Runoff Patterns Research Articles

Assessment and Application of Multi-Source Precipitation Products in Cold Regions Based on the Improved SWAT Model

In hydrological modeling, the accuracy of precipitation data and the reflection of the model’s physical mechanisms are crucial for accurately describing hydrological processes. Identifying reliable data sources and exploring reasonable hydrological evolution mechanisms for hydrology and water resources research in high-altitude mountainous regions with sparse stations and limited data constitute a significant challenge and focus in the field of hydrology. This study focuses on the Yarkant River Basin in Xinjiang, which originates from glaciers and contains a substantial amount of meltwater runoff. A dynamic glacier melt module considering the synergistic effects of multiple meteorological factors was developed and integrated into the original Soil and Water Assessment Tool (SWAT) model. Four precipitation datasets (ERA5-land, MSWEP, CMA V2.0, and CHM-PRE) were selected to train the model, including remote sensing precipitation products and station-interpolated precipitation data. The applicability of the improved SWAT model and precipitation datasets in the source region of the Yarkant River was evaluated and analyzed using statistical indicators, hydrological characteristic values, and watershed runoff simulation effectiveness. The optimal dataset was further used to analyze glacier evolution characteristics in the basin. The results revealed the following: (1) The improved model fills the gap in glacier runoff simulation with respect to the original SWAT model, with the simulation results more closely aligning with the actual runoff variation patterns in the study area, better describing the meltwater runoff process. (2) CMA V2.0 precipitation data has the best applicability in the study area. This is specifically reflected in the rationality of the spatial and temporal distribution patterns of the inverted precipitation, the accuracy observed in capturing precipitation events and actual precipitation characteristics, the goodness of fit in driving hydrological models, and the observed precision in reflecting the composition of watershed runoff, all of which are superior to those pertaining to other precipitation products. (3) The glacier melt calculated using the improved SWAT model informed by CMA V2.0 shows that during the study period, the basin formed a pattern with a positive–negative glacier balance demarcation at 36.5° N, featuring melting at higher latitudes and accumulation at lower latitudes. The results of this study are of significant importance for hydrometeorological applications and hydrological and water resources research in this region.

Integration of Generative-Adversarial-Network-Based Data Compaction and Spatial Attention Transductive Long Short-Term Memory for Improved Rainfall–Runoff Modeling

This work presents a novel approach to rainfall–runoff modeling. We incorporate GAN-based data compaction into a spatial-attention-enhanced transductive long short-term memory (TLSTM) network. The GAN component reduces data dimensions while retaining essential features. This compaction enables the TLSTM to capture complex temporal dependencies in rainfall–runoff patterns more effectively. When tested on the CAMELS dataset, the model significantly outperforms benchmark LSTM-based models. For 8-day runoff forecasts, our model achieves an NSE of 0.536, compared to 0.326 from the closest competitor. The integration of GAN-based feature extraction with spatial attention mechanisms improves predictive accuracy, particularly for peak-flow events. This method offers a powerful solution for addressing current challenges in water resource management and disaster planning under extreme climate conditions.

Reassessing water resource challenges in a changing climate in the Kagera transboundary river basin, Tanzania

The dynamic shifts in climatic patterns, particularly alterations in precipitation, have significantly impacted water resource availability and accessibility. These climate-induced changes affect hydrological processes, encompassing runoff, groundwater replenishment, water demand, and biophysical patterns within river basins. This study seeks to unravel the multifaceted implications encountered and challenges in water resource management within the river basin. It employed a survey-based approach for data collection, including Key informant interviews (KI), consultation methods and documentary reviews. The study utilized Statistical Package for the Social Sciences (SPSS) and Microsoft Excel for quantitative analysis and thematic synthesis for qualitative insights, including local community perspectives. The findings reveal substantial fluctuations in precipitation, protracted droughts, severe flooding, and other climatic extremes. The observed annual mean rainfall of 832 mm, with a standard deviation of 248, a standard error of 361, and a skewness of −945, accentuates the pronounced variability in mean precipitation, exhibiting a declining trend indicative of climatic shifts (R2 = 0.1797 and P = 0.002). The challenges are limited access to hydrological and meteorological data, undermining prediction accuracy, infrastructure planning, and response strategies for water resource allocation and management. The absence of unified approaches, influenced by the politics of riparian states, feeble coordination, conflicting sectoral policies, and ambiguous priorities, hinders effective transboundary water resource management. The study addresses these complexities and advocates for institutionalizing robust water management strategies that capitalize on existing resources and foster innovative capacities among critical stakeholders. Central to these efforts is harmonizing and enforcing policies, regulations, and laws governing water use, coupled with a concerted focus on inclusive governance to ensure the sustainability of water resource management. Such effort entails synchronized strategies, programs, plans, and livelihood practices underpinned by active stakeholder involvement in pivotal facets of water resources management.

Applying Low-Impact Development Techniques for Improved Water Management in Urban Areas

Worldwide, the increase in impervious surfaces due to urbanization has led to significant water cycle issues such as groundwater depletion, urban heat islands, and flooding. To address these challenges, Low-Impact Development (LID) techniques are increasingly being applied in stormwater management. This study focuses on Ulsan, designated as a water cycle model city in South Korea, with a particular emphasis on the highly urbanized Okgyo drainage watershed. Using the Stormwater Management Model (SWMM) version 5.1, long-term runoff simulations were conducted to evaluate the effects of LID implementation on water cycle change rates and recovery rates. The model incorporates detailed hydrological and hydraulic parameters, including inflow, runoff, infiltration, and evapotranspiration for six subcatchments within the watershed. The SWMM was calibrated and validated using 30 years of historical rainfall data (1987–2016) from the Ulsan weather station. Calibration and validation processes used the NRCS-CN (Curve Number) method to ensure accuracy in simulating runoff patterns and water balance. The study specifically evaluated the effectiveness of two LID techniques: bioretention and permeable pavements. Three scenarios were modeled: bioretention applied to 5% of the area, permeable pavements applied to 5% of the area, and a combined application of both techniques. The results showed that the combined scenario provided the best outcome, with a 7.80% reduction in surface runoff and a 14.56% improvement in water cycle health. The LID application scenario confirmed the potential to achieve the water cycle management target of handling 25.5 mm of rainfall. These findings demonstrate that the introduction of LID techniques in public spaces can significantly enhance water management. This research provides insights into effective water cycle management methods tailored to specific urban land uses, laying a foundation for future urban planning and sustainable development.

Simulating runoff changes and evaluating under climate change using CMIP6 data and the optimal SWAT model: a case study

This study examines the influence of climate change on hydrological processes, particularly runoff, and how it affects managing water resources and ecosystem sustainability. It uses CMIP6 data to analyze changes in runoff patterns under different Shared Socioeconomic Pathways (SSP). This study also uses a Deep belief network (DBN) and a Modified Sparrow Search Optimizer (MSSO) to enhance the runoff forecasting capabilities of the SWAT model. DBN can learn complex patterns in the data and improve the accuracy of runoff forecasting. The meta-heuristic algorithm optimizes the models through iterative search processes and finds the optimal parameter configuration in the SWAT model. The Optimal SWAT Model accurately predicts runoff patterns, with high precision in capturing variability, a strong connection between projected and actual data, and minimal inaccuracy in its predictions, as indicated by an ENS score of 0.7152 and an R2 coefficient of determination of 0.8012. The outcomes of the forecasts illustrated that the runoff will decrease in the coming years, which could threaten the water source. Therefore, managers should manage water resources with awareness of these conditions.

Revealing the impact of wildfires on groundwater quality: Insights from Sierra de la Culebra (Spain)

Wildfires induce changes in soil and vegetation composition, significantly impacting the hydrological cycle and altering future runoff and infiltration patterns. Ash residue on the ground can infiltrate the subsoil along with water, leading to modifications in groundwater hydrochemistry. Climate change and summer heatwaves can create favourable conditions for severe wildfires, such as the one that occurred in Zamora, Spain, in 2022. Fourteen simultaneous points of origin across various locations in Zamora triggered the worst environmental disaster in this province, as well as the largest fire recorded in the history of Spain. Following the severe wildfires in Sierra de la Culebra, Zamora, groundwater samples were obtained to compare the hydrochemistry with pre-fire background data spanning several years. A general decline in pH across all sampling points was observed, most notably at Z1, likely due to its very high permeability and leaching of organic acids from burned vegetation. Increases in major ions such as SO42− and NO3− were detected at Z1-2, while HCO3− levels decreased, indicating possible oxidation of soil organic matter and the introduction of wildfire-derived organic acids into the groundwater system. Elevated concentrations of Na+, K+, Mg2+, and Ca2+ were observed at Z3, suggesting possible ash residue infiltration. Despite the severity of the wildfires, the results indicate that there were no significant long-lasting impacts on groundwater quality overall. This finding suggests that the groundwater systems in the study area are resilient to such environmental catastrophes.

Cover Crop Effects on Surface Runoff and Subsurface Flow in Rainfed Hillslope Farming and Connections to Water Quality

Abstract: Surface runoff and subsurface flow patterns were monitored in hillside runoff plots in almond and olive orchards with soils covered with spontaneous plants over two hydrological years. The experimental runoff plots were located on the south flank of the Sierra Nevada (Lanjarón, SE Spain) at 580 m a.s.l. with an area of 40 m2 (10 m × 4 m). The surface and subsurface discharge were collected and measured at different soil depths (0, 5, 10, 25, and 50 cm), and the dissolved nutrient concentrations (NO3–N, NH4–N, PO4–P, and K) were determined. According to the findings, the subsurface flow pathways drained most of the rainfall water compared with surface runoff, which was affected by plant cover. The influence of rainfall intensity (I30) on surface runoff was more meaningful than that on subsurface flow. Throughout the monitoring period, the runoff coefficients at soil depths of 0, 5, 10, 25, and 50 cm averaged 0.04, 0.11, 0.14, 0.17, and 0.18, respectively. Subsurface flow was one of the dominant pathways for N and K loss, whereas P loss mainly occurred via surface runoff. Moreover, the concentrations in subsurface flow were higher than the recommended level for standard water quality for NO3–N, NH4–N, and PO4–P. Subsurface flow was the main route of dissolved nutrient delivery, making these nutrients available to the root systems of trees, where nutrient uptake is more likely to occur. Thus, by lessening surface runoff and encouraging surface vegetation coverage to facilitate the recycling of nutrients and buffer the rainfall’s impact on the soil surface, nutrient loss control can be achieved.

Land Use Change Impacts over the Indus Delta: A Case Study of Sindh Province, Pakistan

Land use and land cover changes (LULCCs) are vital indicators for assessing the dynamic relationship between humans and nature, particularly in diverse and evolving landscapes. This study employs remote sensing (RS) data and machine learning algorithms (MLAs) to investigate LULCC dynamics within the Indus River Delta region of Sindh, Pakistan. The focus is on tracking the trajectories of land use changes within mangrove forests and associated ecosystem services over twenty years. Our findings reveal a modest improvement in mangrove forest cover in specific areas, with an increase from 0.28% to 0.4%, alongside a slight expansion of wetland areas from 2.95% to 3.19%. However, significant increases in cropland, increasing from 22.76% to 28.14%, and built-up areas, increasing from 0.71% to 1.66%, pose risks such as altered sedimentation and runoff patterns as well as habitat degradation. Additionally, decreases in barren land from 57.10% to 52.7% and a reduction in rangeland from 16.16% to 13.92% indicate intensified land use conversion and logging activities. This study highlights the vulnerability of mangrove ecosystems in the Indus Delta to agricultural expansion, urbanization, resource exploitation, and land mismanagement. Recommendations include harmonizing developmental ambitions with ecological conservation, prioritizing integrated coastal area management, reinforcing mangrove protection measures, and implementing sustainable land use planning practices. These actions are essential for ensuring the long-term sustainability of the region’s ecosystems and human communities.

Comparison of Process-Driven SWAT Model and Data-Driven Machine Learning Techniques in Simulating Streamflow: A Case Study in the Fenhe River Basin

Hydrological modeling is a crucial tool in hydrology and water resource management for analyzing runoff evolution patterns. In this study, the process-driven soil and water assessment tool (SWAT) model and data-driven machine learning techniques (XGBoost, random forest, LSTM, BILSTM, and GRU) were employed to simulate runoff at monthly and daily intervals in the Fenhe River basin, situated in the middle reaches of the Yellow River, respectively. The SWAT model demonstrated effective performance in simulating runoff at various scales, with the coefficient of determination (R2) exceeding 0.80 and the Nash–Sutcliffe efficiency (NSE) surpassing 0.79. Sensitivity analysis reveals varying degrees of sensitivity among the model parameters. Furthermore, the deep learning techniques (LSTM, BILSTM, and GRU) exhibited superior simulation generalization capabilities compared to the SWAT model across various scales. Additionally, the generalization abilities of traditional machine learning techniques (XGBoost and random forest) were comparable to the SWAT model. This indicates that deep learning techniques demonstrate remarkable stability and generalization capabilities across various scales. This analysis was motivated by the use of external continuous time series data as input and the application of deep learning techniques to internal mechanisms. Moreover, an integrated modeling approach was used to enhance simulation accuracy by combining the SWAT model with machine learning techniques. The results indicate that the integrated modeling approach improves simulation performance across various scales compared to the single-model approach. This research is significant for improving the efficiency of water resource utilization and management in the Fenhe River basin.

Hydrological Response of Bamboo Plantations on Soil–Water Dynamics in Humid and Semi-Arid Coastal Region of Kenya

Soils and water are major resources that drive a country’s economy, and therefore should be conserved and utilized sustainably. However, in Kenya, these two resources are facing huge depletion and degradation due to anthropogenic factors and climate change. Bamboo species, especially on large plantations, can significantly alter ecological, hydrological, and biogeochemical processes in the long term. This study aimed to evaluate the effects of different species of bamboo and tree plantations on important soil–water processes like infiltration, bulk density, runoff, and soil loss in Kenya. The research was conducted at two sites (Gede in Arabuko Sokoke forest and at Baolala, in Kilifi County) managed by the Kenya Forestry Research Institute (KEFRI). The Arabuko Sokoke forest has a hot–humid coastal climate, while Baolala is a hot semi-arid area with little precipitation. The study involved measurement of soil–water infiltration rates using infiltrometers, installing runoff plots to quantify surface runoff and sediment loss, and analyzing soil properties like bulk density for growing periods for different bamboo and tree species. At the Gede forest site, the 30-year-old Thyrsostachys siamensis and Bambusa bambos plantations recorded the highest infiltration rates. Mature bamboo plantations of T. siamensis and B. bambos recorded higher infiltration rates compared to mature plantations of E. camaldulensis and G. arborea. It was observed that the bamboo plantations manifested lower soil bulk density compared to bare land, which recorded the highest bulk density. At Boalala, infiltration rates were significantly higher in the bamboo species compared to grassland and bare land. The painted bamboo (B. vulgaris vittata) had a slightly higher water infiltration rate compared to B. vulgaris. Runoff and erosion patterns reinforced the benefits of more mature bamboo plantations as well. There was a significant correlation between amount of runoff and collected soil loss through erosion. The data showed reductions in surface runoff volumes and sediment loss as the bamboo plantations aged compared to younger species. Therefore, by enhancing infiltration and reducing runoff and erosion, well-managed bamboo plantations can protect valuable soil resources, improve water recharge, and support sustainable land use over the long term. In conclusion, this study showed the strong potential of bamboo as a soil and water conservation tool in Kenya.

Functional connectivity related to road linear erosion at rainfall event scale in an agricultural watershed on the Loess Plateau

Intersections of road and channel networks assist in better understanding of runoff and sediment transportation process feedback using the hydrological connectivity concept. However, quantifying functional connectivity and exploring the complex relationship between hydrological connectivity and the processes of runoff and sediment associated with road networks have largely been unexamined by field work. Therefore, our team conducted a 3-year monitoring of hydrological connectivity and road erosion from selected road segments in an agricultural watershed on the Loess Plateau. Rainfall events occurring during 2020–2022 were categorized into three types using K-means clustering analysis: small amounts of low-intensity rainfall (SL-R), large amounts of low-intensity rainfall (LL-R), and large amounts of high-intensity rainfall (LH-R). The monitored roads primarily consist of paved roads and unpaved roads (agricultural roads, abandoned roads, and petroleum roads). In our study, the intersections extracted by the road and channel networks were defined as hydrological connections, and sticks wrapped with clean gauze at the hydrological connections were monitored under variable rainfall conditions for functional connectivity assessment. The runoff heights and sediment traces attached to the sticks are alternative hydrological connectivity indicators. The results revealed an obvious increasing trend in the activation frequency and degree of hydrological connectivity as rainfall types transitioned from SL-R to LL-R and LH-R. Agricultural roads had a more intense response to hydrological connectivity between upslope drainage areas and road surfaces, and petroleum roads performed well in response to hydrological connectivity between road surfaces and down hillslopes. Random forest models indicate that PI30 is the most dominant influencing factor of road erosion and hydrological connectivity between road upslope drainage areas and road surfaces. Road surface erosion rates are the primary determinants of hydrological connectivity between road surfaces and down hillslopes. These findings suggest a close interaction between road erosion and hydrological connectivity driven by these off-site effects. They offer additional evidence concerning runoff and sediment patterns and dynamics in relation to road networks and identify hydrological connectivity and road erosion hotspots. Coordinating measures for hydrological connectivity regulation and road erosion mitigation are essential for minimizing hydrological connectivity and road erosion off-site effects within watersheds.

Spatial distribution of rainfall–runoff characteristics and peak lag time in high‐relief meso‐scale mountain catchments where observations are scarce

AbstractClarifying the relationship between rainfall and runoff characteristics in mountainous areas is crucial to improving flood and sediment disaster prediction. This study investigates that relationship, focusing on meso‐scale catchments of 1–10 km2 in high‐relief mountainous regions where conventional observational data are limited. Such research requires observations in multiple adjoining watersheds due to the variability of runoff patterns in mountainous river systems. However, observations are challenging in meso‐scale catchments due to equipment loss in rivers. This study included seven observation sites with catchment areas of 0.16–9.01 km2 and relative elevations exceeding 1000 m within the Higashigochi River basin, a sub‐branch of the Oi River basin in Shizuoka Prefecture, Japan. Water level gauges were strategically installed at each of these locations to prevent loss. Peak lag time (the time difference between the rainfall and water level peaks), which is crucial to flood hydrograph definition and disaster preparedness, was assessed. The results indicate a general trend of increasing peak lag time with larger catchment area, although spatial heterogeneity was observed at small sites. This finding aligns with prior studies in meso‐scale catchments within mountainous regions with relative elevations of 450 m or less, suggesting that in such meso‐scale catchments, larger catchment area tends to result in longer peak lag times, regardless of topographic undulation. Overall, the impact of topographic undulations on peak lag time appears surprisingly modest, indicating that catchment area plays a more significant role in determining peak lag time. The observed heterogeneity of peak lag time could be attributable to the influence of bedrock groundwater discharge.

Gully regulates snowmelt runoff, sediment and nutrient loss processes in Mollisols region of Northeast China

Gully is a prominent indicator of land degradation in agroecosystems, functioning as a crucial pathway connecting upslopes to downstream channels. However, little is known about how gully regulates runoff, sediment, and nutrient loss processes in the catchment during snowmelt. In this study, we monitored these processes in situ at both the gully head (the upslope accumulated catchment of the gully head, CGH) and outlet of two representative and typical gully-dominated catchments (F1 and F2) during snowmelt in Mollisols region of Northeast China. Our results showed that runoff discharge of CGH and outlet exhibited a multi-peak trend during snowmelt, driven by the transition from snow melting to soil thawing. This transition resulted in distinct runoff patterns in both CGH and outlet, with significant differences in their response to air temperature. The total runoff yield of CGH accounted for 57.8 % in F1 and 40.6 % in F2 of the total runoff yield of the outlet. Notably, the peak sediment concentration displayed a marked lag compared to the peak runoff discharge, primarily dominated by the increased sensitivity of gully erosion after the thawing of gully slopes. Gully erosion was the main source of sediment yield in the catchment, contributing 98.2 % in F1 and 96.6 % in F2. Furthermore, nutrient concentrations exhibited a decreasing trend during snowmelt. The comparison of high nutrient concentrations in CGH and relatively low nutrient concentrations in outlet highlighted the gully's role in intercepting and diluting runoff nutrients. Hysteresis analysis confirmed the differential contribution of CGH and gully to nutrient sources. CGH accounting for 50.9 % and 93.3 % of runoff TN and runoff TP loss, while contributing only 8.3 % and 5.8 % to sediment TN and sediment TP loss, respectively. These findings offer valuable insights for effective erosion control and nonpoint source pollution management in gully-dominated agroecosystems during snowmelt.

Architecture and history of uranium-bearing Palaeocene–Eocene strata deposited on the eastern margin of the Peri-Tethys (Chu-Sarysu Basin, south Kazakhstan)

Deciphering the facies and architecture of alluvial successions provides invaluable insights into the interplay of tectonics, climate, eustasy, and autogenic processes affecting terrestrial sedimentary systems. The stratigraphic response of fluvial systems to variations in discharge and sediment-supply regimes is now well-understood and is tied to changes in climate, precipitation patterns, or sediment sources. The uraniferous Chu-Sarysu Basin in south Kazakhstan occupies the tectonically stable Turan Platform on the eastern margin of the Peri-Tethys, and the study of its Palaeocene–Eocene sedimentary fill offers an opportunity to unravel eustatic and climatic controls that drove the architecture of reservoirs hosting economically important deposits. The stratigraphic succession comprises two multistorey, laterally extensive, sheet-like sandstone bodies floored by prominent erosion surfaces and interpreted as the deposits of channel belts. These packages are interstratified with floodplain, coastal-wetland, and marine-embayment complexes reflecting major extra-channel belt avulsions and marginal-marine incursions on the low-gradient alluvial plain. The stratal architecture was controlled by changes in accommodation likely induced by well-documented sea-level changes in the Peri-Tethys during the Palaeogene, which permitted the development of a chronostratigraphic framework. Eustatic variations culminated in the widespread flooding of the Turan Platform in the middle Eocene, reflected by transgressive lags above a wave ravinement surface capping terrestrial and marginal-marine deposits. Marked changes in fluvial facies, floodplain styles, and inferred channel planforms between Palaeocene and Eocene strata suggest a climatic overprint on river discharge and sedimentation. Upper Palaeocene meandering channel deposits encased in well-drained floodplain strata are indicative of perennial discharge under a semi-arid climate, whereas lower Eocene low-sinuosity channel fills, displaying evidence for transcritical flows and abundant in-situ vegetation, point to intermittent runoff patterns consistent with a humid and seasonal climate. An evolution in atmospheric moisture at the Palaeocene–Eocene boundary from arid to humid conditions has been reported across the Tethys region, and linked to global climatic perturbations of the Palaeocene–Eocene Thermal Maximum.

Mapping the vertical heterogeneity of Greenland's firn from 2011–2019 using airborne radar and laser altimetry

Abstract. The firn layer on the Greenland Ice Sheet (GrIS) plays a crucial role in buffering surface meltwater runoff, which is constrained by the available firn pore space and impermeable ice layers that limit deeper meltwater percolation. Understanding these firn properties is essential for predicting current and future meltwater runoff and its contribution to global sea-level rise. While very-high-frequency (VHF) radars have been extensively used for surveying the GrIS, their lower bandwidth restricts direct firn stratigraphy extraction. In this study, we use concurrent VHF airborne radar and laser altimetry data collected as part of Operation IceBridge over the 2011–2019 period to investigate our hypothesis that vertical heterogeneities in firn (i.e. ice layers) cause vertical offsets in the radar surface reflection (dz). Our results, corroborated by modelling and firn core analyses, show that a dz larger than 1 m is strongly related to the vertical heterogeneity of a firn profile and effectively delineates between vertically homogeneous and vertically heterogeneous firn profiles over a depth range of ∼ 4 m. Temporal variations in dz align with climatic events and reveal an expansion of heterogeneous firn between 2011–2013 covering an area of ∼ 350 815 km2, followed by firn replenishment over the years 2014–2019 spanning an area of ∼ 667 725 km2. Our approach reveals the firn evolution of key regions on the Greenland Ice Sheet, providing valuable insights for detecting potential alterations in meltwater runoff patterns.

Evolution of runoff discharge patterns and risk assessment of water inrush in mountainous railroad tunnels

Evolution of runoff discharge patterns and risk assessment of water inrush in mountainous railroad tunnels

Winter/Spring Runoff Is Earlier, More Protracted, and Increasing in Volume in the Laurentian Great Lakes Basin

AbstractWinter/spring runoff has changed in streams worldwide due to climate change, particularly in temperate areas where winter/spring streamflow depends on snowmelt. Such changes potentially affect receiving waters through altered nutrient loading and mixing patterns. The Laurentian Great Lakes are an important freshwater resource and have experienced a myriad of impacts due to climate change. We analyzed 70 years of stream gauge data in the Great Lakes Basin to test for changes in timing, duration, and amount of winter/spring runoff during the period 1950–2019. We found strong evidence for earlier runoff in each of the Great Lakes except Lake Erie, protracted winter/spring runoff throughout the Great Lakes Basin, and a higher runoff depth during the winter‐spring period over time for all watersheds except Lake Superior. Lake Ontario had the greatest change in the date by which 50% of the Jan–May runoff had been discharged (6 days earlier from 1950 to 2019). For winter/spring runoff duration, the most extreme change was observed in Lake Erie (increase of 19 days), and for runoff depth, the greatest change was in the Lake Huron Basin (increase of 3.3 cm). Results were similar for natural and impacted streams. Our results demonstrate dramatic changes in runoff patterns over the last seven decades in the Great Lakes Basin concomitant with previously published changes in precipitation and snowpack. Shifts toward earlier, more protracted, and more voluminous runoff likely change nutrient loading and mixing patterns that influence primary producers, particularly in the nearshore areas of the Great Lakes.

The ecological–hydrological regime of the Han River basin under changing conditions: The coupled influence of human activities and climate change

AbstractBecause of the confluence of human activities and climate change, the hydrological regime in the Han River basin has substantially evolved, necessitating a multi‐faceted, quantitative analysis of the causative factors. Employing cross‐wavelet analysis, we examined nonlinear relationships between runoff and meteorological variables. Additionally, we assessed hydrological indicators via the IHA index and RVA, then quantified the drivers of runoff variations across different time scales using the Budyko hypothesis and a Generalized Regression Neural Network (GRNN) model. The findings reveal the presence of sustained resonance periods within the climate‐runoff system, notably concentrated in 9‐ to 15‐month intervals during the years 1985–1994, 1995–2012, and 2014–2018, with a confidence level of 95%. Overall, the basin exhibited moderate change (41.66%), with 15 indicators displaying varying degrees of moderate to high transformation. These shifts underscore significant ecosystem transformations. The influence of driving factors on runoff varies across temporal scales. On an annual scale, human activities predominantly shape runoff changes (52.35%), while meteorological factors contribute significantly (47.65%). Conversely, at the monthly scale, climate change emerges as the dominant influence on runoff patterns in June and September, with human activities maintaining a principal role in other months, notably exceeding 90% even in November.

AirSUM - Structure from Motion supported stock unearthing Method: Erosion modeling in Viennese vineyards

Soil mobilization is particularly high in viticulture. In Austria and especially in Vienna, soil protection measures are frequently implemented without the vintners knowing how high their erosion rates are and where in particular they can introduce additional measures. New, highly accurate and cost-efficient methods for erosion estimation are expected to improve this situation.A relatively fast and low-cost option is the stock unearthing method (SUM), which provides a rough estimation of erosion based on biomarkers, but neglects the inter-row area. The improved stock unearthing method (ISUM) approach uses additional measurement points in this area and therefore delivers more accurate erosion volumes. Additionally, the use of Structure from Motion (SfM) DEMs provides respectable results on small plots in vineyards. This study combines and evaluates the unmanned aerial vehicle (UAV) SfM approach with the SUM. This results in a millimeter scale resolution and can be applied to larger study areas. The combination of SUM and SfM forms the new airSUM approach that provides a more accurate representation of the recent relief, rendering interpolation techniques unnecessary, reproducing visible runoff patterns, and enhancing erosion volume estimations. The use of airSUM enabled the detection of 32.7 ± 17.5 m3 (avg. of ∼ 84.1 ± 45 t ha−1 yr−1) soil erosion on an area of 700 m2 in a period of 8 years. Erosion hotspots could be modeled mainly in the wheel tracks with depths of up to 20.5 cm parallel to the slope and correspond excellently with field observations. This is partly due to the compaction of the surface, but mainly due to the preferential runoff and erosion. The identification of the erosion hot spots, runoff breaches and consequent runoff concentration allows the precise allocation of mitigation funds to reduce overland flow and erosion.

Bridging the spatiotemporal ice sheet mass change data gap between GRACE and GRACE-FO in Greenland using machine learning method

The Gravity Recovery and Climate Experiment (GRACE) mission terminated its operation in October 2017, and its successor, the GRACE Follow‐On mission, started operation in May 2018, leading to a 11-months data gap. As a prospective way to evaluate the Greenland ice sheet (GrIS) mass change, it would be vital to bridge the data gap. Despite efforts to bridge the gap with machine learning (ML) techniques, there are still two flaws in terms of the lack of spatial distribution and selecting forcing variables. Firstly, we applied the partial least squares regression (PLSR) model to assess the most explanatory variables of GRACE-derived ice mass change, and thus selected satellite altimetry elevation, and runoff, sublimation, rainfall, and snowfall from MAR v3.12 (Modèle Atmosphérique Régionale) model as predictors for predictive models. SVM (Support Vector Machine), BPNN (Back Propagation Neural Network), and MLR (Multiple Linear Regression) are utilized to reconstruct the GRACE-like gridded data. The SVM outperforms the other two predictive models, with 25.6 % of grid cells having Nash-Sutcliffe efficiency (NSE) coefficient above 0.75 and Scaled Root Mean Square Error (RSR) above 0.50. The average RMSE values for all testing periods are 3.86, 4.37, and 5.57 cm, for SVM, BPNN, and MLR, respectively. The reconstructed GRACE-like grid data demonstrated accurate spatiotemporal information with less noise, and exhibited a higher mean correlation of 0.93 across six sub-regions of GrIS, indicating a stronger agreement with original GRACE data compared to other methods. Furthermore, we investigated the changes in precipitation, runoff, and temperature pattern driven by the North Atlantic Oscillation (NAO), Greenland Blocking Index (GBI) and Atmospheric Rivers (ARs) to corroborate the shifts in GrIS mass loss feature. It was found that there was a correlation between precipitation, runoff, and ARs frequency, with mean correlation of 0.48 and 0.51 for wintertime and summertime, respectively. The moderate mass loss rate during the data gap can be attributed to the positive NAO and higher ARs frequency in GrIS. In general, the reconstructed data presented in this study provided a better comprehension of ice mass change and valuable information on how the ice mass responds to climate variations.