Flooding and the interannual variability of hydrologic quantities in the Missouri River basin

The six mainstem reservoirs in the Missouri River basin (MRB) are managed mainly to prevent flooding from snowmelt and heavy rainfall, a goal for which the interannual variabilities of precipitation ($P$), evapotranspiration ($\mathit{ET}$), and surface air temperature ($T_{\mathrm{air}}$) are vitally important. We tested the hypothesis that under the expected higher variability owing to global climate change, the months with the highest contributions to the interannual variability of $P$, $\mathit{ET}$, and $T_{\mathrm{air}}$ in the MRB will remain unchanged and quantified likely temporal trends in these quantities. Using high-resolution, downscaled Coupled Model Intercomparison Project Phase 5 multi-model ensemble data sets, we compared the multi-year ratio of monthly and annual interannual variability and temporal trends in $P$, $\mathit{ET}$, and $T_{\mathrm{air}}$ during 2011–2020 with three future decades. Results showed that the 6 months with the highest interannual variability in $P$ and $\mathit{ET}$ (April–September) are the same in all four decades. However, for $T_{\mathrm{air}}$, only 4 months (December–March) retain their status as highly variable throughout the four decades; September and October variability is exceeded by the variability in other months. This implies that, compared to $P$ and $\mathit{ET}$, the cyclical change in the probabilities of $T_{\mathrm{air}}$ in the MRB is less stable under future global climate change. This finding can be used to consider the need to alter existing strategies for reservoir release while minimizing the likelihood of aggravating flooding below the reservoirs.     

A 21st-Century perspective on snow drought in the Upper Colorado River Basin

In the Upper Colorado River Basin (UCRB), there is a deep reliance on seasonal snowpack for maintenance of water resources. The term “snow drought” has recently emerged to describe periods of anomalously low snowpack. Unique seasonal patterns in precipitation and temperature that drive snow drought can have distinct hydrologic signatures, and these relationships have not been carefully studied in the UCRB. Here we examine snow drought with a new classification scheme using peak snow water equivalent (SWE) and the ratio of basin-wide modeled peak SWE to accumulated (onset to peak) precipitation (SWE/P) that clusters snow drought years into three distinct groups—“warm,” “dry,” and “warm & dry”—that minimize within-group variance. Over the period 1916–2018, we identify 14 warm years ($\overline{P}$ = 160 mm; $\overline{\mathrm{SWE}/P}$ = 0.24), 24 dry years ($\overline{P}$ = 117 mm; $\overline{\mathrm{SWE}/P}$ = 0.35), and 21 warm & dry years ($\overline{P}$ = 94 mm; $\overline{\mathrm{SWE}/P}$ = 0.23). An elevation-based analysis reveals two distinct patterns: warm snow droughts see severe SWE reductions primarily at lower (<2600 m) elevations (65% at lower elevations, 37% overall), whereas “dry” scenarios exhibit a consistent reduction across all elevations (39% overall). Using naturalized streamflow data, we also differentiate snow droughts by their earlier streamflow timing and decreased peakedness (warm: 7 days, 2%; dry: 7 days, 2%; warm & dry: 13 days, 5%). This research provides new insights into snow drought patterns relevant for regional water management.     

Novel dimensionless index for physically based assessment of thermal refugia characterizes off-channel habitat on gravel bed river

In the Willamette River, OR, main channel temperatures can be too warm for cold water fishes, causing fish to concentrate in secondary channel features that provide thermal refugia. However, temperature regimes vary among and within features. Improved understanding of physical processes controlling thermal regimes is needed. This study developed a dimensionless index for assessment of thermal refugia on the upper Willamette River. The novel hyporheic insolation (HIN) index uses minimal field measurements to predict thermal refugia resulting from buffering. Continuous water temperature measurements at one side channel, eight alcoves, and six beaver ponds provided data to ground truth calculated ${ℍ}_{\text{in}}$ predictions. Water temperature records were first used to characterize stratification at sites. Calculation of the Richardson number, an index of stability, showed two well-mixed sites and 13 stratified sites. At stratified sites, calculated ${ℍ}_{\text{in}}$ values characterized the ratio of cooling flux from hyporheic discharge to heat flux from incoming solar radiation. As ${ℍ}_{\text{in}}$ increased, measured temperatures at sites decreased. Despite overall scatter, a logarithmic fit to bin-averaged ${ℍ}_{\text{in}}$ values showed R² = 0.91. Calculations suggest that secondary channel features characterized by stratification and cool hyporheic discharge can provide thermal refugia. Accordingly, the HIN index may serve as a practical tool grounded in physical processes governing temperature across a floodplain.     

Use of Hydrologic Landscape Classification to Diagnose Streamflow Predictability in Oregon

We implement a spatially lumped hydrologic model to predict daily streamflow at 88 catchments within the state of Oregon and analyze its performance using the Oregon Hydrologic Landscape (OHL) classification. OHL is used to identify the physio‐climatic conditions that favor high (or low) streamflow predictability. High prediction catchments (Nash‐Sutcliffe efficiency of (NS) > 0.75) are mainly classified as rain dominated with very wet climate, low aquifer permeability, and low to medium soil permeability. Most of them are located west of the Cascade Mountain Range. Conversely, most low prediction catchments (NS < 0.6) are classified as snow‐dominated with high aquifer permeability and medium to high soil permeability. They are mainly located in the volcano‐influenced High Cascades region. Using a subset of 36 catchments, we further test if class‐specific model parameters can be developed to predict at ungauged catchments. In most catchments, OHL class‐specific parameters provide predictions that are on par with individually calibrated parameters (NS decline < 10%). However, large NS declines are observed in OHL classes where predictability is not high enough. Results suggest higher uncertainty in rain‐to‐snow transition of precipitation phase and external gains/losses of deep groundwater are major factors for low prediction in Oregon. Moreover, regionalized estimation of model parameters is more useful in regions where conditions favor good streamflow predictability.     

Spatial and Temporal Patterns of Low Streamflow and Precipitation Changes in the Chesapeake Bay Watershed

Spatial and temporal patterns in low streamflows were investigated for 183 streamgages located in the Chesapeake Bay Watershed for the period 1939–2013. Metrics that represent different aspects of the frequency and magnitude of low streamflows were examined for trends: (1) the annual time series of seven‐day average minimum streamflow, (2) the scaled average deficit at or below the 2% mean daily streamflow value relative to a base period between 1939 and 1970, and (3) the annual number of days below the 2% threshold. Trends in these statistics showed spatial cohesion, with increasing low streamflow volume at streamgages located in the northern uplands of the Chesapeake Bay Watershed and decreasing low streamflow volume at streamgages in the southern part of the watershed. For a small subset of streamgages (12%), conflicting trend patterns were observed between the seven‐day average minimum streamflow and the below‐threshold time series and these appear to be related to upstream diversions or the influence of reservoir‐influenced streamflows in their contributing watersheds. Using multivariate classification techniques, mean annual precipitation and fraction of precipitation falling as snow appear to be broad controls of increasing and decreasing low‐flow trends. Further investigation of seasonal precipitation patterns shows summer rainfall patterns, driven by the Atlantic Multidecadal Oscillation, as the main driver of low streamflows in the Chesapeake Bay Watershed.     

Climatic Fluctuations and Forecasting of Streamflow in the Lower Colorado River Basin1

Abstract:  Water‐resource managers need to forecast streamflow in the Lower Colorado River Basin to plan for water‐resource projects and to operate reservoirs for water supply. Statistical forecasts of streamflow based on historical records of streamflow can be useful, but statistical assumptions, such as stationarity of flows, need to be evaluated. This study evaluated the relation between climatic fluctuations and stationarity and developed regression equations to forecast streamflow by using climatic fluctuations as explanatory variables. Climatic fluctuations were represented by the Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), and Southern Oscillation Index (SOI). Historical streamflow within the 25‐ to 30‐year positive or negative phases of AMO or PDO was generally stationary. Monotonic trends in annual mean flows were tested at the 21 sites evaluated in this study; 76% of the sites had no significant trends within phases of AMO and 86% of the sites had no significant trends within phases of PDO. As climatic phases shifted in signs, however, many sites had nonstationary flows; 67% of the sites had significant changes in annual mean flow as AMO shifted in signs. The regression equations developed in this study to forecast streamflow incorporate these shifts in climate and streamflow, thus that source of nonstationarity is accounted for. The R² value of regression equations that forecast individual years of annual flow for the central part of the study area ranged from 0.28 to 0.49 and averaged 0.39. AMO was the most significant variable, and a combination of indices from both the Atlantic and Pacific Oceans explained much more variation in flows than only the Pacific Ocean indices. The average R² value for equations with PDO and SOI was 0.15.     

A Hydrologic Data Screening Procedure for Exploring Monotonic Trends and Shifts in Rainfall and Runoff Patterns

A thorough understanding of past and present hydrologic responses to changes in precipitation patterns is crucial for predicting future conditions. The main objectives of this study were to determine temporal changes in rainfall‐runoff relationship and to identify significant trends and abrupt shifts in rainfall and runoff time series. Ninety‐year rainfall and runoff time series datasets from the Gasconade and Meramec watersheds in east‐central Missouri were used to develop data screening procedure to assess changes in the rainfall and runoff temporal patterns. A statistically significant change in mean and variance was detected in 1980 in the rainfall and runoff time series within both watersheds. In addition, both the rainfall and runoff time series indicated the presence of nonstationary attributes such as statistically significant monotonic trends and/or change in mean and variance, which should be taken into consideration when using the time series to predict future scenarios. The annual peak runoff and the annual low flow in the Meramec watershed showed significant temporal changes compared to that in the Gasconade watershed. Water loss in both watersheds was found to be significantly increasing which is potentially due to the increase in groundwater pumping for water supply purposes.     

El Niño‐Southern Oscillation,rainfall variability and sustainable agricultural development in the Ho Municipality,Ghana

El Niño‐Southern Oscillation (ENSO), which occurs in the Equatorial Pacific Ocean, has been identified to have significant influence on rainfall variability throughout the world, especially in the tropics. Such variability in rainfall has implications for agrarian economies, such as that in Ghana. This study therefore sought to demonstrate the effect of ENSO‐induced variability in annual and seasonal rainfall on the development of sustainable agriculture in the Ho Municipality of Ghana. Using 61 years of monthly rainfall data (1955–2015) for the Ho Municipality and ENSO indices, this study showed that 15% of the variability in total annual rainfall is explained by the ENSO phenomena. Mean annual rainfall and rainfall in the major rainy season decreased for El Niño years, in addition to a more variable rainfall compared to that received in La Niña years. The major growing season was observed to be longer in La Niña years and shorter in El Niño years. This means that the potential for crop cultivation will be severely hampered in an El Niño year. Farmers within the municipality are therefore encouraged to harness other complementary water sources for farming activities and also employ water management strategies during El Niño years.     

Characterization of Storm Flow Dynamics of Headwater Streams in the South Carolina Lower Coastal Plain1

Epps, Thomas H., Daniel R. Hitchcock, Anand D. Jayakaran, Drake R. Loflin, Thomas M. Williams, and Devendra M. Amatya, 2012. Characterization of Storm Flow Dynamics of Headwater Streams in the South Carolina Lower Coastal Plain. Journal of the American Water Resources Association (JAWRA) 1‐14. DOI: 10.1111/jawr.12000 Abstract: Hydrologic monitoring was conducted in two first‐order lower coastal plain watersheds in South Carolina, United States, a region with increasing growth and land use change. Storm events over a three‐year period were analyzed for direct runoff coefficients (ROC) and the total storm response (TSR) as percent rainfall. ROC calculations utilized an empirical hydrograph separation method that partitioned total streamflow into sustained base flow and direct runoff components. ROC ratios ranged from 0 to 0.32 on the Upper Debidue Creek (UDC) watershed and 0 to 0.57 on Watershed 80 (WS80); TSR results ranged from 0 to 0.93 at UDC and 0.01 to 0.74 at WS80. Variability in event runoff generation was attributed to seasonal trends in water table elevation fluctuation as regulated by evapotranspiration. Groundwater elevation breakpoints for each watershed were identified based on antecedent water table elevation, streamflow, ROCs, and TSRs. These thresholds represent the groundwater elevation above which event runoff generation increased sharply in response to rainfall. For effective coastal land use decision making, baseline watershed hydrology must be understood to serve as a benchmark for management goals, based on both seasonal and event‐based surface and groundwater interactions.     

Water quality monitoring—Some practical sampling frequency considerations

Water quality monitoring involves sampling a population, water quality, that is changing over time. Sample statistics (e.g., sample mean) computed from data collected by a monitoring network can be affected by three general factors: (1) random changes due to storms, rainfall, etc.; (2) seasonal changes in temperature, rainfall, etc.; and (3) serial correlation or duplication in information from sample to sample. (Closely spaced samples will tend to give similar information).In general, these effects have been noted, but their specific effects on water quality monitoring network design have not been well defined quantitatively. The purpose of this paper is to examine these effects with a specific data set and draw conclusions relative to sampling frequency determinations in network design.The design criterion adopted for this study of effects due to the above factors is the width of confidence intervals about annual sample geometric means of water quality variables. The data base for the study consisted of a daily record of 5 water quality variables at 9 monitoring stations in Illinois for a period of 1 year.Three general regions of frequencies were identified: (1) greater than approximately 30 samples per year where serial correlation plays a dominant role; (2) between approximately 10 and 30 samples per year where the effects of seasonal variation and serial correlation tended to cancel each other out; and (3) less than approximately 10 samples per year where seasonal variation plays a dominant role. In region 2, either seasonal variation and serial correlation should both be considered or both ignored. To consider only seasonal variation introduces more error than ignoring it. These results are network averages (over variables and stations) from one network, thus results for individual variables may deviate considerably from the average and from those for other networks.Financial support for this study was provided, in part, by the U.S. Environmental Protection Agency, grant number R805759-01-0.     

Observed Winter Warming of the Chesapeake Bay Estuary (1949–2002): Implications for Ecosystem Management

A large number of studies have documented 20th century climate variability and change at the global, hemispheric, and regional levels. However, understanding the implications of climate change for environmental management necessitates information at the level of the ecosystem. Historical monitoring data from the Chesapeake Bay estuary were used to identify temporal patterns of estuarine temperature anomalies in the surface (1 m) and subsurface (15 m) between 1949 and 2002. Data indicated a trend in surface and subsurface warming of +0.16°C and +0.21°C per decade, respectively, driven by warming during winter and spring. These trends suggest warming of the estuary since the mid-20th century of approximately 0.8–1.1°C. Estuarine temperatures correlated well with other independent data records for sea surface and surface air temperatures in the region and to a lesser extent, the northern hemisphere. Gross long-term temperature variability in the estuary was consistent with North Atlantic climate variability associated with the prolonged positive North Atlantic Oscillation/Arctic Oscillation and increased anthropogenic radiative forcing, although localized environmental drivers likely are important as well. A simple spatial analysis revealed strong seasonal latitudinal and longitudinal gradients in estuarine temperature as well as a north–south gradient in long-term temperature trends. Continued warming of the estuary will have important implications for ecosystem structure and function as well as attempts to manage existing challenges such as eutrophication and benthic hypoxia. However, such management efforts must be cognizant of the effects of various climate and nonclimate drivers of environmental variability and change operating over different spatial and temporal scales.Published online     

Spatial and Temporal Variations in Eastern U.S. Hydrology: Responses to Global Climate Variability

Coastal ecosystems are dependent on terrestrial freshwater export which is affected by both climate trends and natural climate variability. However, the relative role of these factors is not clear. Here, both climate trends and internal climate variabilities at different time scales are related to variations in terrestrial freshwater export into the eastern United States (U.S.) coastal region. For the recent 35‐year period, the intensified hydro‐meteorological processes (annual precipitation or evapotranspiration) may explain the observed streamflow variability in the northeast. However, in the southeast, streamflow is positively correlated with climate variability induced by the Pacific Ocean conditions (El Nino‐Southern Oscillation [ENSO] and Pacific Decadal Oscillation) rather than Atlantic Ocean conditions (Atlantic Multi‐decadal Oscillation and North Atlantic Oscillation). The centroid location for volume of terrestrial freshwater export integrated along the eastern U.S. has a positive temporal trend and is negatively correlated with ENSO conditions, suggesting the northward trend in freshwater export to U.S. eastern coast may be disturbed by the natural climate variability, especially ENSO conditions, i.e., the center of freshwater mass moves southward (northward) during El Nino (La Nina) years. The results indicate the spatial and temporal variations in freshwater export from the eastern U.S. are affected by both climate change and inter‐annual climate variability during the recent 35‐year period (1980‐2014).     

CLIMATE VARIABILITY AND FLOOD FREQUENCY ESTIMATION FOR THE UPPER MISSISSIPPI AND LOWER MISSOURI RIVERS1

ABSTRACT: This paper considers the distribution of flood flows in the Upper Mississippi, Lower Missouri, and Illinois Rivers and their relationship to climatic indices. Global climate patterns including El Niño/Southern Oscillation, the Pacific Decadal Oscillation, and the North Atlantic Oscillation explained very little of the variations in flow peaks. However, large and statistically significant upward trends were found in many gauge records along the Upper Mississippi and Missouri Rivers: at Hermann on the Missouri River above the confluence with the Mississippi (p = 2 percent), at Hannibal on the Mississippi River (p < 0.1 percent), at Meredosia on the Illinois River (p = 0.7 percent), and at St. Louis on the Mississippi below the confluence of all three rivers (p = 1 percent). This challenges the traditional assumption that flood series are independent and identically distributed random variables and suggests that flood risk changes over time.     

VARIATION IN THE RELATIONSHIP BETWEEN SNOWMELT RUNOFF IN OREGON AND ENSO AND PDO1

ABSTRACT: The value of using climate indices such as ENSO or PDO in water resources predictions is dependent on understanding the local relationship between these indices and streamflow over time. This study identifies long term seasonal and spatial variations in the strength of El Niño Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) correlations with timing and magnitude of discharge in snowmelt streams in Oregon. ENSO is best correlated with variability in annual discharge, and PDO is best correlated with spring snowmelt timing and magnitude and timing of annual floods. Streams in the Cascades and Wallowa mountains show the strongest correlations, while the southernmost stream is not correlated with ENSO or PDO. ENSO correlations are weaker from 1920 to 1950 and vary significantly depending on whether Southern Oscillation Index (SOI) or Niño 3.4 is used. PDO correlations are strong from 1920 to 1950 and weak or insignificant other years. Although there are not consistent increasing or decreasing trends in annual discharge or spring snowmelt timing, there are significant increases in fractional winter runoff that are independent of precipitation, PDO, or ENSO and may indicate monotonic winter warming.     

Application of Artificial Neural Networks to Predict Total Dissolved Solids at the Karaj Dam

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural networks using data from two water quality monitoring stations at the Karaj Dam in Iran. Input data were calcium ions (Ca²⁺), magnesium ions (Mg²⁺), sodium ions (Na⁺), chloride ions (Cl^?), sulfate (), and pH, and the output data were total dissolved solids (TDS). An MLP with one hidden layer containing eight neurons was selected for the upstream water quality station using normalized input data. We developed a second MLP neural network for the downstream station with one hidden layer containing 10 neurons in the hidden layer using normalized input data. Considering applying normalized input data and one hidden layer, the coefficient of determination (R ²) and index of agreement (IA) between the observed and the predicted data for the upstream and downstream monitoring stations using the MLP neural networks were 0.985, 0.84, 0.99, and 0.92, respectively. The RBF neural network with 100 neurons in its hidden layer reached the minimum errors between the observed and the predicted results in upstream and downstream stations. The R ² between observed and predicted data for upstream and downstream monitoring stations for the RBF was 0.999 and 0.998, respectively. Data normalization improved the performance of the MLP neural networks. Sensitivity analysis indicated that magnesium is the most effective water quality parameter for predicting TDS, and sulfate is the second most effective water quality parameter affecting TDS prediction at the Karaj Dam.     

Evidence for Changing Flood Risk in New England Since the Late 20th Century1

Abstract: Long‐term flow records for watersheds with minimal human influence have shown trends in recent decades toward increasing streamflow at regional and national scales, especially for low flow quantiles like the annual minimum and annual median flows. Trends for high flow quantiles are less clear, despite recent research showing increased precipitation in the conterminous United States over the last century that has been brought about primarily by an increased frequency and intensity of events in the upper 10th percentile of the daily precipitation distribution – particularly in the Northeast. This study investigates trends in 28 long‐term annual flood series for New England watersheds with dominantly natural streamflow. The flood series are an average of 75 years in length and are continuous through 2006. Twenty‐five series show upward trends via the nonparametric Mann‐Kendall test, 40% (10) of which are statistically significant (p < 0.1). Moreover, an average standardized departures series for 23 of the study gages indicates that increasing flood magnitudes in New England occurred as a step change around 1970. The timing of this is broadly synchronous with a phase change in the low frequency variability of the North Atlantic Oscillation, a prominent upper atmospheric circulation pattern that is known to effect climate variability along the United States east coast. Identifiable hydroclimatic shifts should be considered when the affected flow records are used for flood frequency analyses. Special treatment of the flood series can improve the analyses and provide better estimates of flood magnitudes and frequencies under the prevailing hydroclimatic condition.     

A Daily Time Series Analysis of StreamWater Phosphorus Concentrations Along an Urban to Forest Gradient

During a 1-year period, we sampled stream water total phosphorus (TP) concentrations daily and soluble reactive phosphorus (SRP) concentrations weekly in four Seattle area streams spanning a gradient of forested to urban-dominated land cover. The objective of this study was to develop time series models describing stream water phosphorus concentration dependence on seasonal variation in stream base flows, short-term flow fluctuations, antecedent flow conditions, and rainfall. Stream water SRP concentrations varied on average by ±18% or ±5.7 μg/L from one week to another, whereas TP varied ±48% or ±32.5 μg/L from one week to another. On average, SRP constituted about 47% of TP. Stream water SRP concentrations followed a simple sine-wave annual cycle with high concentrations during the low-flow summer period and low concentrations during the high-flow winter period in three of the four study sites. These trends are probably due to seasonal variation in the relative contributions of groundwater and subsurface flows to stream flow. In forested Issaquah Creek, SRP concentrations were relatively constant throughout the year except during the fall, when a major salmon spawning run occurred in the stream and SRP concentrations increased markedly. Stream water SRP concentrations were statistically unrelated to short-term flow fluctuations, antecedent flow conditions, or rainfall in each of the study streams. Stream water TP concentrations are highly variable and strongly influenced by short-term flow fluctuations. Each of the processes assessed had statistically significant correlations with TP concentrations, with seasonal base flow being the strongest, followed by antecedent flow conditions, short-term flow fluctuations, and rainfall. Times series models for each individual stream were able to predict ∼70% of the variability in the SRP annual cycle in three of the four streams (r² = 0.57–0.81), whereas individual TP models explained ∼50% of the annual cycle in all streams (r² = 0.39–0.59). Overall, time series models for SRP and TP dynamics explained 82% and 76% of the variability for these variables, respectively. Our results indicate that SRP, the most biologically available and therefore most important phosphorus fraction, has simpler and easier-to-predict seasonal and weekly dynamics.     

FOREST HYDROLOGY AND EXTREME RAINFALL FREQUENCY1

Extreme rainfall frequency analysis provides one means to predict, within certain limits of probability, the average time interval between the recurrences of storms of a specified duration and magnitude. This in turn furnishes the forest hydrologist a valuable tool for engineering design and runoff and erosion forecast. A modification in the application of the annual maximum and annual exceedance series analysis described by V. T. Chow can, for special purposes, lead to an even more useful estimate of extreme events on a seasonal basis. This can be particularly important on small forested headwater watersheds where the runoff response to extreme rainfall may vary considerably with seasonal changes in canopy cover and soil moisture characteristics. Although the application of data covering a relatively short period of record has produced some inconsistencies among the frequency diagrams, under certain circumstances for short-term recurrence interval forecast and for non-critical application the analysis of extreme rainfall frequency from less than 20 years data seems justified.     

A New Flood Index for Use in Evaluation of Local Flood Severity: A Case Study of Small Ungauged Catchments in Korea1

Ahn, Jae Hyun and Hyun Il Choi, 2013. A New Flood Index for Use in Evaluation of Local Flood Severity: A Case Study of Small Ungauged Catchments in Korea. Journal of the American Water Resources Association (JAWRA) 49(1): 1‐14. DOI: 10.1111/jawr.12025 Abstract: The aim of this article is to develop a new index measuring the severity of floods in small ungauged catchments for initial local flood information by the regression analysis between the new flooding index and rainfall patterns. Although a rapid local flood caused by heavy storm in a short period of time is now one of common natural disasters worldwide, such a sudden and violent hydrologic event is difficult to forecast. As local flooding rises rapidly with little or no advance warning, the key to local flood forecasting is to quickly identify when and where local flooding above a threshold is likely to occur. The new flooding index to characterize local floods is measured by the three normalized relative severity factors for the flood magnitude ratio, the rising curve gradient, and the flooding duration time, quantifying characteristics of flood runoff hydrographs. The new flooding index implemented for the two selected small ungauged catchments in the Korean Peninsula shows a very high correlation with logarithm of the 2‐h maximum rainfall depth. This study proposes 30 mm of rainfall in a 2‐h period as a basin‐specific guidance of precaution for the incipient local flooding in the two study catchments. It is expected that the best‐fit regression equation between the new flooding index and a certain rainfall rate can provide preliminary observations, the flood threshold, and severity information, for use in a local flood alert system in small ungauged catchments. Editor's note: This paper is part of a featured series on Korean Hydrology. The series addresses the need for a new paradigm of river and watershed management for Korea due to climate and land use changes.     

Comparison of Two Parametric Methods to Estimate Pesticide Mass Loads in California’s Central Valley1

Saleh, Dina K., David L. Lorenz, and Joseph L. Domagalski, 2010. Comparison of Two Parametric Methods to Estimate Pesticide Mass Loads in California’s Central Valley. Journal of the American Water Resources Association (JAWRA) 00(0):1‐11. DOI: 10.1111/j.1752‐1688.2010.00506.x Abstract: Mass loadings were calculated for four pesticides in two watersheds with different land uses in the Central Valley, California, by using two parametric models: (1) the Seasonal Wave model (SeaWave), in which a pulse signal is used to describe the annual cycle of pesticide occurrence in a stream, and (2) the Sine Wave model, in which first‐order Fourier series sine and cosine terms are used to simulate seasonal mass loading patterns. The models were applied to data collected during water years 1997 through 2005. The pesticides modeled were carbaryl, diazinon, metolachlor, and molinate. Results from the two models show that the ability to capture seasonal variations in pesticide concentrations was affected by pesticide use patterns and the methods by which pesticides are transported to streams. Estimated seasonal loads compared well with results from previous studies for both models. Loads estimated by the two models did not differ significantly from each other, with the exceptions of carbaryl and molinate during the precipitation season, where loads were affected by application patterns and rainfall. However, in watersheds with variable and intermittent pesticide applications, the SeaWave model is more suitable for use on the basis of its robust capability of describing seasonal variation of pesticide concentrations.