scholarly journals Understanding dominant controls on streamflow spatial variability to set up a semi-distributed hydrological model: the case study of the Thur catchment

2020 ◽  
Vol 24 (3) ◽  
pp. 1319-1345
Author(s):  
Marco Dal Molin ◽  
Mario Schirmer ◽  
Massimiliano Zappa ◽  
Fabrizio Fenicia

Abstract. This study documents the development of a semi-distributed hydrological model aimed at reflecting the dominant controls on observed streamflow spatial variability. The process is presented through the case study of the Thur catchment (Switzerland, 1702 km2), an alpine and pre-alpine catchment where streamflow (measured at 10 subcatchments) has different spatial characteristics in terms of amounts, seasonal patterns, and dominance of baseflow. In order to appraise the dominant controls on streamflow spatial variability and build a model that reflects them, we follow a two-stage approach. In a first stage, we identify the main climatic or landscape properties that control the spatial variability of streamflow signatures. This stage is based on correlation analysis, complemented by expert judgement to identify the most plausible cause–effect relationships. In a second stage, the results of the previous analysis are used to develop a set of model experiments aimed at determining an appropriate model representation of the Thur catchment. These experiments confirm that only a hydrological model that accounts for the heterogeneity of precipitation, snow-related processes, and landscape features such as geology produces hydrographs that have signatures similar to the observed ones. This model provides consistent results in space–time validation, which is promising for predictions in ungauged basins. The presented methodology for model building can be transferred to other case studies, since the data used in this work (meteorological variables, streamflow, morphology, and geology maps) are available in numerous regions around the globe.

10.29007/9kpv ◽  
2018 ◽  
Author(s):  
Yang Zhiyong ◽  
Gao Xichao ◽  
Liu Jiahong

A framework of predictions in ungauged basins (PUBs, taking Paniai lakes watershed, Indonesia as an example) for hydropower exploration is developed. In this framework, remote sensing technology and similar watershed method are used to collect necessary meteorological and topographical data for runoff simulation. Besides, a modified physical based distributed hydrological model is developed to consider the characteristics (regulation capacity of the lakes) of the watershed. Finally, considering the modeling purpose, annual average runoff index is used to assess the modeling results. In the case study (Paniai lakes watershed), TRMM precipitation, HWSD soil type, and AVHRR landcover data, combined with meteorological data from two similar watersheds, are collected to drive the modified hydrological model. According to the model results, the simulated potential evapotranspiration capacities and annual average runoff coefficients are consistent between the two cases (modeling with meteorological data of the two similar watersheds), and the simulated annual average runoff coefficients of the two cases are basically consistent with the observed annual average runoff coefficient of another similar watershed located in Indonesia.


2019 ◽  
Author(s):  
Marco Dal Molin ◽  
Mario Schirmer ◽  
Massimiliano Zappa ◽  
Fabrizio Fenicia

Abstract. The development of semidistributed hydrological models that reflect the dominant processes controlling streamflow spatial variability is a challenging task. This study addresses this problem by investigating the case of the Thur catchment (Switzerland), an alpine and pre–alpine catchment that, while having a moderate (1702 km2) extension, presents a large spatial variability in terms of climate, landscape, and streamflow (measured at 10 subcatchments). The methodology for model development consists of a two–stages approach. In a first stage, we use correlation and regression analysis to identify the main influencing factors on the spatial variability of streamflow signatures. Results of this analysis show that precipitation (rainfall or snow) controls signatures of seasonality and water balance, while landscape characteristics (especially geology) control signatures of hydrograph shape (e.g. baseflow index and flashiness index). In a second stage, we use the results of the previous analysis to develop a semidistributed hydrological model that is consistent with the data. Model experiments confirm that only hydrological models that account for the heterogeneity of precipitation and geology produce hydrographs that have signatures similar to the observed ones. These models provide consistent results in space–time validation, which is promising for prediction in ungauged conditions. The presented methodology can be transferred to other case studies, since the data used in this work (meteorological variables, streamflow, morphology and geology maps) is available in many regions around the globe.


2016 ◽  
Vol 154 ◽  
pp. 1010-1017 ◽  
Author(s):  
Vo Ngoc Duong ◽  
Nguyen Quang Binh ◽  
Le Xuan Cuong ◽  
Qiang Ma ◽  
Philippe Gourbesville

2006 ◽  
Vol 3 (4) ◽  
pp. 2175-2208 ◽  
Author(s):  
J. M. Schuurmans ◽  
M. F. P. Bierkens

Abstract. We investigate the effect of spatial variability of daily rainfall on soil moisture, groundwater level and discharge using a physically-based, fully-distributed hydrological model. We focus on the effect of rainfall spatial variability on day-to-day variability of the interior catchment response, as well as on its effect on the general hydrological behavior of the catchment. The study is performed in a flat rural catchment (135 km2) in The Netherlands, where climate is semi-humid (average precipitation 800 mm/year, evapotranspiration 550 mm/year) and rainfall is predominantly stratiform. Both range-corrected radar data (resolution 2.5×2.5 km2) as well as data from a dense network of 30 raingauges are used, observed for the period March–October 2004. Eight different rainfall scenarios, either spatially distributed or spatially uniform, are used as input for the hydrological model. The main conclusions from this study are: (i) using a single raingauge as rainfall input carries a great risk for the prediction of discharge, groundwater level and soil moisture, especially if the raingauge is situated outside the catchment; (ii) taking into account the spatial variability of rainfall instead of using areal average rainfall as input for the model is needed to get insight into the day-to-day spatial variability of discharge, groundwater level and soil moisture content; (iii) to get insight into the general behavior of the hydrological system it is sufficient to use correct predictions of areal average rainfall over the catchment.


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