Search

The Natural Resources Conservation Service (NRCS, formerly the Soil Conservation Service, SCS) unit hydrograph (UH) is one of the most commonly used synthetic UH methods for hydrologic modeling and engineering design all over the world. However, previous studies have shown that the application of the NRCS UH method using the published approach and parameter values does not produce accurate peak discharges or time to peaks. Over- or underestimation of peak discharge using the NRCS UH is also a critical issue for hydrologic design in the State of Indiana. The objective of this work is to adapt the NRCS UH for use in Indiana by analyzing the role of its two key parameters, namely, the peak rate factor (PRF) and the lag time, in creating runoff hydrographs. Based on 120 rainfall–runoff events collected from 30 small watersheds in Indiana between 2000 and 2020, UHs are derived to extract corresponding PRF and lag time. Results show that the mean value of PRF for these UHs is 376 (in English units), which is lower than the standard PRF of 484, and the NRCS lag time equation tends to underestimate the true lag time for more than half of the study watersheds. Stepwise linear regression models were formulated to estimate NRCS UH parameters on the basis of the geomorphic attributes extracted from the study watersheds. Both the statewide and regional regression models show that the main channel slope is a major factor in determining the PRF and lag time. Finally, a customized Indiana unit hydrograph (INUH) is derived with updated PRFs using a gamma distribution. Validation results show that INUH can provide more accurate predictions in terms of the peak discharge and the time to peak (the mean relative error is reduced by around 45% and 6%, respectively) than the original NRCS UH for watersheds in Indiana. [ABSTRACT FROM AUTHOR]

Study region: The Upper Blue Nile Basin in Ethiopia, characterized by its complex hydrological system, is the focus of this study. The basin includes 76 gauged watersheds, which were analyzed to estimate parameters for ungauged locations using regionalization techniques. Study focus: Regression-Based Approach (RBA), Physical Similarity Approach (PSA), and Spatial Proximity Approach (SPA), for estimating GR4J model parameters. A 25 km by 25 km fishnet-based grid was implemented to enable parameter prediction for ungauged watersheds. Principal Component Analysis (PCA) and k-means clustering were used to group gauged watersheds into three homogeneous clusters, with Beressa, Dedessa, and Gilgel Abay selected as pseudo-watersheds for validation. Model performance was evaluated using PBIAS, R², and NSE metrics. New hydrological insights for the region: The RBA outperformed PSA and SPA in parameter transfer, achieving R² values of 0.57, 0.79, and 0.67; PBIAS values of 7.3, −1.5, and 2.6; and NSE values of 0.58, 0.78, and 0.67 for Beressa, Dedessa, and Gilgel Abay, respectively. Incorporating grid-based parameter values further improved model performance, with NSE values of 0.81 for Dedessa, 0.63 for Beressa, and 0.61 for Gilgel Abay. These findings highlight the effectiveness of the grid-based regionalization approach for accurate streamflow prediction in ungauged watersheds within the Upper Blue Nile Basin.

The design of hydraulic structures in the Arctic is complicated by shallow relief, which cause unique runoff processes that promote snow-damming and refreeze of runoff. We discuss the challenges encountered in modeling snowmelt runoff into two coastal freshwater lagoons in Utqiaġvik, Alaska. Stage-frequency curves with quantified uncertainty were required to design two new discharge gates that would allow snowmelt runoff flows through a proposed coastal revetment. To estimate runoff hydrographs arriving at the lagoons, we modeled snowpack accumulation and ablation using SnowModel which in turn was used to force a physically-based hydraulic runoff model (HEC-RAS). Our results demonstrate the successful development of stage-frequency curves by incorporating a Monte Carlo simulation approach that quantifies the variability in runoff timing and volume. Our process highlights the complexities of Arctic hydrology by incorporating significant delays in runoff onset due to localized snow accumulation and melting processes. This methodology not only addresses the uncertainty in snow-damming and refreeze processes which affect the arrival time of snowmelt inflow peaks, but is also adaptable for application in other challenging environments where secondary runoff processes are predominant. [ABSTRACT FROM AUTHOR]

Accurate hydrological predictions are required to prepare for the impacts of climate change, especially in India, which experiences frequent floods and droughts. However, the complex hydrological processes of its distinct watersheds and limited data make it challenging to deliver highly‐performant hydrologic predictions using conventional models. Moreover, it remains uncertain where the limits of predictability are and whether recently‐popular deep learning approaches can offer significant improvements. Here, we tested the first instance of the hydrologic model based on long short‐term memory (LSTM) for 55 Indian watersheds, using a new dataset comprising forcing, attributes, and discharge data. Our results show that the LSTM model provides much‐improved performance compared to conventional models in India, providing a median Nash‐Sutcliffe efficiency (NSE) of 0.56. The LSTM model trained on all the watersheds is more favourable to those trained on individual or homogeneous watersheds, as it benefits from a broader range of hydrological processes and patterns in the input data. However, the LSTM model performs poorly for non‐perennial, large, and semi‐arid climate zone watersheds due to its inability to simulate the complex hydrological processes specific to these environments. Integrating lagged observations with the LSTM model (referred to as DI‐LSTM) improved the predictions in such watersheds and enhanced the median NSE to 0.76 by capturing the temporal dependencies and historical patterns that influence hydrological processes. Overall, the contrast of model performance across watersheds suggests major limitations could be associated with the quality of forcing data, and the slow flow or groundwater processes are highly important in the Indian subcontinent. Notably, both LSTM and DI‐LSTM models performed reasonably well for predictions in ungauged watersheds. The findings of this study demonstrate that data‐sparse countries, too, can benefit from big‐data deep learning and point out further avenues toward model improvements. [ABSTRACT FROM AUTHOR]

Many rivers and streams are ungauged or poorly gauged and predicting streamflow in such watersheds is challenging. Although streamflow signals result from processes with different frequencies, they can be "sparse" or have a "lower‐dimensional" representation in a transformed feature space. In such cases, if this appropriate feature space can be identified from streamflow data in gauged watersheds by dimensionality reduction, streamflow in poorly gauged watersheds can be predicted with a few measurements taken. This study utilized this framework, named data‐driven sparse sensing (DSS), to predict daily‐scale streamflow in 543 watersheds across the contiguous United States. A tailored library of features was extracted from streamflow training data in watersheds within the same climatic region, and this feature space was used to reconstruct streamflow in poorly gauged watersheds and identify the optimal timings for measurement. Among different regions, streamflow in snowmelt‐dominated and baseflow‐dominated watersheds (e.g., Rocky Mountains) was more effectively predicted with fewer streamflow measurements taken. The prediction efficiency in some rainfall‐dominated regions, for example, New England and the Pacific coast, increased significantly with an increasing number of measurements. The spatial variability of prediction efficiency can be attributed to the process‐driven mechanisms and the dimensionality of watershed dynamics. Storage‐dominated systems are lower‐dimensional and more predictable than rainfall‐dominated systems. Measurements taken during periods with large streamflow magnitudes and/or variances are more informative and lead to better predictions. This study demonstrates that DSS can be an especially useful technique to integrate ground‐based measurements with remotely sensed data for streamflow prediction, sensor placement, and watershed classification. Plain Language Summary: Many rivers and stream reaches are ungauged or poorly gauged because streamflow measurement is costly and resource intensive. Predicting the streamflow time‐series in these ungauged or poorly gauged watersheds is still challenging. Here, we use a signal processing technique called data‐driven sparse sensing on a national‐scale streamflow data set across the contiguous United States. We predict streamflow time‐series in each watershed based on existing streamflow data in watersheds nearby, and explore the best times during the year for measuring streamflow. Our analysis shows that data‐driven sparse sensing is an effective tool to predict streamflow time‐series in poorly gauged watersheds based on very few streamflow measurements. The streamflow in watersheds with high snowmelt and high baseflow can be more easily predicted than in other watersheds. Our analysis also shows that the streamflow measurements taken during periods with large streamflow peaks and variances contain more information and are beneficial for making predictions. We conclude that data‐driven sparse sensing can be further used to classify watersheds and to identify the best locations for streamflow gauging. Key Points: We utilize data‐driven sparse sensing to predict daily streamflow and identify the optimal times for streamflow measurement across the contiguous United StatesStreamflow was more effectively predicted in watersheds dominated by snowmelt and baseflow than those dominated by rainfall and quickflowThe optimal sampling times for streamflow prediction by data‐driven sparse sensing are periods with large flow magnitudes and variances [ABSTRACT FROM AUTHOR]

Effective water quality management and reliable environmental modeling depend on the availability, size, and quality of water quality (WQ) data. Observed stream water quality data are usually sparse in both time and space. Reconstruction of water quality time series using surrogate variables such as streamflow have been used to evaluate risk metrics such as reliability, resilience, vulnerability, and watershed health (WH) but only at gauged locations. Estimating these indices for ungauged watersheds has not been attempted because of the high-dimensional nature of the potential predictor space. In this study, machine learning (ML) models, namely random forest regression, AdaBoost, gradient boosting machines, and Bayesian ridge regression (along with an ensemble model), were evaluated to predict watershed health and other risk metrics at ungauged hydrologic unit code 10 (HUC-10) basins using watershed attributes, long-term climate data, soil data, land use and land cover data, fertilizer sales data, and geographic information as predictor variables. These ML models were tested over the Upper Mississippi River Basin, the Ohio River Basin, and the Maumee River Basin for water quality constituents such as suspended sediment concentration, nitrogen, and phosphorus. Random forest, AdaBoost, and gradient boosting regressors typically showed a coefficient of determination R 2 > 0.8 for suspended sediment concentration and nitrogen during the testing stage, while the ensemble model exhibited R 2 > 0.95 . Watershed health values with respect to suspended sediments and nitrogen predicted by all ML models including the ensemble model were lower for areas with larger agricultural land use, moderate for areas with predominant urban land use, and higher for forested areas; the trained ML models adequately predicted WH in ungauged basins. However, low WH values (with respect to phosphorus) were predicted at some basins in the Upper Mississippi River Basin that had dominant forest land use. Results suggest that the proposed ML models provide robust estimates at ungauged locations when sufficient training data are available for a WQ constituent. ML models may be used as quick screening tools by decision makers and water quality monitoring agencies for identifying critical source areas or hotspots with respect to different water quality constituents, even for ungauged watersheds. [ABSTRACT FROM AUTHOR]

Calibration of global hydrological models (GHMs) has been attempted for over two decades; however, an effective and generic calibration method has not been explored. We present a novel framework for calibrating GHMs assuming that parameters can be regionalized by climate similarities. We calibrated four sensitive parameters of the H08 global hydrological model by aggregating the results of 5,000 simulations with randomly generated parameters into 11 Köppen climate classes and using an objective function Nash–Sutcliffe Efficiency (NSE) with random sampling from the proposed parameter distribution. From a 100‐fold split‐sampling test, we found that both the representativeness and robustness of the transferred parameter sets were guaranteed when the upper 5% of the samples were accepted and assign the median of each accepted parameter distribution for the climate class. The simulation with the climate‐based parameters yielded satisfactory (NSE > 0.0) and good (NSE > 0.5) performances at 480 and 234 stations (61.7% and 30.1% of 777 stations), respectively. The storage capacity (SD) and the conductive coefficient (CD) were sensitive to the climate classes and exhibited well‐constrained distributions of the accepted samples, whereas the recession parameters for the subsurface storage (γ and τ) showed little or no explanatory power to climate. The identified parameters for climate classes exhibited consistency with the physical interpretation of soil formation and efficiencies in vapor transfer. The consistency of the identified parameter values with physical underpinnings indicates that the appropriate parameters were determined, which ensured the robustness of parameters, especially when they are transferred to ungauged watersheds. Key Points: We tested the hypothesis that the climate properties exert a dominant influence on parameter similarities at the global scaleThe simulation with the climate‐based parameters yielded improvement from default parametersAppropriate parameter values were determined, and results demonstrate their robustness, especially when applied to ungauged watersheds [ABSTRACT FROM AUTHOR]

Study region: Six small agricultural watersheds in three tropical climatic regions of Ethiopia. Three regions of contrasting climate: highland, midland, and lowland regions with respectively high, middle, and low elevations and rainfalls. Study focus: Lag time (TL) and time of concentration (TC) are two measures of how quickly a stream responds to runoff-producing rainfall. These parameters are the main inputs used to estimate peak flow under flood conditions in ungauged watersheds. Many empirical methods have been proposed to estimate TL and TC, but the validity of none of them has been tested. This study compared 10 commonly used methods by using measured TL and TC. New hydrological insights: Measured median values of TL and TC for 176 rainfall–runoff events were used to evaluate the performance of empirical methods. For individual watersheds, the estimates of TL and TC differed by up to 2.6 h and 4.4 h, respectively. Most of the empirical methods tended to substantially underestimate TL and TC, which would lead to overestimation of runoff volume. TL and TC computed by two methods that consider both overland and channel flow were closest to the measured values of TL and TC, because such mixed flow is typical of tropical climate regions. Our results show the need for caution when empirical methods developed in regions with a particular climatic and geomorphological conditions are applied elsewhere.

Abstract Estimating the minimum streamflows in rivers is essential to solving problems related to water resources. In gauged watersheds, this task is relatively easy. However, the spatial and temporal insufficiency of gauged watercourses in Brazil makes researchers rely on the hydrological regionalization technique. This study’s objective was to compare different hierarchical and non-hierarchical clustering approaches for the delimitation of hydrologically homogeneous regions in the state of Rio Grande do Sul, Brazil, aiming to regionalize the minimum streamflow that is equaled or exceeded in 90% of the time (Q90). The methodological development for the regionalization of Q90 consisted of using regression analysis supported by multivariate statistics. With respect to independent variables for regionalization, this study considered the morphoclimatic attributes of 100 watersheds located in southern Brazil. The results of this study highlighted that: (i) the clustering techniques had the potential to define hydrologically homogeneous regions, in the context of Q90 in the Rio Grande do Sul State, mostly the Ward algorithm associated with the Manhattan distance; (ii) drainage area, perimeter, centroids X and Y, and mean annual total rainfall aggregated important information that increased the accuracy of the cluster; and (iii) the refined mathematical models provided excellent performance and can be used to estimate Q90 in ungauged rivers.

Water is a fundamental natural resource for life on Earth and is the basic component of the hydrological cycle. The evaluation of the amount of available water in a watershed is a requirement for development; and administration of the hydric resources whether it is for supplying water to the population, agriculture, industry or for energy production. Hydrological modeling is one of the principal tools used for estimating flows in ungauged watersheds. In this study the CEQUEAU distributed hydrological model was applied to estimate the flows in four ungauged sites of the Tacotalpa river watershed for the period 1965 to 1999. For the evaluation of the efficiency of the model, three statistics were used: the Nash-Sutcliffe efficiency coefficient (NSE), the percent bias (PBIAS) and the determination coefficient (R2). According to the statistical criteria, the model is very good. CEQUEAU made simulations with a good natural response of the flows of the watershed. [ABSTRACT FROM AUTHOR]

This study primarily aims to develop a method for estimating the range of flood sizes in small and medium ungauged watersheds in local river streams. In practice, several water control projects have insufficient streamflow information. To compensate for the lack of data, the streamflow propagation method (SPM) provides streamflow information for ungauged watersheds. The ranges of flood sizes for ungauged watersheds were generated using a specific flood distribution analysis based on the obtained streamflow data. Furthermore, the influence of rainfall information was analyzed to characterize the patterns of specific flood distributions. Rainfall location, intensity, and duration highly affected the shape of the specific flood distribution. Concentrated rainfall locations affected the patterns of the maximum specific flood distribution. The shape and size of the minimum specific flood distribution were dependent on the rainfall intensity and duration. The Creager envelope curve was used to generate equations for the maximum/minimum specific flood distribution for the study site. The ranges of the specific flood distributions were produced for each watershed size.

In recent years, several empirical and mathematical methods have been developed to estimate runoff, among which the SCS curve number (SCS-CN) method is one of the simplest and most widely used methods. The SCS-CN depends mainly on a CN parameter which corresponds to various soil, land cover, and land management conditions, selected from look-up tables. An application of GIS and RS techniques along with filed investigations made it possible to enhance the method from a lumped one to the level of semi-distributed models in which a specific value can be assigned to each cell in raster maps. The up-to-date procedures require several datasets, field measurements and overlying issues which limits the use of SCS-CN in data-scarce regions. In this research a new method has been developed which estimates the SCS-CN over the catchment with a minimum input dataset and acceptable accuracy and is based on the saturation-excess concept, which is used in the semi-distributed model: TOPMODEL. The proposed method depends on three parameters, including ndrain (soil porosity), z ¯ (average distance to watershed water table surface) and m (which controls the effective depth of the saturated soil) and one input dataset, the so-called topographic index. Results showed that the maximum and minimum differences between the basin-averaged CN based on the GIS and RS techniques and the proposed method for Kasilian and Jong watersheds are 12% and 0.3%, respectively. Also, the findings indicated that, of the three parameters of proposed method, the m parameter plays a key role and that by increasing this parameter the basin-averaged CN tends to decrease and vice versa. Because of the dependence on a topographic index, the proposed method is strongly affected by DEM resolution and there are significant differences between low and high-resolution DEMs. However, for a small scale watershed, similar to Kasilian, using DEMs with resolution lower than 100 m considerably decreases the above differences. As an overall conclusion, the proposed method provides acceptable values of SCS-CN which is important for running rainfall-runoff model in a data-limited or data-scarce regions. In addition, creating the gridded map for CN, which is required in most hydrological models, is one of the most important advantages of the proposed method. [ABSTRACT FROM AUTHOR]

• Shape-based time-series clustering of gauged watersheds solely based on stream flow data. • Random Forests classification of ungauged watersheds to flow-regime classes. • Boruta algorithm identifies crucial watershed attributes used in Random Forests. • Flow-regime similarity does not equal to similarity in individual attributes. • Catchment Attributes and Meteorology for Large-Sample Studies Dataset. Watershed classification is considered necessary for various purposes, including improving the transferability of streamflow information and allowing the generalization of hydrologic theories. In this paper, we proposed a classification method using shape-based time-series clustering techniques to define hydrologically homogeneous classes for a set of 638 gauged watersheds in the conterminous United States. We defined 15 distinct flow-regime classes based on standardized weekly-step mean annual hydrographs of the watersheds analyzed. Most classes showed regionality, though to various degrees. Classes in the Atlantic Coast region showed strong geographical contiguity indicating that spatial proximity may be an appropriate indicator of flow-regime similarity; but on the other hand, nearby watersheds may exhibit different intra-annual variabilities and watersheds far apart from one another may exhibit a similar flow regime. Such cases show that spatial proximity may not be used as a universal indicator of flow-regime similarity. To expand the flow-regime classification to ungauged watersheds, where no streamflow data are available, we proposed a physical-characteristic classification method using the Random Forests algorithm based on a set of physical-climatic attributes. Based on the Boruta feature-selection algorithm, attributes related to snowiness, precipitation seasonality, and green vegetation coverage stood out as the most relevant controls on regime-class memberships. By analyzing the within-class variability of the watershed attributes, we found that watersheds of the same flow-regime class may be very different in terms of individual attribute values indicating that flow-regime dis/similarity may not be equated to dis/similarity in individual watershed attributes; instead, it is the interplay among attributes that determines the streamflow behavior. Overall, methods proposed for the classification of gauged and ungauged watersheds are believed to be of value in a range of applications including selecting gauged donor watersheds for estimating historical streamflow records at ungauged watersheds. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Highlights: [•] A probabilistic constraint based binary CA model is developed to simulate the futuristic scenarios of urban growth. [•] Spatial data exploration techniques were used to derive the transition rules for CA model. [•] NRCS CN method is found to be suitable to assess the runoff characteristics of ungauged catchments. [•] CA model delivers the temporal inputs to the hydrologic model and is consistent with the latter. [•] Integration of these techniques is vital to assess the relative change in the runoff hydrographs on temporal scale. [ABSTRACT FROM AUTHOR]

Abstract: Gaining knowledge on potential climate change impacts on water resources is a complex process which depends on numerical models capable of describing these processes in quantitative terms. Under limited data or ungauged basin conditions, which constrain the modelling approach, a physically based coherent methodological approach is required. The traditional approach to assess flow regime and groundwater recharge impacts, based on coupling general atmosphere–ocean circulation models (GCM) and hydrologic models, has been investigated in the Siurana ungauged catchment (NE Spain). The future A2 (medium-high) and B1 (medium-low) greenhouse gas scenarios and time slices 2013–2037 (2025) and 2038–2062 (2050), developed by the Intergovernmental Panel on Climate Change (IPCC, 2001), have been selected. For scenario simulations, coupled GCM ECHAM5 scenarios, stochastically downscaled outputs and surface–subsurface modelling to simulate changes in water resources were applied to the catchment. Flow regime analysis was assessed by HEC-HMS, a physically based hydrologic model to assess rainfall–runoff in a catchment, while recharge was estimated with VisualBALAN, a distributed model for natural recharge estimation. Simulations show that the projected climate change at the catchment will affect the entire hydrological system with a maximum of 56% reduction of water resources. While subtle changes are observed for the 2025 time slice, the temperature and precipitation forecast for 2050 shows a maximum increase of 2.2°C and a decreased precipitation volume of 11.3% in relation to historical values. Regarding historical values, runoff output shows a maximum 20% decrease, and 18% decrease of natural recharge with a certain delay in relation to runoff and rainfall data. According to the results, the most important parameters conditioning future water resources are changes in climatic parameters, but they are highly dependent on soil moisture conditions. [Copyright &y& Elsevier]

There has been a steady shift towards modeling and model-based approaches as primary methods of assessing watershed response to hydrologic inputs and land management, and of quantifying watershed-wide best management practice (BMP) effectiveness. Watershed models often require some degree of calibration and validation to achieve adequate watershed and therefore BMP representation. This is, however, only possible for gauged watersheds. There are many watersheds for which there are very little or no monitoring data available, thus the question as to whether it would be possible to extend and/or generalize model parameters obtained through calibration of gauged watersheds to ungauged watersheds within the same region. This study explored the possibility of developing regionalized model parameter sets for use in ungauged watersheds. The study evaluated two regionalization methods: global averaging, and regression-based parameters, on the SWAT model using data from priority watersheds in Arkansas. Resulting parameters were tested and model performance determined on three gauged watersheds. Nash-Sutcliffe efficiencies (NS) for stream flow obtained using regression-based parameters (0.53-0.83) compared well with corresponding values obtained through model calibration (0.45-0.90). Model performance obtained using global averaged parameter values was also generally acceptable (0.4 ≤ NS ≤ 0.75). Results from this study indicate that regionalized parameter sets for the SWAT model can be obtained and used for making satisfactory hydrologic response predictions in ungauged watersheds. [ABSTRACT FROM AUTHOR]

Development of accurate surface runoff estimation techniques from ungauged watersheds is relevant in Indian condition due to the non-availability of hydrologic gauging stations in majority of watersheds. Besides this, the high budgetary requirements for installation of gauging stations are another limiting factor in India, which leads to the use of surface runoff estimation techniques for ungauged watersheds. Natural Resources Conservation Services Curve Number (NRCS-CN) method is one of the most widely used methods for quick and accurate estimation of surface runoff from ungauged watershed. Also, the coupling of NRCS-CN techniques with the advanced Geographic Information System (GIS) capabilities automates the process of runoff prediction in timely and efficient manner. Keeping view of this, a GIS interface was developed using the in-built macro programming language, Visual Basic for Applications (VBA) of ArcGIS® tool to estimate the surface runoff by adopting NRCS-CN technique and its three modifications. The developed interface named as Interface for Surface Runoff Estimation using Curve Number techniques (ISRE-CN), was validated using the recorded data for the periods from 1993 to 2001 of a gauged watershed, Banha in the Upper Damodar Valley in Jharkhand, India. The observed runoff depths for different rainfall events in this study watershed was compared with the predicted values of NRCS-CN methods and its three modifications using statistical significance tests. It was revealed that using all the rainfall data for different AMC conditions, the modified CN I performed the best [R2 (coefficient of determination)=0.92; E (model efficiency)=0.89) followed by modified CN III method (R2=0.88; E=0.87), while the modified CN II (R2=0.42; E=0.36) failed to predict accurately the surface runoff from Banha watershed. Moreover, under AMC based estimations, the modified CN I method also performed best (R2=0.95; E=0.95) for AMC II condition, while the modified CN II performed the worst in all the AMC conditions. However, the developed Interface in ArcGIS® needs to be tested in other watershed systems for wider applicability of the modified CN methods [ABSTRACT FROM AUTHOR]

ABSTRACT: A nontraditional application of the Hydrological Simulation Program - FORTRAN (HSPF) model to simulate freshwater discharge to upper Charlotte Harbor along Florida's west coast was performed. This application was different from traditional HSPF applications in three ways. First, the domain of the model was defined based on the hydraulic characteristics of the landforms using small distributed parameter discretization. Second, broad wetland land forms, representing more than 20% of this area, were simulated as reaches with storage-attenuation characteristics and not as pervious land segments (PERLNDs). Finally, the reach flow-tables (F-Tables) were configured in a unique way to be calibrated representing the uncertainty of the storage-attenuation process. Characterizing wetlands as hydrography elements allows flow from the wetlands to be treated as a stage-dependent flux. The study was conducted for the un-gauged portion of the Peace and Myakka rivers in west-central Florida. Due to low gradient tidal influences, a large portion of the basin is un-gauged. The objective of this study was to simulate stream flow discharges and to estimate freshwater inflow from these ungauged areas to upper Charlotte Harbor. Two local gauging stations were located within the model domain and were used for calibration. Another gauge with a shorter period of record was used for verification. A set of global hydrologic parameters were selected and tested using the parameter optimization software (PEST) during the calibration. Model results were evaluated using PEST and well-known statistical indices. The correlation coefficients were very high (0.899 and 0.825) for the two calibration stations. Further testing of this approach appears warranted for watersheds with significant wetlands coverage. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we investigate model parameter uncertainties associated with hydrological process parameterizations and their impacts on model simulations in the Three-Layer Variable Infiltration Capacity (VIC-3L) land surface model. We introduce an alternative subsurface flow parameterization into VIC-3L to reduce the impacts of model parameter uncertainties on model simulations by reducing the number of model parameters that need to be estimated through a calibration process. The new subsurface flow parameterization is based on the concepts of kinematic wave and hydrologic similarity, and has one parameter for calibration. Results from the 12 MOPEX (Model Parameter Estimation Experiment) basins obtained by applying the VIC-3L model with the new subsurface flow formulation show that the performance of the new parameterization is comparable to the original subsurface flow formulation, which has three parameters for calibration. In addition, a probabilistic approach based on Monte Carlo simulations is used to evaluate model performance and uncertainties associated with model parameters over different ranges of streamflow. Studies based on the 12 MOPEX watersheds show that compared to the parameter associated with the new subsurface flow parameterization, the VIC shape parameter (i.e. the b parameter that represents the shape of the heterogeneity distribution of effective soil moisture capacity over a study area) has a larger impact on model simulations and could introduce more uncertainty if not estimated appropriately. Furthermore, investigations on the b parameter suggest that the ensembles (i.e. the mean response and its bounds) from the Monte Carlo simulations could provide reasonable predictions and uncertainty estimates of streamflows, which have important implications for applications to ungauged basins. The study also shows that appropriate reduction of the number of model parameters is an effective approach to reduce the impacts of parameter uncertainties on model simulations. This is more so for applications to ungauged basins or basins with limited data available for calibration. The new subsurface flow parameterization and the probabilistic uncertainty analysis approach are general and can be applied to other modeling studies. [Copyright &y& Elsevier]

Several issues related to the probabilistic and hydraulic structure of the geomorphologic instantaneous unit hydrograph (GIUH) are addressed. These issues are: (1) accuracy of the geomorphologic expressions of the probabilities of surface runoff movement in a watershed, (2) identifying, for a given storm, a representative time-average velocity for surface runoff, (3) estimation of this velocity for the ungauged watersheds and effect of velocity estimation errors on the GIUH predictability, and (4) suitability of incorporating a linear expression for infiltration in the GIUH as well as the estimation problem of the infiltration coefficient and its effect on the reliability of predicted hydrographs. These issues are analyzed through application of the GIUH for two gauged watersheds in the southwestern region of Saudi Arabia. Twelve storm events are used in the analysis and the results are presented. [ABSTRACT FROM AUTHOR]

This study primarily aims to develop a method for estimating the range of flood sizes in small and medium ungauged watersheds in local river streams. In practice, several water control projects have insufficient streamflow information. To compensate for the lack of data, the streamflow propagation method (SPM) provides streamflow information for ungauged watersheds. The ranges of flood sizes for ungauged watersheds were generated using a specific flood distribution analysis based on the obtained streamflow data. Furthermore, the influence of rainfall information was analyzed to characterize the patterns of specific flood distributions. Rainfall location, intensity, and duration highly affected the shape of the specific flood distribution. Concentrated rainfall locations affected the patterns of the maximum specific flood distribution. The shape and size of the minimum specific flood distribution were dependent on the rainfall intensity and duration. The Creager envelope curve was used to generate equations for the maximum/minimum specific flood distribution for the study site. The ranges of the specific flood distributions were produced for each watershed size. [ABSTRACT FROM AUTHOR]

Irrigation return flow (RF) is a critical component of the water cycle in an agricultural watershed, influencing the flow regime of downstream river. As such, it should be accurately quantified when developing water resources management plans and practices. Although many studies have proposed ways to quantify RF, uncertainty in RF estimates has not been determined to improve reliability and credibility. This study examines how conceptual (CON) and physically-based (PHY) parameterization approaches affect RF uncertainty. Results showed that PHY had a smaller amount of RF uncertainty compared to CON, as parameters of the PHY approach could be regulated based on their physical meanings. This study also found that the application of constraints created based on the relationship between the conceptual parameter and physical characteristics of irrigated plots could effectively reduce RF uncertainty made using the CON approach. This study demonstrates the benefits of the physically-based parameterization approach and the application of constraints on conceptual parameters to RF estimation. [ABSTRACT FROM AUTHOR]