Watershed Management 2020: A Clear Vision of Watershed Management

This study offers a practical method for generating random time series of excess rainfall. The uncertainties in rainfall depth, rainfall temporal distribution, and hydrological losses are accounted for. First, the point rainfall or spatially distributed rainfall depth grids are obtained from NOAA Atlas 14. In random selection of rainfall depth, the probability of each recurrence interval as well as the probability distribution of rainfall between the lower and upper confidence limits are considered. Random selection of temporal distribution considers probability of each of the available empirical curves. The curve number (CN) for each grid (or lumped area) is randomly selected from a statistical distribution fit to CN ranging from dry to wet conditions. The selected CN value is then adjusted for rainfall durations shorter than 24 h. The process of excess rainfall time series generation is repeated hundreds of times for to provide a full range of runoff possibilities. A unique property of the proposed method is that all generated excess rainfall time series are equally valid and probable.

Virginia Beach, VA, is a coastal city that is subject to extreme weather events and sea level rise. Storm surges can occur in combination with heavy rainfall from tropical storms, hurricanes, and nor’easters to cause significant flooding damage. An extensive hydrologic and hydraulic modeling analysis was performed as a part of an alternatives evaluation to identify long-term infrastructure improvements and provide resilient flood protection to a 207-acre fully developed drainage area that includes 120-acres of commercial and residential properties located within the 100-year floodplain. Hydraulic models were developed in personal computer stormwater management model (PCSWMM) to define flood reduction benefits for different improvement alternatives. A total of ten (10) alternatives were scored based on the following six (6) criteria: cost/benefit, flood relief along a principal arterial roadway, flood relief along an existing drainage canal and local roadway, public demand, environmental impacts, and acquisition. The highest scoring improvement alternative includes two interconnected stormwater pump stations, an automated tide gate with pumped bypass, canal improvements, and stormwater collection system improvements. Different recommendations were developed for varying levels of available funding up to $32 million to help the City of Virginia Beach work within budget phasing constraints.

Two watersheds were selected for comparison to establish data-driven correlations: the Trinity River in Texas and the Sacramento River in California. The Trinity River drains into the Trinity–San Jacinto Estuary. Lake Livingston Reservoir is located on the Trinity River and is under environmental flow regulations to release a required amount of water to maintain seasonal inflow at a downstream USGS stream gage to ensure water researches the bay and estuary. The Sacramento River drains into the San Francisco Bay. The Keswick Reservoir and Shasta Dam on the Sacramento River have minimum instream flow requirements to maintain the water temperature for downstream fisheries. Correlations were made between inflow, releases, gauged flow, instream and environmental flow requirements, and fisheries data. The results of this research will contribute to anthropogenic research at the National Water Center to enhance the prediction capabilities for better watershed management.

A new watershed model, sedimentation and river hydraulics— Watershed (SRH-W), is developed for event-based runoff and soil erosion prediction. The model is physically based and mesh-distributed. The adopted numerical method addresses issues such as terrain representation and efficient execution, in addition to various traditional algorithms. The mesh cells can be rectangles or polygons and may be coarser than the fine-scale features. Fine terrains are taken into account in the water storage and budget computation. The model development is still a work in progress; this paper focuses on the methodologies adopted, along with the model validation, and application related to the runoff. In particular, SRH-W is applied to the Clear Creek Watershed, Iowa, which has monitoring data for model evaluation. Model details, challenges, and calibration are presented and discussed.

Salt export from agricultural, wetland, and municipal dischargers to the San Joaquin River (SJR) is regulated as part of a comprehensive total maximum daily load (TMDL) for the San Joaquin River Basin (SJRB). The TMDL is intended to identify, quantify, and control sources of salt loading that affect attainment of salinity objectives and protect agricultural beneficial uses of water. Non-point sources of salinity are not amenable to the establishment of fixed monthly salt load allocations because of the diffuse nature of these loads making it difficult to monitor individual discharge points and assign responsibility. Real-time salinity management (RTSM) has been advocated as a novel means of improving compliance with SJR salinity objectives by enhancing the coordination of west-side agricultural and wetland salt loads with salt load assimilative capacity provided by reservoir releases along east-side tributaries to the SJR. RTSM is a concept that relies upon continuous access to real-time flow and electrical conductivity data from SJR Basin sensor networks and the development of a decision support system that extends watershed monitoring to allow salt load assimilative capacity forecasting in real-time. This paper describes some of the web-based information dissemination and data sharing innovations that have been tried and are under development in the implementation phase of the real-time salinity management program with an emphasis on experiences with stakeholder engagement and participation.

Regression models for analyzing nutrients in highway stormwater runoff were formulated in this study using event- and site-specific variables. Data and information for 59 storm events during the rainy seasons of 1999–2003 obtained from three University of California, Los Angeles (UCLA), sites located in southern California were used to formulate regression models for nutrients that include ammonia, total Kjeldahl nitrogen (TKN), phosphate, nitrate, and nitrite based on their event mean concentrations (EMC). Event-specific parameters— total rainfall, maximum intensity of rainfall, duration of runoff, and antecedent dry days— and site-specific parameters— runoff coefficient, area of catchment, and average daily traffic— were used as the independent variables. Four sets of regression forms— linear, exponential, semi-log, and inverse— were considered to evaluate nutrient EMCs. Models developed for the individual and combined UCLA sites were generally good with R² values greater than 0.50 and 0.40, respectively. Total rainfall and antecedent dry days were the two most significant event-specific parameters influencing the nutrient EMCs. Regression models from this study can be useful for the sustainable integrated management of nutrients in stormwater runoff from vehicular-intensive land use such as highways and has great potential to monitor and improve the water quality in the receiving water bodies.

In the 1950s, the United States Department of Agriculture (USDA)–Soil Conservation Service (SCS) developed the empirical runoff curve number (CN) method, providing modelers, planners, and designers a tool for estimating runoff from rainfall accounting for losses such as evaporation, transpiration, infiltration, and surface storage. SCS originally developed the CN method to analyze primarily agricultural watersheds for watershed protection and flood prevention operations Act of 1954 (PL-83-566) projects. Today modelers world-wide use the CN method, or methods based on it, to model agricultural and urban hydrology. For more than 80 years, SCS, and its successor, the Natural Resources Conservation Service (NRCS), worked and continue to work with farmers; ranchers, State, Tribal, and local governments; and other Federal agencies to maintain healthy and productive working landscapes. In 2015, NRCS partnered with the American Society of Civil Engineers (ASCE) to develop updates to four chapters for the NRCS National Engineering Handbook, Part 630, Hydrology (NEH-630), to incorporate the latest research recommendations to revise the Ia/S (initial abstraction/maximum potential storage) ratio. This represents the first significant update to the CN method since its original development. The Curve Number (CN) Task Committee, under the ASCE–Environmental and Water Resources Institute’s Watershed Management Technical Committee was instrumental in this effort. Through the internal review process, NRCS received over 1,000 individual comments and identified items needing further review. NRCS continues to work to incorporate the internal review comments and additional update recommendations from the CN Task Committee. This paper discusses the purpose of the NEH-630, the process of developing the NEH updates, and actions taken to finalize the NEH-630 CN chapters.

Grid generation is an important and challenging preprocessing step for setting up a detailed discrete hydrodynamic model for simulating river flows. Often, resolution of the modeling study is determined by limitations of the grid generation programs. Also, compatibility of grid generators to match with a hydrodynamic model is another challenge. Environmental Protection Agency’s Environmental Fluid Dynamics Code (EFDC) is one of the most popular models used for simulating surface water hydrodynamics and water quality. However, it’s associated Fortran-based GEFDC grid generator for EFDC is complex and requires a substantial amount of mathematical knowledge and craftsmanship to set up a compatible grid. Taking this into consideration, we developed a new algorithm and implemented in a MATLAB based grid generator program that automates reproducible grid generation from a variety of data source formats. As part of this paper, we present the grid generation process using a test model that was set up and executed to produce results and verify compatibility of our grid generator with EFDC model.

Various multi-scale hierarchical river health approaches and assessments have been developed and implemented with a variety of scientific and management purposes. Although many existing biological and geomorphological assessments offer insights into stream function, none were developed for or exclusively capture the functions of streams in the urban environment. Quantitative aspects of existing screening tools are limited in urban streams due to the unique combination of hydrogeomorphic and anthropogenic factors in the urban environment. Analyzing the physical, biological, and social drivers within the concept of the urban stream syndrome coupled with recent advances in stream assessments connects the health of urban streams with past, present, and future impacts. The urban stream assessment procedure (USAP) provides an innovative framework and conceptual model to comprehensively assess stream functions in the urban environment and quantify their health at multiple scales. The specific aim is to identify the highest function and lowest maintenance “version” of an urban stream that is achievable given the context— varying hydrologic regimes, geomorphic conditions, ecological diversity, social contexts, and community goals. It is designed to classify urban stream reaches and corridors according to their hydrological, geomorphological, vegetation characteristics and to support identification of reaches that might be candidates for rehabilitation. Employing a multi-scale framework yields greater success in assessing stream function, diagnosing watershed-wide drivers, and implementing strategies to enhance streams in the urban environment. Furthermore, the USAP framework can assist water resources managers in assessing and managing health specific to urban streams through applicable spatial and relevant parameters and metrics.

In 1997, the EPA created the Mississippi River/Gulf of Mexico Watershed Nutrient Task Force which is commonly known as the hypoxia task force (HTF) to address the growing dead zone problem in the Gulf of Mexico caused by nitrogen and phosphorus. The HTF developed an action plan in 2001 to address the issue but little progress was made on the problem, so they developed another one in 2008. The 2008 plan as a first step called for all states in the Mississippi/Atchafalaya River Basin to complete and implement comprehensive nutrient reduction strategies for all watersheds with significant contributions of nutrients to the surface waters in the basin. All states have developed nutrient reduction strategy plans and have started implementing them. In 2019 the Gulf of Mexico hypoxic zone was 18,005 km², which is the 8th largest in size since 1985 and larger than the hypoxia task force goal of 5,000 km². It is estimated 52% of the nitrogen load to the Gulf of Mexico from the Mississippi watershed is from corn and soybeans and 71% from agriculture. A recent study estimated Iowa stream loads from 1999 to 2016 contributed from 11% to 52% of the nitrate load to the Gulf of Mexico. The majority of the nitrate load is coming from tile drainage. The largest source of nitrogen load from Minnesota is also from tile drainage. The interim goal of the HTF is to reduce nitrogen loads by 20% by 2025. Since 1999 nitrate loads in the Iowa inclusive basins have increased. The five-year running average nitrate load has been above the 2003 nitrate load for 10 consecutive years. Agriculture contributes 82% of the total nitrogen load to Illinois rivers.

Severe water-related disasters occur in Japan on an almost yearly basis due to typhoons and frontal rains; these events are often characterized by evacuation difficulties. Main purpose and final goal of this study were to develop a user-friendly hazard map that give high impacts on the local people who underestimate the flood hazard risks in their town. To overcome the evacuation problems in Japan, we proposed the distribution of high-risk zone information determined using probable maximum flood inundation data. A rainfall runoff and two-dimension flood inundation models were applied to the Toyama City under the several rainfall scenarios in the basins and several embankment failure points along the rivers to compute the probable maximum flood inundation situations. The simulations produced maximum flood inundation depths exceeding 3.3 m in several areas, which we therefore designated as high-risk zones. Finally, we proposed new hazard maps based on probable maximum flood inundation that were designed for easy use by local people.

The USDA Natural Resources Conservation Service (NRCS, formerly Soil Conservation Service) curve number (CN) method for estimating runoff depth from rainfall depth is a ubiquitous technique that is used worldwide in a variety of applications. Since its creation over 60 years ago, it has undergone numerous analyses on both practical and theoretical grounds. Although originally intended to model runoff depth from rainfall design storms on small agricultural and rangeland watersheds, it has been applied to a wide variety of conditions including green roofs, forests, continental scale river basin runoff, and daily time-step models, in applications other than those originally intended. Numerous problems, limitations, needs, and inappropriate extensions have become evident. In late 2015, a volunteer joint work group of the American Society of Civil Engineers (ASCE) and the American Society Agricultural and Biological Engineers (ASABE) members, began a review of the state of knowledge and experiences about CN methodology. The task group of 16 members delivered a set of suggested revisions and updates to the NRCS in late 2017. This presentation will summarize the task group’s activities, efforts, and the suggested revisions and updates. Included in the presentation are how to determine for which watersheds the CN method is applicable, techniques for estimating CN values from rainfall and runoff data, and how to apply a revised (from the historical CN method version) relationship between initial abstraction and CN. The presentation is intended as a report on the task group’s recommendations and to inform and solicit feedback from the audience on the suggested revisions and updates to this widely used methodology. At this time (January 2020), the report’s recommendations have not been formally incorporated in NRCS institutional hydrology design guides.

Antecedent soil moisture (ASM) is one of the most important factors affecting the rainfall-runoff modelling process. Its existence and influence in computing runoff using soil conservation service curve number (SCS-CN) method have been a point of discussion for many decades. In this study, a novel procedure has been proposed to calculate the ASM by modifying the SCS-CN method and verify its applicability by comparing the computed ASM with the observed soil moisture. Natural rainfall, runoff, and soil moisture data from eight small experimental farms with different land-use viz. sugarcane, maize, black gram, and fallow land, located at Roorkee, India, have been utilized. The ASM is computed by optimizing two parameters, i.e., absolute maximum potential retention (S_abs) and initial abstraction coefficient (λ), and the optimization is carried out by minimizing root mean square error (RMSE). Results show that there exists a good correlation between observed and computed ASM for sugarcane, black gram, and maize with maize showing the highest correlation (R²> 0.6). Fallow land shows the least correlation (R² of 0.32), which may be due to the inadequacy of data. This study will be helpful in calculating ASM for ungauged catchments having different land use and will further broaden the applications of SCS-CN method.

A data-driven modeling framework to link hydrologic, hydrodynamic, and water quality inputs to the migratory movements of endangered fish in urban estuarine watersheds is demonstrated. This will be useful in watersheds in which both the flow as well as the water quality have to be managed with sustaining fish stocks as one of the objectives. Additionally, it will allow the evaluation of management practice alternatives to analyze how flows and water quality will influence the habitat usage and population dynamics of economically valuable fish in river basins. Typically, physical processes in watersheds vary over tidal to inter-annual periods, while habitat use by aquatic organisms occur on different biological scales. Data are collected on yet other timescales independent of the underlying mechanisms. Models that incorporate the physical processes to inform biological movements are either purely statistical or include ad hoc migration rules. They are neither able to represent the biophysical mechanisms accurately nor are robust in out-of-sample hydrologic scenarios. In this modeling framework, the migration towards the ocean of juvenile salmon in the California Central Valley is represented at four timescales at which data is available, viz., seasonal/inter-annual, daily/subtidal, hourly to several minutes, and seconds to minutes. The data used include hourly records of salmon movement, flow and water quality available over several years at multiple locations throughout the system, as well as sub-minute salmon movement and water velocity data that is available at critical locations within the system from short-term experiments. By bridging the scales between animal movement, environmental processes, and data availability in this way, key drivers and stressors at the watershed level on animal migration can be identified. By building this process-based framework of animal movement, optimal use of the available data can be made to better inform ecological management and restoration in watersheds.

Hydrologic models using the diffusion-wave approximation of the shallow water equations to simulate overland flow often use grid cells that are too large (10–1,000 m) to capture many of the small scale (1–10 m) streams and drainage features. These drainage features often carry large discharges and can drain watersheds in a relatively short amount of time depending on the slope. Such large discharges can cause severe local flooding if retention facilities are not built into the system and if backwater effects are present due to poor downstream drainages. The regional simulation model (RSM) developed at the South Florida Water Management District (SFWMD) uses a 2-D total variation diminishing Lax Friedrichs (TVDLF) numerical model to simulate both kinematic and diffusive flow conditions present in watersheds. In this model, rapid drainage features are built into the cell using a conceptual model that is parametrized as a rectangular drainage channel. This approach of using the TVDLF method and grid cells with rapid drainage features is used to simulate storm runoff and the water table in Boggy Creek, Central Florida, and the Kalu Oya watershed north of Colombo, Sri Lanka. The results show that the approach is suitable for capturing both kinematic and diffusive streamflow conditions in watersheds and producing narrow streams of kinematic flow normally not visible in GIS maps. The results also include inundation maps in the downstream wetlands. These results are useful in understanding the current system before planning for future scenarios.

Inlet cleaning is a key element of MDOT SHA’s plan to achieve regulatory compliance. Current state and federal protocols assign a credit for TMDL and NPDES compliance. This research presents the current average amount of material removed of 86 Kg/inlet/year (as dry weight) annually. These numbers vary depending on sample collecting time, locations, and weather conditions. The study recommend approaches to optimize their inlet cleaning operations to maximize nutrient, sediment, and trash load reduction credits for TMDL compliance. The results of the monitoring study suggest that seasonality, or time of inlet cleaning, along with the type of material removed from the inlet, provide opportunities to increase the load, or impervious acre equivalent credit from inlet cleaning.

Compared to distributed models, lumped hydrological models for converting rainfall into runoff— such as units hydrographs— require less information, have a relatively simple structure, and do not suffer from over-parameterization. As an input to such models, though, we need to identify a single value of precipitation for the whole watershed, at each time step. Point depth measurements from rain gages are scarce, giving only very limited information regarding the areal coverage of rainfall. Furthermore, the response of a watershed to a rainfall event varies according to antecedent conditions, which can be indexed by considering either its initial soil moisture or else the magnitude of base flow at the beginning of the event. We explore solutions to such issues with a study on an 1,860 km² sub-basin of the Obion-Forked Deer River system in Western Tennessee, which has a USGS stream-gaging station at Owl City. We use National Center for Environmental Prediction (NCEP) stage IV quantitative precipitation estimates (QPE) radar hourly precipitation data to analyze numerous events spanning 2010–2014. These data help us understand the spatial distribution of every rainfall event and identify its effective coverage and duration. To derive direct runoff for the basin, we utilize gaged streamflow data in conjunction with average radar precipitation, but only for those events with a spatial coverage in excess of 80% of the watershed’s surface area. We use the European Space Agency (ESA) climate change initiative (CCI) soil moisture (SM) combined active-passive dataset V4.4 to find daily average soil moisture over our basin; these data give us the pre-existing watershed conditions. Finally, across all of the analyzed events, we check the correlations between the runoff coefficient, rainfall abstraction (ф-index), average precipitation depth, base flow at the beginning of the event, and soil moisture, and specify equations for calculating some of these variables, based on linear regression analyses. The validation of these equations with 11 storms of varying magnitude shows satisfactory results. Instead of representing the complex physical processes of runoff generation, we focus on the variability introduced in the hydrological response by the input rainfall and antecedent conditions. The unit hydrograph, derived from concurrent rainfall and streamflow records, is presented as a parsimonious rainfall-runoff model with very few parameters, that can use spatially varied rainfall data, and can be improved by using either soil moisture or streamflow data, as a measure of initial watershed conditions.

Watershed models of extreme flooding events require accurate and reliable measurements of streamflow for calibration and validation. However, flow rate measurements during floods are inherently uncertain, and physical measurements of velocity during flood conditions are prohibitive in many cases. Therefore, novel methods to measure stream velocity during extreme floods must be considered. This project addresses this challenge through the development of a novel system that utilizes drones, video imaging, and optical flow algorithms to measure velocity in rivers and streams. This system was applied at a case study location on the Menomonee River in Wauwatosa, WI. To remotely sense stream velocity, a DJI Matrice 210 RTK drone equipped with a Zenmuse X5S camera was used to capture video. The video data from the drone was analyzed using an optical flow algorithm, which generates a high-resolution velocity field by computing the optical displacement of pixels between each video frame. This data was compared against in situ measurements of velocity captured with a hand-held velocimeter. Results indicate that the optical flow algorithms estimate the magnitude of surface velocity to within 12% of hand-held measurements. If fully developed, this system could transform the way that streamflow is measured and modeled.

Land cover change alters hydrological response of a watershed and usually results in flooding. This paper studies the effectiveness of porous pavements in reducing the impact of built-up area on surface runoff and peak flow in the Walnut Gulch Experimental Watershed. The built-up area is expanded into high intensity developed surface with 80%–100% imperviousness. The precipitation events between July and October of 2018 (more than 60% of annual rainfall) are used to calibrate the baseline response. Watershed is modeled in Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS), whereas flow routing is performed in Hydrologic Engineering Center-River Analysis System (HEC-RAS). The model is calibrated and validated by using observed discharge hydrograph data at the watershed outlet (location: 31o 43' N, 110o 41' W). The precipitation and runoff observations were obtained from the United States Department of Agriculture (USDA), agricultural research service website. Land cover data was obtained from the United States Geological Survey (USGS) website. Two scenarios were modeled. In the first scenario, built-up area is increased by changing the value of imperviousness to more than 80% in HEC-HMS. In the second scenario, low impact development (LID) is integrated in the model in the form of porous concrete used in the built-up area. Model simulation of the first scenario shows an increased surface runoff volume. Moreover, the lag time between storm peak and discharge peak reduces due to decrease in the infiltration rate. In the second scenario, the surface runoff is significantly reduced due to increased infiltration resulting from LID. The results of this research provide useful insight about potential land cover change impact on the watershed response and can help in land use planning.

Urbanization disrupts the natural water balance by increasing the runoff and reducing infiltration. This study examines the impact of change in impervious surface on runoff response and infiltration of Walnut Gulch Experimental Watershed. The hydrological model of the watershed was developed using Hydrologic Engineering Centre-Geospatial Hydrologic Modeling System (HEC-GeoHMS) software, and simulation was performed using Hydrologic Engineering Centre-Hydrologic Modeling System (HEC-HMS). Precipitation, elevation, and percentage imperviousness data were used as model inputs for the HEC-HMS model. Land cover and soil characteristics of the watershed are used to adjust the curve number value. The model was calibrated and validated using streamflow data acquired from the U.S. Department of Agriculture (USDA) website. The event-based rainfall data was obtained from five meteorological stations for the year 2012. Urbanization what-if scenarios were considered by altering the percentage of impervious surface. Soil conservation service-curve number (SCS-CN) method was used for calculation of loss and SCS-unit hydrograph method to transform the flow. The impact of urbanization on volume of runoff and infiltration rate was studied. The simulation results of HEC-HMS model show that the increase of impervious land cover increases the volume of runoff significantly. In addition, infiltration decreases correspondingly, which can result in a deficit of groundwater supply. The results from this study will be useful to assess the impact of urbanization on runoff and infiltration of the watershed. The analysis of the results also reflects the significance of considering the effects of urbanization in hydrological modeling.

Las Vegas valley has undergone significant development, thus increasing urban flooding. This study analyzes the impacts of urban development on urban flooding in the Flamingo watershed by using a watershed model. The input data includes precipitation, soil characteristics, elevation, and land cover. Urban development is incorporated through increasing percent impervious. Sub-watersheds and streamlines were delineated in ArcGIS using digital elevation model (DEM) dataset. Natural Resources Conservation Service (NRCS) curve-number method was used for the calculation of runoff. The Hydrologic Engineering Center-Hydrologic Management System (HEC-HMS) was used to estimate the discharge hydrograph. The model was calibrated through changing the curve number of the sub-basins. Two urbanization scenarios created with a 5% and 10% increase in impervious surfaces were generated. The results showed that peak discharge occurred earlier due to increase in impervious surfaces. Moreover, the total discharge volume and peak discharge for a given storm event were increasing due to increased imperviousness from urbanization. This study provides useful insight into a hydrological response to urban development that can be helpful in flood remediation.

Land cover change is a source of hydrologic problems across the southwestern United States. This study investigates the effects of land cover change on hydrograph simulation. The study area is the Santa Clara Valley, California. The study employs HEC-HMS software to estimate the runoff rate. Percentage of grass cover and built up land were extracted using the National Land Cover Database (NLCD). Two scenarios were generated using a calibrated model of the watershed. The two scenarios increased the percentage of imperviousness by 5% and 10%, respectively, of each sub-basin from the base scenario. The base scenario was calibrated using a 2017 events. The model was validated using a 43-day rainfall event. Both calibration and validation values are within allowable limits. The study employs the Natural Resources Conservation Service (NRCS) curve number (CN) method to estimate the runoff rates. The land covers were represented by different curve numbers. The results were validated using discharge data of stream gage with a bias of 12.30% and NSE of 0.63. The analysis shows that a 5% (scenario 1) and 10% (scenario 2) increase in the percentage of built-up land increases the runoff rate by 8% and 22%, respectively, due to a decrease in infiltration. Consequently, the peak discharge, for scenarios 1 and 2, was 18% and 48% higher than the base scenario, respectively. Whereas, the time for peak discharge was 3 and 8 h earlier than the base scenario. This study is helpful for urban planners and watershed managers to understand the dynamics of land cover on surface runoff.

Land use/cover (LUC) has changed largely due to urbanization that has been well observed worldwide in recent decades, which impacts the hydrological process, particularly increasing the peak flow and runoff coefficient. To simulate/forecast the flood process in the urbanizing watersheds effectively, the LUC changes need to be incorporated into the flood simulation/forecast hydrological model to reflect this change. In this study, the latest LUC of the urbanizing watershed that is derived from the remote sensing images, is coupled with a distributed hydrological model for watershed flood process simulation. The distributed hydrological model employed is the Liuxihe model, a physically based distributed watershed hydrological model mainly proposed for flood forecasting. In the model, the vegetation type parameters are highly related to the LUC types, so the model parameters could be changed according to the LUC changes, and a more realistic flood simulation result could be achieved. In this study, an urbanizing watershed in southern China has been case studied.

This paper deals with the impact of temporal variation of Land use/Land cover (LULC) and precipitation on runoff estimation for an un-gauged hilly watershed in Kodagu District of Karnataka, India. Sloped Terrain of the watershed and increasing trend of rainfall over the last decade, has been contributing to high runoff values which has ultimately damaged the rich flora and fauna of the region. The total area of the watershed is 459.31 km². The land use/land cover change analysis has been performed for the years 2004, 2007, 2013, and 2018. Remote sensing and GIS techniques have been used to identify the watershed and for developing thematic maps. Very popular, soil conservation service— Curve number (SCS–CN) (USDA), method has been used in this study to estimate the seasonal runoff from the rainfall. The LULC maps have been generated by supervised maximum likelihood classification technique in order to get higher accuracy in the estimation of curve numbers. Class-wise runoff coefficients have been estimated to analyse the response of each class to rainfall. The runoff response for a watershed depends both on precipitation as well as land use/land cover classes. The impact of both the factors has been assessed in the estimation of runoff, and it has been observed that the impact of rainfall factor is more. The runoff values have been obtained for return periods of 10, 20, 30, 50, and 100 years by using the value of runoff coefficient averaged over 15 years.

Climate change induces nonstationarities in the hydrological cycle and on the availability of water resources, which in turn produce impacts on water supply systems and their performances. This requires novel approaches that allow to properly take into account the nonstationarities in precipitation, temperature, and streamflow within reservoir simulation and operation optimization. In this work, we present a modeling chain that is based on the combined use of regional climate models (RCMs) and water supply system simulation. The methodology enables assessing: (1) the potential future changes in precipitation, temperature, and streamflow, and (2) the impacts of such changes on future water supply reliability and vulnerability. The methodology is applied to the Pozzillo reservoir, located in Sicily (Italy), a region that has experienced several droughts in the past. Based on a previous study, we use a selected RCM providing the most reliable data compared to other models of the EURO-CORDEX initiative. RCM precipitation and temperature monthly time series are used, after a bias correction procedure, to generate future reservoir inflow data, according to two Representative Concentration Pathways RCP 4.5 (intermediate emissions scenario) and RCP 8.5 (high emissions scenario). Results indicate that performances of the Pozzillo reservoir in satisfying irrigation needs are predicted to decrease significantly in the future, with impacts of RCP 8.5 higher than RCP 4.5.

This project examines whether there is a positive association between bacterial growth and nutrient loading in subsurface storm drains. Storm drains that are perennially fed by groundwater seepage and/or permanent dewatering infrastructure may provide “incubator” conditions for bacteria proliferation. Grout lined storm drains convey storm runoff and dry weather flows (car wash runoff, lawn runoff, and other nuisance flows) away from urban areas. As runoff enters storm drains, it carries N and P, which serve as electron acceptors and provides necessary energy for bacteria to multiply. Other elements needed to create incubator conditions are carbon rich input and seed bacteria. Vegetation detritus washed into the storm drain during urban watering or from internal algae growth provide a carbon source, and enough wildlife exists in and near storm drains (racoons, bats, rats) to provide the seed bacteria spawning bacteria growth.