Hydrologic engineering

Steve Blanchard called the meeting to order at 9:06 AM (EST) and welcomed everyone. Each person introduced themselves and their organizational affiliation The meeting agenda was approved as listed above. Minutes from April 17, 2008 Meeting The minutes were approved. 4. Status of Action Items from April 17, 2008 Meeting Steve Blanchard updated the status of the action items: a. Email any additional organization business reports to Mary Greene by Monday May 5 -Completed b. Work Group leaders -email a written version of the reports you gave at the meeting to Mary Greene by Monday May 5 so Mary can complete the draft minutes. -Completed c. Email suggestions for a Theme for the 2010 Hydrologic Modeling Conference to Don Frevert by Monday May 5. -Completed d. Steve will contact Carol regarding integrating the SOH and BOR websites to post the previous hydrologic modeling conference proceedings. -Addressed e. Steve will act on the requests from the Extreme Storms Ad Hoc Group to resolve the NOAA/NWS availability for leading the extreme storm update effort. -Addressed f. Email SOH Connections Newsletter submissions to Claudia and Mary by Friday May 23 -the next newsletter is scheduled for early June 2008-Completed 5. Presentation "National Environmental Status and Trends Indicators Project" by Richard Guldin, Director -Quantitative Sciences U.S. Forest Service (Presentation followed by Q&A -total 30 min.) (Presentation Attached) 6. Update on Hydrologic Frequency Analysis Work Group Hydrologic Frequency Analysis Work Group (HFAWG) report Will Thomas summarized the status of the testing for the Expected Moments Algorithm (EMA) and Bulletin 17B techniques. He reported that he received more test results from John England, Bureau of Reclamation, on May 5, 2008. These results were more complete but not significantly different from the February results. The results to date indicate large differences in the 100-year flood estimates for the two methods for a few stations with zero flow years and low outliers. John England indicated that the EMA code needed some additional modifications with respect to the new low-outlier test. Will reported that the data group (

For any river, besides the importance of stagedischarge relationship (rating curve), a stage-discharge-distance relationship is of more significance. The accurate estimation of both relationships along a river reach is considered a key point for various applications of water resources engineering such as operation and management of water resources projects, designing of hydraulic structures, and sediment analysis. In this paper, both relationships were established for the Shatt Al-Hillah river reach by applying multiple linear regression and simple linear regression using least square method for determining regression equations. Twelve gauging stations including three primary and nine secondary stations were considered for this method. Moreover, for evaluating the performance of both regressions, statistical measures such as coefficient of determination, root mean square error, mean square error, and Thiel's factor were used. The study results generally indicate a superior performance of both modeling techniques. MLR model was able to predict and mimic the stage-discharge-distance relationship with a coefficient correlation of about 0.932, while SLR model was able to predict three rating curves for the three primary stations with coefficient correlation of about 0.960, 0.943, and 0.924 respectively.

Natural or human-induced variability emerged from investigation of the traditional stationary assumption regarding extreme precipitation analyses. The frequency of extreme rainfall occurrence is expected to increase in the future and neglecting these changes will result in the underestimation of extreme events. However, applications of extremes accept the stationarity that assumes no change over time. Thus, non-stationarity of extreme precipitation of 5, 10, 15, and 30 minutes and 1-, 3-, 6-, and 24-hour data of 17 station in the Black Sea region were investigated in this study. Using one stationary and three non-stationary models for every station and storm duration, 136 stationary and 408 non-stationary models were constructed and compared. The results are presented as non-stationarity impact maps across the Black Sea Region to visualize the results, providing information about the spatial variability and the magnitude of impact as a percentage difference. Results revealed that nonstationary (NST) models outperformed the stationary model for almost all precipitation series at the 17 stations. The model in which time dependent location and scale parameter used (Model 1), performed better among the three different time variant non-stationary models (Model 1 as time variant location and scale parameters, Model 2 as time variant location parameter, and Model 3 as time variant scale parameter). Furthermore, non-stationary impacts exhibited site-specific behavior: Higher magnitudes of non-stationary impacts were observed for the eastern Black Sea region and the coastal line. Moreover, the non-stationary impacts were more explicit for the subhourly data, such as 5 minutes or 15 minutes, which can be one of the reasons for severe and frequent flooding events across the region. The results of this study indicate the importance of the selected covariate and the inclusion of it for the reliability of the model development. Spatial and temporal distribution of the nonstationary impacts and their magnitude also urges to further investigation of the impact on precipitation regime, intensification, severity.

Extreme flood simulation with synthetic extreme precipitation events raises unavoidable questions about the choice of initial conditions. State-of-the-art extreme flood estimation frameworks propose to address these questions with the help of semicontinuous modeling and reanalysis of simulated state variables. In this context, the present work proposes a new method for the selection of initial conditions for extreme flood simulation. The method is based on generating sets of initial conditions from the matrix of state variables corresponding to a long simulation run of the selected hydrological model. Two sets of initial conditions are obtained: a deterministic set composed of selected state variable quantiles and a stochastic set composed of state variable vectors randomly drawn from the complete state variable matrix. The extreme flood simulations corresponding to both sets are compared in detail, and the stochastic simulations are used in a sensitivity analysis to identify the dominant state variables and possible interactions. The aim hereby is to provide a tool to analyze the role of initial conditions and the importance to account for state variable interactions in extreme flood estimation. The proposed method is applied to probable maximum flood estimation for the Swiss Mattmark Dam catchment with a semilumped hydrological model. The obtained results for this case study show that for high flood peak quantiles, the initial soil saturation is dominating other state variables, and deterministic initial conditions are sufficient to generate extreme floods.

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In cold conditions, early winter precipitation occurs as snowfall and contributes to the accumulating seasonal snowpack. In a warming climate, precipitation may occur as rainfall in mountainous areas. Detecting changes during seasonal transitions is difficult because these may encompass changes in timing, magnitude and phase, which may not be consistent between years. In this study, a sampling window from September to December is used to assess trends in magnitude, frequency and duration (of rainfall, snowfall and runoff events) in 127 climate stations and 128 watersheds with more than 30 years of record across the Rocky Mountains of North America. Rainfall events have increasing frequencies and durations, and magnitude trends are predominantly increasing at mid-latitude and mid-elevation. Snowfall events have the largest numbers of significant trends, with both increasing and decreasing trends in each of magnitude, frequency and duration. Snowfall events have decreasing frequencies and durations in the northern low-elevation sites and increasing frequencies and durations at the southern high-elevation sites. Trends in runoff events are less common than for rainfall and snowfall but are greater than expected by chance, and show similar frequency and duration patterns to snowfall trends. Snowfall and runoff events are decreasing in frequency and duration north of Wyoming and increasing to the south. Snowfall magnitude is generally decreasing to the north and increasing to the south, with runoff magnitude trends showing the reverse. Dans les environnements froids, les pr ecipitations au d ebut de l'hiver sont constitu ees de chutes de neige qui contribuent a l'accumulation de la couche neigeuse hivernale. Dû au r echauffement climatique, ces pr ecipitations peuvent devenir des chutes de pluie dans les r egions montagneuses. D eterminer les variations pendant les p eriodes de transitions entre les saisons est difficile; ces variations, qui ne sont pas consistantes d'une ann ee a l'autre, incluent la date d'occurrence, la quantit e de pr ecipitation, et la phase (neige ou pluie). Dans cette etude, une fenêtre d' echantillonnage de septembre a d ecembre est utilis ee pour evaluer les tendances des quantit es, des fr equences d'occurrence et des dur ees associ ees aux chutes de pluie et chutes de neige, et aux ev enements de ruissellement dans 127 stations m et eorologiques et 128 bassins versants, sur plus de 30 ann ees de donn ees aux travers des Rocheuses d'Am erique du Nord. Les chutes de pluie ont et e de plus en plus fr equentes, et leur dur ee et quantit e ont une tendance principalement a la hausse dans les latitudes et altitudes moyennes. Les chutes de neige ont le plus grand nombre de tendances significatives avec a la fois des tendances a la hausse et a la basse pour leurs quantit es, fr equences et dur ees. Au nord, la fr equence et la dur ee de ces ev enements baissent sur les sites de basse altitude mais elles augmentent sur les sites de haute altitude dans le sud. Pendant la p eriode consid er ee, les ev enements de ruissellement ont des tendances plus faibles que les chutes de pluie et de neige, mais sont plus importantes que seul le hasard pourrait pr evoir et sont similaires en fr equence et en dur ee aux tendances des chutes de neige. Les ev enements de ruissellement et les chutes de neige d ecroissent en fr equence et dur ee dans les r egions qui sont situ ees plus au nord du Wyoming et ils accroissent plus au sud. Pour les chutes de neige, leur quantit e est g en eralement en d eclin dans le nord mais elle s'accroit dans le sud alors que la quantit e des ev enements de ruissellement montre des tendances oppos ees.

De manière à étudier la performance hydraulique sur le long terme de tranchées d'infiltration d'eaux pluviales, une étude a été entreprise visant à évaluer la résistance hydraulique globale ainsi que celles du fond et des parois. La méthode utilisée est basée sur le calage de résistances hydrauliques évènement pluvieux par évènement pluvieux selon le modèle de Bouwer et a été appliquée à une tranchée de démonstration dans un contexte brésilien. Le calage a été mené en minimisant les écarts entre débit d'infiltration mesuré et modélisé et en utilisant des données en continu de pluies, de débits d'entrée, de températures et de hauteurs d'eau dans la tranchée acquises dans le cadre du projet européen Switch. L'étude a montré que la méthodologie et notamment le recours au modèle de Bouwer donnaient de bons résultats. Elle révèle une évolution significative du colmatage sur une année. La résistance globale y a été multipliée par un facteur de plus de 6. Une différence significative a également été constatée entre résistance hydraulique du fond et des parois ; le fond étant le plus prompt au colmatage. Même si le niveau atteint par le colmatage des parois est acceptable par rapport à celui du fond, une augmentation de sa valeur a été détectée ; ce que l'on ne retrouve pas dans la littérature qui rapporte des valeurs plus constantes sur des périodes plus importantes. Des fluctuations brusques de résistance aussi bien pour le fond que pour les parois ont pu être observées qui pourraient être expliquées par des apports de matières en suspension élevés.

This paper presents a study of precision agriculture in the wine industry. While precision viticulture mostly aims to maximise yields by delivering the right inputs to appropriate places on a farm in the correct doses and at the right time, the objective of this study was rather to assess vine biomass differences. The solution proposed in this paper uses aerial imagery as the primary source of data for vine analysis. The first objective to be achieved by the solution is to automatically identify vineyards blocks, vine rows, and individual vines within rows. This is made possible through a series of enhancements and hierarchical segmentations of the aerial images. The second objective is to determine the correlation of image data with the biophysical data (yield and pruning mass) of each vine. A multispectral aerial image is used to compute vegetation indices, which serve as indicators of biophysical measures. The results of the automatic detection are compared against a test field, to verify both vine location and vegetation index correlation with relevant vine parameters. The advantage of this technique is that it functions in environments where active cover crop growth between vines is evident and where variable vine canopy conditions are present within a vineyard block.

The concept of a cascade of n linear reservoirs gives rise to an interesting unit hydrograph ͑UH͒ given by Eq. ͑1͒ in the original paper, which is repeated here for convenience: The authors are perfectly right in trying to simplify the relationship between the conceptual parameters n and K of this UH and its two major graphic characteristics, that is, its peak value, noted q p , and the time of peak occurrence, t p . The relationship is very simple for t p ͑Eq. 2͒ of the original paper but far more complicated for q p ͑Eq.

Many engineers and hydrologists use the curve number method to estimate runoff from ungaged watersheds; however, the method does not explicitly account for the influence of season or forest cutting on runoff. This study of observed rainfall and runoff for small, forested watersheds that span the Appalachian Mountains of the eastern United States showed that curve numbers calibrated for the growing season tended to be smaller than for the dormant season. Forest cutting tended to increase curve numbers. However, the increase in water yield following forest cutting on these watersheds only lasted 1 year to 11 years, thereby limiting the precision of the curve numbers estimated for these brief hydrologic effect periods. This study highlights the need to account for seasonal and forest cutting when estimating runoff from some forested watersheds.

Engineers and hydrologists use the curve number method to estimate runoff from rainfall for different land use and soil conditions; however, large uncertainties occur for estimates from forested watersheds. This investigation evaluates the accuracy and consistency of the method using rainfall-runoff series from 10 small forested-mountainous watersheds in the eastern United States, eight annual maximum series from New Hampshire, West Virginia, and North Carolina, and two partial duration series from Georgia. These series are the basis to compare tabulated curve numbers with values estimated using five methods. For nine of 10 watersheds, tabulated curve numbers do not accurately estimate runoff. One source of the large uncertainty is a consistent decrease in storm-event curve numbers with increasing rainfall. A calibrated constant curve number is suitable for only two of 10 watersheds; the others require a variable watershed curve number associated with different magnitude rainfalls or probabilities of occurrence. Paired watersheds provide consistent curve numbers, indicating that regional values for forested-mountainous watersheds (locally calibrated and adjusted for storm frequency) may be feasible.

To better understand and monitor the hydraulic effects caused by clogging near groundwater production wells, a study was conducted in the unconfined sand aquifer utilized by the City of North Battleford, Saskatchewan, Canada. Detailed investigations were carried out near one production well between the spring of 2007 and the fall of 2008. Results from four pumping tests conducted over this time period indicated no significant temporal changes in hydraulic conductivity within approximately two meters of the pumping well. In contrast, long-term monitoring of hydraulic head differentials did reveal increases with time, and suggested that clogging began to accelerate within a radius of one meter of the well after about one year of continuous pumping. It is concluded that continuous monitoring of hydraulic head in pumping wells and nearby piezometers, combined with breakpoint analyses of head differentials and specify capacity tests, will be more effective than standard pumping tests for detecting temporal and spatial trends in aquifer and well screen clogging.

An exhaustive knowledge of flood risk, vulnerability and exposure in different spatial locations is essential for developing an effective flood mitigation strategy for a watershed. In the present study, a flood risk-vulnerability analysis is performed. All four components of flood vulnerability: (a) physical; (b) economic; (c) infrastructure and (d) social, are evaluated individually using a Geographic Information System (GIS) environment. The proposed methodology estimates the impact on infrastructure vulnerability due to inundation of critical facilities, emergency service stations, and road bridges. The components of vulnerability are combined to determine the overall vulnerability. The patterns of land use and soil type are considered as two major components of flood exposure. Flood hazard maps, overall vulnerability and exposure are used to finally compute the flood risk at different locations in the watershed. The proposed methodology is implemented to six major damage centers in the Upper Thames River watershed, located in south-western Ontario of Canada to assess the flood risk. A web-based information system is developed for systematic presentation of the flood risk, vulnerability and exposures by postal code regions or Forward Sortation Areas (FSAs). The system is designed to provide support for different users, i.e., general public, decision-makers and water management professionals. An interactive analysis tool is developed within the web-based information system to assist in evaluation of the flood risk in response to a change in land use pattern.

A data set comprising rainfall-runoff data gathered at 31 Agricultural Research Service experimental watersheds was used to explore curve number calibration. This exploration focused on the calibrated value and goodness-of-fit as a function of several items: calibration approach, precipitation event threshold, data ordering approach, and initial abstraction value. Calibration methods explored were least-squares, the National Engineering Handbook (NEH) median, and an asymptotic approach. Results were quantified for events exceeding two precipitation thresholds: 0 and 25.4 mm. Natural and frequency-matched data ordering methods were analyzed. Initial abstraction ratios of 0.05 and 0.20 were examined. Findings showed that the least-squares calibration approach applied directly to rainfall-runoff data performed best. Initial abstraction ratios clearly influenced the magnitude of the calibrated curve number. However, neither ratio outperformed the other in terms of goodness-of-fit of predicted runoff to observed runoff. Precipitation threshold experiments produced mixed results, with neither threshold level producing a clearly superior model fit. Frequency-matching was not considered to be a valid analysis approach, but was contrasted with naturally ordered results, indicating a bias toward producing calibrated curve numbers that were 5-10 points larger.

The objective of this study was to investigate the presence of trends in extreme precipitation. (denoted MAXP and defined as the annual maximum daily precipitation depth) time series for coastal northern New England and to assess changes in the magnitude of the socalled "100-year storm". MAXP depths from 48 stations with long, continuous records in Maine, New Hampshire and Massachusetts were analyzed. At those same stations, the number of daily precipitation depths ≥ 2 inches (denoted as GT2in) was also quantified for each year. The seasonally-averaged MAXP was found to be fairly uniform throughout the year, but the frequency of MAXP is highest during August through October, the typical hurricane season in New England. The presence of trends in MAXP and GT2in was evaluated over four time frames

Base flow recession analysis is required to estimate the long-term, reliable component of the hydrograph and water cycle, and for drought management, inflow design and analysis, and contaminant and nutrient transport. Operationally, recursive digital filtering (RDF) techniques are commonly applied; however, questions have been raised about the reliability and parameterization of these methods. A comparison of three base flow separation methods, including two popular RDFs and one hydrologic model, is performed in two base flowdominant subbasins of the Grand River Basin in southern Ontario, Canada. The Eramosa River and Whiteman's Creek subbasins are similar in size (236 and 383 km 2 , respectively) and have similar annual runoff distributions yet significantly different physiography governing runoff generation. Subbasin physiographic characteristics are found to result in significant differences in annual base flow distributions, which are well captured by the hydrologic model but not by the RDFs. Stable water isotopes (SWIs) are applied for verification of base flow separations using two-component mixing model separations and show better visual agreement with a base flow derived from the hydrologic model over the RDF methods. Limitations include insufficient isotope data to quantify a statistically significant model fit and results that are tied to subbasin physiography and basin-specific processes. Results support the application of SWIs for regional hydrograph separation and highlight the need for more efficient, yet physically based base flow separation in regions with complex physiography.

A critical need for farmers, particularly those in arid and semi-arid areas is to have a reliable, accurate and reasonably accessible means of estimating the evapotranspiration rates of their crops in order to optimize their irrigation requirements. Evapotranspiration

Drainage basins in the most parts of world are either partially gauged or completely ungauged. Prediction in ungauged basins is a serious challenge in hydrologic sciences, and there is still abundant work required to realize strong and reliable predictions for such basins. Errors in the prediction of hydrological parameters are seen due to unavailability of sufficient discharge data. In this research work Sub-basin of Kundalika River has been taken as the study area. Firstly we created the DEM by using the ArcGIS technique. To derive SUH we are used traditional synthetic unit hydrograph like Snyder's method. After modeling we developed the unit hydrograph of basin and we predict the discharge of basin.

This study focuses on a technique to improve runoff modeling based on radar-derived rainfall and hydrological model for the whole watershed. GIS tools were used to provide the model parameters for the Upper Bernam River Basin (1090 km2), Malaysia. Virtual rainfall stations are created throughout the UBRB watershed. The rainfall data for these stations are estimated from raw weather radar data using newly developed program called RaDeR ver1.0. For this study, estimated radar rainfall data from Subang weather radar stations were compared and calibrated with actual rain gauge data. Radar-derived rainfall calibration model developed for Subang radar station was y=0.8772x. According to the model developed, the radar rainfall calibration factor (RCf) can be identified as 0.8772. The original estimated radar derived rainfall data should be adjusted before using the calibration factor (RCf). The model gives better correlation when adjusted radar values were used instead of the original radar rainfall values. The model calibration factor increased from 0.464 with R2 of 0.2759** to 0.8772 with R2 of 0.3655***. Finally, the virtual rainfall stations created throughout the river basin produced a more representative rainfall distribution. It is believed that watershed river flow can be better estimated by using radar-derived rainfall data.

A 1D hydrologic-hydraulic model for simulating dual drainage in urban areas is presented. It consists of four modules: (1) rainfall-runoff transformation, (2) one-dimensional (1-D) flow routing on a street network, (3) flow interception at street inlets and (4) flow interaction between surface water on the streets and the underground storm-water system by interfacing with the EPA-SWMM5 engine (Environmental Protection Agency-Storm Water Management Model). The hydrologic model (first module) transforms rainfall to runoff using the kinematic wave approximation and simulates the infiltration process with the Green-Ampt method. The street network model (second module) is based on a finite-volume shock-capturing scheme that solves the fully conservative Saint-Venant equations and can be used to model both subcritical and supercritical flows. The inlet model (third module) uses the HEC-22 relations to compute the amount of water intercepted by inlets. The formulation of boundary conditions at the street crossings is generalized and can be used for any number of streets, any combination of inflowing and outflowing streets, and flow regime (e.g., subcritical and supercritical flows). Flow interaction between surface water on the streets and underground storm-water system is achieved by interfacing the proposed model with EPA-SWMM5. This interaction allows flow to enter from streets to the underground storm-water system and vice versa. The proposed model has several potential applications such as the identification of critical zones for flooding (e.g., zones with high water depths and flow velocities) in urban developments and can be used to take appropriate measures for drainage control (e.g., to increase number and/or size of inlets), to determine the consequences of different degrees of inlet clogging, and to assess flooding hazards through the application of suitable hazard criteria. A summary of criteria used for storm-water hazard assessment is presented. To demonstrate the dual-drainage model's potential an application is performed in a catchment of the metropolitan area of Chicago, IL. The results obtained are promising and show that the model can be a useful tool for storm water management and flooding hazard assessment in urban areas.

The objective of this study is to validate a flow prediction model for a hydrometric station using a multistation criterion in addition to standard single-station performance criteria. In this contribution we used cluster analysis to identify the regional flow height, i.e., waterlevel patterns and validate the output of an artificial neural network (ANN) model of the Alportel River in Portugal. Measurements of precipitation, temperature, and flow height were used as input variables to the ANN model with a lead time of 12 h. The lead time of 12 h is assumed to be appropriate for a short-term hydrological prediction since it is meaningful for physical processes. The ANN model with three inputs, four hidden neurons, and ten epochs was tested using the new model-validation criterion. The high performance of the model (i.e., Nash-Sutcliffe coefficient is equal to 0.922) was confirmed by the cluster-analysis criterion. It can be concluded that a multistation-based approach can be used as an additional validation criterion and might result in a rejection of a model which initially passed a single-station validation criterion.

High rainfall intensity will generate different response on the concrete block pavement (CBP) performance. A study found that larger openings of CBP did not lead more water penetrated. In other study, larger openings can lead greater decrease in runoff velocity. The correlation between the openings, water penetration and runoff velocity has remained unclear. In this study, we investigated hydraulic performance of CBP as an impact of surface roughness condition, under high rainfall intensities, saturated sub-base layer, and various slope surfaces. We conducted experiment using a 2 m by 6 m of rectangular CBP layer with herringbone 90 o and basket-weave pattern. We used a modified dye tracing method in view to monitor the surface flow velocity under various high rainfall intensities. The results showed that hydraulic performance of surface runoff in the CBP layer was more influenced by the surface roughness condition. The roughness condition was very sensitive to the change in surface configuration of the CBP. The relationship between rainfall intensity, surface slope and roughness number followed polynomial functions. A further study is required to investigate the appropriate quality of CBPs, which have high durability applied over a steep slope surface and under high rainfall intensities.

This paper focuses on the flow of water through rockfill porous media and brings out the effect of various parameters on hydraulic conductivity, friction factors, energy losses, drag coefficients, drag forces, and shear forces for such systems. Laboratory tests on rock materials with median diameters 1.1 cm and 1.8 cm and porosity of 42% and 47%, respectively, showed a nonlinear relationship between hydraulic gradient and pore velocity. The Reynolds numbers were always greater than 2000, thus fully justifying turbulent flow conditions. The obtained results showed that the Darcy-Weisbach friction factor ranged between approximately 0.8 and 0.2 with surfacing velocity. It was observed that energy losses were nonlinear to the Reynolds number; furthermore, they changed from 0.02 to 0.14 m along the media length. Drag coefficient was found to be an inverse of the Reynolds number with approximate values from 2.5 to 0.5. The shear forces along the water surface profile varied generally between 0.2 and 1.0 N/m² for all test runs, depending on the flow conditions and the media characteristics. Calculated hydraulic conductivity ranges from 0.05 to 0.15 m/s and can be explained primarily by variations of the flow depth and changes to the particle size distribution along the channel. It is further found that variations of porosity and flow depth produce a perceivable effect on shear forces along the water surface profile within rockfill media. Vegetation on the bed and bank also influences the resistance owing to flow.

River level forecasting has been undertaken by the National Weather Service (NWS) in the Suwannee River basin since the late 1970's. Among others, forecast sites were established in the lower reach of the Santa Fe River, a major tributary to the Suwannee River. Since that time, extensive riverbank and floodplain development has occurred upstream of those forecast sites in the Santa Fe River. During the 1980's and 1990's, some riverine stretches experienced significant flooding events, which at times were not preceded by adequate warning from the existing flood warning network. In response to a request from affected residents, the NWS and the Suwannee River Water Management District (SRWMD) cooperated in establishing the first new forecast site in the basin in over 20 years. Data review and analysis, field surveys, and public meetings were conducted to identify the optimal gage location and establish the appropriate action, cautionary, and flood stages. Data analysis showed the existence of two distinct types of flood events in the basin, one inherent to rainfall over the basin and the second due to backwater effects. The Southeast River Forecast Center (SERFC) has incorporated the new forecast site in the National Weather Service River Forecast System (NWSRFS) and it is in the process of redefining the existing setting for the Santa Fe River. The enhancements in the new setting will include the use of dynamic routing to account for the backwater effects in the Santa Fe River due to flooding in the Suwannee River basin.

Pressure flushing of deposited sediments behind large dam walls causes development of a funnel-shaped crater due to the induced vortex and considerable shear flow around the outlets. Laboratory experiments were conducted to investigate interaction of flow and sediment parameters on the development and final size of the scour cone. The volume of the flushed sediment increases with increase of discharge, and decreases with increase of sediment bulk density and the water level above the sediment. While the water depth over the sediments is the most significant parameter in the collapse of sediments above the intake and the initial development of the cone, sensitivity analysis indicated bulk density of cohesive sediment to be the most effective parameter to determine the final size of the scour cone. Using experimental results, two equations were developed to estimate scour cone volume and length with readily defined and measurable parameters of flow and sediment. The field measurements confirm the capability of the equations for large-scale application and flushing practice.

Manning' n-value varies in vegetation zones of river and flood plains due to variation of vegetation flexibility and flow depth. Study of river behavior and effect of vegetation on river systems are very important for water intake in pumping stations, water treatment plans and wastewater, fish pools in the river banks and construction of structures such as bridges and diversion channels. Purpose of this research is to develop relationships for estimation of non-submerged vegetation roughness in the flood plains and river banks. Number of 182 experiments was conducted on a laboratory flume and effect of parameters on the roughness coefficient was evaluated. Results show a nonlinear decrease of roughness coefficient with increasing of flow rate. Increase of velocity from 0.2 m/s to 2 m/s make Manning's coefficient reduces to one third of the first case. Decrease of roughness coefficient with decrease of submergence ratio and density was low compared to velocity effect.

Fluvial floods drive severe risk to riverine communities. There is a strong evidence of increasing flood hazards in many regions around the world. The choice of methods and assumptions used in flood hazard estimates can impact the design of risk management strategies. In this study, we characterize the expected flood hazards conditioned on the uncertain model structures, model parameters and prior distributions of the parameters. We construct a Bayesian framework for river stage return level estimation using a nonstationary statistical model that relies exclusively on Indian Ocean Dipole Index. We show that ignoring uncertainties can lead to biased estimation of expected flood hazards. We find that the considered model parametric uncertainty is more influential than model structures and model priors. Our results highlight the importance of incorporating uncertainty in river stage estimates, and are of practical use for informing water infrastructure designs in a changing climate.

Extreme floods provide a design basis for flood-sensitive infrastructures. There is strong evidence of climate change to alter the characteristics of extreme floods in the central Himalayan region, Nepal. However, current infrastructure design practices rely on the assumption of stationary flood peak records. Given the nonstationary behavior in extreme floods, traditional infrastructure design specifications may yield poor outcomes. Here we show that assuming climate stationarity can drastically underestimate extreme floods. We find that the uncertainty in extreme flood estimates is driven by complex interaction between uncertainties associated with data record length, model priors and model structures. Our results highlight the importance of incorporating climate nonstationarity into extreme flood estimates, and are of practical use for keeping infrastructure reliable over the service life.

This paper presents a methodology for forecasting seasonal streamflow and is an extension of a previously developed categorical streamflow forecast model that used persistence (i.e., the previous season's streamflow) and El-Southern Oscillation (ENSO) indicators. This newly developed methodology takes Nino persistence, an ENSO indicator, and several Pacific/Indian Ocean sea surface temperature (SST) series as the main predictor variables. Using linear discriminant analysis, the forecast is expressed as probability of exceedance of continuous streamflow amounts. An exceedance probability forecast is continuous and is useful for the design and operation of water resource systems, which require a high degree of system reliability. Application of the forecast model to five Australian catchments shows that persistence is the most important predictor of streamflow for the next season. The other predictors, SSTs and the Southern Oscillation Index, may be more useful for forecasts with longer lead times when the degree of persistence is less noticeable. Finally, it is noteworthy that this generic approach to making an exceedance probability forecast can be used on any predictors and predictands.

Advancements in information technology led environmental scientists to the illusion that efforts should be mainly focused on developing models that reduce uncertainty rather than on models adjusted to the existing uncertainty. As a result, environmental relationships are represented by non‐parsimonious and suboptimal models, which in many instances could be even wrong. The objective of this research was to provide scientists focused on modeling ecosystem processes with a procedure that supplies parsimonious correct results. The procedure transforms the response variable to achieve a linear model and the normality of the residuals. After the parameters of the transformed model are estimated, the bias induced by back‐transforming is corrected. We have computed the bias corrections for 11 of the most popular functions from the power, trigonometric, and hyperbolic families by considering the truncated normal distribution, when necessary. Using generated data, we have shown that the prop...

"Simulation of Surface Runoff Under Future Climate Change
Scenarios and its Application for Water Resources Management
in Palestine"
By Mohammad Omar Ahmad Shakarneh
Like any other arid region, the Palestinian ephemeral catchments, Daraja and Al-Ghar (located in the Jerusalem Desert within the Eastern Rain Shadow Mountains of the West Bank near the Dead Sea), have unique hydrological characteristics and few water supplies. High levels of geographical and temporal variability in rainfall distributions, flash floods, the absence of base flow, and high rates of evapotranspiration define the
hydrological regime in this area. This study aimed to analyze the rainfall-runoff regime statistically to determine the patterns in past climate records over two decades from the studied catchments. Runoffs from both catchments were simulated over the past (1990–2010) data period and future climate scenarios using a hydrological model and three of
these runoff events were used in a flood assessment model to map the flood-prone areas in both catchments. High temporal resolution (5-min) radar rainfall data, field-based stream flow data (from the hydrometric stations installed at the outlets of both catchments), land use land cover, soil types, and topographical data sets (obtained from GeoMOLG, local government’s archives) were used to achieve the objectives of this
study. The statistical analysis of 57 rainfall and runoff events during the 1990-2010 period over both catchments was carried out to explore the past climatic patterns. The results of the analysis revealed that the study area has been affected by climate change mainly in the spatial and temporal variation of rainfall regimes and the associated runoff. The
seasonal and monthly variations in rainfall were clear in which cold winter
Mediterranean cyclones decreased while local warm synoptic cyclones (spring and autumn) increased. This led to extreme sporadic rainfall events in the form of floods.
Al-Ghar catchment experienced more rainfall events, and high flooding than Daraja even though both catchments are neighboring and similar in many physical characteristics. The study area may be subjected to more flooding and extreme rainfall events with long-term spell droughts in the future.
In the second part of the study, 20 rainfall-runoff events were selected (10 for each catchment) out of a total of 57 events for hydrological modeling over the past data period. A hydrological model named “the Hydrologic Engineering Center's Hydrologic Modelling System (HEC-HMS)” was used to simulate the runoff from both catchments. Main inputs to the model included soil properties, land use land cover, stream network, and slope in addition to rainfall data and physical catchment characteristics. The model was calibrated on 5 events and validated on the same number of rainfall-runoff events from both catchments. The model’s efficiency was estimated through accuracy percentage, Nash Sutcliffe (NS), and coefficient of determination (R2). Simulation indicated acceptable model efficiency with NS (R2) values ranging between 0.78‒0.96 (0.79‒0.98) and 0.88‒0.94 (0.91‒0.98) for Daraja and Al-Ghar catchments, respectively, over the entire data period. According to a sensitivity analysis of the HEC-HMS model parameters utilized in this work, Curve Number had a greater impact on the model results than the percentage of impervious area. A calibrated HEC-HMS model was then employed to analyze the future runoff variation under the decreased rainfall scenarios of 3%, 6.5%, and 10% till the end of the century. It was observed that the runoff tends to decrease by 3‒10% under the given rainfall scenarios but is still
capable of generating flash floods during extreme events.
The hydrodynamic model “Hydrologic Engineering Center's River Analysis System (HEC-RAS)” was used to simulate flood inundation in both catchments. Flood extent, velocity, and depth were determined by coupling the HEC-HMS and HEC-RAS models and running with different recurrence periods. The HEC-RAS model was calibrated by  using Manning’s roughness coefficient that best described the water elevation profiles (WEP) at selected channel segments. Using the peak discharges in 1991, 1994, and 2001, WEP within the flow channel was calculated. The occurrence of flood event in 2001, which happens once every century, was deemed the most high-risk event. Its analysis revealed a significant likelihood of flooding, indicating that the stream would exceed its banks and pose a threat to the neighborhood. The projected overtopping and
historical occurrences were compared with good agreement and the HEC-RAS model's average accuracy was ~88% for the entire studied area. The obtained flood inundation map for the three events in both catchments showed that both sides of the stream channels are inundated areas.
Outcomes of the study suggested that the HEC-HMS model calibrated during this study may be used for forecasting the flows in a short time step with a future climatic scenario in similar environments and may be helpful for water resources managers to predict and mitigate the flood risk. This research may be considered as a guideline for nonstructural flood management techniques including flood forecasting, mapping, and risk zoning. It may be effective at reducing the dangers and disasters that floods may bring in the future. However, future work may focus on downscaling the climate scenarios for this region and using those to predict the stream flow. It may enhance the integrity of the results. Future research may also use HEC-HMS to simulate and predict the low
flows in addition to the high flows.

A large set of monthly precipitation data from 43 stations throughout Texas was employed to investigate the spatial variability in the multiscaling properties of wet and dry spells. Special emphasis was given to dry spells which are related to meteorological droughts. Scaling properties deduced from the analysis of dry spells can be used in drought modeling and multiscale temporal variability of droughts. Using moment scaling exponents, scaling properties of wet and dry spells were examined for a median truncation level. No coherent regional differences were found from the spatial depiction of scaling parameters. Wet and dry spells showed different tendencies in simple scaling and multiscaling throughout the study area. Also, significant low frequency patterns of precipitation were found when the wavelet transform was used. Investigation of the relationship between scaling properties and significant cycles of precipitation data showed that annual cycles may contribute to the occurrence of simple scaling mechanism in wet and dry spell sequences. Such characterization of sequences of wet and dry spells is essential for addressing a multitude of hydrological problems, including estimation of flood and drought frequencies, construction of rainfall and runoff relationships, agricultural planning, to name but a few.

The main aim of this study is to improve the underestimation of spatial snowfall distributions by using assimilation. Although measuring snowfall depth is crucial to evaluate snow water resources and predict snowmelt runoff in spring season, it is difficult to measure snow depth correctly with a gauge because wind speed strongly influences the capture ratio. Snowfall observation errors have a significant influence on the accuracy of hydrological model output. An evaluation of the distributed hydrological model was carried out in the Yoneshiro River Basin in Japan with a modification of the model using snow depth data. To reduce the measurement error using the snowmelt-runoff model, an assimilation policy based on the observed snow depth is included in the snow water equivalent (SWE) model at regular intervals. As a result, the assimilation improves the accuracy of both the snow depth estimation and the snowmelt-runoff simulation. The Nash-Sutcliffe coefficient is improved from 0.63 to 0.86 throughout the year and from 0.21 to 0.82 from March to May. The assimilation of snow depth can contribute to improvement of the hydrological model with higher accuracy compared with direct use of gauge data. Also, how to assimilate snow depth, such as an interval of the assimilation and its applicable timing, is discussed. The model suggested in this study can be helpful for water management-related activities and decision making.