Average Nash-Sutcliffe Coefficient Research Articles

Modeling of runoff formation processes with aufeis feed in mountain cryosphere of the north-east of Russia

Aufeis are a key element in the chain of water exchange processes in the permafrost zone. The hydrological role of aufeis in the formation of river flow can be comparable to that of glaciers. Observations of aufeis during the construction of the Baikal-Amur Mainline showed that the share of aufeis runoff in the annual river flow can reach 35% for watersheds with an area of up to 500 km2. Despite the long history of studying aufeis and the results achieved, there are no methods for predicting the development of aufeis processes, as well as hydrological models that take into account the share of aufeis feeding in river runoff. This is due to the lack of observational data on the dynamics and development factors of aufeis. In the last century, long-term studies were carried out on some aufeis in Siberia and the Far East. One of these unique objects is the giant Anmangynda aufeis, which forms in the upper reaches of the Kolyma River basin. The aim of the research is to study the hydrological regime of the Anmangynda river basin and develop a method for accounting of aufeis runoff (module «Aufeis») in the distributed hydrological model «Hydrograph». The module “Aufeis” takes into account two factors of aufeis destruction – under the influence of solar radiation and thermal erosion destruction. The input data is the area of aufeis at the beginning of the warm season and daily meteorological data. The calculation parameters are the coefficients of ice melting and evaporation from ice, as well as the coefficient of the relative area of aufeis depending on the period of destruction, calculated for the modern climatic period. The result of the calculation is the aufeis runoff and the characteristics of aufeis for a given interval. Based on historical data and materials obtained during own field research, the module «Aufeis» was tested. The deviation of the calculated and observed values was 2—10% and 1—9% for the maximum values of the area and volume of the Anmangynda aufeis, respectively. Results of numerical calculations for the period 1967–2022 revealed the dynamics of the contribution of the Anmangynda aufeis to the river flow in different seasons. The expanded functionality of the Hydrograph model has improved the quality of modeling for rivers where aufeis form. For the river basin Anmangynda average Nash-Sutcliffe coefficient and annual runoff error were 0,57 and 13,0% compared to 0,41 and 18,0% when the module «Aufeis» was not used.

Study of Flood Simulation in Small and Medium-Sized Basins Based on the Liuxihe Model

The uneven distribution of meteorological stations in small and medium-sized watersheds in China and the lack of measured hydrological data have led to difficulty in flood simulation and low accuracy in flood forecasting. Traditional hydrological models no longer achieve the forecasting accuracy needed for flood prevention. To improve the simulation accuracy of floods and maximize the use of hydrological information from small and medium-sized watersheds, high-precision hydrological models are needed as a support mechanism. This paper explores the applicability of the Liuxihe model for flood simulation in the Caojiang river basin and we compare flood simulation results of the Liuxihe model with a traditional hydrological model (Xinanjiang model). The results show that the Liuxihe model provides excellent simulation of field floods in Caojiang river basin. The average Nash–Sutcliffe coefficient is 0.73, the average correlation coefficient is 0.9, the average flood peak present error is 0.33, and the average peak simulation accuracy is 93.9%. Compared with the traditional flood hydrological model, the Liuxihe model simulates floods better with less measured hydrological information. In addition, we found that the particle swarm optimization (PSO) algorithm can improve the simulation of the model, and its practical application only needs one representative flood for parameter optimization, which is suitable for areas with little hydrological information. The study can support flood forecasting in the Caojiang river basin and provide a reference for the preparation of flood forecasting schemes in other small and medium-sized watersheds.

Application and Research of Liuxihe Model in the Simulation of Inflow Flood at Zaoshi Reservoir

Floods occur frequently in China, and watershed floods are caused mainly by intensive rainfall, but the spatial distribution of this rainfall is often very uneven. Thus, a watershed hydrological model that enables a consideration of a heterogeneous spatial distribution of rainfall is needed. In this study, a flood forecasting scheme based on the Liuxihe model is established for the Zaoshi Reservoir. The particle swarm optimization (PSO) algorithm is used to optimize the model parameters for flood simulation, and the model’s performance is assessed by a comparison with measured flood data. The spatial distributions of rainfall selected for this study are non-uniform, with much greater rainfall in some areas than in others in some cases. Rainfall may be concentrated in the middle of the basin, in the reservoir area, or in the upstream portion of the basin. The Liuxihe-model-based flood inflow forecasting scheme for the Zaoshi Reservoir demonstrates an excellent simulation effect, with an average peak simulation accuracy of 96.3%, an average peak time of 1.042 h early, and an average Nash–Sutcliffe coefficient of 0.799. Under the condition of an uneven spatial distribution of rainfall, the Liuxihe model simulates floods well. The PSO algorithm significantly improves the model’s simulation accuracy, and its practical application requires only the selection of a typical flood for parameter optimization. Thus, the flood simulation effect of the Liuxihe model is ideal for the watershed above the Zaoshi Reservoir, and the scheme developed in this study can be applied for operational flood forecasting.

Sediment load estimation using a novel regionalization sediment-response similarity method for ungauged catchments

The development and application of reliable hydrological models are challenging in ungauged or poorly gauged catchments. Although several regionalization methods have been developed for predicting hydrological variables in ungauged catchments, a large uncertainty usually exists in the prediction because previous methods typically depended on several static catchment attributes or long-term mean climatic descriptors, thereby neglecting the spatiotemporal variability of the climate. In this study, we propose a new regionalization method called sediment-response similarity (SRS) using the soil and water assessment tool model and self-organizing maps to estimate sediment load in ungauged catchments, considering spatiotemporal variability and the sediment load relation to rainfall characteristics of individual catchments. The performance of SRS was evaluated by comparing it against the conventional regionalization method (i.e., physical similarity) and calibration model results, in four gauged catchments of the lower Mekong River basin. The SRS method showed improved performance over the conventional approach in estimating sediment loads, with an average Nash-Sutcliffe efficiency (NSE) and coefficient of determination (R2) of 0.75 and 0.76, respectively, which were close to the respective values of NSE and R2 = 0.77, of the calibration model. Additionally, the SRS approach showed an error reduction of up to 7 %, compared with that of the physical similarity method, indicating the potential of the proposed regionalization method in estimating catchment-wide sediment load in ungauged catchments. The findings indicate that the SRS method ideally determines the similarity of sediment loads between gauged and ungauged catchments. The SRS approach effectively tackled the main challenge of selecting catchment attributes in the conventional regionalization method and minimizing the uncertainty of sediment prediction in ungauged catchments. This novel SRS method is a promising method for estimating, not only sediment load, but also other hydrological variables and rainfall-driven phenomena in the ungauged catchments globally.

Comparison of two hydro-sediment models based on energy principle of water movement in saturated/unsaturated soils

To study the impact of complex climatic conditions on runoff and sediment, physics-based distributed hydrological models are frequently employed for representation and analysis. In this work, two physics-based hydro-sediment models, namely CASC2D-SED (CASCade 2 Dimensional SEDiment) and GSSHA (Gridded Surface Subsurface Hydrologic Analysis), are used to simulate runoff and sediment in a semi-arid watershed on the Loess Plateau. Examining the degree to which the two models provide comparable or dissimilar outcomes with regard to runoff and sediment casting is an aspect of this investigation that is of utmost importance. Both the models are derived from the principle of mass and energy balance, and arrived at very similar conclusions regarding the infiltration of precipitation and the transport of silt in surface runoff. For typical flood events that occurred between 1981 and 2017, a comparison of modeled data with measured runoff and sediment is carried out. Both models achieved satisfactory results when runoff and sediment simulations were performed over three separate time periods, with average NSE (Nash–Sutcliffe coefficient) values of runoff and sediment are 0.81 and 0.70 respectively. The GSSHA model performs better in terms of the maximum and total amount of runoff and sediment, with an 6.8%, 8.6%, and 6.7% increase, respectively, in the average Rq (relative error of peak flow) Rd (relative error of runoff depth), and Rs (relative error of peak sediment values). In contrast, the CASC2D-SED model simulates better in terms of the Tq (time difference of peak discharge), and Ts (time difference of peak sediment), with the mean time difference of peak flow and sediment values being 0.27 and 1.02 higher, respectively. This shows that the CASC2D-SED model can simulate the timing of runoff and sediment more accurate.

A two-step calibration framework for hydrological parameter regionalization based on streamflow and remote sensing evapotranspiration

• We proposed a two-step calibration based parameter regionalization method. • This method combines spatial proximity method and RS ET based parameter calibration method. • The proposed regionalization method achieves the overall best model performance. • The accuracy of RS ET is the main factor affecting the performance of the proposed method. Runoff prediction in ungauged catchments is an important and challenging research topic in the field of hydrology. However, traditional parameter regionalization methods may not be applicable in regions with sparsely distributed hydrological stations. Remote sensing (RS) evapotranspiration (ET) products provide valuable information for model parameter estimations. This study aimed to improve the accuracy of hydrological modeling in ungauged catchments by combining the traditional spatial proximity (SP) method with the RS ET-based parameter calibration method to form a two-step calibration-based regionalization method (SP-bcET method using bias-corrected RS ET data and SP-rawET method using raw RS ET data). The proposed regionalization scheme is demonstrated by using the variable infiltration capacity model in the Yangtze River Basin, China, and its performance was evaluated and compared with the SP method and two RS ET-based parameter calibration schemes (bcET method using bias-corrected RS ET data and rawET method using raw RS ET data). The results indicate that the bcET method performs better than the rawET method, the SP method outperforms the RS ET-based parameter calibration method, and the SP-bcET and SP-rawET methods achieve the best overall performance among the five methods. Taking the simulated streamflow at a daily scale in the calibration period as an example, the average Nash-Sutcliffe coefficient of efficiency for the SP, bcET, rawET, SP-bcET, and SP-rawET methods were 0.69, 0.61, 0.56, 0.71, and 0.71, respectively. Moreover, the SP-bcET and SP-rawET methods improved the simulation of the temporal dynamics and spatial variability of soil moisture anomalies. The accuracy of the RS ET data was one of the most dominant factors affecting the performance of the proposed parameter regionalization scheme. The SP-bcET method is expected to perform better than the SP method in warmer catchments or when gauged catchments are sparely distributed. The findings demonstrate the great potential of combining the traditional parameter regionalization and RS ET-based parameter calibration methods in hydrological simulations in ungauged catchments.

Copula-based probabilistic spectral algorithms for high-frequent streamflow estimation

The recent increase in population growth and urbanisation demands for real-time riverine water management to fulfil the daily-scale domestic and ecological water needs, necessitating for high-frequent streamflow estimation at any river section. Although there are several conventional methods available in the literature, using these methods to obtain streamflow information at finer spatiotemporal resolutions is not feasible. Moreover, streamflow estimation using single or multi-satellite remote sensing approaches is still in the experimental stage. As advancement in the existing approaches, this study advocates two novel models, namely CMOD and CFUS, which use the Frank copula-based single MODIS satellite data and copula-based multi-satellite MODIS-Landsat fusion data, respectively. These developed models fit a box-centre matrix to the pixel ratios of water (within the river at the gauging station) and land (in the riparian zone) in the near-infrared spectrum. Two other approaches using the stand-alone MODIS data (MOD model) and enhanced spatiotemporal fusion of MODIS-Landsat datasets (FUS model) are also developed to inter-compare with the CMOD and CFUS models for reproducing the daily time series of streamflow at three gauging stations on the Brahmani River in eastern India. The calibration and validation results reveal that the 30 m resolution CFUS model is the best approach for daily-scale streamflow estimation with sufficient accuracy with an average Nash-Sutcliffe Coefficient (NSC) of 0.92 followed by the CMOD (NSC = 0.80), FUS (NSC = 0.78), and MOD (NSC = 0.66) models. Hence, the CFUS model can be used as a potential tool for the next-generation hydrometry in semi-gauged river basins for riverine water resources assessment and inter-state water-sharing conflict resolution.

Catchment-scale hydrology simulations using inter-variable multi-parameter terrain descriptions

Global simulations of hydrology and hydraulics combine network-type routing with hydrological simulations on large Hydrological Response Units (HRUs). In order to efficiently simulate the hydrological response of HRUs, empirical equations are generally applied to averaged physical parameters or empirical constants. The usage of physically-based equations on a spatial description of the HRU could provide benefits in accuracy and scenario assessment. In this research we develop a multi-parameter inter-variable terrain description to simulate HRU hydrology and flow. Water is distributed over these classifications and used for application of physically-based equations. A mass-balanced scheme is presented that uses parameter inter-variability matrices to update the distributions of water. Furthermore, the method is used to simulate processes using physically-based equations that result in fluxes from and to other classes within a distribution, resulting in a semi-spatial process description within an HRU. Finally, physically-based equations are thus applied to a dynamic semi-spatial HRU description using multiple inter-variable physical parameters. A variety of study cases on hypothetical and real events are performed to highlight the behavior and sensitivity of the model, as well as comparison to existing methods. Un-calibrated comparison with full spatial flow simulations show reasonable agreement with a Nash-Sutcliffe coefficient of 0.47. Comparison to simulations of real events on catchments in Northern China and Central Spain show good comparability with an average Nash-Sutcliffe coefficient of 0.56. The performance of the model indicates good applicability when considering the efficiency of the method. Simulation time is in the ranges from fractions of seconds on consumer hardware without dependency of the area of the catchment. Additionally, the model uses physical parameters as input and physically-based descriptions of processes. This results in interactions between infiltration and flow that alter flow dynamics and hydrograph shapes in similar manners to full spatial hydrology and flow models. Finally, the method provides the possibility of linked additional processes such as groundwater and erosion, and could be applied on a global scale.

Assessment of the hydrological response of an urban watershed to rainfall-runoff events in different land use scenarios - Belo Horizonte, MG, Brazil.

Intense urbanization results in greater soil sealing and a consequent increase in surface runoff. When high soil sealing rates and high slopes are encountered in small catchments, high volume and short duration precipitation events increase the probability of flash floods. This study evaluates the hydrological response of an urban catchment with regard to rainfall-runoff events in the following scenarios: current land use, without a detention basin which has been part of the system since the 1970s, with the maximum soil sealing allowed by legislation, and with green infrastructure implantation in 100%, 50% and 10% of the suitable area. Hydrological modelling was performed using the Storm Water Management Model (SWMM). Six level gauges installed along the length of the stream provided the data used in model calibration and validation. The model calibration process provided adequate results: the average Nash-Sutcliffe coefficient was 0.72, the mean error of peak flow was 11% and the mean error of the runoff volume was 12%. Rainfall based on 2-, 10-, 50- and 100-year events were simulated for each scenario, as well as two observed events. In the scenario without the detention basin, an average elevation of 10% in the peak flow was observed in the catchment outlet. In the scenario with maximum soil sealing, the catchment outlet peak flow increased by 30% on average. On the other hand, in the scenario with green infrastructure implementation in 100%, 50% and 10% of the available areas, the outlet peak flow was reduced by 60%, 30% and 5%, respectively. Results indicated the efficiency of the detention basin to reduce flooding, the importance of green area preservation to reduce peak flows, as well as the catchment potential of green infrastructure implementation and the hydrological benefits that they can provide, increasing infiltration and reducing runoff volume and peak flow.

Improvement and application research of the SRM in alpine regions.

The simulation of snowmelt runoff in alpine mountainous areas is of great significance not only for the risk assessment of snowmelt flood in spring and summer, but also for the development and management of water resources in the basin. An improved snowmelt runoff model (SRM) is constructed based on the analysis of change characteristics of climate, runoff, and snow and ice cover in the middle and upper reaches of the Taxkorgan River in Xinjiang Province, China. Because of the large evaporation in the study basin, the evaporation loss is added to the model. The SRM and the improved SRM are calibrated and verified by using data such as temperature, precipitation, water vapor pressure, and snow-covered area (SCA) ratio in the study basin from 2002 to 2012. The results show that, compared with the SRM, the average Nash-Sutcliffe coefficient (NSE) of annual runoff simulation increases from 0.80 to 0.86 in the calibration and increases from 0.74 to 0.83 in the validation through the improved model, and the average runoff error reduces from - 12.8 to 1.32% in the calibration and reduces from - 20.0 to - 11.51% in the validation. After adding the measured flow rate for real-time correction, the average NSE of annual runoff simulation increases from 0.91 to 0.93 and the average annual runoff error reduces from - 7.76 to - 3.91% in the calibration. The average NSE increases from 0.85 to 0.89 and the average runoff error reduces from - 12.35 to - 2.76% in the validation. It indicates that the SRM structure with increased evaporation loss is more in line with the actual situation. The short-term simulation effect of the model is greatly improved by adding the measured flow rate for real-time correction. At the same time, the improved SRM and the hypothetical climate change scenario are used to analyze the impact analysis of the snowmelt runoff simulation in the partial wet year. The results show that in the case of rising temperature, the ice and snow ablation period is prolonged, and the annual runoff also changes significantly in time distribution. It is of guiding significance for the influence of climate change on the runoff of recharged rivers with ice-snow meltwater in the other alpine regions.

AltEx: An open source web application and toolkit for accessing and exploring altimetry datasets

Understanding the spatial and temporal distribution of hydrologic variables, such as streamflow, is important for sustainable development, especially with global population growth and climate variations. Typical monitoring of streamflow is conducted using in situ gauging stations; however, stations are costly to setup and maintain, leading to data gaps in regions that cannot afford gauges. Satellite data, including altimetry data, are used to supplement in situ observations and in some cases supply information where they are lacking. This study introduces an open-source web application to access and explore altimetry datasets for use in water level monitoring, named the Altimetry Explorer (AltEx). This web application, along with its relevant REST API, facilitates access to altimetry data for analysis, visualization, and impact. The data provided through AltEx is validated using thirteen gauges in the Amazon Basin from 2008 to 2018 with an average Nash-Sutcliffe Coefficient and root mean square error of 0.78 and 1.2 m, respectively. Access to global water level data should be particularly helpful for water resource practitioners and researchers seeking to understand the long-term trends and dynamics of global water level and availability. This work provides an initial framework for a more robust and comprehensive platform to access future altimetry datasets and support research related to global water resources.

Assessment of the Performance of Satellite-Based Precipitation Products for Flood Events across Diverse Spatial Scales Using GSSHA Modeling System

Accurate precipitation measurements for high magnitude rainfall events are of great importance in hydrometeorology and climatology research. The focus of the study is to assess the performance of satellite-based precipitation products against a gauge adjusted Next-Generation Radar (NEXRAD) Stage IV product during high magnitude rainfall events. The assessment was categorized across three spatial scales using watershed ranging from ~200–10,000 km2. The propagation of the errors from rainfall estimates to runoff estimates was analyzed by forcing a hydrologic-model with the satellite-based precipitation products for nine storm events from 2004 to 2015. The National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) Morphing Technique (CMORPH) products showed high correlation to the NEXRAD estimates in all spatial domains, and had an average Nash-Sutcliffe coefficient of 0.81. The Global Precipitation Measurement (GPM) Early product was inconsistent with a very high variance of Nash-Sutcliffe coefficient in all spatial domains (from −0.46 to 0.38), however, the variance decreased as the watershed size increased. Surprisingly, Tropical Rainfall Measuring Mission (TRMM) also showed a very high variance in all the performance statics. In contrast, the un-corrected product of the TRMM showed a relatively better performance. The errors of the precipitation estimates were amplified in the simulated hydrographs. Even though the products provide evenly distributed near-global spatiotemporal estimates, they significantly underestimate strong storm events in all spatial scales.

Multisource hydrologic modeling uncertainty analysis using the IBUNE framework in a humid catchment

Hydrologic cycle is a complex system associated with both certain and uncertain constituents. The propagation of confidence bounds from different uncertainty sources to model output is of great significance for hydrologic modeling. In this paper, we applied the integrated bayesian uncertainty estimator to quantify the effects of parameter, input and model structure uncertainty on hydrologic modeling progressively. Two hydrologic models (Xinanjiang model and TOPMODEL) were applied to a humid catchment under three scenarios. Case I: the shuffled complex evolution metropolis (SCEM-UA) algorithm was conducted to determine the posterior parameter distribution of hydrologic models and analyze the corresponding forecast uncertainty. Case II: input uncertainty was also considered by assuming rain depth bias follows a normal distribution, and integrated with SCEM-UA. Case III: Simulations from two models were combined by the Bayesian model averaging to fully quantify multisource uncertainty effects. Results suggested that, from Case I to II, the containing ratio (percentage of observed streamflow enveloped by 95% confidence interval) obviously increased by an average magnitude of 10% for the study period 2000–2006. Besides, it also found that the width of 95% confidence interval became wider and narrower for Xinanjiang model and TOPMODEL, respectively, from Case I to II. This may indicate that the uncertainty of TOPMODEL results was more remarkable than Xinanjiang model in Case I. By combining results from two models, model structure uncertainty was also considered in Case III. The accuracy of uncertainty bounds further improved with the containing ratio of 95% confidence interval >95%. In addition, the optimized deterministic results from the uncertainty analysis showed that the average Nash–Sutcliffe coefficient increased continually from Case I to II and III (0.82, 0.84 and 0.90, respectively) for the study period. The analysis demonstrated the improvement of modeling accuracy when extra uncertainty sources were also quantified, and this finding also proved the applicability of IBUNE framework in hydrologic modeling.

Calibrating a flow model in an irrigation network: Case study in Alicante, Spain

The usefulness of models depends on their validation in a calibration process, ensuring that simulated flows and pressure values in any line are really occurring and, therefore, becoming a powerful decision tool for many aspects in the network management (i.e., selection of hydraulic machines in pumped systems, reduction of the installed power in operation, analysis of theoretical energy recovery). A new proposed method to assign consumptions patterns and to determine flows over time in irrigation networks is calibrated in the present research. As novelty, the present paper proposes a robust calibration strategy for flow assignment in lines, based on some key performance indicators (KPIF) coming from traditional hydrological models: Nash-Sutcliffe coefficient (non-dimensional index), root relative square error (error index) and percent bias (tendency index). The proposed strategy for calibration was applied to a real case in Alicante (Spain), with a goodness of fit considered as “very good” in many indicators. KPIF parameters observed present a satisfactory goodness of fit of the series, considering their repeatability. Average Nash-Sutcliffe coefficient value oscillated between 0.30 and 0.63, average percent bias values were below 10% in all the range, and average root relative square error values varied between 0.65 and 0.80.

Water balance and flow rate discharge on a receiving water body: Application to the B-XII Irrigation District in Spain

Summary The quantification of the main water balance components becomes necessary to diminish the pollutants load from drainage, and its harmful effect on the environment, aggravated within a context of increasing water scarcity. As a first approach to the hydrological study of the 15,000 ha B-XII Irrigation District in Spain, a conceptual lumped model entitled WATEN has been developed, aiming to calculate the monthly flow rate discharge to the Guadalquivir River over the period 2002–2012. The model requires as inputs, irrigation, precipitation and potential crop evapotranspiration. Main model parameters are the total and readily available moisture in the soil, the effective rainfall and the irrigation efficiency. Energy consumption for drainage discharge was used for calibration. Both classical optimization and a robust approach based on Monte Carlo were performed. In order to diminish computational requirements, Monte Carlo was not haphazardly applied, but conducted on a similar manner to genetic algorithms, entitled Parameters Estimation on Driven Trials (PEDT). The model attained an average Nash–Sutcliffe coefficient e 2 ≅ 0.90 between observed and estimated drainage discharge. It was detected a significant crop evapotranspiration reduction compared to potential values. The volume of water discharged to the river might be sufficient for leaching irrigation water salts.

Application of spatially varying storage capacity model for runoff parameterization in semi-arid catchment

This paper introduces the method of designation of water storage capacity for each grid cell within a catchment, which considers topography, vegetation and soil synthetically. For the purpose of hydrological process simulation in semi-arid regions, a spatially varying storage capacity (VSC) model was developed based on the spatial distribution of water storage capacity and the vertical hybrid runoff mechanism. To verify the applicability of the VSC model, both the VSC model and a hybrid runoff model were used to simulate daily runoff processes in the catchment upstream of the Dianzi hydrological station from 1973 to 1979. The results showed that the annual average Nash-Sutcliffe coefficient was 0.80 for the VSC model, and only 0.67 for the hybrid runoff model. The higher annual average Nash-Sutcliffe coefficient of the VSC model means that this hydrological model can better simulate daily runoff processes in semi-arid regions. Furthermore, as a distributed hydrological model, the VSC model can be applied in regional water resource management.

Accuracy of Hourly Water Temperatures in Rivers Calculated from Air Temperatures

Water temperature is a critical variable for water quality control and management. The primary objective of this paper was to develop and compare simple methods to estimate hourly water temperatures in rivers. The wave function (WF) model, originally used to calculate hourly air temperature, was modified and applied to eight Alabama rivers. The results show significant improvement by using the modified WF model instead of direct linear and non-linear (polynomial and logistic) regression models with time lags (4–5 h). The average Nash–Sutcliffe coefficient (NS) used to evaluate model accuracy for the eight rivers improved from 0.71 for the linear model to 0.89 for the modified WF model with NS for most rivers exceeding 0.90. A lumped modified WF model was also developed by combining water temperature data for all eight rivers and can be applied for rivers in Alabama when no observed water temperatures are available to develop a site-specific WF model. The procedure to develop a modified WF model can be applied to other regions.

Quantitative water resources assessment of Qinghai Lake basin using Snowmelt Runoff Model (SRM)

A Snowmelt Runoff Model (SRM) is used to simulate streamflow from snowmelt in the Buha watershed within the Qinghai Lake basin for the hydrologic years 2003-2009 (from September to August). Two different precipitation products are used as inputs to examine the model and their performances: station measured precipitation and gridded APHRODITE-precipitation, with two different snow cover products, namely, flexible multiday combined MODIS Terra-Aqua (MODISMC) maps, and the standard MODIS/Terra 8-day composite (MOD10A2) product. Results show that the MODISMC product with a higher temporal resolution of 2.2 days obtains slightly small bias in runoff volumes than MOD10A2 does for basin- and zone-wide simulations. The average Nash-Sutcliffe coefficient of determination (R-2) is 0.73 (0.70-0.75) for basin- and zone-wide simulations. The simulated runoff with APHRODITE-precipitation is well correlated with measured runoffs (R-2 = 0.76). The multivariate regression analysis indicates that lake level changes are significantly associated (95% level) with the following single variables: measured runoff discharge from the Buha watershed (R-m), simulated runoff (R-s), simulated runoff fraction from rainfall (R-s-rainfall), the snow cover area at zone B of the Buha watershed, or with a combination of multivariates: R-s-rainfall + P-L (precipitation over the lake) and R-m + P-L + Evaporation. These results suggest that precipitation plays a dominant role in lake level variations. Snowmelt contributed runoff shows no significant contribution to lake level variation (13% of the total runoff). This study suggests that APHRODITE-precipitation data can be used for snowmelt simulations and water resource management on the Tibetan Plateau where precipitation gauges are limited, and that the SRM is an effective tool for estimating snowmelt discharge and water resource management in high mountain regions. (C) 2014 Elsevier B.V. All rights reserved.

Development and application of a storage–release based distributed hydrologic model using GIS

► A tightly coupled grid based GIS model is developed using a simple storage release approach. ► The proposed model is tested on three watersheds in Indiana. ► The model results are compared with outputs of HEC-HMS and time variant SDDH. ► The results outperform HEC-HMS and SDDH in terms of hydrograph shape and magnitude. The objective of this paper is to present the development and application of a grid based hydrologic model using an object oriented framework within geographic information systems (GIS). The proposed model is called Storage Released based Distributed Hydrologic Model (STORE DHM). Development of STORE DHM is a part of a broader objective to develop a modular hydrologic modeling system within GIS. In this paper, the conceptual framework of STORE DHM including its application to three watersheds in Indiana is presented. The model is tested in the study watersheds by first calibrating it against observed flow hydrograph for a single event, and then verifying it for three additional storm events using both point (gauged) and continuous (radar) rainfall data. Results show that SOTRE DHM is able to predict runoff hydrographs for different types of events in terms of storm duration, peak flow magnitude and time-to-peak. The average Nash–Sutcliffe coefficient is greater than 0.8 for all runoff hydrograph during validation, and the errors in peak flow and time-to-peak predictions are within 5% and 15%, respectively. In addition, STORE DHM output is compared with outputs from two hydrologic models including Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) and time variant Spatially Distributed Direct Hydrograph travel time method (SDDH). Results from these comparisons show that STORE DHM outperforms both HEC-HMS and SDDH in terms of overall hydrograph shape and flow magnitude. Although the results from application of STORE DHM are encouraging, the work presented in this paper is just an initial step towards developing a comprehensive tightly coupled GIS modeling framework for hydrology.

A Comparison of Low Flow Estimates in Ungauged Catchments Using Regional Regression and the HBV-Model

Estimates of a low flow index in ungauged catchments calculated by a regional regression model and a regional hydrological model were compared for a study region southwestern Norway. The regression method was based on a relationship between the low flow index and an optimal set of catchment descriptors, established using stepwise linear regression for homogeneous subregions. Subregions were distinguished according to the season in which the lowest flow occurs, summer (May to October) or winter (November to April), and the average July temperature was found to be the best index for determining the low flow season for ungauged catchments. Catchment descriptors characterising the presence of lakes and bogs, in addition to catchment length and indicators of climatic conditions, were found to be important in the regression models. A cross-validation procedure was used to evaluate the predictive performance of the model in ungauged catchments. A gridded version of HBV, a daily rainfall-runoff model was also applied as a regional hydrological model and was calibrated using the average Nash–Sutcliffe coefficient for log-transformed streamflow as the calibration criterion. A comparison of the two methods in 21 independent catchments indicates that the regression method generally gives better estimates of Qc in ungauged catchments than does the HBV model, particularly in those catchments with the lowest Qc values.