Evaluating the drivers of seasonal streamflow in the U.S. Midwest

Streamflows have increased notably across the U.S. Midwest over the past century, fueling a debate on the relative influences of changes in precipitation and land cover on the flow distribution. Here we propose a simple modeling framework to evaluate the main drivers of streamflow rates. Streamflow records from 290 long-term USGS stream gauges were modeled using five predictors: precipitation, antecedent wetness, temperature, agriculture, and population density. We evaluated which predictor combinations performed best for every site, season and streamflow quantile. The goodness-of-fit of our models is generally high and varies by season (higher in the spring and summer than in the fall and winter), by streamflow quantile (best for high flows in the spring and winter, for low flows in the fall, and good for all flow quantiles in summer), and by region (better in the southeastern Midwest than in the northwestern Midwest). In terms of predictors, we find that precipitation variability is key for modeling high flows, while antecedent wetness is a crucial secondary driver for low and median flows. Temperature improves model fits considerably in areas and seasons with notable snowmelt or evapotranspiration. Last, in agricultural and urban basins, harvested acreage and population density are important predictors of changing streamflow, and their influence varies seasonally. Thus, any projected changes in these drivers are likely to have notable effects on future streamflow distributions, with potential implications for basin water management, agriculture, and flood risk management

On the impact of gaps on trend detection in extreme streamflow time series

Streamflow time series often contain gaps of varying length and location. However, the influence of these gaps on trend detection is poorly understood and cannot be estimated a priori in trend-detection studies. We simulated the effects of varying gap size (1, 2, 5, and 10 years) and location (one quarter, one third, and half of the way) on the detection rate of significant monotonic trends in annual maxima and peaks-over-threshold, based on the most commonly-used trend tests in time series of varying length (from 15 to 150 years) and trend magnitude (β1). Results show that, in comparison with the complete time series, the loss in trend detection rate tends to grow with (i) increasing gap size, (ii) increasing gap distance from the middle of the time series, (iii) decreasing β1 slope, and (iv) decreasing time series length. Based on these findings, we provide objective recommendations and cautionary remarks for maximal gap allowance in trend detection in extreme streamflow time series

Enhancing the predictability of seasonal streamflow with a statistical-dynamical approach

Seasonal streamflow forecasts facilitate water allocation, reservoir operation, flood risk management, and crop forecasting. They are generally computed by forcing hydrological models with outputs from general circulation models (GCMs) or using large-scale climate indices as predictors in statistical models. In contrast, hybrid statistical-dynamical forecasts (combining statistical methods with dynamical climate predictions) are still uncommon and their skill is largely unknown. Here, we conduct systematic forecasting of seasonal streamflow using eight GCMs from the North-American Multi-Model Ensemble, 0.5-9.5 months ahead, at 290 streamgauges in the U.S. Midwest. Probabilistic forecasts are developed for low to high streamflow using predictors that reflect climatic and anthropogenic influences. Results indicate that GCM forecasts of climate and antecedent climatic conditions enhance seasonal streamflow predictability; while land cover and population density predictors decrease biases or enhance skill in certain catchments. This paper paves the way for novel forecasting approaches using dynamical GCM predictions within statistical frameworks

Climatology of flooding in the United States

Flood losses in the United States have increased dramatically over the course of the past century, averaging US$7.96 billion in damages per year for the 30-year period ranging from 1985 to 2014. In terms of human fatalities, floods are the second largest weather-related hazard in the United States, causing an average of 82 deaths per year between 1986 and 2015. Given the wide-reaching impacts of flooding across the United States, the evaluation of flood-generating mechanisms and of the drivers of changing flood hazard are two areas of active research. Flood events can be driven by a variety of physical mechanisms, including rain and snowmelt, frontal systems, monsoons, intense tropical cyclones, and more generic cyclonic storms. However, flood frequency analysis has traditionally been based on statistical analyses of the observed flood distributions that rarely distinguish among these physical flood-generating processes. In reality, flood frequency distributions are often characterized by ‘mixed populations’ arising from multiple flood-generating mechanisms, which can be challenging to disentangle. Temporal changes in the frequency and magnitude of flooding have also been the subject of a large body of work in recent decades. The science has moved from a focus on the detection of trends and shifts in flood peak distributions towards the attribution of these changes, with particular emphasis on climatic and anthropogenic factors, including urbanisation and changes in agricultural practices. A better understanding of these temporal changes in flood peak distributions, as well as of the physical flood-generating mechanisms, will enable us to move forward with the estimation of future flood design values in the context of both climatic and anthropogenic change

Recent trends in U.S. flood risk

Flooding is projected to become more frequent as warming temperatures amplify the atmosphere’s water holding capacity and increase the occurrence of extreme precipitation events. However, there is still little evidence of regional changes in flood risk across the USA. Here, we present a novel approach assessing the trends in inundation frequency above the National Weather Service’s four flood level categories in 2,042 catchments. Results reveal stark regional patterns of changing flood risk that are broadly consistent above the four flood categories. We show that these patterns are dependent on the overall wetness and potential water storage, with fundamental implications for water resources management, agriculture, insurance, navigation, ecology, and populations living in flood-affected areas. Our findings may assist in a better communication of changing flood patterns to a wider audience compared with the more traditional approach of stating trends in terms of discharge magnitudes and frequencies

Examination of changes in annual maximum gage height in the continental United States using quantile regression

This study focuses on the detection of temporal changes in annual maximum gage height (GH) across the continental United States and their relationship to changes in short- and long-term precipitation. Analyses are based on 1805 U.S. Geological Survey records over the 1985-2015 period and are performed using quantile regression. Trends were significant only at a limited number of sites, with a higher number of detections at the tails of the distribution. Overall, we found only weak evidence that the annual maximum GH records have been changing over the continental United States during the past 30 years, possibly due to a weak signal of change, large variability, and limited record length. In addition to trend detection, we also assessed to what extent these changes can be attributed to storm total rainfall and long-term precipitation. Our findings indicate that temporal changes in GH maxima are largely driven by storm total rainfall across large areas of the continental United States (east of the 100th meridian, U.S. West Coast). Long-term precipitation accumulation, on the other hand, is a strong flood predictor in regions where snowmelt is an important flood generating mechanism (e.g., northern Great Plains, Rocky Mountains), and is overall a relatively less important predictor of extreme flood events

Analyses Through the Metastatistical Extreme Value Distribution Identify Contributions of Tropical Cyclones to Rainfall Extremes in the Eastern United States

AbstractTropical cyclones (TCs) generate extreme precipitation with severe impacts across large coastal and inland areas, calling for accurate frequency estimation methods. Statistical approaches that take into account the physical mechanisms responsible for these extremes can help reduce the estimation uncertainty. Here we formulate a mixed‐population Metastatistical Extreme Value Distribution explicitly incorporating non‐TC and TC‐induced rainfall and evaluate its implications on long series of daily rainfall for six major U.S. urban areas impacted by these storms. We find statistically significant differences between the distributions of TC‐ and non‐TC‐related precipitation; moreover, including mixtures of distributions improves the estimation of the probability of extreme precipitation where TCs occur more frequently. These improvements are greater when rainfall aggregated over durations longer than one day are considered

Weighting of NMME temperature and precipitation forecasts across Europe

Multi-model ensemble forecasts are obtained by weighting multiple General Circulation Model (GCMs) outputs to heighten forecast skill and reduce uncertainties. The North American Multi-Model Ensemble (NMME) project facilitates the development of such multi-model forecasting schemes by providing publicly-available hindcasts and forecasts online. Here, temperature and precipitation forecasts are enhanced by leveraging the strengths of eight NMME GCMs (CCSM3, CCSM4, CanCM3, CanCM4, CFSv2, GEOS5, GFDL2.1, and FLORb01) across all forecast months and lead times, for four broad climatic European regions: Temperate, Mediterranean, Humid-Continental and Subarctic-Polar. We compare five different approaches to multi-model weighting based on the equally weighted eight single-model ensembles (EW-8), Bayesian updating (BU) of the eight single-model ensembles (BU-8), BU of the 94 model members (BU-94), BU of the principal components of the eight single-model ensembles (BU-PCA-8) and BU of the principal components of the 94 model members (BU-PCA-94). We assess the forecasting skill of these five multi-models and evaluate their ability to predict some of the costliest historical droughts and floods in recent decades. Results indicate that the simplest approach based on EW-8 preserves model skill, but has considerable biases. The BU and BU-PCA approaches reduce the unconditional biases and negative skill in the forecasts considerably, but they can also sometimes diminish the positive skill in the original forecasts. The BU-PCA models tend to produce lower conditional biases than the BU models and have more homogeneous skill than the other multi-models, but with some loss of skill. The use of 94 NMME model members does not present significant benefits over the use of the 8 single model ensembles. These findings may provide valuable insights for the development of skillful, operational multi-model forecasting systems

Evaluation of the skill of North-American multi-model ensemble (NMME) global climate models in predicting average and extreme precipitation and temperature over the continental USA

This paper examines the forecasting skill of eight Global Climate Models (GCMs) from the North-American Multi-Model Ensemble (NMME) project (CCSM3, CCSM4, CanCM3, CanCM4, GFDL2.1, FLORb01, GEOS5, and CFSv2) over seven major regions of the continental United States. The skill of the monthly forecasts is quantified using the mean square error skill score. This score is decomposed to assess the accuracy of the forecast in the absence of biases (potential skill) and in the presence of conditional (slope reliability) and unconditional (standardized mean error) biases. We summarize the forecasting skill of each model according to the initialization month of the forecast and lead time, and test the models’ ability to predict extended periods of extreme climate conducive to eight ‘billion-dollar’ historical flood and drought events. Results indicate that the most skillful predictions occur at the shortest lead times and decline rapidly thereafter. Spatially, potential skill varies little, while actual model skill scores exhibit strong spatial and seasonal patterns primarily due to the unconditional biases in the models. The conditional biases vary little by model, lead time, month, or region. Overall, we find that the skill of the ensemble mean is equal to or greater than that of any of the individual models. At the seasonal scale, the drought events are better forecasted than the flood events, and are predicted equally well in terms of high temperature and low precipitation. Overall, our findings provide a systematic diagnosis of the strengths and weaknesses of the eight models over a wide range of temporal and spatial scales

Spatial And Temporal Modeling Of Radar Rainfall Uncertainties

It is widely acknowledged that radar-based estimates of rainfall are affected by uncertainties (e.g., mis-calibration, beam blockage, anomalous propagation, and ground clutter) which are both systematic and random in nature. Improving the characterization of these errors would yield better understanding and interpretations of results from studies in which these estimates are used as inputs (e.g., hydrologic modeling) or initial conditions (e.g., rainfall forecasting). Building on earlier efforts, the authors apply a data-driven multiplicative model in which the relationship between true rainfall and radar rainfall can be described in terms of the product of a systematic and random component. The systematic component accounts for conditional biases. The conditional bias is approximated by a power-law function. The random component, which represents the random fluctuations remaining after correcting for systematic uncertainties, is characterized in terms of its probability distribution as well as its spatial and temporal dependencies. The space-time dependencies are computed using the non-parametric Kendall\u27s τ measure. For the first time, the authors present a methodology based on conditional copulas to generate ensembles of random error fields with the prescribed marginal probability distribution and spatio-temporal dependencies. The methodology is illustrated using data from Clear Creek, which is a densely instrumented experimental watershed in eastern Iowa. Results are based on three years of radar data from the Davenport Weather Surveillance Radar 88 Doppler (WSR-88D) radar that were processed through the Hydro-NEXRAD system. The spatial and temporal resolutions are 0.5. km and hourly, respectively, and the radar data are complemented by rainfall measurements from 11 rain gages, located within the catchment, which are used to approximate true ground rainfall. © 2013 Elsevier B.V