Since 1950, a large number of high-head storage hydropower plants (HPP) in the Alps have met the demand for peak load energy in the European power grid (Schleiss, 2007). In Switzerland, for example, 32% of the total electricity in 2010 was produced by storage hydropower plants. Water retention in large reservoirs and concentrated turbine operations allow electricity to be produced on demand. Sudden opening and closing of the turbines produce highly unsteady flow conditions in the riverdownstream of the power house (Moog, 1993). This so-called hydropeaking is the major hydrological alteration in Alpine regions (Petts, 1984; Poff et al., 1997). Due to the unpredictability and intensity they cause, sub-daily hydropeaking events disturb the natural discharge regime, which is a key factor in ecological quality and the natural abiotic structure of ecosystems (Parasiewicz et al., 1998; Bunn and Arthington, 2002). These disturbances directly affect riverine biological communities (Young et al., 2011). Frequent and fast fluctuations change hydraulic parameters, e.g., flow depth, velocity and bed shear stress (Petts and Amoros, 1996), and thus influence fish habitat availability, stability and quality. Salmonid populations are less abundant and have reduced population size in rivers with hydropeaking (Moog, 1993; Gouraud et al., 2008). In headwaters of Alpine rivers, brown trout (Salmo trutta fario) is one of the species most impacted by dam operations. Without appropriate flow shelter habitat, the hydropeaking-impacted flowregime becomes energetically costly for fish and affects their over-wintering survival (Scruton et al., 2003; Scruton et al., 2008). Spawning areas are faced with the risk of dewatering, and young of the year (YOY) shore habitat is displaced or lost (Liebig et al., 1998; Saltveit et al., 2001). Success in natural reproduction and YOY survival are key factors for the fish population’s natural renewal.
Since 1950, a large number of high-head storage hydropower plants (HPP) in the Alps have met the demand for peak load energy in the European power grid (Schleiss, 2007). In Switzerland, for example, 32% of the total electricity in 2010 was produced by storage hydropower plants. Water retention in large reservoirs and concentrated turbine operations allow electricity to be produced on demand. Sudden opening and closing of the turbines produce highly unsteady flow conditions in the riverdownstream of the power house (Moog, 1993). This so-called hydropeaking is the major hydrological alteration in Alpine regions (Petts, 1984; Poff et al., 1997). Due to the unpredictability and intensity they cause, sub-daily hydropeaking events disturb the natural discharge regime, which is a key factor in ecological quality and the natural abiotic structure of ecosystems (Parasiewicz et al., 1998; Bunn and Arthington, 2002). These disturbances directly affect riverine biological communities (Young et al., 2011). Frequent and fast fluctuations change hydraulic parameters, e.g., flow depth, velocity and bed shear stress (Petts and Amoros, 1996), and thus influence fish habitat availability, stability and quality. Salmonid populations are less abundant and have reduced population size in rivers with hydropeaking (Moog, 1993; Gouraud et al., 2008). In headwaters of Alpine rivers, brown trout (Salmo trutta fario) is one of the species most impacted by dam operations. Without appropriate flow shelter habitat, the hydropeaking-impacted flowregime becomes energetically costly for fish and affects their over-wintering survival (Scruton et al., 2003; Scruton et al., 2008). Spawning areas are faced with the risk of dewatering, and young of the year (YOY) shore habitat is displaced or lost (Liebig et
countries, constitutes a highly complexsystem. With several large dams, namely Kariba, Cahora Bassa, Kafue Gorge and Itezhi-Tezhi, the basin’s hydrology is also influenced by vast wetlands with high ecological value such as the Barotse plains, the Mana Pools or the Kafue flats. The African DAms ProjecT (ADAPT) is an interdisciplinary research project aiming to develop an integrated set of methods that help assessing the ecological and socio-economic effects of dams. A comprehensive evaluation and characterization of the flow regimes before and after the dam’s construction is a stepping stone towards this goal. The analysis is based on historical data, taking into account the evolution of existing reservoirs and hydropower plants. Three indicators are considered to describe the flow regimes in the basin. They allow quantifying the seasonal transfer of the water, the sub- weekly flow fluctuations and the intensity and frequency of the flow changes. In a further stage, a semi-distributed conceptual hydrological model will be built to simulate the flowregime with and without dams for actual and future hydrological scenarios.
The study of pedestrian bottleneck is crucial from the perspective of level of service and evacuation. In the last few years, bottleneck and itsimpact on flow characteristics of pedestrians have gained significant attention. Several controlled experiments in laboratory conditions have been performed at normal conditions to gain more insights [1-3]. Some experimental studies were performed at various stress levels, as an attempt to replicate real field conditions . However, the ability of participants in such experiments to reproduce real behavior and motivation levels of crowd even at normal condition is doubtful. Also in a laboratory setup, one can test combinations of experimental variables, whereas in a field data collection the conditions may not always be favorable.
The purpose of this research is to clarify the behavior of excess flow in improved river channel. The target area is a part of the river channel of Shirakawa River, which is a bend located between 18.5km to 19.4km away from river mouth and have been improved. The model experiment was conducted first; water was flush at the peak discharge of flood (probability of once in 150 years) and the process of sandbar formation was reproduced. In the next, the size of sediment particle was changed, and the relationship between both flow discharge and particle size, and sedimentation was studied in order to reveal the behavior of river channel and capacity of improved river channel under heavy flow.
As renewable generation (e.g. wind power) cannot be pre- dicted with acceptable accuracy 24 to few hours ahead of operation , operators have to cope with these uncertainties in one way or another. In this context, a possible approach (not relying on probabilistic models) consists in checking whether, given some range of uncertainties (e.g. defined as intervals on bus active/reactive power injections), the worst- case with respect to each contingency is still controllable by appropriate combinations of preventive and corrective actions. To tackle this problem Ref.  sets-up a broader framework in the form of a three-stage decision making process under uncertainty including slow strategic controls that need to be committed several hours ahead in time (e.g. starting up a power plant, postponing maintenance works), fast preventive controls that may be launched in real-time operation (e.g. generation rescheduling) and corrective (or emergency) controls can still be taken in the post-contingency state (e.g. generation rescheduling, network switching, phase shifter actions, etc.). The computation of worst-case scenarios is an essential task of this approach. The worst-cases that cannot be covered by preventive/corrective controls require strategic actions that can be computed using the approach presented in .
I also want to thank Arjan Tuijnder as my daily supervisor at Arcadis. Thank you for being there almost every day to answer my questions. I have learned a lot from you. You always required to formulate complicated questions as clear as possible. This has helped me a lot in writing the report. Thank you Pepijn for your help during this period too. You were always willing to help and check formulas if I asked to. We were both situated in a different location so our communication was mainly by email. And yet your response was always very quick which is very much appreciated. Thank you Bart for your help. You always were able to clearly explain questions regarding complicated subjects such as turbulence or non- hydrostatic flow. I want to thank dhr. Ribberink. Your feedback was very constructive and has helped to reshape my report in a structured way. Your flexibility regarding the research process is very much appreciated. I finally want to thank my lovely wife Jacomijn for her support. Especially in the last stage of my research almost my whole life was devoted to it. And still you always helped and supported me and never complained about me doing my work. I’m very grateful and astonished by the way you have helped me through.
Procedures for removing seasonal factors from data fall into two broad camps: empirical and model-based techniques. Empirical techniques use sta- tistical smoothing methods without presupposing that the data are generated by an underlying model. The procedure developed at the U.S. Census Bu- reau, Variant X-11, see Shishkin et al (1967), is still the major part of adjust- ment procedures used in most statistical agencies. An excellent treatment of the way the program is assembled can be found in Hylleberg (1992), Ghysels and Perron (1993) and Wallis (1982). The more recent X-12 ARIMA, see Findley et al (1998), builds on X-11, improving diagnostics, the treatment of outliers and enabling the use of ARIMA-generated out of sample values in the smoothing. Such techniques, however, have a number of drawbacks for econometric work. Firstly, the use of moving averages with long lags and leads means that a definitive figure for the adjusted series will not be available for a number of years. Secondly, the procedure provides no insight into what seasonality might be and no framework in which we can examine its relationship with the trend component. Thirdly, these procedures smooth data in connection with the rest of the sample leaving a series of data-points which are no longer independent realisations. The number of degrees of free- dom lost will depend on a number of issues including the choice of procedure for smoothing outliers and trading day effects.
period. As shown in Fig. 9c, on average, predictions from the different scenarios indicate an increased frequency of low precipitation events in the future compared to the baseline time period. For example, the relative frequency of daily pre- cipitations less than 15 mm increases from 85 % for the base- line time period to 93–95 % for the long-term climate. On the other hand, the daily precipitation corresponds to a 99th percentile value decrease from 72.2 mm for the baseline time period to 25.5–30.2 mm for the long-term climate. There- fore, on a broader time scale (an average of 2 decades in this study), we can expect regular low-level river discharge in the future compared to the present climate. Figure 9d shows the seasonal change in low-flow events compared to 7Q10 es- timations for the baseline time period. While the predicted low-flow events during autumn and winter remain relatively unchanged, a significant increase in low-flow events can be observed for the spring and summer seasons, which further intensify from the short-term climate to the long-term cli- mate by the end of the 21st century. For example, the an- nual low-flow events, on average, will increase by 16 and 15 days during the spring and summer seasons, respectively, in the long-term climate in comparison to the average for the baseline period. Therefore, according to the results shown in Fig. 9b and d, we can conclude that, even though the mag- nitude of the minimum river discharge increases under the changing climate, variations in water discharge can increase significantly, leading to a future with regular low-flow events. 3.4 Shift in river discharge timing attributed to earlier
Thus, it is crucial to investigate the causes of damage to weirs and drop struc- tures in small rivers, and to find methods to secure their stability  . Various methods have been proposed by researchers to reduce the damage caused by ar- tificial structures -. However, there are few studies to improve the design standard by comparing with existing design standard formulas. In this respect, the present study examined the scour characteristics of a downstream weir ac- cording to change in upstream river bed slopes through hydraulic experiment using a small river weir model. Based on the test results, the aim of this study is ultimately to complement and improve the shortcomings of design standards for the length of the downstream apron for small river weirs. In this study, we inves- tigated the characteristics of the downstream scour of the river owing to the change in the upstream slope through a hydraulic experiment. The ultimate goal of this study is to overcome the deficiencies of the design standards for down- stream water basins of small rivers or streams.
Abstract. Given the increasing impacts of flooding in Jakarta, methods for assessing current and future flood risk are required. In this paper, we use the Damagescanner- Jakarta risk model to project changes in future river flood risk underscenarios of climate change, land subsidence, and land use change. Damagescanner-Jakarta is a simple flood risk model that estimates flood risk in terms of annual ex- pected damage, based on input maps of flood hazard, expo- sure, and vulnerability. We estimate baseline flood risk at USD 186 million p.a. Combining all future scenarios, we simulate a median increase in risk of + 180 % by 2030. The single driver with the largest contribution to that increase is land subsidence (+126 %). We simulated the impacts of cli- mate change by combining two scenarios of sea level rise with simulations of changes in 1-day extreme precipitation totals from five global climate models (GCMs) forced by the four Representative Concentration Pathways (RCPs). The re- sults are highly uncertain; the median change in risk due to climate change alone by 2030 is a decrease by − 46 %, but we simulate an increase in risk under 12 of the 40 GCM–RCP– sea level rise combinations. Hence, we developed probabilis- tic risk scenarios to account for this uncertainty. If land use change by 2030 takes places according to the official Jakarta Spatial Plan 2030, risk could be reduced by 12 %. However, if land use change in the future continues at the same rate as the last 30 years, large increases in flood risk will take place. Finally, we discuss the relevance of the results for flood risk management in Jakarta.
The means of annual 1-, 3-, 7-, 30- and 90-day minimum and maximum decrease significantly between the two periods. All values in the post-impact period were smaller than the pre-impact values. For example, the annual 1-day minima flow had a decrease rate of 99.0%, the annual 1-day maxima flow had a decrease rate of 27.2, and the 1-day maxima deviation reached 72.2% (Fig. 3). Note that zero flow days, which would be expected to cause substantial mortality of aquatic organisms and threaten to alter ecological quality and continuity in the long term, increased dramatically between the pre- and post-impact periods, from 8 to 26. Results indicate that the daily, weekly, monthly and quarterly maximum/minimum flow cycles are negatively influenced by dams and sluice regulation.
the in-stream processes of water retention (Heidbüchel et al., 2012). To determine how differences in geomorphologic settings influence spatial heterogeneity in transport and retention of nutrient, research has suggested that a network perspective is needed to understand how connectivity, residence times, and reactivity interact to influence dissolved nutrient processing in hierarchical river systems (Stewart et al., 2011). Beyond the traditional insights of nonlinear processes using 1-D, 2-D or 3-D hydrodynamics equations, other nonlinear statistical approach such as the Boosted Regression Trees (BRT) is becoming to play a part in hydrodynamic studies (Ouedraogo and Vanclooster, 2016; Toprak and Cigizoglu, 2008; Toprak et al., 2014). The BRT model, which combines the advantages of regression trees and boosted adaptive method, has been widely applied in studies on ecological traits and species distributions (Zimmermann et al., 2010). Due to its powerful functionality and feasibility, BRT modelling has being increasingly applied recently in other environmental issues, too (Roe et al., 2005). Related topics such as natural flow regimes, groundwater and hydraulic conductivity (Jorda et al., 2015; Naghibi et al., 2016; Snelder et al., 2009), soil science (Martin et al., 2009; Jalabert et al., 2009), air pollution (Carslaw et al., 2009), energy (Kusiak et al., 2010), or climate change (Shabani et al., 2016) etc. has been applied with the BRT modelling.
regional variability is large. As expected, RCP2.6 shows the smallest area with a decrease in low flows globally (40 % of the world), while for RCP8.5 the decrease in the threshold is more severe (52 %). Differences in these trends are larger among continents and climate types (see Fig. A1 in the Ap- pendix for the climate regions used in this study) than among the GCMs; the latter in general show high agreement on the directionality of the change. For the equatorial and warm temperate climate (A and C) the low flows will decrease in 62–77 % of the area, while for the snow and polar climates (D and E) the low flows will increase in 54–90 % of the area (depending on RCP scenario). In these colder regions the in- creased low flows are mainly due to larger snowmelt and in- creased precipitation. However, a seasonal shift in low flows was observed for these regions where the snowmelt will oc- cur earlier in the season under a warmer climate. This leads to reduced low flows late in summer, which was observed by the decreasing trend in July to September for most of the Northern Hemisphere (Fig. 2).
Geographically, the Hailiutu catchment is a part of the Maowusu semi-desert. However, the catchment is mainly covered by xeric shrubland (Fig. 2), which occupies around 88 % of the surface area (Table 2). The crop land mixed with wind-breaking trees occupies only 3 % of the total surface area. Most crop lands are located in the river valley and in the Bulang sub-catchment. Grassland areas can be found in local depressions where groundwater is near to the surface. The catchment is characterized by a semi-arid continental cli- mate. The long-term annual average of daily mean tempera- ture from 1961 to 2006 is 8.1 ◦ C with the highest daily mean temperature of 38.6 ◦ C recorded in 1935 and the lowest value of − 32.7 ◦ C observed in 1954. The monthly mean daily air temperature is below zero in the winter time from November until March (Fig. 3a). The growing season starts in April and lasts until October. The mean value of the annual sunshine hours is 2926 h (Xu et al., 2009). The mean annual precip- itation for the period 1985 to 2008 is 340 mm a −1 , the max- imum annual precipitation at Wushenqi is 616.3 mm a −1 in 2002, and the minimum annual precipitation is 164.3 mm a −1 in 1999 (Wushenqi meteorological station monitoring data, 1985–2008). Majority of precipitation occurs in June, July, August and September (Fig. 3b). The mean annual pan evaporation (recorded from evaporation pan with a diame- ter of 20 cm) is 2184 mm a −1 (Wushenqi metrological sta- tion, 1985–2004). The monthly pan evaporation significantly increases from April, reaches highest in May to July, and decreases from August (Fig. 3c). The mean monthly dis- charges at Hanjiamao station vary from 0.86 m 3 s −1 in April to 11.6 m 3 s −1 in August (Fig. 3d).
In this work, the FVM is selected to solve 2D-SWEs on the Cartesian mesh. By two tests presented in this paper, the scheme demonstrated to behave satisfactorily with respect to their effectiveness and robustness in simulating total and partial dam-break flow over complex topographies, which can be able to work with real case study. The dam-break flood flow from the Nam Chien reservoir is simulated by using the presented model to obtain outflow hydrograph and flooding map. This study indicated that the proposed numerical model is an indispensable tool for calculating and simulating dam-break scenarios.
Abstract Despite the numerous benefits of hydropower production, this renewable energy source can have serious negative consequences on the environment. For example, dams act as barriers for the longitudinal migration of organ- isms and transport of particulate matter. Accelerated siltation processes in the receiving river reduce the vertical connec- tivity between river and groundwater. Hydropeaks, caused by short-term changes in hydropoweroperation, result in a negative impact on both habitat and organisms, especially during winter months when natural discharge is low and almost constant. In this study, we report the current deficits present in the River Rhone from two different scientific perspectives – fish ecology and hydrology. Potential reha- bilitation solutions in synergy with flood protection mea- sures are discussed. We focus on the effects of hydropeaking in relation to longitudinal and vertical dimensions and dis- cuss local river widening as a potential rehabilitation tool. The fish fauna in the Rhone is characterized by a highly unnatural structure (low diversity, impaired age distribution). A high correlation between fish biomass and monotonous morphology (poor cover availability) was established. Tracer hydrology provided further details about the reduced permeability of the riverbank, revealing a high degree of siltation with K values of about 4.7 × 10 −6 m s −1 .
A simple GA is used in this study. Descriptions of GA can be found in Deb . GA is a population based optimization algorithm in which each individual rep- resents a solution. The initial population is generated randomly. GA involves coding of decision variables, tness evaluation of solutions, and genetic operations for nding new solutions. Binary coding is used in this study for representing decision variables. Fitness of solutions is evolved using the objective function and violation of constraints. When a constraint is violated, a big penalty is added with objective function value so as to make it less t. With the known tness values, using the GA operators crossover and mutation, a new population is generated. GA has been used for reservoir operation by many studies, while few have applied it for optimizing operations of hydropower reservoir [30,31]. 3.2. Articial Bee Colony (ABC) algorithm ABC algorithm has been used since 2005 and is a swarm intelligence based optimization algorithm. This algorithm is briey explained below and detailed de- scriptions and applications can be found elsewhere [32- 34]. In real bee colony, the foraging is performed by bees that are classied as scout bees, employed bees, and onlooker bees. First, the scout bees ex- plore the honey source by random search. After a period, scout bees return to the hive with the honey collected and perform a dance called waggle dance. The bees communicate the information (amount of honey, direction, and distance of source) about the honey source they have identied through the waggle dance to the onlooker bees waiting in the dance area. Some of the onlooker bees select to explore the neighborhood of a good honey source indicated in the waggle dance based on its quality. By waggle dance, the bees that nd good source attract onlookers and go back to the neighborhood of the same source accompanied by the attracted onlookers. The number of attracted onlookers depends on the quality of the source identied, which is indicated by the waggle dance. A bee, which goes to the neighborhood of the source already identied by it, is called an employed bee. A bee returns and stays in the hive as a scout bee after exhausting the search in an area. Some of the scout bees decide to perform random search. These processes continue repeatedly.
Conicts between water supply objectives and keeping enough head in the reservoirs for ecient power generation is the main issue in the long-term planning models. In this study, the stochastic model, known as Demand Driven Stochastic Dynamic Programming (DDSP), for hydropower reservoir operation optimiza- tion, is applied. DDSP is an extension of the Bayesian Stochastic Dynamic Programming (BSDP) model de- veloped by Karamouz and Vasiliadis 5]. In BSDP, a discrete Markov Process describes the transition of an inow from one period to the next. In addition, in BSDP, Bayesian Decision Theory (BDT) is used to develop and continuously update, prior to posterior probabilities, to capture the natural and forecast uncer- tainties. The stochastic model, developed by Vasiliadis and Karamouz 6], is extended for a two hydropower- reservoirs system (parallel system). More details about the objective function and constraints added to the DDSP model are presented in the following sections.
1 - magnitude of monthly water conditions, group 2 – magnitude and duration of annual extremes, group 3 – timing of annual extremes, group 4 – frequency and duration of high and low pulses and group 5 – rate and frequency of change in conditions. Richter et al. (1997) proposed the Range of Variability Approach (RVA) to assess the hydrological alteration (HA). This method enables to evaluate the degree of alteration by giving a target range for each indicator. In order to improve the interpretation of the RVA approach Richter et al. (1998) developed three-class scale representing low, moderate and high alteration of hydrological regime. The assessment of river hydrologic regime alteration by means of IHA method is conducted on the basis on daily flow series from the period before and after dam con- struction. The data obtained from the first gauge station located downstream the reservoir are used mainly [Richter et al. 1996, Yu et al. 2015].