Terms Of Nash-Sutcliffe Efficiency Research Articles

On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow

Abstract. In northern cold-temperate countries, a large portion of annual streamflow is produced by spring snowmelt, which often triggers floods. It is important to have spatial information about snow variables such as snow water equivalent (SWE), which can be incorporated into hydrological models, making them more efficient tools for improved decision-making. The present research implements a unique spatial pattern metric in a multi-objective framework for calibration of hydrological models and attempts to determine whether raw SNODAS (SNOw Data Assimilation System) data can be utilized for hydrological model calibration. The spatial efficiency (SPAEF) metric is explored for spatially calibrating SWE. Different calibration experiments are performed combining Nash–Sutcliffe efficiency (NSE) for streamflow and root-mean-square error (RMSE) and SPAEF for SWE, using the Dynamically Dimensioned Search (DDS) and Pareto Archived Dynamically Dimensioned Search multi-objective optimization (PADDS) algorithms. Results of the study demonstrate that multi-objective calibration outperforms sequential calibration in terms of model performance (SWE and discharge simulations). Traditional model calibration involving only streamflow produced slightly higher NSE values; however, the spatial distribution of SWE could not be adequately maintained. This study indicates that utilizing SPAEF for spatial calibration of snow parameters improved streamflow prediction compared to the conventional practice of using RMSE for calibration. SPAEF is further implied to be a more effective metric than RMSE for both sequential and multi-objective calibration. During validation, the calibration experiment incorporating multi-objective SPAEF exhibits enhanced performance in terms of NSE and Kling–Gupta efficiency (KGE) compared to calibration experiment solely based on NSE. This observation supports the notion that incorporating SPAEF computed on raw SNODAS data within the calibration framework results in a more robust hydrological model. The novelty of this study is the implementation of SPAEF with respect to spatially distributed SWE for calibrating a distributed hydrological model.

Does the performance enhancement through multi-model averaging at the catchment outlet gets translated to the interior ungauged points?

Accurate prediction of streamflow is essential at both the outlet and interior points for effective water resource management. This study aimed to investigate whether improved simulation results at the catchment outlet through Multi-Model Averaging (MMA) can be translated to interior ungauged points in a catchment. To achieve this, the study considered ensemble combinations of 81 conceptual hydrologic models and calibrated each model using four objective functions for two study areas (Jagdalpur and Wardha subbasins of Godavari basin). Six MMA methods were applied to evaluate their performance at interior points in the catchment. The findings revealed that, when compared to the best performing hydrologic model, the best MMA method showed an improvement in terms of Nash-Sutcliffe efficiency, ranging from 2.2% to 3.8% at the interior point of Jagdalpur and 2% to 12% at the interior points of Wardha. MMA methods with KGE-calibrated models as input exhibited better performance in terms of NSE, while those using LogarithmicNSE-calibrated and parent models as input ensembles showed better performance in terms of Percentage bias and Skill Score. Bayesian model averaging and Genetic Algorithm based averaging methods were identified as the most appropriate MMA methods, and the study findings suggest that the performance of these techniques varies at different interior points based on their spatial and temporal variation of flow characteristics. Overall, this study would be beneficial for better flow prediction at internal ungauged points, which is crucial for effective water resource management in areas where there is limited data or where new developments have occurred.

Streamflow simulation using conceptual and neural network models in the Hemavathi sub-watershed, India

Water is one of the most valuable natural resources and a major element of a state's and country's socio-economic growth. The world's water resources and India are under huge pressure because of rising demand and a limited supply. Proper water management is the only solution for ensuring a close gap between demand and supply. Hydrological modeling offers an answer to this issue by establishing relationships between different hydrological processes. Several models have been developed in the past decade to simulate the rainfall and runoff relations. Some models are simple conceptual models based on spatially distributed event-based or continuous and artificial intelligence (AI) models. This study aims to compare two conceptual daily-based models and one AI model developed for the Hemavathi sub-watershed in the Cauvery Basin (India). Two daily runoff models are implemented using conceptual models, i.e., Sacramento and the Australian water balance model (AWBM) using Rainfall-Runoff Library (RRL) tool and Feed forward Backpropagation neural network (FFBPNN) model. The models were calibrated for daily streamflow values from 1990 to 2006 and then validated from 2007 to 2015. The effectiveness of model runoff predictions is evaluated using statistical parameters such as Nash-Sutcliffe efficiency (NSE) and Correlation coefficient (CC) values. The NSE values for the FFBPNN is 0.88 (calibration) and 0.74 (validation), Sacramento model is 0.66 (calibration) and 0.48 (validation), and 0.63 (calibration) and 0.44 (validation) for the AWBM model. From the obtained results, the FFBPNN model performs well in terms of NSE and CC compared to Sacramento and AWBM models.

Investigating the impact of calibration timescales on streamflow simulation, parameter sensitivity and model performance for Indian catchments

ABSTRACT Hydrological model calibration is a quintessential step in model development, and the time scale of calibration depends on the application. However, the implications of choice of time scale of calibration have not been explored extensively. Here, we evaluate the effect of the time scale of calibration on model sensitivity, best parameter ranges, and predictive uncertainty for three river basins using the Soil and Water Assessment Tool (SWAT) model. Multiple models were set up for three different catchments from southern India. Our results showed that the sensitivity of the parameters, best parameter ranges, and model performance are conditioned on the time scale of calibration. The models calibrated at coarser time scales marginally outperformed the models calibrated at fine time scale in terms of Nash-Sutcliffe efficiency and percentage bias. Transfer of parameters across scales (both from coarse to fine and from fine to coarse) have a general tendency to worsen the model performance in all three catchments, with few exceptions.

Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale

Statistical analyses and descriptive characterizations are sometimes assumed to be offering information on time series forecastability. Despite the scientific interest suggested by such assumptions, the relationships between descriptive time series features (e.g., temporal dependence, entropy, seasonality, trend and linearity features) and actual time series forecastability (quantified by issuing and assessing forecasts for the past) are scarcely studied and quantified in the literature. In this work, we aim to fill in this gap by investigating such relationships, and the way that they can be exploited for understanding hydroclimatic forecastability and its patterns. To this end, we follow a systematic framework bringing together a variety of –mostly new for hydrology– concepts and methods, including 57 descriptive features and nine seasonal time series forecasting methods (i.e., one simple, five exponential smoothing, two state space and one automated autoregressive fractionally integrated moving average methods). We apply this framework to three global datasets originating from the larger Global Historical Climatology Network (GHCN) and Global Streamflow Indices and Metadata (GSIM) archives. As these datasets comprise over 13,000 monthly temperature, precipitation and river flow time series from several continents and hydroclimatic regimes, they allow us to provide trustable characterizations and interpretations of 12-month ahead hydroclimatic forecastability at the global scale. We first find that the exponential smoothing and state space methods for time series forecasting are rather equally efficient in identifying an upper limit of this forecastability in terms of Nash-Sutcliffe efficiency, while the simple method is shown to be mostly useful in identifying its lower limit. We then demonstrate that the assessed forecastability is strongly related to several descriptive features, including seasonality, entropy, (partial) autocorrelation, stability, (non)linearity, spikiness and heterogeneity features, among others. We further (i) show that, if such descriptive information is available for a monthly hydroclimatic time series, we can even foretell the quality of its future forecasts with a considerable degree of confidence, and (ii) rank the features according to their efficiency in explaining and foretelling forecastability. We believe that the obtained rankings are of key importance for understanding forecastability. Spatial forecastability patterns are also revealed through our experiments, with East Asia (Europe) being characterized by larger (smaller) monthly temperature time series forecastability and the Indian subcontinent (Australia) being characterized by larger (smaller) monthly precipitation time series forecastability, compared to other continental-scale regions, and less notable differences characterizing monthly river flow from continent to continent. A comprehensive interpretation of such patters through massive feature extraction and feature-based time series clustering is shown to be possible. Indeed, continental-scale regions characterized by different degrees of forecastability are also attributed to different clusters or mixtures of clusters (because of their essential differences in terms of descriptive features).

Assessment of the WRF-Hydro uncoupled hydro-meteorological model on flashy watersheds of the Grande Terre tropical island of New Caledonia (South-West Pacific)

Study regionSix watersheds of the main island of New Caledonia. Study focusIn the context of a projected reduction of extreme precipitation events by ~ 20% by 2100 combined to large land cover variability, a distributed hydrological approach was adopted over New Caledonia. The WRF-Hydro model was implemented using a one-way WRF meteorological forcing, with rainfall being spatially interpolated from rain gauge observations using the Thiessen polygon and IDEW methods. The Dynamically Dimensioned Search algorithm was applied for calibration on three contrasted flash-floods. Two-year validation allowed the dynamics of the watersheds to be studied and compared. New hydrological insights for the regionGood results in terms of Nash-Sutcliffe efficiency were reached on all watersheds (NSE ≥ 0.6). WRF-Hydro showed consistent water budget components for two flash-floods with 90% correlation between simulated and observed runoff coefficients of all basins. Adjusted physical parameters matched known watershed morphological and hydrological features. Both methods of rainfall interpolation produced similar NSE but strongly different water distributions, with simulated soil moisture changes varying by up to 60% during flash-floods. Spatial patterns of soil moisture and flood decomposition led to improved understanding of watershed behavior and to a revision of infiltration properties for one of them. Overall, WRF-Hydro proved to be a suitable framework to better understand hydrological processes and efficiently handle prospective modifications of New Caledonia’s land cover and climate regimes.

Analyzing the Suitability of Remotely Sensed ET for Calibrating a Watershed Model of a Mediterranean Montane Forest

The ability to spatially characterize runoff generation and forest health depends partly on the accuracy and resolution of evapotranspiration (ET) simulated by numerical models. A possible strategy to increase the accuracy and resolution of numerically modeled ET is the use of remotely sensed ET products as an observational basis for parameter estimation (model calibration) of those numerical models. However, the extent to which that calibration strategy leads to a realistic representation of ET, relative to ground conditions, is not well understood. We examined this by comparing the spatiotemporal accuracy of ET from a remote sensing product, MODIS MOD16A2, to that from a watershed model (SWAT) calibrated to flow measured at an outlet streamgage. We examined this in the upper Kings River watershed (3999 km2) of California’s Sierra Nevada, a snow-influenced watershed in a Mediterranean climate. We assessed ET accuracies against observations from three eddy-covariance flux towers at elevations of 1160–2700 m. The accuracy of ET from the stream-calibrated watershed model surpassed that of MODIS in terms of Nash-Sutcliffe efficiency (+0.36 versus −0.43) and error in elevational trend (+7.7% versus +81%). These results indicate that for this particular experiment, an outlet streamgage would provide a more effective observational basis than remotely sensed ET product for watershed-model parameter estimation. Based on analysis of ET-weather relationships, the relatively large errors we found in MODIS ET may be related to weather-based corrections to water limitation not representative of the hydrology of this snow-influenced, Mediterranean-climate area.

Modelación hidrológica horaria en la cuenca alta del río Fuerte, Sinaloa, México

Introduction: Mexico uses hydrological models to determine floods, evaluate land use change scenarios, evaluate climate change scenarios, and define federal zones, among other applications. However, the models are rarely calibrated beforehand, which increases uncertainty in the design of structures and hydraulic standards. Objective: To build a hydrological model for the watershed of the Fuerte River, Mexico, of extreme rainfall events occurred in 2009, 2011, 2015, 2016 and 2017. Methodology: Five extreme rainfall events were considered for this study. The hydrologic model was design using the HEC-HMS program, and calibrated at the Tubares hydrometric station. The runoff curve number methodology and the Clark unit hydrograph were used. Results: The results collected in four of the five events were positive; the Nash-Sutcliffe efficiency (NSE) ranged between 0.22 and 0.52. The temporal behavior of the river, at times when it moved away from the flow value, preserved the variation trend. Limitations of the study: The study reaches the Tubares hydrometric station, Chihuahua, without including the downstream dams in Sinaloa. Originality: There are few hydrological studies that generate a calibrated and, therefore, reliable hourly model. Conclusions: The hourly hydrologic model had an acceptable performance in four of the five predicted events in terms of NSE and root mean square error (RMSE).

A novel framework to determine the usefulness of satellite-based soil moisture data in streamflow prediction using dynamic Budyko model

Soil moisture plays an important role in partitioning rainfall into runoff and evapotranspiration. Due to advancements in remotely sensed soil moisture data acquisition techniques, many soil moisture data assimilation (SMDA) studies have been conducted to improve streamflow prediction. It is thus expected that the outcome of a SMDA exercise will be determined by the quality of soil moisture data in hand and the hydrology of the catchment. Our study begins with the hypothesis that it is possible to determine the usefulness of a satellite-based soil moisture data product for areas where paramaterizing a complex model is difficult due to availability of limited information. To this end, we use satellite-based GLDAS root-zone soil moisture data with dynamic Budyko (DB), rainfall-runoff model, to improve streamflow prediction for 60 US basins. Our results suggest that there is a reasonably good one-to-one or universal relationship between instantaneous dryness-index (φ), the key state variable of the DB model, and root-zone soil moisture (θ), which implies that the model can directly use soil moisture information for predicting streamflow. To check the robustness of the universal φ-θ relationship, we also developed basin specific φ-θ relationships. Model performance, expressed in terms of Nash-Sutcliffe efficiency (NSE), improved in 34 basins when we considered the universal φ-θ relationship and in 51 basins when we considered the basin specific relationships. Multiple linear regression (MLR) analysis reveals that change in NSE due to the use of soil moisture information is predicted quite well by certain basin characteristics. In particular, it is found that available water capacity and forest area positively influence NSE, whereas average sand, silt and clay contents, latitude, and longitude affect it negatively. A slightly stronger MLR relationship was observed while considering soil moisture deficit (the difference between saturated soil moisture and actual soil moisture) in place of soil moisture. From the stepwise MLR analysis, it is observed that the vegetation and runoff generation mechanism control the parameters of the φ-θ relationship. Overall, our study provides a framework to determine the suitability of remotely-based soil moisture data for hydrological modelling.

Activation soil moisture accounting (ASMA) for runoff estimation using soil conservation service curve number (SCS-CN) method

In this study, the concept of activation soil moisture (ASM) has been conceptualised by coupling the Soil Moisture Accounting (SMA) concept with the static infiltration component (Fc) for simulating rainfall-runoff process. The ASM has been defined as the height of soil moisture barrier (or the amount of soil moisture deficit), which must be fulfilled before runoff can start. Most of the SCS-CN inspired methods, including the original one do not consider ASM in their formulation to simulate rainfall-runoff process. To account for ASM, here, we develop an activation soil moisture accounting (ASMA) based method (ASMA-SCS-CN) by coupling the SMA concept of Michel-Vazken-Perrin (MVP) method with the static infiltration (Fc) based Mishra-Singh (MS) method, which presents a fuller picture of SMA system. The performance of the ASMA-SCS-CN method is compared with the original SCS-CN method, MS method and MVP method by applying a large dataset of 56,343 storm events from 164 small to large watersheds in the United States using goodness-of-fit statistics in terms of Nash-Sutcliffe efficiency (NSE), the root mean square error (RMSE), normalized RMSE (nRMSE), percent bias (PBIAS), mean absolute error (MAE), standard error (SE) and RMSE-observations standard deviation ratio (RSR). The ASMA-SCS-CN method has the highest median value of NSE (0.71; varying from 0.11 to 0.97) with inter-quartile range (IQR) as (0.62–0.80) followed by MVP with NSE (0.67; varying from 0.10 to 0.0.96) and IQR as (0.57–0.74), MS with NSE (0.61; varying from 0.02 to 0.97) and IQR range as (0.46–0.72), and SCS-CN with NSE (0.58; varying from 0,01 to 0.92) and IQR as (0.44–0.69). The ASMA-SCS-CN method is found to have lowest mean and median values of RMSE, nRMSE, MAE, SE and RSR than the MVP, MS and SCS-CN method. The PBIAS values of the ASMA-SCS-CN and MVP methods are lower than that of MS and SCS-CN method. In addition, the performance of all four methods is further evaluated based on the watershed characteristics such as landuse, soil type, drainage area, and mean rainfall and the results show that in all cases the ASMA-SCS-CN method performs much better than the rest of the methods. Overall, the improved performance of ASMA-SCS-CN can be attributed to the inclusion of SMA along with the static infiltration component for representing the complete picture of SMA system in modelling rainfall-runoff process.

Neural network modeling for groundwater-level forecasting in coastal aquifers

Advances in the artificial intelligence-based models can act as robust tools for modeling hydrological processes. Neural network architectures coupled with learning algorithms are considered as useful modeling tools for groundwater-level fluctuations. Emotional artificial neural network coupled with genetic algorithm (EANN-GA) is one such novel hybrid neural network which has been used in the present study for the forecasting of groundwater levels at three sites (Site H3, Site H4.5, and Site H9) in a coastal aquifer system. This study was conceived to address and investigate the efficiency of the ensemble model (EANN-GA) for forecasting one-month ahead groundwater level and to compare its performance with emotional artificial neural network (EANN), generalized regression neural network (GRNN), and the conventional feedforward neural network (FFNN). Variations in the rainfall, tidal levels, and groundwater levels are selected as inputs for the development of EANN-GA, EANN, GRNN, and FFNN models. Suitable goodness-of-fit criteria such as Nash–Sutcliffe efficiency (NSE), bias, root mean squared error (RMSE), and graphical indicators are used for assessing the efficiency of the developed models. The improvement in the performance of the EANN-GA model over the developed EANN, GRNN, and FFNN models in terms of NSE is 0.81, 6.02, and 9.56% at Site H3; 4.35, 5.50, and 22.68% at Site H4.5; and 1.05, 7.18, and 21.75% at Site H9. Thus, it can be inferred that the EANN-GA model outperforms the developed EANN model, GRNN model, and FFNN model. Further, this paper examines the predictive capability of extreme events by the EANN-GA, EANN, GRNN, and FFNN models. The RMSE values of the EANN-GA model at all peak points are found as 0.27, 0.23, and 0.10 m at sites H3, H4.5, and H9, respectively, and the results indicate superior performance of EANN-GA model. To check the generalization ability of the developed EANN-GA models, they are validated with the data of another site (Site I2) located in the same coastal aquifer. Superior prediction capability and generalization ability make the EANN-GA model a better alternative for predicting groundwater levels. Overall, this study demonstrates the effectiveness of EANN-GA in modeling spatio-temporal fluctuations of groundwater levels. It is also concluded that the EANN-GA model yields remarkably better predictions of extreme events, and hence, it could be a promising technique for developing alarm systems for real-world water problems.

Evaluation and uncertainty attribution of the simulated streamflow from NoahMP-RAPID over a high-altitude mountainous basin

Accurate hydrological simulations in high-altitude mountainous basins are important for optimizing reservoir operations in hydropower production and flood control. However, the simulations experience substantial uncertainties from the sparse precipitation measurements over complex mountainous topography and the imperfect hydrological process representations over the heterogeneous surface. At the Daduhe river basin, which is a high-altitude mountainous river basin located in the eastern slope of the Tibetan Plateau, daily streamflow over the 2-year period of 2014 and 2015 is simulated using the Noah land surface model with multi-parameterizations (NoahMP) and Routing Application for Parallel computation of Discharge (RAPID) coupled model driven by the Chinese Meteorological Forcing Dataset (CMFD). Two different runoff parameterization schemes of NoahMP are used for the simulations: SIMGM, which is derived from TOPMODEL and parameterizes surface runoff using water table depth, and NOAH, in which surface runoff is parameterized using soil moisture content based on the Philip infiltration model. The simulated streamflow is evaluated at two mainstream gauges on the Daduhe river, Tongjiezi and Longtoushi, in terms of Nash-Sutcliffe Efficiency (NSE), correlation coefficient, bias, and standard deviation. The uncertainty of the simulated streamflow is attributed to different runoff parameterization in NoahMP, the flow wave celerity parameter of RAPID, and the forcing CMFD precipitation data. Results show that the NOAH runoff parameterization scheme outperforms the SIMGM scheme. NOAH satisfactorily reproduced the observed streamflow anomaly and flood timing at the two mainstream gauges, and the correlation coefficients between the simulated and observed streamflow are above 0.85. The NSE for NOAH is approximately 0.3. The mathematical decomposition of NSE reveals that the relatively low NSE value is mainly attributed to the significant streamflow bias. Without the bias, NSE can be improved to approximately 0.7. The bias shows little dependency on the flow wave celerity parameter of RAPID and the runoff parameterization of NoahMP, whereas it closely corresponds to the forcing CMFD precipitation data bias. Comparing the estimated precipitation from Budykos curve and the observed streamflow, CMFD significantly underestimates the basin-averaged precipitation, which leads to the significant streamflow underestimation in the hydrological simulations. CMFD does not correctly represent the orographic effects on precipitation in the Daduhe river basin. Comparing the rain gauge measurements located in low-altitude river valleys, the CMFD precipitation is significant underestimated, suggesting a significantly underestimation in high-altitude precipitation. This study shows that streamflow bias can be reduced by densifying rain gauges at high altitudes. Despite the bias, the NoahMP RAPID coupled model can satisfactorily reproduce streamflow anomalies and flood timing in mountainous basins. As a dam model is already included, the coupled model can be further used to optimize reservoir operations for hydropower production and flood control.

Normalized fractional adaptive methods for nonlinear control autoregressive systems

The trend of applying mathematical foundations of fractional calculus to solve problems arising in nonlinear sciences, is an emerging area of research with growing interest especially in communication, signal analysis and control. In the present study, normalized fractional adaptive strategies are exploited for automatic tuning of the step size parameter in nonlinear system identification based on Hammerstein model. The brilliance of the methodology is verified by mean of viable estimation of electrically stimulated muscle model used in rehabilitation of paralyzed muscles. The dominance of the schemes is established by comparing the results with standard counterparts in case of different noise levels and fractional order variations. The results of the statistical analyses for sufficient independent runs in terms of Nash-Sutcliffe efficiency, variance account for and mean square error metrics validated the consistent accuracy and reliability of the proposed methods. The proposed exploitation of fractional calculus concepts makes a firm branch of nonlinear investigation in arbitrary order gradient-based optimization schemes.

Performance of Frequently Used Interpolation Methods to Predict Spatial Distribution of Selected Soil Properties in an Agricultural Watershed in Ethiopia

A soil map of a watershed provides a wealth of knowledge and can be vital for implementing site-specific soil managements. Hence, watershed-based soil assessment was conducted to select an optimum spatial interpolation method, while aiming for sustainable soil managements. Intensive soil sampling was undertaken to investigate the performance of ordinary kriging (OK), inverse distance weighting (IDW), and radial basis functions (RBF) for predicting the spatial distribution of soil texture, pH, soil organic carbon (SOC), and available phosphorus (AP). The 72 ha study area was divided into a 100 x 100 m grid and approximately at the center of each grid, topsoil samples (roughly from 10-25cm depth) were collected from over 75 locations across the entire watershed. The exponential and Gaussian models were best fitted in the semivariogram of measured soil. The performance of each interpolation method was assessed quantitatively in terms of Nash-Sutcliffe efficiency (E), coefficient of determination (R2), and index of agreement (d). The interpolated maps generated based on the highest value of E displayed OK was best performed for SOC and sand. RBF was most suitable for mapping of AP and clay, while IDW gave better results when applied to pH. The highest value of R2, E, and d (0.51, 0.51, and 0.83, respectively) resulted from the spatial interpolation of AP. Overall, the cross-validation statistics for each interpolation method showed there was no single method that significantly outperformed the others. Therefore, one of the interpolation methods could be applied for surface map generation in future studies of similar regions.

Improvement of Hydrological Simulations by Applying Daily Precipitation Interpolation Schemes in Meso-Scale Catchments

Ground-based precipitation data are still the dominant input type for hydrological models. Spatial variability in precipitation can be represented by spatially interpolating gauge data using various techniques. In this study, the effect of daily precipitation interpolation methods on discharge simulations using the semi-distributed SWAT (Soil and Water Assessment Tool) model over a 30-year period is examined. The study was carried out in 11 meso-scale (119–3935 km2) sub-catchments lying in the Sulejów reservoir catchment in central Poland. Four methods were tested: the default SWAT method (Def) based on the Nearest Neighbour technique, Thiessen Polygons (TP), Inverse Distance Weighted (IDW) and Ordinary Kriging (OK). =The evaluation of methods was performed using a semi-automated calibration program SUFI-2 (Sequential Uncertainty Fitting Procedure Version 2) with two objective functions: Nash-Sutcliffe Efficiency (NSE) and the adjusted R2 coefficient (bR2). The results show that: (1) the most complex OK method outperformed other methods in terms of NSE; and (2) OK, IDW, and TP outperformed Def in terms of bR2. The median difference in daily/monthly NSE between OK and Def/TP/IDW calculated across all catchments ranged between 0.05 and 0.15, while the median difference between TP/IDW/OK and Def ranged between 0.05 and 0.07. The differences between pairs of interpolation methods were, however, spatially variable and a part of this variability was attributed to catchment properties: catchments characterised by low station density and low coefficient of variation of daily flows experienced more pronounced improvement resulting from using interpolation methods. Methods providing higher precipitation estimates often resulted in a better model performance. The implication from this study is that appropriate consideration of spatial precipitation variability (often neglected by model users) that can be achieved using relatively simple interpolation methods can significantly improve the reliability of model simulations.

Solving Richards Equation for snow improves snowpack meltwater runoff estimations in detailed multi-layer snowpack model

Abstract. The runoff from a snow cover during spring snowmelt or rain-on-snow events is an important factor in the hydrological cycle. In this study, three water balance schemes for the 1 dimensional physically-based snowpack model SNOWPACK are compared to lysimeter measurements at two alpine sites with a seasonal snow cover, but with different climatological conditions: Weissfluhjoch (WFJ) and Col de Porte (CDP). The studied period consists of 14 and 17 yr, respectively. The schemes include a simple bucket-type approach, an approximation of Richards Equation (RE), and the full RE. The results show that daily sums of snowpack runoff are strongly related to a positive energy balance of the snow cover and therefore, all water balance schemes show very similar performance in terms of Nash-Sutcliffe efficiency (NSE) coefficients (around 0.63 and 0.72 for WFJ and CDP, respectively) and r2 values (around 0.83 and 0.72 for WFJ and CDP, respectively). An analysis of the runoff dynamics over the season showed that the bucket-type and approximated RE scheme release meltwater slower than in the measurements, whereas RE provides a better agreement. Overall, solving RE for the snow cover yields the best agreement between modelled and measured snowpack runoff, but differences between the schemes are small. On sub-daily time scales, the water balance schemes behave very differently. In that case, solving RE provides the highest agreement between modelled and measured snowpack runoff in terms of NSE coefficient (around 0.48 at both sites). At WFJ, the other water balance schemes loose most predictive power, whereas at CDP, the bucket-type scheme has an NSE coefficient of 0.39. The shallower and less stratified snowpack at CDP likely reduces the differences between the water balance schemes. Accordingly, it can be concluded that solving RE for the snow cover improves several aspects of modelling snow cover runoff, especially for deep, sub-freezing snow covers and in particular on the sub-daily time scales. The additional computational cost was found to be in the order of a factor of 1.5–2.

광릉 원두부 유역 생태수문과정의 통합을 위한 지역 생태수문 모사 시스템(RHESSys)의 모수화와 적용

Despite the close linkage in changes between the ecological and hydrological processes in forest ecosystems, an integrative approach has not been incorporated successfully. In this study, based on the vegetation and hydrologic data of the Gwangneung headwater catchment with the Geographic Information System, we attempted such an integrated approach by employing the Regional Hydro-Ecologic Simulation System (RHESSys). To accomplish this, we have (1) constructed the input data for RHESSys, (2) developed an integrated calibration system that enables to consider both ecological and hydrological processes simultaneously, and (3) performed sensitivity analysis to estimate the optimum parameters. Our sensitivity analyses on six soil parameters that affect streamflow patterns and peak flow show that the decay parameter of horizontal saturated hydraulic conductivity and porosity decay by depth (PD) had the highest sensitivity. The optimization of these two parameters to estimate the optimum streamflow variation resulted in a prediction accuracy of 0.75 in terms of Nash-Sutcliffe efficiency (NSec). These results provide an important basis for future evaluation and mapping of the watershed-scale soil moisture and evapotranspiration in forest ecosystems of Korea.

EVALUATION OF THE INFLUENCE OF UNCERTAINTY IN RAINFALL AND DISCHARGE DATA ON HYDROLOGICAL MODELING

The objectives of this study are to analyze the influence of systematic and random error inrainfall data, discharge data, and the spatial representation of rainfall data on the performance of a distributed hydrologicalmodel, BTOPMC. A framework for uncertainty analysis was developed using a Monte Carlo approach, which was applied to the Kalu River basin in Sri Lanka. Findings show that a systematic error exceeding +/-10% in rainfall or discharge data is detrimental to model results. A random error with standarddeviation is equal to10% ofrainfall or discharge isnot substantial. Calibrationof parameters can compensate for some error. The impact of systematic error in rainfall in terms of Nash-Sutcliffe Efficiency (NSE) is higher than that in discharge. The impact of random error in discharge in terms of NSE is higher than that inrainfall. The impact of the random error is the lowest for the gauge network of the highest density, but the impact of systematic error is not the lowest for this case.