Total Streamflow Research Articles

Assessing Streamflow Sensitivity to Precipitation Variability in Karst‐Influenced Catchments With Unclosed Water Balances

AbstractKarst hydrological models are widely used for simulating groundwater dynamics at the aquifer scale. However, modeling streamflow of a topographic catchment that is partially covered by karst is rarely reported. This absence is due to difficulties of properly considering the strong differences of karstic and nonkarstic hydrodynamics and the widespread occurrence of unclosed water balances in karstic regions due to intercatchment groundwater flow (IGF). In this study, we present a new approach that uses hydrological signatures to identify important natural processes and appropriate model structures for the simulation of karst‐influenced catchments. We account for karstic IGF and apply our approach to six karst‐influenced catchments in Europe and the Middle East. We estimate the contributions of karstic and nonkarstic parts to the total streamflow sensitivity. With different model structures identified at different sites, our approach reduces water balance errors by 18% and reduces the root mean squared error by 20% for catchments where the IGF is deemed important. We find that IGF modifies the contribution of karstic areas to the total streamflow sensitivity. The total streamflow sensitivity increases under gaining conditions and decreases under losing conditions for catchments with larger karstic discharge elasticity, while it shows the converse change for catchments with smaller karstic discharge elasticity. Modeling streamflow at karst‐influenced catchments requires the consideration of differences between karstic and nonkarstic areas, and the IGF matters for catchments with unclosed water balances.

Quantifying contributions of snowmelt water to streamflow using graphical and chemical hydrograph separation

AbstractIn this study, we characterize the snowmelt hydrological response of nine headwater watersheds in southeast Wyoming by separating streamflow into three components using a combination of tracer and graphical approaches. First, continuous 15‐min records of specific conductance (SC) from 2016 to 2018 were used to separate streamflow into annual contributions, representing water that contributes to streamflow in a given year that entered the watershed in the same year being considered, and perennial contributions, representing water that contributes to streamflow in a given year that entered the watershed in previous years. Then, diurnal streamflow cycles occurring during the snowmelt season were used to graphically separate annual contributions into rapid diurnal snowmelt contributions, representing water with the relatively fastest hydrological response and shortest residence time, and delayed annual contributions, representing water with relatively longer residence time in the watershed before becoming streamflow. On average, mean annual total streamflow was comprised of between 22% and 46% perennial contributions, 7% and 14% rapid diurnal snowmelt contributions, and 46% and 55% delayed annual contributions across the watersheds. A hysteresis index describing SC‐discharge patterns indicated that, annually, most watersheds showed negative, concave, anti‐clockwise hysteretic direction suggesting faster flow pathways dominate streamflow on the rising limb of the annual hydrograph relative to the falling limb. At the daily timescale during snowmelt‐induced diurnal streamflow cycles, hysteresis was negative, but with a clockwise direction, implying that rapid diurnal snowmelt contributions generated from the concurrent daily snowmelt, with lower SC, arrived after delayed annual contribution peaks and preferentially contributed on the falling limb of diurnal cycles. South‐facing watersheds were more susceptible to early season snowmelt at slower rates, resulting in less annual and more perennial contributions. Conversely, north‐facing watersheds had longer snow persistence and larger proportions of annual contributions and rapid diurnal snowmelt contributions. Watersheds with surficial geology dominated by glacial deposits had a lower proportion of rapid diurnal snowmelt contributions compared to watersheds with large percentages of bedrock surficial geology.

Modeling the Snowmelt Runoff Process of the Tizinafu River Basin, Northwest China, with GLDAS Data and Bayesian Uncertainty Analysis

AbstractThe climate of the Tizinafu River basin is characterized by low temperature and sparse precipitation, and snow and glacier melt serve as the main water resource in this area. Modeling the snowmelt runoff process has great significance for local ecosystems and residents. The total streamflow of the Tizinafu River basin was divided into surface streamflow and baseflow. The surface streamflow was estimated using the routing model (RM) with Noah runoff data from Global Land Data Assimilation (GLDAS), and the parameter uncertainty of the RM was quantified through Markov chain Monte Carlo simulation. Additionally, the 10 commonly used baseflow separation methods of four categories [digital filter, hydrograph separation program (HYSEP), baseflow index, and Kalinlin methods] were used to generate the baseflow and were then evaluated by their performance in total streamflow simulation. The results demonstrated that the RM driven by GLDAS runoff data could reproduce the runoff process of the Tizinafu River basin. RM-Hl (local minimum HYSEP method) achieved the best performance in the total streamflow simulation, with Nash–Sutcliffe efficiency (NSE) coefficients of 0.82 and 0.93, relative errors of −0.40% and 10.50%, and observation inclusion ratios C of 62.07% and 68.52% for the calibration and verification periods, respectively. The local minimum HYSEP method was most suitable for describing the baseflow of the Tizinafu River basin among the 10 baseflow separation methods. However, digital filter methods exhibited weak performance in baseflow separation.

Recharge and baseflow constrained by surface-water and groundwater chemistry: case study of the Chari River, Chad basin

The Chari-Logone watershed is the only hydrologically active part of the Lake Chad Basin (Central Africa). The Chari-Logone River and Lake Chad exchange water with the surrounding unconfined aquifers of sedimentary and crystalline rock types. In this study, the groundwater contribution to stream flow was quantified by taking advantage of a comprehensive hydrological and chemical database from the 1970s for the upper catchment, which has no equivalent to date. The study area was limited to the larger Chari catchment where the exchanges are mainly oriented from the aquifers to the stream. Upon identification of the mixing poles using end-member mixing analysis (EMMA), and a Monte Carlo inversion of a monthly mixing mass-balance model, an annual averaged base flow of 59 ± 10% of the total stream flow was estimated for 1969–1973. Then, a hydrological model accounting for baseflow and surface flow was calibrated using a 47-year-long monthly discharge time series, and the 4-years’ monthly averaged chemical data available. The simulations yielded an average baseflow of 64% of the annual discharge. The time-constant of the reservoir model (τ = 0.2 years) used to simulate the baseflow was interpreted in terms of hydraulic diffusivity, yielding a value in the order of 101 m2 s−1. This large value corresponds to a highly productive bedrock and thick sedimentary aquifers. The main contribution to the Chari stream flows is restricted to a 140,000-km2 subcatchment located in the southern tropical zone, where the recharge rate reaches 72 ± 6 mm year−1.

A data-driven analysis of frequent patterns and variable importance for streamflow trend attribution

Identifying key driving forces for streamflow variation is essential for improving sustainable water resource management in terms of understanding how changes in the watershed translate to changes in streamflow. In this study, the relationships between trends in total annual streamflow and trends in watershed characteristics across the contiguous U.S. during 1981-2016 are investigated with data from 2,621 USGS gages. The regions of homogeneous hydrologic change, i.e. watersheds that are undergoing similar statistically significant streamflow trends, are delineated and frequent pattern mining (i.e. Apriori algorithm) and variable importance (i.e. Random Forest) are used to derive the key driving forces for these regions. As expected, the trends in streamflow are highly associated with the trends of precipitation. In contrast, the influences of anthropogenic factors vary substantially across regions. Particularly, the influence of water use change tends to be significant in the regions dominated by agricultural land, e.g. Dakotas. The importance of land use change is highlighted in the regions with relatively large forest coverage, e.g. Northeast. However, these important identified water use changes are not frequently associated with the increasing streamflow in sub-regions, e.g. Great Lakes, and thus the significance of the water use impacts are site-specific. Therefore, the changes in climate and land use are frequently and importantly identified together in the sub-regions with increasing streamflow, which can be collectively used to discover the major causes of the streamflow trends in those regions. Although the impacts of changing water use are highlighted in the Southwest, climate trends are primarily responsible for the decreasing streamflow.

Seasonal recharge of spring and stream waters in a karst catchment revealed by isotopic and hydrochemical analyses

Quantifying and understanding recharge behavior of aquifers in complex hydrogeological systems is challenging, which limits our ability to manage water resources in karstic areas. In this study, we analyzed the seasonal recharge sources and processes of a stream, an intermittent spring, and a perennial spring in a small dolomitic catchment. Weekly monitoring of stable isotopes and chemical characteristics and daily hydrological data of these waters was performed in 2017 and 2018. There were broad seasonal variations in rainfall isotopes, with more negative values observed in the wet season and more positive values observed in the dry season, while narrow ranges were observed in spring and stream waters. Such values plotting on a LMWL represented a homogeneous mixing of rainfall without the effective evaporation effect. Hydrograph separation showed that the mean proportion of old water was approximately 94% for the springs and stream, which indicated a mixing mechanism in recharge processes. The mean residence time was approximately 23 weeks for spring 1, 201 weeks for spring 2, and 43 weeks for stream. The significant difference between springs was attributed to the combined effects of relatively higher proportions of rocky outcrops, thinner soil–epikarst, and better karstic development in the aquifer of spring 1, which enhanced the sensitivity to rainfall. The stream was recharged by waters from hillslopes, which mixed extensively in the depression, accompanied by soil–epikarst interaction. However, only approximately 1.5% of total stream flow was recharged by springs annually, and most of the stream was recharged by water through underground paths, based on the discharge analyses. Moreover, stream was recharged by subsurface flows, which were considerably affected by soil, leading to the fluctuating stream discharge characteristics during the wet season. The results suggest that greater attention should be paid to the roles of near-surface soil–epikarst architecture in hydrological processes.

Evaluation of baseflow modelling structure in monthly water balance models using 443 Australian catchments

It is critical for monthly water balance models (MWBMs) to achieve realistic hydrological modelling of total flow and its components (i.e. quick flow and baseflow) in practical application. Various methods have been developed to improve the performances of the three flow components by focusing on calibration procedures. However, the understanding of runoff partitioning structure in MWBMs for better performances is still very limited, especially whether the storage-discharge relationship is linear or nonlinear at monthly time scale. In this study, model structures for baseflow simulation in 5 widely used MWBMs are reviewed and modified from a linear storage-discharge relationship to a nonlinear exponential storage-discharge relationship to achieve realistic baseflow simulation in 443 catchments from Australia with diverse hydro-climatic conditions. The performances of original and modified models are evaluated and compared through four assessment criteria including Nash-Sutcliffe efficiency (NSE), logarithmic form of NSE (NSE(log)), Pearson correlation coefficient (r) and Bias (B). Basically, the original models with linear storage-discharge relationship perform satisfactorily in simulating total streamflow and quick flow, but degrade remarkably for simulating baseflow with an underestimation of −60 ± 36% in all study catchments. The modified MWBMs with nonlinear storage-discharge relationship significantly outperform the original ones for simulating both total streamflow and baseflow. The assessment criteria NSE, NSE(log), r and B of total streamflow improve in 82 ± 4.0% (mean ± 1 standard deviation of 5 MWBMs), 72 ± 4.7%, 76 ± 4.5% and 51 ± 2.4% study catchments, respectively. The NSE(log) and r of baseflow simulated using the modified MWBMs have improved in 68 ± 4.6% and 83 ± 4.1% catchments with median improvement of 0.17 ± 0.03 and 0.14 ± 0.03, respectively. It suggests that the exponential nonlinear storage-discharge relationship is more capable for MWBMs to capture storage-discharge dynamics than the linear one at monthly time scale. This study highlights that, at monthly time scale, the nonlinearity in catchment storage-discharge relationship is a very important factor for MWBMs performance and more studies are required to reveal catchment monthly runoff generation mechanisms.

Accounting for Temporal Variability of Streamflow in Estimates of Travel Time

Retention, processing, and transport of solutes and particulates in stream corridors are influenced by the travel time of streamflow through stream channels, which varies dynamically with discharge. The effects of streamflow variability across sites and over time cannot be addressed by time-averaged models if parameters are based solely on the characteristics of mean streamflow. We develop methods to that account for the effects of streamflow variability on travel time and compare our estimates with to flow-weighted (“effective”) travel time at 100 streams in the southeastern United States. Velocity time series were generated for each stream from multiple-year (median 15.5 years), high-frequency (15-minute interval) records of instantaneous streamflow and field measurements of velocity and inverted to produce time series of specific travel time [T/L]. The effective travel times for streams are 60 to 90% of the specific travel time of mean streamflow because a large fraction of the total streamflow volume is discharged during higher flows with higher velocities. We find that adjusting the specific travel time of mean streamflow at a site by a factor of 0.81 generally accounts for the effect of a skewed streamflow distribution, but at-site estimates of the coefficient of variation of streamflow are necessary to resolve differences in streamflow variability between streams or changes in variability over time. For example, the effective travel time of urban streams is less than the effective travel of forested streams in the southeastern United States as a result of increased streamflow variability in urban streams. Effective travel time accounts for both the variation in velocity with streamflow and the large fraction of streamflow discharged during high flows in most streams and provides time-averaged models with limited capability to account for effects of streamflow variability that otherwise they lack. This capability is needed for continental-scale modeling where streamflow variability is not uniform because heterogeneous surficial geology, hydro-climatology, and vegetation and applications where streamflow variability is not stationary as a response to climate change or hydrologic alteration.

Assessing hydrological responses to reforestation and fruit tree planting in a sub-tropical forested watershed using a combined research approach

Fruit tree planting is often considered as one of reforestation practices in the past decades in many parts of China because it increases forest coverage while gaining short-term economic benefits. However, the effects of large-scale fruit tree planting on water resources at the watershed scale are rarely assessed, especially in subtropical China. In this study, the forested watershed (Jiujushui watershed, 261.4 km2), located in Southeastern China, was selected to examine the responses of major flow components including baseflow, surface runoff, and total streamflow to both reforestation (e.g., Cunninghamia lanceolata (Lamb.) Hook and Pinus massoniana Lamb.) and fruit tree planting (e.g., Citrus reticulate Blanco) practices. The study period (1961–2016) was split into three sub-periods including reference, reforestation and fruit tree planting. The combined approach including pair-wise method and separation of relative contributions was adopted in this study. The former was used to test the statistical relations between forest changes and flow components, while the latter (sensitivity-based and time trend methods) was applied to quantitatively separate the relative contributions of forest changes and climatic variability to each flow component so that the effects of forest changes (reforestation and fruit tree planting) on flow components can be quantified and compared. Results from the pair-wise technique showed that reforestation and fruit tree planting had no significant effects on total streamflow. However, reforestation significantly increased base flow by 15% (p = 0.03), while fruit tree planting did not. In contrast, surface runoff was significantly increased by fruit tree planting (p = 0.048), while it was not by reforestation. The results from the separation technique showed that in the reforestation period, the relative contributions of reforestation to surface runoff, baseflow and total streamflow were 23.9%, 67.3% and 50.9%, respectively, in comparison with 76.1%, 32.7% and 49.1% from climate variability, highlighting that reforestation had greater effects on base flow. However, fruit tree planting accounted for 54.4%, 48.8% and 57.3% of total streamflow, baseflow and surface runoff variations, while climatic variability were responsible for 45.6%, 51.3% and 42.7%, respectively in the fruit tree planting period, demonstrating that fruit tree planting had greater effects on surface runoff. Thus, the results from both techniques clearly demonstrated that forest recovery through reforestation effectively increased baseflow, while fruit tree planting caused increasing of surface runoff. We also conclude that the combination approach provides a robust and complementary research framework for assessing the hydrological effects of reforestation in a single watershed.

An Aridity Index‐Based Formulation of Streamflow Components

AbstractDirect runoff and baseflow are the two primary components of total streamflow, and their accurate estimation is indispensable for a variety of hydrologic applications. While direct runoff is the quick response stemming from surface and shallow subsurface flow paths and is often associated with floods, baseflow represents the groundwater contribution from stored sources (e.g., groundwater) to streams and is crucial for environmental flow regulations, and water supply, among others. L'vovich (1979) proposed a two‐step water balance partitioning, where precipitation is divided into direct runoff and catchment wetting, followed by the disaggregation of the latter into baseflow and evapotranspiration. Here, we investigate the role of the aridity index (ratio between mean‐annual potential evapotranspiration and precipitation) in controlling the long‐term (mean‐annual) fluxes of direct runoff and baseflow. We present an analytical solution beginning with similar assumptions as proposed by Budyko (1974), leading to two complementary expressions for the two fluxes. The aridity index explained 77% and 89% of variability in direct runoff and baseflow from 378 catchments within the continental United States, while our formulations were able to reproduce the patterns of water balance partitioning proposed by L'vovich (1979) at the mean‐annual timescale. Our approach can be used to further understand how climate and landscape controls the terrestrial water balance at mean‐annual timescales, while also representing a step toward the prediction of baseflow and direct runoff at ungauged basins.

A Climatic Perspective on the Impacts of Global Warming on Water Cycle of Cold Mountainous Catchments in the Tibetan Plateau: A Case Study in Yarlung Zangbo River Basin

Global warming has a profound influence on global and regional water cycles, especially in the cold mountainous area. However, detecting and quantifying such changes are still difficult because noise and variability in observed streamflow are relatively larger than the long-term trends. In this study, the impacts of global warming on the catchment water cycles in the Yarlung Zangbo River Basin (YZRB), one of most important catchments in south of the Tibetan Plateau, are quantified using a climatic approach based on the relationship between basin-scale groundwater storage and low flow at the annual time scale. By using a quantile regression method and flow recession analysis, changes in low flow regimes and basin-scale groundwater storage at the Nuxia hydrological station are quantified at the annual time scale during 1961–2000. Results show annual low flows (10th and 25th annual flows) of the YZRB have decreased significantly, while long-term annual precipitation, total streamflow, and high flows are statistically unchanged. Annual lowest seven-day flow shows a significantly downward trend (2.2 m3/s/a, p < 0.05) and its timing has advanced about 12 days (2.8 day/10a, p < 0.1) during the study period. Estimated annual basin-scale groundwater storage also shows a significant decreasing trend at a rate of 0.079 mm/a (p < 0.05) over the study period. Further analysis suggests that evaporation increase, decreased snow-fraction, and increased annual precipitation intensity induced by the rising temperature possibly are the drivers causing a significant decline in catchment low flow regimes and groundwater storage in the study area. This highlights that an increase in temperature has likely already caused significant changes in regional flow regimes in the high and cold mountainous regions, which has alarming consequences in regional ecological protection and sustainable water resources management.

The effect of the ‘Las Maquinas’ wildfire of 2017 on the hydrologic balance of a high conservation value Hualo (Nothofagus glauca (Phil.) Krasser) forest in central Chile

The large wildfires of January 2017 burned more than 500,000 ha, including areas of high conservation value native forest, in central Chile. Runoff, streamflow and water balance were measured in a native forest catchment for 7 years before and 2 years after the fires. The annual peak flows and runoff coefficients decreased after the fire as did summer flows and total streamflow. These reductions in flow were despite rainfall being higher in the two years after the fire than in the two years before the fire. The forest re-sprouted vigorously after the fire and evapotranspiration in the second year after the fire was approximately two thirds of the rate before the fire. While the forest water use is recovering rapidly, streamflow in the two years after the fire was reduced relative to the seven years before the fire. In the two years after the fires, more than 50% of rainfall is not accounted for by either evapotranspiration or by streamflow. Potential explanations for this gap in the water balance include recovery in storage after the fire and increased bypass flow below the measuring weirs.

Globally partitioning the simultaneous impacts of climate-induced and human-induced changes on catchment streamflow: A review and meta-analysis

Streamflow (Q) change is simultaneously driven by climate-induced changes and various human activities. To understand their respective roles, a large number of partitioning studies were conducted. However, each of the scattered case studies may only provide local-to-regional understanding of the contributions from climate-induced changes and human-induced changes to streamflow variations over various study periods. To address if it is possible to reveal the relative contribution of climate-induced changes (dQ_CIC) and human-induced changes (dQ_HIC) from the spatially distributed case studies, and then to predict regional to global spatial pattern of streamflow changes due to climate-induced changes, we reviewed 103 studies providing access to 1321 catchment cases, and performed a meta-analysis which characterized how that population varied with the number of catchment cases per study and the length of study period, and analyzed if and to what extent the bias of that population was influenced by various study factors. Possible catchment streamflow change, relative to the 1960s−2000s, was projected for the 2040s–2060s. Our meta-analysis suggested that, over the past decades, total streamflow change (i.e. dQ_Tot), on average, was −11.9 mm.a−1, and streamflow changes due to dQ_CIC and dQ_HIC were + 3.6 ± 48.1 mm.a−1 and −15.5 ± 44.4 mm.a−1, respectively. We also found that several study factors including catchment size, partitioning method, and climate type (mainly mean annual precipitation) were not important sources of bias for the meta-analysis, as they each only explained a small portion of the variability in dQ_CIC or dQ_HIC. Although study periods of the case studies and climate oscillation influenced the magnitude and direction of dQ_CIC (and dQ_HIC), dQ_CIC showed a strongly temporal invariant dependence on dP, with a ratio of 0.48 for the fitted linear line (p < 0.05). The mean changing rate of dQ_CIC (i.e. dQ_CIC/dt) over the past decades was +0.07 ± 4.13 mm.a−2. Our study heightened the imperative to perform analysis with respect to the spatial variation of dQ response to climate perturbations and/or variation across various regions, and to disentangle hydrological contribution of interactions/feedbacks between climate-induced change and land-use/land-cover changes from the other anthropogenic changes in future partitioning studies for a better understanding of dQ response to climate-induced change.

Recent Trends in Individual and Multivariate Compound Flood Drivers in Canada's Coasts

AbstractOver half of the global population and the majority of the cities in coastal zones are at risk of coastal flooding. Changes in the occurrence of individual extremes and the interactions between hydrological and coastal variables can exacerbate flood risks. While extensive research has been conducted to understand and predict different types of flood hazards in isolation, spatial and temporal trends and variability of compound flooding, that is, flooding caused by multiple drivers, remain an open question. This study investigates the individual and joint temporal variations of multiple drivers that can cause compound flooding in Canada's coasts including total water level, storm surge, precipitation, and streamflow. Long‐term changes in the frequency and intensity of extremes are analyzed over the Atlantic, Pacific, and the Great Lakes regions. Univariate and multivariate trend tests including Mann Kendall, Covariance Inversion Test, Covariance Sum Test, and Covariance Eigenvalue Test are applied. In addition, a new multivariate index based on the contributing flood drivers transformed into a probability space is proposed, and its application to study compound flooding is investigated. Overall, results show increased risks of individual and compound flooding over the Atlantic coast and varying trends in the Pacific and Great Lakes regions. The multivariate trend indices show consistent results in most scenarios. The proposed index provides a simple and flexible measure to analyze the spatial and temporal variation of compound flooding risks at different thresholds. The results highlight the importance of considering nonstationary compound flood events to develop resilience strategies in coastal environments.

The Utility of Optical Satellite Winter Snow Depths for Initializing a Glacio‐Hydrological Model of a High‐Elevation, Andean Catchment

AbstractInformation about end‐of‐winter spatial distribution of snow depth is important for seasonal forecasts of spring/summer streamflow in high‐mountain regions. Nevertheless, such information typically relies upon extrapolation from a sparse network of observations at low elevations. Here, we test the potential of high‐resolution snow depth data derived from optical stereophotogrammetry of Pléiades satellites for improving the representation of snow depth initial conditions (SDICs) in a glacio‐hydrological model and assess potential improvements in the skill of snowmelt and streamflow simulations in a high‐elevation Andean catchment. We calibrate model parameters controlling glacier mass balance and snow cover evolution using ground‐based and satellite observations, and consider the relative importance of accurate estimates of SDICs compared to model parameters and forcings. We find that Pléiades SDICs improve the simulation of snow‐covered area, glacier mass balance, and monthly streamflow compared to alternative SDICs based upon extrapolation of meteorological variables or statistical methods to estimate SDICs based upon topography. Model simulations are found to be sensitive to SDICs in the early spring (up to 48% variability in modeled streamflow compared to the best estimate model), and to temperature gradients in all months that control albedo and melt rates over a large elevation range (&gt;2,400 m). As such, appropriately characterizing the distribution of total snow volume with elevation is important for reproducing total streamflow and the proportions of snowmelt. Therefore, optical stereo‐photogrammetry offers an advantage for obtaining SDICs that aid both the timing and magnitude of streamflow simulations, process representation (e.g., snow cover evolution) and has the potential for large spatial domains.

Long-Term Hydro–Climatic Trends in the Mountainous Kofarnihon River Basin in Central Asia

Hydro–climatic variables play an essential role in assessing the long-term changes in streamflow in the snow-fed and glacier-fed rivers that are extremely vulnerable to climatic variations in the alpine mountainous regions. The trend and magnitudinal changes of hydro–climatic variables, such as temperature, precipitation, and streamflow, were determined by applying the non-parametric Mann–Kendall, modified Mann–Kendall, and Sen’s slope tests in the Kofarnihon River Basin in Central Asia. We also used Pettitt’s test to analyze the changes during the 1951–2012 and 1979–2012 time periods. This study revealed that the variations of climate variables have their significant spatial patterns and are strongly regulated by the altitude. From mountainous regions down to plain regions, the decadal temperature trends varied from −0.18 to 0.36 °C/decade and the variation of precipitation from −4.76 to −14.63 mm yr−1 per decade. Considering the temporal variation, the temperature trends decreased in winter and significantly increased in spring, and the precipitation trends significantly decreased in spring but significantly increased in winter in the high-altitude areas. As consequence, total streamflow in headwater regions shows the obvious increase and clear seasonal variations. The mean monthly streamflow decreased in fall and winter and significantly increased in the spring and summer seasons which can be attributed to the influence of global warming on the rapid melting of snow and ice. Although the abrupt change points in air temperature and precipitation occurred around the 1970s and 1990s in the low-altitude areas and 2000s in the high-altitude areas during the 1951–2012 and 1979–2012 periods, the general trends of hydro–climatic variables keep consistent. This study benefits water resource management, socio–economic development, and sustainable agricultural planning in Tajikistan and its downstream countries.

Flood Flow Frequency Analysis to Estimate Potential Floodplain Nitrogen Treatment during Overbank Flow Events in Urban Stream Restoration Projects

Stream restoration for mitigation purposes has grown rapidly since the 1980s. As the science advances, some organizations (Chesapeake Bay Program, North Carolina Department of Environmental Quality) have approved or are considering providing nutrient credits for stream restoration projects. Nutrient treatment on floodplains during overbank events is one of the least understood processes that have been considered as part of the Chesapeake Bay Program’s Stream Restoration Nutrient Crediting program. This study analyzed ten years of streamflow and water quality data from five stations in the Piedmont of North Carolina to evaluate proposed procedures for estimating nitrogen removal on the floodplain during overbank flow events. The volume of floodplain flow, the volume of floodplain flow potentially treated, and the nitrogen load retained on the floodplain were calculated for each overbank event, and a sensitivity analysis was completed. On average, 9% to 15% of the total annual streamflow volume accessed the floodplain. The percentage of the average annual volume of streamflow potentially treated ranged from 1.0% to 5.1%. Annually, this equates to 0.2% to 1.0% of the total N load retained/removed on the floodplain following restoration. The relatively low nitrogen retention/removal rates were due to a majority of floodplain flow occurring during a few large events each year that exceeded the treatment capacity of the floodplain. On an annual basis, 90% of total floodplain flow occurred during half of all overbank events and 50% of total floodplain flow occurred during two to three events each year. Findings suggest that evaluating only overbank events may lead to undervaluing stream restoration because treatment is limited by hydrologic controls that restrict floodplain retention time. Treatment is further governed by floodplain and channel size.

Simulating Groundwater-Streamflow Connections in the Upper Colorado River Basin.

In mountain, snow driven catchments, snowmelt is supposed to be the primary contribution to river streamflows during spring. In these catchments the contribution of groundwater is not well documented because of the difficulty to monitor groundwater in such complex environment with deep aquifers. In this study we use an integrated hydrologic model to conduct numerical experiments that help quantify the effect of lateral groundwater flow on total annual and peak streamflow in predevelopment conditions. Our simulations focus on the Upper Colorado River Basin (UCRB; 2.8 × 105 km2 ) a well-documented mountain catchment for which both streamflow and water table measurements are available for several important sub-basins. For the simulated water year, our results suggest an increase in peak flow of up to 57% when lateral groundwater flow processes are included-an unexpected result for flood conditions generally assumed independent of groundwater. Additionally, inclusion of lateral groundwater flow moderately improved the model match to observations. The correlation coefficient for mean annual flows improved from 0.84 for the no lateral groundwater flow simulation to 0.98 for the lateral groundwater flow one. Spatially we see more pronounced differences between lateral and no lateral groundwater flow cases in areas of the domain with steeper topography. We also found distinct differences in the magnitude and spatial distribution of streamflow changes with and without lateral groundwater flow between Upper Colorado River Sub-basins. A sensitivity test that scaled hydraulic conductivity over two orders of magnitude was conducted for the lateral groundwater flow simulations. These results show that the impact of lateral groundwater flow is as large or larger than an order of magnitude change in hydraulic conductivity. While our results focus on the UCRB, we feel that these simulations have relevance to other headwaters systems worldwide.

Assessing basin storage: Comparison of hydrometric‐ and tracer‐based indices of dynamic and total storage

AbstractStorage is a fundamental but elusive component of drainage basin function, influencing synchronization between precipitation input and streamflow output and mediating basin sensitivity to climate and land use/land cover (LULC) change. We compare hydrometric and isotopic approaches to estimate indices of dynamic and total basin storage, respectively, and assess inter‐basin differences in these indices across the Oak Ridges Moraine (ORM) region of southern Ontario, Canada. Dynamic storage indices for the 20 study basins included the ratio of baseflow to total streamflow (baseflow index BFI), Q 99 flow and flow duration curve (FDC) slope. Ratios of the standard deviation of the streamflow stable isotope signal relative to that of precipitation were determined for each basin from a 1 year bi‐weekly sampling program and used as indicators of total storage. Smaller ratios imply longer water travel times, smaller young water fractions (F yw, &lt; ~2–3 months in age) in streamflow and greater basin storage. Ratios were inversely related to BFI and Q 99, and positively related to FDC slope, suggesting longer travel times and smaller F yw for basins with stable baseflow‐dominated streamflow regimes. Inter‐basin differences in all indices reflected topographic, hydrogeologic and LULC controls on storage, which was greatest in steep, forest‐covered headwaters underlain by permeable deposits with thick and relatively uniform unsaturated zones. Nevertheless, differential sensitivity of indices to controls on storage indicates the value of using several indices to capture more completely how basin characteristics influence storage. Regression relationships between storage indices and basin characteristics provided reasonable predictions of aspects of the streamflow regime of test basins in the ORM region. Such relationships and the underlying knowledge of controls on basin storage in this landscape provide the foundation for initial predictions of relative differences in streamflow response to regional changes in climate and LULC.

Large-scale baseflow index prediction using hydrological modelling, linear and multilevel regression approaches

Baseflow is critical for water balance budget, water resources management, and environmental evaluation. Prediction of baseflow index (BFI), the ratio of baseflow to total streamflow, has a great significance in unravelling the baseflow characteristics for large scale trajectory. Therefore, this study compares BFI predictive performance derived from a new multilevel regression approach along with two other commonly used approaches: hydrological modelling (SIMHYD, a simplified version of the HYDROLOG model, and Xinanjiang model), and linear regression (traditional linear regression, and alternative traditional regression considers the second-order interaction). The multilevel regression approach does not only group the catchments into the four climate zones (arid, tropics, equiseasonal and winter rainfall), but also considers inter-catchment and inter-climate zone variances. Likewise, calibration and two regionalisation techniques namely spatial proximity and integrated similarity are used to obtain the BFI from hydrological modelling approach. Correspondingly, the traditional linear regression technique estimates BFI establishing linear regressions between catchment attributes and four climate zones. Then, all the three approaches are evaluated against combined average estimation from four well-parameterised baseflow separation methods (Lyne-Hollick (LH), United Kingdom Institute of Hydrology (UKIH), Chapman-Maxwell (CM) and Eckhardt (ECK)) at 596 catchments across Australia for 1980–2012. The findings show that the multilevel regression has greatly improved the performance of BFI prediction in comparison to other methods. In particular, the two calibrated and regionalised hydrological models perform worst in predicting BFI with a Nash-Sutcliffe Efficiency (NSE) of −8.44 and −2.58 along with an absolute percent bias (PBIAS) of 81% and 146% (overestimation of baseflow), respectively. However, the traditional linear regression remains in intermediate position with the NSE of 0.57 and bias of 25. In addition, alternative traditional regression also shows very close proximity. In contrast, the multilevel regression approach shows the best performance with the NSE of 0.75 and bias of 19%. The study also demonstrates that the multilevel regression approach can improve BFI prediction, and shows potential for being used in the prediction of other hydrological signatures in large-scale.