Streamflow Hydrographs Research Articles

Optimization of the SWAT+ model to adequately predict different segments of a managed streamflow hydrograph

ABSTRACT Complete representation of rainfall–runoff responses in complex, large watersheds using a single-objective parameterization approach in watershed models is often unachievable. In this study we present a calibration approach for the SWAT+ model that independently fits model parameters for different flow segments of the hydrograph. The approach is demonstrated for the Feather River, California, USA, using daily streamflow from the Lake Oroville Reservoir outlet gage. Results show that when model parameters were independently fitted for different flow segments the KGE, NSE, PBIAS, and RSR values improved to 0.96, 0.99, −3.3, and 0.10, respectively, compared to 0.72, 0.66, −9.30, and 0.53, respectively, achieved under a multiobjective and full hydrograph (average hydrograph) calibration. The results highlight that when considering the average hydrograph and flow duration curves, a more balanced representation of both poorly and well-performing segments is achieved, emphasizing the importance of segment-specific parameterization and multi-objective evaluation for accurately representing different flow conditions.

Technical note: Testing the connection between hillslope-scale runoff fluctuations and streamflow hydrographs at the outlet of large river basins

Abstract. A series of numerical experiments were conducted to test the connection between streamflow hydrographs at the outlet of large watersheds and the time series of hillslope-scale runoff yield. We used a distributed hydrological routing model that discretizes a large watershed (∼ 17 000 km2) into small hillslope units (∼ 0.1 km2) and applied distinct surface runoff time series to each unit that deliver the same volume of water into the river network. The numerical simulations show that distinct runoff delivery time series at the hillslope scale result in indistinguishable streamflow hydrographs at large scales. This limitation is imposed by space-time averaging of input flows into the river network that are draining the landscape. The results of the simulations presented in this paper show that, under very general conditions of streamflow routing (i.e., nonlinear variable velocities in space and time), the streamflow hydrographs at the outlet of basins with Horton–Strahler (H–S) order 5 or above (larger than 100 km2 in our setup) contain very little information about the temporal variability of runoff production at the hillslope scale and therefore the processes from which they originate. In addition, our results indicate that the rate of convergence to a common hydrograph shape at larger scales (above H–S order 5) is directly proportional to how different the input signals are to each other at the hillslope scale. We conclude that the ability of a hydrological model to replicate outlet hydrographs does not imply that a correct and meaningful description of small-scale rainfall–runoff processes has been provided. Furthermore, our results provide context for other studies that demonstrate how the physics of runoff generation cannot be inferred from output signals in commonly used hydrological models.

A comparative analysis of continuous and event-based hydrological modeling for streamflow hydrograph prediction

A comparative analysis of continuous and event-based hydrological modeling for streamflow hydrograph prediction

A hydrologic similarity-based parameters dynamic matching framework: Application to enhance the real-time flood forecasting

Conventional hydrological modeling is usually based on the assumption that parameters are statically time-invariant. However, recent studies suggest that the influences of climate change and human interventions have made this hypothesis questionable. Meanwhile, machine learning techniques are increasingly used to extract patterns and insights from the ever-increasing hydrometeorological data. Here, we proposed a hybrid framework (HSPDM) to improve the precision of real-time flood forecasting in response to nonstationary conditions. It utilizes multiple machine learning techniques to dynamically retrieve calibrated hydrological model parameters from historical similar floods, thus continuously obtaining hourly time-variant parameters in real-time flood forecasting operations. Using the Quzhou Basin in China as a case study, the effectiveness and advancement of HSPDM framework was examined. Three schemes, including traditional time-invariant parameters (scheme 1), hourly time-variant parameters (scheme 2), and probabilistic forecasting scheme (scheme 3), were built for comparison purpose. The results were summarized as follows: (1) The proposed framework can successfully identify continuous flood subsequence with a high retrieval accuracy (1.74) and acceptable time consumption (175.05 s) by adopting k-means, K-Nearest Neighbor (KNN), and embedding-based subsequence matching (EBSM) method. (2) Compared to scheme 1, scheme 2 provided more reliable forecasting results with higher accuracies, in terms of the general goodness-of-fit (higher NSE value) and reproducing flood peak and flood process. (3) The streamflow hydrographs forecasted by scheme 2 fell exactly in the predictive uncertainty bounds of scheme 3 and even showed superiority compared to a preferred deterministic forecast (Q50) of scheme 3. The major scientific contribution of this study lies in advancing the technique of real-time flood forecasting based on the hourly time-variant model parameters, thereby strengthening our understanding of model behaviors under changing conditions. The proposed framework can also act as a new alternative for flood control and ultimately contribute to the mitigation of flooding disasters.

Determining the response of riparian vegetation and river morphology to drought using Google Earth Engine and machine learning

Riparian vegetation composition and channel morphology are susceptible to long-term alterations caused by external stressors, including climate-change-induced droughts and engineered infrastructures. The objectives of this study were to (1) quantify trends in riparian vegetation and channel/floodplain morphology over large spatial (∼290 km) and temporal scales (∼30 years) and (2) investigate the relationships between hydroclimatic drivers and changes in riparian vegetation and channel morphology. We implemented a random forest classifier via a machine learning technique in Google Earth Engine. The study area was a 290 km reach of the Rio Grande located in New Mexico, USA. We used the combination of remotely sensed data and products (e.g., Landsat imagery, Normalized Difference Vegetation Index (NDVI), and land cover) to characterize vegetation, vegetation cover changes, and river morphology shifts from 1984 to 2020. The trend analysis revealed increased vegetated areas and NDVI (0.0004/yr) during long-term drought. The channel experienced a reduction in width associated with vegetation encroachment and the formation of stable vegetated islands. The streamflow hydrograph characteristics were positively correlated with vegetation cover and channel morphology. Our study contributes novel insights into the long-term riparian ecosystem dynamics under drought stress, informing drought impact mitigation and ecosystem management in arid and semi-arid regions.

A Case Study on GIS Based Method to Develop Geographically Distributed Synthetic Unit Hydrograph using Geo-Spatial Tools in Ungauged Sub-watershed of Ambika River Basin, Gujarat

Abstract In this work, using Snyder, SCS, and the geographically distributed approaches, synthetic unit hydrographs are developed for the recurring flood-impacted ungauged sub-watershed in the Ambika river basin of Gujarat. Due to the scarcity of observed rainfall-runoff data, enforcement of effective flood control measures have not been implemented in this area. Hence the districts in the Ambika river basin regularly experience unexpected floods during the monsoon season, resulting in loss of life and property. The unit hydrograph of a watershed is essential for developing a flood hydrograph, which is useful for flood forecasting. The majority of Indian watersheds are not gauged, and there is not enough rainfall-runoff data to develop a unit hydrograph. However, there are several approaches available that may be used to develop synthetic unit hydrographs using different regionally based empirical equations, and some of them are mentioned above. Using geospatial tools is one of the GIS-based methods for obtaining synthetic unit hydrographs that solely take into account the digital elevation model (DEM) which eliminates the use of regional empirical constants. The geographically distributed synthetic unit hydrograph (GDSUH) approach is employed here on the idea of distributed time-area unit hydrograph and spatial distribution of overland and channel flow velocity. The cumulative travel time map has been divided into isochrones to develop a time-area curve and the resulting unit hydrograph. In this technique, a pure translation flow process is assumed. This approach is most appropriate for watersheds of smaller size. Hence, in this present study, a smaller, unmeasured sub-watershed with an area of 46 km2 is chosen, and the synthetic unit hydrographs are derived using each of the three approaches. The findings from this study were analyzed to determine the most appropriate method for this region. It was observed that the GDSUH is more suitable in comparison to the other two conventional synthetic unit hydrograph approaches. In smaller and un-gauged watersheds, this method gives a conceptual model for forecasting stream flow hydrographs that will capture the geographically distributed nature of the rainfall-runoff process. Additionally, it has the advantages of being simple, quick, inexpensive, and time-saving, especially for smaller regions that lack rainfall-runoff data.

A hybrid machine learning-based multi-DEM ensemble model of river cross-section extraction: Implications on streamflow routing

Accurate representation of river channel and floodplain geometries is of prime concern for streamflow routing and flood inundation modelling under inadequate surveyed river cross-section data-condition, in many rivers across the globe. Due to global coverage and easy accessibility, the public domain medium resolution (1-arc second) DEMs such as SRTM, ASTER, and ALOS-AW3D30, with a linear vertical bias-correction approach, are widely used for hydrodynamic modelling of upland river networks. In this context, this study, for the first time, developed a hybrid machine learning-based Multi-DEM Ensemble (MDE) approach to simulate bias-corrected cross-sections using open source DEMs-extracted river cross-sections. Additionally, the hydraulic suitability of the simulated cross-sections and associated uncertainties were analysed. For this, the routing of streamflow and water level was performed using MIKE11-HD for a low-gradient flood-prone deltaic river basin of Eastern India (∼9000 km2) consisting of 19 river distributary systems. A comprehensive evaluation of the developed hybrid machine learning-based MDE models revealed an excellent performance of the Random Forest-Particle Swarm Optimisation-MDE (RF-PSO-MDE) model (NSE: 0.89–0.95; RMSE: 1.75–2.23 m) and Deep Neural Network-Particle Swarm Optimisation-MDE (DNN-PSO-MDE) model (NSE: 0.89–0.93; RMSE: 2.09–2.27 m) followed by the Support Vector Machine-Particle Swarm Optimisation-MDE (SVM-PSO-MDE) model (NSE: 0.87–0.92 and RMSE: 2.22–2.50 m). The MIKE11-HD simulations using RF-PSO-MDE and DNN-PSO-MDE model-simulated cross-sections were in close agreement with the observed streamflow and water level hydrographs, together with excellent matching of the flood peak, time to flood peak, and streamflow regimes. Predictive uncertainties analysis by the quantile regression technique confirmed an acceptable range of the Mean Prediction Interval and Percentage Interval Coverage Probability for the MIKE11-HD-RFMDE and MIKE11-HD-DNNMDE model variants including the MIKE11-HD-S model. Conclusively, the developed MDE models have potential for generating river cross-sections under sparse bathymetric conditions for the accurate simulation of streamflow and water level estimates using public domain digital elevation models.

The persistence of snow on the ground affects the shape of streamflow hydrographs over space and time: a continental-scale analysis

Snow persistence (SP) is a widely available remotely-sensed measure of snowpack accumulation and ablation, reflecting the duration of snow presence on the ground in a given year. Available local-scale studies showed that SP is associated with the average magnitude of streamflow. However, despite the intuitive relationship between SP and catchment storage/release functioning, the spatial and temporal links between the persistence of snow on the ground and the shape and functionality of streamflow hydrographs were not studied empirically and were not generalized to diverse climatic settings. This study empirically explores the spatial and temporal links that SP has with measures of hydrograph shape and variability during low-flow and high-flow conditions across continent-wide gradients of aridity and seasonality. In arid in-phase and wet out-of-phase climates, higher SP is spatially associated with a damper (i.e., less flashy) streamflow hydrograph during low-flow and high-flow conditions. This is shown by a larger ratio of baseflow to average flow, a larger ratio of extreme low-flow to average flow, lower low-flow variability, and lower high-flow variability. While SP is spatially associated with a damped hydrograph in both arid/in-phase and wet/out-of-phase climates, this effect is stronger in the former region. For example, the size of the nonlinear impact of SP on reducing low-flow and high-flow variabilities is larger in arid in-phase climates (−7.64, −3.44, respectively) than in wet out-of-phase climates (−4.34, −2.02, respectively). Temporal analyses for “typical snow-rich” catchments show that years with relatively higher SP may lead to relatively flashier streamflow hydrographs, with lower baseflow indices, lower ratios of extreme low-flow to average flow, higher ratios of extreme high-flow to average flow and higher high-flow variability. Such results 1) demonstrate the utility of SP as a globally available descriptor of streamflow hydrograph shape and variability in a wide diversity of climatic conditions, 2) highlight that climate-driven snow loss may lead to substantial changes to hydrograph form and functionality, and 3) indicate that space-time symmetry may not be a valid assumption in hydrology.

Identifying hydrologic signatures associated with streamflow depletion caused by groundwater pumping

AbstractGroundwater pumping can reduce streamflow in nearby waterways (‘streamflow depletion’), a process which must be accounted for in integrated management of surface and groundwater resources. However, causal identification of streamflow depletion from hydrographs alone is challenging because pumping impacts are masked by other drivers of hydrologic variability. To identify potential indicators of streamflow depletion, we used synthetic hydrographs and an analytical streamflow depletion model to assess potential pumping impacts on specific hydrograph characteristics (‘hydrologic signatures’) for 215 streamgages spanning the conterminous United States (CONUS). We found that streamflow depletion commonly impacts signatures associated with seasonal and annual low flows and low flow recessions. The largest impacts occurred during dry years, suggesting streamflow depletion may be evident in dry years even where impacts are unmeasurable in wet years. Random forest models indicated that streamflow depletion could significantly impact Annual, Summer, and Fall signatures in most streams. Our finding that multiple hydrologic signatures are consistently responsive to streamflow depletion across CONUS suggests that the underlying hydrological processes linking pumping to streamflow reductions are consistent across diverse settings, information that will aid in identifying indicators of streamflow depletion from streamflow hydrographs.

Understanding the hydrological response of a headwater-dominated catchment by analysis of distributed surface–subsurface interactions

We computationally explore the relationship between surface–subsurface exchange and hydrological response in a headwater-dominated high elevation, mountainous catchment in East River Watershed, Colorado, USA. In order to isolate the effect of surface–subsurface exchange on the hydrological response, we compare three model variations that differ only in soil permeability. Traditional methods of hydrograph analysis that have been developed for headwater catchments may fail to properly characterize catchments, where catchment response is tightly coupled to headwater inflow. Analyzing the spatially distributed hydrological response of such catchments gives additional information on the catchment functioning. Thus, we compute hydrographs, hydrological indices, and spatio-temporal distributions of hydrological variables. The indices and distributions are then linked to the hydrograph at the outlet of the catchment. Our results show that changes in the surface–subsurface exchange fluxes trigger different flow regimes, connectivity dynamics, and runoff generation mechanisms inside the catchment, and hence, affect the distributed hydrological response. Further, changes in surface–subsurface exchange rates lead to a nonlinear change in the degree of connectivity—quantified through the number of disconnected clusters of ponding water—in the catchment. Although the runoff formation in the catchment changes significantly, these changes do not significantly alter the aggregated streamflow hydrograph. This hints at a crucial gap in our ability to infer catchment function from aggregated signatures. We show that while these changes in distributed hydrological response may not always be observable through aggregated hydrological signatures, they can be quantified through the use of indices of connectivity.

Development of channel transmission loss function for WRF_HYDRO modeling of semi-arid regions: The case of wadi Faria, Palestine

In arid and semi-arid regions, the quantitative understanding of the processes of runoff generation and prediction of the streamflow hydrographs and their transmission to the catchment outlet are among the most basic challenges of hydrology. To address these challenges, hydrological modeling has been used to gain understanding of the hydro-logical systems and to provide the required inputs needed for sustainable water resources management. Further-more, coupling hydrological models with climatic models provides even more understanding of the influence of the climatic parameters on hydrological processes and systems, and hence on runoff generation mechanism. In this research, WRF-Hydro (the Weather Research Forecasting model coupled with the Distributed Hydrologic Modeling System) was used for streamflow forecasting in the Faria catchment. Faria (320 km2), located in the northeastern part of the West Bank, Palestine, is a gauged catchment where rainfall and streamflow data are available for more than ten years. This study aims to assess the functionality of the WRF-Hydro to predict reliable streamflow hydro-graphs at the upper part of the catchment. Additionally, a new channel loss function was incorporated into WRF-Hydro to model the amount of water lost in the ephemeral (loosing) streams of the catchment. Hence, the rainfall and streamflow data for the rainy season; 2017-2018.were utilized. Results indicate that WRF-Hydo model is well functioning, but model calibration and validation is still required to further improve the model performance.

Soil moisture routing modeling of targeted biochar amendment in undulating topographies: an analysis of biochar's effects on streamflow

<abstract><p>The effect of biochar on hydrologic fluxes was estimated using a single hillslope version of a gridded soil moisture routing (SMR) model. Five grid cells were aligned linearly with varied slopes to simulate a small undulating hillslope with or without a restrictive layer beneath the soil profile. Biochar amendments (redwood sawdust and wheat straw biochar) at concentrations of 0%, 4%, and 7% were applied to the topmost grid-cell by mass of dry soil. Simulated streamflow hydrographs for restricted and non-restricted soil profiles were manually calibrated with measured Palouse River streamflow data. Evapotranspiration, percolation, lateral flow, baseflow, and streamflow were all modeled yearly. Two generally reported field capacities (FC) in literature at −6 and −33 kPa were considered to assess the effect of biochar. Field capacity considered at −6 kPa corresponds to higher moisture content, and hence higher moisture storage capacity between FC and permanent wilting point than at −33 kPa. At −6 kPa FC, biochar effectively increased evapotranspiration and reduced the lateral flow of the system. Increased soil porosity from biochar amendment enhanced the water holding capacity of the soil and plant available water. These mechanisms impacted the streamflow generated from the system indicating positive outcomes from biochar amendment in both restricted and non-restricted soil profiles. Biochar amendment showed an order of magnitude smaller effects with −33 kPa FC compared to −6 kPa FC; the increased porosity appeared to be less influential at lower field capacity values. Additionally, the results showed that the over-application of coarse biochar might negatively affect retaining soil moisture. These findings point to positive results for using biochar as a water management strategy if applied less than 7% in this study, but further exploration is needed to find the optimum level of biochar with different biochar and soil properties.</p></abstract>

Application of the HEC-RAS Program in the Simulation of the Streamflow Hydrograph for Air Lakitan Watershed

Floods are an issue that results in losses, and thus attempts to solve the problem of flooding are attempts to minimize losses. To mitigate the losses incurred as much as possible, there are several approaches to deal with the loss through incident management and its impacts. The objective of this study is to investigate the variations in water level within Air Lakitan Watershed to monitor the fluctuations of water level for preventing flooding issues in the future. This research was carried out by analyzing the hydrodynamics of flow in irrigation canals in Irrigation Area II with an area of 928 hectares using the HEC-RAS program with rainfall data and flow hydraulics data. The study was carried out in Air Lakitan Watershed in Sumber Harta District, Musi Rawas Regency, Sumatra, Indonesia. Each portion of the studied irrigation canal’s water level and velocity, as measured by a current meter, are shown on graphs, as are the study’s overall conclusions for each observation station along the channel. Simulated data was acquired using a river crossing that is not filled and a discharge of 0.024 m3/s. From this research, it can be concluded that the Log-Pearson Type III distribution is the frequency distribution that matches the hydrological analysis in the research area. This method can be applied in analyses of river levels in other areas with heavy rainfall. Therefore, the water level upstream and downstream is the same at 0.41 m with a discharge of 1 m3/s; the river cross-section downstream with an existing discharge of 0.024 m3/s produces water height as high as 0.08 m and with a flow rate of 0.783 m/s, the water level at the downstream cross-section is filled up to 0.75 m high, and the water level downstream of the irrigation channel is up to 0.40 m.

Spatiotemporal variations of river water turbidity in responding to rainstorm-streamflow processes and farming activities in a mountainous catchment, Lai Chi Wo, Hong Kong, China

River turbidity is an important factor in evaluating environmental water quality, and turbidity dynamics can reflect water sediment changes. During rainfall periods, specifically in mountainous areas, river turbidity varies dramatically, and knowledge of spatiotemporal turbidity variations in association with rainfall features and farming activities is valuable for soil erosion prevention and catchment management. However, due to the difficulties in collecting reliable field turbidity data during rainstorms at a fine temporal scale, our understanding of the features of turbidity variations in mountainous rivers is still vague. This study conducted field measurements of hydrological and environmental variables in a mountainous river, the Lai Chi Wo river, in Hong Kong, China. The study results revealed that variations of turbidity graphs during rainstorms closely match variations of streamflow hydrographs, and the occurrence of the turbidity peaks and water level peaks are almost at the same time. Moreover, the study disclosed that the increasing rates of the turbidity values are closely related to the rainfall intensity at temporal scales of 15 and 20 min, and the impact of farming activities on river turbidity changes is largely dependent on rainfall intensity. In the study area, when the rainfall intensity is larger than 35 mm/hr at a time interval of 15 min, the surface runoff over the farmland would result in higher river water turbidity downstream than that upstream. The study results would enrich our understanding of river water turbidity dynamics at minute scales and be valuable for further exploration of the river water environment in association with turbidity.

Cyber-enabled autocalibration of hydrologic models to support Open Science

Automatic calibration (autocalibration) of models is a standard practice in hydrologic sciences. However, hydrologic modelers, while performing autocalibrations, spend considerable amount of time in data pre-processing, coding, and running simulations rather than focusing on science questions. Such inefficiency, as this paper outlines, stems from: (i) platform dependence, (ii) limited computational resource, (iii) limited programming literacy, (iv) limited model structure and source code literacy, and (v) lack of data-model interoperability in the so-called autocalibration process. By expanding and enhancing an existing web-based modeling platform SWATShare, developed for the Soil and Water Assessment Tool (SWAT) hydrologic model, this paper demonstrates a generalizable pathway to making autocalibration efficient via cyberinfrastructure (CI) solutions. SWATShare is a collaborative platform for sharing and visualization of SWAT models, model results, and metadata online. This paper describes the front and back end architectures of SWATShare for enabling efficient SWAT model autocalibration on the web. In addition, this paper also demonstrates three implementation case studies to validate the autocalibration workflow and results. Results from these implementations show that SWATShare autocalibration can produce streamflow hydrograph and parameters that are comparable with commonly used offline SWATCUP calibration outputs. In some instances, the parameter values from SWATShare calibration are more physically relevant than those from SWATCUP. Although the discussion in this paper is in the context of SWAT and SWATShare, the conceptual and technical design presented here can be used as an Open Science blueprint for similar CI-enabled developments in other hydrologic models, and more importantly, in other domains of Earth system sciences.

AN INTEGRATED STUDY USING GIS AND SWAT METHOD FOR RAINFALL RUNOFF MODELLING IN KULSI WATERSHED, ASSAM

The present study is based on the estimation of runoff using GIS and SWAT in the Kulsi Watershed, Assam. Soil and Water assessment tool (SWAT) is a physically based distributed parameter model which was developed to predict runoff, sediment, erosion from specific watersheds under different input conditions. For understanding the rainfall-runoff behavior of the basin, SWAT hydrological model was used and divided in 19 subwatersheds and 188 Hydrogical Response Units. The watershed comprises of four Land use classifications (River/water bodies, Open forest, dense forests and barren land) with three soil classes. The delineated area of the study is found to be 1754.39 km². The study indicates that the annual rainfall runoff relationship correlates with magnitude of total runoff. From the calibration output, the simulated stream flow hydrograph deviates slightly with the observed stream flow hydrograph. The model was auto calibrated and validated using SWAT-CUP SUFI2 for 10 year i.e. from 1988-1997 .The average Nash Sutcliffe efficiency (NSE) for monthly calibration and validation was found to be 0.18 and 0.70 respectively. The coefficient of determination (R²) value for monthly calibration and validation was found to be 0.31 and 0.73 and RSR value for calibration and validation was found to be 0.91 and 0.55 respectively. The study was also carried out to perform the sensitivity analysis of different parameters responsible for stream flow generation. Based on the analysis, SCS runoff curve number (CN2.mgt), Groundwater "revap" coefficient (GW_REVAP.gw), Groundwater delay days (GW_DELAY.gw), Saturated hydraulic conductivity (SOL_AWC.sol), Base flow alpha factor (ALPHA_BF.gw), Threshold depth of water in the shallow aquifer required for return flow to occur (mm) (GWQMN.gw) were found to be the most sensitive parameters. Overall the SWAT model performance was found to be satisfactory for stream flow simulation and the calibrated model can be used for runoff generation and sustainable water resource projects and development.

Prediction of Runoff in Watersheds Located within Data-Scarce Regions

The interest in the use of mathematical models for the simulation of hydrological processes has largely increased especially in the prediction of runoff. It is the subject of extreme research among engineers and hydrologists. This study attempts to develop a simple conceptual model that reflects the features of the arid environment where the availability of hydrological data is scarce. The model simulates an hourly streamflow hydrograph and the peak flow rate for any given storm. Hourly rainfall, potential evapotranspiration, and streamflow record are the significant input prerequisites for this model. The proposed model applied two (2) different hydrologic routing techniques: the time area curve method (wetted area of the catchment) and the Muskingum method (catchment main channel). The model was calibrated and analyzed based on the data collected from arid catchment in the center of Jordan. The model performance was evaluated via goodness of fit. The simulation of the proposed model fits both (a) observed and simulated streamflow and (b) observed and simulated peak flow rate. The model has the potential to be used for peak discharges’ prediction during a storm period. The modeling approach described in this study has to be tested in additional catchments with appropriate data length in order to attain reliable model parameters.

Use of streamflow indices to identify the catchment drivers of hydrographs

Abstract. Time irreversibility or temporal asymmetry refers to the steeper ascending and gradual descending parts of a streamflow hydrograph. The primary goal of this study is to bring out the distinction between streamflow indices directly linked with rising limbs and falling limbs and to explore their utility in uncovering processes associated with the steeper ascending and gradual descending limbs of the hydrograph within the time-irreversibility paradigm. Different streamflow indices are correlated with the rising and falling limbs and the catchment attributes. The key attributes governing rising and falling limbs are then identified. The contribution of the work is on differentiating hydrographs by their time irreversibility features and offering an alternative way to recognize primary drivers of streamflow hydrographs. A series of spatial maps describing the streamflow indices and their regional variability in the Contiguous United States (CONUS) is introduced here. These indices complement the catchment attributes provided earlier (Addor et al., 2017) for the CAMELS data set. The findings of the study revealed that the elevation, fraction of precipitation falling as snow and depth to bedrock mainly characterize the rising limb density, whereas the aridity and frequency of precipitation influence the rising limb scale parameter. Moreover, the rising limb shape parameter is primarily influenced by the forest fraction, the fraction of precipitation falling as snow, mean slope, mean elevation, sand fraction, and precipitation frequency. It is noted that falling limb density is mainly governed by climate indices, mean elevation, and the fraction of precipitation falling as snow; however, the recession coefficients are controlled by mean elevation, mean slope, clay, the fraction of precipitation falling as snow, forest fraction, and sand fraction.

Challenges of Hydrological Engineering Design in Degrading Permafrost Environment of Russia

The study shows that the current network of hydrometeorological observation in the permafrost zone of Russia is insufficient to provide data for the statistical approaches adopted at the state level for engineering surveys and calculations. The alternative to the financially costly and practically impossible expansion of the monitoring network is the development of hydrological research stations and the implementation of new methods for calculating streamflow characteristics based on mathematical modeling. The data of the Kolyma Water-Balance Station, the first research basin in the world in a permafrost environment (1948–1997), and the process-based hydrological model Hydrograph are applied to simulate streamflow hydrographs in remote mountainous permafrost basins. The satisfactory results confirm that mathematical modeling may substitute or replace statistical approaches in the conditions of extreme data insufficiency. The improvement of the models in a changing climate requires the renewal of historical observations at currently abandoned research stations in Russian permafrost regions. The study is important for forming the state policy in climate change adaptation and mitigation measures.

An Assessment of the Impacts of Snowmelt Rate and Continuity Shifts on Streamflow Dynamics in Three Alpine Watersheds in the Western U.S.

In semiarid to arid regions of the western U.S., river flow availability and variability are highly subject to shifts in snow accumulation and ablation in alpine watersheds. This study aims to examine how shifts in snowmelt rate (SMR) and snow continuity, an indicator of the consistent existence of snow on the ground, affect snow-driven streamflow dynamics in three alpine watersheds in the U.S. Great Basin. To achieve this end, the coupled hydro-ecological simulation system (CHESS) is used to simulate river flow dynamics, and multiple snow metrics are calculated to quantify the variation of SMR and snow continuity, the latter of which is measured by snow persistence (SP), snow residence time (SRT), and snow season length (SSL). Then, a new approach is proposed to partition streamflow into snow-driven and rain-driven streamflow. The statistical analyses indicate that the three alpine watersheds experienced a downward trend in SP, SRT, SSL, and SMR during the study period of 1990–2016 due to regional warming. As a result, the decrease in SMR and the decline in snow continuity shifted the occurrence day of 25% and 50% of the snow-driven cumulative discharge, as well as peak discharge, toward an earlier occurrence. Moreover, the magnitudes of snow-driven annual streamflow, summer baseflow, and peak discharge also decreased due to the declined snow continuity and the reduced SMR. Overall, by using multiple snow and flow metrics, and by partitioning streamflow into snow-driven and rain-driven flow via the newly proposed approach, we found that SMR and snow continuity determine the streamflow hydrographs and magnitudes in the three alpine watersheds. Given that warming can significantly affect snow dynamics in alpine watersheds in semiarid to arid regions, this has important implications for water resource management in the snow-dominated region when facing future climate warming.