Streamflow Estimates Research Articles

Influence of reservoir management on Guadiana streamflow regime

Study regionGuadiana international catchment, located in the Iberian Peninsula. Study focusThe Guadiana River catchment has registered an intense and speed up modification of its natural regime over the last few decades to cope with water scarcity. Such modifications pose a serious challenge to the development of improved management tools as information on available water resources is often not detailed enough. This study aimed to simulate the flow regime of the Guadiana River using the MOHID-Land model during a 30 years period. Two scenarios were defined by considering (or not) inputs from reservoirs. Model performance was assessed by comparing simulated and measured monthly streamflow at 24 hydrometric stations influenced (9 stations) or not (15 stations) by reservoir management. New hydrological insights for the regionStreamflow simulations produced quite acceptable estimates for most of the hydrometric stations not influenced by reservoirs. However, results also showed the importance of non-simulated processes (e.g., groundwater abstractions) to the accurate prediction of the Guadiana flow regime. For the stations influenced by reservoir management, model simulations generally overestimated streamflow measurements. Information related to reservoir management needs thus to be made clearer for achieving better results, which can only be accomplished when national water agencies improve their cooperation protocols. Further model limitations were also addressed as well as the necessary developments for improving streamflow estimates in future modeling applications.

Estimation of Streamflow with Incomplete Soil Dataset in Krasioa Basin Using Soil-Landscape Evaluation Approach and SWAT Model

Data on soil properties are indispensable for process-based hydrological modeling. Soil information of Thailand is primarily provided by the Land Development Department (LDD), nevertheless soil property data are available only in arable land whose slope is less than 35%. The steep-slope land was generally labeled as Slope Complex (SC), there is no information available. This paper demonstrated the application of soil-landscape evaluation approach for predicting the missing properties of soil which resulted on enhancement of model performance in streamflow estimation in Krasioa Basin by the Soil and Water Assessment Tool (SWAT) model. The physical properties of soil-soil thickness, fraction of soil particles (clay, sand, organic matter) were predicted using the Soil-Landscape Estimation and Evaluation Program (SLEEP). The additional properties of soil including bulk density, hydraulic conductivity, and available water content were estimated using the pedo-transfer functions (ROSETTA). It was found that SLEEP model could provide consistent information on physical properties of soil. The SWAT model performance in streamflow simulation at the Krasioa Reservoir was improved using the proposed approach. Appropriate model inputs can generate reasonable output. Model performance can further be improved by calibration.

A comparison of snowmelt‐derived streamflow from temperature‐index and modified‐temperature‐index snow models

AbstractReliable estimation of the volume and timing of snowmelt runoff is vital for water supply and flood forecasting in snow‐dominated regions. Snowmelt is often simulated using temperature‐index (TI) models due to their applicability in data‐sparse environments. Previous research has shown that a modified‐TI model, which uses a radiation‐derived proxy temperature instead of air temperature as its surrogate for available energy, can produce more accurate snow‐covered area (SCA) maps than a traditional TI model. However, it is unclear whether the improved SCA maps are associated with improved snow water equivalent (SWE) estimation across the watershed or improved snowmelt‐derived streamflow simulation. This paper evaluates whether a modified‐TI model produces better streamflow estimates than a TI model when they are used within a fully distributed hydrologic model. It further evaluates the performance of the two models when they are calibrated using either point SWE measurements or SCA maps. The Senator Beck Basin in Colorado is used as the study site because its surface is largely bedrock, which reduces the role of infiltration and emphasizes the role of the SWE pattern on streamflow generation. Streamflow is simulated using both models for 6 years. The modified‐TI model produces more accurate streamflow estimates (including flow volume and peak flow rate) than the TI model, likely because the modified‐TI model better reproduces the SWE pattern across the watershed. Both models also produce better performance when calibrated with SCA maps instead of point SWE data, likely because the SCA maps better constrain the space‐time pattern of SWE.

Streamflow calculation for medium-to-small rivers in data scarce inland areas

Inland streamflow estimation is essential in global water supply and environment protection. In data-scarce areas a highly efficient way of estimating streamflow is through remote sensing methods. However, high requirement of most previous methods on ground-measured data hinder their wide use in data-scarce areas. Therefore, this paper presented a new framework for estimation of streamflow in medium-to-small rivers with few ground measurements by using high-resolution unmanned aerial vehicle (UAV) imagery. A new Virtual Hydraulic Radius (VHR) method was proposed to complement AMHG (at-many-stations hydraulic geometry), a method not requiring any ground measurements when global parameters are used (global–AMHG) in large-scaled rivers but yielding great uncertainties in smaller scaled rivers, thus creating a VHR-AMHG method for medium-to-small rivers. The accuracy verification of the proposed method was performed by comparing it to field measurement data and the global parameters of the original AMHG (global–AMHG). Results showed that the root mean square error calculated from VHR-AMHG was 32.15 m3/s, while that from global–AMHG was 305.65 m3/s, indicating that the VHR-AHRG method yields a significantly higher accuracy for streamflow estimation for medium-to-small rivers. We found that regardless of the size of the river, AMHG is not applicable for rivers having excessively small b values in the equation w = aQb (low-b rivers). For medium-to-small rivers with b < 0.25, AMHG is not recommended. The accuracy of the original AMHG method is limited by the initial value of the model parameters and the condition that the congruent discharge (Qc) has to be within the range of observational discharge. The initial value setting of the model parameters significantly impacts the calculation accuracy. The VHR-AMHG method is able to overcome the deficiencies of the original AMHG, i.e. being overly dependent on the initial value setting with long-series known discharge data. It also eliminates the limitation of the Qc condition, as it achieves a higher accuracy for rivers in which Qc does not satisfy the condition compared to using global-AMHG on rivers that actually meet the condition, thus greatly expanding its usage scope. Thus VHR-AMHG method can provide detailed data on the spatial and temporal distribution of regional and national streamflow for governments and stakeholders, and offer scientific data support for wisely making water supply polices and sustainably protecting eco-environment.

Using Steady‐State Backwater Analysis to Predict Inundated Area from National Water Model Streamflow Simulations

AbstractNational Water Model (NWM) simulates the hydrologic cycle and produces streamflow forecasts for 2.7 million reaches in the National Hydrography Dataset for continental United States (U.S.). NWM uses Muskingum–Cunge channel routing, which is based on the continuity equation. However, the momentum equation also needs to be considered to obtain more accurate estimates of streamflow and stage in rivers, especially for applications such as flood‐inundation mapping. Here, we used a steady‐state backwater version of Simulation Program for River NeTworks (SPRNT) model. We evaluated SPRNT’s and NWM’s abilities to predict inundated area for the record flood of Hurricane Matthew in October 2016. The Neuse River experienced record‐breaking floods and was well‐documented by U.S. Geological Survey. Streamflow simulations from NWM retrospective analysis were used as input for the SPRNT simulation. Retrospective NWM discharge predictions were converted to stage. The stages (from both SPRNT and NWM) were utilized to produce flood‐inundation maps using the Height Above Nearest Drainage method which uses the local relative heights to find out the local draining potentials and provide spatial representation of inundated area. The inundated‐area accuracies for NWM and SPRNT (based on comparison to a remotely sensed dataset) were 65.1% and 67.6%, respectively. These results show using steady‐state SPRNT results in a modest improvement of inundation‐forecast accuracy compared to NWM.

Assessing Retrospective National Water Model Streamflow with Respect to Droughts and Low Flows in the Colorado River Basin

AbstractStreamflow monitoring in the Colorado River Basin (CRB) is essential to ensure diverse needs are met, especially during periods of drought or low flow. Existing stream gage networks, however, provide a limited record of past and current streamflow. Modeled streamflow products with more complete spatial and temporal coverage (including the National Water Model [NWM]), have primarily focused on flooding, rather than sustained drought or low flow conditions. Objectives of this study are to (1) evaluate historical performance of the NWM streamflow estimates (particularly with respect to droughts and seasonal low flows) and (2) identify characteristics relevant to model inputs and suitability for future applications. Comparisons of retrospective flows from the NWM to observed flows from the United States Geological Survey stream gage network over 22 years in the CRB reveal a tendency for underestimating low flow frequency, locations with low flows, and the number of years with low flows. We found model performance to be more accurate for the Upper CRB and at sites with higher precipitation, snow percent, baseflow index, and elevations. Underestimation of low flows and variable model performance has important implications for future applications: inaccurate evaluations of historical low flows and droughts, and less reliable performance outside of specific watershed/stream conditions. This highlights characteristics on which to focus future model development efforts.

Daily streamflow prediction using optimally pruned extreme learning machine

Daily streamflow prediction is important for flood warning, navigation, sediment control, reservoir operations and environmental protection. The current paper examines the prediction and estimation capability of a new heuristic method, optimally pruned extreme learning machine (OP-ELM) model, for daily streamflows of Fujiangqiao and Shehang stations at Fujiang River. Prediction accuracy of OP-ELM method is compared with other soft computing models, i.e. adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO), multivariate adaptive regression splines (MARS) and M5 model tree (M5Tree) using cross validation technique. Prediction results of the both stations reported that the OP-ELM and ANFIS-PSO are the best in modeling daily streamflows of upstream and downstream, respectively. For improving prediction accuracy of the OP-ELM method, various kernel types are tried and the linear, linear + sigmoid + Gaussian and linear + sigmoid provide the best results for both stations. The OP-ELM outperforms the other methods during estimation of downstream streamflow using hydro climatic data as input. The OP-ELM reduces the prediction error of ANFIS-PSO by 12% in estimation of daily streamflow. It is also found that including local data considerably improves the prediction accuracy in estimation of downstream streamflows. The overall results indicate that the OP-ELM method could be successfully used in predicting and estimating daily streamflow by using hydro climatic data as inputs.

Simulated surface and shallow groundwater resources in the Abaya-Chamo Lake basin, Ethiopia using a spatially-distributed water balance model

Study regionThe volcano-tectonic lakes basin of Abaya-Chamo is part of the Main Ethiopian Rift system and exhibits large variations in geomorphology, physiography and climate between the rift floor and the plateau. Study focusDespite the importance of streamflow for water resources management and planning in the basin, many of the rivers there are ungauged. To make quantitative estimates of streamflow for spatially resolved water availability in such a highly heterogeneous environment, therefore, requires numerical modeling. This study is the first to quantify the surface and shallow groundwater resources in Abaya-Chamo, and to validate the physically fully distributed hydrologic model WetSpass under highly data-limited conditions, in a complex two-lake environment. New hydrological insightsSimulated total river flow and estimated baseflow were verified at 15 gauging stations, with a good agreement. The WetSpass model is shown to be suitable for such a complex setting with a correlation coefficient of 0.95 and 0.97 for total flow and baseflow respectively at a statistically significant level (p-value < 0.05). The simulated annual water budget reveals that 74.6% of the 22.1 billion lit/yr in total precipitation in the basin is lost through evapotranspiration, 15.7% through surface runoff, and only 9.7% recharges the groundwater system. The simulations also revealed the surface runoff and groundwater recharge are the most sensitive to soil textural class, while evapotranspiration depends more strongly on land use.

Investigating the hydraulic role of a large buried valley network on regional groundwater flow

An understanding of the three-dimensional hydrostratigraphic architecture of a buried valley network, in particular the continuity of permeable units within their fill, is needed to evaluate the resource potential and role of buried valley aquifers in regional groundwater flow. In this study, a facies modeling algorithm within the exploration and production software Petrel was used to construct a geological model of a buried valley network located in the central Peace Region in Northeast British Columbia, Canada. Facies were interpreted using resistivity inversion results from an airborne electromagnetic survey (SkyTEM) and borehole gamma-ray and lithology logs. This detailed geological model and a simplified geological model of the buried valley network were used to develop two interpretive numerical groundwater flow models in MODFLOW. Calibration was performed using baseflow estimates derived using a novel approach whereby upper and lower bounds for baseflow indices were combined with streamflow estimates from the Northeast Water Tool (NEWT), a hydrological modeling tool developed for water allocation for industrial use by the British Columbia Oil and Gas Commission. The modeling results suggest that permeable deposits exist within the buried valleys, but are not regionally connected throughout the whole network, and thus do not play a significant role in the regional groundwater flow regime. However, locally extensive permeable deposits occur within the buried valleys and may offer viable water sources for domestic use or to replace existing surface-water licenses for industrial use.

Distributive rainfall–runoff modelling to understand runoff-to-baseflow proportioning and its impact on the determination of reserve requirements of the Verlorenvlei estuarine lake, west coast, South Africa

Abstract. River systems that support high biodiversity profiles are conservation priorities worldwide. Understanding river ecosystem thresholds to low-flow conditions is important for the conservation of these systems. While climatic variations are likely to impact the streamflow variability of many river courses into the future, understanding specific river flow dynamics with regard to streamflow variability and aquifer baseflow contributions is central to the implementation of protection strategies. While streamflow is a measurable quantity, baseflow has to be estimated or calculated through the incorporation of hydrogeological variables. In this study, the groundwater components within the J2000 rainfall–runoff model were distributed to provide daily baseflow and streamflow estimates needed for reserve determination. The modelling approach was applied to the RAMSAR-listed Verlorenvlei estuarine lake system on the west coast of South Africa, which is under threat due to agricultural expansion and climatic fluctuations. The sub-catchment consists of four main tributaries, Krom Antonies, Hol, Bergvallei and Kruismans. Of these, Krom Antonies was initially presumed the largest baseflow contributor, but was shown to have significant streamflow variability attributed to the highly conductive nature of the Table Mountain Group sandstones and Quaternary sediments. Instead, Bergvallei was identified as the major contributor of baseflow. Hol was the least susceptible to streamflow fluctuations due to the higher baseflow proportion (56 %) as well as the dominance of less conductive Malmesbury shales that underlie it. The estimated flow exceedance probabilities indicated that during the 2008–2017 wet cycle average lake inflows exceeded the average evaporation demand, although yearly rainfall is twice as variable in comparison to the first wet cycle between 1987 and 1996. During the 1997–2007 dry cycle, average lake inflows are exceeded 85 % of the time by the evaporation demand. The exceedance probabilities estimated here suggest that inflows from the four main tributaries are not enough to support Verlorenvlei, with the evaporation demand of the entire lake being met only 35 % of the time. This highlights the importance of low-occurrence events for filling up Verlorenvlei, allowing for regeneration of lake-supported ecosystems. As climate change drives increased temperatures and rainfall variability, the length of dry cycles is likely to increase into the future and result in the lake drying up more frequently. For this reason, it is important to ensure that water resources are not over-allocated during wet cycles, hindering ecosystem regeneration and prolonging the length of these dry cycle conditions.

Streamflow regionalization using a similarity approach in ungauged basins: Application of the geo-environmental signatures in the Karkheh River Basin, Iran

Reliable estimation of streamflow especially in ungauged basins is most important for environmental management and planning. Regionalization of streamflow using either the physical attributes or spatial proximities of linked gauged and ungauged basins are considered to be standard techniques for estimating streamflow in ungauged basins. Unfortunately, regionalization of modeling parameters based solely on physical similarities or spatial proximity between gauged and ungauged basins is not always adequate. The hydrologic similarities between basins are related to the basin's response to precipitation, which it is referred to various conditions of basins, so considering the comprehensive characteristics from basins is very important to achieve a knowledge of the hydrology of the basin. Therefore, this study aimed to consider a comprehensive set of signatures with the fuzzy clustering approach. Similar basins were determined by considering key geo-environmental signatures, including topographical/morphological characteristics, soil properties, land use types, remote sensing (RS)-based indices, and climate indices. Redundancy in the selected geo-environmental signatures was assessed using factor analysis. Parameters of lumped and semi-distributed rainfall-runoff models were calibrated in the most similar donor basin (DB) to the ungauged basin (UB) and validated by matching them to the pseudo ungauged basin (PUB). Streamflow estimation results from the pseudo ungauged basins demonstrated that the applied procedure and semi-distributed rainfall-runoff model was an efficient method to estimate streamflow in the ungauged basins.

Hierarchical Bayesian Model for Streamflow Estimation at Ungauged Sites via Spatial Scaling in the Great Lakes Basin

AbstractThis study presents a regional, probabilistic framework for estimating streamflow via spatial scaling for the purposes of informing water balance estimates in the Great Lakes basin. The fra...

Monthly streamflow estimation in ungauged catchments of northern Algeria using regionalization of conceptual model parameters

Streamflow prediction in ungauged basins is considered as a real challenge worldwide. Considerable efforts have been devoted to tackle this issue. However, no single method can be considered as the best solution and employed everywhere. So, the main objective sought for this work is to provide continuous monthly streamflow information in ungauged catchments in northern Algeria, needed for planning and water resources management. Thus, the first part of this work is devoted to the evaluation of the performance of a monthly water balance‑based rainfall-runoff model, namely GR2M, in gauged catchments in northern Algeria. The rainfall-runoff modeling step allowed the delineation of the zones, within the study area, where the GR2M model is suitable. In order to delineate homogeneous regions, prior to regionalization of the GR2M model’s parameters, two classification techniques have been assessed (principal component analysis (PCA) and self-organization maps (SOM)). Regional models between the model’s parameters and features of the catchments have been derived. The rainfall-runoff modeling results show that the water balance‑based GR2M model is not recommended for semi-arid regions. On the other hand, the regionalization results show that the regionalized models, based on the SOM classification technique, led to better performances than those obtained by PCA. For most of the catchments, considered as ungauged, the monthly and annual streamflow values estimated using the SOM-regionalised models are very promising.

Propagation of structural uncertainty in watershed hydrologic models

Structural, parametric and measurement uncertainties are inherent in watershed hydrology models. Among these, structural uncertainty results from simplified representation of reality in models and has proven to be particularly challenging to characterize. Often, flow measurements are available at the outlet or a few other locations within a watershed, and distributed model parameters are calibrated to replicate observed streamflow values. Subsequently, one can quantify uncertainty in streamflow estimation at the measured locations. The objective of this study is the quantification of structural and parametric uncertainty at unmeasured locations using a calibrated watershed hydrologic model. While propagation of parametric uncertainty to unmeasured locations is a relatively simple task in a Bayesian framework, this study is focused on the problem of structural uncertainty propagation. We propose to treat the hydrologic model as a stochastic function by treating the parameters of the model as random quantities, even in the limit of infinite information through data, thus enabling us to accommodate the uncertainty due to structural errors. A mathematical formulation of the method and applications over two different watersheds, using hourly and daily flow data, are presented. The structural uncertainty band at ungauged locations enveloped all the streamflow observations at daily-scale and missed most of the observations at hourly-scale. Structural uncertainty varies spatially at unmeasured locations within the watershed. Consequently, streamflows estimated at unmeasured locations may be accompanied by wide uncertainty bands implying the need for caution when using them.

Estimating minimum streamflow from measurements at ungauged sites in regions with streamflow‐gauging networks

AbstractEstimation of low flows in rivers continues to be a vexing problem despite advances in statistical and process‐based hydrological models. We develop a method to estimate minimum streamflow at seasonal to annual timescales from measured streamflow based on regional similarity in the deviations of daily streamflow from minimum streamflow for a period of interest. The method is applied to 1,019 gauged sites in the Western United States for June to December 2015. The gauges were clustered into six regions with distinct timing and magnitude of low flows. A gamma distribution was fit each day to the deviations in specific discharge (daily streamflow divided by drainage area) from minimum specific discharge for gauges in each region. The Kolmogorov–Smirnov test identified days when the gamma distribution was adequate to represent the distribution of deviations in a region. The performance of the gamma distribution was evaluated at gauges by comparing daily estimates of minimum streamflow with estimates from area‐based regression relations for minimum streamflow. Each region had at least 8 days during the period when streamflow measurements would provide better estimates than the regional regression equation, but the number of such days varied by region depending on aridity and homogeneity of streamflow within the region. Synoptic streamflow measurements at ungauged sites have value for estimating minimum streamflow and improving the spatial resolution of hydrological model in regions with streamflow‐gauging networks.

Streamflow regimes and geologic conditions are more important than water temperature when projecting future crayfish distributions

Ongoing changes in climate are expected to alter current species’ habitat and potentially result in shifts in species distributions. While climatic conditions are important to a species’ ability to persist in an area, for many taxa, other environmental factors, such as geology, land cover, and topography, are also important for providing suitable habitat. Furthermore, aquatic species experience changes in climatic conditions through the effect precipitation and air temperature have on streamflow regimes and water temperature. In this study, species distribution models (SDMs) for ten stream-dwelling crayfish species were generated using a maximum entropy approach across the Mobile River Basin in the southeastern United States. SDMs were developed using model-generated contemporary estimates of streamflow and water temperature as well as geologic, topographic, and land cover data. Future distributions were then projected using global climate model (GCM) projections of streamflow and water temperature. Geology, topography, and streamflow appear to be more important predictors of suitable habitat than water temperature for crayfish species within the Mobile River Basin. Species distributions regulated by limited influences from stream flow and water temperature displayed relatively small changes in projected future habitat distributions based on various GCM scenarios. When shifts in species distributions were projected into the future, these shifts did not appear to follow a northward retreat or expansion, likely due to the limited impact of water temperature on the modeled distributions of suitable habitat for these species. Furthermore, species’ habitat distribution responses among future climate scenarios were variable within and among species and did not vary unidirectionally with increased severity of climate change as realized through increased warming patterns.

Identification of the Most Suitable Probability Distribution Models for Maximum, Minimum, and Mean Streamflow

Hydrological studies are useful in designing, planning, and managing water resources, infrastructure, and ecosystems. Probability distribution models are applied in extreme flood analysis, drought investigations, reservoir volumes studies, and time-series modelling, among other various hydrological studies. However, the selection of the most suitable probability distribution and associated parameter estimation procedure, as a fundamental step in flood frequency analysis, has remained the most difficult task for many researchers and water practitioners. This paper explains the current approaches that are used to identify the probability distribution functions that are best suited for the estimation of maximum, minimum, and mean streamflows. Then, it compares the performance of six probability distributions, and illustrates four fitting tests, evaluation procedures, and selection procedures through using a river basin as a case study. An assemblage of the latest computer statistical packages in an integrated development environment for the R programming language was applied. Maximum likelihood estimation (MLE), goodness-of-fit (GoF) tests-based analysis, and information criteria-based selection procedures were used to identify the most suitable distribution models. The results showed that the gamma (Pearson type 3) and lognormal distribution models were the best-fit functions for maximum streamflows, since they had the lowest Akaike Information Criterion values of 1083 and 1081, and Bayesian Information Criterion (BIC) values corresponding to 1087 and 1086, respectively. The Weibull, GEV, and Gumbel functions were the best-fit functions for the annual minimum flows of the Tana River, while the lognormal and GEV distribution functions the best-fit functions for the annual mean flows of the Tana River. The choices of the selected distribution functions may be used for forecasting hydrologic events and detecting the inherent stochastic characteristics of the hydrologic variables for predictions in the Tana River Basin. This paper also provides a significant contribution to the current understanding of predicting extreme hydrological events for various purposes. It indicates a direction for hydro-meteorological scientists within the current debate surrounding whether to use historical data and trend estimation techniques for predicting future events with issues of non-stationarity and underlying stochastic processes.

미계측 유역에서의 수문 분석을 위한 글로벌 격자형 기후 자료의 활용성 평가

We assessed the usability of satellite and reanalysis data for estimating streamflow amount in an ungauged watershed in consideration of the spatial distribution of precipitation. We used CHIRPS, PERSIANN-CDR, CFSR, ERA-Interim, and MERRA2 to obtain grid-based global data and daily GHCN as measured data for comparison. Evaluation results of daily GHCN data through comparison to the ASOS data from Gangneung station adjacent to Chungju Dam basin showed a greater than 60% missing data rate. The GHCN data underestimated extreme rainfall events caused by typhoons and overestimated rainfall events in certain years compared to the nromal range. In the comparison of grid-based global data, MERRA2 data showed the best reproducibility, with a 0.86 correlation coefficient, on the daily time scale. The SWAT model was applied to Chungju Dam basin for estimating streamflow and showed -16.1% total error when the MERRA2 data was used as model input without bias-correction. However, both total error and RMSE were decreased when the monthly biases of MERRA2 data at the weather stations were corrected by comparing to the 30-year average values. In addition, the biases were spatially interpolated for areas in the basin without a station. Total error was slightly improved to -13.1% when the bias was corrected using the GHCN data from Gangneung station, while the total error showed a satisfactory -6.3% when using ASOS data from Daegwallyeong station for streamflow estimation after bias-correction. In the absence of measured weather data, it is desirable to use the most appropriate grid-based data after bias-correction through comparison with neighboring weather stations to reflect the characteristics of the area.

Accuracy of crowdsourced streamflow and stream level class estimates

ABSTRACTStreamflow data are important for river management and the calibration of hydrological models. However, such data are only available for gauged catchments. Citizen science offers an alternative data source, and can be used to estimate streamflow at ungauged sites. We evaluated the accuracy of crowdsourced streamflow estimates for 10 streams in Switzerland by asking citizens to estimate streamflow either directly, or based on the estimated width, depth and velocity of the stream. Additionally, we asked them to estimate the stream level class by comparing the current stream level with a picture that included a virtual staff gauge. To compare the different estimates, the stream level class estimates were converted into streamflow. The results indicate that stream level classes were estimated more accurately than streamflow, and more accurately represented high and low flow conditions. Based on this result, we suggest that citizen science projects focus on stream level class estimates instead of streamflow estimates.

Hydrology induces intraspecific variation in freshwater fish morphology under contemporary and future climate scenarios

Predicting future changes in habitat-associated species traits is an important step in understanding the ecological and evolutionary consequences of environmental change. However, models projecting phenotypic responses to future climate change typically assume populations will respond similarly across the range of a species, while local adaptation and spatial variation in environmental changes are rarely considered. In this study, among-population phenotypic variability was coupled with geographic variation in anticipated hydrologic changes to examine patterns of population-level phenotypic changes expected under future climatic change. To estimate phenotypic responses to watershed hydrology, phenotype-environment associations between body shape and contemporary streamflow were quantified among populations of six species of fishes (Cyprinidae). Future streamflow estimates (2070–2099) were then used to project body shapes within populations, assuming the same phenotype-environment relationships. All species exhibited significant associations between body shape and contemporary streamflow discharge and variability. However, these relationships were not consistent, even among species occupying similar vertical positions in the water column. When these phenotype-environment relationships were projected into future streamflow conditions, populations are not expected to respond uniformly across the species' ranges, and all but one species exhibited projected morphologies outside of the current range of morphological variation. These findings suggest local adaptation and spatial heterogeneity in environmental changes interact to influence variation in the degree of expected phenotypic responses to climate change at both the species and population level.