scholarly journals Seasonality of hydrological model spin-up time: a case study using the Xinanjiang model

2016 ◽  
Author(s):  
Mohammad M. Rahman ◽  
Minjiao Lu ◽  
Khin H. Kyi
Keyword(s):  
Soil Moisture ◽  
Equilibrium State ◽  
River Basin ◽  
Input Data ◽  
Hydrological Model ◽  
Initial Conditions ◽  
Aridity Index ◽  
Adjustment Process ◽  
Data Sets ◽  
Xinanjiang Model

Abstract. The internal adjustment process of a hydrological model followed by an unusual initial condition is known as the model spin-up. And the time required for a complete adjustment is termed as the model spin-up time. Model results for the duration of this spin-up progression are greatly impacted by the initial conditions, and often impractical or erroneous. The speed of this adjustment process is affected by the characteristics of the input data sets and their persistence. This study discusses the variability and seasonality of hydrological model spin-up time against the aridity of the river basin using multi-year climatologies for 18 river basins distributed relatively snow-free regions of the USA. The Xinanjiang model was run with each of all available year input data sets with two extreme initial conditions (saturated and unsaturated) and thereafter detected the model equilibrium state based on the Mahalanobis distance between the soil moisture states of two model runs. The seasonality of model spin-up was investigated by conducting multiple simulations that start from different time of a year. The basin average soil moisture memory (SMM) timescale (Rahman et al., 2015) and basin aridity index was estimated and thereafter investigated their relationship with the average model spin-up time. Analysis suggests that the spin-up time highly varies with the simulation starting time and the dryness of the river basin. Overall, in all basins, model achieves the equilibrium state quickly while the simulation starts in late autumn (October–November). On the other hand, model equilibrates slowly while simulation starts in spring (March–May). Wet basin shows stronger variability of the model spin-up time (mean range 154 days) throughout the year as compared with that of dry basins (mean range 78 days). The mean spin-up time is shorter for wet basins (154 days) and longer for dry basins (233 days). The spin-up times are 3–7 times longer than the SMM timescale. The basin-wise mean spin-up time shows linear and exponential relationship with the SMM timescale and the basin aridity index respectively. The relationship offers predictability of model spin-up time from widely available potential evaporation and precipitation data sets.

scholarly journals Multi-site calibration and validation of SWAT with satellite-based evapotranspiration in a data-sparse catchment in southwestern Nigeria

2019 ◽  
Vol 23 (2) ◽  
pp. 1113-1144 ◽  
Author(s):  
Abolanle E. Odusanya ◽  
Bano Mehdi ◽  
Christoph Schürz ◽  
Adebayo O. Oke ◽  
Olufiropo S. Awokola ◽  
...  
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Model Calibration ◽  
Hydrological Model ◽  
Assessment Tool ◽  
Potential Evapotranspiration ◽  
Swat Model ◽  
Water Balance Components ◽  
Southwestern Nigeria ◽  
Calibration And Validation

Abstract. The main objective of this study was to calibrate and validate the eco-hydrological model Soil and Water Assessment Tool (SWAT) with satellite-based actual evapotranspiration (AET) data from the Global Land Evaporation Amsterdam Model (GLEAM_v3.0a) and from the Moderate Resolution Imaging Spectroradiometer Global Evaporation (MOD16) for the Ogun River Basin (20 292 km2) located in southwestern Nigeria. Three potential evapotranspiration (PET) equations (Hargreaves, Priestley–Taylor and Penman–Monteith) were used for the SWAT simulation of AET. The reference simulations were the three AET variables simulated with SWAT before model calibration took place. The sequential uncertainty fitting technique (SUFI-2) was used for the SWAT model sensitivity analysis, calibration, validation and uncertainty analysis. The GLEAM_v3.0a and MOD16 products were subsequently used to calibrate the three SWAT-simulated AET variables, thereby obtaining six calibrations–validations at a monthly timescale. The model performance for the three SWAT model runs was evaluated for each of the 53 subbasins against the GLEAM_v3.0a and MOD16 products, which enabled the best model run with the highest-performing satellite-based AET product to be chosen. A verification of the simulated AET variable was carried out by (i) comparing the simulated AET of the calibrated model to GLEAM_v3.0b AET, which is a product that has different forcing data than the version of GLEAM used for the calibration, and (ii) assessing the long-term average annual and average monthly water balances at the outlet of the watershed. Overall, the SWAT model, composed of the Hargreaves PET equation and calibrated using the GLEAM_v3.0a data (GS1), performed well for the simulation of AET and provided a good level of confidence for using the SWAT model as a decision support tool. The 95 % uncertainty of the SWAT-simulated variable bracketed most of the satellite-based AET data in each subbasin. A validation of the simulated soil moisture dynamics for GS1 was carried out using satellite-retrieved soil moisture data, which revealed good agreement. The SWAT model (GS1) also captured the seasonal variability of the water balance components at the outlet of the watershed. This study demonstrated the potential to use remotely sensed evapotranspiration data for hydrological model calibration and validation in a sparsely gauged large river basin with reasonable accuracy. The novelty of the study is the use of these freely available satellite-derived AET datasets to effectively calibrate and validate an eco-hydrological model for a data-scarce catchment.

scholarly journals Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1387 ◽  
Author(s):  
Le ◽  
Ho ◽  
Lee ◽  
Jung
Keyword(s):  
Neural Network ◽  
River Basin ◽  
Input Data ◽  
Short Term Memory ◽  
Flood Forecasting ◽  
Data Sets ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Hoa Binh

Flood forecasting is an essential requirement in integrated water resource management. This paper suggests a Long Short-Term Memory (LSTM) neural network model for flood forecasting, where the daily discharge and rainfall were used as input data. Moreover, characteristics of the data sets which may influence the model performance were also of interest. As a result, the Da River basin in Vietnam was chosen and two different combinations of input data sets from before 1985 (when the Hoa Binh dam was built) were used for one-day, two-day, and three-day flowrate forecasting ahead at Hoa Binh Station. The predictive ability of the model is quite impressive: The Nash–Sutcliffe efficiency (NSE) reached 99%, 95%, and 87% corresponding to three forecasting cases, respectively. The findings of this study suggest a viable option for flood forecasting on the Da River in Vietnam, where the river basin stretches between many countries and downstream flows (Vietnam) may fluctuate suddenly due to flood discharge from upstream hydroelectric reservoirs.

scholarly journals On the sources of hydrological prediction uncertainty in the Amazon

2012 ◽  
Vol 16 (9) ◽  
pp. 3127-3137 ◽  
Author(s):  
R. C. D. Paiva ◽  
W. Collischonn ◽  
M. P. Bonnet ◽  
L. G. G. de Gonçalves
Keyword(s):  
Data Assimilation ◽  
River Basin ◽  
Hydrological Model ◽  
Initial Conditions ◽  
State Variables ◽  
Streamflow Prediction ◽  
Amazon River Basin ◽  
Forecast Uncertainty ◽  
Ensemble Streamflow Prediction ◽  
Hydrological Forecast

Abstract. Recent extreme events in the Amazon River basin and the vulnerability of local population motivate the development of hydrological forecast systems using process based models for this region. In this direction, the knowledge of the source of errors in hydrological forecast systems may guide the choice on improving model structure, model forcings or developing data assimilation systems for estimation of initial model states. We evaluate the relative importance of hydrologic initial conditions and model meteorological forcings errors (precipitation) as sources of stream flow forecast uncertainty in the Amazon River basin. We used a hindcast approach that compares Ensemble Streamflow Prediction (ESP) and a reverse Ensemble Streamflow Prediction (reverse-ESP). Simulations were performed using the physically-based and distributed hydrological model MGB-IPH, comprising surface energy and water balance, soil water, river and floodplain hydrodynamics processes. The model was forced using TRMM 3B42 precipitation estimates. Results show that uncertainty on initial conditions plays an important role for discharge predictability, even for large lead times (∼1 to 3 months) on main Amazonian Rivers. Initial conditions of surface waters state variables are the major source of hydrological forecast uncertainty, mainly in rivers with low slope and large floodplains. Initial conditions of groundwater state variables are important, mostly during low flow period and in the southeast part of the Amazon where lithology and the strong rainfall seasonality with a marked dry season may be the explaining factors. Analyses indicate that hydrological forecasts based on a hydrological model forced with historical meteorological data and optimal initial conditions may be feasible. Also, development of data assimilation methods is encouraged for this region.

Catchment scale prediction of soil moisture trends from Cosmic Ray Neutron Rover Surveys using machine learning

2020 ◽  
Author(s):  
Erik Nixdorf ◽  
Marco Hannemann ◽  
Uta Ködel ◽  
Martin Schrön ◽  
Thomas Kalbacher
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Random Forest ◽  
River Basin ◽  
Hydrological Model ◽  
Cosmic Ray ◽  
Moisture Distribution ◽  
Catchment Scale ◽  
Forest Model ◽  
Soil Moisture Distribution

<p>Soil moisture is a critical hydrological component for determining hydrological state conditions and a crucial variable in controlling land-atmosphere interaction including evapotranspiration, infiltration and groundwater recharge.</p><p>At the catchment scale, spatial- temporal variations of soil moisture distribution are highly variable due to the influence of various factors such as soil heterogeneity, climate conditions, vegetation and geomorphology. Among the various existing soil moisture monitoring techniques, the application of vehicle-mounted Cosmic Ray Sensors (CRNS) allows monitoring soil moisture noninvasively by surveying larger regions within a reasonable time. However, measured data and their corresponding footprints are often allocated along the existing road network leaving inaccessible parts of a catchment unobserved and surveying larger areas in short intervals is often hindered by limited manpower.</p><p>In this study, data from more than 200 000 CRNS rover readings measured over different regions of Germany within the last 4 years have been employed to characterize the trends of soil moisture distribution in the 209 km<sup>2</sup> large Mueglitz River Basin in Eastern Germany. Subsets of the data have been used to train three different supervised machine learning algorithms (multiple linear regression, random forest and artificial neural network) based on 85 independent relevant dynamic and stationary features derived from public databases.  The Random Forest model outperforms the other models (R2= ~0.8), relying on day-of-year, altitude, air temperature, humidity, soil organic carbon content and soil temperature as the five most influencing predictors.</p><p>After test and training the models, CRNS records for each day of the last decade are predicted on a 250 × 250 m grid of Mueglitz River Basin using the same type of features. Derived CRNS record distributions are compared with both, spatial soil moisture estimates from a hydrological model and point estimates from a sensor network operated during spring 2019. After variable standardization, preliminary results show that the applied Random Forest model is able to resemble the spatio-temporal trends estimated by the hydrological model and the point measurements. These findings demonstrate that training machine learning models on domain-unspecific large datasets of CRNS records using spatial-temporally available predictors has the potential to fill measurement gaps and to improve soil moisture dynamics predictions on a catchment scale.</p>

scholarly journals ESA CCI Soil Moisture Assimilation in SWAT for Improved Hydrological Simulation in Upper Huai River Basin

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yongwei Liu ◽  
Wen Wang ◽  
Yuanbo Liu
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Hydrological Model ◽  
European Space Agency ◽  
Low Flow ◽  
Observation Error ◽  
Rainfall Runoff ◽  
Huai River Basin ◽  
Huai River ◽  
Streamflow Simulation

The assimilation of satellite soil moisture (SM) products with coarse resolution is promising in improving rainfall-runoff modeling, but it is largely impacted by the data assimilation (DA) strategy. This study performs the assimilation of a satellite soil moisture product from the European Space Agency (ESA) Climate Change Initiative (CCI) in a physically based semidistributed hydrological model (SWAT) in the upper Huai River basin in China, with the objective to improve its rainfall-runoff simulation. In this assimilation, the ensemble Kalman filter (EnKF) is adopted with full consideration of the model and observation error, the rescaling technique for satellite SM, and the regional applicability of the hydrological model. The results show that the ESA CCI SM assimilation generally improves the streamflow simulation of the study catchment. It is more effective for low-flow simulation, while for very high-flow/large-flood modeling, the DA performance shows uncertainty. The less-effective performance on large-flood simulation lies in the relatively low dependence of rainfall-runoff generation on the antecedent SM as during which the SM is nearly saturated and the runoff is largely dominated by precipitation. Besides, the efficiency of DA is deteriorated by the dense forest coverage and the complex topography conditions of the basin. Overall, the ESA CCI SM assimilation improves the streamflow simulation of the SWAT model in particular for low flow. This study provides an encouragement for the application of the ESA CCI SM in water management, especially over low-flow periods.

scholarly journals Pedotransfer function application for estimation of soil hydrophysical properties using parametric methods

2008 ◽  
Vol 53 (No. 4) ◽  
pp. 149-157 ◽  
Author(s):  
S. Matula ◽  
K. Špongrová
Keyword(s):  
Soil Moisture ◽  
Hydraulic Conductivity ◽  
Soil Profile ◽  
Input Data ◽  
Transfer Functions ◽  
Organic Matter Content ◽  
Pressure Head ◽  
Matter Content ◽  
Pedotransfer Functions ◽  
Data Sets

Soil hydraulic properties are needed as input data to describe and simulate the transport of water and solutes in the soil profile. The most important characteristics are the soil moisture retention curve (SMRC) &theta;(<i>h</i>) and the hydraulic conductivity function <i>k</i>(&theta;) or <i>k</i>(<i>h</i>), where &theta; is the soil moisture content, <i>h</i> is the pressure head and <i>k</i> is the hydraulic conductivity. SMRC represents the amount of water remaining in the soil under equilibrium conditions and is unique for each soil. The measurement of SMRC is laborious and time-consuming and so there are not enough data available sometimes. Various SMRC estimation models have been proposed and used extensively to overcome this problem. Other more easily available soil properties, such as particle size distribution, organic matter content, soil structure and bulk density, were used for the estimation of SMRC. Bouma and van Lanen (1987) called these models &ldquo;transfer functions&rdquo;, and later on they were called &ldquo;pedotransfer functions&rdquo;. This study is based on European works by Wösten et al. (1998, 1999), and others. The pedotransfer functions derived by Wösten et al. (1998) were used in the first part of the study. In the second part, the authors derived their own pedotransfer functions for the sites where all necessary data were available. The methodology of data processing was similar to that used by Wösten et al. (1998) for continuous pedotransfer functions. The use of continuous pedotransfer functions was tested on data sets from several sites in the Czech Republic (Cerhovice, Černičí, Brozany, Ovesná Lhota, Tupadly, Džbánov, Podlesí and Žichlínek). Unfortunately, the available Czech data sets are not as large as the data sets used in Wösten&rsquo;s work. Quite good new estimates of SMRC (expressed as pF curves) were found e.g. for the Cerhovice and Černičí sites; the estimates for a man-made soil profile in Brozany and for natural soils in Ovesná Lhota, Tupadly, Džbánov, Podlesí and Žichlínek were less successful, partly because of insufficient input data. The applications of continuous pedotransfer functions derived by Wösten et al. (1998) for the Czech data sets were not very successful, either. The quality and size of the input data sets are critical factors for a successful use of pedotransfer functions.

HydroGFD3: a climatological and real-time updated hydrological forcing dataset

2020 ◽  
Author(s):  
Peter Berg ◽  
Fredrik Almén ◽  
Denica Bozhinova ◽  
Riejanne Mook
Keyword(s):  
Real Time ◽  
Observational Data ◽  
Bias Correction ◽  
Hydrological Model ◽  
Initial Conditions ◽  
Correction Method ◽  
Reanalysis Data ◽  
Data Sets ◽  
Initial State ◽  
Data Set

&lt;p&gt;Hydrological forecasting benefits substantially from good initial conditions, which translate information into the forecast. It is therefore important to perform frequent updates of the initial state of the model before the forecast, which demands good meteorological forcing data. For a continental or global hydrological model, it is difficult to find observational data sets which fulfill the requirements of (i) long time series for calibration and spin up, (ii) consistent quality, (iii) at least daily time steps, and (iv) at least data for temperature and precipitation. HydroGFD3 is a new data set that fulfills all the criteria and provides real-time updated data.&lt;/p&gt;&lt;p&gt;HydroGFD3 builds upon the ERA5 reanalysis data set, and performs a bias correction for each new produced month. In contrast to earlier versions (Berg et al., 2018), HydroGFD3 is based on a multi-source climatological background, upon which individual days are produced by adding anomalies from different freely available monthly global observational data sets. These are then disaggregated based on the ERA5 reanalysis. For production redundancy and local tailoring, HydroGFD3 is produced in several tiers, each using different observational data sets originating from GPCC and CPC. Further, intermediate daily updates of the reanalysis through the source ERA5T allow the data set to be updated to within a few days of real-time.&lt;/p&gt;&lt;p&gt;To reach actual real-time, one tier is based on a bias correction method calibrated on the period 1980-2009, which is applied on ERA5T, and further prolonged to current day using the ECMWF deterministic forecasts. The assumption for this to work is that the forecasts have a similar bias as the reanalysis model, which is currently the case. The method also allows bias correction of the forecasts themselves; solving the issue of &amp;#8220;drift&amp;#8221; in the forecasts as the hydrological model adjusts to the (biased) climatological state of the forcing data.&lt;/p&gt;&lt;p&gt;Berg, Peter, Chantal Donnelly, and David Gustafsson. &quot;Near-real-time adjusted reanalysis forcing data for hydrology.&quot; Hydrology and Earth System Sciences 22.2 (2018): 989-1000.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals An Analytical Hydrological Model for the Study of Scaling Issues in Land Surface Modeling

2006 ◽  
Vol 10 (20) ◽  
pp. 1-24 ◽  
Author(s):  
Diandong Ren ◽  
Ann Henderson-Sellers
Keyword(s):  
Soil Moisture ◽  
Steady State ◽  
Analytical Model ◽  
Hydrological Model ◽  
Clay Soil ◽  
Initial Conditions ◽  
Moisture Condition ◽  
Soil Moisture Condition ◽  
Initial Soil Moisture ◽  
Initial Soil

Abstract Besides the atmospheric forcing such as solar radiation input and precipitation, the heterogeneity of the surface cover also plays an important role, especially in the distribution characteristics of the latent heat flux (LE). In this study, scaling issues are discussed based on an analytical hydrological model that describes the transpiration and diffusion processes of soil water. The solution of this analytical model is composed of a transient part that depends primarily on initial conditions and a steady part that depends on the boundary conditions. To know how sensitive the different averaging approaches are to the initial conditions, three initial profiles are chosen that cover the prevailing soil moisture regimes. After analyzing its solution, the study shows that 1) upon reaching the steady state, directly taking an average of soil properties will cause systematic overestimation in the calculation of area-averaged LE. For an initially very dry condition, averaging of a sandy soil and a clay soil can cause a percentage error as large as 40%. 2) For vegetation growing on sandy soils, a direct averaging of the transpiration rate results in persistent overestimation of LE. For vegetation growing on clay soil, however, even after reaching the steady state, averaging of two water extraction weights can be either an overestimation or an underestimation, depending on which two vegetation types are involved. 3) During the interim stage of drying down, averaging of the soil/vegetation properties can lead to either an overestimation or an underestimation, depending on the evolving stage of the soil moisture profile. 4) The initial soil moisture condition matters during the transient stage of drying down. Different initial soil moisture conditions yield different scenarios of underestimation and overestimation patterns and a differing severity of errors. The simplicity of the analytical model and the heuristic initial soil profiles make the generalization easier than using sophisticated numerical models and make the causality mechanism clearer for physical interpretations.

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