Hydrology — Open Access Journal
Hydrology (ISSN 2306-5338) is an international open access journal of hydrology published quarterly online by MDPI.
- Open Access - free for readers, with article processing charges (APC) paid by authors or their institutions.
- High visibility: Indexed in GeoRef; to be covered in Scopus from Vol. 5 (2018).
- Rapid publication: manuscripts are peer-reviewed and a first decision provided to authors approximately 21 days after submission; acceptance to publication is undertaken in 5 days (average values for papers published in this journal in 2017).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
GEV Parameter Estimation and Stationary vs. Non-Stationary Analysis of Extreme Rainfall in African Test Cities►▼ Figures
Hydrology 2018, 5(2), 28; https://doi.org/10.3390/hydrology5020028 - 18 May 2018
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[...] Read more.
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
Development of Monsoonal Rainfall Intensity-Duration-Frequency (IDF) Relationship and Empirical Model for Data-Scarce Situations: The Case of the Central-Western Hills (Panchase Region) of Nepal►▼ Figures
Hydrology 2018, 5(2), 27; https://doi.org/10.3390/hydrology5020027 - 18 May 2018
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[...] Read more.
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
Skill Transfer from Meteorological to Runoff Forecasts in Glacierized Catchments►▼ Figures
Hydrology 2018, 5(2), 26; https://doi.org/10.3390/hydrology5020026 - 15 May 2018
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[...] Read more.
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
Changes in Temperature and Rainfall as a Result of Local Climate Change in Pasadena, California►▼ Figures
Hydrology 2018, 5(2), 25; https://doi.org/10.3390/hydrology5020025 - 10 May 2018
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[...] Read more.
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 Method for Flood Directive Implementation in Ungauged Urban Areas►▼ Figures
Hydrology 2018, 5(2), 24; https://doi.org/10.3390/hydrology5020024 - 20 April 2018
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[...] Read more.
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
Socioeconomic Impact Evaluation for Near Real-Time Flood Detection in the Lower Mekong River Basin►▼ Figures
Hydrology 2018, 5(2), 23; https://doi.org/10.3390/hydrology5020023 - 10 April 2018
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[...] Read more.
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
Impact of Land Use Change on Flow and Sediment Yields in the Khokana Outlet of the Bagmati River, Kathmandu, Nepal►▼ Figures
Hydrology 2018, 5(2), 22; https://doi.org/10.3390/hydrology5020022 - 28 March 2018
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[...] Read more.
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
Future Climate Change Impacts on Streamflows of Two Main West Africa River Basins: Senegal and Gambia►▼ Figures
Hydrology 2018, 5(1), 21; https://doi.org/10.3390/hydrology5010021 - 16 March 2018
This research investigated the effect of climate change on the two main river basins of Senegal in West Africa: the Senegal and Gambia River Basins. We used downscaled projected future rainfall and potential evapotranspiration based on projected temperature from six General Circulation Models[...] Read more.
This research investigated the effect of climate change on the two main river basins of Senegal in West Africa: the Senegal and Gambia River Basins. We used downscaled projected future rainfall and potential evapotranspiration based on projected temperature from six General Circulation Models (CanESM2, CNRM, CSIRO, HadGEM2-CC, HadGEM2-ES, and MIROC5) and two scenarios (RCP4.5 and RCP8.5) to force the GR4J model. The GR4J model was calibrated and validated using observed daily rainfall, potential evapotranspiration from observed daily temperature, and streamflow data. For the cross-validation, two periods for each river basin were considered: 1961–1982 and 1983–2004 for the Senegal River Basin at Bafing Makana, and 1969–1985 and 1986–2000 for the Gambia River Basin at Mako. Model efficiency is evaluated using a multi-criteria function (Fagg) which aggregates Nash and Sutcliffe criteria, cumulative volume error, and mean volume error. Alternating periods of simulation for calibration and validation were used. This process allows us to choose the parameters that best reflect the rainfall-runoff relationship. Once the model was calibrated and validated, we simulated streamflow at Bafing Makana and Mako stations in the near future at a daily scale. The characteristic flow rates were calculated to evaluate their possible evolution under the projected climate scenarios at the 2050 horizon. For the near future (2050 horizon), compared to the 1971–2000 reference period, results showed that for both river basins, multi-model ensemble predicted a decrease of annual streamflow from 8% (Senegal River Basin) to 22% (Gambia River Basin) under the RCP4.5 scenario. Under the RCP8.5 scenario, the decrease is more pronounced: 16% (Senegal River Basin) and 26% (Gambia River Basin). The Gambia River Basin will be more affected by the climate change. Full article
Estimation of Stream Health Using Flow-Based Indices►▼ Figures
Hydrology 2018, 5(1), 20; https://doi.org/10.3390/hydrology5010020 - 15 March 2018
Existing methods to estimate stream health are often location-specific, and do not address all of the components of stream health. In addition, there are very few guidelines to estimate the health of a stream, although the literature and useful tools such as Indicators[...] Read more.
Existing methods to estimate stream health are often location-specific, and do not address all of the components of stream health. In addition, there are very few guidelines to estimate the health of a stream, although the literature and useful tools such as Indicators of Hydrologic Alteration (IHA) are available. This paper describes an approach developed for estimating stream health. The method involves the: (1) collection of flow data; (2) identification of hydrologic change; (3) estimation of some hydrologic indicators for pre-alteration and post-alteration periods; and (4) the use of those hydrologic indicators with the scoring framework of the Dundee Hydrologic Regime Assessment Method (DHRAM). The approach estimates the stream health in aggregate including all of the components, such as riparian vegetation, aquatic species, and benthic organisms. Using the approach, stream health can be estimated at two different levels: (1) the existence or absence of a stream health problem based on the concept of eco-deficit and eco-surplus using flow duration curves; and (2) the estimation of overall stream health using the IHA–DHRAM method. The procedure is demonstrated with a case example of the White Rock Creek watershed in Texas in the United States (US). The approach has great potential to estimate stream health and prescribe flow-based goals for the restoration of impaired streams. Full article
Assessment of Changes in Flood Frequency Due to the Effects of Climate Change: Implications for Engineering Design►▼ Figures
Hydrology 2018, 5(1), 19; https://doi.org/10.3390/hydrology5010019 - 3 March 2018
The authors explore the uncertainty implied in the estimation of changes in flood frequency due to climate change at the basins of the Cedar River and Skunk River in Iowa, United States. The study focuses on the influence of climate change on the[...] Read more.
The authors explore the uncertainty implied in the estimation of changes in flood frequency due to climate change at the basins of the Cedar River and Skunk River in Iowa, United States. The study focuses on the influence of climate change on the 100-year flood, used broadly as a reference flow for civil engineering design. Downscaled rainfall projections between 1960–2099 were used as forcing into a hydrological model for producing discharge projections at locations intersecting vulnerable transportation infrastructure. The annual maxima of the discharge projections were used to conduct flood frequency analyses over the periods 1960–2009 and 1960–2099. The analysis of the period 1960–2009 is a good predictor of the observed flood values for return periods between 2 and 200 years in the studied basins. The findings show that projected flood values could increase significantly in both basins. Between 2009 and 2099, 100-year flood could increase between 47% and 52% in Cedar River, and between 25% and 34% in South Skunk River. The study supports a recommendation for assessing vulnerability of infrastructure to climate change, and implementation of better resiliency and hydraulic design practices. It is recommended that engineers update existing design standards to account for climate change by using the upper-limit confidence interval of the flood frequency analyses that are currently in place. Full article
EMD-Based Predictive Deep Belief Network for Time Series Prediction: An Application to Drought Forecasting►▼ Figures
Hydrology 2018, 5(1), 18; https://doi.org/10.3390/hydrology5010018 - 27 February 2018
Drought is a stochastic natural feature that arises due to intense and persistent shortage of precipitation. Its impact is mostly manifested as agricultural and hydrological droughts following an initial meteorological phenomenon. Drought prediction is essential because it can aid in the preparedness and[...] Read more.
Drought is a stochastic natural feature that arises due to intense and persistent shortage of precipitation. Its impact is mostly manifested as agricultural and hydrological droughts following an initial meteorological phenomenon. Drought prediction is essential because it can aid in the preparedness and impact-related management of its effects. This study considers the drought forecasting problem by developing a hybrid predictive model using a denoised empirical mode decomposition (EMD) and a deep belief network (DBN). The proposed method first decomposes the data into several intrinsic mode functions (IMFs) using EMD, and a reconstruction of the original data is obtained by considering only relevant IMFs. Detrended fluctuation analysis (DFA) was applied to each IMF to determine the threshold for robust denoising performance. Based on their scaling exponents, irrelevant intrinsic mode functions are identified and suppressed. The proposed method was applied to predict different time scale drought indices across the Colorado River basin using a standardized streamflow index (SSI) as the drought index. The results obtained using the proposed method was compared with standard methods such as multilayer perceptron (MLP) and support vector regression (SVR). The proposed hybrid model showed improvement in prediction accuracy, especially for multi-step ahead predictions. Full article
Thermal Regime of A Deep Temperate Lake and Its Response to Climate Change: Lake Kuttara, Japan►▼ Figures
Hydrology 2018, 5(1), 17; https://doi.org/10.3390/hydrology5010017 - 16 February 2018
A deep temperate lake, Lake Kuttara, Hokkaido, Japan (148 m deep at maximum) was completely ice-covered every winter in the 20th century. However, ice-free conditions of the lake over winter occurred three times in the 21st century, which is probably due to global[...] Read more.
A deep temperate lake, Lake Kuttara, Hokkaido, Japan (148 m deep at maximum) was completely ice-covered every winter in the 20th century. However, ice-free conditions of the lake over winter occurred three times in the 21st century, which is probably due to global warming. In order to understand how thermal regime of the lake responds to climate change, a change in lake mean water temperature from the heat storage change was calculated by integrating observed water temperature over water depths and by numerical calculation of heat budget components based on hydrometeorological data. As a result, a temporal variation of lake mean water temperature from the heat budget calculation was very reasonable to that from the observed water temperature (determination coefficient R2 = 0.969). The lowest lake mean temperature for non-freeze was then evaluated at −1.87 °C, referring to the zero level at 6.80 °C. The 1978–2017 data at a meteorological station near Kuttara indicated that there are significant (less than 5% level) long-term trends for air temperature (+0.024 °C/year) and wind speed (−0.010 m/s/year). In order to evaluate the effects of climate change on freeze-up patterns, a sensitivity analysis was carried out for the calculated lake mean water temperature. It is noted that, after two decades, the lake could be ice-free once per every two years. Full article
Groundwater Condition and Management in Kano Region, Northwestern Nigeria►▼ Figures
Hydrology 2018, 5(1), 16; https://doi.org/10.3390/hydrology5010016 - 9 February 2018
This paper provides a broad overview of issues on groundwater condition and management in the Kano region of northwestern Nigeria. The aim is to recommend new management strategies that can ensure sustainable groundwater resource management in the region. To achieve the aim of[...] Read more.
This paper provides a broad overview of issues on groundwater condition and management in the Kano region of northwestern Nigeria. The aim is to recommend new management strategies that can ensure sustainable groundwater resource management in the region. To achieve the aim of the study, various studies on groundwater conducted in the region were reviewed and key issues were identified. The review revealed that groundwater availability varied between the Basement Complex and Chad Formation areas of the region, with the latter having more of the resource than the former region as a result of the migration of groundwater from the Basement complex to the Chad Formation region. The review also revealed a steady annual decrease of groundwater level during the period 2010 to 2013 and the groundwater beneath the floodplains dropped from 9000 Million Cubic Meter (MCM) in 1964 to 5000 MCM in 1987 in the Chad Formation area of the region. The review further revealed that there is poor knowledge regarding the impact of historical and projected climate variability and change on groundwater availability in the region. This is as a result of the lack of sustained time series data on groundwater resource. Thus, there has been little or no integrated management between groundwater excess and deficiency on one hand, and groundwater pollution management on the other hand. Rainwater harvesting, among other approaches, is recommended for sustainable groundwater management in the region. Full article
Quantifying Processes Governing Nutrient Concentrations in a Coastal Aquifer via Principal Component Analysis►▼ Figures
Hydrology 2018, 5(1), 15; https://doi.org/10.3390/hydrology5010015 - 7 February 2018
Submarine groundwater discharge (SGD) is an important source of nutrients to coastal ecosystems. The flux of nutrients associated with SGD is governed by the volumetric discharge of groundwater and the concentrations of nutrients in groundwater within the coastal aquifer. Nutrient concentrations in the[...] Read more.
Submarine groundwater discharge (SGD) is an important source of nutrients to coastal ecosystems. The flux of nutrients associated with SGD is governed by the volumetric discharge of groundwater and the concentrations of nutrients in groundwater within the coastal aquifer. Nutrient concentrations in the coastal aquifer, in turn, are controlled by processes such as mixing, precipitation, adsorption-desorption, the decay of organic material, and nitrogen-fixation/denitrification. In this study, Principal Component Analysis (PCA) was applied to groundwater and ocean water nutrient concentration data from Monterey Bay, California, to identify and rank processes controlling coastal aquifer nutrient concentrations. Mixing with seawater, denitrification, the decay of organic matter, and desorption of phosphate were determined to be the three most important processes accounting for 39%, 19%, 14%, and 12% of the variability, respectively. This study shows how PCA can be applied to SGD studies to quantify the relative contribution of different processes controlling nutrient concentrations in coastal aquifers. Full article
The Climate Change Vulnerability and Risk Management Matrix for the Coastal Zone of The Gambia►▼ Figures
Hydrology 2018, 5(1), 14; https://doi.org/10.3390/hydrology5010014 - 6 February 2018
Global Climate Change is one of the dire challenges facing the international community today. Coastal zones are vulnerable to its impacts. An effective approach with long-term prospects in addressing climate change impacts is it’s mainstreaming into development agenda of sectoral policies. A comprehensive[...] Read more.
Global Climate Change is one of the dire challenges facing the international community today. Coastal zones are vulnerable to its impacts. An effective approach with long-term prospects in addressing climate change impacts is it’s mainstreaming into development agenda of sectoral policies. A comprehensive risk and vulnerability assessment is a pre-requisite to ensure that the right adaptive response is taken for effective integration into developmental plans. The objective of this study is to evaluate and prioritize risks, vulnerability and adaptation issues of current and anticipated impacts of climate change on the coastal zone of The Gambia. The study will also give a methodological contribution for assessing risks, vulnerability and adaptation from the sub-national to local levels. The relevance of this study will be to create a link between the sub-national and local levels in order to facilitate the integration and mainstreaming of climate change into sectoral and local policies for more climate-resilient communities. This will aid in the promotion of strategic investment of constrained developmental resources to actualize successfully dynamic coping strategies, elude ‘maladaptation’ and less compelling responsive measures. A purposive expert sampling technique was used in selecting respondents for the study. The findings of the study reveal that by the end of the 21st century, the climatic variables likely to have the highest impact on the coastal zone of The Gambia are ‘increased flood severity’ and ‘increased temperature’. The coastal zone of The Gambia showed a high vulnerability to these climate change variables. The suggested adaptive response in addressing the impacts of increased flood intensity in the study area includes; improving regulations for restricting agriculture and livestock grazing activities to improve land cover; strengthening of early-warning systems, among others. The suggested adaptive response in addressing the increase in temperature includes: increase crop diversification and rotation to reduce total crop failure; switching to drought-tolerant crop and animal species, among others. Full article
Dynamic Modeling of Surface Runoff and Storm Surge during Hurricane and Tropical Storm Events►▼ Figures
Hydrology 2018, 5(1), 13; https://doi.org/10.3390/hydrology5010013 - 6 February 2018
Hurricane events combine ocean storm surge penetration with inland runoff flooding. This article presents a new methodology to determine coastal flood levels caused by the combination of storm surge and surface runoff. The proposed approach couples the Simulating Waves Nearshore model and the[...] Read more.
Hurricane events combine ocean storm surge penetration with inland runoff flooding. This article presents a new methodology to determine coastal flood levels caused by the combination of storm surge and surface runoff. The proposed approach couples the Simulating Waves Nearshore model and the Advanced Circulation (ADCIRC) model with the Gridded Surface Subsurface Hydrologic Analysis (GSSHA) two-dimensional hydrologic model. Radar precipitation data in a 2D hydrologic model with a circulation model allows simulation of time and spatially varied conditions. The method was applied to study flooding scenarios occurring during the passage of Hurricane Georges (1998) on the east coast of Puerto Rico. The combination of storm surge and surface runoff produced a critical scenario, in terms of flood depth, at this location. The paper describes the data collection process, circulation and hydrologic models, their assemblage and simulation scenarios. Results show that peak flow from inland runoff and peak flow due to storm surge did not coincide in the coastal zone; however, the interaction of both discharges causes an aggravated hazardous condition by increasing flood levels beyond those obtained with storm surge penetration only. Linking of storm surge and hydrologic models are necessary when storm surge conditions occur simultaneously with high precipitation over steep and small coastal watersheds. Full article
The Impact of Climate Change on Water Resource Availability in a Trans-Boundary Basin in West Africa: The Case of Sassandra►▼ Figures
Hydrology 2018, 5(1), 12; https://doi.org/10.3390/hydrology5010012 - 29 January 2018
In the context of climate change in West Africa characterized by a reduction of precipitation, this study was conducted to evaluate the impact of climate change on water resources from now to the end of the 21st century in the transboundary watershed of[...] Read more.
In the context of climate change in West Africa characterized by a reduction of precipitation, this study was conducted to evaluate the impact of climate change on water resources from now to the end of the 21st century in the transboundary watershed of the Sassandra River shared by Guinea and Côte d’Ivoire. Historical and future climate data of Representative Concentration Pathways (RCPs) 4.5 and 8.5 were projected with the Abdus Salam International Centre for Theoretical Physics (ICTP) Regional Climate Model (RegCM4). The hydrological modeling of the river basin was carried out with the conceptual hydrological model, GR2M, a monthly time steps model that allows for the assessment of the discharge of the Sassandra River for each climate scenario according to the time periods 2021–2040 (Horizon 2030), 2041–2060 (Horizon 2050), 2061–2080 (Horizon 2050), and 2061–2080 (Horizon 2090). The results show a reduction in annual discharge when compared to the baseline (1961–1980). For RCP 4.5, the observed values go from −1.2% in 2030 to −2.3% in 2070 and rise to −2.1% in 2090. Concerning RCP 8.5, we saw a variation from −4.2 to −7.9% in Horizons 2030 and 2090, respectively. With the general decrease in rainfall in West Africa, it is appropriate to assess the impact on water resources of the largest rivers (Niger, Gambia, and Senegal) that irrigate the Sahelo–Saharian zone. Full article
Floods and Countermeasures Impact Assessment for the Metro Colombo Canal System, Sri Lanka►▼ Figures
Hydrology 2018, 5(1), 11; https://doi.org/10.3390/hydrology5010011 - 26 January 2018
A 15th-century canal system in the Metro Colombo area of Sri Lanka was studied to identify its capacity in controlling floods. The canal system was modelled by MIKE FLOOD for 10, 25 and 50-year return periods of rainfalls to achieve respective floods. The[...] Read more.
A 15th-century canal system in the Metro Colombo area of Sri Lanka was studied to identify its capacity in controlling floods. The canal system was modelled by MIKE FLOOD for 10, 25 and 50-year return periods of rainfalls to achieve respective floods. The impacts of the considered rainfalls were analyzed considering the flood levels, inundation distributions and affected people. Two simulation scenarios which were based on the river boundary conditions were carried out in the study and they were categorized as favourable and least favorable. It was identified that under the existing conditions, the canal system could handle only a 10-year rainfall flood event under the favourable condition. Therefore, the canal system's sustainability for future anticipated extreme events is suspicious. To mitigate such floods, four countermeasures were introduced and their impacts were analyzed. When the countermeasures were introduced one at a time, the flood water levels were lowered locally and they were not up to the flood safety levels of the surrounding area. When all four countermeasures were introduced together, the flood water levels were significantly lowered below the flood safety levels for a 50-year design rainfall under the favourable condition. The reduction of the inundated area was significant in the case of applying all four countermeasures together. In that case, a 46% inundation area reduction and a 49% reduction in the number of affected people were achieved. Full article
Comparing Machine Learning and Decision Making Approaches to Forecast Long Lead Monthly Rainfall: The City of Vancouver, Canada►▼ Figures
Hydrology 2018, 5(1), 10; https://doi.org/10.3390/hydrology5010010 - 22 January 2018
Estimating maximum possible rainfall is of great value for flood prediction and protection, particularly for regions, such as Canada, where urban and fluvial floods from extreme rainfalls have been known to be a major concern. In this study, a methodology is proposed to[...] Read more.
Estimating maximum possible rainfall is of great value for flood prediction and protection, particularly for regions, such as Canada, where urban and fluvial floods from extreme rainfalls have been known to be a major concern. In this study, a methodology is proposed to forecast real-time rainfall (with one month lead time) using different number of spatial inputs with different orders of lags. For this purpose, two types of models are used. The first one is a machine learning data driven-based model, which uses a set of hydrologic variables as inputs, and the second one is an empirical-statistical model that employs the multi-criteria decision analysis method for rainfall forecasting. The data driven model is built based on Artificial Neural Networks (ANNs), while the developed multi-criteria decision analysis model uses Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach. A comprehensive set of spatially varying climate variables, including geopotential height, sea surface temperature, sea level pressure, humidity, temperature and pressure with different orders of lags is collected to form input vectors for the forecast models. Then, a feature selection method is employed to identify the most appropriate predictors. Two sets of results from the developed models, i.e., maximum daily rainfall in each month (RMAX) and cumulative value of rainfall for each month (RCU), are considered as the target variables for forecast purpose. The results from both modeling approaches are compared using a number of evaluation criteria such as Nash-Sutcliffe Efficiency (NSE). The proposed models are applied for rainfall forecasting for a coastal area in Western Canada: Vancouver, British Columbia. Results indicate although data driven models such as ANNs work well for the simulation purpose, developed TOPSIS model considerably outperforms ANNs for the rainfall forecasting. ANNs show acceptable simulation performance during the calibration period (NSE up to 0.9) but they fail for the validation (NSE of 0.2) and forecasting (negative NSE). The TOPSIS method delivers better rainfall forecasting performance with the NSE of about 0.7. Moreover, the number of predictors that are used in the TOPSIS model are significantly less than those required by the ANNs to show an acceptable performance (7 against 47 for forecasting RCU and 6 against 32 for forecasting RMAX). Reliable and precise rainfall forecasting, with adequate lead time, benefits enhanced flood warning and decision making to reduce potential flood damages. Full article
Merging Real-Time Channel Sensor Networks with Continental-Scale Hydrologic Models: A Data Assimilation Approach for Improving Accuracy in Flood Depth Predictions►▼ Figures
Hydrology 2018, 5(1), 9; https://doi.org/10.3390/hydrology5010009 - 21 January 2018
This study proposes a framework that (i) uses data assimilation as a post processing technique to increase the accuracy of water depth prediction, (ii) updates streamflow generated by the National Water Model (NWM), and (iii) proposes a scope for updating the initial condition[...] Read more.
This study proposes a framework that (i) uses data assimilation as a post processing technique to increase the accuracy of water depth prediction, (ii) updates streamflow generated by the National Water Model (NWM), and (iii) proposes a scope for updating the initial condition of continental-scale hydrologic models. Predicted flows by the NWM for each stream were converted to the water depth using the Height Above Nearest Drainage (HAND) method. The water level measurements from the Iowa Flood Inundation System (a test bed sensor network in this study) were converted to water depths and then assimilated into the HAND model using the ensemble Kalman filter (EnKF). The results showed that after assimilating the water depth using the EnKF, for a flood event during 2015, the normalized root mean square error was reduced by 0.50 m (51%) for training tributaries. Comparison of the updated modeled water stage values with observations at testing locations showed that the proposed methodology was also effective on the tributaries with no observations. The overall error reduced from 0.89 m to 0.44 m for testing tributaries. The updated depths were then converted to streamflow using rating curves generated by the HAND model. The error between updated flows and observations at United States Geological Survey (USGS) station at Squaw Creek decreased by 35%. For future work, updated streamflows could also be used to dynamically update initial conditions in the continental-scale National Water Model. Full article
30 April 2018
Winners of the First MDPI Writing Prize
Winners of the First MDPI Writing Prize
13 March 2018
MDPI Becoming a Member of UKSG
MDPI Becoming a Member of UKSG
Special Issue in HydrologyAdvances in Large Scale Flood Monitoring and Detection Guest Editors: Salvatore Manfreda, Caterina Samela, Alberto Refice, Valerio Tramutoli, Fernando Nardi
Deadline: 30 June 2018
Special Issue in HydrologyWater Quality Monitoring in Streams, Rivers, Lakes and Reservoirs: Novel Methods and Applications Guest Editor: Gustavious Paul Williams
Deadline: 30 September 2018
Special Issue in HydrologyCatchments Hydrology and Sediment Dynamics: Concepts, Measuring and Modelling Guest Editor: Alessio Radice
Deadline: 31 December 2018
Special Issue in HydrologySubmarine Groundwater Discharge and Its Effects Guest Editors: Alanna L. Lecher, Karen Knee, Kimberly Null
Deadline: 31 January 2019