Journal of Hydrologic Engineering

On the basis of laboratory experiments, this paper investigates the effect of different bed forms and vegetated banks on flow velocity, Reynolds stresses, and turbulence intensities. Experimental measurements were made over fifth and sixth dunes in a series of seven of two-dimensional (2D) asymmetric gravel dunes that had lee-slope angles of 28° and 35°, respectively. These dunes had a mean wavelength of 0.96 m, mean dune height of 0.08 m, and width equal to the flume width. Rice stems with a median diameter of 2.7 mm and a stem distribution density of $400\mathrm{stems}/\mathrm{m}$ were used to cover the flume banks for simulating vegetation on river banks. From experimental measurements, the effect of the distance from the vegetated bank on the velocity field, turbulent intensities, and Reynolds stress distribution; the effect of different angles of lee slope of gravel dunes on the characteristics of a flow-in vegetated flume; and the reattachment points for different angles of lee slope of gravel dunes were investigated.

Climatic fluctuations have profound effects on water resources variability in the western United States. The research reported herein centers on streamflow predictability at the medium- and long-range scales in rivers that originate in Colorado. Specifically, we want to improve forecasting seasonal and yearly streamflows based on atmospheric-oceanic forcing factors, such as geopotential height, wind, and sea surface temperature, as well as hydrologic factors, such as snow water equivalent. The approach followed in the study involves searching for potential predictors, applying principal component analysis (PCA) and multiple linear regression (MLR) for forecasting at individual sites, canonical correlation analysis (CCA) for forecasting at multiple sites, and testing the forecasts using various performance measures. The analysis includes comparisons of forecasts by using various combinations of possible predictors, such as hydrologic, atmospheric, and oceanic variables. The study brought into relevance the significant benefits of using atmospheric, oceanic, and hydrological predictors for long-range streamflow forecasting. It has been shown that forecasts based on PCA applied to individual sites give very good results for both seasonal and yearly timescales. We also found that although PCA has been applied on a site-by-site basis, the forecasts approximated well the historical cross correlations, although some underestimation was noted for two sites. Furthermore, the forecasts based on CCA were less efficient than those based on PCA.

Field study is an essential component of hydrologic science because all hydrological studies must be conducted by such observation-based knowledge of real watersheds. Hillslope runoff processes have been intensively investigated, but the flow process at the boundary between the snowpack and ground surface has not been well documented. A field site at the northwest sector of the Ward Creek watershed, Lake Tahoe Basin, was built for observations of overland flow, subsurface stormflow, and channel flow, simultaneously with atmospheric measurements to examine the hydrology at a snow-covered hillslope. Also, the groundwater table under the snowpack was monitored by shallow wells at the hillslope. All field-measured atmospheric data were synthesized with an energy-balance snow model, and the snowmelt rate and energy balance were computed. The results of the analyses indicate that most of the snowmelt water infiltrated into the topsoil layers and that saturated subsurface flow was the largest contributor to the stream channel flow throughout the observation period. However, overland flow or longitudinal flow within the snowpack may still happen even over an unfrozen and unsaturated topsoil on a relatively mild hillslope (16%). The quantification of observations of overland/within snowpack flow showed that the snowpack enhanced the overland runoff, which caused higher peaks in flow discharge and sediment production. Analysis of the major rain-on-snow event of May 7, 2000, revealed that snowmelt water, caused by the energy flux from raindrops, might not be the major contributor to the runoff peak discharge since very little snowmelt was observed during the rain-on-snow event. Consequently, spring storm hydrographs in the Sierra Nevada seem to be affected by the high-soil water in the topsoil because of the daily water supply by snowmelt and by overland/within snowpack flow caused by the capillary suction forces in the snowpack.

Climate change impact studies have generally downscaled large-scale global climate model (GCM) output data; however, few studies have considered downscaling regional climate model (RCM) data. It is unclear whether further downscaling raw RCM data could be beneficial or not in a hydrologic impact study. This study provides some experimental results to address that question. Raw Canadian regional climate model (CRCM4.2) data are downscaled by using a common statistical downscaling method (SDSM) and a data-driven technique called a time-lagged feedforward network (TLFN). Regardless of the downscaling methods and the predictands (e.g., precipitation, temperature), the downscaled CRCM4.2 data are found to be much closer to the observed data than the raw CRCM4.2 data. When the downscaled CRCM4.2 data are used in a hydrologic model (HBV), the model’s ability to accurately simulate streamflow and reservoir inflow is significantly improved as compared to the use of the raw CRCM4.2 data. Simulations of future river flow and reservoir inflow reveal that the general patterns of changes in future flow are quite similar whether downscaled or raw CRCM4.2 data are used. However, the use of downscaled CRCM4.2 data seems to provide more consistent predictions of the magnitude and timing of changes. It appears that the RCM may still suffer from the bias problem inherent to the parent GCM. Further downscaling raw RCM data permits bias correction, improves hydrologic modeling, and provides more consistent changes (magnitude and timing) of future flows.

Analysis of forecasts obtained by a forecasting model called STAFOM, a Muskingum-type model for real-time application, shows that the model provides accurate forecast stage estimates for most of the selected case studies and flood events in the Upper-Middle Tiber River basin in central Italy. However, three main issues affected STAFOM: (1) its kinematic nature, (2) the lateral inflows representation, and (3) the occurrence of sudden fluctuations in water levels observed at the ends of the equipped river reach. Therefore, this simple stage forecasting model is hereby improved by incorporating a methodology relating local stage and remote discharge along river channels. This latter procedure, based on the rating curve model (RCM), is capable of reconstructing a discharge hydrograph at a river site where only the stage is monitored, while the discharge is recorded at another section located far away and for which a significant lateral inflow contribution is expected. Application of the new model, named STAFOM-RCM, to several flood events that occurred along four equipped river reaches of the Upper-Middle Tiber River basin, shows that it improves the stage forecast accuracy both in peak and stage hydrograph primarily for long river reaches, thus allowing consideration of a longer forecast lead time; and hence, avoiding the use of the old two-connecting river branch scheme that amplified the fluctuations in observed water levels.

The generalized likelihood uncertainty estimation (GLUE) methodology has been widely used in many areas as an effective and general strategy for model calibration and uncertainty estimation associated with complex models. The application of GLUE requires a formal definition of a likelihood measure. However, it has been recognized that the choice of a likelihood measure is inherently subjective. This, in turn, introduces a new kind of uncertainty—the uncertainty owing to the lack of knowledge in choosing the true likelihood measure in the GLUE methodology. This study proposes a practical framework to address this uncertainty by using multiple likelihood measures, analogous to considering multiple expert opinions. The final uncertainty probability estimates are then obtained by combining the estimates from individual likelihood measures based on probability theory.