Hydrology, Volume 5, Issue 2 (June 2018) – 9 articles

Understanding the interconnections between oceanic-atmospheric climate variables and regional streamflow of the conterminous United States may aid in improving regional long lead-time streamflow forecasting. The current research evaluates the time-lagged relationship between streamflow of six geographical regions defined from National Climate Assessment and sea surface temperature (SST), 500-mbar geopotential height (Z₅₀₀), 500-mbar specific humidity (SH₅₀₀), and 500-mbar east-west wind (U₅₀₀) of the Pacific and the Atlantic Ocean using singular value decomposition (SVD). The spatio-temporal correlation between streamflow and SST was developed first from SVD and thus obtained correlation was later associated with Z₅₀₀, SH₅₀₀, and U₅₀₀ separately to evaluate the coupled interconnections between the climate variables. Furthermore, the associations between regional streamflow and the El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation, and Atlantic Multidecadal Oscillation were evaluated using the derivatives of continuous wavelet transform. Regional SVD analysis revealed significant teleconnection between several regions and climate variables. The warm phase of equatorial SST had shown a stronger correlation with the majority of streamflow. Both SVD and wavelet analyses concluded that the streamflow variability of the regions in close proximity to the Pacific Ocean was strongly associated with the ENSO. Improved knowledge of teleconnection of climate variables with regional streamflow variability may help in regional water management and streamflow prediction studies. Full article

The purpose of this research was to assess the difference between Soil and Water Assessment Tool (SWAT) simulated pre-development and contemporary developed loading regimes in a mixed-land-use watershed of the central United States (US). Native land cover based on soil characteristics was used to simulate pre-development loading regimes using The Soil and Water Assessment Tool (SWAT). Loading targets were calculated for each major element of a pre-development loading regime. Simulated pre-development conditions were associated with increased retention and decreased export of sediment and nutrients when compared to observed developed conditions. Differences between simulated pre-development and observed developed maximum daily yields (loads per unit area) of suspended sediment (SS), total phosphorus (TP), and total inorganic nitrogen (TIN) ranged from 35.7 to 59.6 Mg km⁻² (SS); 23.3 to 52.5 kg km⁻² (TP); and, 113.2 to 200.8 kg km⁻² (TIN), respectively. Average annual maximum daily load was less during simulated pre-development conditions when compared to observed developed conditions by ranges of 1,307 to 6,452 Mg day⁻¹ (SS), 0.8 to 5.4 kg day⁻¹ (TP), and 4.9 to 26.9 kg day⁻¹ (TIN), respectively. Hydrologic modeling results indicated that the differences in annual maximum daily load were causally linked to land use and land cover influence on sediment and nutrient loading. The differences between SWAT simulated pre-development and observed contemporary loading regimes from this study point to a need for practical loading targets that support contemporary management and integrated flow and pollutant loading regimes. Full article

Nowadays, increased flood risk is recognized as one of the most significant threats in most parts of the world, with recurring severe flooding events causing significant property and human life losses. This has entailed public debates on both the apparent increased frequency of extreme events and the perceived increases in rainfall intensities within climate changing scenarios. In this work, a stationary vs. Non-Stationary Analysis of annual extreme rainfall was performed with reference to the case studies of the African cities of Dar Es Salaam (TZ) and Addis Ababa (ET). For Dar Es Salaam (TZ) a dataset of 53 years (1958–2010) of maximum daily rainfall records (24 h) was analysed, whereas a 47-year time series (1964–2010) was taken into account for Addis Ababa (ET). Both gauge stations rainfall data were suitably fitted by Extreme Value Distribution (EVD) models. Inference models using the Maximum Likelihood Estimation (MLE) and the Bayesian approach were applied on EVD considering their impact on the shape parameter and the confidence interval width. A comparison between a Non-Stationary regression and a Stationary model was also performed. On this matter, the two time series did not show any Non-Stationary effect. The results achieved under the CLUVA (Climatic Change and Urban Vulnerability in Africa) EU project by the Euro-Mediterranean Centre for Climate Change (CMCC) (with 1 km downscaling) for the IPCC RCP8.5 climatological scenario were also applied to forecast the analysis until 2050 (93 years for Dar Es Salaam TZ and 86 years for Addis Ababa ET). Over the long term, the process seemed to be Non-Stationary for both series. Moreover, with reference to a 100-year return period, the IDF (Intensity-Duration-Frequency) curves of the two case-studies were estimated by applying the Maximum Likelihood Estimation (MLE) approach, as a function of confidence intervals of 2.5% and 97.5% quantiles. The results showed the dependence of Non-Stationary effects of climate change to be conveniently accounted for engineering design and management. Full article

Intense monsoonal rain is one of the major triggering factors of floods and mass movements in Nepal that needs to be better understood in order to reduce human and economic losses and improve infrastructure planning and design. This phenomena is better understood through intensity-duration-frequency (IDF) relationships, which is a statistical method derived from historical rainfall data. In Nepal, the use of IDF for disaster management and project design is very limited. This study explored the rainfall variability and possibility to establish IDF relationships in data-scarce situations, such as in the Central-Western hills of Nepal, one of the highest rainfall zones of the country (~4500 mm annually), which was chosen for this study. Homogeneous daily rainfall series of 8 stations, available from the government’s meteorological department, were analyzed by grouping them into hydrological years. The monsoonal daily rainfall was disaggregated to hourly synthetic series in a stochastic environment. Utilizing the historical statistical characteristics of rainfall, a disaggregation model was parameterized and implemented in HyetosMinute, software that disaggregates daily rainfall to finer time resolution. With the help of recorded daily and disaggregated hourly rainfall, reference IDF scenarios were developed adopting the Gumbel frequency factor. A mathematical model [i = a(T)/b(d)] was parameterized to model the station-specific IDF utilizing the best-fitted probability distribution function (PDF) and evaluated utilizing the reference IDF. The test statistics revealed optimal adjustment of empirical IDF parameters, required for a better statistical fit of the data. The model was calibrated, adjusting the parameters by minimizing standard error of prediction; accordingly a station-specific empirical IDF model was developed. To regionalize the IDF for ungauged locations, regional frequency analysis (RFA) based on L-moments was implemented. The heterogeneous region was divided into two homogeneous sub-regions; accordingly, regional L-moment ratios and growth curves were evaluated. Utilizing the reasonably acceptable distribution function, the regional growth curve was developed. Together with the hourly mean (extreme) precipitation and other dynamic parameters, regional empirical IDF models were developed. The adopted approach to derive station-specific and regional empirical IDF models was statistically significant and useful for obtaining extreme rainfall intensities at the given station and ungauged locations. The analysis revealed that the region contains two distinct meteorological sub-regions highly variable in rain volume and intensity. Full article

Runoff predictions are affected by several uncertainties. Among the most important ones is the uncertainty in meteorological forcing. We investigated the skill propagation of meteorological to runoff forecasts in an idealized experiment using synthetic data. Meteorological forecasts with different skill were produced with a weather generator and fed into two different hydrological models. The experiments were repeated for two glacierized catchments of different sizes and morphological characteristics, and for scenarios of different glacier coverage. The results show that for catchments with high glacierization (>50%), the runoff forecast skill is more dependent on the skill of the temperature forecasts than the one for precipitation. This is because snow and ice melt are strongly controlled by temperature. The influence of the temperature forecast skill diminishes with decreasing glacierization, while the opposite is true for precipitation. Precipitation starts to dominate the runoff skill when the catchment’s glacierization drops below 30%, or when the total contribution of ice and snow melt is less than about 60%. The skill difference between meteorological forecasts and runoff predictions proved to be independent from the lead time, and all results were similar for both the considered hydrological models. Our results indicate that long-range meteorological forecasts, which are typically more skillful in predicting temperature than precipitation, hold particular promise for applications in snow- and glacier-dominated catchments. Full article

The City of Pasadena is located in southern California, a region which has a Mediterranean climate and where the vast majority of rainfall occurs between October and April, with the period between January and March being the most intense. A significant amount of the local water supply comes from regional rainfall, therefore any changes in precipitation patterns in the area has considerable significance. Hypothesis: Local climate change has been occurring in the Pasadena area over the last 100 years resulting in changes in air temperature and rainfall. Air Temperatures: Between 1886 and 2016, the air temperature in Pasadena, California has increased significantly, from a minimum of 23.8 °C in the daytime and 8.1 °C at night between 1911 and 1920 to 27.2 °C and 13.3 °C between 2011 and 2016. The increase in nighttime temperature was uniform throughout the year, however daytime temperatures showed more seasonal variation. There was little change in the daytime temperatures for May through July, but more change the rest of the year. For example, the median daytime temperature for June between 1911 and 1920 was 27.9 °C but was 28.7 °C between 2011 and 2016, a difference of 0.8 °C. In contrast, for October for the same periods, the median daytime temperatures were 25.6 °C and 28.9 °C, a difference of 3.3 °C. Rainfall: There has been a change in local rainfall pattern over the same period. In comparing rainfall between 1883 and 1949 and between 1950 and 2016, there appeared to be less rainfall in the months of October, December, and April while other months seemed to show no change in rainfall. For example, between the two periods mentioned above, the median rainfall in October was 12.4 mm and 8.9 mm, respectively, while for December they were 68.6 mm and 40.4 mm. There was comparatively a smaller change in the median volume of rainfall in April (18.8 mm vs. 17.5 mm). However, between 1883 and 2016, there were 13 with less than 1 mm of rain, 12 of which occurred after 1961. In the same line of logic, no measureable amount of rain occurred for 23 Octobers, 15 of those occurring after 1961. Conclusions: As air temperatures increased over the last 100 years in the Pasadena area, rainfall may have decreased in October, December, and April. Full article

An operational framework for flood risk assessment in ungauged urban areas is developed within the implementation of the EU Floods Directive in Greece, and demonstrated for Volos metropolitan area, central Greece, which is frequently affected by intense storms causing fluvial flash floods. A scenario-based approach is applied, accounting for uncertainties of key modeling aspects. This comprises extreme rainfall analysis, resulting in spatially-distributed Intensity-Duration-Frequency (IDF) relationships and their confidence intervals, and flood simulations, through the SCS-CN method and the unit hydrograph theory, producing design hydrographs at the sub-watershed scale, for several soil moisture conditions. The propagation of flood hydrographs and the mapping of inundated areas are employed by the HEC-RAS 2D model, with flexible mesh size, by representing the resistance caused by buildings through the local elevation rise method. For all hydrographs, upper and lower estimates on water depths, flow velocities and inundation areas are estimated, for varying roughness coefficient values. The methodology is validated against the flood event of the 9th October 2006, using observed flood inundation data. Our analyses indicate that although typical engineering practices for ungauged basins are subject to major uncertainties, the hydrological experience may counterbalance the missing information, thus ensuring quite realistic outcomes. Full article

Flood events pose a severe threat to communities in the Lower Mekong River Basin. The combination of population growth, urbanization, and economic development exacerbate the impacts of these events. Flood damage assessments, critical for understanding the effects of flooding on the local population and informing decision-makers about future risks, are frequently used to quantify the economic losses due to storms. Remote sensing systems provide a valuable tool for monitoring flood conditions and assessing their severity more rapidly than traditional post-event evaluations. The frequency and severity of extreme flood events are projected to increase, further highlighting the need for improved flood monitoring and impact analysis. In this study we integrate a socioeconomic damage assessment model with a near real-time flood remote sensing and decision support tool (NASA’s Project Mekong). Direct damages to populations, infrastructure, and land cover are assessed using the 2011 Southeast Asian flood as a case study. Improved land use/land cover and flood depth assessments result in rapid loss estimates throughout the Mekong River Basin. Results suggest that rapid initial estimates of flood impacts can provide valuable information to governments, international agencies, and disaster responders in the wake of extreme flood events. Full article

Land use changes are a key factor for altering hydrological response, and understanding its impacts can help to develop a sustainable and pragmatic strategy in order to preserve a watershed. The objective of this research is to estimate the impact of land use changes on Bagmati river discharge and sediment yield at the Khokana gauging station of the Kathmandu valley outlet. This study analyzes the impact of land use changes from the year 2000 to 2010 using a semi-distributed hydrological, Soil Water Assessment Tool (SWAT) model. The Load Estimator (LOADEST) simulates sediment loads on limited available sediment data. Sensitivity analysis is performed using the ParaSole (Parameter Solution) method within SWAT Calibration and Uncertainty Procedure (SWAT-CUP), which shows that Linear parameters for calculating the maximum amount of sediment that can be re-entrained during channel sediment routing is a most sensitive parameter that affect the hydrological response of the watershed. Monthly discharge and sediment data from 1995 to 2002 are used for calibration and remaining monthly discharge and sediment data from 2003 to 2010 are used for validation. Four statistical parameters including the Coefficient of Determination (R²), Nash–Sutcliffe Efficiency (NSE), RMSE-observations’ standard deviation ratio (RSR), and Percentage Bias (PBIAS) are estimated in order to evaluate the model performance. The results show a very good agreement between monthly measured and simulated discharge data as indicated by R² = 0.88, NSE = 0.90, RSR = 0.34, and PBIAS = 0.03. The model shows nearly the same performance also with sediment data. The land use change data shows about a 6% increase in built-up areas from the years 2000 to 2010, whereas the remaining areas such as Forest, Shrub, Grass, Agriculture, Open Field, and Rivers/Lakes are decreased. The surface runoff contribution to stream flow and sediment yields are increased by 27% and 5% respectively. In the contrary, lateral flow contribution to stream flow and groundwater contribution to stream flow are decreased by 25% and 21%, respectively. Full article