Frequency Of High Flows Research Articles

Investigation of climate change impacts on daily streamflow extremes in Eastern Black Sea Basin, Turkey

This study was undertaken to evaluate how future uncertain climate-related hydrological responses and low and highflow frequencies affect local hydrology up to the 2100, depending on different Global Circulation Models (GCM) and different Concentration Scenarios (CS). To this end, daily total precipitation and daily mean temperature data were used for RCP4.5 and RCP8.5 CSs by employing GCMs (GFDL-ESM2M, HadGEM2-ES and MPI-ESM-MR). In the study, the years 1971–2000 were chosen as the baseline period. Future streamflow predictions were modeled by machine learning. The models were calibrated with baseline period data and future streamflow values were predicted by using the best Streamflow Prediction Model during the 2055s (2041–2070) and 2085s (2071–2100) periods. Flow Duration Curves from the streamflow predictions were obtained and by using them, discharges corresponding to 95% probability of exceedance for lowflow and 5% probability of exceedance for highflow were calculated. According to the findings, it is predicted that there will be decrease of approximately 21% in lowflow discharges and approximately 30% in highflow discharges. This prediction has suggested water resources managers to implement mitigation measures to climate change in Eastern Black Sea Basin and most probably in Türkiye, especially in the context of continuity of aquatic life and sustainable water supply. It is expected that the paper will take a very important place in estimating and evaluating the expected changes in basin hydrology in the coming years, especially in the highflows and lowflows, which are the key concepts in hydroelectric power generation and in flood management.

History cooling events caused migrations of endangered Cathaya argyrophylla into low latitude refuges

Cathaya argyrophylla, referred as “the giant panda of plants”, is an endangered relict conifer species endemic to subtropical mountainous in China. The remanent populations of C. argyrophylla only survive in four refuges, while how the species migrated into these refuges remains unknown. To investigate the genetic structure and history of C. argyrophylla, here we genotyped four natural populations of C. argyrophylla using restriction site-related DNA sequencing (RAD-Seq). The four populations exhibited a relatively high level of nucleotide diversity (π = 0.29–0.33) and low differentiation (Fst = 0.04–0.12), with relatively high gene flow frequency (Nm = 1.86–6.00). We identified two historical migration events from high latitude populations to low latitude ones. The divergence times were corresponded to the starting time of population decline, which coincided with Naynayxungla Glaciation (∼0.8–0.5 Mya), the last glacial period (LGP, ∼115–11.7 kya) and the Holocene (∼10 kya). These results indicate that historical cooling events drove the endemic status of C. argyrphylla and its migration into low latitude refuges. Our findings provide insights into the evolutionary history and genetic diversity of this endangered species.

Quantifying patterns of streamflow peaks over the southeastern United States using a long‐term retrospective data set

AbstractUtilizing simulated daily flow values for 61 948 stream nodes from the National Water Model (NWM) v.2.1 retrospective data set, which covers the period 1979–2020, streamflow peaks (SFP) were determined using an objective threshold method based on the Fisher‐Jenks breakpoint algorithm. This approach provides estimates of low probability, high in‐channel water level conditions. Strong spatial relations in temporal streamflow peak distribution were observed, with four main spatial clusters covering: (1) the central parts of Mississippi to South Carolina; (2) the south and north parts of Mississippi to South Carolina; (3) Florida, the Gulf Coast, and parts of North Carolina and Virginia; and (4) the Atlantic Coast and parts of the Gulf Coast. The first area is characterized by high multiannual SFP occurrence with long total durations, high contribution to total discharge, and occurrence throughout the year with peaks in February and March. Increasing annual peak trends are present locally. The second area is characterized by similar parameters with increased flash flooding potential. In the third area, lower occurrence and share in total discharge are observed, with two peaks of occurrence in the September–October and March periods. In the fourth region, the lowest occurrences and shortest durations are present, along with strong seasonality manifested in September–October occurrences that constitute over 80% of all SFP volumes. Decreasing trends in maximal monthly flows for the June–October period are also observed here. The results provide valuable regionalized information about historical high flow characteristics across the southeast United States, which can be used as a basis for improved long‐term flood prediction based on seasonality models and constitute a baseline for defining regional changes in high flow frequency and distribution.

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.

Unravelling the influence of landscape alteration from flow alteration on benthic macroinvertebrate assemblage response in the Delaware River Basin

Quantifying the effects of streamflow alteration on assemblage response is central to understanding the role humans play in shaping aquatic environments. These changes represent a level of complexity that impedes developing quantitative links between flow and ecological response because stream hydrology is strongly intertwined with natural and anthropogenic factors. Better management outcomes require disentangling these linkages. Benthic macroinvertebrate data were combined with GIS-derived natural and anthropogenic basin characteristics to identify factors associated with changes in flow and assemblage characteristics. Models linking streamflow metrics and macroinvertebrate response at basin- and subregion-scales were developed using boosted regression tree (BRT) analysis. Basin-scale BRT analyses revealed that links between macroinvertebrate response and streamflow metrics were often obscured, whereas more homogeneous subregions were better able to discern relations with flow. Urban land cover was the primary factor accounting for changes in flow characteristics. Elevation, land cover and high flow frequency were the principal variables driving changes in assemblage structure within subregions. Assemblage metrics and traits were equally useful for building response models and were affected similarly by flow alteration. Our results indicate that response models based on upland and lowland subregions performed better than basin-scale models. However, when defining subregions, care should be taken to maintain data sufficiency. Developing practical flow-protection standards that support a balance between human water requirements and ecological integrity requires models that reduce uncertainty and identify management-relevant drivers. However, effective management often differs by location and models developed at the subregion level may be more applicable to management and stakeholder interests.

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.

Increased expiratory flows identify early interstitial lung disease.

Most interstitial lung diseases (ILDs) manifest with a restrictive ventilatory defect as the common physiologic abnormality. Low carbon monoxide diffusing capacity (Dlco) is considered to be the earliest abnormality on pulmonary function tests (PFTs) in patients with ILD. However, its measurement requires complex and expensive equipment. Our study aimed to assess if high expiratory flows are the earliest PFT abnormality in patients with idiopathic pulmonary fibrosis (IPF) and ILD. In a real-world cohort of incident cases with ILD, we identified the initial PFTs on all patients newly diagnosed with ILD at Kingston Health Sciences Center (in Kingston, Ontario, Canada) between 2013 and 2017. The diagnosis of ILD, including IPF, was established as per current guidelines. Among patients with normal forced vital capacity (FVC), total lung capacity (TLC), and Dlco, we assessed the frequency of high expiratory flows defined as forced expiratory volume in 1 s (FEV1)/FVC, FEF25, FEF25-75, FEF75, and peak expiratory flow > 95% confidence limit of normal. We adjusted for emphysema, increased airway resistance, and obesity. We assessed PFTs of 289 patients with ILD; 88 (30%) of them had normal FVC, TLC, and Dlco. Among these, high FEV1/FVC was the most common abnormality in 37% of patients, in 43% of nonobese patients, and in 58% of those with no emphysema and normal airway resistance. Results were similar in the 88 patients with IPF. High FEV1/FVC could allow identifying patients with ILD/IPF in the earliest stages of their disease with simple spirometry, leading to earlier diagnosis and treatment.

A large-sample investigation into uncertain climate change impacts on high flows across Great Britain

Abstract. Climate change may significantly increase flood risk globally, but there are large uncertainties in both future climatic changes and how these propagate into changing river flows. Here, the impact of climate change on the magnitude and frequency of high flows is analysed for Great Britain (GB) to provide the first spatially consistent GB projections to include both climate ensembles and hydrological model parameter uncertainties. We use the latest high-resolution (12 km) regional climate model ensemble from the UK Climate Projections (UKCP18). These projections are based on a perturbed-physics ensemble of 12 regional climate model simulations and allow exploration of climate model uncertainty beyond the variability caused by the use of different models. We model 346 larger (>144 km2) catchments across GB using the DECIPHeR hydrological modelling framework. Generally, results indicated an increase in the magnitude and frequency of high flows (Q10, Q1, and annual maximum) along the western coast of GB in the future (2050–2075), with increases in annual maximum flows of up to 65 % for western Scotland. In contrast, median flows (Q50) were projected to decrease across GB. Even when using an ensemble based on a single regional climate model (RCM) structure, all flow projections contained large uncertainties. While the RCM parameters were the largest source of uncertainty overall, hydrological modelling uncertainties were considerable in eastern and south-eastern England. Regional variations in flow projections were found to relate to (i) differences in climatic change and (ii) catchment conditions during the baseline period as characterised by the runoff coefficient (mean discharge divided by mean precipitation). Importantly, increased heavy-precipitation events (defined by an increase in 99th percentile precipitation) did not always result in increased flood flows for catchments with low runoff coefficients, highlighting the varying factors leading to changes in high flows. These results provide a national overview of climate change impacts on high flows across GB, which will inform climate change adaptation, and highlight the impact of hydrological model parameter uncertainties when modelling climate change impact on high flows.

Exploring the dynamics of flow attenuation at a beaver dam sequence.

Beavers influence hydrology by constructing woody dams. Using a Before After Control Impact experimental design, we quantified the effects of a beaver dam sequence on the flow regime of a stream in SW England and consider the mechanisms that underpin flow attenuation in beaver wetlands. Rainfall-driven hydrological events were extracted between 2009 and 2020, for the impacted (n=612) and control (n=634) catchments, capturing events 7 years before and 3 years after beaver occupancy, at the impacted site. General additive models were used to describe average hydrograph geometry across all events. After beaver occupancy, Lag times increased by 55.9% in the impacted site and declined by 17.5% in the control catchment. Flow duration curve analysis showed a larger reduction in frequency of high flows, following beaver dam construction, with declines of Q5 exceedance levels of 33% for the impacted catchment and 15% for the control catchment. Using event total rainfall to predict peak flow, five generalized linear models were fitted to test the hypothesis that beaver dams attenuate flow, to a greater degree, with larger storm magnitude. The best performing model showed, with high confidence, that beaver dams attenuated peak flows, with increasing magnitude, up to between 0.5 and 2.5m3s-1 for the 94th percentile of event total rainfall; but attenuation beyond the 97th percentile cannot be confidently detected. Increasing flow attenuation, with event magnitude, is attributed to transient floodplain storage in low gradient/profile floodplain valleys that results from an increase in active area of the floodplain. These findings support the assertion that beaver dams attenuate flows. However, with long-term datasets of extreme hydrological events lacking, it is challenging to predict the effect of beaver dams during extreme events with high precision. Beaver dams will have spatially variable impacts on hydrological processes, requiring further investigation to quantify responses to dams across differing landscapes and scales.

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.

Do metapopulations and management matter for relict headwater bull trout populations in a warming climate?

Mountain headwater streams have emerged as important climate refuges for native cold-water species due to their slow climate velocities and extreme physical conditions that inhibit non-native invasions. Species persisting in refuges often do so as fragmented, relict populations from broader historical distributions that are subject to ongoing habitat reductions and increasing isolation as climate change progresses. Key for conservation planning is determining where remaining populations will persist and how habitat restoration strategies can improve biological resilience to enhance the long-term prospects for species of concern. Studying bull trout, a headwater species in the northwestern USA, we developed habitat occupancy models using a data set of population occurrence in 991 natal habitat patches with a suite of novel geospatial covariates derived from high-resolution hydroclimatic scenarios and other sources representing watershed and instream habitat conditions, patch geometry, disturbance, and biological interactions. The best model correctly predicted bull trout occupancy status in 82.6% of the patches and included effects for: patch size estimated as habitat volume, extent of within-patch reaches <9°C mean August temperature, distance to nearest occupied patch, road density, invasive brook trout prevalence, patch slope, and frequency of high winter flows. The model was used to assess 16 scenarios of bull trout occurrence within the study streams that represented a range of restoration strategies under three climatic conditions (baseline, moderate change, and extreme change). Results suggested that regional improvements in bull trout status were difficult to achieve in realistic restoration strategies due to the pervasive nature of climate change and the limited extent of restoration actions given their high costs. However, occurrence probabilities in a subset of patches were highly responsive to restoration actions, suggesting that targeted investments to improve the resilience of some populations may be contextually beneficial. A possible strategy, therefore, is focusing effort on responsive populations near more robust population strongholds, thereby contributing to local enclaves where dispersal among populations further enhances resilience. Equally important, strongholds constituted a small numerical percentage of patches (5%-21%), yet encompassed the large majority of occupied habitat by volume (72%-89%) and their protection could have significant conservation benefits for bull trout.

Assessing the influence of urban greenness and green stormwater infrastructure on hydrology from satellite remote sensing

Green stormwater infrastructure (GSI), which includes features like rain gardens, constructed wetlands, or urban tree canopy, is now widely recognized as a means to reduce urban runoff impacts and meet municipal water quality permit requirements. Many co-benefits of GSI are related to increased vegetative cover, which can be measured with satellite imagery via spectral indices such as the Normalized Difference Vegetation Index (NDVI). In urban landscapes, there remain critical gaps in understanding how urban greenness and GSI influence hydrology. Here, we quantify these relationships to assess the feasibility of tracking the effectiveness of urban greening for improving downstream hydrologic conditions. We combined hydrologic data from the United States Geological Survey (USGS) gauges with an NDVI time series (1985–2019) derived from Landsat satellite imagery, and synthesis of GSI implementation data from a set of 372 urbanized watersheds across the United States. We used a multivariate panel modeling approach to account for spatial and time varying factors (rainfall, temperature, urban cover expansion) in an effort to isolate the relationships of interest. After accounting for expansion of urban boundaries, only 32 watersheds (9%) showed significant greenness trends, a majority of which were reductions. Urban greenness had significant influences on downstream flow responses, so that on average, a 10% greenness increase showed a corresponding reduction of total flow (−3.8%), flow variance (−7.7%), peak flows (−4.7%), high flows (−7.6%), flashiness (−2.2%), and high flow frequency (−1.5%); and a corresponding increase in baseflow (4.3%). For a subset of these watersheds for which GSI data were available (n = 48), the level of GSI implementation showed a significant, but weak influence on urban greenness with a 20% increase in BMP density corresponding to a greenness increase of 0.9%. The study results may support valuation and verification of GSI co-benefits in urbanized landscapes at the watershed scale.

Impact of Climate Change on the Hydrological Regime of the Yarkant River Basin, China: An Assessment Using Three SSP Scenarios of CMIP6 GCMs

Quantification of the impacts of climate change on streamflow and other hydrological parameters is of high importance and remains a challenge in arid areas. This study applied a modified distributed hydrological model (HEC-HMS) to the Yarkant River basin, China to assess hydrological changes under future climate change scenarios. Climate change was assessed based on six CMIP6 general circulation models (GCMs), three shared socio-economic pathways (SSP126, SSP245, SSP370), and several bias correction methods, whereas hydrological regime changes were assessed over two timeframes, referred to as the near future (2021–2049) and the far future (2071–2099). Results demonstrate that the DM (distribution mapping) and LOCI (local intensity scaling) bias correction methods most closely fit the projections of temperature and precipitation, respectively. The climate projections predicted a rise in temperature of 1.72–1.79 °C under the three SSP scenarios for the near future, and 3.76–6.22 °C under the three SSPs for the far future. Precipitation increased by 10.79–12% in the near future, and by 14.82–29.07% during the far future. It is very likely that streamflow will increase during both the near future (10.62–19.2%) and far future (36.69–70.4%) under all three scenarios. The increase in direct flow will be greater than baseflow. Summer and winter streamflow will increase the most, while the increase in streamflow was projected to reach a maximum during June and July over the near future. Over the far future, runoff reached a peak in May and June. The timing of peak streamflow will change from August to July in comparison to historical records. Both high- and low-flow magnitudes during March, April, and May (MAM) as well as June, July, and August (JJA) will increase by varying degrees, whereas the frequency of low flows will decrease during both MAM and JJA. High flow frequency in JJA was projected to decrease. Overall, our results reveal that the hydrological regime of the Yarkant River is likely to change and will be characterized by larger seasonal uncertainty and more frequent extreme events due to significant warming over the two periods. These changes should be seriously considered during policy development.

River runoff in Switzerland in a changing climate – changes in moderate extremes and their seasonality

Abstract. Future changes in river runoff will impact many sectors such as agriculture, energy production, or ecosystems. Here, we study changes in the seasonality, frequency, and magnitude of moderate low and high flows and their time of emergence. The time of emergence indicates the timing of significant changes in the flow magnitudes. Daily runoff is simulated for 93 Swiss catchments for the period 1981–2099 under Representative Concentration Pathway 8.5 with 20 climate model chains from the most recent transient Swiss Climate Change Scenarios. In the present climate, annual low flows typically occur in the summer half-year in lower-lying catchments (&lt;1500 m a.s.l.) and in the winter half-year in Alpine catchments (&gt;1500 m a.s.l.). By the end of the 21st century, annual low flows are projected to occur in late summer and early autumn in most catchments. This indicates that decreasing precipitation and increasing evapotranspiration in summer and autumn exceed the water contributions from other processes such as snowmelt and glacier melt. In lower-lying catchments, the frequency of annual low flows increases, but their magnitude decreases and becomes more severe. In Alpine catchments, annual low flows occur less often and their magnitude increases. The magnitude of seasonal low flows is projected to decrease in the summer half-year in most catchments and to increase in the winter half-year in Alpine catchments. Early time of emergence is found for annual low flows in Alpine catchments in the 21st century due to early changes in low flows in the winter half-year. In lower-lying catchments, significant changes in low flows emerge later in the century. Annual high flows occur today in lower-lying catchments in the winter half-year and in Alpine catchments in the summer half-year. Climate change will change this seasonality mainly in Alpine catchments with a shift towards earlier seasonality in summer due to the reduced contribution of snowmelt and glacier melt in summer. Annual high flows tend to occur more frequent, and their magnitude increases in most catchments except some Alpine catchments. The magnitude of seasonal high flows in most catchments is projected to increase in the winter half-year and to decrease in the summer half-year. However, the climate model agreement on the sign of change in moderate high flows is weak.

The role of input and hydrological parameters uncertainties in extreme hydrological simulations

AbstractQuantifying possible sources of uncertainty in simulations of hydrological extreme events is very important for better risk management in extreme situations and water resource planning. The main objective of this research work is to identify and address the role of input data quality and hydrological parameter sets, and uncertainty propagation in hydrological extremes estimation. This includes identifying and estimating their contribution to flood and low flow magnitude using two objective functions (NSE for flood and LogNSE for low flow), 20,000 Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological parameter sets, and three frequency distribution models (Log‐Normal, Pearson‐III, and Generalized Extreme Value). The influence of uncertainty on the simulated flow is not uniform across all the selected three catchments due to different flow regimes and runoff generation mechanisms. The result shows that the uncertainty in high flow frequency modeling mainly comes from the input data quality. In the modeling of low flow frequency, the main contributor to the total uncertainty is model parameterization. The total uncertainty of QT90 (extreme peak flow quantile at 90‐year return period) quantile shows that the interaction of input data and hydrological parameter sets have a significant role in the total uncertainty. In contrast, in the QT10 (extreme low flow quantile at 10‐year return period) estimation, the input data quality and hydrological parameters significantly impact the total uncertainty. This implies that the primary factors and their interactions may cause considerable risk in water resources management and flood and drought risk management. Therefore, neglecting these factors and their interaction in disaster risk management, water resource planning, and evaluation of environmental impact assessment is not feasible and may lead to considerable risk.Recommendations for Water Resource Managers The role of hydrological parameters and climate input data is significant in flood and low flow estimations and significantly impacts water resources and extremes management. Input data dominantly controlled flood magnitude and frequency, whilst the low flow magnitude and frequency were dominantly affected by both input data quality and hydrological parameters. It is crucial to consider the main features that cause considerable risk in water resource management and extreme risk management. Neglecting the primary factors and their interaction in disaster risk management, water resource planning, and evaluation of environmental impact assessment is not feasible and may lead to considerable risk. Improved extreme water management through a complex modeling approach to better prepare for the impact of extreme high and low flow changes are the best long‐ and short‐term plans and strategies to combat and minimize the risk in water‐related sectors of the local economy.

Modelling the impacts of future coal mining and coal seam gas extraction on river flows: A methodological framework

This manuscript outlines a methodological framework for modelling the hydrological impacts of future coal mining and coal seam gas extraction. Such impacts potentially include changes to both high flow and low flow characteristics such as total volume, high flow rate and frequency, low flow rate and frequency, and cease to flow frequency. In particular, the manuscript proposes a methodology that is suitable for the prediction of cumulative impacts from multiple coal resource developments and for making such predictions at multiple locations in the landscape. The proposed methodology is framed in terms of a structured uncertainty analysis to provide information on the likelihood and potential ranges of various impacts. The approach can be applied under a wide range of data availability, even when there is little to no observed data, whilst additional data can easily be incorporated to better constrain the results. The assessments thus derived can provide regulators with crucial objective information on the potential social, economic, environmental and ecological risks of future developments. Whilst discussed here in terms of coal resource development, this methodology has the potential to be readily adapted and applied to a range of extractive industries, including but not limited to minerals and other sources of unconventional gas. As it stands, the methodology does not explicitly consider impacts on water quality, but could be extended to include this if required.

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.

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.

An evaluation of automated GPD threshold selection methods for hydrological extremes across different scales

This study investigated core components of an extreme value methodology for the estimation of high-flow frequencies from agricultural surface water run-off. The Generalized Pareto distribution (GPD) was used to model excesses in time-series data that resulted from the ‘Peaks Over Threshold’ (POT) method. First, the performance of eight different GPD parameter estimators was evaluated through a Monte Carlo experiment. Second, building on the estimator comparison, two existing automated GPD threshold selection methods were evaluated against a proposed approach that automates the threshold stability plots. For this second experiment, methods were applied to discharge measured at a highly-instrumented agricultural research facility in the UK. By averaging fine-resolution 15-minute data to hourly, 6-hourly and daily scales, we were also able to determine the effect of scale on threshold selection, as well as the performance of each method. The results demonstrate the advantages of the proposed threshold selection method over two commonly applied methods, while at the same time providing useful insights into the effect of the choice of the scale of measurement on threshold selection. The results can be generalised to similar water monitoring schemes and are important for improved characterisations of flood events and the design of associated disaster management protocols.