Mean Annual Flow Research Articles

Reconstructed monthly river flows for Irish catchments 1766-2016.

A 250‐year (1766–2016) archive of reconstructed river flows is presented for 51 catchments across Ireland. By leveraging meteorological data rescue efforts with gridded precipitation and temperature reconstructions, we develop monthly river flow reconstructions using the GR2M hydrological model and an Artificial Neural Network. Uncertainties in reconstructed flows associated with hydrological model structure and parameters are quantified. Reconstructions are evaluated by comparison with those derived from quality assured long‐term precipitation series for the period 1850–2000. Assessment of the reconstruction performance across all 51 catchments using metrics of MAE (9.3 mm/month; 13.3%), RMSE (12.6 mm/month; 18.0%) and mean bias (−1.16 mm/month; −1.7%), indicates good skill. Notable years with highest/lowest annual mean flows across all catchments were 1877/1855. Winter 2015/16 had the highest seasonal mean flows and summer 1826 the lowest, whereas autumn 1933 had notable low flows across most catchments. The reconstructed database will enable assessment of catchment specific responses to varying climatic conditions and extremes on annual, seasonal and monthly timescales.

The Applicability of SWOT’s Non-Uniform Space–Time Sampling in Hydrologic Model Calibration

The Surface Water and Ocean Topography (SWOT) satellite mission, expected to launch in 2022, will enable near global river discharge estimation from surface water extents and elevations. However, SWOT’s orbit specifications provide non-uniform space–time sampling. Previous studies have demonstrated that SWOT’s unique spatiotemporal sampling has a minimal impact on derived discharge frequency distributions, baseflow magnitudes, and annual discharge characteristics. In this study, we aim to extend the analysis of SWOT’s added value in the context of hydrologic model calibration. We calibrate a hydrologic model using previously derived synthetic SWOT discharges across 39 gauges in the Ohio River Basin. Three discharge timeseries are used for calibration: daily observations, SWOT temporally sampled, and SWOT temporally sampled including estimated uncertainty. Using 10,000 model iterations to explore predefined parameter ranges, each discharge timeseries results in similar optimal model parameters. We find that the annual mean and peak flow values at each gauge location from the optimal parameter sets derived from each discharge timeseries differ by less than 10% percent on average. Our findings suggest that hydrologic models calibrated using discharges derived from SWOT’s non-uniform space–time sampling are likely to achieve results similar to those based on calibrating with in situ daily observations.

Quality changes of fluvial sediments impacted by urban effluents in Ushuaia, Tierra del Fuego, southernmost Patagonia

Bottom sediments play a fundamental role in the eutrophication process because they are one of the most active compartments in the biogeochemical cycle of nutrients. In Ushuaia city, Tierra del Fuego, Argentina, sewage effluents have been discharged into natural watercourses without treatment for many years. Organic matter has been introduced until 2016 when improvements in sewage infrastructure were done. The quality of surface sediments from three watercourses with different degree of impact has been studied taking into account contrasting hydrological periods. pH, organic carbon (OC), total nitrogen (TN) and soluble reactive phosphorus (SRP) were measured in austral autumn and spring between 2013 and 2018 in three sections of each watershed. Nutrient content in sediments of upstream sites derives from the contribution of Nothofagus spp. forests through litter-fall. Middle and downstream sites were the most affected by wastewater discharges evidencing that fluvial sediments act as a receptor of the allochthonous organic loads. Particularly, sediments of Buena Esperanza Stream had the lowest mean pH (5.44) and the highest mean concentrations of OC (42.95 mg g−1), TN (2.53 mg g−1) and SRP (0.067 mg g−1), along with the lowest mean annual flow and the largest urbanized area. No differences between hydrological periods were found, although TN and SRP contents were higher in autumn than in the snowmelt season. Minimal differences in nutrients between before and after infrastructure improvement were found. Even when external contributions have been decreased, more time will be necessary to reduce eutrophic conditions of fluvial sediments in southernmost Patagonia.

Does comprehensive evaluation of hydrological models influence projected changes of mean and high flows in the Godavari River basin?

Understanding the projected changes in the mean and high flows remains a significant challenge due to uncertainty arising from global climate models (GCMs) and hydrological models. Moreover, the calibration approaches used for hydrological models can influence the climate change impact assessment. We use the combination of three hydrological models, four global climate models, and two RCPs (2.6 and 8.5) to analyze the projected changes in mean flow, high flow, and the frequency of high flow under the projected future climate in the Godavari River basin (GRB) until the gauge Tekra. The two evaluation approaches: a simple approach (TASK A) based on the calibration and validation at a single streamflow gauge station and a comprehensive approach (TASK B) based on multi-variable and multisite calibration and validation and trend analysis were employed to evaluate the hydrological models. The differences between the projected changes in mean and high flows calculated using models after TASK A and TASK B were estimated. Our results show that the differences can be up to 10–13% in mean annual flow and high flow, and up to 40% in high flow frequency. The comprehensively evaluated hydrological models were chosen for impact assessment, and they project increases in mean and high flows, and the frequency of high flow at all four gauge stations in the GRB. The projected increases are higher under RCP 8.5 and in the End century (2071–2100). Our results demonstrate the importance of the comprehensive evaluation of hydrological models in advance of climate change impact assessment.

Seasonality shift and streamflow flow variability trends in central India

A better understanding of intra/inter-annual streamflow variability and trends enables more effective water resources planning and management for current and future needs. This paper investigates the variability and trends of streamflow data from five stations (i.e. Ashti, Chindnar, Pathgudem, Polavaram, and Tekra) in Godavari river basin, India. The streamflow data were obtained from the Indian Central Water Commission and cover more than 30 years of mean daily records (i.e. 1972–2011). The streamflow data were statistically assessed using Gamma, Generalised Extreme Value and Normal distributions to understand the probability distribution features of data at inter-annual time-scale. Quantifiable changes in observed streamflow data were identified by Sen’s slope method. Two other nonparametric, Mann–Kendall and Innovative Trend Analysis methods were also applied to validate findings from Sen’s slope trend analysis. The mean flow discharge for each month (i.e. January to December), seasonal variation (i.e. Spring, Summer, Autumn, and Winter) as well as an annual mean, annual maximum and minimum flows were analysed for each station. The results show that three stations (i.e. Ashti, Tekra, and Polavaram) demonstrate an increasing trend, notably during Winter and Spring. In contrast, two other stations (i.e. Pathgudem, Chindnar) revealed a decreasing trend almost at all seasons. A significant decreasing trend was observed at all station over Summer and Autumn seasons. Notably, all stations showed a decreasing trend in maximum flows; remarkably, Tekra station revealed the highest decreasing magnitude. Significant decrease in minimum flows was observed in two stations only, Chindnar and Pathgudem. Findings resulted from this study might be useful for water managers and decision-makers to propose more sustainable water management recommendations and practices.

Anthropogenic and Biophysical Controls on Low Flow Hydrology in the Southeastern United States

AbstractThe original paradigm of urban hydrology identified links between urbanization and changes in hydrologic processes. One of the prominent relationships identified was increased impervious surface coverage decreased subsurface recharge and subsequently low flows. This does not consider watershed‐specific biophysical or anthropogenic characteristics that could increase low flows, such as direct discharge from wastewater treatment plants and indirect recharge to groundwater from leaking pipes and infiltration‐based green infrastructure. Here, in an effort to sharpen this original paradigm, we take a statistical approach to evaluate relationships between natural and human‐impacted watershed characteristics and a suite of low flow metrics that describe mean annual low flow magnitudes and interannual variability in 54 watersheds over 15 years in the Piedmont physiographic region of the southeastern United States. We find that annual low flow magnitudes are most strongly correlated to biophysical watershed factors, including positive relationships with topography and precipitation and negative relationships with poorly drained soils. In contrast, interannual variability is positively correlated to land use characteristics, including forest, and negatively related to urbanization. Similarities in best fit models across long‐term average, wet, and dry years indicate a robustness of hydrologic drivers across watershed wetness conditions. Our results also showcase that while the original paradigm of urban hydrology may hold across traditional low flow metrics, such as baseflow index, extreme low flow drivers are complex and counterintuitive. Understanding these first‐order controls on baseflow magnitude and variability across an urbanization gradient is needed to conceptualize the impacts that continued, rapid urbanization will have on low flow dynamics.

Seasonal and Interannual Trends of Oceanographic Parameters over 40 Years in the Northern Adriatic Sea in Relation to Nutrient Loadings Using the EMODnet Chemistry Data Portal

Long-term data series (1971–2015) of physical and biogeochemical parameters were analyzed in order to assess trends and variability of oceanographic conditions in the northern Adriatic Sea (NAS), a mid-latitude shallow continental shelf strongly impacted by river discharges, human activities and climate changes. Interpolation maps and statistical models were applied to investigate seasonal and spatial variability, as well as decadal trends of temperature, salinity, chlorophyll-a and nutrients. This analysis shows that sea surface temperature increased by +0.36% year−1 over four decades. Annual mean flow of the Po River markedly changed due to the occurrence of periods of persistent drought, whereas the frequency of flow rates higher than 3000 m3 s−1 decreased between 2006 and 2015. Moreover, we observed a long-term decrease in surface phosphate concentrations in Po River water (−1.34% year−1) and in seawater (in summer −2.56% year−1) coupled, however, to a significant increase in nitrate concentration in seawater (+3.80% year−1) in almost all seasons. These changes indicate that the nutrient concentrations in the NAS have been largely modulated, in the last forty years, by the evolution of environmental management practices and of the runoff. This implies that further alteration of the marine environment must be expected as a consequence of the climate changes.

Hydrological impacts of climate change on small ungauged catchments – results from a global climate model–regional climate model–hydrologic model chain

Abstract. Climate change is one of the greatest threats currently facing the world's environment. In Norway, a change in climate will strongly affect the pattern, frequency, and magnitudes of stream flows. However, it is challenging to quantify to what extent the change will affect the flow patterns and floods from small rural catchments due to the unavailability or inadequacy of hydro-meteorological data for the calibration of hydrological models and due to the tailoring of methods to a small-scale level. To provide meaningful climate impact studies at the level of small catchments, it is therefore beneficial to use high-spatial- and high-temporal-resolution climate projections as input to a high-resolution hydrological model. In this study, we used such a model chain to assess the impacts of climate change on the flow patterns and frequency of floods in small ungauged rural catchments in western Norway. We used a new high-resolution regional climate projection, with improved performance regarding the precipitation distribution, and a regionalized hydrological model (distance distribution dynamics) between a reference period (1981–2011) and a future period (2070–2100). The flow-duration curves for all study catchments show more wet periods in the future than during the reference period. The results also show that in the future period, the mean annual flow increases by 16 % to 33 %. The mean annual maximum floods increase by 29 % to 38 %, and floods of 2- to 200-year return periods increase by 16 % to 43 %. The results are based on the RCP8.5 scenario from a single climate model simulation tailored to the Bergen region in western Norway, and the results should be interpreted in this context. The results should therefore be seen in consideration of other scenarios for the region to address the uncertainty. Nevertheless, the study increases our knowledge and understanding of the hydrological impacts of climate change on small catchments in the Bergen area in the western part of Norway.

New Zealand River Hydrology under Late 21st Century Climate Change

Climate change is increasingly affecting the water cycle and as freshwater plays a vital role in countries’ societal and environmental well-being it is important to develop national assessments of potential climate change impacts. Focussing on New Zealand, a climate-hydrology model cascade is used to project hydrological impacts of late 21st century climate change at 43,862 river locations across the country for seven hydrological metrics. Mean annual and seasonal river flows validate well across the whole model cascade, and the mean annual floods to a lesser extent, while low flows exhibit a large positive bias. Model projections show large swathes of non-significant effects across the country due to interannual variability and climate model uncertainty. Where changes are significant, mean annual, autumn, and spring flows increase along the west and south and decrease in the north and east. The largest and most extensive increases occur during winter, while during summer decreasing flows outnumber increasing. The mean annual flood increases more in the south, while mean annual low flows show both increases and decreases. These hydrological changes are likely to have important long-term implications for New Zealand’s societal, cultural, economic, and environmental well-being.

The response of three Mediterranean karst springs to drought and the impact of climate change

Karst springs are the exclusive source of water during dry months for many Mediterranean regions. The impact of climate change on the hydrology of Mediterranean karst springs has not received proper attention in the scientific literature. Specifically, the effect of climate change on the drought characteristics of karst springs has not been studied. Here, the response to climate change of three Mediterranean karst springs with different retention times is examined, focusing on the frequency, duration and intensity of their drought events. The Karst-SWAT model is used to quantify the karst springs flow. A set of representative climate change scenarios are used to assess the climate change impact on the springs and surface flow for the period 2019–2098. A non-parametric drought index is modified to estimate the future meteorological and hydrological drought characteristics in comparison to the reference period. The most adverse effects of climate change on the three karst springs are expected to prevail after 2059. Depending on the spring, the mean annual karst flow decreases from 14.2% to 25.1%, the mean number of drought events ranges from a decrease of 8.1% to an increase of 77.5%, the duration of drought events increases from 36.8% to 533%, and the mean monthly water deficit increases from 27.3% to 83.6%, when comparing the period 2059–2098 to the reference period (1979–2018). As the water retention time of the spring increases, the duration and intensity of the droughts are likely to increase more significantly. After 2059, multi-year droughts are forecast for all springs under all scenarios. Both low and high flows will increase, with the former occupying even wet months. The results of this study can be used as a guide for local and regional authorities and water management organizations, to adapt their practices for the mitigation of the impact of climate change on the water resources of Mediterranean karst springs.

Hydropower Potential in the Alps under Climate Change Scenarios. The Chavonne Plant, Val D’Aosta

Present and prospective climate change will likely affect the hydrological cycle in sensitive areas, such as the Alps, thus impacting water-based activities. A most representative example is hydropower production, i.e., exploitation of water to produce energy. In the Italian Alps hydropower is strictly dependent upon water from snow and ice melt, and both are decreasing in response to global warming. Here, we study the effects of potential climate change scenarios at 2100 upon hydropower production from the Chavonne plant, in Valle d’Aosta region of Italy, a run-of-the-river (ROR) plant taking water from two high altitude glacierized catchments of Val di Cogne, and Valsavarenche. We use Poli-Hydro, a state-of-the-art hydrological model to mimic the hydrological budget of the area, including ice and snow melt share. Projections of the hydrological budget were built until 2100 by means of selected climate change scenarios, under proper downscaling. We used runs of three General Circulation Models (GCMs), EC-Earth, CCSM4, and ECHAM6.0 under three Representative Concentration Pathways RCP 2.6, RCP 4.5, and RCP 8.5 from AR5 of IPCC, and of their updated version under four Shared Socio-Economic Pathways SSP1 2.6, SSP2 4.5, SSP3 7.0, and SSP5 8.5 from AR6. We then assessed hydropower production changes against a recent control run CR period (2005–2015). Mean annual flow is estimated at 14.33 m3 s−1 during CR, with ice melt contribution ca. 2%, and snow melt contribution ca. 44%. Ice cover in 2005 was estimated as 19.2 km2, reaching in 2015, 9.93 km2. Mean hydropower production was estimated at 153.72 GWh during the CR. Temperature would largely increase throughout the century (+0.93 °C on average at the half century, +2.45 °C at the end of the century). The ice covered area would be largely depleted (ca. −86%, −94% respectively), with reduced contribution of ice melt (0.23%, <0.1%, respectively) and snow melt (ca. 37%, 33%, respectively). Precipitation would show uncertain patterns, and hence incoming discharge at the plant would erratically vary (−29% to +24% half century, −27% to +59% end of century). Hydropower production displays a large dependence upon monthly discharge patterns, with mostly positive variations (+2.90% on average at half century, +6.95% on average at end of century), with its change driven by exceedance of plant’s capacity.

Seasonal vegetation and management influence overland flow velocity and roughness in upland grasslands

AbstractThere is considerable interest in how headwater management may influence downstream flood peaks in temperate humid regions. However, there is a dearth of data on flow velocities across headwater hillslopes and limited understanding of whether surface flow velocity is influenced by seasonal changes in roughness through vegetation cycles or management. A portable hillslope flume was used to investigate overland flow velocities for four common headwater grassland habitats in northern England: Low‐density Grazing, Hay Meadow, Rank Grassland and Juncus effusus Rush pasture. Overland flow velocity was measured in replicate plots for each habitat, in response to three applied flow rates, with the experiments repeated during five different periods of the annual grassland cycle. Mean annual overland flow velocity was significantly lower for the Rank Grassland habitat (0.026 m/s) followed by Low‐density Grazing and Rushes (0.032 and 0.029 m/s), then Hay Meadows (0.041 m/s), which had the greatest mean annual velocity (examples from 12 L/min flow rate). Applying our mean overland flow velocities to a theoretical 100 m hillslope suggests overland flow is delayed by >1 hr on Rank Grassland when compared to Hay Meadows in an 18 mm storm. Thus grassland management is important for slowing overland flow and delaying peak flows across upland headwaters. Surface roughness was also strongly controlled by annual cycles of vegetation growth, decay, grazing and cutting. Winter overland flow velocities were significantly higher than in summer, varying between 0.004 m/s (Rushes, November) and 0.034 m/s (Rushes, June); and velocities significantly increased after cutting varying between 0.006 m/s (Hay meadows, July) and 0.054 m/s (Hay meadows, September). These results show that seasonal vegetation change should be incorporated into flood modelling, as cycles of surface roughness in grasslands strongly modify overland flow, potentially having a large impact on downstream flood peak and timing. Our data also showed that Darcy‐Weisbach roughness approximations greatly over‐estimated measured flow velocities.

Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability

Delivering useful hydrological forecasts is critical for urban and agricultural water management, hydropower generation, flood protection and management, drought mitigation and alleviation, and river basin planning and management, among others. In this work, we present and appraise a new simple and flexible methodology for hydrological time series forecasting. This methodology relies on (a) at least two individual forecasting methods and (b) the median combiner of forecasts. The appraisal is made by using a big dataset consisted of 90-year-long mean annual river flow time series from approximately 600 stations. Covering large parts of North America and Europe, these stations represent various climate and catchment characteristics, and thus can collectively support benchmarking. Five individual forecasting methods and 26 variants of the introduced methodology are applied to each time series. The application is made in one-step ahead forecasting mode. The individual methods are the last-observation benchmark, simple exponential smoothing, complex exponential smoothing, automatic autoregressive fractionally integrated moving average (ARFIMA) and Facebook’s Prophet, while the 26 variants are defined by all the possible combinations (per two, three, four or five) of the five afore-mentioned methods. The new methodology is identified as well-performing in the long run, especially when more than two individual forecasting methods are combined within its framework. Moreover, the possibility of case-informed integrations of diverse hydrological forecasting methods within systematic frameworks is algorithmically investigated and discussed. The related investigations encompass linear regression analyses, which aim at finding interpretable relationships between the values of a representative forecasting performance metric and the values of selected river flow statistics. We find only loose (but not negligible) relationships between the formed variable sets. These relationships could be exploited for improving (to some extent) future forecasting applications. The results of our big data forecasting experiment are lastly exploited for characterizing –in relative terms– one-year ahead river flow predictability. We believe that our findings have both practical and theoretical implications.

Methods for regional calibration - a case study using the TopNet hydrological model for the Bay of Plenty region, New Zealand

ABSTRACT The regional parameters can be derived from physiographic factors such as topography, soil type, and vegetation. In this study the suitability of five regional parameterisation methods was investigated and assessed to provide guidance on how to select optimal parameter sets. The overarching goal was to improve the accuracy of a hydrological model when applied at the regional scale. The regional parameterisation techniques assessed were: 1) averaging of optimal parameter sets of gauged catchments, 2) calibrating several catchments with a regional performance efficiency, 3) weighted average performance criterion for calibration, 4) largest number of positive Nash-Sutcliffe coefficients and 5) a Bayesian parameter selection method. The parameterisation methods were assessed by applying TopNet in sixteen gauged catchments in the Bay of Plenty region on the North Island of New Zealand, to then predict flows for five test catchments. It was shown that any of the five regionalisation methods considered improved flow predictions of Mean Annual Low Flow when compared to applying the same model with a prescribed spatially distributed set of the key model parameter. The improvement was less pronounced for mean flow and high flow, regardless of the method used and was linked to the hydrogeology class of the catchments.

Analysis of Water System Condition for Monitoring of Critical Area in Jangkok Watershed

An assessment of Jangkok River’s water system conditions is needed to its monitoring watershed criticality to prevent various problems and for conservation purposes. The aim of this research was to assess the condition of Jangkok River’s water system over the past 17 years using the Republic of Indonesia Minister of Forestry Regulation No. 61/Menhut-II/2014 concerning Monitoring and Evaluation of Watershed Management. The assessment results showed that Jangkok is in a good conditions but it tends to deteriorate that was indicated by the tends of increasing Flow Regime Coefficient and Annual Flow Coefficient. The mean of Flow Regime Coefficient’s value of upstream is 65.66 (medium) and downstream is 333.63 (very high), while the increasing rate of 7.72 and 59.13 a year respectively. The mean Annual Flow Coefficient is 0.14 for upstream and downstream 0.22, which increases 0.99% and 0.93% a year respectively. The tendency of the value of the Water Use Index decreased with an annual average of 0.31 and a rate of decline of 0.005 a year. The assessment result shows that Jangkok is GOOD in water system condition but it requires more attention to maintain its function and ensure water sustainability.

Estimation of the River Flow Synchronicity in the Upper Indus River Basin Using Copula Functions

In this study, on the basis of the maximum and mean annual values of flows, dependencies between flows recorded in seven water gauges located in the upper part of the Indus River Basin (IRB) in Pakistan were analyzed. First, the non-parametric Mann–Kendall (M–K) test was used to detect trends in the flows. Next, the Pearson’s correlation coefficient was applied. Then, the selected copulas were used to find joint distributions of the studied time series. In the next stage, the degrees of synchronous and asynchronous occurrence of, respectively, the annual maximum (AMAXF) and mean annual flows (MAF) were calculated. The study revealed that correlations between the flows in selected gauge stations were very strong and statistically significant. These results were confirmed by the synchronicity analysis carried out with the help of the copula functions. The highest relationship was detected in the case of gauges Besham Qila and Kachura on the Indus mainstream, while the lowest was detected in gauges Besham Qila and Naltar on the Naltar River. These findings can be of high practical value in the field of sustainable water resource management, including for flood protection, agricultural water supply, reservoir water storage, and hydropower generation in the IRB.

Assessment of extreme flows and uncertainty under climate change: disentangling the uncertainty contribution of representative concentration pathways, global climate models and internal climate variability

Abstract. Projections of streamflow, particularly of extreme flows under climate change, are essential for future water resources management and the development of adaptation strategies to floods and droughts. However, these projections are subject to uncertainties originating from different sources. In this study, we explored the possible changes in future streamflow, particularly for high and low flows, under climate change in the Qu River basin, eastern China. ANOVA (analysis of variance) was employed to quantify the contribution of different uncertainty sources from RCPs (representative concentration pathways), GCMs (global climate models) and internal climate variability, using an ensemble of 4 RCP scenarios, 9 GCMs and 1000 simulated realizations of each model–scenario combination by SDRM-MCREM (a stochastic daily rainfall model coupling a Markov chain model with a rainfall event model). The results show that annual mean flow and high flows are projected to increase and that low flows will probably decrease in 2041–2070 (2050s) and 2071–2100 (2080s) relative to the historical period of 1971–2000, suggesting a higher risk of floods and droughts in the future in the Qu River basin, especially for the late 21st century. Uncertainty in mean flows is mostly attributed to GCM uncertainty. For high flows and low flows, internal climate variability and GCM uncertainty are two major uncertainty sources for the 2050s and 2080s, while for the 2080s, the effect of RCP uncertainty becomes more pronounced, particularly for low flows. The findings in this study can help water managers to become more knowledgeable about and get a better understanding of streamflow projections and support decision making regarding adaptations to a changing climate under uncertainty in the Qu River basin.

Flow alteration by diversion hydropower in tributaries to the Salween river: a comparative analysis of two streamflow prediction methodologies

ABSTRACT A multi-model approach was applied to reconstruct long-term flow records in 32 ungauged rivers developed with small diversion hydropower stations. Potential hydrologic alteration was assessed for flow records simulated by a catchment similarity model and the multi-criteria Streamflow Prediction under Extreme Data-scarcity (SPED) framework. Model validation based on limited observed data suggests that the SPED flow predictions are substantially more accurate than those generated by the catchment similarity model (NSE of 0.74 and 0.22, respectively and Correlation Coefficient of 0.87 and 0.72, respectively). Both flow prediction techniques indicated that flow signatures were altered substantially by diversion hydropower. Mean annual flows decreased by a mean of 76–86% across the 32 rivers and flow became more predictable in most rivers (47–94% mean increase in predictability). Frequency and duration of high flows decreased and duration of low flow events increased substantially. Slopes of rising hydrograph limbs and recession limbs increased respectively by a mean of 123–161% and 254–720%. While direction of detected flow alteration was similar regardless of model choice, severity of alteration was consistently greater based on the analysis of flows simulated by the multi-objective SPED model. Overall, the agreement of the multi-model analysis indicates that the signal of flow alteration by diversion hydropower in the study rivers supersedes uncertainty associated with flow prediction. While both models may be appropriate for applications such as change detection analysis, prescriptive management actions, such as establishing flow targets for environmental flow regimes, should be based on flow records generated by models adept at simulating rainfall-runoff processes targeted to individual basins, such as SPED.

Black Bass Growth Patterns in Relation to Hydrology in the Arkansas River, Arkansas

AbstractHydrology has been documented as affecting the recruitment of sport fishes. However, the potential cumulative effects of river hydrology on fish growth have not been intensively studied. In 2004, 2005, and 2010, annual growth increments were measured for Largemouth Bass Micropterus salmoides and Spotted Bass M. punctulatus populations from throughout the Arkansas portion of the Arkansas River. During three consecutive years (2007–2009), the lower Arkansas River experienced long durations of high water. Mean annual flows exceeded the 42‐year average by 52%, with summer flows averaging 107% above normal and 29% of the days annually exceeding 2,830 m3/s. Using age‐1–6 cohorts, we compared Largemouth Bass (n = 2,155) and Spotted Bass (n = 833) growth increments across hydrologic conditions occurring during the growth years experienced by these fish. Two‐way ANOVAs using back‐calculated age and growth year warm‐season hydrology (classified as high, average, or low flow based on quartiles from historical April–September hydrographs) as main effects suggested significant hydrologic effects on the growth of both black bass species. A significant interaction between back‐calculated age and growth year April–September hydrology for both black basses further suggested that flow affected growth differently across ages, with decreased annual growth increments detected for the age‐1–3 cohorts. Decreased annual growth occurring during 2007–2009 also was consistent with a 0.5‐year increase in the age at which Largemouth Bass attained 381 mm TL (i.e., the minimum length required for legal harvest) in 2010. Similarly, Spotted Bass required an extra 0.9 year to attain 304 mm TL (i.e., a common minimum length limit). Results suggested that the typically beneficial effects of high‐flow years on black bass populations in large‐river–floodplain systems may be dampened or non‐existent in more highly regulated, impounded river systems, such as the modern‐day Arkansas River.

Future Runoff Analysis in the Mekong River Basin under a Climate Change Scenario Using Deep Learning

In establishing adequate climate change policies regarding water resource development and management, the most essential step is performing a rainfall-runoff analysis. To this end, although several physical models have been developed and tested in many studies, they require a complex grid-based parameterization that uses climate, topography, land-use, and geology data to simulate spatiotemporal runoff. Furthermore, physical rainfall-runoff models also suffer from uncertainty originating from insufficient data quality and quantity, unreliable parameters, and imperfect model structures. As an alternative, this study proposes a rainfall-runoff analysis system for the Kratie station on the Mekong River mainstream using the long short-term memory (LSTM) model, a data-based black-box method. Future runoff variations were simulated by applying a climate change scenario. To assess the applicability of the LSTM model, its result was compared with a runoff analysis using the Soil and Water Assessment Tool (SWAT) model. The following steps (dataset periods in parentheses) were carried out within the SWAT approach: parameter correction (2000–2005), verification (2006–2007), and prediction (2008–2100), while the LSTM model went through the process of training (1980–2005), verification (2006–2007), and prediction (2008–2100). Globally available data were fed into the algorithms, with the exception of the observed discharge and temperature data, which could not be acquired. The bias-corrected Representative Concentration Pathways (RCPs) 4.5 and 8.5 climate change scenarios were used to predict future runoff. When the reproducibility at the Kratie station for the verification period of the two models (2006–2007) was evaluated, the SWAT model showed a Nash–Sutcliffe efficiency (NSE) value of 0.84, while the LSTM model showed a higher accuracy, NSE = 0.99. The trend analysis result of the runoff prediction for the Kratie station over the 2008–2100 period did not show a statistically significant trend for neither scenario nor model. However, both models found that the annual mean flow rate in the RCP 8.5 scenario showed greater variability than in the RCP 4.5 scenario. These findings confirm that the LSTM runoff prediction presents a higher reproducibility than that of the SWAT model in simulating runoff variation according to time-series changes. Therefore, the LSTM model, which derives relatively accurate results with a small amount of data, is an effective approach to large-scale hydrologic modeling when only runoff time-series are available.