Cloud removal methodology from MODIS snow cover product

The Moderate Resolution Imaging Spectroradiometer (MODIS) employed by Terra and Aqua satellites provides spatially snow covered data with 500 m and daily temporal resolution. It delivers public domain data in raster format. The main disadvantage of the MODIS sensor is that it is unable to record observations under cloud covered regions. This is why this study focuses on estimating the pixel cover for cloud covered areas where no information is available. Our step to this product involves employing methodology based on six successive steps that estimate the pixel cover using different temporal and spatial information. The study was carried out for the Kokcha River basin located in northeastern part of Afghanistan. Snow coverage in catchments, like Kokcha, is very important where the melt-water from snow dominates the river discharge in vegetation period for irrigation purposes. Since no snow related observations were available from the region, the performance of the proposed methodology was tested using the cloud generated MODIS snow cover data as possible "ground truth" information. The results show successful performances arising from the methods applied, which resulted in all cloud coverage being removed. A validation was carried out for all subsequent steps, to be outlined below, where each step removes progressively more cloud coverage. Steps 2 to 5 (step 1 was not validated) performed very well with an average accuracy of between 90–96%, when applied one after another for the selected valid days in this study. The sixth step was the least accurate at 78%, but it led to the removal of all remaining cloud cover

Influence of rainfall observation network on model calibration and application

International audienceThe objective in this study is to investigate the influence of the spatial resolution of the rainfall input on the model calibration and application. The analysis is carried out by varying the distribution of the raingauge network. The semi-distributed HBV model is calibrated with the precipitation interpolated from the available observed rainfall of the different raingauge networks. An automatic calibration method based on the combinatorial optimization algorithm simulated annealing is applied. Aggregated Nash-Sutcliffe coefficients at different temporal scales are adopted as objective function to estimate the model parameters. The performance of the hydrological model is analyzed as a function of the raingauge density. The calibrated model is validated using the same precipitation used for the calibration as well as interpolated precipitation based on networks of reduced and increased raingauge density. The effect of missing rainfall data is investigated by using a multiple linear regression approach for filling the missing values. The model, calibrated with the complete set of observed data, is then run in the validation period using the above described precipitation field. The simulated hydrographs obtained in the three sets of experiments are analyzed through the comparisons of the computed Nash-Sutcliffe coefficient and several goodness-of-fit indexes. The results show that the model using different raingauge networks might need recalibration of the model parameters: model calibrated on sparse information might perform well on dense information while model calibrated on dense information fails on sparse information. Also, the model calibrated with complete set of observed precipitation and run with incomplete observed data associated with the data estimated using multiple linear regressions, at the locations treated as missing measurements, performs well. A meso-scale catchment located in the south-west of Germany has been selected for this study

Integration and calibration of a conceptual rainfall-runoff model in the framework of a decision support system for river basin management

International audienceWater balance models provide significant input to integrated models that are used to simulate river basin processes. However, one of the primary problems involves the coupling and simultaneous calibration of rainfall-runoff and groundwater models. This problem manifests itself through circular arguments - the hydrologic model is modified to calculate highly discretized groundwater recharge rates as input to the groundwater model which provides modeled base flow for the flood-routing module of the rainfall-runoff model. A possibility to overcome this problem using a modified version of the HBV Model is presented in this paper. Regionalisation and optimization methods lead to objective and efficient calibration despite large numbers of parameters. The representation of model parameters by transfer functions of catchment characteristics enables consistent parameter estimation. By establishing such relationships, models are calibrated for the parameters of the transfer functions instead of the model parameters themselves. Simulated annealing, using weighted Nash-Sutcliffe-coefficients of variable temporal aggregation, assists in efficient parameterisations. The simulations are compared to observed discharge and groundwater recharge modeled by the State Institute for Environmental Protection Baden-Württemberg using the model TRAIN-GWN

Investigation of the transferability of hydrological models and a method to improve model calibration

International audienceIn order to find a model parameterization such that the hydrological model performs well even under different conditions, appropriate model performance measures have to be determined. A common performance measure is the Nash Sutcliffe efficiency. Usually it is calculated comparing observed and modelled daily values. In this paper a modified version is suggested in order to calibrate a model on different time scales simultaneously (days up to years). A spatially distributed hydrological model based on HBV concept was used. The modelling was applied on the Upper Neckar catchment, a mesoscale river in south western Germany with a basin size of about 4000 km2. The observation period 1961-1990 was divided into four different climatic periods, referred to as "warm", "cold", "wet" and "dry". These sub periods were used to assess the transferability of the model calibration and of the measure of performance. In a first step, the hydrological model was calibrated on a certain period and afterwards applied on the same period. Then, a validation was performed on the climatologically opposite period than the calibration, e.g. the model calibrated on the cold period was applied on the warm period. Optimal parameter sets were identified by an automatic calibration procedure based on Simulated Annealing. The results show, that calibrating a hydrological model that is supposed to handle short as well as long term signals becomes an important task. Especially the objective function has to be chosen very carefully

Spatial correlation of radar and gauge precipitation data in high temporal resolution

A multi-sites precipitation time series generator for engineering designs is currently being developed. The objective is to generate several time series&apos; simultaneously with correct inter-station relationships. Therefore, a model to estimate correlation between stations for arbitrary points in a project area is needed, using rain gauge data as well as radar data. <br><br> Two methods are applied to compare the spatial behaviour of precipitation in both the rain gauge data and the radar data. The first approach is to calculate precipitation intensities from radar reflectivity and use it as gauge data. The results show that the spatial structure in both data sets is similar, but cross correlation varies too much to use radar derived spatial correlation to describe gauge inter-station relationship. Thus, a second approach was tested to account for the differences in the spatial correlation associated to the distribution. Using the indicator time series, cross correlations for different quantiles were calculated from both the rain gauge and radar data. This approach shows that cross correlation varies depending on the chosen quantile. In the lower quantiles, the correlation is very similar in rain gauge and radar data, hence a transfer is possible. This insight is useful to derive cross correlations of rain gauges from radar images. Correlation data for rain gauges thus obtained contains all the information about heterogeneity and anisotropy of the spatial structure of rainfall, which is in the radar data

Influence of rainfall observation network on model calibration and application

The objective in this study is to investigate the influence of the spatial resolution of the rainfall input on the model calibration and application. The analysis is carried out by varying the distribution of the raingauge network. A meso-scale catchment located in southwest Germany has been selected for this study. First, the semi-distributed HBV model is calibrated with the precipitation interpolated from the available observed rainfall of the different raingauge networks. An automatic calibration method based on the combinatorial optimization algorithm simulated annealing is applied. The performance of the hydrological model is analyzed as a function of the raingauge density. Secondly, the calibrated model is validated using interpolated precipitation from the same raingauge density used for the calibration as well as interpolated precipitation based on networks of reduced and increased raingauge density. Lastly, the effect of missing rainfall data is investigated by using a multiple linear regression approach for filling in the missing measurements. The model, calibrated with the complete set of observed data, is then run in the validation period using the above described precipitation field. The simulated hydrographs obtained in the above described three sets of experiments are analyzed through the comparisons of the computed Nash-Sutcliffe coefficient and several goodness-of-fit indexes. The results show that the model using different raingauge networks might need re-calibration of the model parameters, specifically model calibrated on relatively sparse precipitation information might perform well on dense precipitation information while model calibrated on dense precipitation information fails on sparse precipitation information. Also, the model calibrated with the complete set of observed precipitation and run with incomplete observed data associated with the data estimated using multiple linear regressions, at the locations treated as missing measurements, performs well

Hydrological modelling for meso-scale catchments using globally available data

International audienceThis study focuses on modelling water balances for catchments with limited data availability. The objective was to use globally available data for water balance modelling of meso-scale catchments. The study is carried out in two catchments; one having enough data for the performance check of the model and the other with very few data for model validation. Globally available meteorological and geographical data is used for the basic model inputs. Dissaggregation of the global data, both spatially and temporally, was conducted to distribute the available data across the watershed and to attain higher resolution input data for the model. In addition, a glacier module was developed for the regions covered by glaciers. The HBV-IWS model developed at the Institute of Hydraulic Engineering at the University of Stuttgart is applied. The outcomes of the modelling provide noteworthy results for both catchments that can be used in water resources planning and management issues. Moreover, the research presents the potential for modelling water balances using predominantly globally available data and proposes appropriate disaggregation methods for global data usage

Robust estimation of hydrological model parameters

The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives a unique and &lt;i&gt;very best&lt;/i&gt; parameter vector. The parameters of fitted hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on Tukey&apos;s half space depth was used. The depth of the set of &lt;i&gt;N&lt;/i&gt; randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study) for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transfered to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study

Prediction of monsoon rainfall for a mesoscale Indian catchment based on stochastical downscaling and objective circulation patterns

International audienceIn this study a stochastical approach for generating rainfall time series based on objective circulation patterns (CP is applied to the mesoscale Anas catchment in North West India. This CP based approach was developed and successfully applied in the humid and temperate climate of Central Europe. The objective of the study was to find out whether this approach is transferable to a catchment in North West India with a totally different semi arid climate. For the Anas catchment it was possible to identify a CP classification scheme consisting of 12 CPs defined in a window between 5° N 40° E and 35° N 95° E, which explained the space-time variability of observed rainfall at 10 stations in the Anas catchment. Based on the classification scheme, NCAR pressure data from 500 hPa level were classified into a CP time series for the period of 1964?1994, which was in turn used as meteorological forcing for multivariate stochastical rainfall simulations with a daily time step. On the monthly time scale the model performed well. Except for stations Udaigarh and Bhabra the average annual cycle of monthly rainfall and rainy days in a month was matched well. The frequency distributions of monthly rainfall at different stations were also captured well. Correlation coefficients between simulated and observed monthly rainfall were larger than 0.85 at each station. Within a long term simulation of 30 years the model yielded promising predictions for monthly as well as for seasonal rainfall totals, but showed also clear deficiencies in capturing the very extremes and inter-decadal variability of monsoon strength. In this respect, the introduction of additional predictors such as SST anomalies and wind direction classes promised the most substantial model improvements

A review of regionalisation for continuous streamflow simulation

Research on regionalisation in hydrology has been constantly advancing due to the need for prediction of streamflow in ungauged catchments. There are two types of studies that use regionalisation techniques for ungauged catchments. One type estimates parameters of streamflow statistics, flood quantiles in most cases. The other type estimates parameters of a rainfall-runoff model for simulating continuous streamflow or estimates continuous streamflow without using a model. Almost all methods applied to the latter can be applied to the former. This paper reviews all methods that are applied to continuous streamflow estimation for ungauged catchments. We divide them into two general categories: (1) distance-based and (2) regression-based. Methods that fall within each category are reviewed first and followed with a discussion on merits or problems associated with these various methods