in the watershed is critical for better prediction.

A comparison of pre‐ and post‐remediation water quality, Mineral Creek, Colorado

AbstractPre‐ and post‐remediation data sets are used herein to assess the effectiveness of remedial measures implemented in the headwaters of the Mineral Creek watershed, where contamination from hard rock mining has led to elevated metal concentrations and acidic pH. Collection of pre‐ and post‐remediation data sets generally followed the synoptic mass balance approach, in which numerous stream and inflow locations are sampled for the constituents of interest and estimates of streamflow are determined by tracer dilution. The comparison of pre‐ and post‐remediation data sets is confounded by hydrologic effects and the effects of temporal variation. Hydrologic effects arise due to the relatively wet conditions that preceded the collection of pre‐remediation data, and the relatively dry conditions associated with the post‐remediation data set. This difference leads to a dilution effect in the upper part of the study reach, where pre‐remediation concentrations were diluted by rainfall, and a source area effect in the lower part of the study reach, where a smaller portion of the watershed may have been contributing constituent mass during the drier post‐remediation period. A second confounding factor, temporal variability, violates the steady‐state assumption that underlies the synoptic mass balance approach, leading to false identification of constituent sources and sinks. Despite these complications, remedial actions completed in the Mineral Creek headwaters appear to have led to improvements in stream water quality, as post‐remediation profiles of instream load are consistently lower than the pre‐remediation profiles over the entire study reach for six of the eight constituents considered (aluminium, arsenic, cadmium, copper, iron, and zinc). Concentrations of aluminium, cadmium, copper, lead, and zinc remain above chronic aquatic‐life standards, however, and additional remedial actions may be needed. Future implementations of the synoptic mass balance approach should be preceded by an assessment of temporal variability, and modifications to the synoptic sampling protocol should be made if necessary. Published in 2009 by John Wiley & Sons, Ltd.

Streamflow estimation for six UK catchments under future climate scenarios

Possible changes in streamflow in response to climate variation are crucial for anthropological and ecological systems. However, estimates of precipitation under future climate scenarios are notoriously uncertain. In this article, rainfall time series are generated by the generalized linear model (GLM) approach in which stochastic time series are generated using alternative climate model output variables and potential evaporation series estimated by a temperature method. These have been input to a conceptual rainfall–runoff model (pd4-2par) to simulate the daily streamflows for six UK catchments for a set of climate scenarios using seven global circulation models (GCMs) and regional circulation models (RCMs). The performance of the combined methodology in reproducing observed streamflows is generally good. Results of future climate scenarios show significant variability between different catchments, and very large variability between different climate models. It is concluded that the GLM methodology is promising, and can readily be extended to support distributed hydrological modelling.

The effect of the variation of river water levels on the estimation of groundwater recharge in the Hsinhuwei River, Taiwan

Land subsidence is a serious problem in Taiwan’s Yunlin area due to groundwater overpumping. There are safety risks in the high-speed railway structures in the areas of Siluo, Huwei, Tuku, and Yuanchang towns that run from north to south in the Yunlin area. Therefore, it is important to increase the groundwater recharge and to remedy the land subsidence in this area. The purpose of this study is to use the stream-flow estimation model (SF) and the groundwater flow numerical software MODFLOW (MF) to estimate the stream infiltration with consideration to the variation of the river water level in the Hsinhuwei River. The Ferris analytical model (FA) and MF are used to estimate the increased stream infiltration after the water level of the river rises. The hydraulic parameters required for each model are obtained from field observations and laboratory experiments. The results indicate that the assessment of the stream infiltration obtained through the SF and MF models are 264.2 × 104 and 170.9 × 104 m3/year, respectively. When the river water level increases by about 2.5 m, the annual stream infiltration obtained through the FA and MF models significantly increases by 31.6 × 104 and 26.4 × 104 m3/year, respectively. Taken together, the stream storages estimated using these two models indicate that an increasing efficiency of groundwater recharge is within the range of 10.0–18.5%.

Evaluation of NRCS curve number and MODIS time-series proxies for antecedent moisture condition

Soil moisture plays a vital role in a watershed's hydrologic response to a precipitation event. Soil moisture condition antecedent to an event in empirical runoff estimation models, such as the 5-day antecedent moisture model for natural resources conservation service (NRCS) curve number (CN) method, is generalised and spatially inexplicit. This study assesses the potential to parameterise a statistical streamflow estimation model utilising next generation weather radar (NEXRAD) precipitation records and time-series biophysical proxies for soil moisture from moderate resolution imaging spectroradiometer (MODIS) on board Terra satellite. This study is conducted on a 1420 km2 rural watershed in the Guadalupe River basin of southcentral Texas, a basin prone to catastrophic flooding from convective precipitation events. A least squares regression model, accounting for 83% of the variance of observed streamflow for calendar year 2004, was developed based on radar precipitation estimates, land surface temperature, and a vegetation index, on an 8-day interval. Estimated 8-day mean streamflows from the remote sensing model represented an improvement over CN modelled streamflows, developed with the standard and a continuum-based 5-day antecedent moisture condition model. Assessment of remotely sensed proxies of soil moisture has potential for complementing traditional CN method and for estimating streamflow in watersheds for which the CN or other empirical methods are limited by data constraints.

Application of a Developed Grid-Xinanjiang Model to Chinese Watersheds for Flood Forecasting Purpose

A distributed rainfall-runoff model, Grid-Xinanjiang model based on the topographical information of each grid cell extracted from the digital elevation model (DEM), was developed to simulate the hydrologic processes within watersheds. The water exchange among grids within the watershed and the runoff routing along the river drainage networks are taken into consideration in the model. The Grid-Xinanjiang model was applied to the two watersheds in China for flood simulations. The performance of the developed model was compared with that of the original Xinanjiang model. Initial tests show that the Grid-Xinanjiang model can perform well as the original model in terms of estimates of streamflow and of the model efficiency coefficient. All of the qualified ratios relative to peak flow, runoff, and peak time for the two models applied to the Misai watershed and the Xixian watershed are more than 88%. The Grid-Xinanjiang model outperformed the original model in predicting flood runoff and outlet hydrographs, es...

Neural Networks and Wavelet Conjunction Model for Intermittent Streamflow Forecasting

Intermittent streamflow estimates are important for water quality management, planning water supplies, hydropower, and irrigation systems. This paper proposes the application of a conjunction model (neurowavelet) for forecasting daily intermittent streamflow. The neurowavelet conjunction model is improved by combining two methods, discrete wavelet transform and artificial neural networks (ANN), for 1day ahead streamflow forecasting and results are compared with those of the single ANN model. Intermittent streamflow data from two stations in the Thrace Region, the European part of Turkey, in the northwest part of the country are used in the study. The comparison results revealed that the suggested model could significantly increase the forecast accuracy of single ANN in forecasting daily intermittent streamflows. The neurowavelet conjunction model reduced the prediction root mean square errors and mean absolute errors with respect to the single ANN model by 74–65% and 43–12%, and increased the determination coefficient by 47–11%, respectively.

A Fully Distributed Implementation of Mean Annual StreamFlow Regional Regression Equations1

Abstract: Estimates of mean annual streamflow are needed for a variety of hydrologic assessments. Away from gage locations, regional regression equations that are a function of upstream area, precipitation, and temperature are commonly used. Geographic information systems technology has facilitated their use for projects, but traditional approaches using the polygon overlay operator have been too inefficient for national scale applications. As an alternative, the Elevation Derivatives for National Applications (EDNA) database was used as a framework for a fully distributed implementation of mean annual streamflow regional regression equations. The raster “flow accumulation” operator was used to efficiently achieve spatially continuous parameterization of the equations for every 30 m grid cell of the conterminous United States (U.S.). Results were confirmed by comparing with measured flows at stations of the Hydro‐Climatic Data Network, and their applications value demonstrated in the development of a national geospatial hydropower assessment. Interactive tools at the EDNA website make possible the fast and efficient query of mean annual streamflow for any location in the conterminous U.S., providing a valuable complement to other national initiatives (StreamStats and the National Hydrography Dataset Plus).

Performance of Multivariate Adaptive Regression Splines (MARS) in predicting runoff in mid-Himalayan micro-watersheds with limited data / Performances de régressions par splines multiples et adaptives (MARS) pour la prévision d'écoulement au sein de micro-bassins versants Himalayens d'altitudes intermédiaires avecpeu de données

Steep topography and land-use transformations in Himalayan watersheds have a major impact on hydrological characteristics and flow regimes, and greatly affect the perenniality and sustainability of water resources in the region. To identify the appropriate conservation measures in a watershed properly, and, in particular, to augment flow during lean periods, accurate estimation of streamflow is essential. Due to the complexity of rainfall—runoff relationships in hilly watersheds and non-availability of reliable data, process-based models have limited applicability. In this study, data-driven models, based upon the Multiple Adaptive Regression Splines (MARS) technique, were employed to predict streamflow (surface runoff, baseflow and total runoff) in three mid-Himalayan micro-watersheds. In addition, the effect of length of historical records on the performance of MARS models was critically evaluated. Though acceptable MARS models could be developed with a 2-year data set, their performance improved considerably with a 3-year data set. Various indicators of model performance, such as correlation coefficient, average deviation, average absolute deviation and modelling efficiency, showed significant improvement for simulation of surface runoff, baseflow and total flow. To further analyse the versatility and general applicability of the MARS approach, 2-year data sets were used to develop the model and test it on a third-year data set to assess its performance. The models simulated the surface runoff, baseflow and total flow reasonably well and can be reliably applied in ungauged small watersheds under identical agro-climatic settings.

Assessing the Capability of a Regional-Scale Weather Model to Simulate Extreme Precipitation Patterns and Flooding in Central Texas

Abstract A regional-scale weather model is used to determine the potential for flood forecasting based on model-predicted rainfall. Extreme precipitation and flooding events are a significant concern in central Texas, due to both the high occurrence and severity of flooding in the area. However, many current regional prediction models do not provide sufficient accuracy at the watershed scale necessary for flood mitigation efforts. The Weather Research and Forecasting (WRF) model, created with the purpose of improving upon the current fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5), is specifically designed for regional grid spacings of 1–10 km. Previous research by the authors resulted in the development of a regional-scale prediction system over the San Antonio River basin, using a geographic information system (GIS) database, a hydrologic model, and a hydraulic model. Observed precipitation drives the prediction system; the authors hypothesize that the WRF model has the potential to predict flooding, at a lead time of several days, with a level of accuracy near that of observed precipitation. Causes of model error are also investigated, to determine the relative errors caused by model physics, initialization interval, buffer zone and domain size, and small-amplitude random errors. Results show that the Betts–Miller–Janjić cumulus and Lin et al. microphysics schemes, 48-h initialization interval, and two-domain configuration covering minimal ocean and having a parent-to-nest area ratio of greater than 10 best simulates a recent (July 2002) large storm event over the San Antonio River basin. This particular storm was selected because it produced extreme rainfall volumes and intensities, and also because its meteorological characteristics are typical of central Texas storm events. Location errors in rainfall are most significant because of their typically nonlinear patterns (increasing location error does not linearly modify streamflow output). Errors in intensity and timing show a more predictable (linear) watershed response that may be useful in the estimation of streamflow ranges for flood forecasting.

Proximal watershed validation of a remote sensing-based streamflow estimation model

Remotely sensed time series of landscape biophysical states and radar estimates of precipitation are assessed in a statistical streamflow estimation model in four regionally proximate south-central Texas watersheds. Sandies Creek watershed (1420 km2) served as calibration of a streamflow estimation model based on 8-day composited time series of land surface (radiant) temperature (LST) and a vegetation moisture stress index (MSI) from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite imagery products. These time series were hypothesized to serve as proxies for soil moisture condition antecedent to streamflow-generating precipitation events as estimated by NEXRAD (Next Generation Weather Radar) precipitation products. A linear multiple regression statistical model yielding estimation equation for observed flows at Sandies Creek was validated in 3 additional proximal watersheds of varying spatial dimension (860 km2 - 2940 km2), soils, land cover, and climatology. The validation yielded encouraging results as assessed with Pearson's r, Nash-Sutcliffe's E, and relative volume error or bias. Equivalent performance of the calibrated model was seen in the watershed mostly adjacent to Sandies Creek, the mostly similar one in terms of size, land cover, precipitation, and soils. Performance of the model diminished slightly in the more distal, and climatologically-environmentally dissimilar watersheds. The estimation model was re-calibrated for all three validation watersheds, with positive results. The contribution of LST uncertainty and NEXRAD precipitation bias to model discrepancies were evaluated along with discussion of known sources of model error.

Estimation of streamflow by slope regional dependency function

Abstract. Kriging is one of the most developed methodologies in the regional variable modeling. However, one of its drawbacks is that the influence radius can not be determined by this method. In which distance and in what ratio that pivot station is influenced from adjacent sites is rather often encountered problem in practical applications. Regional weighting functions obtained from available data consist of several broken lines. Each line has different slopes which represent the similarity and the contribution of adjacent stations as a weighting coefficient. The approach in this study is called as Slope Regional Dependency Function (SRDF). The main idea of this approach is to express the variability in value differences γ and distances together. Originally proposed SRDF and Trigonometric Point Cumulative Semi-Variogram (TPCSV) methods are used to predict streamflow. TPCSV and Point Cumulative Semi-Variogram (PCSV) approaches are also compared with each other. Prediction performance of all the three methods revealed a relative error less than 10% which is acceptable for most engineering applications. It is shown that SRDF outperforms PCSV and TPCSV with very high differences. It can be used for missing data completion, determination of measurement sites location, calculation of influence radius, and determination of regional variable potential. The proposed method is applied for the 38 stream flow measurement sites located in the Mississippi River basin.

Estimation of peak streamflows through channel geometry / Estimation de pics de débit fluviatiles à l'aide de la géométrie des cours d'eau

Knowledge of peak discharge is essential for safe and economical planning and design of hydraulic structures. In India, as in most developing countries, the majority of river basins are either sparsely gauged or not gauged at all. The gauged records are also of short length (generally 15–30 years), therefore development of robust models is necessary for estimation of streamflows. Various studies reveal that flood estimation through channel geometry is an alternative method for ungauged catchments. It is appropriate for use where flow characteristics are poorly related to catchment area and other catchment characteristics. In the present study, stream geometry parameters for 42 river sites in central-south India were used; calibration equations were developed with data for 35 stations and tested on data for the remaining seven stations. The relationships developed between mean discharge and channel geometry parameters provide an alternative technique for estimation of mean annual channel discharge.

Impact of rainfall estimation uncertainty on streamflow estimations for catchments Wye and Tyne in the United Kingdom

AbstractThe main sources of uncertainty in rainfall‐runoff models are found in the input variables where the largest impact on estimated streamflow is generally given by the catchment rainfall. Hence, by quantifying the uncertainty of catchment rainfall, it is possible to provide an estimate, albeit conservative, of streamflow estimation uncertainty. In this article, 42 series of catchment average rainfall were used to estimate monthly streamflow for two river catchments in the United Kingdom over a period of 8 years, these were as follows: a composite series based on weighted rain gauge series, a series based on interpolated gridded rainfall data and an ensemble of 40 series, based on simulated rainfall grids conditioned on observed rainfall statistics. This study shows that, on average, the uncertainty in monthly streamflow, as given from the mean range of monthly simulated data, were approximately 28 and 42% of the long‐term mean for the two catchments respectively. By quantifying uncertainty in streamflow due to catchment rainfall uncertainty it was possible to identify contributions of other factors to the total streamflow estimation uncertainty. For example, streamflow uncertainty was, in this experiment, shown to respond also to the absolute magnitude of the streamflow, a consequence of the model being optimized for low flows. Copyright © 2008 Royal Meteorological Society

River flow forecasting and estimation using different artificial neural network techniques

This paper demonstrates the application of different artificial neural network (ANN) techniques for the estimation of monthly streamflows. In the first part of the study, three different ANN techniques, namely, feed forward neural networks (FFNN), generalized regression neural networks (GRNN) and radial basis ANN (RBF) are used in one-month ahead streamflow forecasting and the results are evaluated. Monthly flow data from two stations, Gerdelli Station on Canakdere River and Isakoy Station on Goksudere River, in the Eastern Black Sea region of Turkey are used in the study. Based on the results, the GRNN was found to be better than the other ANN techniques in monthly flow forecasting. The effect of periodicity on the model's forecasting performance was also investigated. In the second part of the study, the performance of the ANN techniques was tested for river flow estimation using data from the nearby river.