Low-flow Frequency Research Articles

Non-stationary low flow frequency analysis under climate change

Analysis of low river flows provides important information for effective management of water resources in a region. Despite the critical importance of understanding low flow dynamics, there is a gap in the literature regarding the use of non-stationary models to analyze low flow data under climate change in Turkey. In this research, low flow series from 80 measuring stations in Turkey are investigated by employing both stationary and non-stationary models based on the Generalized Additive Models for Location, Scale and Shape (GAMLSS). For constructing non-stationary models, 31 explanatory variables consisting of time, precipitation, temperature and atmospheric oscillation indices were used to model the parameters of the chosen distributions. The results show that stationary models are more successful at 7 stations, while non-stationary models are more successful at 73 stations. Comparisons between non-stationary models showed that for most stations, the best performing models were non-stationary models with annual precipitation as covariates. In addition, successful results were obtained when Western Mediterranean Oscillation and North Atlantic Oscillation indices were used as explanatory variables. Additionally, this study investigated 20 and 50-year return levels by fitting the non-stationary frequency distribution models for low flows over historical and projection periods under SSP2-4.5 and SSP5-8.5 climate scenarios. GAMLSS incorporated annual total precipitation, which is the most effective explanatory variable for low flows, as a covariate, and thus changes in low flows were analyzed. The results show that decreases are expected in low flows, except for the stations in the upper Euphrates basin compared to the historical period.

Comparative Study of Low Flow Frequency Analysis Using Bivariate Copula Model at Soyanggang Dam and Chungju Dam

A univariate analysis that relies solely on precipitation data in low flow frequency analysis is a technique to express meteorological drought, so it is limited to analyzing the characteristics of hydrological drought related to available water resources. In addition, if the data for the model calibration are insufficient, the uncertainty of a single variable limits the construction of a reliable model. To improve this problem, a frequency analysis was performed by constructing a bivariate copula model as a multivariate model with a high correlation between variables targeting reservoir inflows. The methodology utilizes the theory of runs to identify low flow events, establishing a threshold based on the mandatory regional water supply plan, and determining the low flow duration and cumulative water deficit. The Gumbel copula function, effective in capturing correlations between hydrological variables, was applied to derive a joint bivariate probability distribution, facilitating the calculation of combined low flow event return periods. This study compared low flow frequencies at Soyanggang dam (’74–’22) and Chungju dam (’86–‘22), which are in the same Han River basin but have different capacities and water demands, using a bivariate copula model. The top four extreme low flow events for the two adjacent dam basins did not occur in the same year and, in the years of the extreme low flow events at one of the two dam basins, there was an insignificant magnitude at the remaining dam basin. This result is noteworthy because it shows that the possibility of extreme low flow events appearing simultaneously in both watersheds is not as high as expected. The operational efficiency can be improved by setting the coordinated operation rules of the two reservoirs using the copula dependency structure.

Low Flow Frequency Analysis at Triang River Streamflow Station

Low flow analysis is essential for determining the communities’ water supply. As water supply is a necessity for human daily survival, it is crucial to recognize that the availability of water can have a significant impact on local livelihoods and the sustainability of local populations. Triang River is a tributary of the Pahang River in Peninsular Malaysia. The existence of the Triang Water Intake and Treatment Plant along the Triang River stream serves to determine the low flow magnitudes and frequency curves for the Triang River streamflow station using the Weibull, Gringorten, and Cunnane plotting position formulas. Based on 20 years of historical data, this study analyzed the annual minimum streamflow data for 1-, 4-, 7-, and 30-day durations as well as the average recurrence intervals (ARIs) of 2-, 5-, 10-, 20-, 50-, and 100-year streamflow data. These three plotting position formulas were successfully used to estimate the low flow magnitudes for the various durations and different ARIs. The findings can serve as a guide for developing future water resource projects that take into account the Triang River’s low flow data.

Hydrological Modelling to Inform Forest Management: Moving Beyond Equivalent Clearcut Area

Forest disturbance can alter the hydrologic conditions of a watershed, including the frequency, magnitude, and timing of peak and low flows. Equivalent Clearcut Area (ECA) has been routinely used in watershed assessments to estimate hydrologic alteration due to forest disturbance. ECA analyses typically rely on broad regional assumptions, qualitative observations, and/or expert judgement, making it difficult to provide accurate quantitative estimates of hydrologic change. Process-based hydrological models offer an improved approach since they replicate watershed processes, can simulate land cover and climate change scenarios, and provide quantitative estimates of hydrologic change, including at ungauged points of interest. A workflow using a regionally calibrated hydrological model to investigate forest disturbance and future climate change scenarios is demonstrated. Results are contrasted with ECA-based outputs and emphasize that in addition to the amount of forest disturbance, watershed physical characteristics and the location of disturbance within a watershed influence the hydrologic response. This approach provides forest managers with quantitative outputs that support risk-based forest management decisions and presents a substantial improvement over ECA-based methods. Keywords: hydrological modelling, ECA-based analysis, watershed assessment, forest disturbance, cumulative effects, risk-based forest management

Comprehensive evaluation of environmental flows in the Yangtze River regulated by two large dams

The ecological sustainability of rivers and floodplains depends on their natural flows. Dam construction on rivers has dramatically altered the hydrologic regimes, in turn affecting aquatic ecosystem integrity. The environmental flow components was applied to exam the impacts of the two dams (i.e., the Gezhouba Dam and the Three Gorges Dam) on downstream flows of the Yangtze River, China. Due to the operation of the two dams, the Yangtze River's flow regimes became homogenous through increasing streamflow during the dry season and decreasing streamflow in the flood season. The monthly low flows from January to April significantly increased by 14-31%, while October and November low flows significantly decreased by 18-25%. We observed the decreased duration and frequency of extreme low flows and floods, particularly the disappearance in large floods. These homogenous flows strongly influence the aquatic biodiversity. To alleviate the hydrologic alteration and minimize the negative ecological influence, low flows should be lower and floods should be reestablished as a natural state. The flow characteristics of duration, timing and rate of change should also be incorporated into the environmental flow recommendations. Our results could provide a potential solution to the tradeoffs between water resources exploitation and natural water requirement in the highly regulated rivers.

Research and Application of the Calculation Method of River Roughness Coefficient with Vegetation

The roughness coefficient is a comprehensive parameter reflecting river resistance, which is widely used in the planning and design of river regulation and flood control projects. In recent years, as the upstream water conservancy and hydro-power projects have been put into operation, the frequency of low flow in the middle and lower reaches has increased, and the frequency of flood flow has decreased. All kinds of vegetation in the river floodplain grow luxuriantly, which causes a change in the river resistance and roughness coefficient. The present study was carried out with theoretical analysis and laboratory tests. A formula for the roughness coefficient calculation was derived based on the momentum equilibrium equation and momentum exchange between the vegetation layer and upper layer. The relationship between the depth-averaged velocity within the vegetation layer and depth-averaged velocity of the whole flow was analyzed. The reliability of the formula was verified by a large amount of previous experimental data. Based on the derived formula, the variation law of the roughness coefficient with vegetation density, vegetation height, and water depth were obtained. For the emerged vegetation flow, the Manning coefficient tended to increase with the increase in the vegetation density and water depth. For the submerged vegetation flow, the Manning coefficient showed a trend of decreasing with the increase in the water depth and increased with the increase in the vegetation height. Finally, the derived formula was applied in the Yueyang reach of the Yangtze River and the Duliujian River. The study can be applied in the fields of water level-flow discharge relationship analysis and the water surface line calculation of vegetated rivers.

Quantifying climate change impacts on low flows of small high mountain watersheds: A nonstationary approach

Study regionThe Utah Wasatch Mountains along the eastern Great Basin and the western Rocky Mountains. Study focusChanges in seasonal precipitation patterns and summer evaporation rates due to increases in temperatures will have severe impacts on low flows of the mountainous watersheds affecting downstream water availability and thus impacting ecosystems and drinking water supplies under future climate change. Reliable prediction of climate change impacts on future low flows needs to consider both the extremity of climatic variables and the physical characteristics of the watersheds. This study analyzes low flows of small mountainous watersheds addressing nonstationarity of climatic parameters and estimates the impact of climate change on low flow, combining nonstationarity and outcome of a physically distributed hydrologic model. MACA statistically downscaled climate data have been used as inputs to the DHSVM model to estimate future low flows for both near term (2036–2044) and distant future (2091–2099) under RCP4.5 and 8.5 scenarios.New Hydrological Insights for the Region: Nonstationary models with time (5 watersheds) or temperature (2 watersheds) as the covariate in the location-scale provided the best performance. This study demonstrated that RCP4.5 has more severe impact on the low flow frequency and volume in the near future than RCP8.5. In contrast, the effect of RCP8.5 is more dominant on the low flow regimes in the distant future than RCP4.5.

Technical note: Statistical generation of climate-perturbed flow duration curves

Abstract. Assessing the robustness of a water resource system's performance under climate change involves exploring a wide range of streamflow conditions. This is often achieved through rainfall–runoff models, but these are commonly validated under historical conditions with no guarantee that calibrated parameters would still be valid in a different climate. In this note, we introduce a new method for the statistical generation of plausible streamflow futures. It flexibly combines changes in average flows with changes in the frequency and magnitude of high and low flows. It relies on a three-parameter analytical representation of the flow duration curve (FDC) that has been proved to perform well across a range of basins in different climates. We rigorously prove that, for common sets of streamflow statistics mirroring average behaviour, variability, and low flows, the parameterisation of the FDC under this representation is unique. We also show that conditions applied to these statistics for a solution to exist are commonly met in practice. These analytical results imply that streamflow futures can be explored by sampling wide ranges of three key flow statistics and by deriving the corresponding FDC in relation to model basin response across the full spectrum of flow conditions. We illustrate this method by exploring in which hydro-climatic futures a proposed run-of-river hydropower plant in eastern Turkey is financially viable. Results show that, contrary to approaches that modify streamflow statistics using multipliers applied uniformly throughout a time series, our approach seamlessly represents a large range of futures with increased frequencies of both high and low flows. This matches expected impacts of climate change in the region and supports analyses of the financial robustness of the proposed infrastructure to climate change. We conclude by highlighting how refinements to the approach could further support rigorous explorations of hydro-climatic futures without the help of rainfall–runoff models.

A Hybrid Approach for Regional Low-Flow Frequency Analysis for the Upper Tigris and Euphrates Basin

The Tigris and Euphrates basin is a challenging watershed because its water resources are critical for five countries. Therefore, this research analyzed and estimated the low flow index and its risk of occurrence. Based on 30 unregulated stations in the upper Tigris and Euphrates basin, different low flow indexes were estimated. A multivariate and hybrid principle component regression (PCR) regional model was used to assign the relationship between basin characteristics and low flow indexes in the homogeneous regions. The model performance showed that the principle component regression outperformed the regression models. The jackknife procedure indicated that the model performance for short-duration low flows is weak, but it is relatively good for long-duration low flows. This study suggests that hydrologic drought of the Tigris and Euphrates basin should be investigated more carefully for better water resources management in the changing socio-environmental system of the Middle East.

A mixed distribution approach for low-flow frequency analysis – Part 2: Comparative assessment of a mixed probability vs. copula-based dependence framework

Abstract. In climates with a warm and a cold season, low flows are generated by different processes, which violates the homogeneity assumption of extreme value statistics. In this second part of a two-part series, we extend the mixed probability estimator of the companion paper (Laaha, 2023) to deal with dependency of seasonal events. We formulate a copula-based estimator for seasonal minima series and examine it in a hydrological context. The estimator is a valid generalization of the annual probability estimator and provides a consistent framework for estimating return periods of summer, winter, and annual events. Using archetypal examples we show that differences in the mixed estimator are always observed in the upper part of the distribution, which is less relevant for low-flow frequency analysis. The differences decrease as the return period increases so that both models coincide for the severest events. In a quantitative evaluation, we test the performance of the copula estimator on a pan-European data set. We find a large gain of both mixed distribution approaches over the annual estimator, making these approaches highly relevant for Europe as a whole. We then examine the relative performance gain of the mixed copula versus the mixed distribution approach in more detail. The analysis shows that the differences in the 100-year event are actually minimal. However, the differences in 2-year events are considerable in some of the catchments, with a relative deviation of −15 % to −25 % in the most affected regions. This points to a prediction bias of the mixed probability estimator that can be corrected using the copula approach. Using multiple regression models, we show that the performance gain can be well explained on hydrological grounds, with weak seasonality leading to a high potential for corrections and strong seasonal correlation reinforcing the need to take this potential into account. Accordingly, the greatest differences can be observed in mid-mountain regions in cold and temperate climates, where rivers have a strongly mixed low-flow regime. This finding is of particular relevance for event mapping, where regional severity can be misinterpreted when the seasonal correlation is neglected. We conclude that the two mixed probability estimators are quite similar, and both are conceptually more adequate than the annual minima approach for mixed summer and winter low-flow regimes. In regions with strong seasonal correlation the mixed copula estimator appears most appropriate and should be preferred over the mixed distribution approach.

Simulating hydrological extremes for different warming levels–combining large scale climate ensembles with local observation based machine learning models

Climate change has a large influence on the occurrence of extreme hydrological events. However, reliable estimates of future extreme event probabilities, especially when needed locally, require very long time series with hydrological models, which is often not possible due to computational constraints. In this study we take advantage of two recent developments that allow for more detailed and local estimates of future hydrological extremes. New large climate ensembles (LE) now provide more insight on the occurrence of hydrological extremes as they offer order of magnitude more realizations of future weather. At the same time recent developments in Machine Learning (ML) in hydrology create great opportunities to study current and upcoming problems in a new way, including and combining large amounts of data. In this study, we combined LE together with a local, observation based ML model framework with the goal to see if and how these aspects can be combined and to simulate, assess and produce estimates of hydrological extremes under different warming levels for local scales. For this, first a new post-processing approach was developed that allowed us to use LE simulation data for local applications. The simulation results of discharge extreme events under different warming levels were assessed in terms of frequency, duration and intensity and number of events at national, regional and local scales. Clear seasonal cycles with increased low flow frequency were observed for summer and autumn months as well as increased high flow periods for early spring. For both extreme events, the 3C warmer climate scenario showed the highest percentages. Regional differences were seen in terms of shifts and range. These trends were further refined into location specific results. The shifts and trends observed between the different scenarios were due to a change in climate variability. In this study we show that by combining the wealth of information from LE and the speed and local relevance of ML models we can advance the state-of-the-art when it comes to modeling hydrological extremes under different climate change scenarios for national, regional and local scale assessments providing relevant information for water management in terms of long term planning.

The potential impact of delivery mode and breastfeeding on oral Candida species carriage in a population of young adults.

In early life, children are exposed to microorganisms from maternal and environmental sources, which influence the development of their microbiome throughout life. Several studies have demonstrated the influence of the delivery mode and breastfeeding on the oral microbiome of children, mostly regarding bacterial colonization. However, their influence on the oral fungal carriage is still underexplored. This study aimed to assess the association of the delivery and feeding mode with the oral carriage of yeasts in adulthood. Fungal oral carriage was evaluated by collecting unstimulated saliva in 185 healthy dental students (mean age of 21.51±1.55 years old; 81.6% females). Yeast identification was performed by culture in ChromAgar Candida medium and sequencing of the 18S genes and ITS regions for determination of the species. Demographic and clinical data of each participant was recorded through questionnaires and oral examinations were performed in a subgroup of participants (n=49). Candida species were isolated in 37.5% of all participants. The prevalence of yeasts in the oral cavity was significantly higher in those who were born by vaginal delivery compared to those born by caesarean-section (p=0.035), whereas no statistically significant differences were observed regarding breastfeeding (p=0.398). Low salivary flow rate and frequency of dental visits also were associated with oral yeast carriage (p<0.05). Our study suggests a possible impact of the type of delivery on fungal colonization, which is sustained throughout life due to oral health-related factors.

A mixed distribution approach for low-flow frequency analysis – Part 1: Concept, performance, and effect of seasonality

Abstract. In seasonal climates with a warm and a cold season, low flows are generated by different processes so that the annual extreme series will be a mixture of summer and winter low-flow events. This leads to a violation of the homogeneity assumption for all statistics derived from the annual series and gives rise to inaccurate conclusions. In this first part of a two-paper series, a mixed distribution approach to perform frequency analysis in catchments with mixed low-flow regimes is proposed. We formulate the theoretical basis of the mixed distribution approach for the lower extremes based on annual minima series. The main strength of the model is that it allows the user to estimate return periods of summer low flows, winter low flows, and annual return periods in a theoretically sound and consistent way. Using archetypal examples, we show how the model behaves for a range of low-flow regimes, from distinct winter and summer regimes to mixed regimes where seasonal occurrence in summer and winter is equally likely. The examples show in a qualitative way the loss in accuracy one has to expect with conventional extreme value statistics performed with the annual extremes series. The model is then applied to a comprehensive Austrian data set to quantify the expected gain of using the mixed distribution approach compared to conventional frequency analysis. Results indicate that the gain of using a mixed distribution approach is indeed large. On average, the relative deviation is 21 %, 39 %, and 63 % when estimating the low flow with a 20-, 50-, and 100-year return period. For the 100-year event, 75 % of stations show a performance gain of &gt;10 %, 41 % of stations &gt; 50 %, and 25 % of stations &gt; 80.6 %. This points to a broad relevance of the approach that goes beyond highly mixed seasonal regimes to include the strongly seasonal ones. We finally correlate the performance gain with seasonality indices in order to show the expected gain conditional to the strength of seasonality expressed by the ratio of average summer and winter low flow seasonality ratio (SR). For the 100-year event, the expected gain is about 70 % for SR=1.0, 20 % for SR=1.5, and 10 % for SR=2.0. The performance gain is further allocated to the spatial patterns of SR in the study area. The results suggest that the mixed estimator is relevant not only for mountain forelands but to a much wider range of catchment typologies. The mixed distribution approach provides one consistent approach for summer, winter, and annual probabilities and should be used by default in seasonal climates with a cold winter season where summer and winter low flows can occur.

Plotting Position for Low Flow Frequency Analysis at Jempol River Streamflow Station

Low flow frequency analysis is an important technique for calculating the probability of river flow availability during crucial low flow seasons, such as drought. It is also useful to predict the low flow information of a river in volume and frequency for future planning and construction of water resource projects since the low flow has a significant impact on water quality, water supply, and river sustainability conditions. Therefore, this study was performed to determine the low flow frequency analysis of the Jempol River streamflow station using Weibull and Gringorten plotting position formulas. The average recurrence interval (ARI) of 2-, 5-, 10-, 25-, 50-, and 100-year were determined based on 18 years of historical streamflow data. The study illustrates the annual minimum flow magnitude of 1-, 4-, 7-, and 30-day durations for the study area. It shows that the Gringorten formula tends to give higher estimation values up to 18% compared to the Weibull formula when the flow duration increase. Therefore, the outcome of this study could be used in the future for any project related to the low flow information of Jempol River.

Stochastic Watershed Model Ensembles for Long‐Range Planning: Verification and Validation

AbstractDeterministic watershed models (DWMs) are used in nearly all hydrologic planning, design, and management activities, yet they cannot generate streamflow ensembles needed for hydrologic risk management (HRM). The stochastic component of DWMs is often ignored in practice, leading to a systematic bias in extreme events. Since traditional stochastic streamflow models used in HRM struggle to account for anthropogenic change, there is a need to convert DWMs into stochastic watershed models (SWMs) to generate ensembles for use in HRM. A DWM can be converted to an SWM using a post‐processing (pp) approach to add error to the DWM predictions. Many pp methods advanced in the area of flood forecasting are useful in HRM and for correcting extreme event biases. Selecting a suitable error model for pp is challenging due to nonnormality, skewness, heteroscedasticity, and autocorrelation. We develop a parsimonious pp method based on an autoregressive (AR) model of the logarithm of the ratio of the observations and simulations, which leads to AR model residuals that are approximately symmetric and independent. We document the value of pp for improving flood and low flow frequency analysis and we reintroduce the concepts of verification and validation of stochastic streamflow ensembles to ensure that the SWM can reproduce both statistics it was and was not designed to reproduce, respectively. These concepts are illustrated on a Massachusetts basin using the USGS Precipitation Runoff Modeling System, with an additional analysis indicating the approach may be applicable to 1,225 other sites across the United States.

Monthly flow indicators can be used to infer daily stream flow behaviour across Australia

Flow indicators based on daily flow data are commonly used to characterize the hydrologic regimes of rivers and to investigate links between flow and ecological condition. Discharge data measured at a daily timestep is considered essential for such investigations because there is an underlying premise that longer timesteps (e.g. monthly) are not suitable for capturing the variability in flow behaviour of most relevance to ecologically important processes. This paper investigates this assumption by examining the information content of daily and monthly streamflow data for a diversity of perennial, intermittent and ephemeral flow regimes across Australia, using a novel approach of comparing daily and monthly flow indicators rather than observed flow timeseries. Results show that many flow indicators derived using daily and monthly data are strongly correlated (Spearman ρ greater than 0.9). Partial least squares regression shows that the majority of daily flow indicators can be predicted using monthly flow indicators, and that as few as six monthly flow indicators can be used to estimate a wide range of daily flow characteristics, including those relating to flow magnitudes, and durations of high and low flow events. Daily indicators relating to the frequency and seasonal timing of high and low flows cannot be predicted as accurately (R2 between 0.4 and 0.9), suggesting that information on these facets of the flow regime is partially lost when aggregating to a monthly timestep. Results from this study point to the utility of monthly-based flow indicators in river ecology, opening new opportunities to overcome limitations in gauge network coverage and data resolution in many parts of the world.

Low flow frequency analysis of Tawi River discharge at Jammu location

In present study Tawi River was selected for low flow frequency analysis (LFFA). Tawi River is left bank tributary of Chenab River. LFFA is useful to measure various parameters as mean annual runoff (MAR); mean daily flow (MDF), Q20, Q50, Q90, Q20/Q90, Q50/Q90, Q90/Q50; the average of annual series of minimum ten-day average (MAM10); and the average of annual series of minimum seven-day average (MAM7), 10Q10, 10Q2, 7Q10 and 7Q2. By separating, the 40 years discharge data in 20-20 years' time period, significant change is observed; 15% change observed in MCM, 12.97% change in Q20 and 12.32% change in Q50. Flow duration curves show significant change in discharge of the Tawi River particularly during 5-25% exceedance probability. Monthly discharges show mixed trends with increase in discharges as 42.68%, 81.47% and 42.91% during October, November and June months and decrease in discharges as 13% to 48% during January, March, April, July, August, and December.

Nonstationary flood and low flow frequency analysis in the upper reaches of Huaihe River Basin, China, using climatic variables and reservoir index as covariates

Conventional water infrastructure designs for flood and low flows are usually based on the assumption of stationarity of extreme events. However, recent evidence suggests that the influences of climate variability and human activities have made the hypothesis of stationarity questionable. In this study, we used the generalized additive models for location, scale, and shape (GAMLSS) to construct a nonstationary model in which the parameters of the selected distributions were modelled as a function of climatic variables (i.e., climate indices and precipitation) and/or the reservoir index (RI). The nonstationary models were then used to analyse annual flood and low flow frequency at four hydrological stations in the upper reaches of the Huaihe River Basin, including Dapoling (DPL), Changtaiguan (CTG), Zhuganpu (ZGP), and Xixian (XX) stations. Annual floods were represented by the maximum daily streamflow in each year, and low flows were represented by the 95th quantile of the daily streamflow (Q95) in each year. The change point and trend analysis revealed that the flood series of the ZGP station and the low flow series of the DPL and XX stations exhibited significant downward and upward trends (p < 0.1)), respectively. The low flow series of the ZGP station showed a significant change point in 1980 (p < 0.1). GAMLSS modelling results showed that, in comparison with stationary models, nonstationary models that included precipitation and the Arctic Oscillation climate index as covariates for the gamma distribution location parameter provided a superior description of the flood series at the four stations. Nonstationary models that incorporated precipitation and/or RI as covariates for the Weibull distribution parameters fit the low flow series better than stationary models at all stations. Furthermore, we found that nonstationary models outperformed stationary models in terms of flood frequency analysis, covering all flood observation points and capturing the generally decreasing trend in flood series, as well as a decrease in the scatter of estimated flood value magnitudes. For the low flow frequency analysis, the comparison results showed that the nonstationary and stationary models performed identically for the DPL, CTG, and XX stations, where no significant change point was detected. However, for the ZGP station, where a significant change point was detected, the nonstationary models performed better than the stationary models and could accurately capture the changes in the magnitude of the estimated low flow values before and after the change point. Overall, the proposed nonstationary model can serve as a tool for nonstationary frequency analysis of flood and low flow series under the influence of climate variability and reservoir regulations, thus providing a reference for regional water infrastructure design.

Pre- and post-dam river water temperature alteration prediction using advanced machine learning models.

Dams significantly impact river hydrology by changing the timing, size, and frequency of low and high flows, resulting in a hydrologic regime that differs significantly from the natural flow regime before the impoundment. For precise planning and judicious use of available water resources for agricultural operations and aquatic habitats, it is critical to assess the dam water’s temperature accurately. The building of dams, particularly several dams in rivers, can significantly impact downstream water. In this study, we predict the daily water temperature of the Yangtze River at Cuntan. Thus, this work reveals the potential of machine learning models, namely, M5 Pruned (M5P), Random Forest (RF), Random Subspace (RSS), and Reduced Error Pruning Tree (REPTree). The best and effective input variables combinations were determined based on the correlation coefficient. The outputs of the various machine learning algorithm models were compared with recorded daily water temperature data using goodness-of-fit criteria and graphical analysis to arrive at a final comparison. Based on a number of criteria, numerical comparison between the models revealed that M5P model performed superior (R2 = 0.9920, 0.9708; PCC = 0.9960, 0.9853; MAE = 0.2387, 0.4285; RMSE = 0.3449, 0.4285; RAE = 6.2573, 11.5439; RRSE = 8.0288, 13.8282) in pre-impact and post-impact spam, respectively. These findings suggest that a huge wave of dam construction in the previous century altered the hydrologic regimes of large and minor rivers. This study will be helpful for the ecologists and river experts in planning new reservoirs to maintain the flows and minimize the water temperature concerning spillway operation. Finally, our findings revealed that these algorithms could reliably estimate water temperature using a day lag time input in water level. They are cost-effective techniques for forecasting purposes.

Modelling restoration of natural flow regimes in dam impaired systems: Biomorphodynamic effects and recovery times

Dams affect the natural flow regime by altering the magnitude, timing and frequency of high and low flows. Many river ecosystems impaired by dams are currently being restored. Restoration success is difficult to quantify and is often assessed by comparing the restored system to an unimpaired static ‘reference’ system. However, restoring a river to past environmental conditions and assessing restoration success by comparing it to a static situation neglects natural system dynamics and non-linear, adaptive system responses. With this modelling study we evaluate long-term changes in river morphology, morphodynamics, riparian vegetation cover and habitat suitability of two fish species and two types of wetland vegetation in a meandering gravel bed river after removal of an upstream dam and complete restoration of the natural flow regime. We assessed the ecological and hydromorphodynamic recovery of systems impaired by two different dam operating regimes and three different time periods the dam was present by comparing these to a dynamic undisturbed situation. Modelling results show that recovery potential depends on how much the system has been changed by the dam and the system state at the start of the restoration, rather than the duration of the pressure. Even if the conditions shortly after restoration are comparable to pre-disturbance conditions, there can still be a time-lag in the system response where the future state of the restored system continues to deviate from the undisturbed situation. When this happens, the system can develop into an alternative dynamic equilibrium where recovery becomes increasingly difficult. These results stress the importance of considering natural variability in restored systems as well as in reference systems, requiring detailed spatio-temporal monitoring to assess restoration effects.