Momcilo Markus

Storm frequency estimates and their temporal distributions are important in determining estimates of runoff or peak flow rates in many engineering and hydrological problems. Illinois State Water Survey Bulletin 70 has been serving as the design rainfall standard in Illinois since its publication in 1989. Although Bulletin 70 represented the best available data at the time of its publication, the standards needed to be reevaluated and updated after more than three decades and with the growing evidence of the nonstationary nature of heavy precipitation. The trends in heavy precipitation in Illinois prompted the creation of a new frequency study named Bulletin 75, providing precipitation frequencies for event durations ranging from 5 min to 10 days and for recurrence intervals ranging from 2 months to 500 years. The results are presented for the same 10 geographic sections in Illinois as in Bulletin 70 to maintain the continuity of hydrologic studies and compatibility with regulations. The primary goal of this paper is to outline some of the key methodological issues and challenges, to compare the results with the previous sources, and to highlight the effects of the changing precipitation standards on the development of amendments to existing ordinances. Lake County in Illinois, as one of the most affected urban areas with the highest change in heavy precipitation, was selected to illustrate issues related to the application of modified precipitation standards.

Rainfed agricultural systems have become more vulnerable to climate change due to their significant dependence on natural precipitation. Drastic changes in precipitation, superimposed with anthropogenic activities, including land use land cover change, can modify the hydrologic response, especially in agricultural watersheds. In this study, Fisher Information and cumulative sum charts (CUMSUM) methods were applied to detect the hydrologic regime shifts in six watersheds in Illinois, USA. The regime shift analysis identified shifts in streamflow regime in three agricultural watersheds, mainly around the 1970s; whereas, no significant change in streamflow was observed for forest-dominated watersheds. Furthermore, the Budyko framework was used to determine the relative contribution in streamflow alterations (i.e., regime shifts in streamflow) for the agricultural watersheds, which evidenced significant shifts in streamflow. The Budyko analysis inferred that alterations in streamflow could be primarily attributed to anthropogenic activities with a comparatively lower contribution from climate in agricultural watersheds. The relative contribution from anthropogenic activities were 71.66%, 81.46%, and 74.04%; whereas, the relative contribution from climate were 28.34%, 18.54%, and 25.96% for the Sangamon, Vermillion, and Skillet agricultural watersheds, respectively. The techniques used and the results obtained from the study would be helpful for future research in assessing the vulnerability and impact of management practices in a highly managed agricultural watershed.

This study investigated the accuracy and suitability of several methods commonly used to estimate riverine nitrate loads at eight watersheds located southwest of Lake Erie in the Midwestern United States. This study applied various regression methods, including a regression estimator with five, six, and seven parameters, an estimator enhanced by composite, triangular, and rectangular error corrections with residual and proportional adjustment methods, the weighted regressions on time, discharge, and season (WRTDS) method, and a simple linear interpolation (SLI) method. Daily discharge and nitrate concentration data were collected by the National Center for Water Quality Research. The methods were compared with subsampling frequencies of 6, 12, and 24 times per year for daily concentrations, daily loads, and annual loads. The results indicate that combinations of the seven-parameter regression method with composite residual and rectangular residual adjustments provided the best estimates under most of the watershed and sampling frequency conditions. On average, WRTDS was more accurate than the regression models alone, but less accurate than those models enhanced by residual adjustments, except for the most urbanized watershed, Cuyahoga. SLI was the most accurate in the Vermilion and Maumee watersheds. The results also provide some information about the effects of rating curve shape and slope, land use, and record length on model performance.

The long short-term memory (LSTM) model has been widely used for a broad range of applications entailing the estimation of variables in different fields to improve water quality management in rivers. The main objectives of this study are (1) to develop a novel LSTM-based model for the estimation of nitrate-N loads, which adversely affect water resources, and (2) to evaluate the performance of the model by comparing it with that of Monte Carlo sub-sampling and the weighted regressions on time discharge and season (WRTDS) model. We evaluated the model performance using various numbers of hidden layers, ranging from one to four, in the LSTM model to determine the appropriate number of hidden layers; furthermore, we applied the sampling frequencies of 6, 12, and 24 to assess their impact. Seven polluted river basins in the United States were used for analysis, and the relative root mean squared error (rRMSE) and the mean percentage error (MPE) metrics were applied for the validation of the model estimates. The proposed model achieved accurate nitrate-N load estimates using three to four hidden layers, and improved model performance was observed when the sampling frequency was increased. The differences among the results obtained using the LSTM model were examined based on a binning technique via a log-log plot of nitrate-N concentration against discharge. The binning analysis showed that the slope obtained from the average rates of discharge and low discharge values apparently influenced the estimates. Furthermore, box plot analyses of the statistical indices such as rRMSE and MPE demonstrate that the LSTM model seems to exhibit better performance than the WRTDS model. The results of the examination demonstrate that the LSTM model may be a good alternative with regard to estimating nitrate-N loads for the control of water quality constituents.

Climate projections indicate that in many regions of the world the risk of increased flooding or more severe droughts will be higher in the future. To account for these trends, hydrologists search for the best planning and management measures in an increasingly complex and uncertain environment. The collection of manuscripts in this Special Issue quantifies the changes in projected hydroclimatic extremes and their impacts using a suite of innovative approaches applied to regions in North America, Asia, and Europe. To reduce the uncertainty and warrant the applicability of the research on projections of future floods and droughts, their continued development and testing using newly acquired observational data are critical.

Many studies have projected that as the climate changes, the magnitudes of extreme precipitation events in the Northeastern United States are likely to continue increasing, regardless of the emission scenario. To examine this issue, we analyzed observed and modeled daily precipitation frequency (PF) estimates in the Northeastern US on the rain gauge station scale based on both annual maximum series (AMS) and partial duration series (PDS) methods. We employed four Coupled Model Intercomparison Project Phase 5 (CMIP5) downscaled data sets, including a probabilistic statistically downscaled data set developed specifically for this study. The ability of these four data sets to reproduce the observed features of historical point PF estimates was compared, and the two with the best historical accuracy, including the newly developed probabilistic data set, were selected to produce projected PF estimates under two CMIP5-based emission scenarios, namely Representative Concentration Pathway 4.5 (RCP4.5) and Representative Concentration Pathway 8.5 (RCP8.5). These projections indeed demonstrate a likely increase in PF estimates in the Northeastern US with noted differences in magnitudes and spatial distributions between the two data sets and between the two scenarios. We also quantified how the exceedance probabilities of the historical PF estimate values are likely to increase under each scenario using the two best performing data sets. Notably, an event with a current exceedance probability of 0.01 (a 100-year event) may have an exceedance probability for the second half of the 21st century of ≈0.04 (a 27-year event) under the RCP4.5 scenario and ≈0.05 (a 19-year event) under RCP8.5. Knowledge about the projected changes to the magnitude and frequency of heavy precipitation in this region will be relevant for the socio-economic and environmental evaluation of future infrastructure projects and will allow for better management and planning decisions.