Cross-spectral analysis of rainfall and runoff for Raritan and Mullica river basins in New Jersey

Cross-correlation and cross-spectral analysis were employed in the analysis of rainfall and runoff in two river basins: the Raritan and Mullica River basins in New Jersey. Cross-covariance and coherence were studied in the correlograms for the following correlation cases: (a) rainfall-runoff for each one basin separately; (b) rainfall-rainfall analysis for two main meteorological stations in each one of the basins; (c) runoff-runoff for two main gaging stations in each one of the basins. From the estimates of the coherence at various frequencies the cross-spectral analysis shows a highly nonlinear relationship between rainfall and runoff in Raritan and Mullica River basins. A poor coherence observed at the annual cycles for each basin makes it difficult to predict the annual oscillations of runoff from those of rain-fall by a linear regression model. The high coherence between rainfall (or runoff) at the first station and rainfall (or runoff) at the second station within the same basin at almost all frequencies establishes an accurate prediction on a linear basis.     

Scaling and physiographic controls on streamflow behaviour on the Precambrian Shield, south-central Ontario

Relationships explaining streamflow behaviour in terms of drainage basin physiography greatly assist efforts to extrapolate streamflow metrics from gauged to ungauged basins in the same landscape. The Dorset Environmental Science Centre (DESC) has monitored streamflow from 22 small basins (3.4–190.5 ha) on the Precambrian Shield in south-central Ontario, in some cases since 1976. The basins exhibit regional coherence in their interannual response to precipitation; however, there is often a poor correlation between streamflow metrics from basins separated by as little as 1 km. This study assesses whether inter-basin variations in such metrics can be explained in terms of basin scale and physiography. Several characteristics (annual maximum, minimum and average flow) exhibited simple scaling with basin area, while magnitude, range and timing of annual maximum daily runoff showed scaling behaviour consistent with the Representative Elementary Area (REA) concept. This REA behaviour is partly attributed to convergence of fractional coverage of the two dominant and hydrologically-contrasting land cover types in the DESC region with increasing basin size. Three Principal Components (PCs) explained 82.4% of the variation among basin physiographic properties, and several runoff metrics (magnitude and timing of annual minimum daily runoff, mean number of days per year with 0 streamflow) exhibited significant relationships with one or more PC. Significant relationships were obtained between basin quickflow (QF) production and the PCs on a seasonal and annual basis, almost all of which were superior to simple area-based relationships. Basin physiography influenced QF generation via its control on slope runoff, water storage and hydrologic connectivity; however, this role was minimized during Spring when QF production in response to large rain-on-snow events was relatively uniform across the DESC basins. The PC-based relationships and inter-seasonal changes in their form were consistent with previous research conducted at point, slope and basin scales in the DESC region, and perceptions of key hydrological processes in these small basins may not have been as readily obtained from scaling studies using streamflow from larger basins. This process understanding provides insights into scaling behaviour beyond those derived from simple scaling and REA analyses. The physiography of the study area is representative of large portions of the Precambrian Shield, such that basin streamflow behaviour could potentially be extended across much of south-central Ontario. This would assist predictions of streamflow conditions at ungauged locations, development and testing of hydrological models for this landscape, and interpretation of inter-basin and intra-annual differences in hydrochemical behaviour on the southern Precambrian Shield.     

Dynamic storage: a potential metric of inter‐basin differences in storage properties

The potential for dynamic storage to serve as a metric of basin behaviour was assessed using data from five drainage basins with headwaters on the thick sand and gravel deposits of the Oak Ridges Moraine in southern Ontario, Canada. Dynamic storage was directly correlated with the ratio of variability of δ²H in streamflow relative to that in precipitation. This ratio has previously been shown to be inversely related to basin mean transit time (MTT), suggesting an inverse relationship between dynamic storage and MTT for the study basins. Dynamic storage was also directly correlated with interannual variability in stream runoff, baseflow and baseflow:runoff ratio, implying that basins with smaller dynamic storage have less interannual variability in their streamflow regimes. These preliminary results suggest that dynamic storage may serve as a readily derived and useful metric of basin behaviour for inter‐basin comparisons. Copyright © 2016 John Wiley & Sons, Ltd.     

Regionalizing mean annual flow and daily flow variability for basin‐scale sediment and nutrient modelling

River discharges vary strongly through time and space, and quantifying this variability is fundamental to understanding and modelling river processes. The river basin is increasingly being used as the unit for natural resource planning and management; to facilitate this, basin‐scale models of material supply and transport are being developed. For many basin‐scale planning activities, detailed rainfall‐runoff modelling is neither necessary nor tractable, and models that capture spatial patterns of material supply and transport averaged over decades are sufficient. Nevertheless, the data to describe the spatial variability of river discharge across large basins for use in such models are often limited, and hence models to predict river discharge at the basin scale are required. We describe models for predicting mean annual flow and a non‐dimensional measure of daily flow variability for every river reach within a drainage network. The models use sparse river gauging data, modelled grid surfaces of mean annual rainfall and mean annual potential evapotranspiration, and a network accumulation algorithm. We demonstrate the parameterization and application of the models using data for the Murrumbidgee basin, in southeast Australia, and describe the use of these predictions in modelling sediment transport through the river network. The regionalizations described contain less uncertainty, and are more sensitive to observed spatial variations in runoff, than regionalizations based on catchment area and rainfall alone. Copyright © 2006 John Wiley & Sons, Ltd.     

Application and evaluation of a snowmelt runoff model in the Tamor River basin,Eastern Himalaya using a Markov Chain Monte Carlo (MCMC) data assimilation approach

Previous studies have drawn attention to substantial hydrological changes taking place in mountainous watersheds where hydrology is dominated by cryospheric processes. Modelling is an important tool for understanding these changes but is particularly challenging in mountainous terrain owing to scarcity of ground observations and uncertainty of model parameters across space and time. This study utilizes a Markov Chain Monte Carlo data assimilation approach to examine and evaluate the performance of a conceptual, degree‐day snowmelt runoff model applied in the Tamor River basin in the eastern Nepalese Himalaya. The snowmelt runoff model is calibrated using daily streamflow from 2002 to 2006 with fairly high accuracy (average Nash–Sutcliffe metric ~0.84, annual volume bias < 3%). The Markov Chain Monte Carlo approach constrains the parameters to which the model is most sensitive (e.g. lapse rate and recession coefficient) and maximizes model fit and performance. Model simulated streamflow using an interpolated precipitation data set decreases the fractional contribution from rainfall compared with simulations using observed station precipitation. The average snowmelt contribution to total runoff in the Tamor River basin for the 2002–2006 period is estimated to be 29.7 ± 2.9% (which includes 4.2 ± 0.9% from snowfall that promptly melts), whereas 70.3 ± 2.6% is attributed to contributions from rainfall. On average, the elevation zone in the 4000–5500 m range contributes the most to basin runoff, averaging 56.9 ± 3.6% of all snowmelt input and 28.9 ± 1.1% of all rainfall input to runoff. Model simulated streamflow using an interpolated precipitation data set decreases the fractional contribution from rainfall versus snowmelt compared with simulations using observed station precipitation. Model experiments indicate that the hydrograph itself does not constrain estimates of snowmelt versus rainfall contributions to total outflow but that this derives from the degree‐day melting model. Lastly, we demonstrate that the data assimilation approach is useful for quantifying and reducing uncertainty related to model parameters and thus provides uncertainty bounds on snowmelt and rainfall contributions in such mountainous watersheds. Copyright © 2013 John Wiley & Sons, Ltd.     

UTILIZATION OF THE RESULTS FROM REPRESENTATIVE AND EXPERIMENTAL BASINS WITH A VIEW TO THE MANAGEMENT OF WATER RESOURCES / Utilisation des résultats obtenus sur les bassins représentatifs et expérimentaux en vue de l'aménagement des ressources en eau

Abstract

The management of water resources requires knowledge of the spatial and temporal distribution of surface and groundwater resources, and an assessment of the influence of man on the hydrological regime.

For small water courses regional estimates can be made from representative basins which offer guidelines (1) for the computation of mean annual flow and in some cases for the determination of the statistical distribution of the annual flow; (2) for the computation of the 10-year flood maximum discharge and volume.

An example concerning the tropical African Sahel is given. From a general study of the daily precipitation, a simple rainfall/runoff model used on a daily basis and calibrated on data from representative basins, and also the direct comparison of results from 55 representative basins, statistical distribution curves were established for annual runoff based on mean annual precipitation and the geomorphological characteristics of the basins.

Another example concerning tropical Africa west of Congo presents a methodology for the computation of the 10-year flood (maximum discharge and volume).

The systematic study of 60 representative basins makes it possible to plot the runoff coefficient R/P as a function of basin climate, mean slope and soil permeability. Other curves are used to determine the time of rise and the base time of the hydrograph as a function of the basin area and the mean slope.

The experimental basin is a good tool for the assessment of the influence of man on hydrological parameters. An example shows the influence of land use on the regression between annual precipitation and annual runoff.     

Analytical model for a geomorphological instantaneous unit hydrograph

The rainfall‐runoff modelling of a river basin can be divided into two processes: the production function and the transfer function. The production function determines the proportion of gross rainfall actually involved in the runoff. The transfer function spreads the net rainfall over time and space in the river basin. Such a transfer function can be modelled using the approach of the geomorphological instantaneous unit hydrograph (GIUH). The effectiveness of geomorphological models is actually revealed in rainfall‐runoff modelling, where hydrologic data are desperately lacking, just as in ungauged basins. These models make it possible to forecast the hydrograph shape and runoff variation versus time at the basin outlet. This article is an introduction to a new GIUH model that proves to be simple and analytical. Its geomorphological parameters are easily available on a map or from a digital elevation model. This model is based on general hypotheses on symmetry that provide it with multiscale versatile characteristics. After having validated the model in river basins of very different nature and size, we present an application of this model for rainfall‐runoff modelling. Since parameters are determined relying on real geomorphological data, no calibration is necessary, and it is then possible to carry out rainfall‐runoff simulations in ungauged river basins. Copyright © 2006 John Wiley & Sons, Ltd.     

Addressing uncertainty in reflectivity‐rainfall relations in mountain watersheds during summer convection

The use of precipitation estimates from weather radar reflectivity has become widespread in hydrologic predictions. However, uncertainty remains in the use of the nonlinear reflectivity–rainfall (Z‐R) relation, in particular for mountainous regions where ground validation stations are often lacking, land surface data sets are inaccurate and the spatial variability in many features is high. In this study, we assess the propagation of rainfall errors introduced by different Z‐R relations on distributed hydrologic model performance for four mountain basins in the Colorado Front Range. To do so, we compare spatially integrated and distributed rainfall and runoff metrics at seasonal and event time scales during the warm season when convective storms dominate. Results reveal that the basin simulations are quite sensitive to the uncertainties introduced by the Z‐R relation in terms of streamflow, runoff mechanisms and the water balance components. The propagation of rainfall errors into basin responses follows power law relationships that link streamflow uncertainty to the precipitation errors and streamflow magnitude. Overall, different Z‐R relations preserve the spatial distribution of rainfall relative to a reference case, but not the precipitation magnitude, thus leading to large changes in streamflow amounts and runoff spatial patterns at seasonal and event scales. Furthermore, streamflow errors from the Z‐R relation follow a typical pattern that varies with catchment scale where higher uncertainties exist for intermediate‐sized basins. The relatively high error values introduced by two operational Z‐R relations (WSR‐57 and NEXRAD) in terms of the streamflow response indicate that site‐specific Z‐R relations are desirable in the complex terrain region, particularly in light of other uncertainties in the modelling process, such as model parameter values and initial conditions. Copyright © 2012 John Wiley & Sons, Ltd.     

Annual runoff assessment in arid and semiarid Mediterranean watersheds under the Budyko's framework

The solution of many practical water problems is strictly connected to the availability of reliable and widespread information about runoff. The estimation of mean annual runoff and its interannual variability for any basin over a wide region, even if ungauged, would be fundamental for both water resources assessment and planning and for water quality analysis. Starting from these premises, the main aim of this work is to show a new approach, based on the Budyko's framework, for mapping the mean annual surface runoff and deriving the probability distribution of the annual runoff in arid and semiarid watersheds. As a case study, the entire island of Sicily, Italy, is here proposed. First, time series data of annual rainfall, runoff, and reconstructed series of potential evapotranspiration have been combined within the Budyko's curve framework to obtain regional rules for rainfall partitioning between evapotranspiration and runoff. Then this knowledge has been used to infer long‐term annual runoff at the point scale by means of interpolated rainfall and potential evapotranspiration. The long‐term annual runoff raster layer has been obtained at each pixel of the drainage network, averaging the upstream runoff using advanced spatial analysis techniques within a GIS environment. Furthermore, 2 alternative methods are here proposed to derive the distribution of annual runoff, under the assumption of negligible interannual variations of basin water storage. The first method uses Monte Carlo simulations, combining rainfall and potential evapotranspiration randomly extracted from independent distributions. The second method is based on a simplification of the Budyko's curve and analytically provides the annual runoff distribution as the derived distribution of annual rainfall and potential evapotranspiration. Results are very encouraging: long‐term annual runoff and its distribution have been derived and compared with historical records at several gauged stations, obtaining satisfactory matching.     

Comparative study of neural network,fuzzy logic and linear transfer function techniques in daily rainfall‐runoff modelling under different input domains

This paper compares artificial neural network (ANN), fuzzy logic (FL) and linear transfer function (LTF)‐based approaches for daily rainfall‐runoff modelling. This study also investigates the potential of Takagi‐Sugeno (TS) fuzzy model and the impact of antecedent soil moisture conditions in the performance of the daily rainfall‐runoff models. Eleven different input vectors under four classes, i.e. (i) rainfall, (ii) rainfall and antecedent moisture content, (iii) rainfall and runoff and (iv) rainfall, runoff and antecedent moisture content are considered for examining the effects of input data vector on rainfall‐runoff modelling. Using the rainfall‐runoff data of the upper Narmada basin, Central India, a suitable modelling technique with appropriate model input structure is suggested on the basis of various model performance indices. The results show that the fuzzy modelling approach is uniformly outperforming the LTF and also always superior to the ANN‐based models. Copyright © 2010 John Wiley & Sons, Ltd.     

Dissecting the effect of rainfall variability on the statistical structure of peak flows

This study examines the role of rainfall variability on the spatial scaling structure of peak flows using the Whitewater River basin in Kansas as an illustration. Specifically, we investigate the effect of rainfall on the scatter, the scale break and the power law (peak flows vs. upstream areas) regression exponent. We illustrate why considering individual hydrographs at the outlet of a basin can lead to misleading interpretations of the effects of rainfall variability. We begin with the simple scenario of a basin receiving spatially uniform rainfall of varying intensities and durations and subsequently investigate the role of storm advection velocity, storm variability characterized by variance, spatial correlation and intermittency. Finally, we use a realistic space–time rainfall field obtained from a popular rainfall model that combines the aforementioned features. For each of these scenarios, we employ a recent formulation of flow velocity for a network of channels, assume idealized conditions of runoff generation and flow dynamics and calculate peak flow scaling exponents, which are then compared to the scaling exponent of the width function maxima. Our results show that the peak flow scaling exponent is always larger than the width function scaling exponent. The simulation scenarios are used to identify the smaller scale basins, whose response is dominated by the rainfall variability and the larger scale basins, which are driven by rainfall volume, river network aggregation and flow dynamics. The rainfall variability has a greater impact on peak flows at smaller scales. The effect of rainfall variability is reduced for larger scale basins as the river network aggregates and smoothes out the storm variability. The results obtained from simple scenarios are used to make rigorous interpretations of the peak flow scaling structure that is obtained from rainfall generated with the space–time rainfall model and realistic rainfall fields derived from NEXRAD radar data.     

Application of a land surface model for simulating rainfall streamflow hydrograph: 2. Comparison with hydrological models

Rainfall runoff hydrographs for 12 river basins ∼10³ km² in area, simulated using land surface model SWAP, are compared with analogous hydrographs obtained using hydrological models that took part in the International Model Parameter Estimation Experiment project and demonstrated the best results. All models were calibrated against data on daily river runoff from each basin over a 20-year period (1960–1979). Optimized model parameters were used to simulate runoff hydrographs for the following 19 years (1980–1998). The comparison of the modeled hydrographs for 12 basins in different calculational periods demonstrated that the SWAP model can simulate river runoff with an accuracy comparable with that of hydrological models.     

A note on the representation of nonlinearity in runoff process by the general hydrologic system model

The parameters of a nonlinear model, a case of General Hydrologic System (GHS) model, proposed by Kulandaiswamy are examined by using 116 storms in 11 basins. A study of the relationship of storage at peak and peakflow indicates: (i) the existence of nonlinearity and (ii) the ranges of nonlinearity and linearity in the runoff process in a basin system. The derivation of nonlinear term in the model from the storage-discharge relationship, is explained. Graphical multiple correlations and mathematical relationships between model parameters and the parameters representing rainfall characteristics are also obta ned to facilitate the flood hydrograph estimation due to a future storm in a basin. The storage loop and the surface runoff hydrograph are reproduced using the nonlinear storage equation and the nonlinear surface runoff model respectively. For this purpose, the derivation of model parameters from the multiple correlations and the mathematical relationships between the model parameters and the parameters representing rainfall characteristics, is explained and focussed in this note.     

Modelling streamflow trends for a watershed with limited data: case of the Litani basin,Lebanon

Abstract

Streamflow variability in the Upper and Lower Litani basin, Lebanon was modelled as there is a lack of long-term measured runoff data. To simulate runoff and streamflow, daily rainfall was derived using a stochastic rainfall generation model and monthly rainfall data. Two distinct synthetic rainfall models were developed based on a two-part probabilistic distribution approach. The rainfall occurrence was described by a Markov chain process, while the rainfall distribution on wet days was represented by two different distributions (i.e. gamma and mixed exponential distributions). Both distributions yielded similar results. The rainfall data were then processed using water balance and routing models to generate daily and monthly streamflow. Compared with measured data, the model results were generally reasonable (mean errors ranging from 0.1 to 0.8?m³/s at select locations). Finally, the simulated monthly streamflow data were used to investigate discharge trends in the Litani basin during the 20th century using the Mann-Kendall and Sen slope nonparametric trend detection methods. A significant drying trend of the basin was detected, reaching a streamflow reduction of 0.8 and 0.7 m³/s per decade in January for the Upper and Lower basin, respectively.

Editor D. Koutsoyiannis; Associate editor Sheng Yue

Citation Ramadan, H.H., Beighley, R.E., and Ramamurthy, A.S., 2012. Modelling streamflow trends for a watershed with limited data: case of the Litani basin, Lebanon. Hydrological Sciences Journal, 57 (8), 1516–1529.     

An investigation of lag times for rainfall–runoff–sediment yield events in small river basins / Une analyse des temps de réponse d'événements pluie–débit–transport solide au sein de petits bassins versants

Abstract

River basin lag time (LAG), defined as the elapsed time between the occurrence of the centroids of the effective rainfall intensity pattern and the storm runoff hydrograph, is an important factor in determining the time to peak and the peak value of the instantaneous unit hydrograph, IUH. In the procedure of predicting a sedimentgraph (suspended sediment load as a function of time), the equivalent parameter is the lag time for the sedimentgraph (LAG_s ), which is defined as the elapsed time between the occurrence of the centroids of sediment production during a storm event and the observed sedimentgraph at the gauging station. Results of analyses of rainfall, runoff and suspended sediment concentration event data collected from five small Carpathian basins in Poland and from a 2.31-ha agricultural basin, in central Illinois, USA have shown that LAG_s was, in the majority of cases, smaller than LAG, and that a significant linear relationship exists between LAG_s and LAG.     

Conceptually-based shot noise modeling of streamflows at short time interval

A conceptual-stochastic approach to short time runoff data modelling is proposed, according to the aim of reproducing the hydrological aspects of the streamflow process and of preserving as much as possible the dynamics of the process itself. This latter task implies preservation of streamflow characteristics at higher scales of aggregation and, within a conceptual framework, involves compatibility with models proposed for the runoff process at those scales. At a daily time scale the watershed response to the effective rainfall is considered as deriving from the response of three linear reservoirs, respectively representing contributions to streamflows of large deep aquifers, with over-year response lag, of aquifers which run dry by the end of the dry season and of subsurface runoff. The surface runoff component is regarded as an uncorrelated point process. Considering the occurrences of effective rainfall events as generated by an independent Poisson process, the output of the linear system represents a conceptually-based multiple shot noise process. Model identification and parameter estimation are supported by information related to the aggregated runoff process, in agreement to the conceptual framework proposed, and this allows parameter parsimony, efficient estimation and effectiveness of the streamflow reproduction. Good performances emerged from the model application and testing made with reference to some daily runoff series from Italian basins.     

Conceptually-based shot noise modeling of streamflows at short time interval

A conceptual-stochastic approach to short time runoff data modelling is proposed, according to the aim of reproducing the hydrological aspects of the streamflow process and of preserving as much as possible the dynamics of the process itself. This latter task implies preservation of streamflow characteristics at higher scales of aggregation and, within a conceptual framework, involves compatibility with models proposed for the runoff process at those scales. At a daily time scale the watershed response to the effective rainfall is considered as deriving from the response of three linear reservoirs, respectively representing contributions to streamflows of large deep aquifers, with over-year response lag, of aquifers which run dry by the end of the dry season and of subsurface runoff. The surface runoff component is regarded as an uncorrelated point process. Considering the occurrences of effective rainfall events as generated by an independent Poisson process, the output of the linear system represents a conceptually-based multiple shot noise process. Model identification and parameter estimation are supported by information related to the aggregated runoff process, in agreement to the conceptual framework proposed, and this allows parameter parsimony, efficient estimation and effectiveness of the streamflow reproduction. Good performances emerged from the model application and testing made with reference to some daily runoff series from Italian basins.     

Use of daily precipitation uncertainties in streamflow simulation and forecast

Among other sources of uncertainties in hydrologic modeling, input uncertainty due to a sparse station network was tested. The authors tested impact of uncertainty in daily precipitation on streamflow forecasts. In order to test the impact, a distributed hydrologic model (PRMS, Precipitation Runoff Modeling System) was used in two hydrologically different basins (Animas basin at Durango, Colorado and Alapaha basin at Statenville, Georgia) to generate ensemble streamflows. The uncertainty in model inputs was characterized using ensembles of daily precipitation, which were designed to preserve spatial and temporal correlations in the precipitation observations. Generated ensemble flows in the two test basins clearly showed fundamental differences in the impact of input uncertainty. The flow ensemble showed wider range in Alapaha basin than the Animas basin. The wider range of streamflow ensembles in Alapaha basin was caused by both greater spatial variance in precipitation and shorter time lags between rainfall and runoff in this rainfall dominated basin. This ensemble streamflow generation framework was also applied to demonstrate example forecasts that could improve traditional ESP (Ensemble Streamflow Prediction) method.     

Study on snowmelt runoff simulation in the Kaidu River basin

Alpine snowmelt is an important generation mode for runoff in the source region of the Tarim River basin, which covers four subbasins characterized by large area, sparse gauge stations, mixed runoff supplied by snowmelt and rainfall, and remarkably spatially heterogeneous precipitation. Taking the Kaidu River basin as a research area, this study analyzes the influence of these characteristics on the variables and parameters of the Snow Runoff Model and discusses the corresponding determination strategy to improve the accuracy of snowmelt simulation and forecast. The results show that: (i) The temperature controls the overall tendency of simulated runoff and is dominant to simulation accuracy, as the measured daily mean temperature cannot represent the average level of the same elevation in the basin and that directly inputting it to model leads to inaccurate simulations. Based on the analysis of remote sensing snow maps and simulation results, it is reasonable to approximate the mean temperature with 0.5 time daily maximum temperature. (ii) For the conflict between the limited gauge station and remarkably spatial heterogeneity of rainfall, it is not realistic to compute rainfall for each elevation zone. After the measured rainfall is multiplied by a proper coefficient and adjusted with runoff coefficient for rainfall, the measured rainfall data can satisfy the model demands. (iii) Adjusting time lag according to the variation of snowmelt and rainfall position can improve the simulation precision of the flood peak process. (iv) Along with temperature, the rainfall increases but cannot be completely monitored by limited gauge stations, which results in precision deterioration.     

Study on snowmelt runoff simulation in the Kaidu River basin

Alpine snowmelt is an important generation mode for runoff in the source region of the Tarim River basin, which covers four subbasins characterized by large area, sparse gauge stations, mixed runoff supplied by snowmelt and rainfall, and remarkably spatially heterogeneous precipitation. Taking the Kaidu River basin as a research area, this study analyzes the influence of these characteristics on the variables and parameters of the Snow Runoff Model and discusses the corresponding determination strategy to improve the accuracy of snowmelt simulation and forecast. The results show that: (i) The temperature controls the overall tendency of simulated runoff and is dominant to simulation accuracy, as the measured daily mean temperature cannot represent the average level of the same elevation in the basin and that directly inputting it to model leads to inaccurate simulations. Based on the analysis of remote sensing snow maps and simulation results, it is reasonable to approximate the mean temperature with 0.5 time daily maximum temperature. (ii) For the conflict between the limited gauge sta-tion and remarkably spatial heterogeneity of rainfall, it is not realistic to compute rainfall for each elevation zone. After the measured rainfall is multiplied by a proper coefficient and adjusted with runoff coefficient for rainfall, the measured rainfall data can satisfy the model demands. (iii) Adjusting time lag according to the variation of snowmelt and rainfall position can improve the simulation precision of the flood peak process. (iv) Along with temperature, the rainfall increases but cannot be completely monitored by limited gauge stations, which results in precision deterioration.