Regional Streamflow Research Articles

Improved streamflow simulations in hydrologically diverse basins using physically informed deep learning models

Physically informed deep learning models, especially Long Short-Term Memory (LSTM) networks, have shown promise in large-scale streamflow simulations. However, an in-depth understanding of the relative contribution of physical information in deep learning models has been missing. Using a large-sample testbed of 220 catchments in hydrologically diverse regions of the Indian subcontinent, we quantify the impact of incremental addition of physical information on model performance using multiple variants of the LSTM model based on various combinations of static catchment attributes and simulated land surface states. We found that LSTM models trained with catchment geophysics as additional input outperformed the base LSTM model in terms of the nationwide median Kling-Gupta Efficiency (KGE) of in-sample catchments, increasing KGE from 0.32 to 0.60. Additionally, the model retained significant prediction skill in out-of-sample catchments, demonstrating that a pre-trained LSTM model can be a powerful tool to predict streamflow in data-scarce regions.

Hydrological connectivity drives intra- and inter-annual variation in water quality in an intermittent stream network in a mixed land use catchment under drought

We monitored the spatio-temporal variation of connectivity and linked water quality (WQ) in an intermittent stream network draining a mixed land use, lowland catchment in NE Germany. The monitoring period (2018–2022) coincided with four years of variable hydroclimate, though all years had negative rainfall anomalies compared to the long-term average. Correspondingly, streamflow became more intermittent (in terms of both the longevity and frequency of no-flows), with prolonged periods of no surface water flow in the summer and autumn. Despite inter-annual variation in hydroclimate and length of no-flow periods, in each of the four years the catchment showed three distinct seasonal phases of hydrological connection and disconnection in the channel network which has important implications for WQ. Autumn and early winter were characterised by a connecting phase as spatially variable streamflows were initialized in response to rising water tables following increased rainfall and reduced evapotranspiration as temperatures declined. The winter and early spring were charactered by a fully connected phase of the channel network where streamflows increased at the time of lowest temperatures. The late spring and early summer were characterized by a disconnecting phase as flow gradually ceased and the channel network began to fragment. A wetland in the centre of the catchment saw both the earliest and latest expression of streamflow, with the lower catchment downstream of this taking the longest to connect. The WQ is typical for a eutrophic lowland catchment and spatial variation is primarily related to soils and land use. During the connecting phase, stream WQ reflected that of groundwater though mobilization of solutes from the rewetting riparian area and channel bed also occurred. During the fully connected phase, streamwater was enriched by NO3 from soilwater and agricultural drainage. During the disconnecting phase, lower flows and higher temperatures increased the intensity of in-stream biogeochemical interactions with mobilization of P, Fe and Mn associated with declining oxygen levels and release of dissolved organic carbon (DOC) concentrations. Inter-annual variations in WQ related to how hydroclimate and antecedent catchment wetness regulated the initiation, longevity and cessation of connection each winter. Future climate change is likely to drive increasing intermittency in streamflow in many lowland regions with implications for local and downstream ecosystem services.

Effects of Climate Change and Human Activities on Streamflow in Arid Alpine Water Source Regions: A Case Study of the Shiyang River, China

Climate change and human activities were identified as the primary drivers of streamflow in arid alpine regions. However, limitations in observational data have resulted in a limited understanding of streamflow changes in these water sources, which hinders efforts to adapt to ongoing climate change and to formulate effective streamflow management policies. Here, we use the four main tributaries in the upper reach of the Shiyang River in China as a case study to investigate the long-term trends in streamflow within arid alpine water sources, quantifying the individual contributions of climate change and human activities to these changes. The findings revealed that temperatures and precipitation in arid alpine regions have risen over the past 40 years. Although the warming trend has been significant, it has slowed in recent years. Nevertheless, three-quarters of the rivers are experiencing a decline in streamflow. The land types within the watershed remain relatively stable, with land use and cover change (LUCC) primarily occurring in the Gulang River watershed. Climate change has significantly affected streamflow change in high and rugged terrains, with an influence exceeding 70%. For example, Jingta River showed an impact of 118.79%, Zamu River 84.00%, and Huangyang River 71.43%. Human-driven LUCC, such as the expansion of cultivated and urban land, have led to increased water consumption, resulting in reduced streamflow. This effect is particularly pronounced in the low-lying and gently undulating areas of the Gulang River, where LUCC account for 78.68% of the change in streamflow. As human activities intensify and temperatures continue to rise, further declines in streamflow are projected, highlighting the urgent need for effective water resource management. These insights highlight the urgent need for targeted mitigation and adaptation strategies to confront the water scarcity challenges faced by these vulnerable regions.

Predicting future impacts of climate and land use change on streamflow in the middle reaches of China's Yellow River

With increasing temperatures, changing weather patterns and ongoing development, it is becoming increasingly important to clarify the evolution mechanism of future regional streamflow processes and their controlling factors. In this study, an integrated framework for watershed streamflow prediction based on a Global Climate Model (GCM), the Patch-generating Land Use Simulation model (PLUS), and the Soil and Water Assessment Tool (SWAT) was proposed in the middle Yellow River. The results indicate that, compared with the baseline period (1989–2018), levels of precipitation and maximum and minimum temperatures are expected to increase in the next 30 years, resulting in a warmer and wetter regional climate. Under various climate scenarios, the annual streamflow is projected to increase by 49.2–115.1%. The acreage of various land types may have tended to be saturated, and the main land types such as cropland, forest and grassland have little change (−6.6%-0.6%), so the impact on streamflow will be correspondingly reduced. Under various land use scenarios, the annual streamflow is projected to increase by 5.0%–7.3%. The annual average streamflow trends under the combined climate and land use scenarios are consistent with the climate change scenarios, while the mean values corresponding to the combined scenarios are higher than those of the single scenario. Findings show that climate change is the main driver influencing streamflow, with a contribution of 86.3%–95.1%. This study deepens understanding of the change pattern and influence mechanism of the streamflow process, which can provide a scientific basis for the development and refinement of regional ecological construction plans.

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 streamflow extremes, while the perturbed simulations demonstrated the potential for substantially higher values 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.

The Streamflow Response to Multi‐Day Warm Anomaly Events: Sensitivity to Future Warming and Spatiotemporal Variability by Event Magnitude

AbstractPersistent warm temperature anomalies can drive streamflow in regions where snow and glacier melt are important constituents of streamflow. However, the spatiotemporal variability of the streamflow response depends on both the magnitude of the forcing temperature anomalies and the nature of the underlying hydrological system. Here we ask: when, where, and for what magnitude of temperature anomalies will the streamflow response change most rapidly under warming? We use observed streamflow and temperature for 868 basins across Canada to quantify the streamflow response during warm temperature anomalies and how such responses vary in space, time, and by anomaly magnitude. We first identify two temporal modes of the streamflow response, one in autumn and one in spring, the relative strength of which varies by climate. We then use sinusoidal approximations of seasonal temperature cycles to characterize the sensitivity of such modes to changes in annual temperature. At individual basins, we find that relative to moderate warm events, the streamflow response to more extreme warm events is more sensitive to changes in mean annual temperatures, and this sensitivity is greatest in the coastal, southern, and central regions of Canada. Our results have implications for how the hydrological impacts of extreme events, such as heatwaves, will change in space and time under future climate change.

Using national hydrologic models to obtain regional climate change impacts on streamflow basins with unrepresented processes

Climate change is increasingly impacting water availability. National-scale hydrologic models simulate streamflow resulting from many important processes, but often without processes such as human water use and management activities. This work explores and tests methods to account for such omitted processes using one national-scale hydrologic model. Two bias correction methods, Flow Duration Curve (FDC) and Auto-Regressive Integrated Moving Average (ARIMA), are tested on streamflow simulated by the US Geological Survey National Hydrologic Model (NHM-PRMS), which omits irrigation pumping. A semi-arid agricultural case study is used. FDC and ARIMA perform better for correcting low and high flows, respectively. A hybrid method performs well at both low and high flows; typical Nash-Sutcliffe values increased from <-1.00 to about 0.75. Results suggest methods with which national-scale hydrologic models can be bias-corrected for omitted processes to improve regional streamflow estimates. Utility of these correction methods in simulation of future projections is discussed.

Mechanisms of Suprapermafrost Groundwater Recharge Streamflow in Alpine Permafrost Regions: Insights From Young Water Fraction Analysis

AbstractThis study investigates the temporal processes of suprapermafrost groundwater (SPG)‐supplied streamflow in alpine permafrost regions, aiming to fill the gap in understanding this process from a water‐age perspective. Precipitation, streamflow, and SPG samples were collected from the Three‐Rivers Headwaters Region (TRHR). We defined the physical meaning of Fyw (the young water fraction) of the SPG and calculated it for the first time. The results showed that in the TRHR, the SPG mean travel time (MTT) was 159 days, and approximately 46.4% of SPG was younger than 77 days, whereas the streamflow MTT was 342 days, and approximately 12.2% of the streamflow was younger than 97 days. The correlation analysis revealed that various climatic factors played dominant roles in the recharge time variations of the SPG‐supplied streamflow within the TRHR. The SPG recharge rate did not significantly affect the streamflow Fyw; however, the thickness of the active layer ultimately controlled the SPG transit time distribution. Regression analysis further demonstrated the nonlinear impact of precipitation, average temperature, and average freezing days on SPG Fyw, which is closely related to seasonal freeze–thaw heat conduction and groundwater heat advection in the active layer. During the initial ablation period, the streamflow was primarily recharged by young SPG, resulting in a short‐tail travel time distribution. Our findings provide valuable insights into runoff generation and concentration processes in permafrost regions and have important implications for water resource management.

Comparative analysis of HEC-HMS and SWAT hydrological models for simulating the streamflow in sub-humid tropical region in India.

Assessment of water availability in sub-humid regions is important due to distinct climatic and environmental conditions. In this study, Soil and Water Assessment Tool (SWAT) and Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) models have been assessed in simulating streamflows in the sub-humid tropical Kabini basin in Kerala, India, spanning 1260 km2. Calibration and validation utilized daily weather data from 1997 to 2015 from the Muthankera gauging station. The study investigated the impact of routing methods on runoff simulation in the ArcSWAT, exploring Muskingum and Variable Storage methods. Evaluation metrics encompassed Nash-Sutcliffe Efïciency (NSE), Coefficient of Determination (R2), Percent bias (PBIAS), RMSE-observations standard deviation ratio (RSR), and Peak Percent Threshold Statistics (PPTS) approach for high-flow values. The result indicates that HEC-HMS outperforms SWAT concerning R2 and NSE values during daily calibration and validation. Monthly simulations showed HEC-HMS closely aligning with SWAT (Variable storage), outperforming SWAT (Muskingum). The PPTS approach proved effective in simulating high-flow values. Both models exhibited proficiency in streamflow analysis within the study area, promising predictive potential for future hydrological studies in sub-humid regions.

Hydrological changes in a plain basin in central Argentina following expansion of rainfed agriculture and climate change

AbstractThe characterization of long‐term streamflow in regions undergoing climatic change and agricultural expansion is relevant for achieving sustainable development goals and for assessing the vulnerability of water‐dependent populations and agricultural activities. The objective of this work was to characterize the temporal patterns of water yield in the plain basin of the Carcarañá River (33,063 km2), located in central Argentina and to analyse its relationship with a fast expansion of rainfed cultivation and climate change. The streamflow data for the period 1980–2020 were analysed in conjunction with climatic data (rainfall, reference evapotranspiration), satellite data (NDVI) and cropping statistics (sown area of summer crops) data. The annual water yield averaged ~10% of the rainfall and showed a clear upward trend throughout the study period, both in absolute terms and relative to rainfall (i.e., runoff coefficient), which was not explained by rainfall or reference evapotranspiration temporal patterns. Conversely, we found that the trend in water yield was positively associated with the agricultural area (p &lt; 0.05), which more than doubled during the study period (from 29% to 66%). Likewise, the mean NDVI of the basin, a proxy for primary productivity and vegetation transpiration, has decreased steadily over the last 20 years (p &lt; 0.05). The separation between base flow and quick flow suggested that both flows increased during the analysed period (p &lt; 0.05), though the latter would have been more relevant in explaining the trend observed in total flow. Taken together, our results suggest that agricultural expansion, rather than climate change, is the dominant factor explaining the hydrological changes observed in the study basin. Understanding the key role of land use in shaping the hydrology of a landscape is critical to developing policies and practices for more efficient and sustainable use of environmental resources.

Response of Streamflow to Future Land Use and Cover Change and Climate Change in the Source Region of the Yellow River

This study utilizes meteorological and leaf area index (LAI) data for three shared socioeconomic pathways (SSP1–2.6, SSP2–4.5, and SSP5–8.5) from four general circulation models (GCMs) of the sixth climate model intercomparison project (CMIP6) spanning from 2015 to 2099. Employing calibrated data and incorporating future land use data under three SSPs, the distributed hydrology soil vegetation model (DHSVM) is employed to simulate streamflow in the source region of the Yellow River (SRYR). The research aims to elucidate variations in streamflow across different future scenarios and to estimate extreme streamflow events and temporal distribution changes under future land use and cover change (LUCC) and climate change scenarios. The main conclusions are as follows: The grassland status in the SRYR will significantly improve from 2020 to 2099, with noticeable increases in temperature, precipitation, and longwave radiation, alongside a pronounced decrease in wind speed. The probability of flooding events increases in the future, although the magnitude of the increase diminishes over time. Both LUCC and climate change contribute to an increase in the multi-year average streamflow in the region, with respective increments of 48.8%, 24.5%, and 18.9% under SSP1–2.6, SSP2–4.5, and SSP5–8.5. Notably, the fluctuation in streamflow is most pronounced under SSP5–8.5. In SSP1–2.6, the increase in streamflow during the near future (2020–2059) exceeds that of the distant future (2059–2099). Seasonal variations in streamflow intensify across most scenarios, leading to a more uneven distribution of streamflow throughout the year and an extension of the flood season.

Characteristics of glacier ice melt runoff in three sub-basins in Urumqi River basin, eastern Tien Shan

Study regionThe Urumqi River basin located in eastern Tien Shan in Central Aisa Study focusGlacier runoff plays a pivotal role in water resources and stabilizing streamflow in mountainous regions. To assess the characteristics of glacier ice melt runoff in sub-basins within a single basin, three sub-basins with glacier ratios varying from 4% to 46% in the Urumqi River basin are investigated. Through the simulation by HBV light model on the basis of the observed meteorological and hydrological data. The characteristics and behaviour of glacier ice melt runoff in the three sub-basins are analysed. New hydrological insights for the regionIt was found that both the contribution ratios of ice melt runoff and glacier runoff increase linearly with the increasing glacier ratio for the three catchments, rather than logarithmically or exponentially as observed in previous studies. This is due to the relatively high contributions of ice melt and glacier runoff to river flow in a catchment characterized by high elevation and extensive glacier coverage (Catchment 1), resulting from the coincidence of summer precipitation maxima with snow and ice melt in this region. The coefficient of variations (CV) of river flow tends to decrease with the decreasing glacier ratio in sub-basins in the Urumqi River basin, indicating that river flow becomes more stable as it flows farther from the headwater in the Urumqi River basin. The lowest glacierized Catchment 3 exhibited the minimum CV value, demonstrating a stable outflow.

Spatial‐Temporal Differentiation of Supra‐ and Sub‐Permafrost Groundwater Contributions to River Runoff in the Eurasian Arctic and Qinghai‐Tibet Plateau Permafrost Regions

AbstractSupra‐ and sub‐permafrost groundwater are the two main components of groundwater in permafrost regions. However, due to the lack of groundwater observational data, the spatial‐temporal differentiation of these groundwater components in permafrost basins remains unclear. Based on flow data from 17 hydrological stations in five permafrost rivers within the Eurasian Arctic and Qinghai‐Tibet Plateau permafrost regions, this study tries to determine the proportion of supra‐ and sub‐permafrost groundwater through the corresponding relationship between baseflow separation and baseflow index. The results showed that the annual average contribution of supra‐ and sub‐permafrost groundwater in river runoff to total streamflow in the Yangtze River source basin was 36.81% and 14.56%, respectively. Correspondingly, the Yellow River source basin was 36.58% and 24.46%, the Ob River basin was 37.05% and 26.83%, the Yenisei River basin was 28.80% and 36.56%, and the Lena River basin was 39.13% and 9.54%. Over the past 50–80 years, the ratio of sub‐permafrost groundwater discharge to river runoff and the flux of sub‐permafrost groundwater have shown an increasing trend in all study basins, which was significantly affected by air temperature and permafrost area. Relative contribution of supra‐permafrost groundwater exhibits a significant positive correlation with precipitation and permafrost area. Air temperature has both positive and negative effects on supra‐permafrost groundwater discharge, leading to a rising or falling trend of supra‐permafrost groundwater discharge. In the future, it is necessary to further explore the complex effects of groundwater discharge variations on streamflow in permafrost regions under climate warming.

Examining the complex relations between climate and streamflow in the mid-atlantic region of the United States

Abstract We explored the complex relations between climate and streamflow in the Mid-Atlantic region of the United States. In 124 watersheds across this region, we quantified spatial and temporal variation in air temperature (AT), precipitation (P), and streamflow (Q) from 1981 through 2020. Upward directional trends in monthly values of AT, P, and Q indicated an increase of 0.27–1.9 degrees Celsius, 0.12–1.9 millimeters day−1, and 0.01–7.1 cubic meters s−1 day−1, respectively, over the 40-year period. Comparison of the first 20 years to the last 20 years of data indicated an acceleration in the trend slopes in AT, P, and Q. Changes also were observed in temporal trends in the center of volume (CV) of both P and Q, which generally occurred later in the year; the 7-day mean low flow increased, and the annual day of occurrence of the 7-day mean low flow occurred earlier in the year. Principal components analysis revealed differences in P, Q, CVP, and CVQ trends in watersheds with median elevations greater than and less than 400 meters, as well as by latitude. A seasonal analysis revealed that P increased throughout the study area in spring, summer, and fall but decreased in winter. AT, P, and Q have broadly increased across the region over the 40-year period, and the temporal, spatial, and seasonal changes in P have affected Q. Results highlight the strong couplings between climatic variability and watershed responses.

Retracted: Spatiotemporal convolutional long short-term memory for regional streamflow predictions

Rainfall-runoff (RR) modelling is a challenging task in hydrology, especially at the regional scale. This work presents an approach to simultaneously predict daily streamflow in 86 catchments across the US using a sequential CNN-LSTM deep learning architecture. The model effectively incorporates both spatial and temporal information, leveraging the CNN to encode spatial patterns and the LSTM to learn their temporal relations. For training, a year-long spatially distributed input with precipitation, maximum temperature, and minimum temperature for each day was used to predict one-day streamflow. The trained CNN-LSTM model was further fine-tuned for three local sub-clusters of the 86 stations, assessing the significance of fine-tuning in model performance. The CNN-LSTM model, post fine-tuning, exhibited strong predictive capabilities with a median Nash-Sutcliffe efficiency (NSE) of 0.62 over the test period. Remarkably, 65% of the 86 stations achieved NSE values greater than 0.6. The performance of the model was also compared to different deep learning models trained using a similar setup (CNN, LSTM, ANN). An LSTM model was also developed and trained individually to predict for each of the stations using local data. The CNN-LSTM model outperformed all the models which was trained regionally, and achieved a comparable performance to the local LSTM model. Fine-tuning improved the performance of all models during the test period. The results highlight the potential of the CNN-LSTM approach for regional RR modelling by effectively capturing complex spatiotemporal patterns inherent in the RR process.

Effects of altered streamflow on macroinvertebrate taxonomic richness and composition in the Goulburn River, Australia

River systems have been stressed by the construction of dams and regulation structures which influence aquatic ecosystem integrity. Previous studies considered the general significance of regional streamflow regimes for aquatic communities, but they did not investigate the influence of specific components of flow regimes on aquatic ecosystems under the combined impact of regulation and extreme drought events, limiting our ability to design and implement precise environmental flow management strategies. This study aims to quantify the relationship between macroinvertebrate biotic indices and ecologically important streamflow characteristics derived from five natural flow regime components by investigating the spatiotemporal variation in the macroinvertebrate assemblage in regulated and unregulated reaches and identifying specific flow indices that have a direct impact on macroinvertebrates in the Goulburn basin in Victoria, Australia during the Millennium Drought period. The relationship between dominant flow metrics and macroinvertebrates indices was investigated using boosted regression trees (BRT). The results revealed a significant difference in hydrological variability between regulated and unregulated reaches. The regulated reaches demonstrated reduced hydrological variability during low flow periods, and rapid increase in discharge during high flow periods when compared to unregulated reach. Unregulated reach had 38% more taxa richness than regulated reach impacted by hydropeaking. Eight indicator taxa were identified in the unregulated reach, and they exhibited a higher Stream Invertebrate Grade Number Average Level (SIGNAL 2) score, indicating that they were highly sensitive species. The maximum flow in June was the most important flow parameter that influences the macroinvertebrate indices as per the BRT model. Better management of environmental flows will benefit from identifying which aspects of the natural flow regime impact stream ecosystems and predicting the consequences of altered flow regimes on aquatic ecosystems.

Modeling the impact of climate change on streamflow in glacier/snow-fed northern Tianshan basin

Study regionUrumqi River headwater region in eastern Tianshan, central Asia. Study focusClimate change is anticipated to accelerate glacier shrinkage and alter hydrological conditions, causing variations in the runoff patterns in the catchment and significantly threatening the regional water resources. However, few models exhibit adequate performance to simulate both surface alterations and glacier/snow runoff. Therefore, this study combined the glacier module with the Soil and Water Assessment Tool (SWAT) model to estimate the effect of climate change on the streamflow in the Urumqi River headwater region. The Urumqi River Headwater region is representative because of its long data series, viatal location, and local water availability, and it contains the longest-observed reference glacier (Urumqi Glacier No.1) in China, which spans the period from 1958 to the present. New hydrological insights for the regionThe SWAT model performed satisfactorily for both calibration (1983–2005) and validation (2006–2016) periods with a Nash-Sutcliffe efficiency (NSE) greater than 0.80. The water balance analysis suggested that the snow/glacier melt contributed approximately 25% to the water yield. At the end of the 21st century, the temperature would increase by 2.4–3.8 °C while the precipitation would decrease by 1–2% under two future scenarios (ssp245 and ssp585). Thus, a 34–36% reduction in streamflow was projected due to above climate change impacts. This information would contribute to the development of adaptation strategies for sustainable water resource management.

A hydrologic and river ice modelling framework for assessing ungauged sub-basin streamflow impact in a large cold-region river basin

ABSTRACT Modelling snow- and ice-affected streamflow in cold regions is challenging. Neglecting the streamflow from ungauged regions/sub-basins of a river basin in the inflow boundaries of a river ice model adds further uncertainties. This study combined a river ice model (River1D) with a hydrological model (Soil and Water Assessment Tool, SWAT) to investigate the impacts of ungauged sub-basin streamflow on peak flow simulation under both open water and river ice breakup conditions in the Peace River Basin (PRB). Results showed that ungauged sub-basins of the PRB can greatly affect peak flow simulation of the River1D for both open water and river ice breakup events, especially for flood events. Although they represent only about 26.54% of the whole modelled area in the PRB, the SWAT model simulated results show that the ungauged sub-basins can contribute nearly 50% of peak flow for the open water and river ice breakup flood events.

Improved prediction of monthly streamflow in a mountainous region by Metaheuristic-Enhanced deep learning and machine learning models using hydroclimatic data

Improved prediction of monthly streamflow in a mountainous region by Metaheuristic-Enhanced deep learning and machine learning models using hydroclimatic data

Impacts of longleaf pine (Pinus palustris Mill.) on long-term hydrology at the watershed scale

Threats from climate change and growing populations require innovative solutions for restoring streamflow in many regions. In the arid western U.S., attempts to increase streamflow (Q) through forest management have had mixed results, but these approaches may be more successful in the eastern U.S. where greater precipitation (P) and lower evapotranspiration (ET) offer greater potential to increase Q by reducing ET. Longleaf pine (Pinus palustris Mill.) (LLP) woodlands, once the dominant land cover in the southeastern United States, often have lower ET than other forest types but it is unclear how longleaf pine cover impacts watershed-scale hydrology. To address this question, we analyzed 21 gaged rural watersheds. We estimated annual water balance ET (ETwb) as the difference between precipitation (P) and streamflow (Q) between 1989 and 2021 and quantified low flow rates (7Q10) among watersheds with high and low LLP cover. To control for climate variability among watersheds, we compared variation in hydrology metrics with biotic and abiotic variables using the Budyko equation (ETBudyko) to understand the differences between the two ET estimates (∆ET). Watersheds with 15–72 % LLP cover had 17 % greater mean annual Q, 7 % lower annual ETwb, and 92 % greater 7Q10 low flow rates than watersheds with <3 % LLP. LLP cover decreased ET and increased Q by 2.4 mm or 0.15 % Q/P per 1 % of watershed area, but only when LLP was managed as open woodlands. Our results demonstrate that ecological forest restoration in these systems, which entails mechanical thinning and re-introduction of low-intensity prescribed fire to maintain open woodlands, and enhance understory diversity, can contribute to decreases in ET and increases in Q in eastern forests.