Two new journal publications in hydrology (European Water)

06/11/2015 18:53

Ilia's two most recent papers on: 1) the performance of multi-basin hydrological models at the large scale, and 2) a SCS-CN rainfall-runoff model toolkit have been published in European Water Journal:

Andersson, J.C.M., I.G. Pechlivanidis, D. Gustafsson, C. Donnelly, and B. Arheimer (2015), Key factors for improving large-scale hydrological model
performance, Eur. Water, 49, 77-88.

Pechlivanidis, I.G., S. Anastasiadis, and D.F. Lekkas (2015), Development and testing of the MWBMT toolbox to predict runoff response at the poorly gauged catchment of Mornos, Greece, Eur. Water, 49, 3-18.

 

 

Abtract for article 1:

This study focuses on identifying key factors that influence large-scale hydrological model performance. It draws on experiences from modelling in the Arctic, West Africa, and Europe with the HYPE model (HYdrological Prediction of the Environment). We use multiple evaluation criteria to analyse the influence of catchment delineation, climate input data, model parameterisation, and water management. For each factor, we compare the model performance of reference models and refined models using time-series of observed river discharge ranging from ten to a thousand stations (depending on application). The results show that all investigated factors influence model performance to varying extents. Accurately representing catchment size is critical. European stations with small deviation between modelled and published catchment size performed better than stations with large deviations, even after aligning the stations to the modelled topography (NSE: +10%, aRE: −12%). Refining the climate input data substantially increased model performance in a number of cases. In the Niger River basin, the simulation of both daily discharge dynamics and cumulative volumes improved significantly (NSE: +40%, aRE: −26% on average). In Europe, a refined precipitation data set resulted in a similar performance enhancement (NSE: +2%, aRE: −8%) as a refined temperature data set (NSE: +2%, aRE: −4%), on average. However, the temperature refinement was more consistent spatially. Linking lake parameters to spatially varying hydrological characteristics improved model performance across the Arctic domain (NSE: +11%, aRE: −8%). Refining infiltration capacities in the Niger basin improved both flow dynamics (NSE: +60%) and cumulative volumes (aRE: −40%) through modified flow paths and enhanced evaporation. Irrigation water management in the Arctic only affected model performance locally. Model performance was generally better in large and wet catchments with high runoff coefficients compared with relatively small, dry catchments with low runoff. These factors are also likely to affect model performance in other areas of the world.

 

Abtract for article 2:

 

Prediction of runoff response in ungauged catchments has been one of the key issues in water science. Despite the considerable effort, the hydrologic community has devoted over the past decade, there is still need to develop robust frameworks capable of representing the dynamic behaviours of catchment processes (e.g. streamflow). Runoff prediction has been particularly challenging in the poorly gauged or usually ungauged Greek catchments, whereas predictions are subject to significant uncertainty due to erroneous and/or limited available data. The Athens Water Supply and Sewerage Company (EYDAP SA) has funded a project to measure, and simulate the hydrological fluxes (e.g. rainfall, snowfall, streamflow, evaporation) of the 560 km2 Mornos catchment, Greece, that supplies the metropolitan area of Athens with fresh water. In this study, we present the Mornos Water Balance Model Toolbox (MWBMT) recently developed to simulate the hydrological processes in the catchment. Moreover, we try to upgrade the model performance by assimilating information provided by EYDAP SA (i.e. monthly runoff contribution and water level at the reservoir). The rainfall-runoff model structures are based on the Soil Conservation Service Curve Number (SCS-CN) method, whereas topographical, geological and land use information is used to complement parameter identification. In addition, a simple water balance equation is used to estimate the change in the reservoir’s storage. The analysis is based on daily data from the available 15-month period to drive the original SCS-CN model aiming to support the reservoir’s water management. Finally, we present the sources of error/uncertainty causing poor model performance over different time periods and point towards ways for improvement.