Catchment variability and parameter estimation in multi-objective regionalisation of a rainfall–runoff model

This study attempts to examine if catchment variability favours regionalisation by principles of catchment similarity. Our work combines calibration of a simple conceptual model for multiple objectives and multi-regression analyses to establish a regional model between model sensitive parameters and physical catchment characteristics (PCCs). The objective is to test robustness of regionalisation by assessing if generalisation of a wide range of climatic, topographic and physiographic settings in a regional model favours simulation of stream flow at ungauged catchments. Constraints in this work are very stringent performance measures for selection of catchments to establish the regional model and the selection of only PCCs that are available through the database of the National River Flow Archive in the United Kingdom. As such some PCCs have been ignored that have proven to be effective in other studies. For this study 56 well-gauged catchments in England and Wales are used. For model calibration and runoff simulation of ungauged catchments the HBV model is applied. Optimum parameter sets are derived for 48 catchments through Monte Carlo Simulation using an 8-year simulation period. This study aims to adequately simulate all aspects of the hydrograph at the ungauged catchment and therefore four single objective functions are combined in a multi-objective function. After calibration, 17 catchments that are widely spread over England and Wales are selected to establish relationships for seven selected model parameters using 14 PCCs (area, mean elevation, hypsometric integral, catchment shape, standard average annual rainfall, five types of land use and four classes of hydrogeology). Single and multiple regression analysis are applied to identify these relationships. For six model parameters statistically significant relationships could be established three of which are plausible on the basis of hydrologic interpretation. The established relationships are validated at eight gauged catchments that are spread over the UK and cover a large range of values of catchment descriptors. These catchments are assumed ungauged and results revealed that, in general, model parameters determined by the established regional relationships do not perform better as compared to default parameter values. Similar results are obtained for additional validation runs using catchments that are not used in the regionalisation procedure. Since these parameters are based on model performance assessments in a wide range of catchment settings, this suggests that large variability in settings of PCCs does not favour regionalisation. Therefore, for selected catchments the applicability of regionalisation by principles of catchment similarity for HBV model parameters may be questioned

Calibration of a semi-distributed hydrological model using discharge and remote sensing data

The objective of this study is to present an approach to calibrate a semi-distributed hydrological model using observed streamflow data and actual evapotranspiration time series estimates based on remote sensing data. First, daily actual evapotranspiration is estimated using available MODIS satellite data, routinely collected meteorological data, and applying the SEBS algorithm. Second, the semi-distributed hydrological model HBV is calibrated and validated using the estimated evapotranspiration and observed discharge. This is done for multiple sub-basins of the Karkheh River basin in Iran. The Nash-Sutcliffe coefficient (NS) is calculated for each sub-basin. Maximum and minimum NS values for the calibration using observed discharge are 0.81 and 0.23, respectively, and using estimated evapotranspiration 0.61 and 0.46, respectively. The comparison of model simulations with multiple observed variables increases the probability of selecting a parameter set that represents the actual hydrological situation of the basin. The new calibration approach can be useful for further applications, especially in data-sparse river basin

Diurnal rainfall variability over the Upper Blue Nile Basin: a remote sensing based approach

In this study we aim to assess the diurnal cycle of rainfall across the Upper Blue Nile (UBN) basin using satellite observations from Tropical Rainfall Measuring Mission (TRMM). Seven years (2002-2008) of Precipitation Radar (PR) and TRMM Microwave Imager (TMI) data are used and analyses are based on GIS operations and simple statistical techniques. Observations from PR and TMI reveal that over most parts of the basin area, the rainfall occurrence and conditional mean rain rate are highest between midand late-afternoon (15:00-18:00 LST). Exceptions to this are the south-west and south-eastern parts of the basin area and the Lake Tana basin where midnight and early morning maxima are observed. Along the Blue Nile River gorge the rainfall occurrence and the conditional mean rain rate are highest during the night (20:00-23:00 LST). Orographic effects by large scale variation of topography, elevation and the presence of the UBN river gorge were assessed taking two transects across the basin. Along transects from north to south and from east to west results indicate increased rainfall with increase of elevation whereas areas on the windward side of the high mountain ranges receive higher amount of rainfall than areas on the leeward side. As such, mountain ranges and elevation affect the rainfall distribution resulting in rain shadow effect in the north-eastern parts of Choke-mountain and the ridges in the north-east of the basin. Moreover, a direct relation between rainfall occurrence and elevation is observed specifically for 17:00-18:00 LST. Further, results indicate that the rainfall distribution in the deeply incised and wide river gorge is affected with relatively low rainfall occurrence and low mean rainfall rates in the gorge areas. Seasonal mean rainfall depth is highest in the south-west area and central highlands of the basin while areas in the north, north-east and along the Blue Nile gorge receive the least amount of rainfall. Statistical results of this work show that the diurnal cycle of rainfall occurrence from TRMM estimates show significant correlation with the ground observations at 95% confidence level. In the UBN basin, the PR conditional mean rain rate estimates are closer to the ground observations than the TMI. Analysis on mean wet season rainfall amount indicates that PR generally underestimates and TMI overestimates the ground observed rainfall