scholarly journals Impact of Rain Gauges Distribution on the Runoff Simulation of a Small Mountain Catchment in Southern Ecuador

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1169 ◽  
Author(s):  
Adrián Sucozhañay ◽  
Rolando Célleri
Keyword(s):  
Hydrological Modeling ◽  
Rainfall Variability ◽  
Model Performance ◽  
Rainfall Data ◽  
Model Structure ◽  
Mountain Regions ◽  
Rainfall Estimation ◽  
Runoff Simulation ◽  
Rain Gauges ◽  
The Impact

In places with high spatiotemporal rainfall variability, such as mountain regions, input data could be a large source of uncertainty in hydrological modeling. Here we evaluate the impact of rainfall estimation on runoff modeling in a small páramo catchment located in the Zhurucay Ecohydrological Observatory (7.53 km2) in the Ecuadorian Andes, using a network of 12 rain gauges. First, the HBV-light semidistributed model was analyzed in order to select the best model structure to represent the observed runoff and its subflow components. Then, we developed six rainfall monitoring scenarios to evaluate the impact of spatial rainfall estimation in model performance and parameters. Finally, we explored how a model calibrated with far-from-perfect rainfall estimation would perform using new improved rainfall data. Results show that while all model structures were able to represent the overall runoff, the standard model structure outperformed the others for simulating subflow components. Model performance (NSeff) was improved by increasing the quality of spatial rainfall estimation from 0.31 to 0.80 and from 0.14 to 0.73 for calibration and validation period, respectively. Finally, improved rainfall data enhanced the runoff simulation from a model calibrated with scarce rainfall data (NSeff 0.14) from 0.49 to 0.60. These results confirm that in mountain regions model uncertainty is highly related to spatial rainfall and, therefore, to the number and location of rain gauges.

scholarly journals Spatio-Temporal Hydrological Model Structure and Parametrization Analysis

2021 ◽  
Vol 9 (5) ◽  
pp. 467
Author(s):  
Mostafa Farrag ◽  
Gerald Corzo Perez ◽  
Dimitri Solomatine
Keyword(s):  
Hydrological Modeling ◽  
Model Building ◽  
Model Performance ◽  
Model Description ◽  
Generation Model ◽  
Model Structure ◽  
Runoff Generation ◽  
Spatial Sensitivity ◽  
Spatio Temporal ◽  
Set Up

Many grid-based spatial hydrological models suffer from the complexity of setting up a coherent spatial structure to calibrate such a complex, highly parameterized system. There are essential aspects of model-building to be taken into account: spatial resolution, the routing equation limitations, and calibration of spatial parameters, and their influence on modeling results, all are decisions that are often made without adequate analysis. In this research, an experimental analysis of grid discretization level, an analysis of processes integration, and the routing concepts are analyzed. The HBV-96 model is set up for each cell, and later on, cells are integrated into an interlinked modeling system (Hapi). The Jiboa River Basin in El Salvador is used as a case study. The first concept tested is the model structure temporal responses, which are highly linked to the runoff dynamics. By changing the runoff generation model description, we explore the responses to events. Two routing models are considered: Muskingum, which routes the runoff from each cell following the river network, and Maxbas, which routes the runoff directly to the outlet. The second concept is the spatial representation, where the model is built and tested for different spatial resolutions (500 m, 1 km, 2 km, and 4 km). The results show that the spatial sensitivity of the resolution is highly linked to the routing method, and it was found that routing sensitivity influenced the model performance more than the spatial discretization, and allowing for coarser discretization makes the model simpler and computationally faster. Slight performance improvement is gained by using different parameters’ values for each cell. It was found that the 2 km cell size corresponds to the least model error values. The proposed hydrological modeling codes have been published as open-source.

scholarly journals Impact of model structure on flow simulation and hydrological realism: from a lumped to a semi-distributed approach

2017 ◽  
Vol 21 (8) ◽  
pp. 3937-3952 ◽  
Author(s):  
Federico Garavaglia ◽  
Matthieu Le Lay ◽  
Fréderic Gottardi ◽  
Rémy Garçon ◽  
Joël Gailhard ◽  
...  
Keyword(s):  
Hydrological Model ◽  
Snow Water Equivalent ◽  
Flow Simulation ◽  
Evaluation Framework ◽  
Model Structure ◽  
Runoff Simulation ◽  
Snow Cover Area ◽  
Mountainous Regions ◽  
Distributed Approach ◽  
The Impact

Abstract. Model intercomparison experiments are widely used to investigate and improve hydrological model performance. However, a study based only on runoff simulation is not sufficient to discriminate between different model structures. Hence, there is a need to improve hydrological models for specific streamflow signatures (e.g., low and high flow) and multi-variable predictions (e.g., soil moisture, snow and groundwater). This study assesses the impact of model structure on flow simulation and hydrological realism using three versions of a hydrological model called MORDOR: the historical lumped structure and a revisited formulation available in both lumped and semi-distributed structures. In particular, the main goal of this paper is to investigate the relative impact of model equations and spatial discretization on flow simulation, snowpack representation and evapotranspiration estimation. Comparison of the models is based on an extensive dataset composed of 50 catchments located in French mountainous regions. The evaluation framework is founded on a multi-criterion split-sample strategy. All models were calibrated using an automatic optimization method based on an efficient genetic algorithm. The evaluation framework is enriched by the assessment of snow and evapotranspiration modeling against in situ and satellite data. The results showed that the new model formulations perform significantly better than the initial one in terms of the various streamflow signatures, snow and evapotranspiration predictions. The semi-distributed approach provides better calibration–validation performance for the snow cover area, snow water equivalent and runoff simulation, especially for nival catchments.

scholarly journals The Role of the Spatial Distribution of Radar Rainfall on Hydrological Modeling for an Urbanized River Basin in Japan

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1703 ◽  
Author(s):  
Shakti P. C. ◽  
Tsuyoshi Nakatani ◽  
Ryohei Misumi
Keyword(s):  
River Basin ◽  
Spatial Resolution ◽  
Hydrological Modeling ◽  
Distribution Patterns ◽  
Rainfall Data ◽  
Model Parameters ◽  
Radar Rainfall ◽  
Rain Events ◽  
The Impact ◽  
Hydrological Applications

Recently, the use of gridded rainfall data with high spatial resolutions in hydrological applications has greatly increased. Various types of radar rainfall data with varying spatial resolutions are available in different countries worldwide. As a result of the variety in spatial resolutions of available radar rainfall data, the hydrological community faces the challenge of selecting radar rainfall data with an appropriate spatial resolution for hydrological applications. In this study, we consider the impact of the spatial resolution of radar rainfall on simulated river runoff to better understand the impact of radar resolution on hydrological applications. Very high-resolution polarimetric radar rainfall (XRAIN) data are used as input for the Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) to simulate runoff from the Tsurumi River Basin, Japan. A total of 20 independent rainfall events from 2012–2015 were selected and categorized into isolated/convective and widespread/stratiform events based on their distribution patterns. First, the hydrological model was established with basin and model parameters that were optimized for each individual rainfall event; then, the XRAIN data were rescaled at various spatial resolutions to be used as input for the model. Finally, we conducted a statistical analysis of the simulated results to determine the optimum spatial resolution for radar rainfall data used in hydrological modeling. Our results suggest that the hydrological response was more sensitive to isolated or convective rainfall data than it was to widespread rain events, which are best simulated at ≤1 km and ≤5 km, respectively; these results are applicable in all sub-basins of the Tsurumi River Basin, except at the river outlet.

scholarly journals Hydrological Modeling Approach Using Radar-Rainfall Ensemble and Multi-Runoff-Model Blending Technique

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 850 ◽  
Author(s):  
Lee ◽  
Kang ◽  
Joo ◽  
Kim ◽  
Kim ◽  
...  
Keyword(s):  
Hydrological Modeling ◽  
Radar Data ◽  
Rainfall Data ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Radar Rainfall ◽  
Topographical Effects ◽  
Reservoir Regulation ◽  
Regulation Model ◽  
Runoff Model

The purpose of this study is to reduce the uncertainty in the generation of rainfall data and runoff simulations. We propose a blending technique using a rainfall ensemble and runoff simulation. To create rainfall ensembles, the probabilistic perturbation method was added to the deterministic raw radar rainfall data. Then, we used three rainfall-runoff models that use rainfall ensembles as input data to perform a runoff analysis: The tank model, storage function model, and streamflow synthesis and reservoir regulation model. The generated rainfall ensembles have increased uncertainty when the radar is underestimated, due to rainfall intensity and topographical effects. To confirm the uncertainty, 100 ensembles were created. The mean error between radar rainfall and ground rainfall was approximately 1.808–3.354 dBR. We derived a runoff hydrograph with greatly reduced uncertainty by applying the blending technique to the runoff simulation results and found that uncertainty is improved by more than 10%. The applicability of the method was confirmed by solving the problem of uncertainty in the use of rainfall radar data and runoff models.

scholarly journals Urban Runoff Simulation: How Do Land Use/Cover Change Patterning and Geospatial Data Quality Impact Model Outcome?

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2715
Author(s):  
Amnah Elaji ◽  
Wei Ji
Keyword(s):  
Land Use ◽  
Data Quality ◽  
Metropolitan Area ◽  
Hydrological Modeling ◽  
Urban Landscape ◽  
Urban Runoff ◽  
Geospatial Data ◽  
Rainfall Data ◽  
Runoff Simulation ◽  
Radar Rainfall

With the increase in global urbanization, satellite imagery and other types of geospatial data have been extensively used in urban landscape change research, which includes environmental modeling in order to assess the change impact on urban watersheds. For urban hydrological modeling, as a focus of this study, several related research questions are raised: (1) How sensitive are runoff simulation to land use and land cover change patterning? (2) How will input data quality impact the simulation outcome? (3) How effective is integrating and synthesizing various forms of geospatial data for runoff modeling? These issues were not fully or adequately addressed in previous related studies. With the aim of answering these questions as research objectives, we conducted a spatial land use and land cover (LULC) change analysis and an urban runoff simulation in the Blue River watershed in the Kansas City metropolitan area between 2003 and 2017. In this study, approaches were developed to incorporate the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model with remote sensing, geographic information systems (GIS), and radar rainfall data. The impact of data quality on the model simulation outcome was also analyzed. The results indicate that there are no significant differences between simulated runoff responses in the two study years (2003 and 2017) due to spatial and temporal heterogeneity of urbanization processes in the region. While the metropolitan area has been experiencing remarkable urban development in the past few decades, the gain in built-up land in the study watershed during the study period is insignificant. On the other hand, the gain in vegetated land caused by forestation activities is offset by a decrease in farmland and grassland. The results show that increasing spatial data resolution does not necessarily or noticeably improve the HEC-HMS model performance or outcomes. Under these conditions, using Next Generation Weather Radar (NEXRAD) rainfall data in the simulation provides a satisfactory fit in hydrographs’ shapes, peak discharge amounts and time after calibration efforts, while they may overestimate the amount of rainfall as compared with gauge data. This study shows that the developed approach of synthesizing satellite, GIS, and radar rainfall data in hydrological modeling is effective and useful for incorporating urban landscape and precipitation change data in dynamic flood risk assessment at a watershed level.

scholarly journals The effect of a hydrological model structure and rainfall data on the accuracy of flood description in an upland catchment

2015 ◽  
Vol 47 (4) ◽  
pp. 305-320 ◽  
Author(s):  
Andrzej Wałęga ◽  
Leszek Książek
Keyword(s):  
Hydrological Model ◽  
Model Performance ◽  
Rainfall Data ◽  
Lumped Parameter Model ◽  
Distributed Parameter ◽  
Model Structure ◽  
Related Data ◽  
Distributed Parameter Model ◽  
Lumped Parameter ◽  
Starting Point

Abstract The effect of a hydrological model structure and rainfall data on the accuracy of flood description in an upland catchment. The aim of this paper was to determine the influence of a hydrological model structure and rainfall- -related data on flood parameters obtained from a simulation. The study included an upland river Stobnica, right tributary of the Wisłok. The following assumptions were investigated: (i) the greater number of rainfall stations, the more accurate a flood description, i.e. the resulting hydrograph much better describes the actual flood, (ii) a distributed parameter model provides a more precise description of a catchment response to rainfall than a lumped parameter model. All calculations were performed using HEC-HMS 3.4 software. The analyses showed that increasing the number of rainfall stations slightly improved the model performance (by on average 4.1%). Furthermore, it was showed that in the catchment characterized by low topographical variability and stable land use, more reliable flood simulation results were obtained in the lumped parameter model than in the distributed parameter model. Considering the calibration process slightly improved the model performance, irrespective of its structure and the number of rainfall stations. Multivariate analysis of variance (MANOVA) revealed that the resulting differences in the model efficiency for individual variants were not significant. Considering limited empirical evidence on rainfall-runoff episodes, uncertainty of these results is probably high and thus they should be treated as a starting point for further studies.

Spatio Temporal Variability of Rainfall Across Western Libya from 1979 to 2009

2020 ◽  
Vol 6 (3) ◽  
pp. 235-244
Author(s):  
Ali Salem Eddenjal
Keyword(s):  
Time Series ◽  
Rainfall Variability ◽  
Statistical Significance ◽  
Regional Scale ◽  
Policy Makers ◽  
Rain Gauges ◽  
Spatio Temporal ◽  
Annual Scale ◽  
Increasing Trends ◽  
The Impact

Aims: To understand the links between climate variability and hydrology in western Libya. Background: This study represents the first comprehensive assessment of rainfall variability in western Libya at a regional scale. Objective: To assess temporal and spatial variability of rainfall in western Libya, based on data (1979-2009) from 16 rain gauges. Methods: The non-parametric Mann-Kendall method and Sen’s slop estimator were used to define changes in rainfall series and their statistical significance. Results: Coastal and mountainous time series showed decreasing trends at the annual, autumn, and spring scales, with very few exceptions. Notably, winter showed increasing trends, with the significant values of 1.94 and 0.88 mm/year at Sirt and Nalut, respectively. Desert stations showed increasing trends, especially at the annual scale, with the greatest significant increase on the order of 1.19 mm/year in Ghadames. For the regional rainfall trend analysis, annual, spring and autumn rainfalls decreased in the coastal and mountainous zones, with the highest significant decrease of 1.94 mm/year. Again, winter rainfall showed increasing trend over the whole study domain. Conclusion: Although most time series showed a tendency towards more drier conditions, most of the detected trends were statistically non-significant. This study will provide guidance for policy makers in their future planning to mitigate the impact of drought.

Using the WRF Model in an Operational Streamflow Forecast System for the Jordan River

2011 ◽  
Vol 51 (2) ◽  
pp. 285-299 ◽  
Author(s):  
Amir Givati ◽  
Barry Lynn ◽  
Yubao Liu ◽  
Alon Rimmer
Keyword(s):  
High Resolution ◽  
Hydrological Modeling ◽  
Wrf Model ◽  
Assessment Process ◽  
Jordan River ◽  
Modeling Tools ◽  
Streamflow Forecast ◽  
Rain Gauges ◽  
The Impact ◽  
Good Agreement

AbstractThe Weather Research and Forecasting (WRF) model was employed to provide precipitation forecasts during the 2008/09 and 2009/10 winters (wet season) for Israel and the surrounding region where complex terrain dominates. The WRF precipitation prediction has been coupled with the Hydrological Model for Karst Environment (HYMKE) to forecast the upper Jordan River streamflow. The daily WRF precipitation forecasts were verified against the measurements from a dense network of rain gauges in northern and central Israel, and the simulation results using the high-resolution WRF indicated good agreement with the actual measurements. The daily precipitation amount calculated by WRF at rain gauges located in the upper parts of the Jordan River basin showed good agreement with the actual measurements. Numerical experiments were carried out to test the impact of the WRF model resolution and WRF microphysical schemes, to determine an optimal model configuration for this application. Because of orographic forcing in the region, it is necessary to run WRF with a 4–1.3-km grid increment and with sophisticated microphysical schemes that consider liquid water, ice, snow, and graupel to produce quality precipitation predictions. The hydrological modeling system that ingests the high-resolution WRF forecast precipitation produced good results and improved upon the operational streamflow forecast method for the Jordan River that is now in use. The modeling tools presented in this study are used to support the water-resource-assessment process and studies of seasonal hydroclimatic forecasting in this region.

scholarly journals Areal rainfall estimation using moving cars – computer experiments including hydrological modeling

2016 ◽  
Author(s):  
E. Rabiei ◽  
U. Haberlandt ◽  
M. Sester ◽  
D. Fitzner ◽  
M. Wallner
Keyword(s):  
Spatial Resolution ◽  
Measurement Errors ◽  
Laboratory Experiments ◽  
Hydrological Modeling ◽  
Computer Experiments ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Temporal And Spatial ◽  
Areal Rainfall

Abstract. The need for high temporal and spatial resolution precipitation data for hydrological analyses has been discussed in several studies. Although rain gauges provide valuable information, a very dense rain gauge network is costly. As a result, several new ideas have been emerged to help estimating areal rainfall with higher temporal and spatial resolution. Rabiei et al. (2013) observed that moving cars, called RainCars (RCs), can potentially be a new source of data for measuring rainfall amounts. The optical sensors used in that study are designed for operating the windscreen wipers and showed promising results for rainfall measurement purposes. Their measurement accuracy has been quantified in laboratory experiments. Considering explicitly those errors, the main objective of this study is to investigate the benefit of using RCs for estimating areal rainfall. For that, computer experiments are carried out, where radar rainfall is considered as the reference and the other sources of data, i.e. RCs and rain gauges, are extracted from radar data. Comparing the quality of areal rainfall estimation by RCs with rain gauges and reference data helps to investigate the benefit of the RCs. The value of this additional source of data is not only assessed for areal rainfall estimation performance, but also for use in hydrological modeling. The results show that the RCs considering measurement errors derived from laboratory experiments provide useful additional information for areal rainfall estimation as well as for hydrological modeling. Even assuming higher uncertainties for RCs as obtained from the laboratory up to a certain level is observed practical.

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