Catchment Model Research Articles

Transfer station choice in a multimodal transit system: An empirical study

Connectivity between modes and lines constitutes a core aspect for multimodal transport system user experiences. Using a large-scale transit smart card dataset, we reveal transfer station choices in bus–rail intermodal trips frequently deviate from the “nearest-station” heuristics, motivating research to better explain real transfer behaviors and define more realistic station catchment areas. The investigation is centered on modeling transfers from bus to rail using multinomial logistic regression discrete choice models applied to 200k observations from four weeks of transit smart card observations. The results show that: (1) for trips requiring an intermodal transfer, riders select the nearest rail station in only 40% of cases; (2) commuters’ transfer station selections are jointly influenced by the trip attributes of all involved modes, with the bus ride distance contributing the most, followed by the walking distance; and (3) commuters’ sensitivity to travel distances exhibits nonlinear trends: sensitivity to bus ride distance dampens gradually as a function of trip length, while the sensitivity to walking distance exhibits a concave shape peaking at around 0.6 km. Catchment area modeling reveals that (4) location attributes (e.g., distance from the city center) play a greater role in defining the station catchment area than other attributes (e.g., land use and built environment). Specifically, the catchment area radius is larger for stations located further away from the city center or from the nearest interchange station. Moreover, (5) stations with more amenities (e.g., higher density of shops and greater accessibility) are likely to attract commuters from a larger area. This study strengthens the connections between station selection patterns and station catchment area analysis, to better guide intermodal transit design and operations, such as improving predictions of passenger flows at transfer stations.

DEVELOPING A «LAKE – CATCHMENT» INFORMATION ANALYSIS SYSTEM AS A METHOD FOR BASIC RESEARCH AND A TOOL FOR DECISION-MAKING (LAKE ONEGO CASE STUDY)

An information analysis system (IAS) is being developed to assess the state and predict the dynamics of a lake-catchment system. Forecasting of the state of the lake ecosystem in the context of global climate change and active exploitation of its resources (water, energy, recreational and biological resources, transport) is done using a three-dimensional mathematical model of the lake ecosystem, coupled with a model of runoff from the catchment. Lake and catchment models are combined with a database and a knowledge base to form a single IAS. The prognostic assessments of the seasonal and long-term dynamics of the lake ecosystem performed using the IAS serve as the basis for identifying the possible economic, social and cultural consequences of regional and global changes. Operational IAS is necessary to create a management decision support system.

Effects of the Long-Term Climate Change and Selective Discharge Schemes on the Thermal Stratification of a Large Deep Reservoir, Xin’anjiang Reservoir, China

The effects of global warming and precipitation changes on water temperature and thermocline parameters, such as thermocline depth, thickness, and strength, were assessed. A catchment model, coupled with a reservoir thermal model with meteorological input calculated by a downscaled general circulation model (GCM) projection under three representative concentration pathways (RCPs), was applied to the Xin’anjiang Reservoir, located in southeast China. The results indicate that water temperature in each layer increased (decreased) with the rise (decline) in air temperature, especially the surface water temperature. There was a significant negative (positive) correlation between thermocline depth (strength) and air temperature during the period of stratification weakness. The most sensitive phenomenon of water temperature-to-precipitation changes occurred in the middle layer (depth = 30 m). Additionally, the thermocline depth and thickness increased with decreases in hydraulic residence time, which were caused by precipitation increases. According to the simulation experiments driven by RCP outputs, mean water temperature in each water layer in the future (2096–2100) has a strong response to increases in air temperature, which is projected to increase by 0.11–0.62 °C for RCP2.6, 0.76–1.19 °C for RCP4.5, and 1.50–2.35 °C for RCP8.5, compared to the baseline (2012–2016). However, mean water temperature in each water layer from 2096 to 2100 underwent a slight decrease caused by precipitation changes, with a 0.03–0.25 °C decrease for RCP2.6, 0.07–0.40 °C for RCP4.5, and 0.04–0.29 °C for RCP8.5, compared to 2012–2016. The mean thermocline depth in the future (2096–2100) will be significantly decreased, while the mean thermocline thickness will be slightly increased. Over a multiyear timescale, the impacts of air temperature changes are stronger than those induced by precipitation variations. However, the effects of hydraulic residence time changes caused by precipitation changes (especially rainstorm) should be considered in the management of deep reservoirs.

Modelling Subarctic watershed dissolved organic carbon response to hydroclimatic regime

Shifts in hydroclimatic regimes associated with global climate change may impact freshwater availability and quality. In high latitudes of the northern hemisphere, where vast quantities of carbon are stored terrestrially, explaining landscape-scale carbon (C) budgets and associated pollutant transfer is necessary for understanding the impact of changing hydroclimatic regimes. We used a dynamic modelling approach to simulate streamflow, DOC concentration, and DOC export in a northern Canadian catchment that has undergone notable climate warming, and will continue to for the remainder of this century. The Integrated Catchment model for Carbon (INCA-C) was successfully calibrated to a multi-year period (2012–2016) that represents a range in hydrologic conditions. The model was subsequently run over 30-year periods representing baseline and two future climate scenarios. Average discharge is predicted to decrease under an elevated temperature scenario (22–27 % of baseline) but increase (116–175 % of baseline) under an elevated temperature and precipitation scenario. In the latter scenario the nival hydroclimatic regime is expected to shift to a combined nival and pluvial regime. Average DOC flux over 30 years is predicted to decrease (24–27 % of baseline) under the elevated temperature scenario, as higher DOC concentrations are offset by lower runoff. Under the elevated temperature and precipitation scenario, results suggest an increase in carbon export of 64–81 % above baseline. These increases are attributed to greater connectivity of the catchment. The largest increase in DOC export is expected to occur in early winter. These predicted changes in DOC export, particularly under a climate that is warmer and wetter could be part of larger ecosystem change and warrant additional monitoring efforts in the region.

SWMM stormwater quality model developed to assess the performance of best management practices: a case study of the town of Jasper in Canada

Stormwater quality management is essential for the protection of aquatic life of receiving waterbodies. This study presents a unique case study, whereby a stormwater management model (SWMM) was developed to assess the performances of street sweeping and infiltration trenches to reduce total suspended solids (TSS) loads in the Town of Jasper located in Jasper National Park, AB, Canada. Two catchment models were developed for the Town using long-term meteorological data, as well as watershed and drainage system data provided by the Municipality of Jasper. The quality parameters were calibrated so that TSS loads at the outfalls were within a 20% error of monitoring data. The performance of proposed best management practices was assessed using single-event and continuous data simulations. The results indicated that a combined sweeping/trench control scenario would generate the highest TSS removal efficiency of up to 51%.

Upscaling point-scale soil hydraulic properties for application in a catchment model using Bayesian calibration: An application in two agricultural regions of New Zealand

Hydrological modeling for landscape and catchment scale applications requires upscaling of soil hydraulic parameters which are generally only available at point scale. We present a case study where hourly root zone soil water content and drainage observations from nine flat, pastoral sites (Waikato and Canterbury regions in New Zealand) were used to develop an upscaling approach to parameterize the soil water balance module of the TopNet catchment model, based on scaling multi-layer soil profile information from the national soil data base, S-map, to the single-layer soil profile used in TopNet. Using a Bayesian calibration approach, the hydraulic behavioral parameters of TopNet's soil water balance module were identified. Of the eleven calibration parameters considered three were found to be insensitive to data (stress point, unsaturated hydraulic conductivity and infiltration rate); three were correlated and could be determined from specific soil water content observations (wilting point, field capacity and drainable water); and five were correlated and could be determined from combined specific soil water content and drainage observations (drainage rate, saturated hydraulic conductivity profile, effective soil depth, soil water holding capacity and wetting front suction). Based on the eight correlated parameters, upscaling functions were then developed to derive suitable model parameters from S-map-hydro for each site. The validity of the upscaling functions was verified at each site. The approach used in this research can be used to parameterize the TopNet model at other similar locations, and also provides a transferable framework to parameterize other catchment-scale hydrology models where point-scale soil hydraulic data available.

Satellite-derived spatiotemporal data on imperviousness for improved hydrological modelling of urbanised catchments

• Performance of both lumped and distributed conceptual rainfall-runoff models were improved dramatically when temporal variations in impervious surface fraction (ISF) were used as input to these models. • Both SIMHYD and AWBM could be used for assessing urbanisation effect on streamflow of urbanized catchments. • Both lumped and distributed conceptual rainfall-runoff models showed similar performance in simulation of daily streamflows for the small urban catchment tested. • Given the observed rainfall for the study catchment, one percent increase in impervious surface fraction would lead to 6.9–7.3 mm (2.9–3%) increase in the annual streamflow during 1991–2018. Conceptual rainfall-runoff models have been used extensively to simulate streamflow in urban catchments. However, spatiotemporal changes in the impervious surface area, as an important land use indicator for urban environment, has not been used explicitly as input into conceptual rainfall-runoff models. In this study, spatiotemporal impervious surface fraction (ISF) data were incorporated into two lumped and distributed (1 km) models, namely SIMHYD and AWBM in an urbanized catchment in southeast Queensland, Australia. A sub-pixel classification technique was used to derive ISF from 6 LandSat images between 1988 and 2015. Daily rainfall-runoff models were calibrated with ISF as an input variable. To evaluate model performance for different simulation periods, four model runs including M1 (lumped models with constant ISF), M2 (lumped models with temporally varying ISF), M3 (distributed models with fixed spatial ISF) and M4 (distributed models with spatiotemporal ISF and climate data). Results showed that (1) the sub-pixel classification algorithm can be used to accurately derive ISF data with a mean absolute error (MAE) ranging from 0.07 to 0.12 for 6 images, (2) ISF in the catchment has increased gradually from 12.2% to 34.6% from 1988 to 2015, (3) the performance of SIMHYD and AWBM were not improved noticeably when distributed climate and ISF data were used, (4) simulation capability of lumped and distributed SIMHYD and AWBM were improved (e.g., increase in NSE from 0.68 to 0.73, and decrease in PBIAS from −30.2% to 1.2% for lumped SIMHYD) when temporal (M2) and/or spatiotemporal (M4) ISF data (5-year) were used, (5) difference in model performance is small and insignificantly whether annual or 5-yearly ISF data were used and (6) annual streamflow would increase by 6.9–7.3 mm (equal to 2.9–3%) in response to every 1% increase in ISF for the catchment tested. The methodology developed in this study can be applied for assessment of the effect of urbanisation on regional water balance.

Assessing the role of location and scale of Nature Based Solutions for the enhancement of low flows

ABSTRACT Water resources management during drought is a significant challenge worldwide, particularly for upland areas. Additionally, variations in water availability are becoming more extreme with climate change. Nature Based Solutions (NBS) e.g. Runoff Attenuation Features (RAFs) could provide an alternative to hard-engineering. Using more natural processes, flow pathways are intercepted and attenuated in features during wet periods, increasing infiltration opportunity and thus water availability for use later. NBS research has primarily focused on flood mitigation, but little is known about low flow impacts; knowledge is required on where and at what scale to implement NBS. To explore these questions, we used a physically-based catchment model (MIKE SHE) integrated with a hydraulic river model (MIKE 11) to evaluate scenarios with varying RAF volumes and locations. We applied this to an intensively monitored upland Scottish catchment (0.9 km2) where 40 RAFs (∼2m3 storage each) were installed for low flow enhancement. Model results showed installed RAFs increase recharge (∼0.1%), groundwater contribution to streamflow (∼4%) and low flows (∼1%) and reduce high (∼5%) and mean flows (∼2%), suggesting RAFs could be used to mitigate extreme flows. The scenarios revealed that RAF location (primarily soil type) and scale (total storage volume and spread of features) were both important. Doubling installed RAF volumes increased impact on low flows by ∼25% and high flows by ∼40%, although lower additional benefits were predicted with further storage increases. RAFs had greater impact in freely-draining soils than poorly-draining, however distributing the same storage volume across many smaller RAFs over greater areas (both soil types) provided the largest effect. Absolute changes observed were relatively small, and given model uncertainty, should be treated with caution. Nevertheless, the direction of change was clear and given ecological systems and water supply rely on small margins of change, even slight increases in low flows will likely be beneficial.

What Is the Contribution of Urban Trees to Mitigate Pluvial Flooding?

Hydrological modeling is commonly used in urban areas for drainage design and to estimate pluvial flood hazards in order to mitigate flood risks and damages. In general, modelers choose well-known and proven models, which are tailored to represent the runoff generation of impervious areas and surface runoff. However, interception and other vegetation-related processes are usually simplified or neglected in models to predict pluvial flooding in urban areas. In this study, we test and calibrate the hydrological model LEAFlood (Landscape and vEgetAtion-dependent Flood model), which is based on the open source ‘Catchment Modeling Framework’ (CMF), tailored to represent hydrological processes related to vegetation and includes a 2D simulation of pluvial flooding in urban areas using landscape elements. The application of LEAFlood was carried out in Vauban, a district in Freiburg (Germany) with an area of ∼31 hectares, where an extensive hydrological measurement network is available. Two events were used for calibration (max intensity 17 mm/h and 28 mm/h) and validation (max intensity 25 mm/h and 14 mm/h), respectively. Moreover, the ability of the model to represent interception, as well as the influence of urban trees on the runoff, was analyzed. The comparison of observed and modeled data shows that the model is well-suited to represent interception and runoff generation processes. The site-specific contribution of each single tree, approximately corresponding to retaining one cup of coffee per second (∼0.14 L/s), is viewed as a tangible value that can be easily communicated to stakeholders. For the entire study area, all trees decrease the peak discharge by 17 to 27% for this magnitude of rainfall intensities. The model has the advantage that single landscape elements can be selected and evaluated regarding their natural contribution of soil and vegetation to flood regulating ecosystem services.

Impact of spatial distribution information of rainfall in runoff simulation using deep learning method

Abstract. Rainfall-runoff modeling is of great importance for flood forecast and water management. Hydrological modeling is the traditional and commonly used approach for rainfall-runoff modeling. In recent years, with the development of artificial intelligence technology, deep learning models, such as the long short-term memory (LSTM) model, are increasingly applied to rainfall-runoff modeling. However, current works do not consider the effect of rainfall spatial distribution information on the results. Focusing on 10 catchments from the Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) dataset, this study compared the performance of LSTM with different look-back windows (7, 15, 30, 180, 365 d) for future 1 d discharges and for future multi-day simulations (7, 15 d). Secondly, the differences between LSTMs as individual models trained independently in each catchment and LSTMs as regional models were also compared across 10 catchments. All models are driven by catchment mean rainfall data and spatially distributed rainfall data, respectively. The results demonstrate that regardless of whether LSTMs are trained independently in each catchment or trained as regional models, rainfall data with spatial information improves the performance of LSTMs compared to models driven by mean rainfall data. The LSTM as a regional model did not obtain better results than LSTM as an individual model in our study. However, we found that using spatially distributed rainfall data can reduce the difference between LSTM as a regional model and LSTM as an individual model. In summary, (a) adding information about the spatial distribution of the data is another way to improve the performance of LSTM where long-term rainfall records are absent, and (b) understanding and utilizing the spatial distribution information can help improve the performance of deep learning models in runoff simulations.

A new agro-hydrological catchment model to assess the cumulative impact of small reservoirs

Small-reservoir development is a challenging issue in agricultural catchments facing water scarcity. An integrated, new and original agro-hydrological model considering small reservoirs, MHYDAS-Small-Reservoirs, is presented. The model explicitly represents relevant spatial scales (plot, small-reservoir, stream reach, groundwater, and catchment scales) and the agronomic and hydrological links between these scales at which agriculture-hydrology interactions occur. After numerical verification, the model is evaluated by applying it to a 19-km2 catchment. The model satisfactorily simulates the annual stream runoff (within 6%) and daily stream runoff (Nash efficiency = 0.47) but tends to overestimate the crop yield (+21%). Simulations, one under actual basin conditions and one under virtual conditions, were carried out. This highlighted the potential of the model to predict the local and cumulative impacts of small reservoirs. Hence, MHYDAS-Small-Reservoirs is a promising model for land use planning and water management of agricultural catchments containing small reservoirs.

The Impact of Rainfall -Runoff on the Performance of Main Irrigation Canal and Cross Drainage of Omo-Kuraz I and II Sugar Project (The Case of Omo-Gibe Basin)

Flood is serious economic losses like damage main irrigation canal and installed infrastructure on sugar project. The objective of this research was to provide an estimation of rainfall-runoff effect on main canal irrigation scheme. The hydrograph characteristics of observed and simulated events are compared using various evaluation criteria consisting of Nash-Sutcliffe coefficient (NSE) is between 0.8 and 0.9, relative volume error (RVE) between -10 and 10%, and coefficient of determination (r2) is between 0.8 and 0.9 during calibration and validation period. Among the catchments of the basin that are used in the regional modeling work and nine Model Parameters are selected among those commonly used in regionalization studies. Four parameter regionalization methods (linear regression, spatial proximity, area ratio and sub-basin mean) were applied to transfer model parameter values from the gauged to the un-gauged catchments except area ratio method. In regional model, gauged catchments Model Parameters and Physical catchment characteristics of un-gauged catchments were used to develop the equations in order to predict discharge of un-gauged catchments. To have better understanding of model parameter performance, the sensitivity analysis of nine model parameters were performed manually by trial and error. The evaluation demonstrations that time of concentration (TC), storage coefficient(R), maximum storage (MS) and constant rate (CR) are more sensitive than others. Therefore, spatial proximity method is recommended in predicting discharge for un-gauged catchments for this research and obtained 85.1m3/s, 1.6m3/s, 9m3/s, 0.6m3/s,1.1m3/s, 14.3m3/s and 28.2m3/s peak flow for cross drainage three, culvert outlet four, culvert outlet three, culvert outlet two, culvert outlet one, cross drainage thirteen and cross drainage fourteen un-gauged sub catchments respectively. To determined predicted peak discharge using L-moment method in flood frequency analysis of selected gauged catchment. Finally, flood damage protection structures like dyke must be recommended for temporary, but redesign depend on peak discharge is necessary. Keywords: HEC-HMS, Regionalization, Stream flow simulation, L-moment, un-gauged catchments, Omo-Gibe sub-basin DOI: 10.7176/CER/14-2-02 Publication date: April 30 th 2022

Automating rainfall recording: Ensuring homogeneity when instruments change

Sub-daily rainfall is used in modelling of small catchments, design of urban drainage, calculating soil erosion, understanding the water balance of vegetated catchments, and assessing the impact of climate change on short duration storms. Hence it is critical to ensure that sub-daily rainfall records are homogeneous, but with a global shift to automated measurement of rainfall, there has been a change in the rainfall recording instrumentation. Although changes in instrumentation should be documented, in many cases this is not the case and station metadata are missing. As result, studies of rainfall patterns, particularly those investigating trends, often cannot report their treatment of possible inhomogeneities and must assume the data is suitable for further analysis.Here, we argue that standard quality assurance methods for checking inhomogeneities in rainfall may not identify a change in the rainfall record due to a change in instrumentation. Through testing (1) an aggregation of rainfall to the coarsest instrument resolution, (2) removal of rainfall below a minimum rainfall depth, (3) removal of rainfall below a minimum accumulation (event) depth, and (4) a combination of the above adjustments, we present recommendations for ensuring sub-daily rainfall is homogenous with minimum alteration to the rainfall record.We assess the proposed methods using the case study of Australia’s sub-daily rainfall monitoring network, where, in 1996 the pluviograph network was switched to tipping buckets, creating a large-sample test bed with a known systematic instrumentation change. Our results show that event-based statistics are considerably more sensitive to instrument change than annually aggregated rainfall statistics and hence standard methods for quality assurance may not identify possible inhomogeneities. We suggest an aggregation of the data to the coarsest instrument resolution, with the removal of small rainfall accumulations, can alleviate inhomogeneities for event-based statistics, whereas, for annual statistics, aggregation to the coarsest instrument resolution is sufficient. In the absence of a known instrumentation change, removing rainfall at or below the instrument resolution is also a viable technique for improving rainfall record homogeneity for event-based statistics. These findings will aid future sub-daily rainfall studies by justifying the use of a minimum rainfall or event depth for subsequent analysis.

A retrospective on hydrological catchment modelling based on half a century with the HBV model

Abstract. Hydrological catchment models are important tools that are commonly used as the basis for water resource management planning. In the 1960s and 1970s, the development of several relatively simple models to simulate catchment runoff started, and a number of so-called conceptual (or bucket-type) models were suggested. In these models, the complex and heterogeneous hydrological processes in a catchment are represented by a limited number of storage elements and the fluxes between them. While computer limitations were a major motivation for such relatively simple models in the early days, some of these models are still used frequently despite the vast increase in computational opportunities. The HBV (Hydrologiska Byråns Vattenbalansavdelning) model, which was first applied about 50 years ago in Sweden, is a typical example of a conceptual catchment model and has gained large popularity since its inception. During several model intercomparisons, the HBV model performed well despite (or because of) its relatively simple model structure. Here, the history of model development, from thoughtful considerations of different model structures to modelling studies using hundreds of catchments and cloud computing facilities, is described. Furthermore, the wide range of model applications is discussed. The aim is to provide an understanding of the background of model development and a basis for addressing the balance between model complexity and data availability that will also face hydrologists in the coming decades.

Application of a novel cascade-routing and reinfiltration concept with a Voronoi unstructured grid in MODFLOW 6, for an assessment of surface-water/groundwater interactions in a hard-rock catchment (Sardon, Spain)

Integrated hydrological modelling (IHM) can reliably characterize surface-water/groundwater interactions in complex hydrological systems such as hard-rock systems (HRS), located in water-limited environments (WLE). Such HRS-WLE conditions are represented by Sardon catchment (~80 km2) in Spain, where the MODFLOW 6 modelling environment was tested, applying the following improvements as compared to previous works in that catchment: a new conceptual model, driving forces redefined based on remote sensing data, an unstructured Voronoi grid, and, most importantly, a novel cascade-routing and reinfiltration (CRR) concept. In the standard MODFLOW 6, rejected infiltration and groundwater exfiltration have always been considered as sinks (evaporation). However, in reality, that water can not only evaporate but also reinfiltrate back to the subsurface or move as runoff towards drainage water bodies. The CRR improves surface–unsaturated-zone interactions and also surface-water/groundwater interactions. The standard and new capacities of MODFLOW 6 are presented in the transient model of the Sardon catchment, calibrated using 7 years of daily groundwater heads and streamflows. The results showed: the large spatio-temporal variability of the groundwater fluxes, the substantial role of groundwater exfiltration, the low catchment storage, the fast reaction of the water table and streams to rainfall, and the mosaic character of the net recharge. These characteristics are typical for HRS-WLEs with a shallow water table. MODFLOW 6 has many improvements compared to previous MODFLOW versions, so with the proposed CRR concept (still can be improved), the single-environment MODFLOW 6 has modelling capacity comparable with multienvironment IHMs, while being more flexible and more efficient.

Urban land-use land-cover extraction for catchment modelling using deep learning techniques

AbstractThroughout the world, the likelihood of floods and managing the associated risk are a concern to many catchment managers and the population residing in those catchments. Catchment modelling is a popular approach to predicting the design flood quantiles of a catchment with complex spatial characteristics and limited monitoring data to obtain the necessary information for preparing the flood risk management plan. As an important indicator of urbanisation, land use land cover (LULC) plays a critical role in catchment parameterisation and modelling the rainfall–runoff process. Digitising LULC from remote sensing imagery of urban catchment is becoming increasingly difficult and time-consuming as the variability and diversity of land uses occur during urban development. In recent years, deep learning neural networks (DNNs) have achieved remarkable image classification and segmentation outcomes with the powerful capacity to process complex workflow and features, learn sophisticated relationships and produce superior results. This paper describes end-to-end data assimilation and processing path using U-net and DeepLabV3+, also proposes a novel approach integrated with the clustering algorithm MeanShift. These methods were developed to generate pixel-based LULC semantic segmentation from high-resolution satellite imagery of the Alexandria Canal catchment, Sydney, Australia, and assess the applicability of their outputs as inputs to different catchment modelling systems. A significant innovation is using the MeanShift clustering algorithm to reduce the spatial noise in the raw image and propagate it to the deep learning network to improve prediction. All three methods achieved excellent classification performance, where the MeanShift+U-net has the highest accuracy and consistency on the test imagery. The final suitability assessment illustrates that all three methods are more suitable for the parameterisation of semi-distributed modelling systems rather than the fully distributed modelling systems, where the MeanShift+U-net should be adopted for image-based impervious area extraction of urban catchment due to its superior prediction accuracy of 98.47%.

An Exploration of Bayesian Identification of Dominant Hydrological Mechanisms in Ungauged Catchments

AbstractHydrological modeling of ungauged catchments, which lack observed streamflow data, is an important practical goal in hydrological sciences. A major challenge is to identify a model structure that reflects the hydrological processes relevant to the catchment of interest. This study contributes a Bayesian framework for identifying individual model mechanisms (process representations) from flow indices regionalized to the catchment of interest. We extend a method previously introduced for mechanism identification in gauged basins, by formulating the inference equations in the space of (regionalized) flow indices and by accounting for posterior parameter uncertainty. A flexible hydrological model is used to generate candidate mechanisms and model structures, followed by statistical hypothesis testing to identify “dominant” (more a posterior probable) model mechanisms. The proposed method is illustrated using real data and synthetic experiments based on 92 catchments from northern Spain, from which 16 catchments are treated as ungauged. 624 hydrological model structures from the flexible framework FUSE are employed. In real data experiments, the method identifies a dominant mechanism in 27% of 112 trials (processes and catchments). The most identifiable process is routing, whereas the least identifiable processes are percolation and unsaturated zone processes. In synthetic experiments, where “true” mechanisms are known, the reliability of method varies from 60% to 95% depending on the combined regionalization and hydrological error; the probability of making an identification remains stable at around 25%. More broadly, the study contributes perspectives on hydrological mechanism identification under data‐scarce conditions; limitations and opportunities for improvement are outlined.

Functional Multi‐Scale Integration of Agricultural Nitrogen‐Budgets Into Catchment Water Quality Modeling

AbstractDiffuse Nitrogen pollution from agriculture maintains high pressures on groundwater and aquatic ecosystems. Further mitigation requires targeted measures that reconcile agricultural interests in environmental protection. However, the agriculture‐related processes of catchment N modeling remain poorly defined due to discipline‐specific data and knowledge gaps. Using field‐experimental data, crop N uptake responses to fertilizer management were parsimoniously conceptualized and integrated into a catchment diffuse‐N model. The improved catchment modeling further facilitated integration with agricultural budget‐based assessments. The integrated analysis in a mesoscale catchment disentangled contrasting agri‐environment functional mechanisms in typically flashy chemodynamic and transport‐limited chemostatic export regimes. Moreover, the former was actively responsive to interannual climatic variability and agricultural practices; the latter exhibited drought‐induced enhancement of N enrichment, which could likely be mitigated through reduced fertilization. This interdisciplinary integration of data and methods provided an insightful evidence base for multi‐sector targeted measures, especially under cumulative impacts of changing climate and fertilizer‐use intensities.

Are maps of nitrate reduction in groundwater altered by climate and land use changes?

Abstract. Nitrate reduction maps have been used routinely in northern Europe for calculating the efficiency of remediation measures and the impact of climate change on nitrate leaching. These maps are, therefore, valuable tools for policy analysis and mitigation targeting. Nitrate reduction maps are normally based on output from complex hydrological models and, once generated, are largely assumed constant in time. However, the distribution, magnitude, and efficiency of nitrate reduction cannot necessarily be considered stationary during changing climate and land use as flow paths, nitrate release timing, and their interaction may shift. This study investigates the potential improvement of using transient nitrate reduction maps, compared to a constant nitrate reduction map that is assumed during land use and climate change, both for nitrate loads and the spatial variation in reduction. For this purpose, a crop and soil model (DAISY) was set up to provide nitrate input to a distributed hydrological model (MIKE SHE) for an agricultural catchment in Funen, Denmark. Nitrate reduction maps based on an observed dataset of land use and climate were generated and compared to nitrate reduction maps generated for all combinations of four potential land use change scenarios and four future climate model projections. Nitrate reduction maps were found to be more sensitive to changes in climate, leading to a reduction map change of up to 10 %, while land use changes effects were minor. The study, however, also showed that the reduction maps are products of a range of complex interactions between water fluxes, nitrate use, and timing. What is also important to note is that the choices made for future scenarios, model setup, and assumptions may affect the resulting span in the reduction capability. To account for this uncertainty, multiple approaches, assumptions, and models could be applied for the same area. However, as these models are very time consuming, this is not always a feasible approach in practice. An uncertainty of the order of 10 % on the reduction map may have major impacts on practical water management. It is, therefore, important to acknowledge if such errors are deemed acceptable in relation to the purpose and context of specific water management situations.

A Bayesian Modelling Framework for Integration of Ecosystem Services into Freshwater Resources Management

Models of ecological response to multiple stressors and of the consequences for ecosystem services (ES) delivery are scarce. This paper describes a methodology for constructing a BBN combining catchment and water quality model output, data, and expert knowledge that can support the integration of ES into water resources management. It proposes “small group” workshop methods for elucidating expert knowledge and analyses the areas of agreement and disagreement between experts. The model was developed for four selected ES and for assessing the consequences of management options relating to no-change, riparian management, and decreasing or increasing livestock numbers. Compared with no-change, riparian management and a decrease in livestock numbers improved the ES investigated to varying degrees. Sensitivity analysis of the expert information in the BBN showed the greatest disagreements between experts were mainly for low probability situations and thus had little impact on the results. Conversely, in our applications, the best agreement between experts tended to occur for the higher probability, more likely, situations. This has implications for the practical use of this type of model to support catchment management decisions. The complexity of the relationship between management measures, the water quality and ecological responses and resulting changes in ES must not be a barrier to making decisions in the present time. The interactions of multiple stressors further complicate the situation. However, management decisions typically relate to the overall character of solutions and not their detailed design, which can follow once the nature of the solution has been chosen, for example livestock management or riparian measures or both.