World Environmental and Water Resources Congress 2018: Watershed Management, Irrigation and Drainage, and Water Resources Planning and Management

The runoff loss of nitrogen and phosphorus in farmland results in severe surface water pollution. Runoff losses of nitrogen and phosphorus could be effectively assessed with the empirical model. Using the power function model with the LTS and S estimation, we fitted and evaluated the regression relationship between runoff and runoff losses of soil erosion and various nitrogen and phosphorus under different crop covers at Xinmaqiao Experimental Station located in Huaibei Plain of China. The results showed a good power function of regression between runoff and losses of soil erosion and nitrogen, respectively, and the coefficient of determination r² varied from 0.52–0.94 and followed the order of soil erosion > total nitrogen loss > dissolved nitrogen loss > particulate nitrogen loss. The fitting exponent parameter b of the soil erosion in fallow, cotton, and corn fields and nitrogen loss in fallow field were all greater than 1.0, and the soil erosion and nitrogen loss from these fields could be controlled by reducing the runoff. High phosphorus concentrations in corn, cotton, and soybean fields occur during small runoff events, and concentration of particulate phosphorus does not change much with increased runoff, whereas the concentrations of total phosphorus and dissolved phosphorus decrease. The concentrations of total phosphorus, dissolved phosphorus, and particulate phosphorus in fallow fields increase with runoff.

Determination of the vegetation coverage (VC) plays an important role in different aspects of watershed management. Remote sensing products can provide estimates of VC; however, the uncertainty of the data acquisition and processing has always challenged the usability of spaceborne imagery data. In this study, moderate resolution imaging spectroradiometer (MODIS) products used to model VC of three basins: Lower Fox (LF) in Wisconsin, Lower East Fork White (LEFW) in Indiana, and Lower San Joaquin River (LSJR) in California. MODIS MOD13Q1 product is used to obtain VC based on enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI). However, the commonly used threshold value of 0.4, used to differentiate vegetated from non-vegetated areas, does not necessarily yield to accurate estimates when compared to the National Land Cover Dataset (NLCD). A statistical criterion—determined based on probability distributions of VC—used to filter pixels with low vegetation index (VI) values during the time of peak-vegetation as non-vegetated. The same approach implemented to determine vegetated areas during the low-vegetation season. These pixels were left out of analysis—regardless of the VI value—and their VC condition was determined based on the group of filtered data they belong to. Then, VC was calculated for the remaining pixels and results were integrated with those of the filtered areas. Application of the suggested technique improved VC estimations. The errors of VC estimations based on NLCD-VI comparison were reduced by 0%, 3%, and 33% for EVI and 17%, 6%, and 2% for NDVI in LF, LEFW, and LSJR basins, respectively.

Walker River Irrigation District (WRID) is located in Nevada around the community of Yerington and contains approximately 95,000 hectares (235,000 acres), 32,000 hectares (80,000 acres) of which are irrigated. The district operates a canal system centered around the three forks of the Walker River. Starting in 2009, the district has worked with the Irrigation Training and Research Center (ITRC) to modernize its system by improving the accuracy of the measured diversions from the Walker River and enhancing the real-time control capabilities of the water managers. A “package” was developed for the headworks of canals diverting water from the river, consisting of a combination of the following based on site conditions: a Replogle flume for flow measurement, a self-contained motorized slide gate(s) in a district-standard configuration, a remote terminal unit (RTU) for automatic control of the slide gate(s) and communication with the office base station in Yerington (SCADA), an ITRC flap gate and modifications to the existing spill structure, sediment control features, and off-grid solar power for the entire site. To date, variations of the package have been successfully applied at over ten sites throughout the district, with more sites in progress and planned for the future. Modifications to the initial package have been made over the last eight years to reflect advances in technology, challenges found at completed sites, as well as additional needs identified at the sites. The district plans on continuing the modernization based on the success of the program to date.

Secondary and tertiary treated municipal wastewater has been used to irrigate alfalfa and grains on approximately 4,000 acres in Southern California via center pivots. In addition to coordinating fluctuating treated water supply and crop demands, this program has focused on balancing two competing goals: (a) maximizing crop yield and (b) maintaining compliance with relatively strict environmental discharge permit requirements that limit the transport of nutrients, such as nitrates, past the root zone. For example, minimizing nitrate movement through precise irrigation promotes salts accumulation in the root zone. Subsequently, crop yield and nutrient uptake rates are impacted. In that context a combination of good management strategies, excellent irrigation hardware, and the selective use of technology have proven to be essential tools for success. This paper will detail some of the short and long-term challenges of managing soil health and crop production under local regulatory obligations.

The image of Dr. Poland of the United States Geological Survey (USGS) standing next to a marked powerline in 1977 is circulated as a striking example of land subsidence. This and other historical data are common references for modern subsidence studies. Because of the significance of this data, recent work was conducted to analyze the historical data as well as independently measure the magnitude of subsidence since 1975, within the same study area southwest of Mendota, CA. The results indicate that local subsidence in the area of the photo is less than 0.5 meters between 1975 and 2017. Furthermore, the results identify inconsistencies that may challenge the historical certainty of some local subsidence data. Procedures and methods used in the survey will also be discussed.

In a global scenario of climate change, water scarcity, and population growth it is imperative to optimize crop water management. One way is by calibrating a physically-based crop evapotranspiration (ET) model, in the so-called one-step approach; as opposed to the two step approach that uses reference evapotranspiration a crop coefficients. The Penman-Monteith (1965) ET model uses a surface resistance (r_s) term. This variable describes the resistance (stomatal, leaf, and canopy) to crop water transpiration and water evaporation from the soil surface. If the crop is not transpiring at a potential rate, the r_s resistance depends on the water status of the soil and vegetation. The stomatal resistance is influenced by climate and by water availability. However, this influence is different from crop to crop. The resistance increases when the crop is stressed and when the soil water availability limits ET. In this study, surface resistance was parameterized, for grain corn grown in Iowa, considering plant response to soil, plant, and environmental conditions, and not just to a well-watered and uniform corn surface. The independent variables that yielded a larger correlation with r_s were: net radiation, photosynthetic active radiation, crop height, leaf area index, percent cover, albedo, an advection function, canopy temperature, and surface aerodynamic resistance. These variables were used to obtain “r_s” models (linear, logarithmic, and exponential) which were evaluated with ET values derived from eddy covariance data. The applicability of the parameterized corn “r_s” models is discussed in contrast with “r_s” models developed for other crops.

A two and three-dimensional hydrothermal numerical model were developed and calibrated for Laurance Lake, Oregon, USA. The numerical scheme in the two-dimensional laterally averaged hydrodynamic and water quality model, CE-QUAL-W2, was adapted for a three-dimensional model, called W3, to solve the governing equations of continuity, free surface equation, and momentum. In order to perform an equivalent comparison between the results of the two and three-dimensional model, both models were run using the same meteorological data, boundary conditions of flow and temperature, and surface heat exchange algorithms. The reservoir water levels and temperature were calibrated over the simulation period. Even though the calculated error statistics of the 3D model were slightly higher compared to the 2D model, the numerical predictions of both models had good agreement with field data. Conditions where a 2D or a 3D model is better suited to a lake or reservoir were discussed.

The Brazilian interconnected hydropower system consists of a linked network of 151 medium and large reservoirs, and is complemented by a large set of small hydropower, thermal, and wind power plants, with a total installed capacity of 162 GW. Energy, flood control, public water supply, irrigation, animal raising, wastewater disposal, navigation, and environment protection are among the multiple water uses of the system. In Brazil, hydropower is the predominant source of energy supply, representing from 71% to 93% of total energy consumption in the country during the last 17 years. The reservoir storage in the multi-reservoir system varies from 286,174 mm³ to 573,600 mm³, and the forebay area varies from 22,715 km² to 37,894 km². Installed energy production and storage capacities, and hydrological conditions vary significantly among different regions in the country. Evaporation losses from the storage reservoir are significant and must be accurately estimated. In this paper we use the HIDROTERM, a nonlinear programming optimization model previously developed for planning the operation of the Brazilian hydrothermal system, to estimate the loss of energy production in the system due to evaporation loss. We use different hydrological scenarios and different initial conditions to study the impact of energy loss among different reservoirs and regions in the country. The study is important for planning purposes, especially when the system is undergoing severe droughts.

Water demand management strategies, such as more efficient end-use technology, alternative water resources, and demand response policies have shown promise in reducing consumption and shifting peak demands. Improving demand estimation through smart metering, as well as the potential savings from implementing various demand management strategies on the household, regional, and national scales, have been studied in literature. However, there is a lack of research on how these evolving water demand patterns affect the performance of water distribution networks on a network-wide scale. In this study, three residential demand patterns were used: 1) standard diurnal, 2) flattened demand, representing the response to economic incentives or technological mechanisms, and 3) reduced demand, which models the response to efficient end-use technology and alternative water sources. The network dynamics associated with the three scenarios were investigated using a hydraulic simulator. Four metrics are suggested to assess the network-wide performance in each scenario: water loss, peak flow, water age, and energy loss. Results from simulations using a real-world network indicated that decreased consumption and variance in demand had a negative or negligible impact on water loss and age, but there is a potential for savings in terms of reduced energy losses. The results highlight the implications on water management and planning from the network infrastructure and utility side.

China is currently engaged in a nationwide electricity market reform, aiming to reduce electricity price and improve clean energy consumption. In many areas, thousands of members are participating these special markets; they are all facing with bidding decision-making problems in these unanticipated markets. Due to the particularity of market rules and the shortness of data history and real-time info, plants’ bidding is lacking in theoretical support, their fate in the market is unknown. In this paper, an evolutionary game based electricity market-bidding model is proposed, in which all market members are bidding under limited market info. Each bidding strategy is an exploration of the market. The power plants take ‘market’ itself as their game opponent, instead of ‘other plants’ in normal game theory models. They adjust decisions according to market feedback and attempt to get some payoff from next market. Loss-making power plants will be acquired by other plants and inherit their bidding strategies. By simulating Chinese typical electricity market, the most competitive strategies are found and power generation groups are formed. Furthermore, this paper takes into account the possible impact of different type of demand and the supply-demand ratio. Comparing with constant demand, the demand curve can prevent clearing price from rising effectively. With a certain demand curve and the acquisition mechanism, the market systems achieve stability, in which the clearing point fluctuate little and periodically, the final clearing price is much lower than that before the reform, and the reform goal was achieved.

With the overall promotion of the new round of power system reform in China, constructions of electricity markets in most provinces are becoming more mature, attracting more power generation groups to participate in the electricity market. In electricity market, current scheduling methods of cascade reservoirs for power generation groups is not adapted to the newly established competitive environment. To address the profit maximization problem for power generation groups in day-ahead market, this paper proposes a medium- and long-term optimal scheduling model for cascade reservoirs, taking complex hydraulic and electric couplings into consideration. The day-ahead market transaction price of cascade reservoirs is forecasted through electricity price prediction method based on support vector machine algorithm. The adaptive hybrid particle swarm optimization algorithm is used to solve the proposed model. The simulated results of the case study based on cascade reservoirs in Lancang River show that the method proposed in this paper is reliable and reasonable. It provides a theoretical basis for power generation groups to optimize the dispatching of cascade reservoirs in day-ahead electricity market.

Many water resources management (WRM) problems are similar in nature, and yet they continuously appear in different forms and in different geographical locations. Understanding a malfunctioning system structure is essential for developing sustainable solutions, which should be further examined using detailed quantitative models. Persistent WRM problems can be explained at the strategic level using a set of generic system structures, commonly known as system archetypes. These causal-descriptive conceptual models can be used as diagnostic tools to identify weak links and problematic feedback loops in management schemes, facilitating the dissemination of policy-relevant insights for addressing the weaknesses. This paper illustrates the use of generic structures in water resources systems for uncovering the root cause of some ubiquitous WRM problems and potential unintended consequences of short-sighted solutions. The utility of system archetypes is discussed in terms of gaining big-picture systemic insights, developing effective technological solutions and complementary management policies, and monitoring sustainable system trajectories.

The newly-reformed Yunnan electricity market has been operating successfully for three years with more and more abundant trade varieties and perfect trading mechanism. The vitality of the market is increasing year by year. Market participants are gradually exploring reasonable bidding strategies, and the accurate electricity price forecasting is a joint demand for all the market members. With comprehensive considerations of the social economic background, the structure of the power system, the market development state, and other factors in Yunnan, this paper analyzes various factors and rules that have effects on the electricity price in day-ahead market. Furthermore, a successful application of an auto regressive moving average (ARMA) model to electricity price forecasting (EPF) is presented. The case study based on real data of day-ahead market in Yunnan shows that the proposed model could reflect the characteristics of electricity price and the results prove this model’s effectivity.

Understanding various types of consumers and their respective consumption patterns, changes in their water and electricity consumption trends in response to conservation strategies, and identifying the most influential variables influencing water-electricity usage, are key to support management and conservation strategies. In this work, our goal is to characterize the consumption of five building classifications on a university campus, specifically, academic, education, dormitory, operations, and recreational buildings through daily water and electricity usage collected over several years. Water-electricity demand data grouped based on similar usage characteristics promotes the development of water-electricity demand models and informs utility managers on end-use consumption patterns to support management and conservation strategies. In this study, we have focused on data collection and analysis to identify common attributes of university buildings with similar observed performance over time. Water and electricity demand profiles for 71 buildings across UT Austin’s campus, were aggregated by considering several consumption representative features, such as per capita demand, demand per square foot, and the buildings’ functional purpose. Housing was found to be the largest and least efficient water consumer on campus and had the most consistent seasonal demand profiles. Research laboratories were found to be the largest and least efficient energy consumer on campus. Water demand seems to be heavily human influenced, while energy demand is more likely influenced by automated systems like heating, cooling, and lighting. This suggests that different strategies should be targeted for water and electricity conservation. Dormitory water-electricity demand was correlated with occupancy and time of the year (i.e. academic seasons). As building size and total demand increased the efficiency of water-electricity use per square foot decreased. The methods employed in this study indicate that simple feature extraction can yield insights into water-electricity demand profiles that can inform effective conservation strategies.

This paper details the evaluation of potential climate change impacts on consumptive water use of a proposed power plant cooling system. A water budget model that includes the hydrologic elements and plant water usages was utilized to assess the climate change impacts on long term water use on a reservoir/lake system. The evaluation focuses on key regulatory parameters of reservoir water level and flow release to downstream users. The Third National Climate Assessment (NCA) report was used as the basis to project trends of climate change indicators. Among the climate indicators, the long-term trend of increasing air temperature was found to have the most direct and significant effect on water-use. The effect of projected rising air temperature was evaluated for two scenarios: a lower emissions scenario with a lower increase in dry bulb temperature and a higher emissions scenario with a correspondingly higher increase in dry bulb temperature. The results indicate an increase in release of minimum flow from the reservoir decrease in water level. In addition, long term average and maximum evaporation rates from the plant cooling system also show an increasing trend.

With growing fiscal pressures at the Federal level and ongoing discussions about the role of the Federal government in flood management, there is a need to better understand how flood risk and resilience vary across the United States. This paper describes a national flood risk characterization tool (NFRCT) developed for the U.S. Army Corps of Engineers (USACE). The NFRCT relies on data from the Federal Emergency Management Agency (FEMA), the U.S. Census, the U.S. Geological Survey (USGS), the USACE, and other sources. FEMA’s mapping of 1% annual chance exceedance (ACE) flood zones is used with the USGS National Elevation Dataset to estimate a distribution of flood depths for each flood zone. Flood zones are then overlaid with census blocks to estimate population and building exposure. Exposure is assumed to be proportional to the areal overlap of census blocks and floodzones, with the population coming from census data and the building inventory coming from FEMA’s HAZUS. Finally, damages are estimated for exposed buildings using standard depth-damage functions from FEMA and USACE. Exposure and damage estimates are summed to counties and hydrologic unit code-8 watersheds for comparing risk across different areas of the United States.

Water is unique among natural resources in the wide variety of allocations: human use and environmental use; direct use/market-good and non-use/non-market good; and as a private good and a public good. Environmental flows are essential for estuary and bay ecosystems, but come with tradeoffs and/or externalities associated with use allocation. The State of Texas has recognized the importance of environmental flows for the heath of its bays and estuaries and has enacted legislative actions. Regulatory and market-based strategies have been implemented for the allocation of water resources as a private good. This paper looks at how economic impacts and values can be estimated for water resources allocated for environmental flows as related to Texas water policy, uses case studies to demonstrate examples of how dollar values can be estimated, and describes how value and costs can be estimated using various methods.

This paper aims to show the use of rainfall runoff modeling as an important methodology to optimize investments for flood control in the city of São Paulo. The city of São Paulo is the largest city in Brazil and one of the five largest cities in the world. However, this megacity has a chronic flood problem that occurs every year, especially during the summer. This huge problem affects millions of people generating great economic and social damages. Due to its limited budget, the investments in flood control must be as effective as possible to bring benefits to the maximum number of people. In this sense, a methodology was developed for the diagnosis, evaluation, and classification of each available works for flood control. The rainfall runoff model SWMM was the fundamental tool for these analyses. The use of the model allowed evaluating different scenarios with different flood control works and in this way, it was possible to build different alternatives to solve the flood impacts in the basin. Each alternative had its impacts evaluated, as well as costs and number of people affected. As a result, this methodology consists in an important tool for planning and management of small urban catchments. To illustrate this methodology, this paper shows its application in the Jaguare Basin located in the west area of São Paulo City.

The study developed a preliminary system dynamics model to explore several water management scenarios in the major river systems of the Indus River Basin (IRB) in Pakistan. The performance of the two major reservoirs i.e., Tarbela and Mangla, located on the Indus and Jhelum Rivers, respectively, were evaluated under different flow conditions. Empirical relationships between upstream and downstream locations of each of the existing reservoirs and barrages were developed and were validated against historical flow data. Inflows to the reservoirs were varied across a wide range. The environmental flows in terms of the net contribution to the Arabian Sea, the most downstream location of the IRB, were also calculated under each of the scenarios. The results suggest that the existing conditions can be improved by proper regulations of the reservoirs under different scenarios. However, with the high variation in seasonal flows, infrastructural development will be required to tackle extreme conditions.

Increase in the demand of food and fiber due to a rapid increase in population has imposed heavy stress on the water resource of the world, which has given rise to competition for this resource particularly in those areas where usage has exceeded the limits of availability. With the passage of time, this competition not only intensified but has escalated to the position of conflict, moreover improper management and inequitable distribution has added more stress on the water resources. The Indus Basin Irrigation System (IBIS) Pakistan being the largest contiguous irrigation system is not an exception to twin problem of improper management and inequitable distribution. Water management and distribution in the IBIS are governed by the Indus Water Treaty (IWT) among the India and Pakistan, and via Water Apportionment Accord (WAA) within the provinces in Pakistan. In this paper, we have developed a basin scale decision support system using water evaluation and planning (WEAP) modeling framework to evaluate the water distribution among provinces based on Water Apportionment Accord and suggest the way towards conflict management and mitigation. WEAP model simulates the reservoir (Tarbela, Mangla, and Chashma), rivers, canal, and link canals at the supply nodes; and canal command areas for agriculture and domestic demand nodes. Simulating the water system historically for 23 years from 1991 to 2013, it was observed that every year Sindh has experienced deficiency and cut off from its allocated share. Punjab has also faced deficiency and cut off from an allocated share in every Kharif Season but in a lower proportion, whereas Punjab has extracted excess of its share 11 times in 23 years in Rabi Season.

In the California Sacramento-San Joaquin Delta (Delta), levees reduce risk of flooding and potential for injury, loss of life, property, and infrastructure damage. Levees can also improve water supply reliability and water quality, and protect aquatic habitats. The Delta Plan, adopted in 2013, recommends that the Delta Stewardship Council (Council) develop a delta levees investment strategy (DLIS) to identify funding priorities for State investments in Delta levees. Among the economic considerations in establishment of funding priorities are (1) cost allocation procedures, and (2) determination of Reclamation District (RD) ability to pay (ATP) for levee improvements. The authors present a practical method for estimation of ATP for levee investments based on readily available RD financial information, including annual levee expenditures and income streams derived from sale of crops and rent value of agricultural land. A pilot application of the method is described and, based on the results, potential strategies suggested for: (1) cooperative financing of levee investments, and (2) adjustment of subventions (State support of annual levee maintenance expenses) to reduce acute inequalities in ATP.

Shortage of electric energy is one of the major problems in developing countries, especially in Southeast Asia. The energy producing companies are facing a challenge of meeting increasing demand with significant peak and base load differences with hydro-electric projects. The variability in energy demand (hourly and seasonal) and seasonal changes in streamflow availability to harness energy complicate the design and regulation of a hydro-electric project. A system dynamics model was developed for a hydro-electric project in Nepal with the variable energy demand and the availability of streamflow as the primary control parameters. Analyses were conducted to determine the effect on electricity production from the plant in a year among the various scenarios. An installed capacity of 12 megawatts and storage capacity of 750,000 cubic meters was determined to be a threshold for steep increment in electricity production for the simulated demand. The growth in national installed capacity showed non-linear decrease in electric production from a plant while an increase in electricity demand and its variation showed a non-linear increase. Such models for hydro-electric plants may be helpful in selecting the appropriate storage and installed capacity under variable demand and supply conditions and possible future scenarios.

With the records of recent decline in water level of Lake Mead, for the operation and management of lake it is important to consider the full range of future scenarios involving climate change and water demand due to increasing population. This study evaluates the effect of climate change and varying water demands on Lake Mead storage and water levels. It deals with the various dynamic variables such as metrological and socio-economic factors that impact the water storage capacity at Lake Mead and estimates what the lake storage and water levels would be in future by varying these parameters over a wide range. A system dynamics tool STELLA was used to develop the model, which simulated the historic, present, and future Lake Mead levels in response to changing inflows and outflows. System dynamics is a computer-aided approach used in studying complex systems by analyzing the interaction between various elements within that system. Climatic parameters that affect the water volume were integrated into the model using secondary data and model was calibrated. The results indicate that by using the calibrated model and applying the forecasted inflow for the simulation period, the shortage criteria and dam water regulation guidelines, we will be able to anticipate the water level changes at Lake Mead and assess what management scenarios will allow the water levels in the Lake Mead to be maintained at a higher level than what it has experienced during the recent droughts.

This study explores the use of non-homogeneous hidden Markov models (NHMMs) to forecast streamflows into the Orós Reservoir, located in the State of Ceará, Northeastern Brazil. These models employ hydro-climatic information from a given year to predict the next year inflow. The Orós Reservoir is a key infrastructure in the water resources system of the state, where most rivers present very high temporal variability, which is a challenge for water managers. Streamflow forecast systems at the climate scale (months ahead) can be very useful for the challenging process of water allocation in the region. This paper explores the use of NHMMs to provide a probabilistic one-year ahead forecast of the mean annual flow for the Orós Reservoir. Results based on a forecast verification study based on 60 years of independent data show that NHMMs with two or three hidden states, employing the NINO3 climate index as exogenous variable, provides forecasts with an average value of the continuous ranked probability skill score of about 0.2–0.25, meaning that they are better than climatology. It is also evident that those models performed much better during dry years than during wet years, which may be related to lack of other climate indices.

Mining water billing data allows utilities to stratify their customers based on amount of water consumed and provide insights on customers’ usage patterns and changes in behavior in response to external factors. In this study, the mean monthly summer water use of about 15,000 single family residential customers was mined to explore how their behavior changes with respect to changes in weather conditions, especially during a drought year. It was found that customers significantly increase their outdoor water use in an effort to protect their landscape, but would revert back to their pre-drought usage once the drought is over. Further, educating customers on water conservation practices and sprinkler adjustment, based on weather conditions, can help in reducing water consumption.

Lake Champlain is a valuable resource in the state of Vermont. More than 600,000 people live in the Lake Champlain basin and millions visit each year to enjoy its waters and other natural and historic features. Not only recognized as the sixth (6^th) largest freshwater lake in the United States, Lake Champlain’s watershed has been designated by the United Nations as an International Biosphere Reserve. Excess phosphorus from a variety of sources has impaired the water quality of Lake Champlain for many years. The leading source of phosphorus is agriculture (41%), followed by river instability (21%), and forestland (16%). In 2002, Vermont prepared a plan to reduce phosphorus loadings by developing a total maximum daily load (TMDL). A TMDL places a cap on the maximum amount of phosphorus that is allowed to enter the lake and meet Vermont's water quality standards. EPA disapproved the Vermont 2002 Lake Champlain Phosphorus TMDL in 2011. On June 17, 2016, EPA established a new phosphorus TMDL for the twelve (12) Vermont segments of Lake Champlain. The TMDL was developed in collaboration with the Vermont Agency of Natural Resources, Department of Environmental Conservation, and the Vermont Agency of Agriculture, Food, and Markets (VAAFM). This paper presents background information on the TMDL and a critical analysis of the TMDL.

This study demonstrates how geospatial analysis tools can be effectively used to improve drought monitoring and management. Understanding spatial variation of drought is vital in drought monitoring and management. Drought indices, generally derived from station-wide meteorological and hydrological data are commonly used to monitor and assess droughts. These indices are generally reported for specific spatial scales such as climate division, county, hydrological unit etc. This study involves spatial interpolation of drought data and quantification of the uncertainty in drought reporting at different spatial scales. The standardized precipitation index (SPI) with one month time step, a metrological drought index, from 57 stations across the State of North Dakota (ND), U.S., was used. Two interpolation methods, inverse distance weighting (IDW) and ordinary Kriging (OK) were evaluated for their performance using selected cross-validation parameters. The variability within each of the three spatial scales: county, climate division, and state were quantified based on interpolated values. This study found that (a) IDW with power one, and (b) OK with circular semivariogram model can be used for interpolation of the selected data obtained from the current distribution of stations. Significant variation of drought was found within each spatial scale. Predicted values of SPI were compared with reported values from commonly used drought reporting sources. A discussion of uncertainty in drought reporting at spatial units of different size is also included.

Due to lack of watershed-wide hydrologic data from in situ platforms, whether they are real time or historical, water management has been quite challenging. Benefiting from the high spatio-temporal resolution and near-global coverage, satellite-based precipitation estimates (SPEs) provide an opportunity to enhance present hydrologic prediction capability for a watershed or river basin. In the present study, three different SPEs i.e., PERSIANN, CMORPH, and TRMM precipitation datasets have been assessed over gauge-based India Meteorological Department (IMD) gridded dataset employing statistical and contingency table methods for an agriculture-based Marol watershed of India. The detailed evaluation has been carried out for the years 1998–2013 on daily, monthly, seasonal, and yearly basis with a spatial resolution of 0.25×0.25 latitude/longitude. SPEs performed reasonably well with the IMD gauge-based gridded dataset, although significant biases were observed. In general, the rainfall detection capabilities of SPEs were found better during monsoon season than non-monsoon season and annual time scale. A good correlation was found between watershed-averaged monthly precipitations of TRMM dataset against IMD gauge-based gridded dataset (R² = 0.77) as compared to CMORPH (R² = 0.63) and PERSIANN (R² = 0.56) datasets. Overall, performance of TRMM precipitation dataset was found superior over CMORPH and PERSIANN datasets, however, due to error and bias in SPEs considerable inter-annual and seasonal variations were observed. The analysis indicates that the use of SPEs can be a compensating approach after suitable bias correction and it has potential for hydrologic simulation in data-sparse regions.

Urbanization has become a global trend over the past century. Urbanization, causing significant changes on watershed land use/cover (LUC) types, changes the hydrological characteristics of urbanized watersheds. Surface peak flow and runoff coefficient are the most impacted hydrological index. The increased peak flow and higher runoff coefficient requires increased flood mitigation capacities. To better adapt to this change, quantitatively determining the effect of urbanization on the hydrological processes is extremely important for watershed planning and management. This article studied the impact of urbanization on the peak flow and runoff coefficient of Xinji River in southern China, a highly urbanized watershed. The Liuxihe model, a physically based, distributed hydrological model proposed for watershed flood forecasting is employed to simulate the flood processes of Xinji River, and the LUCs at 12 time periods derived from satellite remote sensing imagery are used to estimate the model parameters induced by urbanization. The results show that from 1987 to 2015, in less than 30 years, the urban land area of Xinji River increased from 0.8% to 45.1%, a rapid urbanization. Land use change has significantly changed the flood characteristics of Xinji River. The peak flow in 2015 had an average increase of 67.83 m³/s than that in 1987, while the average runoff coefficient increased 0.289.

Wave propagation speeds and attenuation rates of flood waves or tidal waves traveling along canals depend on the hydraulic properties of the canal, the seepage parameters of the underlying aquifer, and the overbank flow parameters. Analytical solutions to this problem are useful when understanding the propagation of these waves along canals and rivers. These solutions are also useful when testing integrated hydrologic models. For coupled one-dimensional hydraulic and two-dimensional groundwater numerical models to be accurate: a) the numerical model of the St. Venant equation must be accurate; b) the numerical model for the two-dimensional groundwater model must be accurate; c) the internal boundary conditions of the one- and two-dimensional model interfaces must have the correct flow continuity and head compatibility conditions; and d) the discretization for the one- and two-dimensional models have to be adequate. The analytical solutions developed in this paper will be used to verify the accuracy of the regional simulation model (RSM) that is used to simulate complex hydrologic systems. Results of the analysis show that three-dimensionless parameters are responsible for the analytical solutions for wave propagation characteristics.

Karachi, the biggest city of Pakistan, has undergone rapid urbanization since the creation of Pakistan in 1947. The city is expanding with an alarming rate placing pressure on the available natural resources and services with rising number of natural hazards. Urbanization brings not only multiple environmental challenges but also causes socioeconomic issues. One of the current environmental problems due to urbanization of Karachi is blockages in its natural drainage system causing localized floods after heavy rainfalls. In this paper, landuse/cover (LULC) changes of the Karachi city and their impacts on natural drains of one of its three major watersheds, Malir River Basin, over the last 23 years (1993–2015) have been studied. Historical satellite data and remote sensing tools were utilized to examine LULC changes of the Malir River Basin. The U.S.-based first earth observing satellite series of Landsat were selected for this purpose. Shuttle Radar Topographic Mission’s Digital Elevation Model (DEM) along with the topographic sheets of 1975 was utilized to delineate the streams of the Malir Basin using geographical information system (GIS) tools. GIS overlay analyses were done to identify the blockages in the riverbed and flood damages. For flood simulation, historical (1985 to 2014) rainfall gauge observations of Pakistan Meteorological Department (PMD) were acquired. The results of the study showed that a significant portion of the riverbed, delineated from 1975 topographical sheets, has currently been occupied by urban and agricultural lands which have severely affected the natural drainage of stormwater causing localized floods in the basin.

Government agencies responsible for managing water depend on numerical models to simulate the detailed hydraulics and hydrology over areas under their jurisdiction. These models play a key role in making planning and operational decisions. The regional simulation model (RSM) developed at the South Florida Water Management District (SFWMD) is such a model used for simulating flood control and water supply scenarios. It uses object-oriented design methods to fully integrate 1-D and 2-D surface and groundwater flow features. Fully integration is used for reducing the error in coupling. RSM uses an implicit total variation diminishing Lax-Friedrics (TVDLF) method to solve both full and approximate shallow water equations under extremely flat and steep conditions. Functionality was added recently to the model to simulate wetland hydrology, local hydrologic features, and detailed transitional flow resulting from micro-topography and local drainage. The RSM model is applied to the Kissimmee River Basin in South Florida to capture the dominant hydrologic and hydrologic response features needed for understanding high and low flow regimes in the basin. Following basic physical principles and maintaining parameter parsimony were important considerations during the application. The results show that the ability to simulate transitional flow using physically based representations of micro-topographic features was important for producing the discharge hydrograph correctly using the model. The results also show the overall distribution of flow during flood events, water levels at all locations, and detailed flow vector patterns resulting from surface, sub-surface, and transitional flow.

Flooding in major urban areas are due to the intense rainfall and insufficient capacity of storm sewers system to suitably route the peak flow within the watershed. With increase in imperviousness due to urbanization and increase in precipitation due to changing climatic condition, storm water management has become crucial and equally challenging. Suitable approach for prediction of peak flow based upon the available historical precipitation data, land use condition, soil type, and suitable routing method is necessary. This study focused on the use of Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) for the prediction of peak flow condition in Irwin Creek watershed (15.46 sq.km) located in Charlotte, North Carolina. The urbanized area is prone to flooding on intensified storm events as evidenced recently during the Hurricane Matthew. Using precipitation data from United States Geological Survey (USGS) rain gauge stations, hydrological model was developed in HEC-HMS. Using geospatial hydrologic modeling extension (HEC-Geo HMS) in Arc GIS, watershed for Irwin Creek with 13 sub-watersheds was created. Curve number grid for each sub watershed was prepared in Arc GIS from the land cover and soil group data. Basin model file prepared from HEC-Geo HMS containing all the required hydrological parameters was imported into HEC-HMS. USGS streamflow station data was used for the calibration and validation of the model. 6 hr. 100 yr. storm event produced by National Oceanic and Atmospheric Administration (NOAA) was routed through calibrated model to generate the peak flow of the watershed. Additionally, the impact of climate change on precipitation resulting the peak flow was assessed using climate data developed by North American Regional Climate Change Assessment Program (NARCCAP). Delta change factor of 1.18 was calculated for this study from Hadley Regional Model 3—Hadley Center Coupled Global Climate Model, a regional climate model— global climate model paired model used by NARCCAP. Through the critical analysis of the peak runoff generated by the calibrated model for NOAA rainfall data and NARCAAP rainfall data, 43% increase in peak flow was determined for 18% increase in storm depth. The study highlighted the significance of consideration of climate change factor that is likely to result in increased peak discharge in the existing urban watersheds. Hence, the results from this study suggested the necessity of suitable runoff-reduction approaches like low impact development for the further development of the study area.

Hurricane-induced flooding is a recurring phenomenon causing severe damages to lives and properties in southern coastal states. The magnitude of rainfall observed during Harvey Hurricane in August 2017 was the all-time maximum for some of the regions in Houston, TX. This research focuses on modeling different recurrence frequency flows in a selected river reach along with the Harvey flooding condition. The statistical part involves the flood frequency analysis using generalized extreme value distribution. The floodplain analysis is performed by developing a hydraulic model using Hydraulic Engineering Center’s River Analysis System and Esri ArcMap software. Historical flow data and terrain geometric data are extracted from United States Geological Survey. A river reach of Buffalo Bayou is selected and analyzed for several flow scenarios, and flood inundation map of the affected region is developed. The model is calibrated and validated for river stage by adjusting Manning’s roughness coefficient. The evaluation of each flood stage and comparison with the Harvey flooding stage is expected to provide valuable information for floodplain management, riverside development, and for future flood assessment plans in the adjoining areas.

The hydrologic response of a watershed mostly depends on factors such as land use, soil type, and climatic inputs. Watershed modeling requires information on several soil properties, such as, texture, taxonomy, soil moisture, number of layers, and hydraulic conductivity. Several models are available that allow users to simulate the streamflow for a particular watershed using land use, topography, soil properties, and weather data. Better understanding of the future streamflows and water quality is essential for the long-term planning and management of water resources. The prediction of future flows and other hydrological parameters can be performed using different available modeling tools. Over the years, modeling of a watershed has significantly developed with the introduction of different sets of soil data such as Soil Survey Geographic (SSURGO) and State Soil Geographic (STATSGO). Both SSURGO and STATSGO data are compatible with GIS-enabled interfaces and can be used in both distributed and lumped hydrologic models. Soil and water assessment tool (SWAT) is one such tool that allow the use of soil properties data to estimate the runoff process of a watershed. The predictability of hydrological models vary significantly with the variation in input data resolution. The primary objective of this study was to compare the SWAT-simulated outputs: stream flow; and water quality parameters, for SSURGO and STASTGO data. The study was performed in the watershed of Lower Cumberland-Sycamore in Tennessee. The simulation results has demonstrated that the effectiveness of the model prediction depends mostly on the type and quality of input soil data, and suggested that the higher resolution data yields better model results when compared with observed data.

It is evident that the south coastal regions of the U.S. have been hit hard with frequently occurring tropical storms and hurricanes in recent times. These storms have been more disastrous because they resulted in high intensity rainfall events, which ultimately caused flooding in the region. Flows larger than a 500-year flood have occurred in some regions, which justifies the Federal Emergency Management Agency’s updated policy of incorporating 500-year return period flows in flood-risk assessment studies. Recently, Hurricane Harvey has hit different regions in Texas causing extensive damages to lives and properties. This study focused on performing the flood frequency analysis along the Colorado River in Texas and developing the floodplain maps for Columbus, Wharton, and Bay City in Texas. The frequency analysis was performed to generate flows for different return periods—10, 100, and 500 years and the corresponding floodplains were delineated using Civil Geo-HECRAS, developed by Chris Maeder of CivilGEO team. The result in terms of flood extent and depth were compared with the inundation extent resulting from the Hurricane Harvey. This study can assist managers and policy makers in delineating high risk areas, planning for stormwater management, and vulnerability assessment of the infrastructures.

The meteorologic station provides precipitation, air temperature, dew point, etc. for hydrology and water quality modeling in the watershed. However, the meteorologic stations are absent in some watersheds, which brings difficulty for the watershed modeling. In this study, the method of constructing the virtual meteorologic station in the watershed is developed. This can overcome the difficulty of the absence of meteorologic station and makes it possible for watershed modeling. The data from the precipitation stations inside the objective watershed and the weather stations close to the objective watershed are used to construct the virtual meteorologic station inside the objective watershed. The delineation tool in the better assessment science integrating point and nonpoint (BASINS) is used to delineate the objective watershed. The hydrological simulation program—FORTRAN (HSPF), incorporated in BASINS, is used to model the stream flows in the watershed. The National Elevation Data (NED) terrain dataset is used for watershed delineation. In this study it is found the modeled stream flows and water quality with the virtual meteorologic station in the watershed have reasonable agreements with the observations. The method of constructing the virtual meteorologic station developed in this study can be used for the modeling of hydrology and water quality in the watersheds where the meteorologic stations are absent.

Midwestern states have agreed to reduce nutrient releases to combat Gulf of Mexico hypoxia by focusing on the approximately 80% of N and 50% of P loading resulting from agriculture. In support of this effort, we are working with The Wetlands Initiative to develop “Pocket Wetlands” to capture and treat drain tile water by microbial denitrification and nutrient sequestration. Two wetlands were built, W₁ in 2015 and W₂ in 2017, in high-yield corn/soybean cropland in IL. Inlet and outlet flow and nutrient data have been used to assess treatment performance and learn lessons about construction and operation to increase nutrient removal. NO₃^--N levels averaged 11.7 mg/l, and SRP averaged 0.18 mg/l in W₁ during 2016 with a DIN/SRP ratio >65. Overall, 1100 kg of N was removed in 2016 from W₁, but with relatively low efficiency. In the second year, W₁ nitrate removal efficiency increased significantly with total removal of 1700 kg of N and 200 kg of P. Lessons learned from the operation of W₁ have been employed in the construction and operation of wetland W₂. Data from initial operation in W₂ demonstrate the importance of proper conditions to support the microbial denitrification process.

Depression-dominated regions have a unique undulating topography which contains depressions, potholes, and wetlands. These topographic features are periodically ponded and have different functions such as flood mitigation, nutrient and sediment retention, and habitat provision. Accounting for such detailed topographic features alters the hydrologic modeling results. However, the structure of many macro-scale hydrologic models requires adequately lumped or simplistic approaches rather than detailed, process-based approaches. This study is aimed to identify a relationship between ponding area and ponding storage of depression-dominated regions by conducting a set of topo-statistical analyses. The Red River Basin was chosen in this study to represent a depression-dominated watershed. To extract topographic characteristics of the selected study area and quantify topographic indices, the depression-dominated delineation (D-cubed) algorithm was utilized. Based on the topographic analyses, a regression model was developed, and the utility of the model was tested by the analysis of variance (F test) and other statistics. The delineation results highlighted the presence of a hierarchical relationship among depressions which led to proposing a regression model between ponding area and ponding storage. Testing the global usefulness of the regression model indicated that it was statistically helpful for prediction of ponding area. The results also suggest that variations in ponding area over the study area can be explained by ponding storage and other topographic indices. The topo-statistical analyses used to develop the regression model can be used in macro-scale hydrologic models to improve the modeling for depression-dominated regions.

Daily streamflow records are the basis for many water resources related studies and are almost always taken as free of error. However, streamflow data are not actually measured in the field, but estimated based on daily measurements of water level in conjunction with the rating curve. As the rating curve is only an approximation of the real relationship between water levels and discharge values, daily streamflow data contain uncertainties. The quantitative assessment of these uncertainties is important to obtain a more realistic description of the uncertainties in many water resources related studies. Bayesian inference is very attractive in this case because it can easily incorporate the often imprecise knowledge available on the hydraulic behavior of the river into the analysis, providing a natural way to not only evaluate the uncertainties in the streamflow sample, but also to consider these uncertainties in the estimated hydrologic variable of interest, such as flood quantiles, reservoir yield, water quality parameters, etc. This paper presents a fully Bayesian model capable of incorporating imprecise knowledge on the hydraulic behavior of the river, when available, to estimate the uncertainties in the daily streamflow data. The method was applied to a gauge station in the Madeira River with an abundance of hydrologic knowledge and gauging data, providing an opportunity to understand how prior knowledge on the hydraulics of the river reach, and the amount of measurement data affects uncertainties in the predicted streamflow data.

Streamflow predication is one of the important tasks in water management. It is needed in operation and optimization of water resources, flood control, preventing dam failure, and design of hydraulic structures. Accuracy of these predictions have great influence on the water resource management and decision making. Various model and tool packages have been developed for simulation and prediction of streamflow, and soil and water assessment tool (SWAT) is one of the most applicable package. Simulation results of the SWAT indicate that this tool has deficiencies in simulating peak streamflow events of the cold regions which are generated by snow melting processes. Since global temperature is projected to be increased and the phenomena will change the snow melting characteristics in the snow dominant areas, such as the time of first melt and rate of melting. The concurrence of these processes and high precipitation will cause more flooding problems in these regions. In this study, we used a method for improving the SWAT simulation results. This method is to develop the SWAT-CUP in the steps of calibration and validation. Improved simulation results showed about 50% raised in simulating results.

Flow directions are important because they may be used to derive flow paths. These parameters are requisite in terrain-based flood mapping methods. Prior algorithms of flow direction generation are based in digital elevation models alone. Implicit in these methods is the assumption that elevation is the sole determinant in flow directions; however, such assumption may not be valid in complex landscapes such as in urban areas, where flow directions may be better described by the two-dimensional shallow water equations. A new approach for generating flow directions is therefore described. In the proposed approach, flow directions were derived using a hyper-resolution, hydrodynamic flood simulation instead of digital elevation models alone. With a city block in Austin, Texas, as a test case, results were compared to the widely used D∞ algorithm. It was concluded that elevation difference still greatly affects flow paths; nevertheless, flow is still a complex phenomenon in an urban landscape. The result therefore supports continued application of terrain-based models in rural areas but suggests that the use of hydrodynamic models is a more robust procedure in generating flow directions and flow paths and therefore deserves consideration for future research.

Hydrologists are concerned about possible climate change and trends in flood-flow frequency relationships. A proposal is to estimate the trends in the mean (sometimes also the variance) of floods over the period of record and employ the estimated trends to project the flood-risk distribution’s parameters into the future. How large do trends need to be for that to be attractive? This paper reports a Monte Carlo study based on the log-Pearson type 3 (LP3) distribution with estimated skewness coefficient, considering several variations of such a dynamic flood frequency analysis, and the estimation of parameters. With a sample size of 40 and small trends (within ±0.25% per year), the stationarity moment estimator is about the best among realistic moment methods across all cases considered regardless of the skewness coefficient; for larger trends one should incorporate the trend in the log-mean of the LP3 distribution; only for ±1% per year or more-extreme-trends in both the mean and variance was it advantageous to estimate the trends in both the log-mean and log-variance of the annual maximum series. The results show that the LP3 coefficient of skewness (which was assumed to be a constant and was estimated) has considerable effects on the efficiency of quantile and exceedance probability estimators in a dynamic world.

The 2010 floods in the Indus River system in Pakistan has been one of the devastating river flood disasters that affected more than 14 million people in Pakistan. Sindh, one of its provinces and being lower riparian, faced the worst that Flood 2010 had to offer. Absence of flood inundation maps and flood forecasting techniques made this disaster into catastrophe, especially in Sindh. In this study, a 2D hydrodynamic model was developed for lower Indus River reach- Kotri Barrage to Arabian Sea—to delineate the flood inundation maps and identify the locations that are vulnerable to future flooding. The study mainly utilized HEC-RAS 2D along with Arc-GIS and HEC-GeoRAS tools, for data preprocessing. Recent and freely available ALOS World 3D-30m (AW3D30) Digital Surface Model (DSM), land cover derived Manning’s roughness coefficients, observed cross-sections interpolated bathymetry and upstream flow hydrograph were also parameterized in the model. The model was calibrated with 2010 flood and validated with 2015 discharges. The results identified six vulnerable locations that pose a serious threat in the study area in case of future floods. The results from this study can be used for flood risk assessment, flood management, and evacuation planning.

Design and operation of hydraulic infrastructures require the assessment of quantiles of peak flow discharge, which is generally carried out through the fitting of a parametric probability function to an observed series of peak flows. However, in many cases, peak flow series are only partially available, or not available at all, while records of mean daily flows are generally more common. Indeed the mean daily flow can be considered as a right censored data with respect to peak flows, being the former always less or equal than the latter. Therefore, techniques for parameter estimation for censored data, usually applied for paleo-flood analysis, are potentially applicable. In the paper, we investigate whether including information from available mean daily flows series may increase the reliability of the estimated peak flow quantiles. In particular, the analysis is carried out with reference to three gauged stations within the Simeto River Basin, the largest watershed in Sicily, Italy, for which contemporaneous observations of peak flows and mean daily flows are available. Several combinations of series of peak flows and mean daily flows of different lengths are analysed by means of a resampling approach.

KINEROS2 model was applied to simulate watershed erosion in Barry M. Golden Range (BMGR)—West in Yuma, Arizona. The range is approximately 1000-square-mile, and land cover is primarily shrub/scrub and barren land. It consists of three major watersheds: Yuma Desert Wash, Coyote Wash, and Mohawk Wash. If using the default parameters in KINEROS2, none of the simulated erosion depth in the sub-basins matched the observations. The average simulated erosion depth are two orders of magnitude smaller than the observations. Therefore, a stepwise secant method was used to calibrate the key modeling parameters including saturated hydraulic conductivity, Manning’s roughness, mean capillary drive, rainsplash coefficient, and cohesion coefficient. The calibrated results yielded only 11%, 30%, and 36% sub-basins matched the observations satisfactorily in Yuma Desert Wash, Coyote Wash, and Mohawk Wash, respectively. Results in the majority of sub-basins were still smaller than the observations even though the parameters that could yield the maximum erosion were used. This indicates the limited capability of KINEROS2 in simulating watersheds in arid and semi-arid region.

Restorations of vegetation in damaged grassland ecosystems is a challenging task for researchers and requires better understanding of nutrient availability and dynamics. The stoichiometric properties of plant carbon (C), nitrogen (N), and phosphorus (P), and their relationships with soil were studied in different grassland communities i.e., desert steppe, semi-desert steppe, mountain meadows, mountain meadow steppes, and alpine meadows in northwestern, China. The result showed that C contents in below ground parts were lower than in above ground parts from the temperate steppe community. The average values of total C in plant of the above ground (A-ground) and below ground (B-ground) (0~20 cm) were 346.1 g.kg⁻¹ and 341.8 g.kg⁻¹, respectively. The average values of N and P in plant leaves were 13.6 g.kg⁻¹ and 1.7 g.kg⁻¹, respectively. For the below ground part, the average values of N and P were 9.5 g.kg⁻¹ and 1.4 g.kg⁻¹, respectively. By comparison with the mean N and P contents of leaves in China, N contents in the study area were lower, while the P contents were consistent with average values at national level. The average A-ground and B-ground of the chemometrics N/P ratio were 7.5 and 7.1, respectively. N is a limiting factor and it should be taken into consideration for the appropriate supply of nitrogen. This study could provide the theoretical basis for vegetation restorations of damaged ecosystems and support for further protecting grassland and ecosystems.

The study objective was to develop rainfall frequency estimates for seasonal (3-month) and annual duration for the Tombigbee River Basin (TRB) in Mississippi and Alabama and implement results to determine the probability to end a drought in the study area. Research goal was accomplished by following the procedure outlined by Hosking and Wallis and using the International Center for Integrated Water Resources (ICIWaRM) Regional Analysis Frequency Tool (ICI-RAFT). Regional frequency methods based on L-moment were derived for the Pearson type III (PE3), log-Pearson type III (LPE), log-normal (LNO), and the general extreme value (GEV) distributions. For the winter season and the 12-month duration dataset, the PE3 distribution clearly provided best fit when the TRB was considered a homogeneous region. For the summer and fall seasons duration analysis, the LNO and the GEV distributions, respectively, met the criterion of |z|≤1.64, which corresponds to the failure to reject the hypothesized distribution at a confidence interval of 90%. The determination of the probability of precipitation to end a drought was based on the published maps and datasets from the NOAA-NCDC. A regional index flood model was developed for the four 3-month seasonal duration and for the 12-month duration. One regional scheme (TRB as one homogeneous region) was validated and used to the performance of the complete analysis. The regional frequency method and the use of ICI-RAFT are convenient for delivering reliable and reproducible estimates of rainfall frequencies over areas with big extension such as the TRB. Combining the evaluated methodology and computational tools, producing rainfall intensity-frequency-duration estimates would help in updating existing estimates, which are considered for use in the design of storm water management facilities and water supply. Research results can be used as an aid in decisions regarding implementation of water conservation or allocation.

Major coastal storms originating in the Gulf of Mexico and the Atlantic Ocean often bring significant precipitation inland, creating the possibility for coincident peak stream discharges and elevated coastal flood water levels. Federal and state agency guidance cautions the design engineers to be aware of this issue. However, in practice, the two hazards have predominately been treated independently, with existing analysis methods that are either too complex or produce counterintuitive results. Recent hurricanes, including Harvey/Irma/Maria (2018), have highlighted the need to improve the analysis of coincident surge and rainfall-runoff. This paper identifies key physical processes and provides recommended methodologies to estimate frequency flood elevations along tidal reaches of streams. Four approaches are presented as having the potential to analyze tidal stream reaches along the Atlantic and Gulf coasts and the Great Lakes. Although recommendations focus on the eastern part of the U.S., the methodologies can be applied elsewhere.

This paper presents a technical approach to link the watershed model and surface water quality model to study the effect of pollutant loads from upland watershed on the water quality of the receiving water body. The AnnAGNPS watershed model, developed at the USDA ARS, National Sedimentation Laboratory (NSL), is applied to simulate the loads of water, sediment, and nutrients from upland watersheds. In this model, the effects of land use/land cover, soil properties, climate, agriculture management, etc. on the watershed loads are considered. The computed results are used as boundary conditions for CCHE_WQ, a water quality model developed at the National Center for Computational Hydroscience and Engineering (NCCHE), to simulate the water quality concentration in water bodies. In this model, the effects of sediment on the water quality constituents are considered, and the distributions of nutrients, chlorophyll, and dissolved oxygen in the water body can be obtained. This technical approach is tested using Beasley Lake watershed in the Mississippi Delta as a study site. The lake water quality is monitored by NSL, and the measured data is used to calibrate and validate the numerical model. In this lake, sediment concentration is relatively high, so the sediment-associated water quality processes need to be taken into account. This research provides a useful tool to assess long term impacts of watershed nutrient and sediment loads on the water quality of the receiving water bodies.