Soil Moisture Prediction for Huaihe River Basin Using Hydrological Model XXT and TOPMODEL

2013 ◽  
Vol 433-435 ◽  
pp. 1817-1820 ◽  
Author(s):  
Jing Wen Xu ◽  
Jun Fang Zhao ◽  
Peng Wang ◽  
Shuang Liu
Keyword(s):  
Soil Moisture ◽  
Hydrological Model ◽  
Temporal Change ◽  
Agricultural Drought ◽  
Hydrological Models ◽  
Huaihe River ◽  
Change Pattern ◽  
The Mean ◽  
The Huaihe River ◽  
Future Work

Soil moisture plays an important role in agricultural drought predicting. Hydrological models can be employed to forecast soil moisture. In order to get better predicted soil moisture information, we use two basin hydrological models, i.e. XXT and TOPMODEL, to forecast the soil moisture for Huaihe River watershed. The performance of both the two models was tested in the Linyi watershed with a drainage area of 10040 km2, a tributary of the Huaihe river, China. The results show that the soil moisture simulated by the XXT is more agree with the observed ones than that simulated by TOPMODE compared to the filed observed soil moisture at 10 cm or the mean ones of 10 cm, 20 com, and 40 cm from surface, and that the predicted soil moisture by both the models has the similar trend and temporal change pattern with the observed one. However, both the models need to be improved in soil moisture forecasting in the future work.

scholarly journals Ensemble long-term soil moisture forecast using hydrological modeling

RBRH ◽  
2020 ◽  
Vol 25 ◽  
Author(s):  
Bibiana Rodrigues Colossi ◽  
Carlos Eduardo Morelli Tucci
Keyword(s):  
Soil Moisture ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Computing Time ◽  
Ensemble Member ◽  
Lead Times ◽  
Hydrologic Simulation ◽  
The Mean ◽  
Group B

ABSTRACT Long-term soil moisture forecasting allows for better planning in sectors as agriculture. However, there are still few studies dedicated to estimate soil moisture for long lead times, which reflects the difficulties associated with this topic. An approach that could help improving these forecasts performance is to use ensemble predictions. In this study, a soil moisture forecast for lead times of one, three and six months in the Ijuí River Basin (Brazil) was developed using ensemble precipitation forecasts and hydrologic simulation. All ensemble members from three climatologic models were used to run the MGB hydrological model, generating 207 soil moisture forecasts, organized in groups: (A) for each model, the most frequent soil moisture interval predicted among the forecasts made with each ensemble member, (B) using each model’s mean precipitation, (C) considering a super-ensemble, and (D) the mean soil moisture interval predicted among group B forecasts. The results show that long-term soil moisture based on precipitation forecasts can be useful for identifying periods drier or wetter than the average for the studied region. Nevertheless, estimation of exact soil moisture values remains limited. Forecasts groups B and D performed similarly to groups A and C, and require less data management and computing time.

scholarly journals Predicting Rainfall and Runoff Through Satellite Soil Moisture Data and SWAT Modelling for a Poorly Gauged Basin in Iran

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 594 ◽  
Author(s):  
Majid Fereidoon ◽  
Manfred Koch ◽  
Luca Brocca
Keyword(s):  
Soil Moisture ◽  
Hydrological Model ◽  
Assessment Tool ◽  
Swat Model ◽  
Rainfall Data ◽  
Coefficient Of Determination ◽  
Hydrological Models ◽  
Streamflow Prediction ◽  
Soil Moisture Data ◽  
Monthly Runoff

Hydrological models are widely used for many purposes in water sector projects, including streamflow prediction and flood risk assessment. Among the input data used in such hydrological models, the spatial-temporal variability of rainfall datasets has a significant role on the final discharge estimation. Therefore, accurate measurements of rainfall are vital. On the other hand, ground-based measurement networks, mainly in developing countries, are either nonexistent or too sparse to capture rainfall accurately. In addition to in-situ rainfall datasets, satellite-derived rainfall products are currently available globally with high spatial and temporal resolution. An innovative approach called SM2RAIN that estimates rainfall from soil moisture data has been applied successfully to various regions. In this study, first, soil moisture content derived from the Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E) is used as input into the SM2RAIN algorithm to estimate daily rainfall (SM2R-AMSRE) at different sites in the Karkheh river basin (KRB), southwest Iran. Second, the SWAT (Soil and Water Assessment Tool) hydrological model was applied to simulate runoff using both ground-based observed rainfall and SM2R-AMSRE rainfall as input. The results reveal that the SM2R-AMSRE rainfall data are, in most cases, in good agreement with ground-based rainfall, with correlations R ranging between 0.58 and 0.88, though there is some underestimation of the observed rainfall due to soil moisture saturation not accounted for in the SM2RAIN equation. The subsequent SWAT-simulated monthly runoff from SM2R-AMSRE rainfall data (SWAT-SM2R-AMSRE) reproduces the observations at the six gauging stations (with coefficient of determination, R² > 0.71 and NSE > 0.56), though with slightly worse performances in terms of bias (Bias) and root-mean-square error (RMSE) and, again, some systematic flow underestimation compared to the SWAT model with ground-based rainfall input. Additionally, rainfall estimates of two satellite products of the Tropical Rainfall Measuring Mission (TRMM), 3B42 and 3B42RT, are used in the calibrated SWAT- model after bias correction. The monthly runoff predictions obtained with 3B42- rainfall have 0.42 < R2 < 0.72 and−0.06 < NSE < 0.74 which are slightly better than those obtained with 3B42RT- rainfall, but not as good as the SWAT-SM2R-AMSRE. Therefore, despite the aforementioned limitations, using SM2R-AMSRE rainfall data in a hydrological model like SWAT appears to be a viable approach in basins with limited ground-based rainfall data.

scholarly journals Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa

2008 ◽  
Vol 12 (3) ◽  
pp. 751-767 ◽  
Author(s):  
T. Vischel ◽  
G. G. S. Pegram ◽  
S. Sinclair ◽  
W. Wagner ◽  
A. Bartsch
Keyword(s):  
South Africa ◽  
Remote Sensing ◽  
Soil Moisture ◽  
Hydrological Model ◽  
Regional Scale ◽  
Remotely Sensed ◽  
Hydrological Models ◽  
Good Correspondence ◽  
Physically Based

Abstract. The paper compares two independent approaches to estimate soil moisture at the regional scale over a 4625 km2 catchment (Liebenbergsvlei, South Africa). The first estimate is derived from a physically-based hydrological model (TOPKAPI). The second estimate is derived from the scatterometer on board the European Remote Sensing satellite (ERS). Results show a good correspondence between the modelled and remotely sensed soil moisture, particularly with respect to the soil moisture dynamic, illustrated over two selected seasons of 8 months, yielding regression R2 coefficients lying between 0.68 and 0.92. Such a close similarity between these two different, independent approaches is very promising for (i) remote sensing in general (ii) the use of hydrological models to back-calculate and disaggregate the satellite soil moisture estimate and (iii) for hydrological models to assimilate the remotely sensed soil moisture.

A new numerical model for simulating top surface soil moisture and runoff

2018 ◽  
Vol 35 (3) ◽  
pp. 1344-1363 ◽  
Author(s):  
Jiongfeng Chen ◽  
Wan-chang Zhang
Keyword(s):  
Soil Moisture ◽  
Stream Flow ◽  
Hydrological Model ◽  
Surface Soil ◽  
Flow Simulation ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Surface Soil Moisture ◽  
Content Type ◽  
Simulation Results

PurposeThis paper aims to construct a simplified distributed hydrological model based on the surveyed watershed soil properties database.Design/methodology/approachThe new established model requires fewer parameters to be adjusted than needed by former hydrological models. However, the achieved stream-flow simulation results are similar and comparable to the classic hydrological models, such as the Xinanjiang model and the TOPMODEL.FindingsGood results show that the discharge and the top surface soil moisture can be simultaneously simulated, and that is the exclusive character of this new model. The stream-flow simulation results from two moderate hydrological watershed models show that the daily stream-flow simulation achieved the classic hydrological results shown in the TOPMODEL and Xinanjiang model. The soil moisture validation results show that the modeled watershed scale surface soil moisture has general agreement with the obtained measurements, with a root-mean-square error (RMSE) value of 0.04 (m3/m3) for one of the one-measurement sites and an averaged RMSE of 0.08 (m3/m3) over all measurements.Originality/valueIn this paper, a new simplified distributed hydrological model was constructed.

scholarly journals The Role of Hydrological Signatures in Calibration of Conceptual Hydrological Model

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3401
Author(s):  
Eva Melišová ◽  
Adam Vizina ◽  
Linda R. Staponites ◽  
Martin Hanel
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Goodness Of Fit ◽  
Hydrological Model ◽  
Hydrological Models ◽  
Balance Assessment ◽  
The Czech Republic ◽  
Hydrological Signatures

Determining an optimal calibration strategy for hydrological models is essential for a robust and accurate water balance assessment, in particular, for catchments with limited observed data. In the present study, the hydrological model Bilan was used to simulate hydrological balance for 20 catchments throughout the Czech Republic during the period 1981–2016. Calibration strategies utilizing observed runoff and estimated soil moisture time series were compared with those using only long-term statistics (signatures) of runoff and soil moisture as well as a combination of signatures and time series. Calibration strategies were evaluated considering the goodness-of-fit, the bias in flow duration curve and runoff signatures and uncertainty of the Bilan model. Results indicate that the expert calibration and calibration with observed runoff time series are, in general, preferred. On the other hand, we show that, in many cases, the extension of the calibration criteria to also include runoff or soil moisture signatures is beneficial, particularly for decreasing the uncertainty in parameters of the hydrological model. Moreover, in many cases, fitting the model with hydrological signatures only provides a comparable fit to that of the calibration strategies employing runoff time series.

Assimilation of soil moisture data for improving streamflow prediction: Is there a role for the hydrological model structure?

2020 ◽  
Author(s):  
Aruna Kumar Nayak ◽  
Basudev Biswal ◽  
Kulamulla Parambath Sudheer
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Hydrological Model ◽  
Assimilation Efficiency ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
The Past ◽  
Soil Moisture Data

&lt;p&gt;Soil moisture data assimilation has found increased applicability in hydrology due to easily available remotely sensed soil moisture data. Numerous studies in the past have explored the possibility of assimilating soil moisture information for improving streamflow forecasting. The general understanding is that if better soil moisture data can provide better streamflow forecast. However, to our knowledge no study has so far focused on understanding if the hydrological model itself has a role in assimilation of soil moisture data. In this regard, here we use three different conceptual hydrological models for soil moisture assimilation: (1) Dynamic Budyko (DB), (2) GR4J, and (3) PDM model. Assimilation of GLDAS observed soil moisture is carried out for four MOPEX basins using Ensemble Kalman Filter. DB model&amp;#8217;s performance improved after soil moisture data assimilation for all the study basins. However, deterioration in performance was observed for GR4J and PDM for all the basins after the assimilation exercise. The performance of the assimilated models is evaluated in terms of Assimilation Efficiency (AE), which was found to be varying from 17.11 to 22.56%, from -20.98 to -41.29%, and from -8.4 to -38.23%, respectively, for DB, GR4J, and PDM. Overall, our results highlight the importance of the hydrological models structure in a soil moisture data assimilation exercise.&lt;/p&gt;

scholarly journals APPLICATION OF A MACRO GRID BASED HYDROLOGICAL MODEL TO THE HUAIHE RIVER BASIN IN CHINA

2001 ◽  
Vol 45 ◽  
pp. 127-132 ◽  
Author(s):  
Y. TACHIKAWA ◽  
T. KAWAKAMI ◽  
Y. ICHIKAWA ◽  
M. SHIIBA ◽  
K. TAKARA
Keyword(s):  
River Basin ◽  
Hydrological Model ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
The Huaihe River ◽  
Grid Based

scholarly journals Response of Agricultural Drought to Meteorological Drought: A Case Study of the Winter Wheat above the Bengbu Sluice in the Huaihe River Basin, China

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2805
Author(s):  
Chao Gao ◽  
Cai Chen ◽  
Yi He ◽  
Tian Ruan ◽  
Gang Luo ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Time Lag ◽  
Growth Period ◽  
Meteorological Drought ◽  
Agricultural Drought ◽  
Underlying Surface ◽  
Huaihe River ◽  
Moisture Index ◽  
Geographical Factors ◽  
The Huaihe River

This study investigated the responses of winter wheat to drought for the above part of the Bengbu Sluice in the Huaihe River based on the daily scale dataset of 60 meteorological stations from 1961–2015. Crop water deficit index (CWDI) and relative moisture index (M) were used to examine the winter wheat drought and meteorological drought, respectively. We then analyzed the spatial-temporal evolution characteristics of these two kinds of drought to calculate the time lag of winter wheat drought to meteorological drought, and finally discuss the relationship between the time lag of winter wheat drought to meteorological drought and the underlying surface geographical factors, and drew the following conclusions. (1) In terms of time scale, for CWDI, except for the filling and mature period, the CWDI at other growth periods showed a slight downward trend; for M, there was no significant change in the interannual trend of each growth period. In terms of spatial scale, the proportion of above moderate drought level in each station of CWDI and M presented a decreasing feature from north to south. (2) The time lag of winter wheat drought to meteorological drought was the shortest (3.21 days) in the greening and heading period and the longest in the over-wintering period (84.35 days). (3) The correlation between the geographical factors and the time lag of winter wheat drought in each growth period was better than 0.5. The high-value points of the relation between the underlying surface geographical factors and the time lag of winter wheat drought were mostly distributed in the mountainous areas with poor soil field capacity and at a greater depth of shallow groundwater, high elevation and steep slope in the areas with aspects to the east and northeast, and the northern areas with less precipitation and lower temperature.

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