Hydrology, Volume 5, Issue 1 (March 2018) – 21 articles

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 (F_agg) 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

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

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

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

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

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

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

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

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

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

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

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

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

Utah Lake is highly eutrophic with large phosphorous inflows and a large internal phosphorous reservoir in the sediment. There are debates over whether this phosphorous is from geologic or more recent anthropologic sources. This study characterizes total phosphorous in geologic and current lake sediments to attempt to address that question. The average total phosphorous concentrations in the lake sediment were 666 ppm, with most samples in the 600 to 800 ppm range with a few larger values. Concentrations in historic geologic sediments were not statistically different from lake sediments. A spatial analysis showed that phosphorous distributions appeared continuous from the lake to the shore and that high and low values could be attributed to areas of seeps and springs (low) or feed lots and waste water discharge (high). These results indicate that geologic sediments without anthropogenic impacts are not statistically different than current lake sediments. The high values indicate that internal natural phosphorous loadings could be significant and the impaired state may be relatively insensitive to external anthropogenic loadings. If this is the case, then mitigation efforts to address anthropogenic sources may have minimal impacts. This case study presents an impaired water body where non-anthropogenic nutrient sources are significant and shows that reservoir management decisions should consider these non-anthropogenic phosphorous sources relative to anthropogenic sources. This study can serve as a template for evaluating the importance of geologic phosphorous sources for management decisions. Full article

Developing aquifers as underground water supply reservoirs is an advantageous approach applicable to meeting water management objectives. Aquifer storage and recovery (ASR) is a direct injection and subsequent withdrawal technology that is used to increase water supply storage through injection wells. Due to site-specific hydrogeological quantification and evaluation to assess ASR suitability, limited methods have been developed to identify suitability on regional scales that are also applicable at local scales. This paper presents an ASR site scoring system developed to qualitatively assess regional and local suitability of ASR using 9 scored metrics to determine total percent of ASR suitability, partitioned into hydrogeologic properties, operational considerations, and regulatory influences. The development and application of a qualitative water well suitability method was used to assess the potential groundwater response to injection, estimate suitability based on predesignated injection rates, and provide cumulative approximation of statewide and local storage prospects. The two methods allowed for rapid assessment of ASR suitability and its applicability to regional and local water management objectives at over 280 locations within 62 watersheds in Washington, USA. It was determined that over 50% of locations evaluated are suitable for ASR and statewide injection potential equaled 6400 million liters per day. The results also indicate current limitations and/or potential benefits of developing ASR systems at the local level with the intent of assisting local water managers in strategic water supply planning. Full article

Flooding often has a negative impact on society. In particular, widespread flood events can cause a lot of damage. These events are often spatially and temporally heterogeneous and should be duly considered for an appropriate analysis of flooding. Therefore, a conditional multivariate approach is adapted and applied in order to (i) contribute to a better understanding of the spatial characteristics of fluvial floods and (ii) to deliver sets of synthetically generated flood events. The present paper focuses on a simulation procedure consisting of careful data preparation and selection and the application of a conditional multivariate approach. The conditional approach is adapted to account for the seasonality of runoff data. Model checks attuned to the model are presented to ensure the consistence of simulated and observed data. The Austrian Province Vorarlberg was chosen as the study area. A thorough data analysis of runoff time series showed that the hydrological behaviour is characterized by a strong seasonality that was considered within the applied modelling procedure. The analysis of the spatial dependence of high river flows identified regions where floods likely occur simultaneously and regions with low spatial dependence. The main result of the modelling procedure, a large set of widespread flood events, was successfully generated. Full article

Many studies have shown that isotope data are valuable for hydrological model calibration. Recent developments have made isotope analyses more accessible but event sampling still involves significant time and financial costs. Therefore, it is worth to study how many isotope samples are needed for hydrological model calibration and what the most informative sampling times are. In this study, we used synthetic data to investigate how systematic errors in the precipitation, streamflow and the isotopic composition of precipitation affect the information content of stream isotope samples for model calibration. The results show that model performance improves significantly when two or three isotope samples are used for calibration and that the most informative samples are taken on the falling limb. However, when there are errors in the rainfall isotopic composition, rising limb samples are more informative. Data errors caused the most informative samples to be more clustered and to occur earlier in the event compared to error free data. These results provide guidance on when to sample events for model calibration and thus help to reduce the cost and effort in obtaining useful data for model calibration. Full article

A collection of 514 stable isotope water samples from the Atacama Desert is being reassessed geostatistically. The evaluation reveals that adjacent Andean catchments can exhibit distinct δ¹⁸O and δ²H value ranges in meteoric waters, despite similar sample altitudes of up to 4000 m above sea level (a.s.l.). It is proposed that the individual topographic features of each catchment at the western Andean Precordillera either inhibit or facilitate vapor mixing processes of easterly and westerly air masses with different isotopic compositions. This process likely causes catchment-specific isotope value ranges in precipitations (between −7‰ and −19‰ δ¹⁸O) that are being consistently reflected in the isotope values of groundwater and surface waters of these catchments. Further, due to evaporation-driven isotopic fractionation and subsurface water mixing, isotope samples of the regional Pampa del Tamarugal Aquifer plot collectively parallel to the local meteoric water line. Besides, there is no evidence for hydrothermal isotopic water-rock interactions. Overall, the observed catchment-dependent isotope characteristics allow for using δ¹⁸O and δ²H as tracers to delineate regionally distinct groundwater compartments and associated recharge areas. In this context, δ¹⁸O, δ²H and ³H data of shallow groundwater at three alluvial fans challenge the established idea of recharge from alluvial fans after flash floods. Full article

This study focuses on Laos, a landlocked nation located in South-East Asia with sub-tropical climate and highly seasonal rainfall distribution. Laos is one of the world’s least developed countries, and currently witnesses an unprecedented level of development that is highly reliant on its natural resources, including groundwater. There is currently very limited data and no nationwide assessment of shallow (<30 m) groundwater resources to support sustainable management. This study provides a first step towards addressing this issue by (i) identifying the major aquifer units of the country; (ii) integrating localized data and regional maps into an assessment of the groundwater potential; and (iii) producing quantitative maps of key hydrogeological indicators. Eight aquifer units have been described and evaluated: (i) Basement aquifers, (ii) Volcanic aquifers, (iii) Schists, (iv) Paleozoic sedimentary, (v) Karsts, (vi) Limestones, (vii) Mesozoic sedimentary and (viii) Alluvial sediments. The Mesozoic sandstones and the Alluvial aquifers are the most extensive and productive hydrogeological systems in the country. The Volcanic and Karstic aquifers, although poorly known, might also have important potential. This assessment, along with the maps of quantitative aquifer indicators, provide a significant improvement in both spatial resolution and accuracy compared to previously available information. It will likely support improved management plans and the identification of areas with higher potential for groundwater development. Full article

This study presents a comprehensive trend analysis of precipitation, temperature, and runoff extremes in the Central Valley of California from an operational perspective. California is prone to those extremes of which any changes could have long-lasting adverse impacts on the society, economy, and environment of the State. Available long-term operational datasets of 176 forecasting basins in six forecasting groups and inflow to 12 major water supply reservoirs are employed. A suite of nine precipitation indices and nine temperature indices derived from historical (water year 1949–2010) six-hourly precipitation and temperature data for these basins are investigated, along with nine indices based on daily unimpaired inflow to those 12 reservoirs in a slightly shorter period. Those indices include daily maximum precipitation, temperature, runoff, snowmelt, and others that are critical in informing decision making in water resources management. The non-parametric Mann-Kendall trend test is applied with a trend-free pre-whitening procedure in identifying trends in these indices. Changes in empirical probability distributions of individual study indices in two equal sub-periods are also investigated. The results show decreasing number of cold nights, increasing number of warm nights, increasing maximum temperature, and increasing annual mean minimum temperature at about 60% of the study area. Changes in cold extremes are generally more pronounced than their counterparts in warm extremes, contributing to decreasing diurnal temperature ranges. In general, the driest and coldest Tulare forecasting group observes the most consistent changes among all six groups. Analysis of probability distributions of temperature indices in two sub-periods yields similar results. In contrast, changes in precipitation extremes are less consistent spatially and less significant in terms of change rate. Only four indices exhibit statistically significant changes in less than 10% of the study area. On the regional scale, only the American forecasting group shows significant decreasing trends in two indices including maximum six-hourly precipitation and simple daily intensity index. On the other hand, runoff exhibits strong resilience to the changes noticed in temperature and precipitation extremes. Only the most southern reservoir (Lake Isabella) shows significant earlier peak timing of snowmelt. Additional analysis on runoff indices using different trend analysis methods and different analysis periods also indicates limited changes in these runoff indices. Overall, these findings are meaningful in guiding reservoir operations and water resources planning and management practices. Full article