Hydrological Predictions For The Environment Research Articles

To tame a land: Limiting factors in model performance for the multi-objective calibration of a pan-European, semi-distributed hydrological model for discharge and sediments

Study RegionEurope Study FocusThe semi-distributed Hydrological Predictions for the Environment (HYPE) model for the European domain, E-HYPE4, was calibrated for discharge and sediments within a framework allowing for evaluation of the factors limiting model performance. Calibration was conducted using a multi-phase approach during which an initial multi-objective discharge/sediment calibration was followed by an exhaustive sediment calibration in which combinations of sediment routines were assessed. During each calibration phase, ensembles of parameter sets and model routines were simultaneously evaluated against discharge, evapotranspiration, and sediment observations. In total, 20,000 parameter sets were evaluated during the discharge calibration, and a further 20,000 model setups were assessed during the sediment calibration. New Hydrological Insights for the RegionModel performance was best with a highly regionalized model, and the largest drop in achievable performance occurred when transitioning from an ensemble of candidates to a single model setup. Much of the performance gains from a highly regionalized model could, however, be achieved with a much less regionalized model. Inclusion of sediments as a calibration objective provided more value than that of evapotranspiration in regards to reducing equifinality in the calibration for discharge. Evaluation of the erosion routines indicated that an index-based routine performed equally well as a more complex, process-based routine. Finally, analysis of model performance by subbasin attributes revealed the dominant factors — such as landuse, glaciers, abstractions/regulations, groundwater, and lakes/wetlands — affecting model biases for various regions.

Process based calibration of a continental-scale hydrological model using soil moisture and streamflow data

Study regionNelson Churchill River Basin (NCRB), Canada, and USA. Study FocusSoil temperature and moisture are essential variables that fluctuate based on soil depth, controlling several sub-surface hydrologic processes. The Hydrological Predictions for the Environment (HYPE) model’s soil profile depth can vary up to four meters, discretized into three soil layers. Here, we further discretized the HYPE subsurface domain to accommodate up to seven soil layers to improve the representation of subsurface thermodynamics and water transfer more accurately. Soil moisture data from different locations across NCRB are collected from 2013 to 2017 for model calibration. We use multi-objective optimization (MOO) to account for streamflow and soil moisture variability and improve the model fidelity at a continental scale. New hydrological insightsOur study demonstrates that MOO significantly improves soil moisture simulation from the median Kling Gupta Efficiency (KGE) of 0.21–0.66 without deteriorating the streamflow performance. Streamflow and soil moisture simulation performance improvements are statistically insignificant between the original three-layer and seven-layer discretization of HYPE. However, the finer discretization model shows improved simulation in sub-surface components such as the evapotranspiration when verified against reanalysis products, indicating a 12 % underestimation of evapotranspiration from the three-layer HYPE model. The improvement of the discretized HYPE model and simulating the soil temperature at finer vertical resolution makes it a prospective model for permafrost identification and climate change analysis.

Evaluation of overland flow modelling hypotheses with a multi-objective calibration using discharge and sediment data.

Conceptual hydrological models can move towards process-oriented modelling when addressing broader issues than discharge modelling alone. For instance, water quality modelling generally requires understanding of both pathways and travel times which might not be easily identified because observations at the outlet aggregate all processes at the catchment scale. In this study we tested if adding a second kind of observation, specifically sediment data, can help distinguish overland flow from total discharge. We applied a multi-objective calibration on both discharge and suspended sediment concentration simulation performance to the World-Wide Hydrological Predictions for the Environment (HYPE) model for 111 catchments spread over the USA. Results show that in comparison to two calibrations made one after the other, the multi-objective calibration leads to a significant improvement on the simulation performance of suspended sediments without a significant impact on the performance of discharge. New modelling hypotheses for overland flow calculations are proposed and resulted in similar discharge performances as the original one but with fewer parameters, which reduces equifinality and can prevent unwarranted model complexity in data-poor areas.

Assessing temporal variability of lake turbidity and trophic state of European lakes using open data repositories

Water turbidity is one of the more important water quality parameters that is strictly linked with the productivity of the lake and is commonly used as an indicator of the trophic state. However, limited field data availability across wide geographic gradients may hinder the conduction of large scale longitudinal studies. In this study, time series of lake turbidity and trophic state index (TSI) between 2002 and 2012 were obtained from the Copernicus Lake Water products to create a large longitudinal dataset of lake variables for 22 European lakes. The dataset was combined with estimates of nutrient concentrations and surface water temperature obtained from the Hydrological Predictions for the Environment (HYPE) and ERA5-Land data repositories, that were used as environmental predictors. Hence, the validity of the lake water quality parameters was tested by a) exploring their spatial and temporal variability and b) identifying associations with the environmental predictors. For this purpose, seasonal Mann-Kendall tests were applied to find significant inter-annual trends of turbidity and TSI for each lake, and generalized additive models (GAMs) were employed to identify the main parameters that shape their temporal dynamics. Although we did not find significant inter-annual changes, our findings highlighted the strong influence of seasonality and surface water temperature in defining the temporal variability patterns in most of the lakes. In addition, the importance of nutrients varied among lakes as several lakes exhibited narrow nutrient gradients reflecting relatively stable nutrient conditions during the examined period. Other lake intrinsic factors, such as local climate and biotic interactions, are important drivers of shaping turbidity and nutrient dynamics. This study highlighted the usefulness of combining lake data from large repositories in conducting large scale spatial studies as a valuable asset for future lake research and management purposes.

Exploring the potential for parameter transfer from daily to hourly time step in the HYPE model for Sweden

ABSTRACT Catchments with response time shorter than a day require simulations at sub-daily resolution for flood forecasting. Long-term hydrological data with sub-daily time resolution is lacking for many catchments and, thus, it is worthwhile to investigate the potential in using hydrological model parameters calibrated at a daily temporal resolution to simulate sub-daily runoff. This study investigates the potential of directly transferring the parameters from the one-day Hydrological Predictions for the Environment (HYPE) model for Sweden to a one-hour time step version. A sensitivity analysis and calibration of the one-hour model were also performed to explore the possibility of achieving improved model performance through an additive and multiplicative scaling of the parameters. Directly transferring the parameters led to a negligible loss in performance for all 147 catchments evaluated here. Scaling of the parameters did not lead to improvement for slow-responding catchments, while the increase in performance was negligible for catchments with sub-daily response time.

Remote sensing-aided rainfall–runoff modeling in the tropics of Costa Rica

Abstract. Streamflow simulation across the tropics is limited by the lack of data to calibrate and validate large-scale hydrological models. Here, we applied the process-based, conceptual HYPE (Hydrological Predictions for the Environment) model to quantitatively assess Costa Rica's water resources at a national scale. Data scarcity was compensated for by using adjusted global topography and remotely sensed climate products to force, calibrate, and independently evaluate the model. We used a global temperature product and bias-corrected precipitation from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) as model forcings. Daily streamflow from 13 gauges for the period 1990–2003 and monthly Moderate Resolution Imaging Spectroradiometer (MODIS) potential evapotranspiration (PET) and actual evapotranspiration (AET) for the period 2000–2014 were used to calibrate and evaluate the model applying four different model configurations (M1, M2, M3, M4). The calibration consisted of step-wise parameter constraints preserving the best parameter sets from previous simulations in an attempt to balance the variable data availability and time periods. The model configurations were independently evaluated using hydrological signatures such as the baseflow index, runoff coefficient, and aridity index, among others. Results suggested that a two-step calibration using monthly and daily streamflow (M2) was a better option than calibrating only with daily streamflow (M1), with similar mean Kling–Gupta efficiency (KGE ∼ 0.53) for daily streamflow time series, but with improvements to reproduce the flow duration curves, with a median root mean squared error (RMSE) of 0.42 for M2 and a median RMSE of 1.15 for M1. Additionally, including AET (M3 and M4) in the calibration statistically improved the simulated water balance and better matched hydrological signatures, with a mean KGE of 0.49 for KGE in M3–M4, in comparison to M1–M2 with mean KGE < 0.3. Furthermore, Kruskal–Wallis and Mann–Whitney statistical tests support a similar model performance for M3 and M4, suggesting that monthly PET-AET and daily streamflow (M3) represents an appropriate calibration sequence for regional modeling. Such a large-scale hydrological model has the potential to be used operationally across the humid tropics informing decision-making at relatively high spatial and temporal resolution.

Simulated impacts of relative climate change and river discharge regulation on sea ice and oceanographic conditions in the Hudson Bay Complex

In this analysis, we examine relative contributions from climate change and river discharge regulation to changes in marine conditions in the Hudson Bay Complex using a subset of five atmospheric forcing scenarios from the Coupled Model Intercomparison Project Phase 5 (CMIP5), river discharge data from the Hydrological Predictions for the Environment (HYPE) model, both naturalized (without anthropogenic intervention) and regulated (anthropogenically controlled through diversions, dams, reservoirs), and output from the Nucleus for European Modeling of the Ocean Ice-Ocean model for the 1981–2070 time frame. Investigated in particular are spatiotemporal changes in sea surface temperature, sea ice concentration and thickness, and zonal and meridional sea ice drift in response to (i) climate change through comparison of historical (1981–2010) and future (2021–2050 and 2041–2070) simulations, (ii) regulation through comparison of historical (1981–2010) naturalized and regulated simulations, and (iii) climate change and regulation combined through comparison of future (2021–2050 and 2041–2070) naturalized and regulated simulations. Also investigated is use of the diagnostic known as e-folding time spatial distribution to monitor changes in persistence in these variables in response to changing climate and regulation impacts in the Hudson Bay Complex. Results from this analysis highlight bay-wide and regional reductions in sea ice concentration and thickness in southwest and northeast Hudson Bay in response to a changing climate, and east-west asymmetry in sea ice drift response in support of past studies. Regulation is also shown to amplify or suppress the climate change signal. Specifically, regulation amplifies sea surface temperatures from April to August, suppresses sea ice loss by approximately 30% in March, contributes to enhanced sea ice drift speed by approximately 30%, and reduces meridional circulation by approximately 20% in January due to enhanced zonal drift. Results further suggest that the offshore impacts of regulation are amplified in a changing climate.

Data- and model-driven determination of flow pathways in the Piako catchment, New Zealand

Quantifying flow pathways within a larger catchment can help improve diffuse pollution management strategies across subcatchments. But, spatial quantification of flow pathway contributions to catchment stream flow is very limited, since it is challenging to physically separate water from different paths and very expensive to measure, especially for larger areas. To overcome this problem, a novel, combined data and modelling approach was employed to partition stream flow in the Piako catchment, New Zealand, which is a predominantly agricultural catchment with medium to high groundwater recharge potential. The approach comprised a digital filtering technique to separate baseflow from total stream flow, machine learning to predict a baseflow index (BFI) for all streams with Strahler 1st order and higher, and hydrological modelling to partition the flow into five flow components: surface runoff, interflow, tile drainage, shallow groundwater, and deep groundwater. The baseflow index scores corroborated the spatial distributions of the flow pathways modelled in 1st order catchments. Average depth to groundwater data matched well with BFI and Hydrological Predictions for the Environment (HYPE) modeled flow pathway partitioning results, with deeper water tables in areas of the catchment predicted to have greater baseflow or shallow and deep groundwater contributions to stream flow. Since direct quantification of flow pathways at catchment-scale is scarce, it is recommended to use soft data and expert knowledge to inform model parameterization and to constrain the model results. The approach developed here is applicable as a screening method in ungauged catchments.

Simulating river regulation and reservoir performance in a continental-scale hydrologic model

This study develops a novel reservoir regulation routine, incorporated into a continental-scale hydrologic model in the Nelson, Churchill, Yenisey, Ob, and Lena basins. This regulation routine is integrated into the Hydrological Predictions for the Environment (HYPE) hydrologic model, used for continental-scale applications. Applying this daily timestep regulation routine at 19 reservoirs in the Arctic Ocean watershed, performance is shown to improve upon the reservoir regulation currently available in the HYPE model when testing outflow and storage Nash Sutcliffe Efficiencies (NSEs). Improvements stem from intra-annually variable storage rule curves and a variety of stage-dependent outflow functions, improving simulation skill (median NSE increases of 0.18 over 21 reservoir outflow records and 0.49 over 19 reservoir storage records). This new, reservoir regulation routine is suitable for continental-scale modelling by deriving varying, rather than fixed, threshold water surface levels and associated outflow rules in a programmatic way for multiple reservoirs.

Modelling of the Discharge Response to Climate Change under RCP8.5 Scenario in the Alata River Basin (Mersin, SE Turkey)

This study investigates the impacts of climate change on the hydrological response of a Mediterranean mesoscale catchment using a hydrological model. The effect of climate change on the discharge of the Alata River Basin in Mersin province (Turkey) was assessed under the worst-case climate change scenario (i.e., RCP8.5), using the semi-distributed, process-based hydrological model Hydrological Predictions for the Environment (HYPE). First, the model was evaluated temporally and spatially and has been shown to reproduce the measured discharge consistently. Second, the discharge was predicted under climate projections in three distinct future periods (i.e., 2021–2040, 2046–2065 and 2081–2100, reflecting the beginning, middle and end of the century, respectively). Climate change projections showed that the annual mean temperature in the Alata River Basin rises for the beginning, middle and end of the century, with about 1.35, 2.13 and 4.11 °C, respectively. Besides, the highest discharge timing seems to occur one month earlier (February instead of March) compared to the baseline period (2000–2011) in the beginning and middle of the century. The results show a decrease in precipitation and an increase in temperature in all future projections, resulting in more snowmelt and higher discharge generation in the beginning and middle of the century scenarios. However, at the end of the century, the discharge significantly decreased due to increased evapotranspiration and reduced snow depth in the upstream area. The findings of this study can help develop efficient climate change adaptation options in the Levant’s coastal areas.

Spatial validation of a semi-distributed hydrological nutrient transport model

Semi-distributed hydrological and water quality models are increasingly used as innovative and scientific-based management tools. However, their application is usually restricted to the gauging stations where they are originally calibrated, limiting their spatial capability. In this study, the semi-distributed hydrological water quality model HYPE (HYdrological Predictions for the Environment) was tested spatially to represent nitrate-N (NO3--N) and total phosphorus (TP) concentrations and loads of the nested and heterogeneous Selke catchment (463 km2) in central Germany. First, an automatic calibration procedure and uncertainty analysis were conducted using the DiffeRential Evolution Adaptive Metropolis (DREAM) tool to simulate discharge, NO3--N and TP concentrations. A multi-site and multi-objective calibration approach was applied using three main gauging stations, covering the most important hydro-meteorological and physiographical characteristics of the whole catchment. Second, the model’s capability was tested to represent further internal stations, which were not initially considered for calibration. Results showed that discharge was well represented by the model at all three main stations during both calibration (1994–1998) and validation (1999–2014) periods with lowest Nash-Sutcliffe Efficiency (NSE) of 0.71 and maximum Percentage BIAS (PBIAS) of 18.0%. The model was able to reproduce the seasonal dynamics of NO3--N and TP concentrations with low predictive uncertainty at the three main stations, reflected by PBIAS values in the ranges from −16.1% to 6.4% and from −20.0% to 11.5% for NO3--N and TP load simulations, respectively. At internal stations, the model could represent reasonably well the seasonal variation of nutrient concentrations with PBIAS values in the ranges from −9.0% to 14.2% for NO3--N and from −25.3% to 34.3% for TP concentration simulations. Overall, results suggested that the spatial validation of a nutrient transport model can be better ensured when a multi-site and multi-objective calibration approach using archetypical gauging stations is implemented. Further, results revealed that the delineation of sub-catchments should put more focus on hydro-meteorological conditions than on land-use features.

A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network

The increasing penetration of non-programmable renewable energy sources (RES) is enforcing the need for accurate power production forecasts. In the category of hydroelectric plants, Run of the River (RoR) plants belong to the class of non-programmable RES. Data-driven models are nowadays the most widely adopted methodologies in hydropower forecast. Among all, the Artificial Neural Network (ANN) proved to be highly successful in production forecast. Widely adopted and equally important for hydropower generation forecast is the HYdrological Predictions for the Environment (HYPE), a semi-distributed hydrological Rainfall–Runoff model. A novel hybrid method, providing HYPE sub-basins flow computation as input to an ANN, is here introduced and tested both with and without the adoption of a decomposition approach. In the former case, two ANNs are trained to forecast the trend and the residual of the production, respectively, to be then summed up to the previously extracted seasonality component and get the power forecast. These results have been compared to those obtained from the adoption of a ANN with rainfalls in input, again with and without decomposition approach. The methods have been assessed by forecasting the Run-of-the-River hydroelectric power plant energy for the year 2017. Besides, the forecasts of 15 power plants output have been fairly compared in order to identify the most accurate forecasting technique. The here proposed hybrid method (HYPE and ANN) has shown to be the most accurate in all the considered study cases.

Hydrological modeling of freshwater discharge into Hudson Bay using HYPE

This study details the enhancement and calibration of the Arctic implementation of the HYdrological Predictions for the Environment (HYPE) hydrological model established for the BaySys group of projects to produce freshwater discharge scenarios for the Hudson Bay Drainage Basin (HBDB). The challenge in producing estimates of freshwater discharge for the HBDB is that it spans over a third of Canada’s continental landmass and is 40% ungauged. Scenarios for BaySys require the separation between human and climate interactions, specifically the separation of regulated river discharge from a natural, climate-driven response. We present three key improvements to the modelling system required to support the identification of natural from anthropogenic impacts: representation of prairie disconnected landscapes (i.e., non-contributing areas), a method to generalize lake storage-discharge parameters across large regions, and frozen soil modifications. Additionally, a unique approach to account for irregular hydrometric gauge density across the basins during model calibration is presented that avoids overfitting parameters to the densely gauged southern regions. We summarize our methodologies used to facilitate improved separation of human and climate driven impacts to streamflow within the basin and outline the baseline discharge simulations used for the BaySys group of projects. Challenges remain for modeling the most northern reaches of the basin, and in the lake-dominated watersheds. The techniques presented in this work, particularly the lake and flow signature clusters, may be applied to other high latitude, ungauged Arctic basins. Discharge simulations are subsequently used as input data for oceanographic, biogeochemical, and ecosystem studies across the HBDB.

A New National Water Quality Model to Evaluate the Effectiveness of Catchment Management Measures in England

This investigation reports on a new national model to evaluate the effectiveness of catchment sensitive farming in England, and how pollution mitigation measures have improved water quality between 2006 and 2016. An adapted HYPE (HYdrological Predictions for the Environment) model was written to use accurate farm emissions data so that the pathway impact could be accounted for in the land phase of transport. Farm emissions were apportioned into different runoff fractions simulated in surface and soil layers, and travel time and losses were taken into account. These were derived from the regulator’s ‘catchment change matrix’ and converted to monthly load time series, combined with extensive point source load datasets. Very large flow and water quality monitoring datasets were used to calibrate the model nationally for flow, nitrogen, phosphorus, suspended sediments and faecal indicator organisms. The model was simulated with and without estimated changes to farm emissions resulting from catchment measures, and spatial and temporal changes to water quality concentrations were then assessed.

A New Fully Distributed Model of Nitrate Transport and Removal at Catchment Scale

AbstractHydrological water quality models have gained wide acceptance from environmental scientists and water managers to address deterioration of surface water quality. Higher spatiotemporal accuracy of such models is increasingly required for better understanding the functional heterogeneity of catchments and improving management decisions at different governance levels. However, balancing spatial representation and model complexity remains challenging. We present a new flexibly designed, fully distributed nitrate transport and removal model (mHM‐Nitrate) at catchment scale. The model was developed mainly based on the mesoscale Hydrological Model (mHM) and the Hydrological Predictions for the Environment (HYPE) model. The mHM‐Nitrate model was tested in the Selke catchment (Central Germany), which is characterized by heterogeneous physiographic and land‐use conditions, using adequate observed hydrological and nitrate data at three nested gauging stations. Long term (1997–2015) daily simulations showed that the model well reproduced the seasonal dynamics of biweekly nitrate observations in forested, agricultural and urban areas. High‐frequency measurements (2010‐2015) were additionally used to validate model performance of simulating short‐term changes in stream‐water concentrations that reflect changes in runoff partitioning and event‐based dilution effects. Uncertainty analysis confirmed the model's robustness. Moreover, model calculations showed that mean terrestrial nitrate input/output (in total 105 kg ha−1 yr−1) and in‐stream removal (8% of mean nitrate load) were in comparable ranges with literature, respectively. The new mHM‐Nitrate model is capable of providing detailed spatial information on nitrate concentrations and fluxes, which can motivate more specific catchment investigations on nitrate transport processes and provide guidance on spatially differentiated agricultural practices and measures.

Near-real-time adjusted reanalysis forcing data for hydrology

Abstract. Extending climatological forcing data to current and real-time forcing is a necessary task for hydrological forecasting. While such data are often readily available nationally, it is harder to find fit-for-purpose global data sets that span long climatological periods through to near-real time. Hydrological simulations are generally sensitive to bias in the meteorological forcing data, especially relative to the data used for the calibration of the model. The lack of high-quality daily resolution data on a global scale has previously been solved by adjusting reanalysis data with global gridded observations. However, existing data sets of this type have been produced for a fixed past time period determined by the main global observational data sets. Long delays between updates of these data sets leaves a data gap between the present day and the end of the data set. Further, hydrological forecasts require initializations of the current state of the snow, soil and lake (and sometimes river) storage. This is normally conceived by forcing the model with observed meteorological conditions for an extended spin-up period, typically at a daily time step, to calculate the initial state. Here, we present and evaluate a method named HydroGFD (Hydrological Global Forcing Data) to combine different data sets in order to produce near-real-time updated hydrological forcing data of temperature and precipitation that are compatible with the products covering the climatological period. HydroGFD resembles the already established WFDEI (WATCH Forcing Data–ERA-Interim) method (Weedon et al., 2014) closely but uses updated climatological observations, and for the near-real time it uses interim products that apply similar methods. This allows HydroGFD to produce updated forcing data including the previous calendar month around the 10th of each month. We present the HydroGFD method and therewith produced data sets, which are evaluated against global data sets, as well as with hydrological simulations with the HYPE (Hydrological Predictions for the Environment) model over Europe and the Arctic regions. We show that HydroGFD performs similarly to WFDEI and that the updated period significantly reduces the bias of the reanalysis data. For real-time updates until the current day, extending HydroGFD with operational meteorological forecasts, a large drift is present in the hydrological simulations due to the bias of the meteorological forecasting model.

Process refinements improve a hydrological model concept applied to the Niger River basin

AbstractThis study examines a method to improve a process‐oriented hydrological model concept applied to another region than it was first developed for. In principle, we propose to analyse and refine each major hydrological process separately, sequentially, and iteratively. To test the method, the HYPE model concept (HYdrological Predictions for the Environment, originally developed for Sweden) was here applied to the data‐sparse Niger River basin in West Africa. Errors in the baseline Niger‐HYPE model were analysed to identify inadequately described processes. These process descriptions were subsequently isolated and refined through a set of experiments focusing on concept development, input data enhancement, and multivariable calibration. The refinements were guided by in situ discharge observations, earth observations, local expert knowledge, and previous studies. The results show that the original model concept could simulate the annual cycle of discharge, but not the magnitudes or daily dynamics (56‐station average Nash‐Sutcliffe Efficiency = −1). The main processes requiring improved descriptions were precipitation, evaporation, surface runoff, infiltration, soil storage, reservoir regulations, aquifer recharge, and flooding and river‐atmosphere exchange in the Inner Niger Delta. Of these, evaporation, flooding and river‐atmosphere exchange differ so much between Sweden and the Niger River that the model concept had to be refined. All refinements were synthesized in a new model version (Niger‐HYPE2.0) performing significantly better across the basin (56‐station average Nash‐Sutcliffe Efficiency = 0.4). This study demonstrates the danger of applying a model off the shelf, and the obligation to carefully evaluate and revise process descriptions when applying a model concept to a new region. Moreover, the results indicate that our approach to separately, sequentially, and iteratively refine processes together with local experts can substantially improve process‐oriented hydrological models.

Regional climate model and model output statistics method uncertainties and the effect of temperature and precipitation on future river discharges in Scandinavia

We analyze the importance of regional climate models (GCM-RCMs) and model output statistics (MOS) methods as uncertainty sources for future changes of various hydrological variables in Scandinavia. The Hydrological Predictions for the Environment (HYPE) model, driven with daily mean temperature and precipitation, is used to simulate changes in river discharges and other hydrological components from the present-day climate (1980–2009) to mid-21st century conditions (2041–2070). The results show that GCM-RCM differences explain most of the spread in the simulated changes in the annual mean cycle of river discharge. At seasonal level, MOS-method uncertainties are most important during the winter and spring, which is likely explained by the sensitivity of snow processes to the representation of daily variability in the MOS methods. To gain physical insights into the physical processes, the relative importance of changes to temperature or precipitation on changes in surface hydrology are also assessed. In most regions of Scandinavia, changes to temperature explain most of the changes in river discharge volumes and spring peaks. Precipitation changes only have a secondary role in modulating these changes. Again, these results are mostly explained by changes in snow processes in winter and increases in evapotranspiration in summer.

Evaluating the HYPE model for estimating groundwater recharge in a groundwater dominated catchment in Poland

Hydrological models can be useful tools simulating climate and land use changes and their impact on nutrients outflows from a catchment area. One of them is the HYPE (HYdrological Predictions for the Environment) water quality model applicable to different spatial scales. Groundwater recharge via infiltrating precipitation is a significant water budget component. The rate of groundwater recharge in the HYPE model is estimated from the water balance in soils. The Kocinka river catchment is one of the test areas in the BONUS-Soils2Sea project where HYPE model modelling was carried out. A hydrograph, among others, is one of the modelling results and, based on it, the recharge rate of groundwater was determined. This value was compared with groundwater recharge rates estimated by the infiltration method used for the Groundwater Vulnerability Map of Poland.

Coupled hydro-meteorological modelling on a HPC platform for high-resolution extreme weather impact study

Abstract. Impact-focused studies of extreme weather require coupling of accurate simulations of weather and climate systems and impact-measuring hydrological models which themselves demand larger computer resources. In this paper, we present a preliminary analysis of a high-performance computing (HPC)-based hydrological modelling approach, which is aimed at utilizing and maximizing HPC power resources, to support the study on extreme weather impact due to climate change. Here, four case studies are presented through implementation on the HPC Wales platform of the UK mesoscale meteorological Unified Model (UM) with high-resolution simulation suite UKV, alongside a Linux-based hydrological model, Hydrological Predictions for the Environment (HYPE). The results of this study suggest that the coupled hydro-meteorological model was still able to capture the major flood peaks, compared with the conventional gauge- or radar-driving forecast, but with the added value of much extended forecast lead time. The high-resolution rainfall estimation produced by the UKV performs similarly to that of radar rainfall products in the first 2–3 days of tested flood events, but the uncertainties particularly increased as the forecast horizon goes beyond 3 days. This study takes a step forward to identify how the online mode approach can be used, where both numerical weather prediction and the hydrological model are executed, either simultaneously or on the same hardware infrastructures, so that more effective interaction and communication can be achieved and maintained between the models. But the concluding comments are that running the entire system on a reasonably powerful HPC platform does not yet allow for real-time simulations, even without the most complex and demanding data simulation part.