Journal of Hydrologic Engineering

The geographically nonuniform sea-level change has increased the importance of assessing sea-level variability and the factors controlling it on regional scales. This study provides a framework, based on the rules governing an artificial neural network (ANN), to identify an ensemble of large-scale meteorological variables (MVs), which significantly affect long-term monthly water-level anomalies (MWLA) in northern coast of the Persian Gulf (1990–2013). Horizontal spatial grid cells of $10°\times 10°$, bounded between (0-100°E and 0-70°N), create a surface control to address the patterns of six MVs consisting of zonal and meridional wind velocity, total precipitable water, 1,000–500 hPa thickness, relative humidity, and air temperature at surfaces of 300 and 700 hPa, respectively. Additionally, 14 representative marine regions are also taken into consideration to assess the potential impact of sea surface temperature (SST) and sea-level pressure (SLP) on local sea-level variability. The multicollinearity problem is effectively tackled by principal components analysis, which classified the MVs into the independent categories. A neural network-based pruning algorithm under a statistical hypothesis test is introduced to discern redundant factors, and then estimate the relative importance of each of the significant predictors in simulating the MWLA. The pruning algorithm detected the nine meteorological components, which are able to predict up to 56% of the total variance in the MWLA. Moreover, it is found that more than half of the predicted variability is manifested by zonal wind, SST, and SLP patterns.

This study assessed trends in flows of return periods of 5, 10, and 20 years and in the peak annual flow (PAF). The U.S. Geological Survey (USGS) stream-gauging stations in the northeastern United States ($n=93$) were analyzed, including 34 gauges in the USGS’s Hydroclimatic Data Network 2009 (HCDN), which are mostly unaffected by land-use change. For each gauge, flood flows were determined for a series of sequential blocks, each 10 years long. Approximately 26% of HCDN gauges and approximately 17% of non-HCDN gauges showed a statistically significant ($\mathrm{alpha}=0.05$) increasing trend for at least one return period on the basis of the Mann-Kendall test; no gauge showed a decreasing trend. The percentage of gauges with increasing trends was approximately the same, regardless of return period. Regarding magnitude of these statistically significant trends assessed via Sen slope, the average rate of increase for gauges was $\sim 1.8\%$ per year, again regardless of return period, which is larger than the average rate for PAF (0.9%). Rates were approximately equal for HCDN and non-HCDN gauges. The difference in trend magnitude between PAF and flood flows indicates that a study of trends in PAF may not produce results that correspond to flows of higher return periods.

This paper compares two data assimilation methods: state–parameter assimilation and output assimilation in improving streamflow forecasting using the Soil and Water Assessment Tool (SWAT) model. The state–parameter assimilation is performed by updating the stored water content and soil curve number with the extended Kalman filter (EKF); the output assimilation is carried out by updating the model output errors with autoregressive (AR) models. The performances of the two data assimilation techniques are compared for a dry year and a wet year, and it is found that whereas both methods significantly improve forecasting accuracy, their performances are influenced by the hydrological regime of the particular year. During the wet year, the average root-mean-square error (RMSE) for seven days forecasts is improved from 670.46 to $420.42{\mathrm{m}}^3/\mathrm{s}$ when output assimilation is used, and to $367.60{\mathrm{m}}^3/\mathrm{s}$ when state–parameter assimilation is used. The Nash–Sutcliffe coefficient (NSC) is improved from 0.63 to 0.85 and 0.88, respectively; the mean error (ME) is improved from $-375.83{\mathrm{m}}^3/\mathrm{s}$ to $-131.68{\mathrm{m}}^3/\mathrm{s}$ and $-129.11{\mathrm{m}}^3/\mathrm{s}$, respectively. For shorter forecast leads (1–4 days), the state–parameter assimilation outperforms output assimilation in both dry and wet years. For longer forecast leads (5–7 days), the output assimilation could provide better results in the wet year. A hybrid method that combines state–parameter assimilation and output assimilation performs very well in both dry and wet years according to all three indicators.

As one of the most important components of hydrologic models, routing module determines model performance to a large degree. In this study, the authors proposed a fully distributed linear reservoir routing scheme that is more compatible to fully distributed hydrologic models than the previous version of the cell-to-cell routing scheme proposed in the coupled routing and excess storage (CREST) model. The new scheme effectively solves issues existing in the previous version of the cell-to-cell routing scheme in CREST, including the tendency to underestimate channel flow and discontinuous flow values along the river after storms. Tested over three basins with two in the United States and one in China at spatial resolutions from 250 m to 1 km and temporal resolutions from hourly to daily, the proposed scheme is proved spatiotemporal scalable. At daily scale, the Nash-Sutcliffe coefficient (NSCE), correlation coefficient (CC), and absolute relative bias range from 0.65, 0.82, and 10.3% to 0.90, 0.95, and 1.57% in the three tested basins respectively. At hourly scale, CREST v2.1 successfully replicated 11 out of 12 flood events during 2002–2013 in the Tar and Kankakee Rivers with mean NSCE, CC, and absolute differences of peaks being (0.78, 0.95, and 10.9%).

Canal seepage is an important cause of irrigation water losses. Specific seepage losses of Gongan lateral and Zuosi field canals, which are located in the Hetao Irrigation District of Inner Mongolia in China, are calculated in this study. Specifically, canal seepage losses are calculated by field experiments, empirical formulas, the Kostiakov formula, and numerical simulations. The ponding method is rarely utilized in large irrigation canals and canals with many branches or high slopes. The results of the field experiment are utilized to compare and analyze the calculation results of three other methods. When the empirical formulas are employed to calculate canal seepage losses, more related factors are considered; hence, more-accurate calculation results are obtained. The first empirical formula is mostly used for canals that do not adopt seepage control measures or apply simple forms for seepage control. The second empirical formula is suitable for the case wherein the entire flow section of a canal has been paved with a soil impermeable layer and canal seepage is in a free state. When the Kostiakov formula is employed to calculate canal seepage losses, the parameter values may differ significantly from the actual conditions. Thus, a large difference exists between the calculation results of the Kostiakov formula and those of the field experiment. The Kostiakov formula is widely utilized in the planning and design of canals, as well as being applied to seepage loss estimation during canal water conveyance and distribution under normal canal operating conditions. The numerical method is utilized to calculate canal seepage losses by simulating the actual situation in the site. Except for the result of seepage loss for the lined Gongan lateral canal, the simulation results of seepage losses for the unlined Gongan lateral and Zuosi field canals and the lined Zuosi field canal are relatively reasonable. If sufficient data of the study area can be obtained and finite-element software can meet the computing requirements, the numerical method can be applied to any channel.

Although the soil and water assessment tool (SWAT) is a physically based hydrologic simulator, it has many parameters that cannot be measured directly in the field, but must be obtained through a model calibration process. Model calibration is thus an essential task to obtain the optimal parameter values, which match simulations with observations as closely as possible. This study used the Zenne River Basin (Belgium) as a case study, which experiences high spatial heterogeneity in terms of geological formation, groundwater recharge, and rainfall-runoff responses. Therefore, the objectives of this paper were to calibrate the SWAT model on the basis of different calibration techniques and identify which technique is suitable for such a heterogeneous basin so that the calibrated SWAT can be used as a tool for integrated management of the Zenne River Basin. Prior to calibration, the sensitive parameters were identified on the basis of a detailed sensitivity analysis (SA) of the Latin hypercube one-factor-at-a-time (LH-OAT) technique and increased sampling intervals. Then, SWAT was calibrated by using single-site calibration (SSC) at the watershed outlet; sequential calibration (SC), calibration from upstream to downstream; and simultaneous multisite calibration (SMSC), where data of two flow gauging stations were simultaneously used in a single calibration. It was found that at least 200 sampling intervals should be considered for the LH-OAT SA method to obtain converged rankings of SWAT parameters. In addition, to well capture the spatial variability of heterogeneous catchment and achieve stable sensitivity ranking, simultaneous multisite SA technique is important. Streamflow findings suggested that the SC and the SMSC techniques provided very good results and significantly improved model performance, but the SSC results were merely satisfactory. Though the results of SC and SMSC techniques were similar, the SMSC was selected over SC because it simultaneously handles the entire catchment spatial variability by assigning different parameter values and allows data information communication among stations in a single calibration. It was thus concluded that simultaneous multisite calibration should be considered for catchments with a high spatial variability like the Zenne River Basin.

This paper conducts a catchment-scale analysis of extreme rainfall events of the reference (1961–1990) and three future climate periods (2013–2039, 2040–2070, and 2071–2100) for Rize Province, Turkey. The extreme value theory (EVT) is applied to analyze observational and projected extreme rainfall data including regional climate model (RCM) outputs guided by two general circulation models (GCM) under SRES-A2 and RCP8.5 greenhouse gas scenarios. A new rapid and effective bias correction method is also developed and applied to adjust the climate models simulations. The EVT analysis results demonstrated significant differences between the model runs for both the reference and future periods with considerable spatial variability in rainfall extremes. Based upon the assembled mean results, approximately a 30% decrease in the median value of extreme rainfall events is projected over the study region for the near future, 2013–2039, and middle of the century. This change dramatically decreases to 15% of its historic value at the end of the century. The results from the implemented GCM-RCM combinations revealed that more intense rainfalls are produced by the combination forced by the RCP8.5 scenario. In addition, the results from goodness-of-fit tests among different distribution models showed that general extreme value distribution can be used appropriately to characterize the behavior of projected extreme rainfalls over the study region.

Under natural conditions, stormwater runoff in much of the semiarid Southwest drains through a network of unlined stream channels called arroyos. Dry during most of the year, arroyos are transformed into raging rivers for short periods of time following intense rain events. As stormwater travels downstream, a portion of the flow is lost to the highly permeable arroyo bed. The purpose of this study was to quantify these so-called transmission losses for a 13-km reach of one New Mexico arroyo. Infiltration rates were tested in the field using a double-ring infiltrometer. Test results varied considerably from 3.0 to $19.6\mathrm{cm}/\mathrm{h}$, with a median rate of $9.4\mathrm{cm}/\mathrm{h}$. Additionally, three stream-gauging stations were installed along the arroyo; for two storms in 2015, they measured a dramatic decrease in peak discharge (91 and 84%, respectively) and runoff volume (90 and 80%, respectively). Gauge data was used to successfully simulate transmission losses in a hydrologic model of the drainage system; the average loss rate for the arroyo was found to be $3.8\mathrm{cm}/\mathrm{h}$. On average, infiltrometer results overestimated reach-scale loss rates by 60%.

This technical note is an extension of a previously reported hydrologic study of vegetative filter strips to calculate sediment trap efficiencies. It presents a set of charts developed using predetermined numerical solutions to the governing equations for overland flow, infiltration, and mass balance of suspended sediments in overland flow. Kinematic-wave equations are used for overland flow, the Green and Ampt equations are employed for infiltration, and the Aberdeen equation is adopted for sediment removal. The governing equations are written and solved numerically in terms of a set of dimensionless variables. The solutions thus obtained are generalized using the principle of hydrologic similarity and presented in chart form. The results show that the trap efficiency is affected by the subsurface conditions, surface conditions, and sediment characteristics. The charts presented can be used to quantify the impact of each parameter. Among the typical hydrological design parameters, the flow length has the highest impact on trap efficiency.