Hydrology, Volume 5, Issue 4 (December 2018) – 18 articles

This study evaluates the impacts of land use and climate changes on daily discharge in Ouémé river basin at Bétérou outlet. Observed rainfall and temperature over 2002–2008 and land use data of 2003 and 2007 were used. Corrected rainfall and temperature data, under RCP4.5 and RCP8.5 scenarios from regional climate model REMO were considered. Two land use scenarios from RIVERTWIN project were used. The first one, Land Use A (LUA), is characterized by stronger economic development, controlled urbanization, implementation of large-scale irrigation schemes, and 3.2% population growth per year. The other one, Land Use B (LUB), is characterized by a weak national economy, uncontrolled settlement, and farmland development as well as 3.5% population growth per year. Four climate and land use combined scenarios (LUA + RCP4.5, LUA + RCP8.5; LUB + RCP4.5, and LUB + RCP8.5) were used for forcing LISFLOOD hydrological model to estimate future discharges at 2050. As a result, during calibration and validation, the LISFLOOD model showed high ability to reproduce historical flows of Ouémé River at Bétérou outlet with Nash–Sutcliffe efficiencies greater than 90%. Future discharges simulations show general increase for all land use and climate combined scenarios for all time horizons until 2050. The increase is more exacerbated under the combined scenarios using LUB than the ones using LUA. Increase of river discharge varies between 7.1% and 52% compared to the mean of the reference period 2002–2004. These findings highlight growing challenges for water resources managers and planners. Moreover, they emphasize the need to address potential climate and land use changes’ impact on water resources. Then, developing water management plans, strategies to reduce flooding risks must be considered. Full article

In recent years, advanced methods for measuring riverbank migration have been used to understand the process of river planform evolution. However, the role of the so-called outer secondary cell in the hydraulic pattern in bank erosion remains unclear. For this purpose, a natural river meander with high curvature bends and steep riverbanks was chosen to quantify bank migration by high-resolution terrestrial laser scanning of three patches along two river bends in four time intervals. The first two time intervals were seasonal, from spring to autumn, and with relatively few water level changes, whereas the third and fourth time intervals were short, just before and after single flood peak events. The yielded point clouds were filtered and digital elevation models (DEMs) were created. These DEMs were used to analyze bank retreat, riverbank morphology, and slope gradient changes in order to understand the role of the outer secondary cell in these processes. In addition, it is shown that storm events causing short peaks in river discharge are less important for river migration than longer-lasting medium discharge. Full article

Evaluating the impact of climate change on sediment yield has become one of the major topics in climate research. The purpose of this study was to investigate sediment yield contribution to lake volume change under changing climatic conditions in the Central Rift Valley Basin. The ensemble mean of five regional climate models (RCMs) in the coordinated regional climate downscaling experiment (CORDEX)-Africa was considered for the purpose of this study. The climate variables (precipitation, minimum and maximum temperatures) in RCMs were bias corrected against observed data (1985–2016) using linear scaling (LS), power transformation (PT), variance of scaling (VS), and quantile mapping (QM). Two emission scenarios, the Representative Concentration Pathways, RCP4.5 and RCP8.5, were considered for the future scenario period (2041–2070). Better results were obtained when the ensemble values of the bias correction methods were used. Hence, the projected values of climate variables after bias correction were used in the Soil and Water Assessment Tool (SWAT) hydrological model to estimate the sediment yield contribution to lake volume change due to climate change. The results show that the average projected precipitation will decrease by 7.97% and 2.55% under RCP4.5 and RCP8.5, respectively. On average, the maximum temperature will increase by 1.73 °C and 2.36 °C under RCP4.5 and RCP8.5, respectively, while the minimum temperature will increase by 2.16 °C and 3.07 °C under RCP4.5 and RCP8.5, respectively. The average annual sediment yield contributions to Lake Ziway were 431.05 ton/km² and 322.82 ton/km² for the Meki and Ketar rivers, respectively, in the historical period (1985–2010). The study also reveals that the annual sediment yield that was estimated for the Meki River was 323 ton/km² and 382 ton/km² under RCP4.5 and under RCP8.5, respectively. The sediment estimations for the Ketar River were 157 ton/km² and 211 ton/km² under RCP4.5 under RCP8.5, respectively. This will decrease the rate of volume change in Lake Ziway by 38% under RCP4.5 and by 23% under RCP8.5. The results show that the life expectancy of the lake is likely to increase under climate change scenarios. This will help water resources managers make informed decisions regarding the planning, management, and mitigation of the river basins. Full article

Hydrologists use a number of tools to compare model results to observed flows. These include tools to pre-process the data, data frames to store and access data, visualization and plotting routines, error metrics for single realizations, and ensemble metrics for stochastic realizations to calibrate and evaluate hydrologic models. We present an open-source Python package to help characterize predicted and observed hydrologic time series data called hydrostats which has three main capabilities: Data storage and retrieval based on the Python Data Analysis Library (pandas), visualization and plotting routines using Matplotlib, and a metrics library that currently contains routines to compute over 70 different error metrics and routines for ensemble forecast skill scores. Hydrostats data storage and retrieval functions allow hydrologists to easily compare all, or portions of, a time series. For example, it makes it easy to compare observed and modeled data only during April over a 30-year period. The package includes literature references, explanations, examples, and source code. In this note, we introduce the hydrostats package, provide short examples of the various capabilities, and provide some background on programming issues and practices. The hydrostats package provides a range of tools to make characterizing and analyzing model data easy and efficient. The electronic supplement provides working hydrostats examples. Full article

On many tropical reefs, submarine groundwater discharge (SGD) provides a substantial and often overlooked nutrient source to nearshore ecosystems, yet little is known about the impacts of SGD on the biology of reef organisms. To address this, the physiological responses of the endemic rhodophyte Gracilaria coronopifolia and an invasive congener, Gracilaria salicornia, were examined across an SGD gradient in the field and laboratory. Tissue samples of both species were cultured for 16 days along an onshore-offshore SGD gradient at Wailupe, Oahu. G. salicornia tolerated the extremely variable salinity, temperature, and nutrient levels associated with SGD. In marked contrast, half of G. coronopifolia plants suffered tissue loss and even death at SGD-rich locations in the field and in laboratory treatments simulating high SGD flux. Measurements of growth, photosynthesis, and branch development via two novel metrics indicated that the 27‰ simulated-SGD treatment provided optimal conditions for the apparently less tolerant G. coronopifolia in the laboratory. Benthic community analyses revealed that G. salicornia dominated the nearshore reef exposed to SGD compared with the offshore reef, which had a greater diversity of native algae. Ultimately, SGD inputs to coastal environments likely influence benthic community structure and zonation on otherwise oligotrophic reefs. Full article

In this study, 17 hydrologists with different experience in hydrological modelling applied the same conceptual catchment model (HBV) to a Greek catchment, using identical data and model code. Calibration was performed manually. Subsequently, the modellers were asked for their experience, their calibration strategy, and whether they enjoyed the exercise. The exercise revealed that there is considerable modellers’ uncertainty even among the experienced modellers. It seemed to be equally important whether the modellers followed a good calibration strategy, and whether they enjoyed modelling. The exercise confirmed previous studies about the benefit of model ensembles: Different combinations of the simulation results (median, mean) outperformed the individual model simulations, while filtering the simulations even improved the quality of the model ensembles. Modellers’ experience, decisions, and attitude, therefore, have an impact on the hydrological model application and should be considered as part of hydrological modelling uncertainty. Full article

Complete and accurate data are necessary for analyzing and understanding trends in time-series datasets; however, many of the available time-series datasets have gaps that affect the analysis, especially in the earth sciences. As most available data have missing values, researchers use various interpolation methods or ad hoc approaches to data imputation. Since the analysis based on inaccurate data can lead to inaccurate conclusions, more accurate data imputation methods can provide accurate analysis. We present a spatial-temporal data imputation method using Empirical Mode Decomposition (EMD) based on spatial correlations. We call this method EMD-spatial data imputation or EMD-SDI. Though this method is applicable to other time-series data sets, here we demonstrate the method using temperature data. The EMD algorithm decomposes data into periodic components called intrinsic mode functions (IMF) and exactly reconstructs the original signal by summing these IMFs. EMD-SDI initially decomposes the data from the target station and other stations in the region into IMFs. EMD-SDI evaluates each IMF from the target station in turn and selects the IMF from other stations in the region with periodic behavior most correlated to target IMF. EMD-SDI then replaces a section of missing data in the target station IMF with the section from the most closely correlated IMF from the regional stations. We found that EMD-SDI selects the IMFs used for reconstruction from different stations throughout the region, not necessarily the station closest in the geographic sense. EMD-SDI accurately filled data gaps from 3 months to 5 years in length in our tests and favorably compares to a simple temporal method. EMD-SDI leverages regional correlation and the fact that different stations can be subject to different periodic behaviors. In addition to data imputation, the EMD-SDI method provides IMFs that can be used to better understand regional correlations and processes. Full article

Spectral images from remote sensing platforms are extensively used to estimate chlorophyll-a (chl-a) concentrations for water quality studies. Empirical models used for estimation are often based on physical principles related to light absorption and emission properties of chl-a and generally relying on spectral bands in the green, blue, and near-infrared bands. Because the physical characteristics, constituents, and algae populations vary widely from lake to lake, it can be difficult to estimate coefficients for these models. Many studies select a model form that is a function of these bands, determine model coefficients by correlating remotely-measured surface reflectance data and coincidentally measured in-situ chl-a concentrations, and then apply the model to estimate chl-a concentrations for the entire water body. Recent work has demonstrated an alternative approach using simple statistical learning methods (Multiple Linear Stepwise Regression (MLSR)) which uses historical, non-coincident field data to develop sub-seasonal remote sensing chl-a models. We extend this previous work by comparing this method against models from literature, and explore model performance for a region of lakes in Central Utah with varying optical complexity, including two relatively clear intermountain reservoirs (Deer Creek and Jordanelle) and a highly turbid, shallow lake (Utah Lake). This study evaluates the suitability of these different methods for model parameterization for this area and whether a sub-seasonal approach improves performance of standard model forms from literature. We found that while some of the common spectral bands used in literature are selected by the data-driven MLSR method for the lakes in the study region, there are also other spectral bands and band interactions that are often more significant for these lakes. Comparison of model fit shows an improvement in model fit using the data-driven parameterization method over the more traditional physics-based modeling approaches from literature. This suggests that the sub-seasonal approach and exploitation of information contained in other bands helps account for lake-specific optical characteristics, such as suspended solids and other constituents contributing to water color, as well as unique (and season-specific) algae populations, which contribute to the spectral signature of the lake surface, rather than only relying on a generalized optical signature of chl-a. Consideration of these other bands is important for development of models for long-term and entire growing season applications in optically diverse water bodies. Full article

Submarine groundwater discharge (SGD) consists of fresh submarine groundwater discharge (FSGD) and recirculated submarine groundwater discharge (RSGD). In this study, we conducted simultaneous 25-hour time-series measurements of short-lived ²²²Rn and ²²⁴Ra activities at two sites with differing SGD rates in the central Seto Inland Sea of Japan to evaluate SGD rates and their constituents. At both sites, we also quantified the total SGD, FSGD, and RSGD using a seepage meter to verify the water fluxes estimated with ²²²Rn and ²²⁴Ra. SGD rates estimated using ²²²Rn and ²²⁴Ra at the site with significant SGD approximated the total SGD and RSGD measured by the seepage meter. However, SGD rates derived using ²²²Rn at the site with minor SGD were overestimated, since ²²²Rn activity at the nearshore mooring site was lower than that in the offshore area. These results suggest that the coupling of short-lived ²²²Rn and ²²⁴Ra is a powerful tool for quantification of FSGD and RSGD, although it is important to confirm that tracer activities in coastal areas are higher than those in offshore. Full article

Submarine groundwater discharge (SGD) is a global and well-studied geological process by which groundwater of varying salinities enters coastal waters. SGD is known to transport bioactive solutes, including but not limited to nutrients (nitrogen, phosphorous, silica), gases (methane, carbon dioxide), and trace metals (iron, nickel, zinc). In addition, physical changes to the water column, such as changes in temperature and mixing can be caused by SGD. Therefore SGD influences both autotrophic and heterotrophic marine biota across all kingdoms of life. This paper synthesizes the current literature in which the impacts of SGD on marine biota were measured and observed by field, modeling, or laboratory studies. The review is grouped by organismal complexity: bacteria and phytoplankton, macrophytes (macroalgae and marine plants), animals, and ecosystem studies. Directions for future research about the impacts of SGD on marine life, including increasing the number of ecosystem assessment studies and including biological parameters in SGD flux studies, are also discussed. Full article

Drawing a distinction between the suspended solid size and concentration impacts on physical clogging process in the Managed Aquifer Recharge (MAR) systems has been fraught with difficulties. Therefore, the current study was then aimed to statistically investigate and differentiate the impacts of clay-, silt- and sand-sized suspended solids at three concentration levels including 2, 5 and 10 g/L, compared with the clean water (0 g/L), on infiltration rate reducibility. The treatments were compared by virtue of Cohen’s d effect size measure. Furthermore, the competency of Singular Spectrum Analysis (SSA) was evaluated in reconstruction of infiltration rate. Results showed that clay-sized suspended solids were found to be the most important determining factor in physical clogging occurrence. The effect size measure highlighted that a lower concentration level of clay-sized suspended solids, that is, 2 g/L could be more important in trigging the physical clogging than a higher concentration level of silt-sized suspended solids namely 5 g/L. Also, we recognized that concentration level of clay-sized suspended sediments could non-linearly decrease the infiltrability. Also, findings revealed that SSA represented a high level of competency in reconstruction of the infiltration rate under all treatments. Hence, SSA can be quite beneficial to MAR systems for forecasting applications. Full article

Recently, Deer Creek Reservoir (DCR) underwent a large drawdown to support dam reconstruction. This event exposed sediments inundated by the reservoir, since dam completion in the early 1940s. This event allowed us to take sediment data samples and evaluate them for phosphorous (P) content. It is difficult for normal reservoir sediment studies to have sediment samples at high spatial resolution because of access. During the drawdown, we collected 91 samples on a grid 100 m in one direction and 200 m in the other. This grid defined an area of approximately 750,000 m² (185 acre). We took both surface samples, and at some sites, vertical samples. We determined water soluble P for all the samples, and P in four other reservoirs or fractions for 19 samples. Results showed water soluble P in the range of 2.28 × 10⁻³ to 9.81 × 10⁻³, KCl-P from 2.53 × 10⁻³ to 1.10 × 10⁻², NaOH-P from 5.30 × 10⁻² to 4.60 × 10⁻¹, HCl-P from 1.28 × 10⁻¹ to 1.34, and residual (mostly organic) P from 8.23 × 10⁻¹ to 3.23 mg/g. We provide this data set to the community to support and encourage research in this area. We hope this data set will be used and analyzed to support other research efforts. Full article

Accurate meteorological estimates are critical for process-based hydrological simulation and prediction. This presents a significant challenge in mountainous Asia where in situ meteorological stations are limited and major river basins cross international borders. In this context, remotely sensed and model-derived meteorological estimates are often necessary inputs for distributed hydrological analysis. However, these datasets are difficult to evaluate on account of limited access to ground data. In this case, the implications of uncertainty associated with precipitation forcing for hydrological simulations is explored by driving the South Asia Land Data Assimilation System (South Asia LDAS) using a range of meteorological forcing products. MERRA2, GDAS, and CHIRPS produce a wide range of estimates for rainfall, which causes a widespread simulated streamflow and evapotranspiration. A combination of satellite-derived and limited in situ data are applied to evaluate model simulations and, by extension, to constrain the estimates of precipitation. The results show that available gridded precipitation estimates based on in situ data may systematically underestimate precipitation in mountainous regions and that performance of gridded satellite-derived or modeled precipitation estimates varies systematically across the region. Since no station-based data or product including station data is satisfactory everywhere, our results suggest that the evaluation of the hydrological simulation of streamflow and ET can be used as an indirect evaluation of precipitation forcing based on ground-based products or in-situ data. South Asia LDAS produces reasonable evapotranspiration and streamflow when forced with appropriate meteorological forcing and the choice of meteorological forcing should be made based on the geographical location as well as on the purpose of the simulations. Full article

West African basins play a vital role in the socio-economic development of the region. They are mostly trans-boundary and sources of different land use practices. This work attempts to assess the spatio-temporal land use and land cover changes over three South Western African basins (Volta, Mono and Sassandra basins) and their influence on discharge. The land use and land cover maps of each basin were developed for 1988, 2002 and 2016. The results show that all the studied basins present an increase in water bodies, built-up, agricultural land and a decline in vegetative areas. These increases in water bodies and land use are as a result of an increase in small reservoirs, of dugouts and of dam constructions. However, the decline in some vegetative clusters could be attributed to the demographic and socio-economic growth as expressed by the expansion of agriculture and urbanization. The basic statistical analysis of precipitation and discharge data reveals that the mean annual discharge varies much more than the total annual precipitation at the three basins. For instance, in the entire Volta basin, the annual precipitation coefficient of variation (CV) is 10% while the annual discharge CV of Nawuni, Saboba and Bui are 43.6%, 36.51% and 47.43%, respectively. In Mono basin, the annual precipitation CV is 11.5% while the Nangbeto and Athieme annual discharge CV are 37.15% and 46.60%, respectively. The annual precipitation CV in Sassandra basin is 7.64% while the annual discharge CV of Soubre and Dakpadou are 29.41% and 37%, respectively. The discharge varies at least three times much more than the precipitation in the studied basins. The same conclusion was found for all months except the driest months (December and January). We showed that this great variation in discharge is mainly due to land use and land cover changes. Beside the hydrological modification of the land use and land cover changes, the climate of the region as well as the water quality and availability and the hydropower generation may be impacted by these changes in land surfaces conditions. Therefore, these impacts should be further assessed to implement appropriate climate services and measures for a sustainable land use and water management. Full article

The transport and fate of nitrate (NO₃⁻) to in the top 15 cm of a streambed has been well-documented, but an understanding of greater depths is limited. This work examines the transport and fate of nitrate (NO₃⁻) at depths of 30 cm, 60 cm, 90 cm, and 150 cm below the stream-streambed interface. Concentrations of nitrate as nitrogen (NO₃-N) and chloride (Cl⁻) were measured in the waters from the streambed, the stream water, and the groundwater. Mixing models predicted values of ΔNO₃-N, the difference between measured NO₃-N and theoretical NO₃-N. At a 30-cm depth, the mean ΔNO₃-N value was −0.25 mg/L, indicating a deficit of NO₃-N and the removal of NO₃-N from the system. At deeper levels, the values of ΔNO₃-N began to approach zero, reaching a mean value of −0.07 mg/L at 150 cm. The reduction of NO₃-N does not appear to be controlled by vegetation, as it was not correlated to either temperature or visible light. Larger negative ΔNO₃-N values (more removal) occur when stream NO₃-N concentrations are higher and organic matter is present. Full article

Accurate water demand forecasting is essential to operate urban water supply facilities efficiently and ensure water demands for urban residents. This study proposes an extreme learning machine (ELM) coupled with variational mode decomposition (VMD) for short-term water demand forecasting in six cities (Anseong-si, Hwaseong-si, Pyeongtaek-si, Osan-si, Suwon-si, and Yongin-si), South Korea. The performance of VMD-ELM model is investigated based on performance indices and graphical analysis and compared with that of artificial neural network (ANN), ELM, and VMD-ANN models. VMD is employed for multi-scale time series decomposition and ANN and ELM models are used for sub-time series forecasting. As a result, ELM model outperforms ANN model. VMD-ANN and VMD-ELM models outperform ANN and ELM models, and the VMD-ELM model produces the best performance among all the models. The results obtained from this study reveal that the coupling of VMD and ELM can be an effective forecasting tool for short-term water demands with strong nonlinearity and non-stationarity and contribute to operating urban water supply facilities efficiently. Full article

The Ogallala Aquifer has experienced a continuous decline in water levels due to decades of irrigation pumping with minimal recharge. Corn is one of the major irrigated crops in the semi-arid Northern High Plains (NHP) of Texas. Selection of less water-intensive crops may provide opportunities for groundwater conservation. Modeling the long-term hydrologic impacts of alternative crops can be a time-saving and cost-effective alternative to field-based experiments. A newly developed management allowed depletion (MAD) irrigation scheduling algorithm for Soil and Water Assessment Tool (SWAT) was used in this study. The impacts of irrigated farming, dryland farming, and continuous fallow on water conservation were evaluated. Results indicated that simulated irrigation, evapotranspiration, and crop yield were representative of the measured data. Approximately 19%, 21%, and 32% reductions in annual groundwater uses were associated with irrigated soybean, sunflower, and sorghum, respectively, as compared to irrigated corn. On average, annual soil water depletion was more than 52 mm for dryland farming scenarios. In contrast, only 18 mm of soil water was lost to evaporation annually, for the long-term continuous fallow simulation. The fallow scenario also showed 31 mm of percolation for aquifer recharge. Full article

Hydraulic modeling is a fundamental tool for managing and mitigating flood risk. Developing low resolution hydraulic models, providing consistent inundation simulations with shorter running time, as compared to high-resolution modeling, has a variety of potential applications. Rapid coarse resolution flood models can support emergency management operations as well as the coupling of hydrodynamic modeling with climate, landscape and environmental models running at the continental scale. This work sought to investigate the uncertainties of input parameters and bidimensional (2D) flood wave routing simulation results when simplifying the terrain mesh size. A procedure for fluvial channel bathymetry interpolation and floodplain terrain data resampling was investigated for developing upscaled 2D inundation models. The proposed terrain processing methodology was tested on the Tiber River basin evaluating coarse (150 m) to very coarse (up to 700 m) flood hazard modeling results. The use of synthetic rectangular cross sections, replacing surveyed fluvial channel sections, was also tested with the goal of evaluating the potential use of geomorphic laws providing channel depth, top width and flow area when surveyed data are not available. Findings from this research demonstrate that fluvial bathymetry simplification and DTM resampling is feasible when the terrain data resampling and fluvial cross section interpolation are constrained to provide consistent representation of floodplain morphology, river thalweg profile and channel flow area. Results show the performances of low-resolution inundation simulations running in seconds while maintaining a consistent representation of inundation extents and depths. Full article