Streamflow Patterns Research Articles

Comparative analysis of event runoff coefficients and curve numbers in contrasting urban environments based on observed rainfall-runoff data

Recently, there has been a growing recognition that the generic values of runoff coefficient (C) and curve number (CN) from standard lookup tables in the literature may not accurately capture the specific characteristics of a given catchment. This study aims to estimate and compare the event C and CN of two contrasting urban catchments in Ljubljana, Slovenia using observed rainfall-runoff data. Estimated parameter values were compared to the tabulated values from the literature (i.e., ASCE, 1992; USDA-NRCS, 2004). Seasonal changes of C and CN, along with diurnal streamflow patterns, were also analyzed to understand the eco-hydrological processes in the urban forest. The results demonstrated that the urban mixed forest exhibited high variability of Cs across all rainfall events with a mean and median of 0.11 and 0.062, respectively. Most of the C values observed in the urban area are clustered around the central tendency with a mean and median of 0.60. These mean C values in both catchments were lower than the tabulated values in the ASCE (1992) design manual. Mean and median CN values in the urban area were 95.45 and 96.81, respectively, lower than the urban mixed forest’s CN values of 82.69 and 83.95. An asymptotic CN∞ of 90.69 was found for the urban area and 71.71 for the urban mixed forest. Central tendency-derived CN values tend to be slightly higher than the tabulated values from USDA-NRCS (2004) and gridded values from GCN250 (Jaafar et al., 2019), while asymptotic CN∞ values were lower. Using central tendency measures as the single lumped value of C and CN may provide a reasonable representation of the runoff behavior in the studied urban area. However, this may present certain challenges in the urban forest catchment. Additionally, pre-event soil moisture conditions and specific storm characteristics contributed to the observed variations in C and CN. Bi-monthly analysis showed that C and CN were high during the autumn and winter months. Diurnal streamflow pattern is most prevalent during low-flow and precipitation-free periods while exhibiting seasonal structures in terms of the shape and timing of maxima and minima. Local estimation of C and CN allows for a more tailored representation of the catchment’s hydrological behavior.

A blueprint for coupling a hydrological model with fine- and coarse-scale atmospheric regional climate change models for probabilistic streamflow projections

In cold regions, climate change, including warming and changes in snowmelt dynamics, have profound impacts on streamflow patterns, often leading to flooding events. Understanding the projected changes in hydrometeorological factors contributing to streamflow, such as snowmelt runoff and rain-on-snowmelt, is crucial for effective adaptation and mitigation strategies. It is also essential to consider uncertainty in streamflow projections and incorporate this uncertainty into flood predictions. This study presents a blueprint for calculating probabilistic future streamflow and flow duration curves in a mountainous cold region. The methodology integrated forcings from an atmospheric model (WRF) with a hydrological model (MESH) at fine spatial (4 km) and temporal (3-hourly) resolutions. To account for uncertainty, an ensemble of 15 CanRCM4 regional climate simulations with varying boundary conditions for the RCP 8.5 scenario was utilized. A novel method was developed to perturb the CanRCM4 simulations’ precipitation using a WRF Pseudo Global Warming run to incorporate uncertainty. This comprehensive approach revealed significant uncertainty in streamflow driven by internal variability. WRF-driven simulations without uncertainty showed a decrease in future high streamflows, while the perturbed simulations demonstrated the potential for substantially higher streamflows under climate change. Moreover, the study derived probabilistic flow duration curves from the streamflow projections, aiding in estimating flood frequency for floodplain mapping when driving local hydraulic models. The methodology developed in this work can be extended to other river basins in Canada and elsewhere where gridded downscaled climate model outputs are available.

Enhancing streamflow simulations with gated recurrent units deep learning models in the flood prone region with low-convergence streamflow data

Highly accurate streamflow simulations are essential for water resources and river management. However, there are longstanding challenges for streamflow modeling in the hydrology field. As a result, deep learning methods have been introduced as novel tools and are being used in simulation. This paper proposes a novel approach that utilizes unconventional data preprocessing techniques to improve the accuracy of daily streamflow predictions in flood-prone regions. We applied this approach to a flood-prone area in western Iran and simulated daily streamflow using a deep learning Gated Recurrent Unit (GRU) method under various data selection scenarios. These scenarios focused on identifying the optimal combination of input variables, time steps, and outlier removal techniques. The outlier removal methods investigated in this study include Mahalanobis distance, critical section removal, Z-score, and no removal. The average rainfall of the area, data driven precipitation, Normalized Difference Vegetation Index (NDVI), surface soil moisture, groundwater baseflows, and streamflow in the hydrometric station were evaluated using correlation control method, and inputs with the lowest correlation were removed. Based on the results obtained from the deep learning models produced in the research, it was found that the GRU model, with various modified inputs using Z-score removal, had the best performance. The model had an average of Root Mean Squared Error (RMSE) at 5.24 mm and R2 (Coefficient of Determination) at 0.91 during training, while during validation, it had an RMSE of 7.74 mm and R2 of 0.83. Considering using relatively low convergence data in streamflow simulation in this study, it can be said that the listed scenarios showed an appropriate result in dealing with the data and recognizing the complex pattern of the daily streamflow, and the future studies will show the improvement of the GRU models if higher convergence data will be used.

Investigating the response of groundwater and streamflow to rainfall in a tropical catchment using non-parametric methods

The present study investigates the monthly and seasonal patterns of groundwater levels and streamflow in response to rainfall in a humid tropical catchment in central Kerala, India. The trends were investigated at a temporal scale using data from eight groundwater-level observation wells, four stream-gauging stations and rainfall records of three subcatchments, viz. Cheripad, Pala and Kidangoor. The exercise of trend detection was carried out using two widely adopted non-parametric tests, viz. the Mann–Kendall (MK) test, Spearman's Rho (SR) test and a relatively new visual graphical approach, innovative trend analysis (ITA). The results revealed predominantly rising trends (monthly and seasonal scale) within the catchment, across all three hydrological components examined. Notably, the ITA test identified specific changes in the trend direction within the time series (positive to negative or vice versa), which were not observable in the MK or SR tests. Data published in the Groundwater Book of Kerala for 2021–22 corroborated this increasing trend, showing, on average, a rising trend in well water levels throughout the watershed across pre-monsoon and post-monsoon seasons. This trend was further confirmed using ITA by pairing the observations from the groundwater-level monitoring station at KTM-OW-11 with rainfall data from the nearby gauging station at Erattupettah.

Changes in Streamflow Pattern and Complexity in the Whole Yangtze River Basin

Changes in Streamflow Pattern and Complexity in the Whole Yangtze River Basin

Integrated Modeling of Flow, Soil Erosion, and Nutrient Dynamics in a Regional Watershed: Assessing Natural and Human‐Induced Impacts

AbstractCurrent integrated modeling frameworks for simulating nutrient sources and dynamics are inadequate for large regional watersheds dominated by groundwater‐surface water interactions due to their simplistic representations of groundwater. In this study, we develop a coupled model that integrates comprehensive surface water, 3‐D groundwater, soil erosion, and nutrient processes. The model is intended to enhance the understanding of nutrient dynamics and sources in the Pearl River Basin (PRB). The model exhibits satisfactory performance in simulating streamflow and sediment transport patterns, capturing essential seasonal variations in water quality indicators. Hydrological budget assessments from 2002 to 2020 in the PRB reveal that 54% of precipitation drains into the South China Sea as surface water, while groundwater discharge as baseflow accounts for 18% of the streamflow. The nutrient budget for the PRB indicates that non‐point sources are the dominant contributors to both nitrogen (N) and phosphorus (P), ranging between 64% and 90%. Improved sewage collection and treatment have reduced point source nutrient contributions over the evaluation period. Groundwater remains a significant and consistent source of N, contributing between 11% and 19%. Natural disturbances and fertilization have led to an upward trend in river N inputs, while afforestation and sewage reduction efforts have resulted in a downward trend in river P inputs. Increased fertilization emerges as a central concern for the PRB, suggesting cost‐effective mitigation of fertilizer usage a pragmatic solution. The coupled simulation model developed in this study offers a novel systems approach for basin‐wide nutrient analysis and pollution control strategies, considering both natural and human‐induced disturbances.

Exploring climate-change impacts on streamflow and hydropower potential: insights from CMIP6 multi-GCM analysis

ABSTRACT The generation of hydropower is profoundly influenced by shifts in streamflow patterns induced by climate change. This research examines changes in streamflow and the potential surge in hydropower generation over a span of 35 years (2015–2050) at the Bhakra Dam site within the Upper Sutlej River basin. Employing a deep learning methodology, particularly the long short-term memory (LSTM) model, in conjunction with Coupled Model Intercomparison Project (CMIP) 6 multi-global climate model (GCM), facilitates a thorough analysis of these dynamics. Six out of 14 bias-corrected statistically downscaled datasets (0.25° × 0.25° grid resolution) from CMIP6 multi-GCM were selected based on entropy and combined compromise solution techniques. This innovative approach is utilized to assess streamflows and project potential increases in hydropower at the Bhakra Dam site under shared socioeconomic pathways (SSP) scenarios, specifically SSP245 and SSP585. The results indicate a maximum increase of approximately 15% and 17% in mean monthly streamflow under SSP245 and SSP585, respectively. Moreover, dependability flows calculated at Q50, Q75, and Q90 show respective rises of 13%, 16%, and 17% under SSP245 and 21%, 17%, and 18% under SSP585. The projected hydropower potential exhibits an increase of up to 15.9% and 17.3% under SSP245 and SSP585, respectively.

A diagnostic approach to modeling watersheds with human interference

Most watersheds have human impacts that modify hydrological responses differently over a range of timescales. However, these impacts are not accounted for in most hydrological models. Human impacts in watersheds are diverse and case specific, unlike natural hydrologic processes. Incorporating all plausible human impacts comes at a high data acquisition and modeling cost. This raises the question, which human impacts do we need to incorporate to represent observed streamflow patterns at different timescales? To answer this question, we develop a diagnostic approach to modeling watersheds with human interference. This mixed methods approach is informed by the case history and builds on the top-down hydrological modeling approach where process complexity is incrementally added with changing timescales to identify and respond to changing dominant hydrological processes in a given watershed. Here we implement this modeling approach in the East Fork watershed in California, USA for which data on changes in water imports, withdrawals, irrigation and agriculture land cover is available from the early 1940′s, making it an ideal demonstration case. In the East Fork watershed, we find that incorporation of water imports and rights are sufficient to replicate annual patterns of runoff variability, and that adding crop water demand and irrigation enables replication of monthly and daily patterns, while incorporation of groundwater pumping results in negligible improvements. To demonstrate the capabilities of the diagnostic approach in and beyond this case we conducted two computational experiments: checking for needed model structural change and exploring a counterfactual scenario of intensified agriculture.

Intercomparison of deep learning models in predicting streamflow patterns: insight from CMIP6

This research was carried out to predict daily streamflow for the Swat River Basin, Pakistan through four deep learning (DL) models: Feed Forward Artificial Neural Networks (FFANN), Seasonal Artificial Neural Networks (SANN), Time Lag Artificial Neural Networks (TLANN) and Long Short-Term Memory (LSTM) under two Shared Socioeconomic Pathways (SSPs) 585 and 245. Taylor Diagram, Random Forest, and Gradient Boosting techniques were used to select the best combination of General Circulation Models (GCMs) for Multi-Model Ensemble (MME) computation. MME was computed via the Random Forest technique for Maximum Temperature (Tmax), Minimum Temperature (Tmin), and precipitation for the aforementioned three techniques. The best MME for Tmax, Tmin, and precipitation was rendered by Compromise Programming. The DL models were trained and tested using observed precipitation and temperature as independent variables and discharge as dependent variables. The results of deep learning models were evaluated using statistical performance indicators such as root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R2). The TLANN demonstrated superior performance compared to the other models based on RMSE, MSE, MAE, and R2 during training (65.25 m3/s, 4256.97 m3/s, 46.793 m3/s and 0.7978) and testing (72.06 m3/s, 5192.95 m3/s, 51.363 m3/s and 0.7443) respectively. Subsequently, TLANN was utilized to make predictions based on MME of SSP245 and SSP585 scenarios for future streamflow until the year 2100. These results can be used for planning, management, and policy-making regarding water resources projects in the study area.

Investigating the impact of irrigation practices on hydrologic fluxes in a highly managed river basin

Irrigation practices and sources can have significant impacts on water resources and the hydrologic fluxes that control these resources. To better manage water resources and future water supply, the influence of irrigation practices and management on these hydrologic fluxes should be quantified in time and space at varying scales, under potential irrigation management practices. To fulfill this objective, a surface-subsurface modeling approach was applied to simulate watershed-scale hydrologic processes in the Cache la Poudre River Basin, Colorado, USA (4824 km2), in which both surface water irrigation and groundwater irrigation are prevalent. The model chosen for this study is the watershed model SWAT+, using the spatially distributed, physically based groundwater module gwflow, in which unconfined groundwater storage, flows, and interaction with land surface features are simulated using a collection of grid cells that represent control volumes of the aquifer. Major groundwater inflows and outflows include pumping, recharge, groundwater-channel exchange, groundwater-lake exchange, and tile drainage outflow. To investigate the impact of irrigation practices, detailed surface and groundwater irrigation routines and canal-aquifer interactions were added to the SWAT+ source code, requiring information of irrigation sources and irrigation canal locations throughout the river basin. Model calibration and testing was performed using monthly stream discharge and groundwater head. The calibrated model is used to quantify the impact of surface water and groundwater irrigation scenarios on water availability and hydrologic fluxes within the river basin. A total of 22 scenarios were conducted and grouped into five main groups: irrigation source, irrigation amount, irrigation type, canal bed thickness, and partial or full sealing of earthen irrigation canals. Using groundwater as the only irrigation source decreases groundwater discharge to streams (by 14 %) due to lowering groundwater levels; converting flood irrigation to sprinkler irrigation throughout the basin decreases surface runoff by 22 %; and sealing earthen canals leads to a lowering of groundwater levels, which decreases groundwater discharge to streams by 9 %, leading to an overall decrease in streamflow in the Cache la Poudre River and changes to temporal patterns in streamflow. Overall, irrigation amount and type and canal sealing have a small impact on total groundwater storage, compared to changes in the percent of fields irrigated by groundwater pumping. Results are helpful for informed decision-making in agriculture water management and can lead to sustainable, efficient, and equitable use of water resources, helping to address the challenges posed by water scarcity and environmental conservation.

Single-Frame Lagrangian Tracking Of 3-D Acoustic Streaming Flows Using Digital Defocusing Micro Particle Streak Velocimetry

In this study, a single-frame particle streak velocimetry technique is applied to the classic DDPIV(Digital Defocusing Particle Image Velocimetry) concept to achieve a single-frame 3-D Lagrangian tracking of particles in microscopic flows. The shortcoming of images suffering low signal to noise ratio due to pinholes can be avoided. With the streak resolving algorithm, it allows extended exposure time when streak images are taken, and the single-frame approach also eliminates the need for high-cost and complicated synchronization hardware for double-pulse frame-straddling light sources. For each long-exposure, color-coded images received from each pinhole equipped with red, green or blue color filters, the streak-resolving algorithm is applied first to find each streak on the separate color channel, followed by an one-time triplet-matching process to group the red, green and blue streak projections. From the streak triplets, 3-D reconstruction can be done on all the resolved points on the streak, and trajectory fitting can then be applied to resolve the tracer particle trajectories in the field of view with temporal history. This procedure was applied for visualizing a microscale 3-D acoustic streaming flow pattern induced by a longitudinal spine-shaped fin oscillating at 12kHz. The height of the channel is 1mm with the spine of 0.3mm height and a 30 。 tip angle. Resolved flow fields show that the particle streak images can be resolved with the same level of accuracy to the particle tracking method, while the throughput of velocity vectors can be significantly higher due to multiple velocity vectors can be resolved from each streak triplet. The methodology has the potential to be applied to various applications that can capture long-exposure images and possible to resolve higher-order information such as acceleration and forces applied on the particles or the flow.

Streamflow seasonality in a snow-dwindling world.

Climate warming induces shifts from snow to rain in cold regions1, altering snowpack dynamics with consequent impacts on streamflow that raise challenges to many aspects of ecosystem services2-4. A straightforward conceptual model states that as the fraction of precipitation falling as snow (snowfall fraction) declines, less solid water is stored over the winter and both snowmelt and streamflow shift earlier in season. Yet the responses of streamflow patterns to shifts in snowfall fraction remain uncertain5-9. Here we show that as snowfall fraction declines, the timing of the centre of streamflow mass may be advanced or delayed. Our results, based on analysis of 1950-2020 streamflow measurements across 3,049 snow-affected catchments over the Northern Hemisphere, show that mean snowfall fraction modulates the seasonal response to reductions in snowfall fraction. Specifically, temporal changes in streamflow timing with declining snowfall fraction reveal a gradient from earlier streamflow in snow-rich catchments to delayed streamflow in less snowy catchments. Furthermore, interannual variability of streamflow timing and seasonal variation increase as snowfall fraction decreases across both space and time. Our findings revise the 'less snow equals earlier streamflow' heuristic and instead point towards a complex evolution of seasonal streamflow regimes in a snow-dwindling world.

Evaluating the significance of wetland representation in isotope-enabled distributed hydrologic modeling in mesoscale Precambrian shield watersheds

Evaluation of regional-scale models against both streamflow and stable isotopes of water is a relatively new and evolving area of distributed hydrological modeling. While applications of isoWATFLOOD, an isotope-enabled distributed hydrological model are increasing, evaluation of the impacts of model structural features, such as wetland connectivity, on simulation of runoff components is still limited. Wetlands are important water bodies that impact streamflow generation and its spatial variation in Precambrian shield watersheds in parts of Canada and elsewhere. This study investigates the efficiency of isoWATFLOOD in simulating streamflow and instream δ18O and the effects of model percent connected wetlands (%CW) in a mesoscale (∼12,000 km2) Precambrian Shield watershed in Northeastern Ontario, Canada. Model calibration and validation are performed using daily streamflow for five hydrometric gauging stations and biweekly to monthly instream δ18O from 11 river sampling locations from across the watershed. Five model versions with different %CW are generated, calibrated, and validated separately. Results show all models can simulate the timing and pattern of streamflow and δ18O. %CW impacts simulated streamflow during both calibration and validation periods, calibrated parameter sets and simulated internal hydrological processes. Model performance varies spatially with no single value of %CW providing best performance across sub-catchments with 40 % CW providing best mean streamflow simulations in calibration. Increasing %CW (10 % to 40 %) results in higher contribution of lower soil zone water to streamflow that markedly improves baseflow simulation. This study builds on recent isotope-enabled hydrologic simulation capacity of isoWATFLOOD, contributing new examination of model representation of wetland connectivity, a critical component of Precambrian Shield watersheds and evidence of how stable isotopes can contribute to addressing the challenge of equifinality. Results suggest the need to develop model capability for representation of spatial variation in wetland connectivity to recognize this important aspect of heterogeneity in watersheds influencing hydrologic function.

A warming-induced glacier reduction causes lower streamflow in the upper Tarim River Basin

Study areaThree headwater basins of the Tarim River Study focusThis study built upon existing knowledge by incorporating threshold melting temperatures into the Spatial Processes in Hydrology (SPHY) model and decomposing the streamflow changes into individual runoff components. Additional glacier retreat rate data were used to calibrate the improved SPHY, which provides better constrained estimates of glacier response to climate change. We employed bias-corrected simulations from Coupled Model Intercomparison Project Phase 6 (CMIP6) models to drive the improved SPHY under four future scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5). New hydrological insights for the regionModeling results show accelerated glacier melt in the future, with glacier volume at the end of this century projected to be less than 20% of the current level under SSP5–8.5. Furthermore, future streamflow is expected to decrease, most notably in the Karakash River basin under the SSP1–2.6 scenario. The reduction in streamflow, mainly during the summer months, results from decreased glacier melt runoff, which cannot be offset by increased rainfall runoff. Additionally, the implementation of climate change mitigation measures could delay glacier loss, but the dominant component of runoff would still shift from current glacier melt to future rainfall. Plain language summaryGlobal warming accelerates the hydrological cycle, altering the temporal and spatial patterns of streamflow, especially in cold regions, such as the Tarim River Basin. Understanding future streamflow changes is important for the economic development and ecological protection of this region, which will help to manage the risk of water shortages and to plan engineering works to mitigate water shortage crises. We therefore used state-of-the-art climate simulation to project future streamflow changes over three basins of the Upper Tarim River. We found that under all future scenarios, runoff will decrease in these basins. This decrease is mainly due to glacier retreat and cannot be mitigated by anticipated increases in rainfall in the future.

Interpretable and explainable hybrid model for daily streamflow prediction based on multi-factor drivers.

Streamflow time series data typically exhibit nonlinear and nonstationary characteristics that complicate precise estimation. Recently, multifactorial machine learning (ML) models have been developed to enhance the performance of streamflow predictions. However, the lack of interpretability within these ML models raises concerns about their inner workings and reliability. This paper introduces an innovative hybrid architecture, the TCN-LSTM-Multihead-Attention model, which combines two layers of temporal convolutional networks (TCN) followed by one layer of long short-term memory (LSTM) units, integrated with a Multihead-Attention mechanism for predicting streamflow with streamflow causation-driven prediction samples (RCDP), employing local and global interpretability studies through Shapley values and partial dependency analysis. The find_peaks method was used to identify peak flow events in the test dataset, validating the model's generality and uncovering the physical causative patterns of streamflow. The results show that (1) compared to the LSTM model with the same hyperparameter settings, the proposed TCN-LSTM-Multihead-Attention hybrid model increased the R2 by 52.9%, 2.5%, 43.1%, and 10.7% respectively at four stations in the test set predictions using RCDP samples. Moreover, comparing the prediction results of the hybrid model under different samples in Hengshan station, the R2 for RCDP increased by 5.06% and 1.22% compared to streamflow autoregressive prediction samples (RAP) and meteorological-soil volumetric water content coupled autoregressive prediction samples (MCSAP) respectively. (2) Historical streamflow data from the preceding 3days predominantly influences predictions due to strong autocorrelation, with flow quantity (Q) typically emerging as the most significant feature alongside precipitation (P), surface soil moisture (SSM), and adjacent station flow data. (3) During periods of low and normal flow, historical data remains the most crucial factor; however, during flood periods, the roles of upstream inflow and precipitation become significantly more pronounced. This model facilitates the identification and quantification of various hydrodynamic impacts on flow predictions, including upstream flood propagation, precipitation, and soil moisture conditions. It also elucidates the model's nonlinear relationships and threshold responses, thereby enhancing the interpretability and reliability of streamflow predictions.

Impact of Soil Moisture Dynamics and Precipitation Pattern on UK Urban Pluvial Flood Hazards Under Climate Change

AbstractThe diversity of flood‐generating mechanisms superimposed on catchment physiographic features with non‐stationary meteorological drivers makes future flood hazard assessment a grand challenge. To date, many studies have examined patterns in rainfall and streamflow, but far fewer have investigated trends in the other drivers of flooding. The complex transfer function between precipitation and flooding makes it potentially misleading to simply look at the change in rainfall to express the hazard. Furthermore, there are very few studies that have directly used output from km‐scale climate models in flood modeling. Coarse resolution climate data sets may not credibly resolve local climate and weather extremes. Changes in rainfall distribution and antecedent moisture over extended time periods due to climate change have so far been ignored when assessing urban pluvial flood risk. In this paper, an urban flood hazard assessment framework using the latest 2.2 km resolution UK Climate Projections Local is proposed. Global warming induced changes in pluvial flood risks under RCP8.5 are projected, focusing on the impact of changing precipitation patterns and soil moisture dynamics on flood generation. Results indicate a strong increase in the frequency of occurrence of extreme floods, and the resultant future (2060–2080) annual flood volume is expected to increase up to 52.6% relative to 1980–2000 over a major UK urban region, and these patterns are likely to hold more generally elsewhere in the UK. Shifts to a later occurrence of extreme flooding is identified under global warming. Previous studies that have neglected soil moisture dynamics are unlikely to give accurate flood estimates.

Dry, drier, driest: Differentiating flow patterns across a gradient of intermittency

AbstractIntermittent streams exhibit regular patterns of drying and are widespread, but the patterns of drying among streams within geographic proximity are not fully understood. We compared annual patterns of flow and drying among 10 intermittent streams within a single drainage basin and assessed how traditional hydrologic metrics described variation between streams. We installed stream intermittency sensors and evaluated stage height using low‐cost methods and evaluated landscape factors as potential drivers of flow patterns. Intermittent streams varied based on both high‐ and low‐flow metrics, driven by a variety of landscape‐level factors, especially watershed size. Additionally, we compared the observed flow regimes within our system with predictions generated using an established Soil and Water Assessment Tool, finding that modeled streamflow patterns generally underrepresented observed drying within the system.

Determining Streamflow Patterns in North Macedonia Using PCA and AHC Analysis

Determining Streamflow Patterns in North Macedonia Using PCA and AHC Analysis

Greenup Variability Impact on Seasonal Streamflow and Soil Moisture Dynamics in Humid, Temperate Forests

AbstractIn this study, we investigate how seasonal streamflow and soil moisture patterns have responded to variability in vegetation phenology in humid, temperate forested watersheds without significant seasonal snowmelt over the last four decades. We characterize spring streamflow peaks using 50th percentiles of cumulative daily precipitation, streamflow, and soil moisture measurements, and investigate interactions with remotely sensed, greenup anomalies. After removing a dominant precipitation control, 1‐day earlier greenup is usually associated with about 1‐day early spring flow peak at four low‐elevation deciduous catchments using both sequential and multiple linear regressions. This indicates that the strong dependency of seasonal flow regimes on precipitation is mediated by vegetation seasonality, especially by greenup variability. In contrast, we find less significant correlations of the greenup anomalies on flow percentiles from two paired evergreen and two high‐elevation deciduous catchments. At a plot scale, similar correlations were found only at an upslope topographic position, where precipitation also showed tighter coupling with moisture seasonal patterns than downslope. Our study suggests that rainfall‐runoff and rainfall‐soil moisture relations have been closely mediated by vegetation seasonality in deciduous forests, especially by greenup anomalies, but patterned along topoclimate and hillslope gradients. This study emphasizes that it is important to understand phenological responses to ongoing climate change (in both long‐term and interannual variability) for prediction of seasonal flow regimes especially in deciduous forested catchments.

Integrated hydrological modelling of two contrasting watersheds with a terminal reservoir in the Upper Tapi River basin, India

Abstract The present study developed an integrated hydrologic model for sustainable utilisation and water management in two complex watersheds with varying physioclimatic features and reservoirs. The soil and water assessment tool (SWAT) is used for predicting integrated inflows into the Hatnur reservoir from the Burhanpur and Purna watersheds of the Upper Tapi River basin, while outflows are simulated using a rule curve. The influence of watershed complexities on hydrological model parameters and the watershed processes are investigated using extensive multisite and multivariable calibration (1998–2007) and validation (2008–2013) approaches, including sensitivity and uncertainty analyses. The sensitive parameters are related to curve number (CN), groundwater, slope, and main channel characteristics. The annual streamflow (m3/km2/mm of rainfall) in the Burhanpur watershed is 568.7, which is 4.2 times higher than the Purna watershed's streamflow of 136.2. The hypsometric analysis, areal rainfall, and flow duration curves revealed a substantially different streamflow pattern and a larger coefficient of variation in the spatial distribution of water balance components over sub-watersheds in the Burhanpur watershed compared to the Purna watershed due to diverse topographic features. The developed model would be useful for planning controlled releases from the terminal reservoir to mitigate hazards in the downstream reaches of the Tapi River basin.