Large-scale Catchment Research Articles

Integrating material flow analysis into hydrological model for water environment management of large-scale urban-rural mixed catchment

Simultaneous simulation of urban and rural hydrological processes is important for water environment management of mixed land-uses catchments. However, the discharge paths of pollution in the urban drainage system are not described in traditional catchment hydrological models. In this study, an urban-rural water environment (URWE) model is developed through incorporating the material flow analysis (MFA) and the soil and water assessment tool (SWAT) into a general framework. The URWE model is an advancement with respect to traditional hydrological models in terms of simultaneously simulating the urban organized and rural decentralized discharges of pollution. Due to the low data requirement and high computational efficiency of MFA, URWE model is applicable to large-scale catchment with wide urban area. The URWE model is applied to a typical urban-rural mixed catchment, the Dianchi Catchment (China), where the pollution characteristics are analyzed and the pollution control measures are investigated. Results indicate that the URWE model outperforms the conventional SWAT model for both water quantity and quality simulations, with an 8.5 % improvement in average coefficient of determination (R2) and a 67.4 % improvement in average Nash coefficient (NSE). Rural best management practice, rainwater-sewage separation, and storage capacity expansion are identified as the most cost-effective measures for COD, TN, and TP reduction, respectively. Contributions of this study are to improve the accuracy of water environment simulation in urban-rural mixed catchment, as well as to help decision-makers develop synergistic urban-rural water environment management measures.

Tracing sulfate sources in a tropical agricultural catchment with a stable isotope Bayesian mixing model

Sulfate (SO42−) is an essential anion in drinking water and a vital macronutrient for plant growth. However, elevated sulfate levels can impact ecosystem or human health and could be an important indicator of acid rock drainage or pollution. Therefore, monitoring SO42− sources and transport is important for water quality assessments. This study focused on exploring the sources and transformations of SO42− as well as estimating the proportional contribution of the potential SO42− pollutant sources to groundwater and surface water in a tropical river basin, the Densu River Basin. The study used major ions combined with stable sulfur and oxygen isotope compositions and a Bayesian isotope mixing model, MixSIAR. The major ion characteristics indicate that SO42− concentrations remain stable throughout the rainy and dry seasons but originate from diverse sources. The multi-isotope model (δ34SSO4, δ18OSO4) identified four potential SO42− sources: detergent, precipitation, sewage, and sulfate fertilizer. However, the δ34SSO4 and δ18OSO4 values of the fertilizer source signatures overlapped with those of precipitation and sewage. Nevertheless, the contributions from each source were disentangled using the MixSIAR model, which revealed sewage as the most dominant SO42− pollutant in the Densu Basin, accounting for ~47 % of sulfate in groundwater and ~ 56 % of sulfate in surface water. Sulfate fertilizer (~33 %) was the second most important source after sewage for groundwater, while detergent (~23 %) was the second most important source for surface water. The redox processes of bacterial sulfate reduction and sulfide oxidation were determined to have a minimal impact on the sulfur isotope fractionation within the basin. This study highlights the benefits of combining major ions, sulfur isotopes and the MixSIAR model for identifying sources of sulfate. This approach accounts for uncertainties in source contributions which allows for more robust and reliable apportionment of sulfate sources. The study emphasizes the need for effective waste management and pollution control measures to protect water quality and provides vital guidelines on how to partition sulfate sources on a large catchment scale and evidence for making pollution management decisions on water resources.

Impacts of spatiotemporal resolutions of precipitation on flood event simulation based on multimodel structures – a case study over the Xiang River basin in China

Abstract. Accurate flood event simulation and prediction, enabled by effective models and reliable data, are critical for mitigating the potential risk of flood disaster. This study aims to investigate the impacts of spatiotemporal resolutions of precipitation on flood event simulation in a large-scale catchment of China. We use high-spatiotemporal-resolution Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) products and a gauge-based product as precipitation forcing for hydrologic simulation. Three hydrological models (HBV, SWAT and DHSVM) and a data-driven model (long short-term memory (LSTM) network) are utilized for flood event simulation. Two calibration strategies are carried out, one of which targets matching of the flood events, with peak discharge exceeding 8600 m3 s−1 between January 2015 and December 2017, and the other one is the conventional strategy for matching the entire streamflow time series. The results indicate that the event-based calibration strategy improves the performance of flood event simulation compared with a conventional calibration strategy, except for DHSVM. Both hydrological models and LSTM yield better flood event simulation at a finer temporal resolution, especially in flood peak simulation. Furthermore, SWAT and DHSVM are less sensitive to the spatial resolutions of IMERG, while the performance of LSTM obtains improvement when degrading the spatial resolution of IMERG-L. Generally, LSTM outperforms the hydrological models in most flood events, which implies the usefulness of the deep learning algorithms for flood event simulation.

Novel time-lag informed deep learning framework for enhanced streamflow prediction and flood early warning in large-scale catchments

Constrained by the sparsity of observational streamflow data, large-scale catchments face pressing challenges in streamflow prediction and flood management amid climate change. Deep learning excels in simulation performance while flow lag information in data-driven approaches is barely highlighted. In this study, we introduce a time-lag informed deep learning framework for large-scale catchments. Central to this framework is the utilization of flow time-lag information between upstream and downstream subbasins, enabling precise flood forecasting at outlet driven by upstream data. Taking the monsoon-influenced large-scale Dulong-Irrawaddy River Basin (DIRB) as study area, we determined peak flow lag (PFL) days and relative annual flow scale (RAFS) for defined subbasins. By incorporating this time-lag information with historical flow data at different time intervals, we developed the optimal model for DIRB. This model was then applied to evaluate the flood processes in 2008 and 2009, using selected flood indicators. The results indicate that the time-lag information led to significant performance improvements, notably in the LSTM_PFL_RAFS model driven by upstream Hkamti sub-basin data, which achieved a Kling-Gupta Efficiency (KGE) of 0.891 (Nash-Sutcliffe efficiency coefficient, NSE, 0.904), surpassing LSTM's 0.683 (NSE, 0.785). Further integration of historical flow data with specific interval, the optimal model, H(15)_PFL utilizes Hkamti sub-basin data, reached an impressive KGE of 0.948 (NSE, 0.940). This model outperformed standard LSTM in accurately simulating key flood characteristics, including peak flows, initiation times, and durations for the 2008 and 2009 flood events. Notably, H(15)_PFL provides a valuable 15-day lead time for flood forecasting, extending the window for emergency response preparations. Future research that incorporates additional essential catchment features into the framework holds great potential in unraveling the complex mechanisms of hydrological responses to human activities and climate change.

Can a Sparse Network of Cosmic Ray Neutron Sensors Improve Soil Moisture and Evapotranspiration Estimation at the Larger Catchment Scale?

AbstractCosmic‐ray neutron sensors (CRNS) fill the gap between locally measured in‐situ soil moisture (SM) and remotely sensed SM by providing accurate SM estimation at the field scale. This is promising for improving hydrologic model predictions, as CRNS can provide valuable information on SM in the root zone at the typical scale of a model grid cell. In this study, SM measurements from a network of 12 CRNS in the Rur catchment (Germany) were assimilated into the Terrestrial System Modeling Platform (TSMP) to investigate its potential for improving SM, evapotranspiration (ET) and river discharge characterization and estimating soil hydraulic parameters at the larger catchment scale. The data assimilation (DA) experiments (with and without parameter estimation) were conducted in both a wet year (2016) and a dry year (2018) with the ensemble Kalman filter (EnKF), and later verified with an independent year (2017) without DA. The results show that SM characterization was significantly improved at measurement locations (with up to 60% root mean square error (RMSE) reduction), and that joint state‐parameter estimation improved SM simulation more than state estimation alone (more than 15% additional RMSE reduction). Jackknife experiments showed that SM at verification locations had lower and different improvements in the wet and dry years (an RMSE reduction of 40% in 2016 and 16% in 2018). In addition, SM assimilation was found to improve ET characterization to a much lesser extent, with a 15% RMSE reduction of monthly ET in the wet year and 9% in the dry year.

On the reliability of 12 high-resolution precipitation products for process-based hydrological modeling in China

Numerous satellite- and gauge-based precipitation products (SBPs/GBPs) have emerged in recent decades, providing unprecedented opportunities for process-based hydrological modeling. However, their reliability in high-resolution hydrological modeling at continental scales, especially for variables other than streamflow, remains to be investigated. Factors influencing their hydrological performance (e.g., catchment sizes, precipitation products’ spatial resolution and raw observation information) are also under debate. Here, we provide a comprehensive analysis of the hydrological reliability of 12 state-of-the-art precipitation datasets over China using a process-based land surface model at 0.125° resolution. Simulations driven by different precipitation products are evaluated across ∼ 200 basins with different catchment sizes. Results show that all simulations well reproduce the streamflow, soil moisture, evapotranspiration, and snow-depth at monthly timescale. None of the precipitation products provides best results for all variables, but the gauge-based and infrared-microwave-merged satellite products are superior to the infrared satellite products. The gauge-based regional precipitation product based on the 0.25° China precipitation dataset and the 6-km China Land Data Assimilation System (CN051_CLDAS) provides the best performance generally, followed by two global precipitation products from the Global Precipitation Climatology Centre (GPCC V2022) and the Global Precipitation Measurement Integrated Multi-satellite Retrievals (IMERGE-F V6). However, the advantages of CLDAS_CN051 and GPCC V2022 against satellite-based precipitation products are scale-dependent and tend to disappear over large-scale catchments (>106 km2). The spatial resolutions of precipitation products show no relationship with their hydrological performance, but the number of stations used to generate/bias-correct the precipitation is positively correlated with the performance of streamflow and soil moisture simulations. Differences among the climatology of terrestrial water-cycle components caused by using different precipitation products are notable and tend to increase over the dry and cold regions. Snow depth is the variable most sensitive to precipitation datasets, followed by runoff, ET, and soil moisture. This study suggests that using more station observations is more important than increasing spatial resolutions to enhance the applications of precipitation products in high-resolution hydrological modeling.

Water quality in a large complex catchment: Significant effects of land use and soil type but limited ability to detect trends

Globally, significant societal resources are devoted to mitigating negative effects of eutrophication from excessive phosphorus (P) and nitrogen (N) loading. Potential effectiveness of mitigation measures and possible confounding factors are often assessed using studies conducted in headwater catchments. However, success is often evaluated based on trends in river mouth water chemistry. It is not clear how transferrable insights from headwater catchments are to larger rivers. Here, relationships between P and suspended solids (SS) identified in small agricultural headwater catchments were applied to 30 larger, mixed land use catchments draining into Mälaren, a Swedish great lake. Relationships identified in headwater streams between SS concentration, catchment agricultural land percentage and arable land clay content were corroborated for the larger catchments (R2 = 0.59, p-value<0.001. The same was true for connections between SS and particulate P (R2 = 0.74, p-value<0.001). This study highlights the importance of agricultural land, clay content and SS for P transport, on both smaller headwater as well as larger catchment scales, supporting the use of headwater findings on larger, management relevant scales. Consequently, these relationships should be used to target mitigation measures to reduce SS and P losses. To explore the effectiveness of mitigation measures on water quality, we assessed long-term (20 year) trends in tributary water quality and compared these trends to the amount of mitigation measures implemented in the catchment. Overall improving trends were detected using regional Mann Kendall tests, but few decreasing trends in nutrient concentrations were found for individual sites using Generalized Additive Models (GAM). The lack of significant trends and identifiable connections to amount of mitigation measures implemented could be due to several reasons, e.g. insufficient time for recently implemented measures to have an effect, ongoing release of legacy P as well as low areal coverage and poor spatial placement of implemented measures. In addition, trend detection requires large amounts of data and the results should be carefully interpreted and communicated.

Potential assessment of calibration approaches using the SWAT hydrological model for streamflow and sediment yield for a large-scale catchment

ABSTRACT This study explored the potential of three calibration approaches – single-site, multisite, and multisite–multivariable – to simulate the streamflow and sediment yield of the Upper Blue Nile (UBN) river basin using the Soil and Water Assessment Tool (SWAT). Several statistical indices were used to verify the SWAT model performance. While all three approaches reasonably estimate the streamflow and sediment yield, the multisite calibration approach was found to perform better in handling the spatial variability of parameters and their influence on streamflow and sediment yield over the entire catchment. Further, evaluations of hydropower potential at the selected location were conducted to demonstrate the importance of calibration approaches in planning and designing various water resource structures.

Downscaling legacy soil information for hydrological soil mapping using multinomial logistic regression

In South Africa, there is a growing demand for large scale detailed hydrological soil maps for modelling and management purposes. However, imbalanced legacy soil information often impedes the accurate creation of such maps by not being representative of the environmental complexity of large-scale catchments and containing imbalanced soil class distributions, often resulting in the loss of minority soil classes, which are often of great hydrological importance (e.g., wetland and riparian soils). In this study, we proposed a new downscaling approach to handle spatially localised legacy soil data within a larger low resolution legacy soil dataset to create an accurate hydrological soil map of the macro-scale (5790 km2) Sabie-Sand catchment using multinomial logistic regression (MNLR). The spatially localised legacy data was downscaled using k-means clustering and added to the broader legacy dataset. Five levels of legacy soil data were analysed in their representation of environmental covariates using QQ-plots and a Welsh’s t-test and their mapping accuracy using confusion matrix’s and Kappa coefficient statistics. However, MNLR also requires balanced soil classes. The value of the best performing legacy soil dataset was also compared to using all available soil information after both had their soil class distributions fully balanced using Synthetic Minority Oversampling Technique (SMOTE). The 500 ha/observation-SMOTE dataset resulted in the most accurate hydrological soil map with a validation point accuracy of 73% and a Kappa coefficient of 0.60, substantially outperforming the other downscaled soil maps as well as the SMOTE balanced dataset using all available soil information. This was due to the decreased variation between observations and catchment means, where the 500 ha/observation dataset yielded the least variation between soil observation and catchment datasets and well as reducing the class imbalance within the legacy soil data. Downscaling spatially localised legacy soil data for environmental representation is an effective tool to improve digital soil mapping accuracy using MNLR.

Synoptic water isotope surveys to understand the hydrology of large intensively managed catchments

Understanding the hydrology of large catchments has always been a major challenge due to the spatial heterogeneity of climate, geology, topography, soils and land use. However, precise knowledge of water cycling, storage and losses across large scale catchments is required to sustainably meet growing water demands and adjust water management strategies. This study used four large-scale, seasonal synoptic surveys in 2021 to characterize the spatio-temporal patterns of water isotopes (stable water isotopes and radioisotope tritium at natural abundance) to better understand the complex hydrology of the intensively managed 10,000 km2 catchment of the River Spree in Germany. Apart from the upper headwaters, the hydrology of the Spree is heavily regulated by reservoir releases, pumped minewater discharges, extensive wetlands and lakes, water abstractions and urban drainage. Moreover, the catchment is drought-sensitive with potential evapotranspiration (∼800 mm a year) often exceeding annual rainfall (∼600 mm). This is reflected in the isotopic composition of river water: In the steeper, upper headwaters, the river has a “flashy” rainfall-runoff response, with isotopes plotting close to the Global Meteoric Water Line (GMWL) consistent with dominant groundwater sources, but showing more influence by rainfall in winter. However, flows in the midstream parts are complex due to artificially enhanced baseflows and attenuated high flows from extensive reservoir releases and pumped groundwater releases from dewatering of mines. The reservoir waters are isotopically heavier and reflect the effects of open water evaporation. Fractionation effects strengthen downstream as managed wetland areas in the Spreewald and natural lakes further enhance evaporation and attenuate flows. Water abstractions for drinking water and to sustain the Oder-Spree canal reduce flows further. In Berlin, sources of urban runoff and waste water discharges reduce the effects of fractionation, though samples still plot below the meteoric water line. Seasonally, the effects of evaporation in the lower river network are strongest in summer and autumn, though they remain in winter and spring, indicating a large memory effect due to long mean travel times within the river system. Tritium variability along the river reflects inputs of younger and older water in different parts of the river system; though the influence of pumped groundwater means that the mean age of stream water (i.e., time elapsed since rainfall) in the lower river is likely to be >50 years. Climate change, together with increased population and economic growth in Berlin, is likely to increase pressures on the Spree system in future. In the coming years, the anticipated termination of the lignite mining will lead to a reduction in artificial inputs into surface water, which in turn may exacerbate water stress. We demonstrated that longer-term, synoptic isotope studies are valuable in better understanding the hydrology of such complex, large-scale, heavily modified river systems in terms of insights into water sources and mixing processes; thus, providing an evidence base for more sustainable management in the future.

The riparian reactive interface: a climate-sensitive gatekeeper of global nutrient cycles

Riparian zones are critical interfaces to freshwater systems, acting as gateways for the conveyance and modification of macronutrient fluxes from land to rivers and oceans. In this paper, we propose that certain riparian conditions and processes (conceptually ‘Riparian Reactive Interfaces’) may be susceptible to environmental change with consequences of accelerating local nutrient cycling cascading to global impacts on the cycles of carbon (C), nitrogen (N), and phosphorus (P). However, we argue that this concept is insufficiently understood and that research has not yet established robust baseline data to predict and measure change at the key riparian ecosystem interface. We suggest one contributing factor as lack of interdisciplinary study of abiotic and biotic processes linking C, N, and P dynamics and another being emphasis on riparian ecology and restoration that limits frameworks for handling and scaling topography–soil–water–climate physical and biogeochemical observations from plot to large catchment scales. Scientific effort is required now to evaluate riparian current and future controls on global nutrient cycles through multi-nutrient (and controlling element) studies, grounded in landscape frameworks for dynamic riparian behaviour variation, facilitating scaling to catchment predictions.

Quantifying changes and trends of NO3 concentrations and concentration-discharge relationships in a complex, heavily managed, drought-sensitive river system

Stream nitrate nitrogen (NO3) concentrations and concentration-discharge (C-Q) relationships are key indicators of water quality. However, their long-term variability and response to climate change in large-scale catchments are still poorly understood. Here, we report a synoptic survey and long-term observations (2000–2021) in the Spree catchment (10,105 km2), Germany, to identify the spatial–temporal patterns in stream NO3 concentrations and C-Q relationships, as well as the underlying environmental drivers. We found that in the context of gradual reduction of nitrate inputs, the stream NO3 concentrations had a decreasing trend, but showed different magnitudes at different spatial scales (-0.01–0.48 mg L-1 yr−1). In the upstream parts of the catchment - with a high proportion of farmland - high levels of stream NO3 concentration remained with a risk of eutrophication due to the large nitrogen legacy. Especially in winter, stream NO3 concentrations were much higher due to the groundwater export with high NO3 concentrations, a decreased dilution effect of rainfall and vegetation uptake became clear. Consequently, the large nitrogen legacy in the upper catchment resulted in NO3 concentrations not significantly changing with streamflow and showing chemostatic behavior. This was different from NO3 dynamics in the mid- and lower-catchment areas, which were positively related to streamflow, showing a chemodynamic behavior. Further, we found the export patterns (i.e. enrichment vs. dilution) of stream NO3 concentrations strongly correlated with the export regimes (i.e. chemostatic vs. chemodynamic), which were also affected by drought conditions. This is probably due to the decrease in stream depth which would enhance benthic removal leading to a decrease in stream NO3 concentrations. Reduced hydrological connectivity leads to higher spatial heterogeneity of residual NO3 in soil and drainage water, which will result in chemodynamic behavior of stream NO3 concentrations. After rewetting, the export of these high NO3 concentration in soils led to elevated stream NO3 concentrations, resulting in a positive correlation with streamflow. Our work revealed significant heterogeneity in stream NO3 concentrations and C-Q relationships at different spatial–temporal scales in large catchments. Critically, current stream NO3 concentrations and C-Q relationships are likely to respond strongly to future drought, leading to challenges for future land and water management.

Water table depth assimilation in integrated terrestrial system models at the larger catchment scale

As an important source of water for human beings, groundwater plays a significant role in human production and life. However, different sources of uncertainty may lead to unsatisfactory simulations of groundwater hydrodynamics with hydrological models. The goal of this study is to investigate the impact of assimilating groundwater data into the Terrestrial System Modeling Platform (TSMP) for improving hydrological modeling in a real-world case. Daily groundwater table depth (WTD) measurements from the year 2018 for the Rur catchment in Germany were assimilated by the Localized Ensemble Kalman Filter (LEnKF) into TSMP. The LEnKF is used with a localization radius so that the assimilated measurements only update model states in a limited radius around the measurements, in order to avoid unphysical updates related to spurious correlations. Due to the mismatch between groundwater measurements and the coarse model resolution (500 m), the measurements need careful screening before data assimilation (DA). Based on the spatial autocorrelation of the WTD deduced from the measurements, three different filter localization radii (2.5, 5, and 10 km) were evaluated for assimilation. The bias in the simulated water table and the root mean square error (RMSE) are reduced after DA, compared with runs without DA [i.e., open loop (OL) runs]. The best results at the assimilated locations are obtained for a localization radius of 10 km, with an 81% reduction of RMSE at the measurement locations, and slightly smaller RMSE reductions for the 5 and 2.5 km radius. The validation with independent WTD data showed the best results for a localization radius of 10 km, but groundwater table characterization could only be improved for sites &amp;lt;2.5 km from measurement locations. In case of a localization radius of 10 km the RMSE-reduction was 30% for those nearby sites. Simulated soil moisture was validated against soil moisture measured by cosmic-ray neutron sensors (CRNS), but no RMSE reduction was observed for DA-runs compared to OL-run. However, in both cases, the correlation between measured and simulated soil moisture content was high (between 0.70 and 0.89, except for the Wuestebach site).

The Performance of Natural Flood Management at the Large Catchment-Scale: A Case Study in the Warwickshire Stour Valley

The limited understanding of Natural Flood Management (NFM) performance, especially at large hydrological scales, is considered a critical barrier for the further funding and implementation of these nature-based solutions to the increasing international problem of flooding. The publications of the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report and Environment Agency’s National Flood and Coastal Erosion Risk Management Strategy (NFCERMS) for England have shown that extreme weather, including increased likelihood of high magnitude flood events, will occur and will require more novel management methods. This study focused on the ability of co-designed NFM measures to ameliorate downstream fluvial flooding by attenuating catchment response through a highly spatially distributed network of attenuating and roughening measures. Performance was characterised by the ability of NFM to attenuate flood peaks at different spatial scales across a large (187 km2) dendritic catchment, including the lowering of flood peaks and delaying the time-to-peak. Using a coupled modelling methodology and applying it to the upper Stour Valley, Warwickshire-Avon, UK, a rural response to the application of a set of NFM interventions was developed using the hydrodynamic model Flood Modeller Pro and XPSWMM ©. The method demonstrated a means of incorporating local knowledge in a realistic set of NFM schemes, tested to multiple flood risk scenarios (including climate change). Under frequent, smaller design storm events (e.g., Index Flood (QMED) and 3.3% AEP), flood peaks were lowered across all hydrological scales tested (5.8 km2 to 187 km2). As the design flood event severity increases, impact from upstream NFM attenuation on downstream peak response diminished significantly, especially at the largest hydrological scales. However, even at the largest hydrological scale, delays in time-to-peak were noted, increasing the ability of downstream communities to respond and enact flood preparation activities, thus increasing resilience to potential flooding events. While the benefits were limited to large flood events, the modelling indicated that NFM has the potential to reduce downstream flood risk. However, greater integration of observed data to improve model confidence and reduce uncertainty in modelled events is needed, especially the uncertainty associated with using single peaked design storm events from the Flood Estimation Handbook (FEH). This paper proposes a future Before–After Control–Impact (BACI) monitoring programme that could be integrated with models and applied across non-tidally influenced catchments seeking to empirically test the hydrological performance of in-situ NFM.

The Role of Runoff Attenuation Features (RAFs) in Natural Flood Management

Natural Flood Management (NFM) and catchment-based solutions for flood risk management and environmental problems are wide-ranging and complex. Management of fluvial flood risk in the UK is undergoing a fundamental shift, with a change in emphasis from solely working with structural defences to considering catchment-based measures which attenuate flood runoff. At the heart of this change are NFM and nature-based solutions. One key type of intervention is the Runoff Attenuation Feature (RAF): a class of features that targets runoff flow pathways and creates new temporary flow storage (such as ponds and leaky barriers). However, there is currently a lack of evidence for the effectiveness of NFM and RAFs at larger catchment scales and for managing extreme flood events. Nevertheless, there is a strong evidence base to suggest that well-designed RAFs deliver a range of ecosystem services if installed in the correct location. This paper reviews and critiques the role of RAFs and NFM as an interventionist and holistic approach to lowering runoff rates. The link between RAF design types and their relationship to land use and scale is made. Recent novel innovations and attempts to scale up RAFs are discussed. The role of antecedent conditions, groundwater and the change in residence time of processes is highlighted. The uncertainty and complexity of proving NFM effectiveness underpin a view that new thinking in catchment flood management is needed. New research is required, and many questions are raised about RAFs and NFM. The direction of travel is that a positive and proactive NFM community can now embrace the problem. Proof that RAFs and NFM can address flood management is not likely to be resolved without a great deal of further research but confidence that RAFs do beneficial work is growing and an argument for greater amounts of runoff attenuation is made.

Heavy metal habitat: A novel framework for mapping heavy metal contamination over large-scale catchment with a species distribution model

Heavy metal(loid)s (HMs) have been consistently entering the food chain, imposing great harm on environment and public health. However, previous studies on the spatial dynamics and transport mechanism of HMs have been profoundly limited by the field sampling issues, such as the uneven observations of individual carriers and their spatial mismatch, especially over large-scale catchments with complex environment. In this study, a novel methodological framework for mapping HMs at catchment scale was proposed and applied, combining a species distribution model (SDM) with physical environment and human variables. Based on the field observations, we ecologicalized HMs in different carriers as different species. This enabled the proposed framework to model the ‘enrichment area’ of individual HMs in the geographic space (termed as the HM ‘habitat’) and identify their ‘hotspots’ (peak value points) within the catchment. Results showed the output maps of HM habitats from secondary carriers (soil, sediment, and wet deposition) well agreed with the influence of industry contaminants, hydraulic sorting, and precipitation washout process respectively, indicating the potential of SDM in modeling the spatial distributions of the HM. The derived maps of HMs from secondary carriers, along with the human and environmental variables were then input as explanatory variables in SDM to predict the spatial patterns of the final HM accumulation in river water, which was observed to have largely improved the prediction quality. These results confirmed the value of our framework to leverage SDMs from ecology perspective to study HM contamination transport at catchment scale, offering new insights not only to map the spatial HM habitats but also help locate the HM transport chains among different carriers.

Quantitative Attribution Analysis of the Spatial Differentiation of Gully Erosion in the Black Soil Region of Northeast China

Gully erosion is the major soil erosion type in the black soil region of Northeast China. However, studies on multifactor synthesis at a large scale and on the driving mechanism of spatial differentiation are still relatively lacking for gully erosion in this region. In this study, the simulation of gully erosion and its quantitative attribution analysis have been conducted in the Sancha River catchment in Northeast China, based on high-resolution satellite imagery mapping and the geodetector method. A total of 18 indicators in 6 categories, including topography, climate and weather, soil properties, lithology, land use, have been taken into consideration. The influence of each influencing factor and its interactive influence on gully erosion were quantitatively evaluated. The results showed that at the large catchment scale, the submeter images had a strong capacity for the recognition of a permanent gully and obtained satisfactory results. According to the results of the geodetector, lithology and soil type are the main factors that affect the spatial differentiation of gully erosion in the Sancha River basin, because their interpretation power for gully density and gully intensity was close to 10%. The lithology belonged to gray–white matter rhyolite, spherulite rhyolite, and crystal clastic tuff, with the highest gully density and intensity. The interpretation power of the secondary factors, including rainfall erosivity, watershed area, elevation, soil erodibility, land use pattern, slope, and distance from the river, amounted to more than 1%. The interactions among most driving factors showed nonlinear enhancement. The influence of the interaction between lithology and soil type appeared to be the largest. In particular, the lithology of different soil types accounted for 28.7% and 32.5% of the gully density and gully intensity. The interaction of factors had a stronger influence on the spatial differentiation of gully erosion than any single factor.

Global Landslide Forecasting System for Hazard Assessment and Situational Awareness

Landslides triggered by extreme rainfall can be devastating, resulting in loss of life, property, and infrastructure. Landslide forecasting systems provide an opportunity to build awareness of potential hazards and ultimately take preemptive measures. There is currently a dearth of forecasting systems that provide regional or global coverage, but these systems can offer important situational awareness in data-sparse, ungauged, or large-scale catchments. A near global, primarily satellite-based system called the Landslide Hazard Assessment for Situational Awareness (LHASA) provides near real-time estimates of potential landslide hazard and exposure around the world. In this work, a precipitation forecast module is introduced into LHASA to complement the existing LHASA framework and provide an estimate of landslide hazard up to 3 days in advance at 1 km resolution. The model-based Goddard Earth Observing System-Forward Processing (GEOS-FP) precipitation forecast product is used as the forcing input for the model in place of the satellite-based Integrated Multi-satellitE Retrievals for Global Precipitation Mission product. Soil moisture and snow depth from the GEOS-FP assimilated product are also incorporated. The study period January 2020–January 2021 is used to test the model performance against the LHASA near real-time estimates at multiple spatiotemporal scales. Validation of the model is carried out using a collection of rainfall-triggered landslide inventories from around the world as case studies to demonstrate the potential utility and limitations of this system. The rescaling of the GEOS-FP precipitation product is a critical step in incorporating the forecasted precipitation data within LHASA-Forecast (LHASA-F). Combining different streams of forecasted data within the LHASA-F framework shows promise, particularly for larger events at the 1- and 2-days lead time for events. Results indicate that for the case studies evaluated, the LHASA-F is generally able to resolve major landslide events triggered by extreme rainfall, such as from tropical cyclones. The analysis shows that landslide forecast outputs may be represented differently depending on the user’s needs. This framework serves as a first milestone in providing a global predictive view of landslide hazard.

&amp;lt;i&amp;gt;Escherichia coli&amp;lt;/i&amp;gt; concentration, multiscale monitoring over the decade 2011–2021 in the Mekong River basin, Lao PDR

Abstract. Bacterial pathogens in surface waters may threaten human health, especially in developing countries, where untreated surface water is often used for domestic needs. The objective of the long-term multiscale monitoring of Escherichia coli ([E. coli]) concentration in stream water, and that of associated variables (temperature (T), electrical conductance (EC), dissolved oxygen concentration ([DO]) and saturation (DO%), pH (pH), oxidation-reduction potential (ORP), turbidity (Turb), and total suspended sediment concentration ([TSS])), was to identify the drivers of bacterial dissemination across tropical catchments. This data description paper presents three datasets (see “Data availability” section) collected at 31 sampling stations located within the Mekong River and its tributaries in Lao PDR (0.6–25 946 km2) from 2011 to 2021. The 1602 records have been used to describe the hydrological processes driving in-stream E. coli concentration during flood events, to understand the land-use impact on bacterial dissemination on small and large catchment scales, to relate stream water quality and diarrhea outbreaks, and to build numerical models. The database may be further used, e.g., to interpret new variables measured in the monitored catchments, or to map the health risk posed by fecal pathogens.

Reducing MCPA herbicide pollution at catchment scale using an agri-environmental scheme

In river catchments used as drinking water sources, high pesticide concentrations in abstracted waters require an expensive treatment step prior to supply. The acid herbicide 2-methyl-4-chlorophenoxyacetic acid (MCPA) is particularly problematic as it is highly mobile in the soil-water environment following application. Here, an agri-environmental scheme (AES) was introduced to a large-scale catchment (384 km2) to potentially reduce the burden of pesticides in the water treatment process. The main measure offered was contractor application of glyphosate by weed wiping as a substitute for boom spraying of MCPA, supported by educational and advisory activities. A combined innovation applied in the assessment was, i) a full before-after-control-impact (BACI) framework over four peak application seasons (April to October 2018 to 2021) where a neighbouring catchment (386 km2) did not have an AES and, ii) an enhanced monitoring approach where river discharge and MCPA concentrations were measured synchronously in each catchment. During peak application periods the sample resolution was every 7 h, and daily during quiescent winter periods. This sampling approach enabled flow- and time-weighted concentrations to be established, and a detailed record of export loads. These loads were up to 0.242 kg km−2 yr−1, and over an order of magnitude higher than previously reported in the literature. Despite this, and accounting for inter-annual and seasonal variations in river discharges, the AES catchment indicated a reduction in both flow- and time-weighted MCPA concentration of up to 21% and 24%, respectively, compared to the control catchment. No pollution swapping was detected. Nevertheless, the percentage of MCPA occurrences above a 0.1 μg L−1 threshold did not reduce and so the need for treatment was not fully resolved. Although the work highlights the advantages of catchment management approaches for pollution reduction in source water catchments, it also indicates that maximising participation will be essential for future AES.