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The Congo River Basin, located in central Africa, is the second-largest river basin in the world, after the Amazon. It has a drainage area of approximately 3.7 M km2 and is home to 75 million people. A significant part of the population is exposed to recurrent floods and droughts, and climate change is likely to worsen these events. Climate change studies of the Congo River basin have so far focused on annual and seasonal precipitation, but little attention was paid to extreme climatic events. This study aims to assess future changes in rainfall-induced flash floods and drought regimes in the Congo basin from the present day to 2100, using four selected extreme climatic indices as proxies to these two natural disasters. The indices are the total annual precipitation (PCPTOT), the number of days where rainfall is above 20 mm (PCP20), the standardized precipitation index (SPI), and the standardized precipitation-evapotranspiration index (SPEI). The indices were calculated with the statistically downscaled output of eleven Regional Climate Models (RCMs) from the Coordinated Downscaling Experiment (CORDEX-AFRICA) under two Representative Concentration Pathways: RCP 8.5 (high emissions scenario) and RCP 4.5 (moderate emissions scenario). Precipitation and temperature simulated by the RCMs were statistically downscaled using quantile mapping, while wind speed, solar radiation, and relative humidity were projected using K-nearest neighbor downscaling. The evolution of the indices was then assessed between the reference period (1976–2005) and three future periods (2011–2040, 2041–2070, and 2071–2100). Multimodel average results suggest that (i) independently of the scenario and period, PCPTOT and SPI will increase in the north, east, and western extremities of the basin and decrease in the basin’s center. (ii) The maximum increase (+ 24%) and decrease (− 6%) in PCPTOT were both projected under RCP 8.5 in the 2071–2100 period. (iii) PCP20 will increase independently of the period and scenario. Under RCP 8.5, in the 2071–2100 period, PCP20 will increase by 94% on average over the whole watershed. (iv) The SPEI results suggest that in all periods and scenarios, the rise in evapotranspiration due to higher temperatures will offset annual precipitation increases in the north, east, and western extremities of the basin. Increased evaporation will exacerbate the decrease in annual precipitation in the center, leading to increased drought frequency in the entire basin. [ABSTRACT FROM AUTHOR]

The countries sharing the Niger River suffer from poor access to clean water and energy as well as food insecurity. The Niger River Basin Authority is tasked with advancing progress in all these areas while also reducing environmental degradation. To help the basin authority and its investors prioritize portfolio activities that support multiple securities of interest, we developed a mixed-methods approach that engaged basin countries in qualitatively ranking projects to meet energy, environmental and food security goals, complemented by quantitative modelling for the more complex ranking of water and environmental sustainability goals, necessitated by complex upstream–downstream linkages. [ABSTRACT FROM AUTHOR]

While Stochastic Weather Generators (SWGs) are used intensively in climate and hydrological applications to simulate hydroclimatic time series and estimate risks and performance measures linked to climate variability, there have been few investigations into how many realizations are required for a robust estimation of these measures. Given the computational cost and time necessary to force climate-sensitive systems with multiple realizations, the estimation of the optimal number of synthetic time series to generate with a particular SWG for a predefined accuracy when estimating a particular risk or performance measure is particularly important. In this paper, the required number of realizations of five SWGs coupled with a SWAT model (the Soil and Water Assessment Tool) needed in order to achieve a predefined Relative Root Mean Square Error is investigated. The statistical indices used are the mean, standard deviation, skewness, and kurtosis of four hydroclimatic variables: precipitation, maximum and minimum temperature, and annual streamflow obtained for each observed and model-generated time series. While the results vary somewhat across SWGs, variables and indicators, they overall show that the marginal improvement decreases dramatically after 25 realizations. The results also indicate that the benefit of generating more than 100 realizations of climate and streamflow data is very minimal. The methodology presented herein can be applied in further investigations of other set of risk indicators, SWGs, hydrological models, and watersheds to minimize the required workload. [ABSTRACT FROM AUTHOR]

Stochastic weather generators are designed to produce synthetic sequences that are commonly used for risk discovery, as they would contain rare events that can lead to potentially catastrophic impacts on the environment, or even human lives. These time series are sometimes used as inputs to rainfall-runoff models to simulate the hydrological impacts of these rare events. This paper puts forward a method that evaluates the usefulness of weather generators by assessing how the statistical properties of simulated precipitation, temperatures, and streamflow deviate from those of observations. This is achieved by plotting a large ensemble of (1) synthetic precipitation and temperature time series in a Climate Statistics Space, and (2) hydrological indices using simulated streamflow data in a Risk and Performance Indicators Space. Assessment of weather generator's performance is based on visual inspection and the Mahalanobis distance between statistics derived from observations and simulations. A case study was carried out on the South Nations watershed in Ontario, Canada, using five different weather generators: two versions of a single-site Weather Generator, two versions of a multi-site Weather Generator (MulGETS) and the K-Nearest Neighbour weather generator (k-nn). Results show that the MulGETS model often outperformed the other weather generators for that particular study area because: (a) the observations were well centered within a point cloud of the synthetically-generated time series in both spaces, and (b) the points generated using MulGETS had a smaller Mahalanobis distance to the observations than those generated with the other weather generators. The k-nn weather generator performed particularly well in simulating temperature variables, but was poor at modelling precipitation and streamflow statistics. [ABSTRACT FROM AUTHOR]

Two methods for generating streamflow forecasts in a Sahelian watershed, the Sirba basin, were compared. The direct method used a linear relationship to relate sea-surface temperature to annual streamflow, and then disaggregated on a monthly time scale. The indirect method used a linear relationship to generate annual precipitation forecasts, a temporal disaggregation to generate daily precipitation and the SWAT (Soil and Water Assessment Tool) model to generate monthly streamflow. The accuracy of the forecasts was assessed using the coefficient of determination, the Nash-Sutcliffe coefficient and the Hit score, and their economic value was evaluated using the cost/loss ratio method. The results revealed that the indirect method was slightly more effective than the direct method. However, the direct method achieved higher economic value in the majority of cost/loss situations, allowed for predictions with longer lead times and required less information. [ABSTRACT FROM AUTHOR]

The Extended Kalman Filter (EKF) is used to assimilate in situ surface soil moisture and streamflow observation at the outlet of an experimental watershed outlet into a semi-distributed SWAT (Soil and Water Assessment Tool) model. Watershed scale, instead of HRU scale soil moisture was used in state vector to reduce computational burden. Numerical experiments were designed to select the best state vector which consists of streamflow and soil moisture in all vertical soil layers. Compared to open-loop model and direct-insert method, the estimate of both soil moisture and streamflow has been improved by EKF assimilation. The combined assimilation of surface soil moisture and streamflow outperforms the assimilation with only surface soil moisture or streamflow especially in the estimate of full profile soil moisture. The NSC has been improved to 0.63 from −4.45 and the RMSE has been reduced to 12.34 mm from 47.44 mm in open-loop. Such improvement is also reflected in the short term forecast of soil moisture. The improvement of streamflow prediction is relatively moderate in both simulation and forecast mode compared to quality of the soil moisture prediction. The quantification of the model error, especially the error covariance between different state variables, was found to be critical to the estimate of the state variable corresponding to the error covariance. [ABSTRACT FROM AUTHOR]

There is great potential in Data Assimilation (DA) for the purposes of uncertainty identification, reduction and real-time correction of hydrological models. This paper reviews the latest developments in Kalman filters (KFs), particularly the Extended KF (EKF) and the Ensemble KF (EnKF) in hydrological DA. The hydrological DA targets, methodologies and their applicability are examined. The recent applications of the EKF and EnKF in hydrological DA are summarized and assessed critically. Furthermore, this review highlights the existing challenges in the implementation of the EKF and EnKF, especially error determination and joint parameter estimation. A detailed review of these issues would benefit not only the Kalman-type DA but also provide an important reference to other hydrological DA types. Editor D. Koutsoyiannis; Associate editor F. Pappenberger [ABSTRACT FROM AUTHOR]

The objective of the current study is to present a comparison of techniques for the forecasting of low-frequency climate oscillation indices with a focus on the Great Lakes system. A number of time series models have been tested including the traditional autoregressive moving average (ARMA) model, dynamic linear model (DLM), generalized autoregressive conditional heteroskedasticity (GARCH) model, as well as the nonstationary oscillation resampling (NSOR) technique. These models were used to forecast the monthly El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) indices which show the most significant teleconnection with the net basin supply (NBS) of the Great Lakes system from a preliminary study. The overall objective is to predict future water levels, ice extent, and temperature, for planning and decision making purposes. The results showed that the DLM and GARCH models are superior for forecasting the monthly ENSO index, while the forecasted values from the traditional ARMA model presented a good agreement with the observed values within a short lead time ahead for the monthly PDO index. [ABSTRACT FROM AUTHOR]

Land-use conversion models elucidate the complexities and spatial interdependencies of components of land use systems and provide insights into future land-use configurations. In this paper, the 2012-2050 future land-use patterns in the South Nation (SN) River basin, located in eastern Ontario, Canada, were generated with a modified version of the CLUE model and a 2011 reference map. The SN is an example of a basin where some water quality endpoints have dropped below acceptable limits because of a combination of intensive agriculture, urbanization, and climate change. Five historical land-use maps were used to identify the historical trends in generalized land-use classes. Seven demographic and geographic factors were used to derive the spatial distribution of land suitability to each land-use class. The methodology was first validated by simulating land-use changes from 1991 to 2011 starting from the 1991 reference map, and comparing the simulated 2011 map to the 2011 reference map. Then, the 2012-2050 land-uses were generated, assuming historical trends derived from historical reference maps will continue in the future. Environmental impacts of the projected land-use changes were discussed. [ABSTRACT FROM AUTHOR]

Attempts to forecast the final form of a dam-break suffer from various uncertainty sources such as field data (topography, hydraulic and geotechnical parameters), and modeling assumptions. So the result seems to be at least partially chaotic and difficult to match with the objectives of the dam-safety engineers in which the goal is to model dam-break flow evolution in time. On the other hand, the principle of minimal energy dissipation rate makes it possible to predict the shape towards which the geometry of the breach will evolve to under stationary hydraulic conditions. In this article, one uses energy minimization by a genetic algorithm to integrate at the same time the two concepts of random result and convergence towards an equilibrium state. They are used to simulate the submersion failure of a clay dam, and the results agree reasonably with the field data. This approach makes it possible to obtain as realistic breach shapes as those obtained by actual models, and to introduce a random component in the process of dam-breach formation, which remains, however, guided by a physical principle. [ABSTRACT FROM AUTHOR]

Because of uncertainty on inflows, a hydroelectric system may at any time leave its normal zone of operation and fail. This paper proposes a way to continuously measure this risk of failure by presenting it in the form of time-varying indicators. These indicators are very flexible and could be defined according to the manager's needs. They are calculated by stochastic simulation by using inflows scenarios built according to the observed conditions and the statistical behaviour of the river. The utility of this dynamic risk is highlighted by an application on the Cabonga reservoir located in Quebec, Canada. For this reservoir, we define and evaluate six hypothetical management rules. Four of these rules are explicitly related to the instant level of risk on this reservoir. Opposition between risk and system performance was highlighted in a quantitative way. [ABSTRACT FROM AUTHOR]

The Inner Niger Delta (IND) is a complex hydraulic system where the flood dynamics and connectivity between water bodies is the main driver for ecosystem services and economic activities. Therefore, it is of pivotal importance that hydraulic models used to assess ecosystem services and socio-economic usages in the IND are capable of capturing both the inundation and connectivity dynamics. A particularity of the IND is that a strong hysteresis effect can be observed in the stage-discharge relationships at all hydrometric stations in the area. However, existing hydrodynamic models of the IND typically use a static stage-discharge relationship as the downstream boundary condition during both the rise and recession of the flood, which leads to potential inaccuracies when trying to predict the flood extent. This paper explores how the simulation results of the flood and connectivity dynamics in the IND can be improved by using a looped rating curve at the downstream model boundary. The looped rating curve is described using the dimensionless discharges and water levels (DLRC) method. The results show that simulation with DLRC improves the accuracy in predicting floodplain extent and connectivity dynamics between the Niger river system and an important lake in the IND. The improvement in water level predictions decreased steadily with the distance from the downstream boundary of the modelled area. •The paper investigates the effect of looped rating curve on flood extent simulation with a 2D hydrodynamic model. •The model is run with the looped rating curve at the downstream boundary. •Simulation with looped rating improves the accuracy in predicting water level and inundation extent at the downstream area. •The improvement in water level predictions decreased steadily with the distance from the downstream boundary of the area. •The study area: Inner Niger Delta, Mali, West Africa. [ABSTRACT FROM AUTHOR]

Summary The Extended Kalman Filter (EKF) is coupled with the Soil and Water Assessment Tools (SWAT) model in the streamflow assimilation of the upstream Senegal River in West Africa. Given the large number of distributed variables in SWAT, only the average watershed scale variables are included in the state vector and the Hydrological Response Unit (HRU) scale variables are updated with the a posteriori / a priori ratio of their watershed scale counterparts. The Jacobian matrix is calculated numerically by perturbing the state variables. Both the soil moisture and CN2 are significantly updated in the wet season, yet they have opposite update patterns. A case study for a large flood forecast shows that for up to seven days, the streamflow forecast is moderately improved using the EKF-subsequent open loop scheme but significantly improved with a newly designed quasi-error update scheme. The former has better performances in the flood rising period while the latter has better performances in the recession period. For both schemes, the streamflow forecast is improved more significantly when the lead time is shorter. [ABSTRACT FROM AUTHOR]

A significant decrease in mean river flow as well as shifts in flood regimes have been reported at several locations along the River Niger. These changes are the combined effect of persistent droughts, damming and increased consumption of water. Moreover, it is believed that climate change will impact on the hydrological regime of the river in the next decades and exacerbate existing problems. While decision makers and stakeholders are aware of these issues, it is hard for them to figure out what actions should be taken without a quantitative estimate of future changes. In this paper, a Soil and Water Assessment Tool (SWAT) model of the Niger River watershed at Koulikoro was successfully calibrated, then forced with the climate time series of variable length generated by nine regional climate models (RCMs) from the AMMA-ENSEMBLES experiment. The RCMs were run under the SRES A1B emissions scenario. A combination of quantile-quantile transformation and nearest-neighbour search was used to correct biases in the distributions of RCM outputs. Streamflow time series were generated for the 2026–2050 period (all nine RCMs), and for the 2051–2075 and 2076–2100 periods (three out of nine RCMs) based on the availability of RCM simulations. It was found that the quantile-quantile transformation improved the simulation of both precipitation extremes and ratio of monthly dry days/wet days. All RCMs predicted an increase in temperature and solar radiation, and a decrease in average annual relative humidity in all three future periods relative to the 1981–1989 period, but there was no consensus among them about the direction of change of annual average wind speed, precipitation and streamflow. When all model projections were averaged, mean annual precipitation was projected to decrease, while the total precipitation in the flood season (August, September, October) increased, driving the mean annual flow up by 6.9% (2026–2050), 0.9% (2051–2075) and 5.6% (2076–2100). At-test showed that changes in multi-model annual mean flow and annual maximum monthly flow between all four periods were not statistically significant at the 95% confidence level. [ABSTRACT FROM PUBLISHER]

Daily precipitation data from 31 Senegalese stations spanning the period from 1950 to 2007 were used to examine the inter-annual variations of seven rainfall indices: the annual mean precipitation (MEAN); the annual standard deviation of daily precipitation (STD); the frequency of wet days (Prcp1); the maximum number of consecutive dry days (CDD); the maximum 3-day rainfall total (R3D); the wet day precipitation intensity (SDII); and the 90th percentile of rain-day precipitation (Prec90p). The indices were spatially averaged over three agro-climatic regions in Senegal. Trends in the time series of the averaged indices were assessed using both visual examination and a modified version of the Mann-Kendall (MM-K) test. Initially negative significant trends in all seven indices suggest gradually drier conditions over the three agro-climatic regions between 1950 and 1980. In contrast, no significant trends, or even positive significant trends, were observed from the mid-1980s to 2007. The MM-K test was applied to all available data (1950–2007) and the period from 1971 to 2000. While several indices were found to have significant trends towards drier conditions for the 1950–2007 period, only PRCP1 showed a positive significant trend for the 1971–2000 period. The MM-K test did not detect a significant trend for the other indices. It was found that the rainfall deficit and therefore drought is no longer intensifying, and that the region may even become wetter. However, the period covered by the observations is still too short to resolve the question of whether there is now a trend towards wetter conditions.Editor Z.W. Kundzewicz; Associate editor K. Hamed [ABSTRACT FROM AUTHOR]

In this paper, a dispersion relation analysis is employed to investigate the finite volume triangular C-grid formulation for two-dimensional shallow-water equations. In addition, two proposed combinations of time-stepping methods with the C-grid spatial discretization are investigated. In the first part of this study, the C-grid spatial discretization scheme is assessed, and in the second part, fully discrete schemes are analyzed. Analysis of the semi-discretized scheme (i.e. only spatial discretization) shows that there is no damping associated with the spatial C-grid scheme, and its phase speed behavior is also acceptable for long and intermediate waves. The analytical dispersion analysis after considering the effect of time discretization shows that the Leap-Frog time stepping technique can improve the phase speed behavior of the numerical method; however it could not damp the shorter decelerated waves. The Adams–Bashforth technique leads to slower propagation of short and intermediate waves and it damps those waves with a slower propagating speed. The numerical solutions of various test problems also conform and are in good agreement with the analytical dispersion analysis. They also indicate that the Adams–Bashforth scheme exhibits faster convergence and more accurate results, respectively, when the spatial and temporal step size decreases. However, the Leap-Frog scheme is more stable with higher CFL numbers. [ABSTRACT FROM AUTHOR]

Weekly urban water demand forecasting using a hybrid wavelet-bootstrap-artificial neural network approach. This study developed a hybrid wavelet-bootstrap-artificial neural network (WBANN) model for weekly (one week) urban water demand forecasting in situations with limited data availability. The proposed WBANN method is aimed at improving the accuracy and reliability of water demand forecasting. Daily maximum temperature, total precipitation and water demand data for almost three years were used in this study. It was concluded that the hybrid WBANN model was more accurate compared to the ANN, BANN and WANN methods, and can be applied successfully for operational water demand forecasting. The WBANN model simulated peak water demand very effectively. The better performance of the WBANN model indicated that wavelet analysis significantly improved the model's performance, whereas the bootstrap technique improved the reliability of forecasts by producing ensemble forecasts. The WBANN model was also found to be effective in assessing the uncertainty associated with water demand forecasts in terms of confidence bands; this can be helpful in operational water demand forecasting. [ABSTRACT FROM AUTHOR]

Highlights: [•] We update the recent rainfall evolution in Senegal. [•] We highlight different changepoints showing the necessity to integrate recent period. [•] Detecting changepoints increases assumption about the return to a wetter climate. [•] Improving the analysis of rainfall contributes to a better awareness of land use. [Copyright &y& Elsevier]

The article focuses on the detection of inhomogeneities in climatic series using the Bayesian Normal Homogeneity Test (BNHT). It mentions that the sensitivity of BHNT to chosen priors was examined through its application to all data sets. It reveals that the test was very sensitive to the choice of prior probability of no change in homogenous series while less sensitive in single shift series. Moreover, advantages of the technique include the direct inclusion of metadata.

This paper aims to evaluate sources of uncertainty in projected hydrological changes under climate change in twelve large-scale river basins worldwide, considering the mean flow and the two runoff quantiles Q (high flow), and Q (low flow). First, changes in annual low flow, annual high flow and mean annual runoff were evaluated using simulation results from a multi-hydrological-model (nine hydrological models, HMs) and a multi-scenario approach (four Representative Concentration Pathways, RCPs, five CMIP5 General Circulation Models, GCMs). Then, three major sources of uncertainty (from GCMs, RCPs and HMs) were analyzed using the ANOVA method, which allows for decomposing variances and indicating the main sources of uncertainty along the GCM-RCP-HM model chain. Robust changes in at least one runoff quantile or the mean flow, meaning a high or moderate agreement of GCMs and HMs, were found for five river basins: the Lena, Tagus, Rhine, Ganges, and Mackenzie. The analysis of uncertainties showed that in general the largest share of uncertainty is related to GCMs, followed by RCPs, and the smallest to HMs. The hydrological models are the lowest contributors of uncertainty for Q and mean flow, but their share is more significant for Q. [ABSTRACT FROM AUTHOR]

River ice complicates river hydraulics and morphodynamics by adding a new boundary layer to the top of the flow. This boundary layer affects the velocity distribution throughout the depth due to increased flow resistance, and varies the local boundary shear stress on the bed (lower boundary) by adding new shear stress on the upper boundary (under surface of the ice). Variation of shear stress plays an important role in incipient motion of upper and lower boundary materials: sediment motion and transport are directly affected by local boundary shear stress, as are ice cover thickness, condition, and progression. This paper provides estimates of upper and lower boundary shear stress during stable ice cover and the important stage of ice cover breakup using available methods based on continuous field measurements of velocity profiles obtained with a bottom-mounted acoustic Doppler current profiler in the Nelson River, Canada. Boundary shear stresses varied dynamically with transformation of the ice cover, including the presence and removal of slush ice and formation of the ice jams. Estimated upper boundary shear stress varied between 0.52 and 23.87 N=m², whereas lower boundary shear stress varied between 1.51 and 2.40 N=m², with the highest values observed during breakup. These values are compared with estimates from previous laboratory experiments. [ABSTRACT FROM AUTHOR]

In shallow waterways such as the St. Lawrence River, an accurate prediction of the squat is important to ensure a balance between the security and the efficiency of traffic. The Canadian Coast Guard is now studying the squat phenomenon and considering to reassess the actual underkeel clearance standards of the St. Lawrence Waterway. Hence, a field campaign was conducted with 12 deep draft ship sailings, during which the maximal squat was measured with on-the-fly global positioning system. All the variables that may influence the squat (speed, draught, water level, etc.) were also measured. Twenty of the empirical models that are used in practice to predict the squat were tested and the Canadian Coast Guard recommended to either optimize these models or develop new models. Therefore, statistical approaches to model the squat of deep draft ships that navigate on the St. Lawrence Waterway are proposed in this paper. The Eryuzlu model, which is presently used by the Canadian Coast Guard, was optimized by modeling its errors with a stepwise regression. New models were also developed with the regression tree technique. The performance of the statistical models was better than 10 empirical models that are considered the most suitable to predict the maximal squat in the St. Lawrence Waterway. The models built by regression tree gave the best predictions. [ABSTRACT FROM AUTHOR]