Min Time Step Research Articles

Hidden power system inflexibilities imposed by traditional unit commitment formulations

Approximations made in traditional day-ahead unit commitment model formulations can result in suboptimal or even infeasible schedules for slow-start units and inaccurate predictions of actual costs and wind curtailment. With increasing wind penetration, these errors will become economically more significant. Here, we consider inaccuracies from three approximations: the use of hourly intervals in which energy production from each generator is modeled as being constant; the disregarding of startup and shutdown energy trajectories; and optimization based on expected wind profiles. The results of unit commitment formulations with those assumptions are compared to models that: (1) use a piecewise-linear power profiles of generation, load and wind, instead of the traditional stepwise energy profiles; (2) consider startup/shutdown trajectories; and (3) include many possible wind trajectories in a stochastic framework. The day-ahead hourly schedules of slow-start generators are then evaluated against actual wind and load profiles using a model real-time dispatch and quick-start unit commitment with a 5min time step. We find that each simplification usually causes expected generation costs to increase by several percentage points, and results in significant understatement of expected wind curtailment and, in some cases, load interruptions. The inclusion of startup and shutdown trajectories often yielded the largest improvements in schedule performance.

Ultra short‐term PV power forecasting based on ELM segmentation model

With the rapid increase of the installed capacity of photovoltaic (PV) power plants, it is important to forecast PV power output in order to meet the requirements of power systems. Based on extreme learning machine (ELM), this study proposes a methodology of ultra short-term PV power forecasting by using historical power output data, which can predict power output for a prediction horizon of 30 min with 10 min time step. Compared with auto-regressive and moving average (ARMA) time series model and BPAQ3 neural network, ELM provides better prediction accuracy in each time horizons. To further enhance the model performance, the authors analyse the forecasting error distribution in different time intervals during each day, and according to the error expectation, divide the data set into two subsets, then train each subset separately to establish segmentation model. The result shows that ELM segmentation model based on error distribution provides a better performance than using ELM alone.

A rainfall disaggregation scheme for sub-hourly time scales: Coupling a Bartlett-Lewis based model with adjusting procedures

Many hydrological applications, such as flood studies, require the use of long rainfall data at fine time scales varying from daily down to 1min time step. However, in the real world there is limited availability of data at sub-hourly scales. To cope with this issue, stochastic disaggregation techniques are typically employed to produce possible, statistically consistent, rainfall events that aggregate up to the field data collected at coarser scales. A methodology for the stochastic disaggregation of rainfall at fine time scales was recently introduced, combining the Bartlett-Lewis process to generate rainfall events along with adjusting procedures to modify the lower-level variables (i.e., hourly) so as to be consistent with the higher-level one (i.e., daily). In the present paper, we extend the aforementioned scheme, initially designed and tested for the disaggregation of daily rainfall into hourly depths, for any sub-hourly time scale. In addition, we take advantage of the recent developments in Poisson-cluster processes incorporating in the methodology a Bartlett-Lewis model variant that introduces dependence between cell intensity and duration in order to capture the variability of rainfall at sub-hourly time scales. The disaggregation scheme is implemented in an R package, named HyetosMinute, to support disaggregation from daily down to 1-min time scale. The applicability of the methodology was assessed on a 5-min rainfall records collected in Bochum, Germany, comparing the performance of the above mentioned model variant against the original Bartlett-Lewis process (non-random with 5 parameters). The analysis shows that the disaggregation process reproduces adequately the most important statistical characteristics of rainfall at wide range of time scales, while the introduction of the model with dependent intensity-duration results in a better performance in terms of skewness, rainfall extremes and dry proportions.

Modeling the diurnal variability of respiratory fluxes in the Canadian Terrestrial Ecosystem Model (CTEM)

AbstractThe Canadian Terrestrial Ecosystem Model (CTEM) coupled to the Canadian Land Surface Scheme (CLASS) is a dynamic vegetation model that incorporates photosynthesis and respiration submodules among many other physiological processes of the terrestrial biosphere. While the photosynthesis and leaf respiration submodules of CTEM operate at a time step of 30 min, other respiratory fluxes are estimated at a daily time step using the daily‐averaged values of canopy and soil temperature, and soil moisture content. Here we modify CTEM to be able to simulate the diurnal variation of ecosystem respiratory fluxes caused by the diurnal variation in the driving climate data. Simulating respiration at a 30 min time step changed the equilibrium states of primary carbon pools and fluxes. This required changes to some of the model's parameters in order to realistically simulate the equilibrium values of carbon pools and fluxes. The resulting estimated daily and annual carbon fluxes from the modified and original CTEM are similar with small relative differences. The similar annual cycles of daily values of primary CO2 fluxes confirm that modeling the respiratory fluxes at a subdaily time step does not affect the annual cycles of these fluxes. We also demonstrate that the modified version of the model is able to simulate the diurnal cycle of net ecosystem exchange of CO2 fluxes that are broadly comparable to observation‐based estimates at two flux tower sites and exhibit realistic seasonal patterns. Limitations remain in the modeled diurnal patterns of primary land‐atmosphere CO2 fluxes which will form the basis of further model improvements.

SATIN–Satellite driven nowcasting system

Abstract. A precipitation nowcasting system (SATIN) is presented which relies entirely on satellite based precipitation products and rain gauge measurements. Thus, the proposed system is most suitable for areas where ground based radar observations are not available, or potentially suffer from low quality. SATIN delivers analyses on a 1 km grid every 15 min and nowcasts (obtained through motion vectors) in 15 min time steps. Nowcasts are gradually merged with NWP precipitation forecasts. An extensive validation including comparisons to different NWP models yields superior performance for SATIN analyses as well as nowcasts for lead times up to 1 h. Reducing the station density still yields better performance than operationally available NWP's.

Temporal rainfall disaggregation using a multiplicative cascade model for spatial application in urban hydrology

Rainfall time series of high temporal resolution and spatial density are crucial for urban hydrology. The multiplicative random cascade model can be used for temporal rainfall disaggregation of daily data to generate such time series. Here, the uniform splitting approach with a branching number of 3 in the first disaggregation step is applied. To achieve a final resolution of 5 min, subsequent steps after disaggregation are necessary. Three modifications at different disaggregation levels are tested in this investigation (uniform splitting at Δt = 15 min, linear interpolation at Δt = 7.5 min and Δt = 3.75 min). Results are compared both with observations and an often used approach, based on the assumption that a time steps with Δt = 5.625 min, as resulting if a branching number of 2 is applied throughout, can be replaced with Δt = 5 min (called the 1280 min approach). Spatial consistence is implemented in the disaggregated time series using a resampling algorithm. In total, 24 recording stations in Lower Saxony, Northern Germany with a 5 min resolution have been used for the validation of the disaggregation procedure. The urban-hydrological suitability is tested with an artificial combined sewer system of about 170 hectares. The results show that all three variations outperform the 1280 min approach regarding reproduction of wet spell duration, average intensity, fraction of dry intervals and lag-1 autocorrelation. Extreme values with durations of 5 min are also better represented. For durations of 1 h, all approaches show only slight deviations from the observed extremes. The applied resampling algorithm is capable to achieve sufficient spatial consistence. The effects on the urban hydrological simulations are significant. Without spatial consistence, flood volumes of manholes and combined sewer overflow are strongly underestimated. After resampling, results using disaggregated time series as input are in the range of those using observed time series. Best overall performance regarding rainfall statistics are obtained by the method in which the disaggregation process ends at time steps with 7.5 min duration, deriving the 5 min time steps by linear interpolation. With subsequent resampling this method leads to a good representation of manhole flooding and combined sewer overflow volume in terms of hydrological simulations and outperforms the 1280 min approach.

Dwellings Electrical and DHW Load Profiles Generators Development for μCHP Systems using RES Coupled to Buildings Applications

The micro combined heat and power (μCHP) is the simultaneous and decentralized production of thermal and mechanic/electrical energy at low scale (low electrical power output below than 50 kWel). A wood pellet steam engine and a gas (or biogas) Stirling engine μCHP devices have been tested at the laboratory of INSA Strasbourg in order to characterize their performances in steady and unsteady states. Two realistic and dynamic models based on these experimental investigations have been developed in previous works [1,2] in order to predict their energy performances and their pollutant emissions. These models have been implemented in the TRNSYS's numerical environment where an optimization platform has been implemented. Thermal and electrical energy storage systems and energy management controller have been implemented in this platform which is used to optimize the coupling between buildings and this kind of innovative devices by considering energetic, economic and environmental criteria. Dynamic thermal simulations (DTS) only computes dynamic heating loads but the other most crucial parameters of the platform are the DHW load profiles and mainly the electrical load profiles in buildings which needs to be realistic, variable, suitable to the French context and with a low time step (2min). Existing data basis are weakly suited to our platform because of their lack of precision (more than 5min time step), their lack of information (no information about the load profiles for each electrical appliance) or their non-relevance in the French context. The paper deals with the creation of a low time step electrical and DHW load profile generator well adapted to the French context by using a “bottom-up” method aggregating the electrical load of each electrical appliance or specific DHW draw-off by a stochastic way. This work presents the platform and the electrical and DHW load profile generator. A sensitivity analysis shows that low time resolution (2min) is required to obtain reliable and realistic results.

The role of urban vegetation in temperature and heat island effects in Querétaro city, Mexico

Alteration of climatic conditions and the urban heat island effect (UHI) are consequences of increased human population and activities in urban zones. Determining the magnitude of the UHI is important to improve urban planning in medium-size cities like Querétaro. Increase and conservation of vegetated areas is a mitigation option for UHI. Here we characterized both the UHI and the role of vegetation cover over temperature regularization in urban zones. Four local climatic zones were defined: three urban and one rural, each with two plots with low and high canopy cover defined by their average leaf area index (0.5 and 2.0, respectively). Air temperature and relative humidity were measured with data loggers at a 30 min time step from June 2012 to May 2013. Climatic data from six weather stations was also analyzed. Daily mean temperature increased at a rate of 0.75 °C per decade (r2=0.38, P < 0.0001), and this was related to population dynamics (r2=0.52, P < 0.0001). Patterns of air temperature defined a cold and a warm season: July to March and April to June for maximum temperature, and November to March and April to October for minimum temperature. The difference between cold and warm seasons was 5 °C (P < 0.0001). The minimum temperature was similar between canopy cover levels. However, relative humidity was higher in high canopy cover plots. The relationship between UHI and the pervious surface fraction of the city was inversely proportional. The UHI ranged from 0.1 to 5 °C and this magnitude was similar between the warm and cold seasons. Vegetation with high canopy cover had lower temperature at 17:00 LT and higher at 9:00 to 10:00 LT during the warm season. Increasing the urban zone canopy cover by 50% would reduce the UHI by 2.05 °C. In conclusion, vegetation with higher canopy cover improved environmental conditions in terms of relative humidity and regularization of extreme temperatures during the warm season.

Groundwater discharge into an estuary using spatially distributed radon time series and radium isotopes

Summary Quantifying groundwater discharge remains a challenge due to its large temporal and spatial variability. Here, we quantify groundwater discharge into a small estuary using radon (222Rn) and radium isotopes (223Ra and 224Ra). High temporal resolution (30 min time steps) radon observations at 4 time series stations were used to determine where groundwater discharge is prevalent in the estuary, and to reduce mass balance model uncertainties. A three-endmember mixing model was developed based on short-lived radium isotopes (sampled at a single location) to separate the shallow saline and deep fresh sources of the discharging groundwater. The results show that using multiple 222Rn time series stations decreased the overall uncertainty of groundwater discharge estimates from about 41% to 23%. The radon derived groundwater flux was 56 ± 13 and 35 ± 12 cm d−1 in wet and dry conditions, respectively. The spatially distributed stations detected a well-defined small area located four kilometers upstream from the mouth of the estuary as a groundwater discharging hotspot. Estimates based on a 223Ra and 224Ra mass balance resulted in groundwater discharge estimates of 65 ± 18 and 84 ± 48 cm d−1 in the wet and 18 ± 5 and 20 ± 6 cm d−1 in the dry. The mixing model revealed contrasting results for deep vs. fresh groundwater contribution in wet and dry conditions. In wet conditions, deep fresh groundwater discharging into the estuary contributed 65% compared to the shallow saline groundwater (35%), while during dry conditions a larger contribution (80%) was related to shallow groundwater. A comprehensive spatial and temporal sampling strategy can produce groundwater discharge estimates with lower uncertainty and provides additional insight on where groundwater enters surface waters.

Detecting surface runoff location in a small catchment using distributed and simple observation method

Summary Surface runoff is one of the hydrological processes involved in floods, pollution transfer, soil erosion and mudslide. Many models allow the simulation and the mapping of surface runoff and erosion hazards. Field observations of this hydrological process are not common although they are crucial to evaluate surface runoff models and to investigate or assess different kinds of hazards linked to this process. In this study, a simple field monitoring network is implemented to assess the relevance of a surface runoff susceptibility mapping method. The network is based on spatially distributed observations (nine different locations in the catchment) of soil water content and rainfall events. These data are analyzed to determine if surface runoff occurs. Two surface runoff mechanisms are considered: surface runoff by saturation of the soil surface horizon and surface runoff by infiltration excess (also called hortonian runoff). The monitoring strategy includes continuous records of soil surface water content and rainfall with a 5 min time step. Soil infiltration capacity time series are calculated using field soil water content and in situ measurements of soil hydraulic conductivity. Comparison of soil infiltration capacity and rainfall intensity time series allows detecting the occurrence of surface runoff by infiltration-excess. Comparison of surface soil water content with saturated water content values allows detecting the occurrence of surface runoff by saturation of the soil surface horizon. Automatic records were complemented with direct field observations of surface runoff in the experimental catchment after each significant rainfall event. The presented observation method allows the identification of fast and short-lived surface runoff processes at a small spatial and temporal resolution in natural conditions. The results also highlight the relationship between surface runoff and factors usually integrated in surface runoff mapping such as topography, rainfall parameters, soil or land cover. This study opens interesting prospects for the use of spatially distributed measurement for surface runoff detection, spatially distributed hydrological models implementation and validation at a reasonable cost.

Appropriate resolution timescale to evaluate water saving and retention potential of rainwater harvesting for toilet flushing in single houses

The main objective of the paper is to identify the appropriate temporal scale for modeling the behavior of rainwater harvesting tanks in relation to the purpose they are built for, i.e., water saving, stormwater retention potential, etc. A tank water balance model coupled with a specific procedure to determine long-term series of rainfall (tank inflow) and toilet flushes (tank outflow) at different daily and sub-daily resolution timescales was developed. The model was applied to a household case study for which detailed water demand data are available from measurements. Simulations show that the daily scale may be reliably chosen to evaluate the tank water saving efficiency. In contrast, sub-daily resolutions (at least the hourly time step) are needed for the evaluation of the tank retention efficiency to limit inaccuracies, especially for small tanks and for high values of the water demand. Moreover, preliminary results at the 5 min time step show that rainwater tanks can help in reducing the rainfall intensity peak, basically depending on the tank storage and on the rainfall event characteristics.

Rainfall monitoring based on microwave links from cellular telecommunication networks: First results from a West African test bed

AbstractRainfall monitoring based on commercial terrestrial microwave links is tested for the first time in Burkina Faso, in Sahelian West Africa. In collaboration with one national cellular phone operator, Telecel Faso, the attenuation on a 29 km long microwave link operating at 7 GHz was monitored at 1 s time rate for the monsoon season 2012. The time series of attenuation is transformed into rain rates and compared with rain gauge data. The method is successful in quantifying rainfall: 95% of the rainy days are detected. The correlation with the daily rain gauge series is 0.8, and the season bias is 6%. The correlation at the 5 min time step within each event is also high. These results demonstrate the potential interest of exploiting national and regional wireless telecommunication networks for monitoring rainfall in Africa, where operational rain gauge networks are degrading and the hydrometeorological risk increasing.

Stochastic scenario‐based model and investigating size of energy storages for PEM‐fuel cell unit commitment of micro‐grid considering profitable strategies

This paper presents a unit commitment formulation for micro-grid that includes a significant number of grid parallel Proton Exchange Membrane-Fuel Cell Power Plants (PEM-FCPPs) with ramping rate and minimum up/down time constraints. The aim of this problem is to determine the optimum size of energy storage like battery storages and use the efficient hydrogen and thermal energy storages and to schedule the committed units' output power while satisfying practical constraints and electrical/thermal load demand over one day with 15 min time step. In order to best use of multiple PEM-FCPPs, hydrogen storage management is carried out. Also, since the electrical and heat load demand are not synchronised, it could be useful to store the extra heat of PEM-FCPPs in the peak electrical load in order to satisfy delayed heat demands. Due to uncertainty nature of electrical/thermal load, photovoltaic and wind turbine output power and market price, a two-stage scenario-based stochastic programming model, where the first stage prescribes the here-and-now variables and the second stage determines the optima value of wait-and-see variables under cost minimization is implemented. For solving the problem, a new enhanced cuckoo optimisation algorithm is presented and successfully applied to two typical micro-grids. Quantitative results show its usefulness.

Stochastic scenario-based model and investigating size of battery energy storage and thermal energy storage for micro-grid

Energy storage systems (ESS) are designed to accumulate energy when production exceeds demand and to make it available at the user’s request. They can help match energy supply and demand, exploit the variable production of renewable energy sources (e.g. solar and wind), increase the overall efficiency of the energy system and reduce CO2 emissions. This paper presents a unit commitment formulation for micro-grid that includes a significant number of grid parallel PEM-Fuel Cell Power Plants (PEM-FCPPs) with ramping rate and minimum up and down time constraints. The aim of this problem is to determine the optimum size of energy storage devices like hydrogen, thermal energy and battery energy storages in order to schedule the committed units’ output power while satisfying practical constraints and electrical/thermal load demand over one day with 15min time step. In order to best use of multiple PEM-FCPPs, hydrogen storage management is carried out. Also, since the electrical and heat load demand are not synchronized, it could be useful to store the extra heat of PEM-FCPPs in the peak electrical load in order to satisfy delayed heat demands. Due to uncertainty nature of electrical/thermal load, photovoltaic and wind turbine output power and market price, a two-stage scenario-based stochastic programming model, where the first stage prescribes the here-and-now variables and the second stage determines the optima value of wait-and-see variables under cost minimization. Quantitative results show the usefulness and viability of the suggested approach.

Modelling sustainable energy futures for the UK

As a result of signing the Kyoto Agreement the UK will need to reduce carbon emissions to 20% of their 1990 value by 2050. This will require a complete change in power generation over the next 40 years. The system involved is immensely complex, with multiple agents, levels of description, new technologies and new policies and actions. However, here we develop a relatively simple spatial, dynamic model representing a basic part of the problem – the changing geographical distribution of electrical generation capacity in the UK. It runs from 2010 until 2050 and allows the exploration of the different pattern of investments in, and closures of, generation capacity. It was develop as part of the CASCADE project on Smart Grids to provide scenarios for annual changes in generating capacity. It provides generation scenarios for much more complex, multi-agent models, such as that developed in the CASCADE project, that represent the short-term (30min time step) dynamics of the wholesale and retail energy markets. The model allows us to explore different possible pathways to 2050 and the difficulty of the overall endeavour. In order to increase electricity production but reduce CO2 emissions, we shall need to close our current coal/gas generating plants and make a vast investment in new low carbon generating capacity. The model allows us to rapidly the possible consequences of innovations in technologies, and to re-shape plans in the light of as new opportunities and circumstances.

Modeling Soil and Nutrient Runoff Yields from an Italian Vineyard Using SWAT

Abstract. In the Chianti Classico area (Tuscany, Italy), hillside vineyards are usually kept bare of vegetation and are thus prone to soil erosion and nutrient losses. The aim of this study was to develop Soil and Water Assessment Tool (SWAT) model parameters for predicting runoff, sediment, and nitrogen and phosphorus losses from vineyards. Runoff volume and composition were measured during 87 rainfall events from January 2005 to December 2008 in an instrumented up-down slope vineyard at the University of Florence’s Montepaldi Farm in Tuscany, Italy. Harrowed and grass cover interrow management techniques were evaluated. While the applicability of SWAT to simulate soil and nutrient losses was tested at a daily scale, the soil hydrology was tested using the Green-Ampt-Mein-Larson equation with a 10 min time step. The results indicated that the calibrated SWAT model overestimated runoff and soil loss by 15% to 20%. However, these deviations were considered satisfactory to good according to three commonly used statistical indices. The simulations of nitrogen and phosphorus losses were also satisfactory to good according to the statistical indices, one of which, the Nash-Sutcliffe coefficient, had values greater than 0.92 for the calibration and validation phases and for each management technique.

Technical Note: Glacial influence in tropical mountain hydrosystems evidenced by the diurnal cycle in water levels

Abstract. Worldwide, the rapid shrinking of glaciers in response to ongoing climate change is modifying the glacial meltwater contribution to hydrosystems in glacierized catchments. Determining the influence of glacial runoff to streams is therefore of critical importance to evaluate potential impact of glacier retreat on water quality and aquatic biota. This task has challenged both glacier hydrologists and ecologists over the last 20 yr due to both structural and functional complexity of the glacier–stream system interface. Here we propose quantifying the diurnal cycle amplitude of the streamflow to determine the glacial influence in glacierized catchments. We performed water-level measurements using water pressure loggers over 10 months at 30 min time steps in 15 stream sites in 2 glacier-fed catchments in the Ecuadorian Andes (&gt; 4000 m a.s.l.) where no perennial snow cover is observed outside the glaciers. For each stream site, we performed wavelet analyses on water-level time series, determined the scale-averaged wavelet power spectrum at 24 h scale and defined three metrics, namely the power, frequency and temporal clustering of the diurnal flow variation. The three metrics were then compared to the percentage of the glacier cover in the catchments, a metric of glacial influence widely used in the literature. As expected, we found that the diurnal variation power of glacier-fed streams decreased downstream with the addition of non-glacial tributaries. We also found that the diurnal variation power and the percentage of the glacier cover in the catchment were significantly positively correlated. Furthermore, we found that our method permits the detection of glacial signal in supposedly non-glacial sites, thereby revealing glacial meltwater resurgence. While we specifically focused on the tropical Andes in this paper, our approach to determine glacial influence may have potential applications in temperate and arctic glacierized catchments. The measure of diurnal water amplitude therefore appears as a powerful and cost-effective tool to understand the hydrological links between glaciers and hydrosystems better and assess the consequences of rapid glacier shrinking.

A COMPARISON OF THE THERMODYNAMIC EFFICIENCY OF VACUUM TUBE AND FLAT PLATE SOLAR COLLECTOR SYSTEMS / VAKUUMINIŲ IR PLOKŠČIŲJŲ SAULĖS KOLEKTORIŲ SISTEMŲ EKSERGINIŲ EFEKTYVUMŲ PALYGINIMAS

The article presents simulation based exergy analysis used for comparing solar thermal systems applied for preparing domestic hot water. The simulation of flat and vacuum tube solar collector systems was performed in TRNSYS simulation environment. A period of one year under Lithuanian climate conditions was chosen. Simulation was performed on 6 min time step resolution by calculating energy and exergy flows and creating balance calculation. Assessment results at system and element levels have been presented as monthly variation in efficiency. The conducted analysis has revealed that the systems designed to cover equal heat energy demand operates in different exergetic efficiencies. Article in Lithuanian. Santrauka Straipsnyje eksergijos požiūriu nagrinėjamos dvi saulės kolektorių sistemos, skirtos karštam vandeniui gaminti. Plokščiųjų ir vakuuminių saulės kolektorių modeliavimas atliktas TRNSYS programa. Modeliuota vienerių metų periodui Lietuvos klimatinėmis sąlygomis 6 minučių laiko žingsniu skaičiuojant energijos ir eksergijos srautus kiekvienam laiko žingsniui ir sudarant energinius ir ekserginius balansus. Sistemos ir posistemių efektyvumo rodiklių kitimo rezultatai pateikti grafiškai. Išanalizavus akivaizdu, kad pasirinktomis sąlygomis plokščiųjų ir vakuuminių saulės kolektorių sistemos, turinčios tą pačią funkcinę paskirtį, veikia skirtingu efektyvumu.

Spatial and seasonal variations in evapotranspiration over Canada's landmass

Abstract. A 30 yr (1979–2008) dataset of actual evapotranspiration (ET) at 1 km resolution was generated over Canada's landmass by integrating remote sensing land surface data and gridded climate data using the EALCO model run at a 30 min time step. This long-term high-resolution dataset was used to characterize the spatiotemporal variations in ET across Canada. The results show that annual ET varied from 600 mm yr−1 over several regions in the south to less than 100 mm yr−1 in the northern Arctic. Nationally, ET in summer (i.e., June to August) comprised 65% of the annual total amount. ET in the cold season remained mostly below 10 mm month−1 over the country. Negative monthly ET was obtained over the Arctic region in winter, indicating EALCO simulated a larger amount of condensation than ET. Overall, the mean ET over the entire Canadian landmass for the 30 yr was 239 mm yr−1, or 44% of its corresponding precipitation. Comparisons of available ET studies in Canada revealed large uncertainties in ET estimates associated with using different approaches. The scarcity of ET measurements for the diverse ecosystems in Canada remains a significant challenge for reducing the uncertainties; this gap needs to be addressed in future studies to improve capabilities in climate/weather modeling and water resource management.

Separately accounting for uncertainties in rainfall and runoff: Calibration of event-based conceptual hydrological models in small urban catchments using Bayesian method

[1] Uncertainty analysis of hydrological models is usually based on model calibration, and the Bayesian method is a popular way to evaluate the uncertainty. The traditional Bayesian method usually uses lumped model residuals to form the likelihood function, where uncertainty in inputs (rainfall) is not explicitly addressed. This paper compares three approaches based on Bayesian inferences, considering rainfall uncertainty either implicitly or explicitly in calibration. Consistent parameter estimation and reliable quantification of predictive uncertainty are mainly examined. When rainfall uncertainty is explicitly treated in calibration, several rainfall observations at one-minute time steps are grouped to share one multiplier to consider the possible observation errors. The appropriate grouping strategy that balances the representativeness and the complexity of the problem is suggested. The application of the methods considered in this study focuses on small urban catchments (<200 ha) with a small temporal scale (1 min time step), in contrast to most literature studies dealing with larger catchments monitored at larger time steps. It is found that uncertainty in rainfall has a minor contribution to the total uncertainty in runoff estimation, and this minor role can be explained by the low pass filter effect of the linear reservoir model. However, the approach explicitly accounting for input uncertainty results in more informed knowledge for uncertainties related with hydrological model calibrations, which can possibly provide an estimation of uncertainty attributed to rainfall records. It should be noted that rainfall error estimates can compensate model structural uncertainty that is not explicitly addressed in this study.