scholarly journals EVALUATION OF RAINFALL-RUNOFF MODELS FOR MEDITERRANEAN SUBCATCHMENTS

2016 ◽  
Vol XLI-B8 ◽  
pp. 321-325 ◽  
Author(s):  
A. Cilek ◽  
S. Berberoglu ◽  
C. Donmez
Keyword(s):  
Mediterranean Basin ◽  
Eastern Mediterranean ◽  
Ground Cover ◽  
Annual Rainfall ◽  
Hydrological Process ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Data Set ◽  
Erosion Risk ◽  
Rainfall Runoff Model

The development and the application of rainfall-runoff models have been a corner-stone of hydrological research for many decades. The amount of rainfall and its intensity and variability control the generation of runoff and the erosional processes operating at different scales. These interactions can be greatly variable in Mediterranean catchments with marked hydrological fluctuations. <br><br> The aim of the study was to evaluate the performance of rainfall-runoff model, for rainfall-runoff simulation in a Mediterranean subcatchment. The Pan-European Soil Erosion Risk Assessment (PESERA), a simplified hydrological process-based approach, was used in this study to combine hydrological surface runoff factors. In total 128 input layers derived from data set includes; climate, topography, land use, crop type, planting date, and soil characteristics, are required to run the model. Initial ground cover was estimated from the Landsat ETM data provided by ESA. <br><br> This hydrological model was evaluated in terms of their performance in Goksu River Watershed, Turkey. It is located at the Central Eastern Mediterranean Basin of Turkey. The area is approximately 2000 km<sup>2</sup>. The landscape is dominated by bare ground, agricultural and forests. The average annual rainfall is 636.4mm. This study has a significant importance to evaluate different model performances in a complex Mediterranean basin. The results provided comprehensive insight including advantages and limitations of modelling approaches in the Mediterranean environment.

scholarly journals EVALUATION OF RAINFALL-RUNOFF MODELS FOR MEDITERRANEAN SUBCATCHMENTS

2016 ◽  
Vol XLI-B8 ◽  
pp. 321-325
Author(s):  
A. Cilek ◽  
S. Berberoglu ◽  
C. Donmez
Keyword(s):  
Mediterranean Basin ◽  
Eastern Mediterranean ◽  
Ground Cover ◽  
Annual Rainfall ◽  
Hydrological Process ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Data Set ◽  
Erosion Risk ◽  
Rainfall Runoff Model

The development and the application of rainfall-runoff models have been a corner-stone of hydrological research for many decades. The amount of rainfall and its intensity and variability control the generation of runoff and the erosional processes operating at different scales. These interactions can be greatly variable in Mediterranean catchments with marked hydrological fluctuations. <br><br> The aim of the study was to evaluate the performance of rainfall-runoff model, for rainfall-runoff simulation in a Mediterranean subcatchment. The Pan-European Soil Erosion Risk Assessment (PESERA), a simplified hydrological process-based approach, was used in this study to combine hydrological surface runoff factors. In total 128 input layers derived from data set includes; climate, topography, land use, crop type, planting date, and soil characteristics, are required to run the model. Initial ground cover was estimated from the Landsat ETM data provided by ESA. <br><br> This hydrological model was evaluated in terms of their performance in Goksu River Watershed, Turkey. It is located at the Central Eastern Mediterranean Basin of Turkey. The area is approximately 2000 km<sup>2</sup>. The landscape is dominated by bare ground, agricultural and forests. The average annual rainfall is 636.4mm. This study has a significant importance to evaluate different model performances in a complex Mediterranean basin. The results provided comprehensive insight including advantages and limitations of modelling approaches in the Mediterranean environment.

Seasonal ecosystem vulnerability to climatic anomalies in the Mediterranean

2021 ◽  
Author(s):  
Johannes Vogel
Keyword(s):  
Soil Moisture ◽  
Mediterranean Basin ◽  
Eastern Mediterranean ◽  
Western Mediterranean ◽  
Ecosystem Productivity ◽  
Data Set ◽  
Ecosystem Vulnerability ◽  
Dry Conditions ◽  
The Mediterranean ◽  
The Mediterranean Basin

&lt;p&gt;The ecosystems of the Mediterranean Basin are particularly prone to climate change and related alterations in climatic anomalies. The seasonal timing of climatic anomalies is crucial for the assessment of the corresponding ecosystem impacts; however, the incorporation of seasonality is neglected in many studies. We quantify ecosystem vulnerability by investigating deviations of the climatic drivers temperature and soil moisture during phases of low ecosystem productivity for each month of the year over the period 1999 &amp;#8211; 2019. The fraction of absorbed photosynthetically active radiation (FAPAR) is used as a proxy for ecosystem productivity. Air temperature is obtained from the reanalysis data set ERA5 Land and soil moisture and FAPAR satellite products are retrieved from ESA CCI and Copernicus Global Land Service, respectively. Our results show that Mediterranean ecosystems are vulnerable to three soil moisture regimes during the course of the year. A phase of vulnerability to hot and dry conditions during late spring to midsummer is followed by a period of vulnerability to cold and dry conditions in autumn. The third phase is characterized by cold and wet conditions coinciding with low ecosystem productivity in winter and early spring. These phases illustrate well the shift between a soil moisture-limited regime in summer and an energy-limited regime in winter in the Mediterranean Basin. Notably, the vulnerability to hot and dry conditions during the course of the year is prolonged by several months in the Eastern Mediterranean compared to the Western Mediterranean. Our approach facilitates a better understanding of ecosystem vulnerability at certain stages during the year and is easily transferable to other study areas and ecoclimatological variables.&lt;/p&gt;

scholarly journals Sensitivity Analysis to Investigate the Reliability of the Grid-Based Rainfall-Runoff Model

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1839 ◽  
Author(s):  
Mun-Ju Shin ◽  
Yun Choi
Keyword(s):  
Water Movement ◽  
Parameter Sensitivity ◽  
Low Flow ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Rainfall Events ◽  
Flow Calculation ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Grid Based

This study aimed to assess the suitability of the parameters of a physically based, distributed, grid-based rainfall-runoff model. We analyzed parameter sensitivity with a dataset of eight rainfall events that occurred in two catchments of South Korea, using the Sobol’ method. Parameters identified as sensitive responded adequately to the scale of the rainfall events and the objective functions employed. Parameter sensitivity varied depending on rainfall scale, even in the same catchment. Interestingly, for a rainfall event causing considerable runoff, parameters related to initial soil saturation and soil water movement played a significant role in low flow calculation and high flow calculation, respectively. The larger and steeper catchment exhibited a greater difference in parameter sensitivity between rainfall events. Finally, we found that setting an incorrect parameter range that is physically impossible can have a large impact on runoff simulation, leading to substantial uncertainty in the simulation results. The proposed analysis method and the results from our study can help researchers using a distributed rainfall-runoff model produce more reliable analysis results.

scholarly journals Preliminary Study of Computational Time Steps in a Physically Based Distributed Rainfall–Runoff Model

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1269 ◽  
Author(s):  
Yun Choi ◽  
Mun-Ju Shin ◽  
Kyung Kim
Keyword(s):  
Optimal Parameter ◽  
Computational Time ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Stream Network ◽  
Time Step ◽  
Simulation Results ◽  
Rainfall Runoff Model ◽  
Parameter Values ◽  
Runoff Model

The choice of the computational time step (dt) value and the method for setting dt can have a bearing on the accuracy and performance of a simulation, and this effect has not been comprehensively researched across different simulation conditions. In this study, the effects of the fixed time step (FTS) method and the automatic time step (ATS) method on the simulated runoff of a distributed rainfall–runoff model were compared. The results revealed that the ATS method had less peak flow variability than the FTS method for the virtual catchment. In the FTS method, the difference in time step had more impact on the runoff simulation results than the other factors such as differences in the amount of rainfall, the density of the stream network, or the spatial resolution of the input data. Different optimal parameter values according to the computational time step were found when FTS and ATS were used in a real catchment, and the changes in the optimal parameter values were smaller in ATS than in FTS. The results of our analyses can help to yield reliable runoff simulation results.

OIL POLLUTION RISK ASSESSMENT AND PREPAREDNESS IN THE EAST MEDITERRANEAN

2005 ◽  
Vol 2005 (1) ◽  
pp. 259-264 ◽  
Author(s):  
Natalia Martini ◽  
Roberto Patruno
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
Mediterranean Basin ◽  
Oil Spills ◽  
Oil Pollution ◽  
Eastern Mediterranean ◽  
East Mediterranean ◽  
Risk Area ◽  
Data Set ◽  
High Risk Area

ABSTRACT The East Mediterranean is an area of high oil traffic because it is an important transit centre between Middle Eastern/Russian oil and the western European countries/USA. Recent traffic developments show that the importance of this centre is expected to increase. ITOPF and REMPEC carried out a joint risk assessment study of the area. For the purpose of this paper the “East Mediterranean” includes the Adriatic Sea and the East Mediterranean Basin; this was necessary to carry out a comprehensive analysis of the issue, as the oil traffic in the Adriatic is strictly linked with the activities occurring in the East Mediterranean basin. The aim of this study is to test the hypothesis that the East Mediterranean is a high risk area for oil spills. For this analysis the ITOPF oil spill data set was used (1974 to 2003). Results show that the majority of spills involving a quantity of less than 7 tonnes are operational, whereas medium and major spills result from accidents. Crude oil spills appear to have the highest occurrence in each of the spill size categories, with the highest value for major spills; the accident occurrence appears to be closely related to the import flow. A risk analysis for the East Mediterranean has been overdue, as this area is characterised by a very heterogeneous level of preparedness and response, by several sensitive areas, and a lack of active bi/tri-lateral cooperation agreements. It is concluded that the Eastern Mediterranean is a high risk area for tanker spills, and the risk is likely to increase with the predicted increases in tanker traffic.

scholarly journals A note on leveraging synergy in multiple meteorological datasets with deep learning for rainfall-runoff modeling

2020 ◽  
Author(s):  
Frederik Kratzert ◽  
Daniel Klotz ◽  
Sepp Hochreiter ◽  
Grey S. Nearing
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Rainfall Runoff ◽  
Data Set ◽  
Simulation Accuracy ◽  
Meteorological Forcing ◽  
Runoff Modeling ◽  
Long Short Term Memory ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. A deep learning rainfall-runoff model can take multiple meteorological forcing products as inputs and learn to combine them in spatially and temporally dynamic ways. This is demonstrated using Long Short Term Memory networks (LSTMs) trained over basins in the continental US using the CAMELS data set. Using multiple precipitation products (NLDAS, Maurer, DayMet) in a single LSTM significantly improved simulation accuracy relative to using only individual precipitation products. A sensitivity analysis showed that the LSTM learned to utilize different precipitation products in different ways in different basins and for simulating different parts of the hydrograph in individual basins.

scholarly journals Importance of the information content in the study area when regionalising rainfall-runoff model parameters: the role of nested catchments and gauging station density

2020 ◽  
Author(s):  
Mattia Neri ◽  
Juraj Parajka ◽  
Elena Toth
Keyword(s):  
Information Content ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Data Set ◽  
Study Region ◽  
Averaging Methods ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Station Density ◽  
Averaging Techniques

Abstract. The set up of a rainfall-runoff model in a river section where no streamflow measurements are available for its calibration is one of the key research activity for the Prediction in Ungauged Basins (PUB): in order to do so it is possible to regionalise the model parameters based on the information available in gauged sections in the study region. The information content in the data set of gauged river stations plays an essential role in the assessment of the best regionalisation method: this study analyses how the performances of different model regionalisation approaches are influenced by the information richness of the available regional data set, and in particular by its gauging density and by the presence of nested catchments, that are expected to be hydrologically very similar. The research is carried out over a densely gauged dataset covering the Austrian country, applying two different rainfall-runoff models: a semi-distributed version of the HBV model (TUW model), and the Cemaneige-GR6J model. The regionalisation approaches include both methods which transfer the entire set of model parameters from donor catchments, thus maintaining correlation among parameters (output averaging techniques), and methods which derive each target parameter independently, as a function of the calibrated donors’ ones (parameter averaging techniques). The regionalisation techniques are first implemented using all the basins in the dataset as potential donors, showing that the output-averaging methods outperform the parameter-averaging kriging method, highlighting the importance of maintaining the correlation between the parameter values. The regionalisation is then repeated decreasing the information content of the data set, by excluding the nested basins, identified taking into account either the position of the closing section along the river or the percentage of shared drainage area. The parameter-averaging kriging is the method that is less impacted by the exclusion of the nested donors, whereas the methods transferring the entire parameter set from only one donor suffer the highest deterioration, since the single most similar or closest donor is often a nested one. On the other hand, the output-averaging methods degrade more gracefully, showing that exploiting the information resulting from more than one donor increases the robustness of the approach also in regions that do not have so many nested catchments as the Austrian one. Finally, the deterioration resulting from decreasing the station density on the regionalisation was analysed, showing that the output averaging methods using as similarity measure a set of catchment descriptors, rather than the geographical distance, are more capable to adapt to less dense datasets. The study confirms how the predictive accuracy of parameter regionalisation techniques strongly depends on the information content of the dataset of available donor catchments and indicates that the output-averaging approaches, using more than one donor basin but preserving the correlation structure of the parameter set, seem to be preferable for regionalisation purposes in both data-poor and data-rich regions.

scholarly journals Model Calibration Parameter Using Optimization Trial in HEC-HMS for Unda Watershed

2021 ◽  
Vol 930 (1) ◽  
pp. 012040
Author(s):  
G A P Eryani ◽  
I M S Amerta ◽  
M W Jayantari
Keyword(s):  
Simulation Model ◽  
Water Availability ◽  
Resource Planning ◽  
Rainfall Simulation ◽  
Calibration Model ◽  
Hydrological Process ◽  
Rainfall Runoff ◽  
Initial Model ◽  
Runoff Simulation ◽  
Resources Management

Abstract In water resource planning, information on water availability is needed. Nowadays, data on water availability is still difficult to obtain. With technology in the form of a rainfall-runoff simulation model that can predict water availability in the Unda watershed. It can add information about the potential for water in the Unda watershed. It can be used to prepare water resources management in the Unda watershed so that the existing potential can be used sustainably. Based on the rainfall simulation model results in the Unda watershed, it can be concluded that after running the initial model and calibration. The results are obtained R2 value was 0.68 and increased by 9.81% to 0.754. Both the initial model and the calibration model show an efficient R2 value, NASH value increases by 49.93% to 0.713, which includes satisfactory criteria, RMSE value of 1.135 and decreased by 49.47% to 0.758, and the PBIAS value was 44.70% which was classified as unsatisfactory and decreased from 80.24% to 24.80% at the time of calibration which was classified as satisfactory. In general, the overall simulation results are quite good for representing the watershed’s efficient hydrological process.

scholarly journals Investigation of satellite rainfall-driven rainfall–runoff model using deep learning approaches in two different catchments in India

2021 ◽  
Author(s):  
Pavan Kumar Yeditha ◽  
Maheswaran Rathinasamy ◽  
Sai Sumanth Neelamsetty ◽  
Biswa Bhattacharya ◽  
Ankit Agarwal
Keyword(s):  
Deep Learning ◽  
River Basin ◽  
Flood Forecasting ◽  
Precipitation Data ◽  
Rainfall Runoff ◽  
Data Set ◽  
Satellite Precipitation ◽  
Satellite Rainfall ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract Rainfall–runoff models are valuable tools for flood forecasting, management of water resources, and drought warning. With the advancement in space technology, a plethora of satellite precipitation products (SPPs) are available publicly. However, the application of the satellite data for the data-driven rainfall–runoff model is emerging and requires careful investigation. In this work, two satellite rainfall data sets, namely Global Precipitation Measurement-Integrated Multi-Satellite Retrieval Product V6 (GPM-IMERG) and Climate Hazards Group Infrared Precipitation with Station (CHIRPS), are evaluated for the development of rainfall–runoff models and the prediction of 1-day ahead streamflow. The accuracy of the data from the SPPs is compared to the India Meteorological Department (IMD)-gridded precipitation data set. Detection metrics showed that for light rainfall (1–10 mm), the probability of detection (POD) value ranges between 0.67 and 0.75 and with an increasing rainfall range, i.e., medium and heavy rainfall (10–50 mm and &gt;50 mm), the POD values ranged from 0.24 to 0.45. These results indicate that the satellite precipitation performs satisfactorily with reference to the IMD-gridded data set. Using the daily precipitation data of nearly two decades (2000–2018) over two river basins in India's Eastern part, artificial neural network, extreme learning machine (ELM), and long short-time memory (LSTM) models are developed for rainfall–runoff modelling. One-day ahead runoff prediction using the developed rainfall–runoff modelling confirmed that both the SPPs are sufficient to drive the rainfall–runoff models with a reasonable accuracy estimated using the Nash–Sutcliffe Efficiency coefficient, correlation coefficient, and the root-mean-squared error. In particular, the 1-day streamflow forecasts for the Vamsadhara river basin (VRB) using LSTM with GPM-IMERG inputs resulted in NSC values of 0.68 and 0.67, while ELM models for Mahanadhi river basin (MRB) with the same input resulted in NSC values of 0.86 and 0.87, respectively, during training and validation stages. At the same time, the LSTM model with CHIRPS inputs for the VRB resulted in NSC values of 0.68 and 0.65, and the ELM model with CHIRPS inputs for the MRB resulted in NSC values of 0.89 and 0.88, respectively, in training and validation stages. These results indicated that both the SPPs could reliably be used with LSTM and ELM models for rainfall–runoff modelling and streamflow prediction. This paper highlights that deep learning models, such as ELM and LSTM, with the GPM-IMERG products can lead to a new horizon to provide flood forecasting in flood-prone catchments.

scholarly journals Contrasting rainfall-runoff characteristics of floods in desert and Mediterranean basins

2019 ◽  
Vol 23 (6) ◽  
pp. 2665-2678 ◽  
Author(s):  
Davide Zoccatelli ◽  
Francesco Marra ◽  
Moshe Armon ◽  
Yair Rinat ◽  
James A. Smith ◽  
...  
Keyword(s):  
Strong Influence ◽  
Eastern Mediterranean ◽  
Inverse Correlation ◽  
Radar Data ◽  
Moderate Intensity ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Catchment Scale ◽  
Data Set ◽  
Climatic Regions

Abstract. Catchment-scale hydrological studies on drylands are lacking because of the scarcity of consistent data: observations are often available at the plot scale, but their relevance for the catchment scale remains unclear. A database of 24 years of stream gauge discharge and homogeneous high-resolution radar data over the eastern Mediterranean allows us to describe the properties of floods over catchments spanning from desert to Mediterranean climates, and we note that the data set is mostly of moderate intensity floods. Comparing two climatic regions, desert and Mediterranean, we can better identify specific rainfall-runoff properties. Despite the large differences in rainfall forcing between the two regions, the resulting unit peak discharges and runoff coefficients are comparable. Rain depth and antecedent conditions are the most important properties to shape flood response in Mediterranean areas. In deserts, instead, storm core properties display a strong correlation with unit peak discharge and, to a lesser extent, with runoff coefficient. In this region, an inverse correlation with mean catchment annual precipitation suggests also a strong influence of local surface properties. Preliminary analyses suggest that floods in catchments with wet headwater and dry lower section are more similar to desert catchments, with a strong influence of storm core properties on runoff generation.

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