scholarly journals Using globally available soil moisture indicators for flood modelling in Mediterranean catchments

2013 ◽  
Vol 10 (8) ◽  
pp. 10997-11033 ◽  
Author(s):  
C. Massari ◽  
L. Brocca ◽  
S. Barbetta ◽  
C. Papathanasiou ◽  
M. Mimikou ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Weather Forecasting ◽  
Early Warning Systems ◽  
Flood Forecasting ◽  
Rainfall Runoff ◽  
Small Catchment ◽  
Continuous Models ◽  
Available Soil Moisture ◽  
Risk Event ◽  
Mediterranean Regions

Abstract. Floods are one of the most dangerous natural hazards in Mediterranean regions. Flood forecasting tools and early warning systems can be very beneficial to reduce flood risk. Event-based rainfall runoff models are frequently employed for operational flood forecasting purposes because of their simplicity and the reduced number of parameters involved with respect to continuous models. However, the advantages that are related with the reduced parameterization face against the need for a correct initialization of the model, especially in areas affected by strong climate seasonality. On the other hand, the use of continuous models may be very problematic in poorly gauged areas. This paper introduces a simplified continuous rainfall-runoff model, which uses globally available soil moisture retrievals to identify the initial wetness condition of the catchment, and, only event rainfall data to simulate discharge hydrographs. The model calibration involves only 3 parameters. For soil moisture, beside in situ and modelled data, satellite products from the Advanced SCATterometer (ASCAT) and the Advanced Microwave Scanning Radiometer for Earth observation (AMSR-E) sensors are employed. Additionally, the ERA-LAND reanalysis soil moisture product of the European Centre for Medium Range Weather Forecasting (ECMWF) is used. The model was tested in the small catchment of Rafina, 109 km2 located in the Eastern Attica region, Greece. Specifically, fifteen rainfall-runoff events were modelled by considering different configurations for the initial soil moisture conditions. Comparing the performance of the different soil moisture products, it was found that all global indicators allow reproducing fairly well the selected flood events providing much better results than the situation where a constant initial condition is provided. ERA-LAND slightly outperforms the satellite soil moisture products and in general, all the indicators give the same performance obtained by ground and continuously simulated soil moisture data. Due to the wide diffusion of globally available soil moisture retrievals and the small amount of parameters used, the proposed modelling approach is very suitable for runoff prediction in poorly gauged areas.

scholarly journals Using globally available soil moisture indicators for flood modelling in Mediterranean catchments

2014 ◽  
Vol 18 (2) ◽  
pp. 839-853 ◽  
Author(s):  
C. Massari ◽  
L. Brocca ◽  
S. Barbetta ◽  
C. Papathanasiou ◽  
M. Mimikou ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Early Warning Systems ◽  
Flood Forecasting ◽  
Rainfall Runoff ◽  
Flood Events ◽  
Small Catchment ◽  
Continuous Models ◽  
Available Soil Moisture ◽  
Continuous Rainfall

Abstract. Floods are one of the most dangerous natural hazards in Mediterranean regions. Flood forecasting tools and early warning systems can be very beneficial to reducing flood risk. Event-based rainfall–runoff models are frequently employed for operational flood forecasting purposes because of their simplicity and the reduced number of parameters involved with respect to continuous models. However, the advantages related to the reduced parameterization oppose to the need of a correct initialization of the model, especially in areas characterized by strong climate seasonality. In this case, the use of continuous models could be desirable but it is very problematic in poorly gauged areas where continuous rainfall and temperature data are not available. This paper introduces a Simplified Continuous Rainfall–Runoff model (SCRRM), which uses globally available soil moisture retrievals to identify the initial wetness condition of the catchment, and, only event rainfall data to simulate discharge hydrographs. The model calibration involves only three parameters. For soil moisture, besides in situ data, satellite products from the Advanced SCATterometer (ASCAT) and the Advanced Microwave Scanning Radiometer for Earth observation (AMSR-E) sensors were employed. Additionally, the ERA-Land reanalysis soil moisture product of the European Centre for Medium-Range Weather Forecasting (ECMWF) was used. SCRRM was tested in the small catchment of the Rafina River, 109 km2, located in the eastern Attica region, Greece. Specifically, sixteen recorded rainfall–runoff events were simulated by considering the different indicators for the estimation of the initial soil moisture conditions from in situ, satellite and reanalysis data. By comparing the performance of the different soil moisture products, we conclude that: (i) all global indicators allow for a fairly good reproduction of the selected flood events, providing much better results than those obtained from setting constant initial conditions; (ii) the use of all the indicators yields similar results when compared with a standard continuous simulation approach that, however, is more data demanding; (iii) SCRRM is robust since it shows good performances in validation for a significant flood event that occurred on February 2013 (after calibrating the model for small to medium flood events). Due to the wide diffusion of globally available soil moisture retrievals and the limited number of parameters used, the proposed modelling approach is very suitable for runoff prediction in poorly gauged areas.

Digital Elevation Hydrological Modelling in a Small Catchment in South Australia

2003 ◽  
Vol 34 (3) ◽  
pp. 161-178
Author(s):  
H. Sun ◽  
P. S. Cornish ◽  
T. M. Daniell
Keyword(s):  
Soil Moisture ◽  
Spatial Data ◽  
South Australia ◽  
Moisture Distribution ◽  
Rainfall Runoff ◽  
Estimation Errors ◽  
Small Catchment ◽  
Digital Elevation ◽  
Rainfall Runoff Model ◽  
Runoff Model

A rainfall runoff model based on a digital elevation model (DEM) was applied to a small catchment in Happy Valley, South Australia to predict catchment storm runoff. The DEM was used to partition the catchment into several thousand irregular shaped elements. These elements, with an average size of 825 m2 each, form an interconnected one-dimensional flow network for runoff routing. The rainfall runoff model is a kinematic flow model which combines the solving of flow continuity equation and the Manning's equation to generate surface and subsurface runoff. This study improves on the existing rainfall runoff model in several areas. It adds spatial rainfall averaging methods to derive spatial rainfalls for catchment modelling; and it improves the catchment soil moisture representation by developing a boundary wetness index, and relates this index to antecedent catchment flow to derive spatial catchment moisture distribution. Improved runoff predictions were obtained as a result of the improvement in spatial data input and spatial soil moisture representation. The study identifies these improvements as the key areas for better runoff prediction. It demonstrates that where prediction results showed larger than expected variance, it is frequently caused by the inability to derive good spatially distributed input data rather than parameter estimation errors.

scholarly journals The benefit of high-resolution operational weather forecasts for flash flood warning

2008 ◽  
Vol 5 (1) ◽  
pp. 345-377 ◽  
Author(s):  
J. Younis ◽  
S. Anquetin ◽  
J. Thielen
Keyword(s):  
High Resolution ◽  
Weather Forecasting ◽  
Flash Flood ◽  
Flood Forecasting ◽  
Flash Floods ◽  
Rainfall Runoff ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. In Mediterranean Europe, flash flooding is one of the most devastating hazards in terms of human life loss and infrastructures. Over the last two decades, flash floods brought losses of a billion Euros of damage in France alone. One of the problems of flash floods is that warning times are very short, leaving typically only a few hours for civil protection services to act. This study investigates if operationally available shortrange numerical weather forecasts together with a rainfall-runoff model can be used as early indication for the occurrence of flash floods. One of the challenges in flash flood forecasting is that the watersheds are typically small and good observational networks of both rainfall and discharge are rare. Therefore, hydrological models are difficult to calibrate and the simulated river discharges cannot always be compared with ground "truth". The lack of observations in most flash flood prone basins, therefore, lead to develop a method where the excess of the simulated discharge above a critical threshold can provide the forecaster with an indication of potential flood hazard in the area with leadtimes of the order of the weather forecasts. This study is focused on the Cévennes-Vivarais region in the Southeast of the Massif Central in France, a region known for devastating flash floods. The critical aspects of using numerical weather forecasting for flash flood forecasting are being described together with a threshold – exceedance. As case study the severe flash flood event which took place on 8–9 September 2002 has been chosen. The short-range weather forecasts, from the Lokalmodell of the German national weather service, are driving the LISFLOOD model, a hybrid between conceptual and physically based rainfall-runoff model. Results of the study indicate that high resolution operational weather forecasting combined with a rainfall-runoff model could be useful to determine flash floods more than 24 hours in advance.

scholarly journals SHETRAN and HEC HMS Model Evaluation for Runoff and Soil Moisture Simulation in the Jičinka River Catchment (Czech Republic)

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 872
Author(s):  
Vesna Đukić ◽  
Ranka Erić
Keyword(s):  
Soil Moisture ◽  
Czech Republic ◽  
Hydrological Model ◽  
Flash Flood ◽  
Model Performance ◽  
Rainfall Runoff ◽  
The Czech Republic ◽  
River Catchment ◽  
Hydrological Variable ◽  
Model Structures

Due to the improvement of computation power, in recent decades considerable progress has been made in the development of complex hydrological models. On the other hand, simple conceptual models have also been advanced. Previous studies on rainfall–runoff models have shown that model performance depends very much on the model structure. The purpose of this study is to determine whether the use of a complex hydrological model leads to more accurate results or not and to analyze whether some model structures are more efficient than others. Different configurations of the two models of different complexity, the Système Hydrologique Européen TRANsport (SHETRAN) and Hydrologic Modeling System (HEC-HMS), were compared and evaluated in simulating flash flood runoff for the small (75.9 km2) Jičinka River catchment in the Czech Republic. The two models were compared with respect to runoff simulations at the catchment outlet and soil moisture simulations within the catchment. The results indicate that the more complex SHETRAN model outperforms the simpler HEC HMS model in case of runoff, but not for soil moisture. It can be concluded that the models with higher complexity do not necessarily provide better model performance, and that the reliability of hydrological model simulations can vary depending on the hydrological variable under consideration.

EFFECT OF IRRIGATION AND PLANT POPULATION ON YIELD, FRUIT SPECK AND BLOSSOM-END ROT OF PROCESSING TOMATOES

1985 ◽  
Vol 65 (4) ◽  
pp. 1011-1018 ◽  
Author(s):  
C. S. TAN ◽  
B. N. DHANVANTARI
Keyword(s):  
Soil Moisture ◽  
Lycopersicon Esculentum ◽  
Pseudomonas Syringae ◽  
Tomato Fruit ◽  
Plant Population ◽  
Southern Ontario ◽  
Available Soil Moisture ◽  
Blossom End Rot ◽  
Irrigation Effects ◽  
Processing Tomatoes

Two tomato (Lycopersicon esculentum Mill.) cultivars, Heinz-2653 and Campbell-28, were grown on Fox loamy sand in the subhumid region of southern Ontario from 1979 to 1982. Irrigation increased the marketable yields of H-2653 in a dry year, 1982, but not in the other years. Irrigation substantially increased marketable yields of C-28 in 1979 and 1982. Irrigation, when the available soil moisture (ASM) level reached 50%, was no more effective than when the ASM level in the soil was allowed to drop to 25%. Without irrigation yield increased as plant population increased in normal and wet years, but not in a dry year. Blossom-end rot (BER) of C-28 cultivar was markedly reduced by irrigation. Effects of irrigation or plant population treatments on the incidence of fruit speck did not appear to be significant.Key words: Available soil moisture, Lycopersicon esculentum, Pseudomonas syringae pv. tomato, fruit speck

Exploring hydrological processes at a small agricultural catchment in the Czech Republic

2021 ◽  
Author(s):  
Tailin Li ◽  
Nina Noreika ◽  
Jakub Jeřábek ◽  
Tomáš Dostál ◽  
David Zumr
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Hydrological Processes ◽  
Hydraulic Parameters ◽  
Rainfall Runoff ◽  
The Czech Republic ◽  
Mike She ◽  
Stream Discharge ◽  
Soil Hydraulic Parameters ◽  
Soil Hydraulic

<p>A better understanding of hydrological processes in agricultural catchments is not only crucial to hydrologists but also helpful for local farmers. Therefore, we have built the freely-available web-based WALNUD dataset (Water in Agricultural Landscape – NUčice Database) for our experimental catchment Nučice (0.53 km<sup>2</sup>), the Czech Republic. We have included observed precipitation, air temperature, stream discharge, and soil moisture in the dataset. Furthermore, we have applied numerical modelling techniques to investigate the hydrological processes (e.g. soil moisture variability, water balance) at the experimental catchment using the dataset.</p><p>The Nučice catchment, established in 2011, serves for the observation of rainfall-runoff processes, soil erosion and water balance of the cultivated landscape. The average altitude is 401 m a.s.l., the mean land slope is 3.9 %, and the climate is humid continental (mean annual temperature 7.9 °C, average annual precipitation 630 mm). The catchment consists of three fields covering over 95 % of the area. There is a narrow stream which begins as a subsurface drainage pipe in the uppermost field draining the water at catchment. The typical crops are winter wheat, rapeseed, mustard and alfalfa. The installed equipment includes a standard meteorological station, several rain gauges distributed in the area of the basin, and an H flume to monitor the stream discharge, water turbidity and basic water quality indicators. The soil water content (at point scale) and groundwater level are also recorded. Recently, we have installed two cosmic-ray soil moisture sensors (StyX Neutronica) to estimate large-scale topsoil water content at the catchment.</p><p>Even though the soil management and soil properties in the fields of Nučice seem to be nearly homogeneous, we have observed variability in the topsoil moisture pattern. The method for the explanation of the soil water regime was the combination of the connectivity indices and numerical modelling. The soil moisture profiles from the point-scale sensors were processed in a 1-D physically-based soil water model (HYDRUS-1D) to optimize the soil hydraulic parameters. Further, the soil hydraulic parameters were used as input into a 3D spatially-distributed model, MIKE-SHE. The MIKE-SHE simulation has been mainly calibrated with rainfall-runoff observations. Meanwhile, the spatial patterns of the soil moisture were assessed from the simulation for both dry and wet catchment conditions. From the MIKE-SHE simulation, the optimized soil hydraulic parameters have improved the estimation of soil moisture dynamics and runoff generation. Also, the correlation between the observed and simulated soil moisture spatial patterns showed different behaviors during the dry and wet catchment conditions.</p><p>This study has been supported by the Grant Agency of the Czech Technical University in Prague, grant No. SGS20/156/OHK1/3T/11 and the Project SHui which is co-funded by the European Union Project: 773903 and the Chinese MOST.</p>

scholarly journals Forecasting Vegetation Condition with a Bayesian Auto-regressive Distributed Lags (BARDL) Model

2021 ◽  
Author(s):  
Edward E. Salakpi ◽  
Peter D. Hurley ◽  
James M. Muthoka ◽  
Adam B. Barrett ◽  
Andrew Bowell ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Lead Time ◽  
Expert Knowledge ◽  
Research Work ◽  
Early Warning Systems ◽  
Ar Model ◽  
Lead Times ◽  
Vegetation Condition ◽  
Early Action ◽  
Auto Regressive

Abstract. Droughts form a large part of climate/weather-related disasters reported globally. In Africa, pastoralists living in the Arid and Semi-Arid Lands (ASALs) are the worse affected. Prolonged dry spells that cause vegetation stress in these regions have resulted in the loss of income and livelihoods. To curb this, global initiatives like the Paris Agreement and the United Nations recognised the need to establish Early Warning Systems (EWS) to save lives and livelihoods. Existing EWS use a combination of Satellite Earth Observation (EO) based biophysical indicators like the Vegetation Condition Index (VCI) and socio-economic factors to measure and monitor droughts. Most of these EWS rely on expert knowledge in estimating upcoming drought conditions without using forecast models. Recent research has shown that the use of robust algorithms like Auto-Regression, Gaussian Processes and Artificial Neural Networks can provide very skilled models for forecasting vegetation condition at short to medium range lead times. However, to enable preparedness for early action, forecasts with a longer lead time are needed. The objective of this research work is to develop models that forecast vegetation conditions at longer lead times on the premise that vegetation condition is controlled by factors like precipitation and soil moisture. To achieve this, we used a Bayesian Auto-Regressive Distributed Lag (BARDL) modelling approach which enabled us to factor in lagged information from Precipitation and Soil moisture levels into our VCI forecast model. The results showed a ∼2-week gain in the forecast range compared to the univariate AR model used as a baseline. The R2 scores for the Bayesian ARDL model were 0.94, 0.85 and 0.74, compared to the AR model's R2 of 0.88, 0.77 and 0.65 for 6, 8 and 10 weeks lead time respectively.

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