Simulated watershed responses to land cover changes using the Regional Hydro‐Ecological Simulation System

In this work, we used the Regional Hydro‐Ecological Simulation System (RHESSys) model to examine runoff sensitivity to land cover changes in a mountain environment. Two independent experiments were evaluated where we conducted simulations with multiple vegetation cover changes that include conversion to grass, no vegetation cover and deciduous/coniferous cover scenarios. The model experiments were performed at two hillslopes within the Weber River near Oakley, Utah watershed (USGS gauge # 10128500). Daily precipitation, air temperature and wind speed data as well as spatial data that include a digital elevation model with 30 m grid resolution, soil texture map and vegetation and land use maps were processed to drive RHESSys simulations. Observed runoff data at the watershed outlet were used for calibration and verification. Our runoff sensitivity results suggest that during winter, reduced leaf area index (LAI) decreases canopy interception resulting in increased snow accumulations and hence snow available for runoff during the early spring melt season. Increased LAI during the spring melt season tends to delay the snow melting process. This delay in snow melting process is due to reduced radiation beneath high LAI surfaces relative to low LAI surfaces. The model results suggest that annual runoff yield after removing deciduous vegetation is on average about 7% higher than with deciduous vegetation cover, while annual runoff yield after removing coniferous vegetation is on average as about 2% higher than that produced with coniferous vegetation cover. These simulations thus help quantify the sensitivity of water yield to vegetation change. Copyright © 2013 John Wiley & Sons, Ltd.     

Quantitative evaluation of different hydrological modelling approaches in a partly glacierized Swiss watershed

Mountain water resources management often requires hydrological models that need to handle both snow and ice melt. In this study, we compared two different model types for a partly glacierized watershed in central Switzerland: (1) an energy‐balance model primarily designed for snow simulations; and (2) a temperature‐index model developed for glacier simulations. The models were forced with data extrapolated from long‐term measurement records to mimic the typical input data situation for climate change assessments. By using different methods to distribute precipitation, we also assessed how various snow cover patterns influenced the modelled runoff. The energy‐balance model provided accurate discharge estimations during periods dominated by snow melt, but dropped in performance during the glacier ablation season. The glacier melt rates were sensitive to the modelled snow cover patterns and to the parameterization of turbulent heat fluxes. In contrast, the temperature‐index model poorly reproduced snow melt runoff, but provided accurate discharge estimations during the periods dominated by glacier ablation, almost independently of the method used to distribute precipitation. Apparently, the calibration of this model compensated for the inaccurate precipitation input with biased parameters. Our results show that accurate estimates of snow cover patterns are needed either to correctly constrain the melt parameters of the temperature‐index model or to ensure appropriate glacier surface albedos required by the energy‐balance model. Thus, particularly when only distant meteorological stations are available, carefully selected input data and efficient extrapolation methods of meteorological variables improve the reliability of runoff simulations in high alpine watersheds. Copyright © 2011 John Wiley & Sons, Ltd.     

Multiple objective evaluation of a simple phosphorus transfer model

A simple phosphorus (P) transfer model of the Welland catchment, UK, is evaluated against multiple objective functions using a Monte Carlo approach that combines calibration, identifiability, sensitivity and uncertainty analysis. The model is based on simple conceptual rainfall‐runoff and river routing components, combined with estimates of the daily non‐point source load derived from annual landuse‐based export coefficients, disaggregated as a function of the runoff. The model has limited data requirements, consistent with data availability, and is parsimoneous with respect to the number of parameters identified through inverse modelling. The best performing parameter sets capture the main aspects of the observed flow and total P (TP) concentrations and provide a suitable basis for a decision‐support tool. However, a trade‐off is evident between matching the observed flow peaks, flow recessions and TP concentrations simultaneously, highlighting some limitations of the model structure and/or calibration data. Model analysis indicates that daily non‐point source load cannot be described as a function of near‐surface runoff and land use alone, but that other influences, including seasonality, are important. However, further model development to improve performance is likely to introduce additional complexity (in terms of parameter numbers), and hence additional problems of parameter identifiability and output uncertainty, which in turn raises issues of the information content of the available data. Copyright © 2004 John Wiley & Sons, Ltd.     

Prediction uncertainty of conceptual rainfall-runoff models caused by problems in identifying model parameters and structure

Abstract

The uncertainties arising from the problem of identifying a representative model structure and model parameters in a conceptual rainfall-runoff model were investigated. A conceptual model, the HBV model, was applied to the mountainous Brugga basin (39.9 km”) in the Black Forest, southwestern Germany. In a first step, a Monte Carlo procedure with randomly generated parameter sets was used for calibration. For a ten-year calibration period, different parameter sets resulted in an equally good correspondence between observed and simulated runoff. A few parameters were well defined (i.e. best parameter values were within small ranges), but for most parameters good simulations were found with values varying over wide ranges. In a second step, model variants with different numbers of elevation and landuse zones and various runoff generation conceptualizations were tested. In some cases, representation of more spatial variability gave better simulations in terms of discharge. However, good results could be obtained with different and even unrealistic concepts. The computation of design floods and low flow predictions illustrated that the parameter uncertainty and the uncertainty of identifying a unique best model variant have implications for model predictions. The flow predictions varied considerably. The peak discharge of a flood with a probability of 0.01 year^?1, for instance, varied from 40 to almost 60 mm day^?1. It was concluded that model predictions, particularly in applied studies, should be given as ranges rather than as single values.     

Updating of snow depletion curve with remote sensing data

A method for using remotely sensed snow cover information in updating a hydrological model is developed, based on Bayes' theorem. A snow cover mass balance model structure adapted to such use of satellite data is specified, using a parametric snow depletion curve in each spatial unit to describe the subunit variability in snow storage. The snow depletion curve relates the accumulated melt depth to snow‐covered area, accumulated snowmelt runoff volume, and remaining snow water equivalent. The parametric formulation enables updating of the complete snow depletion curve, including mass balance, by satellite data on snow coverage. Each spatial unit (i.e. grid cell) in the model maintains a specific depletion curve state that is updated independently. The uncertainty associated with the variables involved is formulated in terms of a joint distribution, from which the joint expectancy (mean value) represents the model state. The Bayesian updating modifies the prior (pre‐update) joint distribution into a posterior, and the posterior joint expectancy replaces the prior as the current model state. Three updating experiments are run in a 2400 km² mountainous region in Jotunheimen, central Norway (61°N, 9°E) using two Landsat 7 ETM+ images separately and together. At 1 km grid scale in this alpine terrain, three parameters are needed in the snow depletion curve. Despite the small amount of measured information compared with the dimensionality of the updated parameter vector, updating reduces uncertainty substantially for some state variables and parameters. Parameter adjustments resulting from using each image separately differ, but are positively correlated. For all variables, uncertainty reduction is larger with two images used in conjunction than with any single image. Where the observation is in strong conflict with the prior estimate, increased uncertainty may occur, indicating that prior uncertainty may have been underestimated. Copyright © 2006 John Wiley & Sons, Ltd.     

Testing a spatially distributed tracer‐aided runoff model in a snow‐influenced catchment: Effects of multicriteria calibration on streamwater ages

Integrating stable isotope tracers into rainfall‐runoff models allows investigation of water partitioning and direct estimation of travel times and water ages. Tracer data have valuable information content that can be used to constrain models and, in integration with hydrometric observations, test the conceptualization of catchment processes in model structure and parameterization. There is great potential in using tracer‐aided modelling in snow‐influenced catchments to improve understanding of these catchments' dynamics and sensitivity to environmental change. We used the spatially distributed tracer‐aided rainfall‐runoff (STARR) model to simulate the interactions between water storage, flux, and isotope dynamics in a snow‐influenced, long‐term monitored catchment in Ontario, Canada. Multiple realizations of the model were achieved using a combination of single and multiple objectives as calibration targets. Although good simulations of hydrometric targets such as discharge and snow water equivalent could be achieved by local calibration alone, adequate capture of the stream isotope dynamics was predicated on the inclusion of isotope data in the calibration. Parameter sensitivity was highest, and most local, for single calibration targets. With multiple calibration targets, key sensitive parameters were still identifiable in snow and runoff generation routines. Water ages derived from flux tracking subroutines in the model indicated a catchment where runoff is dominated by younger waters, particularly during spring snowmelt. However, resulting water ages were most sensitive to the partitioning of runoff sources from soil and groundwater sources, which was most realistically achieved when isotopes were included in the calibration. Given the paucity of studies where hydrological models explicitly incorporate tracers in snow‐influenced regions, this study using STARR is an important contribution to satisfactorily simulating snowpack dynamics and runoff generation processes, while simultaneously capturing stable isotope variability in snow‐influenced catchments.     

Application of a data‐based mechanistic modelling (DBM) approach for predicting runoff generation in semi‐arid regions

This paper addresses the application of a data‐based mechanistic (DBM) modelling approach using transfer function models (TFMs) with non‐linear rainfall filtering to predict runoff generation from a semi‐arid catchment (795 km²) in Tanzania. With DBM modelling, time series of rainfall and streamflow were allowed to suggest an appropriate model structure compatible with the data available. The model structures were evaluated by looking at how well the model fitted the data, and how well the parameters of the model were estimated. The results indicated that a parallel model structure is appropriate with a proportion of the runoff being routed through a fast flow pathway and the remainder through a slow flow pathway. Finally, the study employed a Generalized Likelihood Uncertainty Estimation (GLUE) methodology to evaluate the parameter sensitivity and predictive uncertainty based on the feasible parameter ranges chosen from the initial analysis of recession curves and calibration of the TFM. Results showed that parameters that control the slow flow pathway are relatively more sensitive than those that control the fast flow pathway of the hydrograph. Within the GLUE framework, it was found that multiple acceptable parameter sets give a range of predictions. This was found to be an advantage, since it allows the possibility of assessing the uncertainty in predictions as conditioned on the calibration data and then using that uncertainty as part of the decision‐making process arising from any rainfall‐runoff modelling project. Copyright © 2001 John Wiley & Sons, Ltd.     

Modelling snow water equivalent and spring runoff in a boreal watershed,James Bay,Canada

The hydrology of boreal regions is strongly influenced by seasonal snow accumulation and melt. In this study, we compare simulations of snow water equivalent (SWE) and streamflow by using the hydrological model HYDROTEL with two contrasting approaches for snow modelling: a mixed degree‐day/energy balance model (small number of inputs, but several calibration parameters needed) and the thermodynamic model CROCUS (large number of inputs, but no calibration parameter needed). The study site, in Northern Quebec, Canada was equipped with a ground‐based gamma ray sensor measuring the SWE continuously for 5 years in a small forest clearing. The first simulation of CROCUS showed a tendency to underestimate SWE, attributable to bias in the meteorological inputs. We found that it was appropriate to use a threshold of 2 °C to separate rain and snow. We also applied a correction to account for snowfall undercatch by the precipitation gauge. After these modifications to the input dataset, we noticed that CROCUS clearly overestimated the SWE, likely as a result of not including loss in SWE because of blowing snow sublimation and relocation. To correct this, we included into CROCUS a simple parameterisation effective after a certain wind speed threshold, after which the thermodynamic model performed much better than the traditional mixed degree‐day/energy balance model. HYDROTEL was then used to simulate streamflow with both snow models. With CROCUS, the main peak flow could be captured, but the second peak because of delayed snowmelt from forested areas could not be reproduced due to a lack of sub‐canopy radiation data to feed CROCUS. Despite the relative homogeneity of the boreal landscape, data inputs from each land cover type are needed to generate satisfying simulation of the spring runoff. Copyright © 2013 John Wiley & Sons, Ltd.     

Development of probability distributions for urban hydrologic model parameters and a Monte Carlo analysis of model sensitivity

This paper proposes an approach to estimating the uncertainty related to EPA Storm Water Management Model model parameters, percentage routed (P_R) and saturated hydraulic conductivity (K_sat), which are used to calculate stormwater runoff volumes. The methodology proposed in this paper addresses uncertainty through the development of probability distributions for urban hydrologic parameters through extensive calibration to observed flow data in the Philadelphia collection system. The established probability distributions are then applied to the Philadelphia Southeast district model through a Monte Carlo approach to estimate the uncertainty in prediction of combined sewer overflow volumes as related to hydrologic model parameter estimation. Understanding urban hydrology is critical to defining urban water resource problems. A variety of land use types within Philadelphia coupled with a history of cut and fill have resulted in a patchwork of urban fill and native soils. The complexity of urban hydrology can make model parameter estimation and defining model uncertainty a difficult task. The development of probability distributions for hydrologic parameters applied through Monte Carlo simulations provided a significant improvement in estimating model uncertainty over traditional model sensitivity analysis. Copyright © 2013 John Wiley & Sons, Ltd.     

Long‐term investigations of the snow cover in a subalpine semi‐forested catchment

To improve spring runoff forecasts from subalpine catchments, detailed spatial simulations of the snow cover in this landscape is obligatory. For more than 30 years, the Swiss Federal Research Institute WSL has been conducting extensive snow cover observations in the subalpine watershed Alptal (central Switzerland). This paper summarizes the conclusions from past snow studies in the Alptal valley and presents an analysis of 14 snow courses located at different exposures and altitudes, partly in open areas and partly in forest. The long‐term performance of a physically based numerical snow–vegetation–atmosphere model (COUP) was tested with these snow‐course measurements. One single parameter set with meteorological input variables corrected to the prevailing local conditions resulted in a convincing snow water equivalent (SWE) simulation at most sites and for various winters with a wide range of snow conditions. The snow interception approach used in this study was able to explain the forest effect on the SWE as observed on paired snow courses. Finally, we demonstrated for a meadow and a forest site that a successful simulation of the snowpack yields appropriate melt rates. Copyright © 2006 John Wiley & Sons, Ltd.     

Assessment of the adequacy of the regional relationships between catchment attributes and catchment response dynamics,calibrated by a multi‐objective approach

This study aimed to evaluate the effectiveness of the regionalization method on the basis of a combination of a parsimonious model structure and a multi‐objective calibration technique. For this study, 12 gauged catchments in the Republic of Korea were used. The parsimonious model structure, requiring minimal input data, was used to avoid adverse effects arising from model complexity, over‐parameterization and data requirements. The IHACRES rainfall‐runoff model was applied to represent the dynamic response characteristics of catchments in Korea. A multi‐objective approach was adopted to reduce the predictive uncertainty arising from the calibration of a rainfall‐runoff model, by increasing the amount of information retrieved from the available data. The regional relationships (or models) between the model parameters and the catchment attributes were established via a multiple regression approach, incorporating correlation analysis and stepwise regression on linear and logarithmic scales. The impacts of the parameters, calibrated by the multi‐objective approach, on the adequacy of regional relationships were assessed by comparison with impacts obtained by the single‐objective approach. The regional relationships were well defined, despite limited available data. The drainage area, the effective soil depth, the mean catchment slope and the catchment gradient appeared to be the main factors for describing the hydrologic response characteristics in the areas studied. The overall model performance of the regional models based on the multi‐objective approach was good, producing reasonable results for high and low flows and for the overall water balance, simultaneously. The regional models based on the single‐objective approach yielded accurate predictions in high flows but showed limited predictive capability for low flows and the overall water balance. This was due to the optimal model parameter estimates when using a single‐objective measure. The parameters calibrated by the single‐objective approach decreased the predictability of the regional models. Copyright © 2013 John Wiley & Sons, Ltd.     

Constraining water table depth simulations in a land surface model using estimated baseflow

Several recent studies have shown the significance of representing groundwater in land surface hydrologic simulations. However, optimal methods for model parameter calibration in order to realistically simulate baseflow and groundwater depth have received little attention. Most studies still use globally constant groundwater parameters due to the lack of available datasets for calibration. Moreover, when models are calibrated, various parameter combinations are found to exhibit equifinality in simulated total runoff due to model parameter interactions. In this study, a simple lumped groundwater model is incorporated into the Community Land Model (CLM), in which the water table is interactively coupled to soil moisture through the groundwater recharge fluxes. The coupled model (CLMGW) is successfully validated in Illinois using a 22-year (1984–2005) monthly observational dataset. Baseflow estimates from the digital recursive filter technique are used to calibrate the CLMGW parameters. The advantage obtained from incorporating baseflow calibration in addition to traditional calibration based on measured streamflow alone is demonstrated by a Monte Carlo-type simulation analysis. Using the optimal parameter sets identified from baseflow calibration, flow partitioning and water table depth simulations using CLMGW are improved, and the equifinality problem is alleviated. For other regions that lack observations of water table depth, the baseflow calibration approach can be used to enhance parameter estimation and constrain water table depth simulations.     

Using atmospheric tracers to reduce uncertainty in groundwater recharge areas

A Monte Carlo-based approach to assess uncertainty in recharge areas shows that incorporation of atmospheric tracer observations (in this case, tritium concentration) and prior information on model parameters leads to more precise predictions of recharge areas. Variance-covariance matrices, from model calibration and calculation of sensitivities, were used to generate parameter sets that account for parameter correlation and uncertainty. Constraining parameter sets to those that met acceptance criteria, which included a standard error criterion, did not appear to bias model results. Although the addition of atmospheric tracer observations and prior information produced similar changes in the extent of predicted recharge areas, prior information had the effect of increasing probabilities within the recharge area to a greater extent than atmospheric tracer observations. Uncertainty in the recharge area propagates into predictions that directly affect water quality, such as land cover in the recharge area associated with a well and the residence time associated with the well. Assessments of well vulnerability that depend on these factors should include an assessment of model parameter uncertainty. A formal simulation of parameter uncertainty can be used to delineate probabilistic recharge areas, and the results can be expressed in ways that can be useful to water-resource managers. Although no one model is the correct model, the results of multiple models can be evaluated in terms of the decision being made and the probability of a given outcome from each model.     

Assessment of a seasonal calibration technique using multiple objectives in rainfall–runoff analysis

The hydrological response characteristics for the catchments in the Republic of Korea are related to a strong seasonality in the rainfall and streamflow distributions with distinct wet and dry seasons. This study aims to improve a model's ability to predict streamflows by minimizing information loss from the available data during the calibration processes. This study assesses calibration techniques incorporating a multi‐objective approach and seasonal calibration. The lumped conceptual rainfall–runoff model IHACRES was applied to selected catchments in Korea. The model was calibrated based on three different methods: the classical approach using a single performance statistic (the single‐objective method), the multi‐objective approach (the multi‐objective method (I)) and the combined approach incorporating multi‐objective and seasonal calibrations (the multi‐objective method (II)). In the multi‐objective approach, the ‘best fit’ models in the calibration period were selected by considering the trade‐offs among multiple statistics. During seasonal calibration, the calibration period was divided into four seasons to investigate whether these calibrated models can improve the model performance with regards to seasonal climate, rainfall and streamflow distributions. The adequacy of the three different calibration methods was assessed through comparison of the variability of model performance in high and low flows and water balance for the entire period and for each seasonal period. The multi‐objective methods yielded more accurate and consistent predictions for high and low flows and water balance simultaneously, compared to the single‐objective method. In particular, the multi‐objective method (II) produces the best modelling capacity to capture the non‐stationary nature of the hydrological response under different climate conditions. The pattern of improvement with the multi‐objective method (II) was generally consistent through the seasons, with the exception of the winter period in the regions partially affected by snow. This exception is due to a potential limitation of the IHACRES model in reflecting the impact of snow on the catchment hydrology. Copyright © 2013 John Wiley & Sons, Ltd.     

Sensitivity analysis and uncertainty assessment in water budgets simulated by the variable infiltration capacity model for Canadian subarctic watersheds

In this study, we evaluate uncertainties propagated through different climate data sets in seasonal and annual hydrological simulations over 10 subarctic watersheds of northern Manitoba, Canada, using the variable infiltration capacity (VIC) model. Further, we perform a comprehensive sensitivity and uncertainty analysis of the VIC model using a robust and state-of-the-art approach. The VIC model simulations utilize the recently developed variogram analysis of response surfaces (VARS) technique that requires in this application more than 6,000 model simulations for a 30-year (1981–2010) study period. The method seeks parameter sensitivity, identifies influential parameters, and showcases streamflow sensitivity to parameter uncertainty at seasonal and annual timescales. Results suggest that the Ensemble VIC simulations match observed streamflow closest, whereas global reanalysis products yield high flows (0.5–3.0 mm day⁻¹) against observations and an overestimation (10–60%) in seasonal and annual water balance terms. VIC parameters exhibit seasonal importance in VARS, and the choice of input data and performance metrics substantially affect sensitivity analysis. Uncertainty propagation due to input forcing selection in each water balance term (i.e., total runoff, soil moisture, and evapotranspiration) is examined separately to show both time and space dimensionality in available forcing data at seasonal and annual timescales. Reliable input forcing, the most influential model parameters, and the uncertainty envelope in streamflow prediction are presented for the VIC model. These results, along with some specific recommendations, are expected to assist the broader VIC modelling community and other users of VARS and land surface schemes, to enhance their modelling applications.     

Estimating source regions for snowmelt runoff in a Rocky Mountain basin: tests of a data‐based conceptual modeling approach

In many mountain basins, river discharge measurements are located far away from runoff source areas. This study tests whether a basic snowmelt runoff conceptual model can be used to estimate relative contributions of different elevation zones to basin‐scale discharge in the Cache la Poudre, a snowmelt‐dominated Rocky Mountain river. Model tests evaluate scenarios that vary model configuration, input variables, and parameter values to determine how these factors affect discharge simulation and the distribution of runoff generation with elevation. Results show that the model simulates basin discharge well (NSCE and R >0.90) when input precipitation and temperature are distributed with different lapse rates, with a rain‐snow threshold parameter between 0 and 3.3 °C, and with a melt rate parameter between 2 and 4 mm °C^?1 d^?1 because these variables and parameters can have compensating interactions with each other and with the runoff coefficient parameter. Only the hydrograph recession parameter can be uniquely defined with this model structure. These non‐unique model scenarios with different configurations, input variables, and parameter values all indicate that the majority of basin discharge comes from elevations above 2900 m, or less than 25% of the basin total area, with a steep increase in runoff generation above 2600 m. However, the simulations produce unrealistically low runoff ratios for elevations above 3000 m, highlighting the need for additional measurements of snow and discharge at under‐sampled elevations to evaluate the accuracy of simulated snow and runoff patterns. Copyright © 2013 John Wiley & Sons, Ltd.     

Land-cover impacts on streamflow: a change-detection modelling approach that incorporates parameter uncertainty

Abstract

The effect of land-use or land-cover change on stream runoff dynamics is not fully understood. In many parts of the world, forest management is the major land-cover change agent. While the paired catchment approach has been the primary methodology used to quantify such effects, it is only possible for small headwater catchments where there is uniformity in precipitation inputs and catchment characteristics between the treatment and control catchments. This paper presents a model-based change-detection approach that includes model and parameter uncertainty as an alternative to the traditional paired-catchment method for larger catchments. We use the HBV model and data from the HJ Andrews Experimental Forest in Oregon, USA, to develop and test the approach on two small (<1 km²) headwater catchments (a 100% clear-cut and a control) and then apply the technique to the larger 62 km² Lookout catchment. Three different approaches are used to detect changes in stream peak flows using: (a) calibration for a period before (or after) change and simulation of runoff that would have been observed without land-cover changes (reconstruction of runoff series); (b) comparison of calibrated parameter values for periods before and after a land-cover change; and (c) comparison of runoff predicted with parameter sets calibrated for periods before and after a land-cover change. Our proof-of-concept change detection modelling showed that peak flows increased in the clear-cut headwater catchment, relative to the headwater control catchment, and several parameter values in the model changed after the clear-cutting. Some minor changes were also detected in the control, illustrating the problem of false detections. For the larger Lookout catchment, moderately increased peak flows were detected. Monte Carlo techniques used to quantify parameter uncertainty and compute confidence intervals in model results and parameter ranges showed rather wide distributions of model simulations. While this makes change detection more difficult, it also demonstrated the need to explicitly consider parameter uncertainty in the modelling approach to obtain reliable results.

Citation Seibert, J. & McDonnell, J. J. (2010) Land-cover impacts on streamflow: a change-detection modelling approach that incorporates parameter uncertainty. Hydrol. Sci. J. 55(3), 316–332.     

Studying the effect of different approaches to estimating model parameters on the accuracy of river runoff simulation by SWAP model

Different approaches to estimating the parameters of SWAP physically based model, which describes heat and water transfer processes in the soil-vegetaion (snow) cover-atmosphere system are examined. In particular, two methods of a priori estimation of parameter values and two variants of their calibration are discussed. The parameter sets obtained by different methods were used to simulate the runoff from 12 experimental catchments in the eastern USA. The calculations were conducted for a 39-year period (1960–1998) with a 3-hour step. The results of calculations were compared with each other and with measured river runoff values in order to identify the parameter set that is optimal for runoff evaluation. A strategy is proposed for a priori parameter estimation in the case of basins where observational data are too poor to enable parameter calibration.     

Modelling the impacts of land-cover change on streamflow dynamics of a tropical rainforest headwater catchment

Abstract

A modelling experiment is used to examine different land-use scenarios ranging from extreme deforestation (31% forest cover) to pristine (95% forest cover) conditions and related Payment for Ecosystem Services (PES) schemes to assess whether a change in streamflow dynamics, discharge extremes and mean annual water balance of a 73.4-km² tropical headwater catchment in Costa Rica could be detected. A semi-distributed, conceptual rainfall–runoff model was adapted to conceptualize the empirically-based, dominant hydrological processes of the study area and was multi-criteria calibrated using different objective functions and empirical constraints on model simulations in a Monte Carlo framework to account for parameter uncertainty. The results suggest that land-use change had relatively little effect on the overall mean annual water yield (<3%). However, streamflow dynamics proved to be sensitive in terms of frequency, timing and magnitude of discharge extremes. For low flows and peak discharges of return periods greater than one year, land use had a minor influence on the runoff response. Below these thresholds (<1-year return period), forest cover potentially decreased runoff peaks and low flows by as much as 10%, and non-forest cover increased runoff peaks and low flows by up to 15%. The study demonstrated the potential for using hydrological modelling to help identify the impact of protection and reforestation efforts on ecosystem services.

Editor Z.W. Kundzewicz

Citation Birkel, C., Soulsby, C., and Tetzlaff, D., 2012. Modelling the impacts of land-cover change on streamflow dynamics of a tropical rainforest headwater catchment. Hydrological Sciences Journal, 57 (8), 1543–1561.     

Addressing snow model uncertainty for hydrologic prediction

Streamflow forecasting methods are moving towards probabilistic approaches that quantify the uncertainty associated with the various sources of error in the forecasting process. Multi-model averaging methods which try to address modeling deficiencies by considering multiple models are gaining much popularity. We have applied the Bayesian Model Averaging method to an ensemble of twelve snow models that vary in their heat and melt algorithms, parameterization, and/or albedo estimation method. Three of the models use the temperature-based heat and melt routines of the SNOW17 snow accumulation and ablation model. Nine models use heat and melt routines that are based on a simplified energy balance approach, and are varied by using three different albedo estimation schemes. Finally, different parameter sets were identified through automatic calibration with three objective functions. All models use the snow accumulation, liquid water transport, and ground surface heat exchange processes of the SNOW17. The resulting twelve snow models were combined using Bayesian Model Averaging (BMA). The individual models, BMA predictive mean, and BMA predictive variance were evaluated for six SNOTEL sites in the western U.S. The models performed best and the BMA variance was lowest at the colder sites with high winter precipitation and little mid-winter melting. An individual snow model would often outperform the BMA predictive mean. However, observed snow water equivalent (SWE) was captured within the 95% confidence intervals of the BMA variance on average 80% of the time at all sites. Results are promising that consideration of multiple snow structures would provide useful uncertainty information for probabilistic hydrologic prediction.