Using high‐resolution isotope data and alternative calibration strategies for a tracer‐aided runoff model in a nested catchment

Testing hydrological models over different spatio‐temporal scales is important for both evaluating diagnostics and aiding process understanding. High‐frequency (6‐hr) stable isotope sampling of rainfall and runoff was undertaken during 3‐week periods in summer and winter within 12 months of daily sampling in a 3.2‐km² catchment in the Scottish Highlands. This was used to calibrate and test a tracer‐aided model to assess the (a) information content of high‐resolution data, (b) effect of different calibration strategies on simulations and inferred processes, and (c) model transferability to <1‐km² subcatchment. The 6‐hourly data were successfully incorporated without loss of model performance, improving the temporal resolution of the modelling, and making it more relevant to the time dynamics of the isotope and hydrometric response. However, this added little new information due to old‐water dominance and riparian mixing in this peatland catchment. Time variant results, from differential split sample testing, highlighted the importance of calibrating to a wide range of hydrological conditions. This also provided insights into the nonstationarity of catchment mixing processes, in relation to storage and water ages, which varied markedly depending on the calibration period. Application to the nested subcatchment produced equivalent parameterization and performance, highlighting similarity in dominant processes. The study highlighted the utility of high‐resolution data in combination with tracer‐aided models, applied at multiple spatial scales, as learning tools to enhance process understanding and evaluation of model behaviour across nonstationary conditions. This helps reveal more fully the catchment response in terms of the different mechanistic controls on both wave celerites and particle velocities.     

Spatially distributed tracer‐aided modelling to explore water and isotope transport,storage and mixing in a pristine,humid tropical catchment

Rapidly transforming headwater catchments in the humid tropics provide important resources for drinking water, irrigation, hydropower, and ecosystem connectivity. However, such resources for downstream use remain unstudied. To improve understanding of the behaviour and influence of pristine rainforests on water and tracer fluxes, we adapted the relatively parsimonious, spatially distributed tracer‐aided rainfall–runoff (STARR) model using event‐based stable isotope data for the 3.2‐km² San Lorencito catchment in Costa Rica. STARR was used to simulate rainforest interception of water and stable isotopes, which showed a significant isotopic enrichment in throughfall compared with gross rainfall. Acceptable concurrent simulations of discharge (Kling–Gupta efficiency [KGE] ~0.8) and stable isotopes in stream water (KGE ~0.6) at high spatial (10 m) and temporal (hourly) resolution indicated a rapidly responding system. Around 90% of average annual streamflow (2,099 mm) was composed of quick, near‐surface runoff components, whereas only ~10% originated from groundwater in deeper layers. Simulated actual evapotranspiration (ET) from interception and soil storage were low (~420 mm/year) due to high relative humidity (average 96%) and cloud cover limiting radiation inputs. Modelling suggested a highly variable groundwater storage (~10 to 500 mm) in this steep, fractured volcanic catchment that sustains dry season baseflows. This groundwater is concentrated in riparian areas as an alluvial–colluvial aquifer connected to the stream. This was supported by rainfall–runoff isotope simulations, showing a “flashy” stream response to rainfall with only a moderate damping effect and a constant isotope signature from deeper groundwater (~400‐mm additional mixing volume) during baseflow. The work serves as a first attempt to apply a spatially distributed tracer‐aided model to a tropical rainforest environment exploring the hydrological functioning of a steep, fractured‐volcanic catchment. We also highlight limitations and propose a roadmap for future data collection and spatially distributed tracer‐aided model development in tropical headwater catchments.     

Using high resolution tracer data to constrain water storage,flux and age estimates in a spatially distributed rainfall‐runoff model

Models simulating stream flow and conservative tracers can provide a representation of flow paths, storage distributions and mixing processes that is advantageous for many predictive purposes. Compared with models that only simulate stream flow, tracer data can be used to investigate the internal consistency of model behaviour and to gain insight into model performance. Here, we examine the strengths and weaknesses of a data‐driven, spatially distributed tracer‐aided rainfall‐runoff model. The model structure allowed us to assess the influence of landscape characteristics on the routing and mixing of water and tracers. The model was applied to a site in the Scottish Highlands with a unique tracer data set; ~4 years of daily isotope ratios in stream water and precipitation were available, as well as 2 years of weekly soil and ground water isotopes. The model structure was based on an empirically based, lumped tracer‐aided model previously developed for the catchment. The best model runs were selected from Monte Carlo simulations based on dual calibration criteria using objective functions for both stream isotopes and discharge at the outlet. Model performance for these criteria was reasonable (Nash–Sutcliffe efficiencies for discharge and isotope ratios were ~0.4–0.6). The model could generally reproduce the variable isotope signals in the soils of the steeper hill slopes where storage was low, and damped isotope responses in valley bottom cells with high storage. The model also allowed us to estimate the age distributions of internal stores, water fluxes and stream flow. Average stream water age was ~1.6 years, integrating older groundwater in the valley bottom and dynamic younger soil waters. By tracking water ages and simulating isotopes, the model captured the changes in connectivity driven by distributed storage dynamics. This has substantially improved the representation of spatio‐temporal process dynamics and gives a more robust framework for projecting environmental change impacts. Copyright © 2016 The Authors Hydrological Processes Published by John Wiley & Sons Ltd.     

Projecting climate change impacts on stream flow regimes with tracer‐aided runoff models ‐ preliminary assessment of heterogeneity at the mesoscale

The northern mid‐high latitudes form a region that is sensitive to climate change, and many areas already have seen – or are projected to see – marked changes in hydroclimatic drivers on catchment hydrological function. In this paper, we use tracer‐aided conceptual runoff models to investigate such impacts in a mesoscale (749 km²) catchment in northern Scotland. The catchment encompasses both sub‐arctic montane sub‐catchments with high precipitation and significant snow influence and drier, warmer lowland sub‐catchments. We used downscaled HadCM3 General Circulation Model outputs through the UKCP09 stochastic weather generator to project the future climate. This was based on synthetic precipitation and temperature time series generated from three climate change scenarios under low, medium and high greenhouse gas emissions. Within an uncertainty framework, we examined the impact of climate change at the monthly, seasonal and annual scales and projected impacts on flow regimes in upland and lowland sub‐catchments using hydrological models with appropriate process conceptualization for each landscape unit. The results reveal landscape‐specific sensitivity to climate change. In the uplands, higher temperatures result in diminishing snow influence which increases winter flows, with a concomitant decline in spring flows as melt reduces. In the lowlands, increases in air temperatures and re‐distribution of precipitation towards autumn and winter lead to strongly reduced summer flows despite increasing annual precipitation. The integration at the catchment outlet moderates these seasonal extremes expected in the headwaters. This highlights the intimate connection between hydrological dynamics and catchment characteristics which reflect landscape evolution. It also indicates that spatial variability of changes in climatic forcing combined with differential landscape sensitivity in large heterogeneous catchments can lead to higher resilience of the integrated runoff response. Copyright © 2012 John Wiley & Sons, Ltd.     

Parameterization and multi‐criteria calibration of a distributed storm flow model applied to a Mediterranean agricultural catchment

The principal challenge in the parameterization of storm flow models for agricultural catchments with an artificial drainage network and fields with different degrees of tillage lies in the parsimonious definition of distributed model parameters in a way that reduces the number of calibration parameters to a justifiable minimum. This paper presents a comprehensive case study for the parameter estimation of a distributed storm flow model applied to an agricultural catchment (0.91 km²) in the Mediterranean region. Model parameterization was combined with procedures for multi‐criteria, multi‐storm calibration, where we automatically calibrated three parameters related to flow velocity and infiltration, and compared single and multi‐storm criteria that are based on discharge volume, peak flow, and the Nash–Sutcliffe coefficient. Multi‐storm calibration yielded a set of parameter values for the simulation batch with best multi‐storm overall performance, which are close to the median values in the pre‐calibration of individual storms. Our results suggest that flow velocities and proportionality of the channel infiltration rate do not vary significantly over the course of 11 years. Copyright © 2012 John Wiley & Sons, Ltd.     

Effects of conditional parameterization on performance of rainfall‐runoff model regarding hydrologic non‐stationarity

Better parameterization of a hydrological model can lead to improved streamflow prediction. This is particularly important for seasonal streamflow forecasting with the use of hydrological modelling. Considering the possible effects of hydrologic non‐stationarity, this paper examined ten parameterization schemes at 12 catchments located in three different climatic zones in east Australia. These schemes are grouped into four categories according to the period when the data are used for model calibration, i.e. calibration using data: (1) from a fixed period in the historical records; (2) from different lengths of historical records prior to prediction year; (3) from different climatic analogue years in the past; and (4) data from the individual months. Parameterization schemes were evaluated according to model efficiency in both the calibration and verification period. The results show that the calibration skill changes with the different historic periods when data are used at all catchments. Comparison of model performance between the calibration schemes indicates that it is worth calibrating the model with the use of data from each individual month for the purpose of seasonal streamflow forecasting. For the catchments in the winter‐dominant rainfall region of south‐east Australia, a more significant shift in rainfall‐runoff relationships at different periods was found. For those catchments, model calibration with the use of 20 years of data prior to the prediction year leads to a more consistent performance. Copyright © 2011 John Wiley & Sons, Ltd.     

Including spatial distribution in a data‐driven rainfall‐runoff model to improve reservoir inflow forecasting in Taiwan

Multi‐step ahead inflow forecasting has a critical role to play in reservoir operation and management in Taiwan during typhoons as statutory legislation requires a minimum of 3‐h warning to be issued before any reservoir releases are made. However, the complex spatial and temporal heterogeneity of typhoon rainfall, coupled with a remote and mountainous physiographic context, makes the development of real‐time rainfall‐runoff models that can accurately predict reservoir inflow several hours ahead of time challenging. Consequently, there is an urgent, operational requirement for models that can enhance reservoir inflow prediction at forecast horizons of more than 3 h. In this paper, we develop a novel semi‐distributed, data‐driven, rainfall‐runoff model for the Shihmen catchment, north Taiwan. A suite of Adaptive Network‐based Fuzzy Inference System solutions is created using various combinations of autoregressive, spatially lumped radar and point‐based rain gauge predictors. Different levels of spatially aggregated radar‐derived rainfall data are used to generate 4, 8 and 12 sub‐catchment input drivers. In general, the semi‐distributed radar rainfall models outperform their less complex counterparts in predictions of reservoir inflow at lead times greater than 3 h. Performance is found to be optimal when spatial aggregation is restricted to four sub‐catchments, with up to 30% improvements in the performance over lumped and point‐based models being evident at 5‐h lead times. The potential benefits of applying semi‐distributed, data‐driven models in reservoir inflow modelling specifically, and hydrological modelling more generally, are thus demonstrated. Copyright © 2012 John Wiley & Sons, Ltd.     

Multi‐site calibration using a grid‐based event rainfall–runoff model: a case study of the upstream areas of the Nakdong River basin in Korea

Most runoff analyses using a grid‐based distributed model use one parameter group calibrated at the outlet of a watershed, instead of dividing the watershed into subwatersheds. Significant differences between the observed value and the simulation result of the subwatersheds can occur if just one parameter group is used in all subwatersheds that have different hydrological characteristics from each other. Therefore, to improve the simulation results of the subwatersheds within a watershed, a model calibrated at every subwatershed needs to be used to reflect the characteristics of each subwatershed. In this study, different parameter groups were set up for one or two sites using a distributed model, the GRM (Grid based Rainfall‐runoff Model), and the evaluations were based on the results of rainfall–runoff analysis, which uses a multi‐site calibration (MSC) technique to calibrate the model at the outlet of each site. The Hyangseok watershed in Naeseong River, which is a tributary of Nakdong River in Korea, was chosen as the study area. The watershed was divided into five subwatersheds each with a subwatershed outlet that was applied to the calibration sites . The MSC was applied for five cases. When a site was added for calibration in a watershed, the runoff simulation showed better results than the calibration of only one site at the most downstream area of the watershed. The MSC approach could improve the simulation results on the calibrated sites and even on the non‐calibrated sites, and the effect of MSC was improved when the calibrated site was closer to the runoff site. Copyright © 2014 John Wiley & Sons, Ltd.     

A semi‐distributed parallel‐type linear reservoir rainfall‐runoff model and its application in Taiwan

The main purpose of this paper is to introduce a semi‐distributed parallel surface rainfall‐runoff conceptual model. In this paper, a general solution of the instantaneous unit hydrograph (IUH) has been derived successfully for N linearly connected reservoirs, each having a different storage constant. The solution is a function of geomorphologic parameters, meteorologic factors and roughness coefficients. The model also takes into account the hydrologic response which is influenced by outflow downstream of a reservoir. For calibration, the shuffled complex evolution (SCE) algorithm is used to search for the global optimal parameters of the model. Because of the parallel structure, the mean roughness parameter of the channel becomes a “conceptual” parameter without a real physical meaning. To evaluate the adaptability of the model adopted, three watersheds around the city of Taipei in Taiwan were chosen to test the effectiveness of the model. The study provides an appropriate rainfall‐runoff model for planning flood mitigation in Taiwan. Copyright © 1999 John Wiley & Sons, Ltd.     

Effectivity of dissolved SF6 tracer for clarification of rainfall–runoff processes in a forested headwater catchment

Understanding rainfall‐runoff processes is crucial for prevention and prediction of water‐related natural disasters. Sulfur hexafluoride (SF₆) is a potential tracer, but few researches have applied it for rainfall‐runoff process studies. We observed multiple tracers including SF₆ in spring water at 1‐ to 2‐hr intervals during rainstorm events to investigate the effectivity of SF₆ tracer in rainfall–runoff studies through the clarification of rainfall–runoff process. The target spring is a perennial spring in a forested headwater catchment with an area of 0.045 km² in Fukushima, Japan. The relationship between the SF₆ concentration in spring water and the spring discharge volume was negative trend; the SF₆ concentration in spring water becomes low as the spring discharge volume increases especially during rainstorms. The hydrograph separation using SF₆ and chloride ion tracers was applied for determining the contribution of principal sources on rainfall–runoff water. It suggested more than 60% contribution of bedrock groundwater at the rainfall peak and high percentage contribution continued even in the hydrograph recession phase. Based on observed low SF₆ concentration in groundwater after heavy rainfall, the replacement of groundwater near the spring with bedrock groundwater is indicated as a mechanism for water discharge with low SF₆ concentration during rainfall events. Consequently, rainstorm events play an important role as triggers in discharging water stored in the deeper subsurface area. In addition, SF₆ tracer is concluded as one of the strongest tracers for examining rainfall–runoff process studies. And, therefore, this study provided new insights into the dynamics of groundwater and its responses to rainfall in terms of SF₆ concentration variance in water in headwater regions.     

Use of multi‐platform,multi‐temporal remote‐sensing data for calibration of a distributed hydrological model: an application in the Arno basin,Italy

Images from satellite platforms are a valid aid in order to obtain distributed information about hydrological surface states and parameters needed in calibration and validation of the water balance and flood forecasting. Remotely sensed data are easily available on large areas and with a frequency compatible with land cover changes. In this paper, remotely sensed images from different types of sensor have been utilized as a support to the calibration of the distributed hydrological model MOBIDIC, currently used in the experimental system of flood forecasting of the Arno River Basin Authority. Six radar images from ERS‐2 synthetic aperture radar (SAR) sensors (three for summer 2002 and three for spring–summer 2003) have been utilized and a relationship between soil saturation indexes and backscatter coefficient from SAR images has been investigated. Analysis has been performed only on pixels with meagre or no vegetation cover, in order to legitimize the assumption that water content of the soil is the main variable that influences the backscatter coefficient. Such pixels have been obtained by considering vegetation indexes (NDVI) and land cover maps produced by optical sensors (Landsat‐ETM). In order to calibrate the soil moisture model based on information provided by SAR images, an optimization algorithm has been utilized to minimize the regression error between saturation indexes from model and SAR data and error between measured and modelled discharge flows. Utilizing this procedure, model parameters that rule soil moisture fluxes have been calibrated, obtaining not only a good match with remotely sensed data, but also an enhancement of model performance in flow prediction with respect to a previous calibration with river discharge data only. Copyright © 2006 John Wiley & Sons, Ltd.     

Influence of soil‐surface types on the overall runoff of the Tabernas badlands (south‐east Spain): field data and model approaches

The Tabernas desert, an extensive badlands area in Almeria province (south‐east Spain), is characterized by a high variability in soil surface cover and soil properties along with important topographical contrasts giving rise to a wide range of hydrological behaviour. A double approach through field monitoring and modelling has been used to ascertain the influence of soil‐surface variability on the overall hydrological response. Small plots were monitored for 3 years to assess runoff from the different surface types. Data provided by the long‐term monitoring of three small catchments formed by different soil surfaces were used to find out the specific contribution of each soil surface to the catchment runoff. A simple spatially distributed model was built to predict runoff generation based on the infiltration rate of each soil‐surface type (defined as terrain units with the same cover, the same soil type and on the same landform). Plot results prove that the soil surface units within the study area behave differently in terms of hydrological response to natural rainfall. These responses are explained by the types of cover, topographical characteristics and soil properties. When runoff events are simple (with one or two runoff peaks), the modelled hydrographs reproduce the hydrographs observed reasonably well, but in complex events (with several runoff peaks) the adjustment is not as good. The model also shows the influence of the spatial distribution of soil surfaces on the overall runoff, aiding exploration of the spatial hydrological relationships among different landscape units. Copyright © 2002 John Wiley & Sons, Ltd.     

Impact of model simplifications on soil erosion predictions: application of the GLUE methodology to a distributed event‐based model at the hillslope scale

In this paper, we analyse how the performance and calibration of a distributed event‐based soil erosion model at the hillslope scale is affected by different simplifications on the parameterizations used to compute the production of suspended sediment by rainfall and runoff. Six modelling scenarios of different complexity are used to evaluate the temporal variability of the sedimentograph at the outlet of a 60 m long cultivated hillslope. The six scenarios are calibrated within the generalized likelihood uncertainty estimation framework in order to account for parameter uncertainty, and their performance is evaluated against experimental data registered during five storm events. The Nash–Sutcliffe efficiency, percent bias and coverage performance ratios show that the sedimentary response of the hillslope in terms of mass flux of eroded soil can be efficiently captured by a model structure including only two soil erodibility parameters, which control the rainfall and runoff production of suspended sediment. Increasing the number of parameters makes the calibration process more complex without increasing in a noticeable manner the predictive capability of the model. Copyright © 2015 John Wiley & Sons, Ltd.     

Spatio‐temporal variability of the isotopic input signal in a partly forested catchment: Implications for hydrograph separation

The isotopic composition of precipitation (D and ¹⁸O) has been widely used as an input signal in water tracer studies. Whereas much recent effort has been put into developing methodologies to improve our understanding and modelling of hydrological processes (e.g., transit‐time distributions or young water fractions), less attention has been paid to the spatio‐temporal variability of the isotopic composition of precipitation, used as input signal in these studies. Here, we investigated the uncertainty in isotope‐based hydrograph separation due to the spatio‐temporal variability of the isotopic composition of precipitation. The study was carried out in a Mediterranean headwater catchment (0.56 km²). Rainfall and throughfall samples were collected at three locations across this relatively small catchment, and stream water samples were collected at the outlet. Results showed that throughout an event, the spatial variability of the input signal had a higher impact on hydrograph separation results than its temporal variability. However, differences in isotope‐based hydrograph separation determined preevent water due to the spatio‐temporal variability were different between events and ranged between 1 and 14%. Based on catchment‐scale isoscapes, the most representative sampling location could also be identified. This study confirms that even in small headwater catchments, spatio‐temporal variability can be significant. Therefore, it is important to characterize this variability and identify the best sampling strategy to reduce the uncertainty in our understanding of catchment hydrological processes.