Catchment Scale Research Articles (Page 1)

A framework for selecting Nature-based Solutions: applications and challenges at the catchment scale.

Impact of impervious surface spatial morphologies on urban waterlogging: Insights from a cascade modeling chain at catchment scale

Abstract. Assessing the transport behaviour of microbes in surface water–groundwater systems is important to prevent contamination of drinking-water resources by pathogens. While wellhead protection area (WHPA) delineation is predominantly based on dye injection tests and advective transport modelling, size exclusion of colloid-sized microbes from the smaller and usually less conductive pore spaces causes a faster breakthrough and thus faster apparent transport of microbes compared to that of solutes. To provide a tool for better assessment of the differences between solute and microbial transport in surface water–groundwater systems, here, we present the implementation of a dual-permeability, two-site kinetic deposition formulation for microbial transport in the integrated surface–subsurface hydrological model HydroGeoSphere (HGS). The implementation considers attachment, detachment, and inactivation of microbes in both permeability regions and allows for multispecies transport. The dual-permeability, two-site kinetic deposition implementation in HGS was verified against an analytical solution for dual-permeability colloid transport. The suitability of the model for microbial transport in integrated surface–subsurface hydrological settings at the wellfield or small headwater catchment scale is demonstrated by two illustrative examples. The first example is a benchmark for integrated rainfall–runoff and streamflow generation modelling to which we added microbial transport from a conceptual manure application, demonstrating the novelty of explicit and coupled microbial and solute transport simulations in an integrated surface–subsurface hydrological scenario. The second example is a multi-tracer flow and transport study of an idealized alluvial riverbank filtration site, in which we simulate in parallel the transport of reactive microbes, conservative 4He, and reactive 222Rn, demonstrating the assessment of mixing ratios, tracer breakthrough curves, and travel times in an integrated manner via multiple approaches. The developed simulation tool represents the first integrated surface–subsurface hydrological simulator for reactive solute and microbial transport and marks an important advancement to unlock and quantify governing microbial transport processes in coupled surface water–groundwater settings. It enables meaningful WHPA delineation and risk assessments of riverbank filtration sites with respect to microbial contamination even under situations of extreme hydrological and microbial stress, such as flood events.

Functional river restoration as a lever for adapting to climate change from an interdisciplinary emblematic showcase on the Upper Rhine.

ABSTRACTEnvironmental site design (ESD) is a stormwater management approach that prioritises the use of infiltration‐based non‐structural techniques to mimic the natural hydrologic cycle by reducing impervious surfaces, slowing runoff and increasing infiltration. Traditional storage‐based stormwater management is designed for flood control by quickly diverting runoff from developed areas. This study compared the effect of ESD and only storage‐based stormwater management practices on the hydrology of an urban watershed in Baltimore County, Maryland, USA. Minebank Run is an 8.47 km2 flashy urban stream with a catchment largely developed without stormwater management. A calibrated SWMM model was used to simulate changes in catchment hydrology resulting from ESD and detention basins over a 54‐year period, from the onset of urbanisation in 1948 to the state of urbanisation in 2001. This approach offers a novel, retrospective perspective by simulating how the watershed hydrology might have changed if ESD had been implemented from the beginning of urban development. The model results were analysed by quantifying and comparing different hydrologic metrics to evaluate runoff quantity and flow variability. Results indicated that although storage ponds performed similarly to ESD in reducing annual maximum peak flows (43% vs. 45% reduction, respectively), ESD reduced mean annual runoff coefficients significantly more than ponds (28% vs. 2.7%, p < 0.0001). The Richards–Baker Flashiness Index was reduced from 0.46 to 0.32 with the implementation of ESD, as compared to 0.36 with detention ponds. This study also tested the hypothesis that the impact of urbanisation on the hydrology of the Minebank Run watershed would have been reduced if it had been developed with ESD. The results indicated that the implementation of ESD would have reduced annual maximum peak flows by an average of 46%, annual mean runoff coefficients by 51% and the Richards–Baker Flashiness Index by 37%, as compared to the as‐is condition. The study provides quantitative insights into the performance of traditional and innovative stormwater management techniques at the catchment scale, illustrating the benefits of a combination of both infiltration practices and detention storage in reducing the hydrologic impacts of urbanisation.

Introducing Normalized Surface-adjusted Precipitation Index (NSPI) for regional drought assessment.

ABSTRACTForest cover within catchments is a widely adopted Nature‐based Solution (NbS) for flood mitigation, offering hydrological benefits such as rainfall interception, enhanced infiltration, and reduced overland flow. Despite its recognized potential, quantitative reviews remain limited, especially at the catchment scale, with effectiveness varying by spatial scale, forest type, and climate. This review synthesizes 50 international case studies involving forest‐based NbS, selected through structured screening based on intervention type, catchment characteristics, and availability of quantitative flood metrics, and presents a detailed bibliometric and content analysis. Forest cover consistently impacts peak flow across catchments of all sizes, with a generalized linear relationship where the effect magnitude is approximately half the forest cover change. For example, a 20% increase in forest cover tends to reduce peak flow by 10% across small, medium, and large catchments. Across a range of catchment sizes, there are only minor differences in the mean peak flow reductions for different event intensities (up to 1% AEP). An asymmetric hydrological response is evident: deforestation consistently increases peak flows, whereas afforestation yields gradual reductions, which are shaped by forest maturity, spatial distribution, and modeling assumptions. Upstream distributed forest placements offer distinct hydrological benefits. These outcomes highlight the importance of conserving mature forests, preventing deforestation, and optimizing forest placement, while acknowledging potential adverse impacts on water availability during dry periods.

This study proposes a two-stage Global Circulation Model (GCM) selection approach that evaluates the performance of CMIP5 GCMs in simulating past rainfall for the Kalu Ganga catchment in Sri Lanka. In the initial selection, GCMs were screened based on their ability to replicate monsoonal rainfall patterns using data from the Global Precipitation Climatology Project (GPCP). The fine selection involved bias-corrected GCM simulations at the catchment scale across three rainfall categories—extreme events, normal rainfall, and no-rain days—validated against the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) dataset. Several statistical performance indicators (PIs) were used to assess GCM performance at both stages, and Multi-Criteria Decision-Making (MCDM) techniques were applied to rank the GCMs accordingly. The results indicated that CanESM2 is most suitable for simulating extreme rainfall events, CNRM-CM5 for normal rainfall, and either CanESM2 or CNRM-CM5 for no-rain days. This highlights that no single GCM performs optimally across all rainfall categories, and researchers should select GCMs tailored to their specific focus on future rainfall extremes, flood risk, or drought analysis.

Riverine evidence for the significant anthropogenic impact on sulfur and carbon biogeochemical cycle: A case study from Xijiang River Basin.

ABSTRACTSediment transport plays a crucial role in water quality at the catchment scale. Yet, access to comprehensive datasets for research on sediment quantity and quality at different spatial scales remains limited. This paper introduces a comprehensive hydro‐sedimentological dataset on the Ardières‐Morcille catchment and scientific observatory (Beaujolais vineyard, France) available for the period 2020–2023. The observatory was monitored at three nested scales: the Saint‐Joseph plot (0.28 ha), the Morcille sub‐catchment (3.9 km2), and the Ardières catchment (143 km2). This dataset includes continuous monitoring of rainfall, water level, and turbidity at the three sites, from which discharge and suspended solids concentrations are derived. In addition, discontinuous yet integrated over time samples were collected for SPM grain size, major elements, and particle‐bound pesticides analysis. This dataset has made it possible to assess orders of magnitude for sediment and particle‐bound pesticides transfers and to interpret scale effects in time and space responsible for these transfers. We expect this dataset to be a valuable resource for the research community, supporting investigations into sediment transport processes, contaminant fluxes, and hydrological dynamics across multiple scales.

Abstract. Long-term observations of spring discharge provide an alternative for estimating the evolution of groundwater resources based on observational data at the catchment scale. Karst springs can be found in large parts of Europe, covering all climate zones of the mid-latitudes. Continuous spring discharge measurements are holistic signals, representing both fast and slow flow components, typical of karst environments. Due to relatively short response times, karst systems are pivotal in improving our understanding of the impact of climate change on groundwater resources. This study analyses observational data (precipitation, temperature, and discharge) from over 50 springs distributed across Europe (AT, FR, GB, SI), offering a continental perspective on groundwater resource changes in karst areas. The work focuses on two periods spanning 20 and 40 years, aiming to detect possible accelerations or moderations in trends over time. For both periods, trend analyses of the observational data were conducted using the Mann–Kendall test and Sen's slope on full time series as well as seasonal data. Additionally, potential process changes were examined through trends in high and low flow values. Structural differences among karst systems were accounted for using two indices related to storage and inertia of the system, which were used to (i) highlight structural differences and (ii) classify karst systems accordingly. The results show that the sensitivity of karst aquifers to climate change is not controlled by their degree of karstification. Long-term trends in spring discharge observed in this study align with the general patterns of river discharge found in the literature. However, the behaviour during the last 20 years diverges from these historical patterns. In this most recent period, increasing temperatures play a more significant role in the evolution of spring discharge than changes in precipitation. These findings are contextualized with consideration of indirect drivers, such as changes in land use or land cover, specific regional conditions, and shifts in groundwater recharge and storage processes. Together, they offer valuable insights for assessing groundwater recharge in the past and in the future.

Abstract Several Alpine river ecosystem services (ES) depend on the streamflow regime, thus they might be affected by multiple stressors such as changing climate and anthropic water uses, with still poorly investigated consequences. We focused on the supply of three ES in an Alpine river, namely habitat provision, recreational activities, and hydroelectricity production from run-of-the-river (RoR) power plants. We applied an integrated hydrological, hydraulic and habitat modeling approach to quantify the effects of climate change (CC) on these services, based on the outcomes of four regional climate models. The paper investigated the effects of water use policies such as the introduction of prescriptions for environmental flow (EF) under the same CC models. We observed that CC significantly affects the river suitability for the supply of ES at the catchment scale, while the introduction of EF releases are relevant at a more local scales (several reaches). Under future scenarios, simulated increasing abstractions for hydroelectricity production from RoR power plants have a stronger effect on white-water rafting and a relatively smaller effect on fish habitat. Quantifying the potential effects of CC and of different strategies of river flow management under these scenarios is a promising approach to support the design of long-term water resources management strategies at catchment and local level.

We investigated the contribution of bedrock groundwater to streamflow as a function of catchment scale in a headwater stream. Synoptic surveys were conducted during hydrologically important periods of the year using multiple environmental tracers in stream water, soil water, and bedrock groundwater, along a first-order montane stream, in west-central Montana. Sampled analytes included 222Rn, used to constrain total subsurface flux, and major and minor elements, used in end-member mixing analysis (EMMA) to identify the contributions of soil and bedrock groundwater to the stream. Partitioning between soil-derived and bedrock-derived groundwater was then analyzed as a function of the incremental and accumulated sub-catchment sizes. Radon results indicated that subsurface water contributions accounted for the majority of streamflow at all surveyed times. EMMA results revealed that the bedrock groundwater contribution to streamflow varied between 26% during peak snowmelt and 44% during late summer. Streamflow generation was dominated by soil groundwater contribution along the entire reach, but the bedrock groundwater contribution increased consistently with accumulated sub-catchment size. However, groundwater contributions were not well-correlated with incremental sub-catchment size. The scale at which increased bedrock groundwater discharge can be correlated with sub-catchment size appears to be >1 km2 for our study. Our results are consistent with a conceptual model where streamflow is predominantly generated by a 3D subsurface nested flow system. Local subsurface heterogeneities control the stream source at local scales but begin to average out at scales >2 km2. Our study indicates that, while soil groundwater is the dominant source, bedrock groundwater remains an important and predictable contributor to streamflow throughout the year, even in a snow-dominated, mountainous headwater catchment.

ABSTRACTRiver temperature is an important control on ecosystem health. Concerns around climate change impacts and availability of lower cost sensors have led to an increase in temperature monitoring of aquatic environments. While equipment biases are typically small compared to daily, seasonal, and spatial temperature variability at catchment scales, they can be large in the context of long‐term temporal trends. Failure to address issues of quality control and calibration can thus affect the detection, direction, and magnitude of reported temperature trends. This study uses a 35‐year river temperature time series, collected in the Girnock Burn, Scotland, to illustrate the potential influence of equipment (datalogger/sensor) bias on reported trends. Non‐linear, seasonally varying trends were observed, with summer months showing the greatest positive trends. Calibration experiments comparing historical and contemporary equipment with a calibrated internal reference logger revealed that older dataloggers (pre‐2007) exhibited coarse reporting resolutions and measurement rounding resulting in positive and negative biases depending on the equipment make and model. Recent dataloggers collected data at a higher reporting resolution, where operational rounding issues were not relevant, and exhibit only relatively minor biases, even before calibration. Comparison of raw and bias‐corrected temperature trends showed that uncorrected time series exhibited moderated temporal trends due to positive biases prior to 1998 and negative biases between 1998 and 2007. This study illustrates the importance of metadata in interpreting long‐term time series and the need for appropriate calibration and quality control procedures for temperature monitoring networks, especially where long‐term trends are of interest.

ABSTRACTStudies on the quantity and quality of the spawning habitats of resident brown trout on the catchment scale of rivers are rare. The spawning habitats of salmonids are locally nested along river corridors and are found throughout variable channel patterns, with a preference for sediments composed of gravel and cobbles and appropriate flow conditions. Geology has a strong influence on both the supply of spawning gravel and the fine sediment supply, which might impact the salmonid reproduction rates due to siltation. The present study addresses shortcomings in the catchment‐scale perspective of brown trout reproduction. The aims of this study were to investigate a representative catchment of the Bohemian Massif both to (i) determine the availability of potential spawning sites in relation to different channel patterns and (ii) determine the possible threats of reduced spawning habitats due to siltation. The aims were tested on extensive field sampling campaigns of the entire river via GIS‐based approaches at the Große Mühl River/Austria for the period from 2013–2023. The results indicate that there are differences in the longitudinal course according to the two parameters river morphology and sediment dynamics. Most spawning habitats and the largest spawning areas were found in the riffle‐pool section, with an average density of 214 m2/km. The risk from siltation in the riffle‐pool section reached the highest level, but these reaches still presented the highest potential for spawning. In the lower plane‐bed reaches, especially in the downstream cascade part, spawning habitats are limited, with a lower risk of siltation, and there are much smaller and fewer spawning habitats, averaging 55 m2/km and 5 m2/km, respectively. Catchment‐scale assessment and sediment regime were shown to be important factors for trout reproduction in the granite and gneiss river system of the Grosse Mühl River in the Bohemian Massif.

Accurate calibration and validation of remote sensing soil moisture products critically depend on high-quality in situ measurements. However, effectively capturing representative soil moisture patterns across heterogeneous catchments using ground-based sensors remains a significant challenge. To address this, we propose a machine-learning-based framework for optimizing soil moisture sensor network deployment at the catchment scale. The framework was validated using Sentinel-1 SAR-derived soil moisture data within a humid catchment in southern China. Results show that a network of nine optimally placed sensors minimized prediction errors (RMSE: 7.20%), outperforming both sparser and denser configurations. The optimized sensor network achieved a 52.45% reduction in RMSE compared to random placement. Moreover, the optimal number of sensors varied with seasonal dynamics: the wet season required 11 sensors due to increased precipitation-induced spatial variability, whereas the dry season could be adequately monitored with only six sensors. The proposed optimization approach offers a cost-effective strategy for collecting reliable in situ data, which is essential for improving the accuracy and applicability of remote sensing products in catchment-scale soil moisture monitoring.

ABSTRACT Large river basins exhibit many local-scale management concerns for which a catchment-based approach is required. This study addresses the need for hydrological and water resources management information at the scale of decision making, the catchment. A hydrology and water resources planning modelling approach is applied to a transboundary river, the Inkisi River in Congo basin, based on six catchments partitioned using the catchment classification framework for the Congo basin. The model, developed for the reference period from 1948 to 2021, generates information on water availability and assesses future scenarios of climate change and water demand through 2100. It is calibrated using 20 years of streamflow data and shows good performance based on objective functions of hydrological model evaluation. Assessment of climate change impacts under emissions scenarios RCP 4.5 and RCP 8.5 indicates a decreasing trend in water availability, highlighting the importance of adaptive water resources management strategies to address future challenges.

Extreme rainstorms are difficult to predict and often result in catchment-scale rainfall flooding, leading to substantial economic losses globally. Enhancing the numerical computational efficiency of flood models is essential for improving flood forecasting capabilities. This study presents an integrated hydrological–hydrodynamic model accelerated using GPU (Graphics Processing Unit) technology to perform high-efficiency and high-precision rainfall flood simulations at the catchment scale. The model couples hydrological and hydrodynamic processes by solving the fully two-dimensional shallow water equations (2D SWEs), incorporating GPU-accelerated parallel computing. The model achieves accelerated rainstorm flooding simulations through its implementation on GPUs with parallel computing technology, significantly enhancing its computational efficiency and maintaining its numerical stability. Validations are conducted using an idealized V-shaped catchment and an experimental benchmark, followed by application to a small catchment on the Chinese Loess Plateau. The computational experiments reveal a strong positive correlation between grid cell numbers and GPU acceleration efficiency. The results also demonstrate that the proposed model offers better computational accuracy and acceleration performance than the single-GPU model. This GPU-accelerated hydrological–hydrodynamic modeling framework enables rapid, high-fidelity rainfall flood simulations and provides critical support for timely and effective flood emergency decision making.

ABSTRACTWorldwide, groundwater dependent ecosystems (GDEs) support rare/endemic species, providing essential habitat and access to water when rivers, wetlands and springs are present. Two decades of research have focused on improving knowledge to describe GDEs and identify their location, providing information to facilitate their protection, especially in areas affected by anthropogenic development of groundwater resources. However, a key knowledge gap persists in understanding water requirements of GDEs, providing challenges to sustainable use of groundwater. More specifically, groundwater management must account for timing of GDE groundwater use (i.e., monthly, seasonally), duration of groundwater reliance, and importantly, volumes of groundwater discharged, particularly by vegetation. Without this knowledge, difficulties remain in setting sustainable volumetric groundwater extraction limits at local, catchment and basin scales. Within, we leverage a unique, extensive on‐ground Australian forest groundwater discharge dataset rare to GDE studies, to validate simulated groundwater discharge estimates from the ‘ABCD’ hydrological model, where monthly model outputs over multiple years provide vegetation groundwater discharge data to specifically examine vegetation groundwater requirements. On‐ground forest data consist of multi‐year, sub‐monthly, plot scale data across 18 sites, taking a water balance approach underpinned by sap flow data and including soil moisture monitoring to quantify groundwater extraction across the ~2700 km2 forest estate. The ABCD model requires low parameterisation and was further developed by incorporating the Budyko Framework to close the water balance (herein, ‘ABCD‐BF model’). Forest groundwater discharge estimates correlated well with on‐ground values (r of 0.7 and RMSE of 0.99 mm day−1). Approximately half the sites returned correlations of > 0.8 and low RMSE of 0.46 mm day−1, indicating the ABCD‐BF model provides suitable groundwater discharge estimates. Thus, the development and application of the ABCD‐BF model provide a pragmatic desktop approach to a significant knowledge gap impeding sustainable groundwater management, thereby enhancing GDE protection. Furthermore, broad‐scale application of the model is possible by employing parameters globally available via remote sensing, enabling desktop impact assessments, enhancing GDE management and protection worldwide.

The quality of water in the Red River is a complex interplay between human-induced changes and inherent natural variables. This research utilized the snapshot sampling approach, garnering water quality data from 45 sampling sites in the Red River and crafting 24 environmental indicators related to land use and inherent natural determinants at the catchment scale. Through Spearman rank correlation and redundancy analyses, relationships among land use, natural variables, and water quality were elucidated. Our variance partitioning revealed differentiated impacts of land use and natural factors on water quality. Pivotal findings indicated superior water quality in the Red River, driven mainly by land use dynamics, which showed a distinct geomorphic gradient. Specific land use attributes, like cropland patch density, grassland’s largest patch index, and urban metrics, were pivotal in explaining variations in parameters such as total nitrogen, ammonia, and temperature. Notably, the configuration of land use had a more profound influence on water quality than merely its components. In terms of natural influences, while topography played a dominant role in shaping water quality, other factors like soil and weather had marginal impacts. Elevation was notably linked with metrics like total phosphorus and suspended solids, whereas precipitation and slope significantly determined electrical conductivity and chlorophyll-a models. In sum, incorporating both land use configurations and natural determinants offers a more comprehensive understanding of water quality disparities in the Red River’s ecosystem. For holistic water quality management, the focus should not only be on the major contributors like croplands and urban areas but also on underemphasized areas like grasslands. Tweaking cropland distribution, recognizing the intertwined nature of land use and natural elements, and tailoring land management based on topographical variations are essential strategies moving forward.