Availability Of Hydrologic Data Research Articles

A New Benchmark on Machine Learning Methodologies for Hydrological Processes Modelling: A Comprehensive Review for Limitations and Future Research Directions

The best practice of watershed management is through the understanding of the hydrological processes. As a matter of fact, hydrological processes are highly associated with stochastic, non-linear, and non-stationary phenomena. Hydrological processes simulation and modeling are challenging issues in the domains of hydrology, climate and environment. Hence, the development of machine learning (ML) models for solving those complex hydrological problems took essential place over the past couple decades. It can be observed, hydrological data availability has increased remarkably, and thus computational resources has led to a resurgence in ML models’ development. It has been witnessed huge efforts on the hydrological processes modeling using the facility of ML models and several review researches have been conducted. Literature studies approved the capacity of ML models in the field of hydrology over the classical “traditional models” based on their forecastability, flexibility, precision, generalization, and modeling execution convergence speed. However, although several potential merits were observed in ML model’s development, several limitations are allied such as the interpretability of those black-box models, the practicality of the ML models in watershed management, and difficulty to explain the physical hydrological processes. In this survey, an exhibition for all the published review articles on the development of ML models for hydrological processes and recognize all the research gaps and potential research direction. The ultimate aim of the current survey is to establish a new milestone for the interested hydrology, environment and climate researchers on the applications of ML models.

Evaluation of the Compatibility of TRMM Satellite Data with Precipitation Observation Data

The availability of hydrological data is one of the challenges associated with developing water infrastructure in different areas. This led to the TRMM (Tropical Precipitation Measurement Mission) design by NASA, which involves using satellite weather monitoring technology to monitor and analyze tropical precipitation in different parts of the world. Therefore, this validation study was conducted to compare TRMM precipitation data with observed precipitation to determine its application as an alternate source of hydrological data. The Kuranji watershed was selected as the study site due to the availability of suitable data. Moreover, the validation analyses applied include the Root Mean Squared Error (RMSE), Nash-Sutcliffe Efficiency (NSE), Coefficient Correlation (R), and Relative Error (RE). These used two calculation forms: one for the uncorrected data and another for the corrected data. The results showed that the best-adjusted data validation from the Gunung Nago station in 2016 was recorded to be RMSE = 62,298, NSE = 0.044, R = 0.902, and RE = 11,328. The closeness of the R-value to one implies that the corrected TRMM data outperforms the uncorrected ones. Therefore, it was generally concluded that the TRMM data matches the observed precipitation data and can be used for hydrological study in the Kuranji watershed

Uncertainty in hydrological modelling: A review

Availability of hydrological data and various soft wares for developing models make easy way to answer frequently asked questions to hydrologists. A great deal of concentration has given to the development of models in the last decades. But the thorough study regarding uncertainty of simulations has not carried out in comparison with the development of models. Uncertainty in models emanates from input data, calibrated data, parameters and from the structure of models. The sources of uncertainty, cause of generation and how these can be dealt with are reviewed here. This also comprises a review about five different methods viz. Monte Carlo sampling, Bayesian approach, Generalized Likelihood Uncertainty Estimation, Bootstrap Approach and Machine learning methods which were applied in the estimation of the model and parameter uncertainty. This will indicate the comparison between the methods which were applied to measure the uncertainty of hydrological models and highlight the strengths and weaknesses of the methods in identifying the usefulness of the models. By the comparison of the methods the improvement of the model reliability, slackening of the prediction error of the hydrological models can be suggested. By a proper quantification of uncertainty of data applied for the building up and evaluation of models, model performance can be improved, cost can be reduced and unambiguous results can lead the proper water resources management.

Prediction of Runoff in Watersheds Located within Data-Scarce Regions

The interest in the use of mathematical models for the simulation of hydrological processes has largely increased especially in the prediction of runoff. It is the subject of extreme research among engineers and hydrologists. This study attempts to develop a simple conceptual model that reflects the features of the arid environment where the availability of hydrological data is scarce. The model simulates an hourly streamflow hydrograph and the peak flow rate for any given storm. Hourly rainfall, potential evapotranspiration, and streamflow record are the significant input prerequisites for this model. The proposed model applied two (2) different hydrologic routing techniques: the time area curve method (wetted area of the catchment) and the Muskingum method (catchment main channel). The model was calibrated and analyzed based on the data collected from arid catchment in the center of Jordan. The model performance was evaluated via goodness of fit. The simulation of the proposed model fits both (a) observed and simulated streamflow and (b) observed and simulated peak flow rate. The model has the potential to be used for peak discharges’ prediction during a storm period. The modeling approach described in this study has to be tested in additional catchments with appropriate data length in order to attain reliable model parameters.

Prediction of the Discharge Flow in a Small Hydropower Station without Hydrological Data Based on SWAT Model

The availability of hydrological data for small hydropower plants is an important prerequisite for reservoir scheduling, reservoir flood control and integrated water resources. To address the problem of a lack of hydrological data in small hydropower plants, this paper proposes a method to predict the power generation flow of small hydropower stations without hydrological data using the Soil and Water Assessment Tool model (SWAT) when the traditional data-driven methods cannot study the problem of power generation flow prediction in small hydropower stations well. The method can use gridded meteorological data as the input of the model to solve the problem of small hydropower stations without meteorological data. The problem that small hydropower plants without hydrological data cannot calibrate the hydrological model is solved by calculating the generation flow through the output of small hydropower station and by using the similarity analysis method to migrate the generation flow of similar small hydropower stations. The model was tested in a watershed in southwest China to demonstrate the effectiveness of the proposed method. The results show that the coefficient of determination between the predicted and measured values of small hydropower stations without information is about 0.84, which achieves a better prediction.

Analysis of hydrological data with correlation matrices: technical implementation and possible applications

Changing political frameworks in addition to novel and more cost-effective means to investigate the subsurface have led to an increase in the availability of hydrological data. This wealth of data, however, poses new challenges in effectively making use of it. Traditional tools such as spreadsheets or proprietary datalogger software often do not scale easily with a larger amount of available datasets, requiring considerable user interaction.Also, comparing different locations and types of data can be difficult and tedious. Thus, a python script is presented that enables the user to quickly visualize and compare different types of data such as for example groundwater levels or precipitation amounts.This is done by first standardizing the data using different drought indices and, subsequently, visualization of correlation matrices or plots of data on maps. This approach can be used for data quality control (identifying erroneous data, classifying data into different types), data comparison (comparing different types of data, such as groundwater and precipitation; comparing different locations) and to visualize and analyze the development of hydrological data and their correlation patterns over time. Prospects and limitations of the approach are illustrated and discussed using various example applications.

Water Cycle and Climate Signals in Africa Observed by Satellite Gravimetry

The availability of hydrologic data is an important step for hydrological modeling and water resource management in the world. Unfortunately, the in situ observations with the right characteristics are very sparse globally, particularly in Africa. Understanding the climate variability of Africa and its prominent role as the heat engine of the global climate system is one of the key goals in climate research. Also, studies show that the time varying of terrestrial water storage contributes significantly to regional climate. In this paper, we have analyzed terrestrial water storage variations from GRACE satellite mission and from GLDAS model over the whole Africa for the period of August 2002 till April 2012. Amplitudes of water storage and rainfall data over some large river basins in Africa have been studied at seasonal and interannual scales. Comparison with the GLDAS model outputs is performed and discussed. Terrestrial Water Storage (TWS) estimates from GRACE and GLDAS show comparable patterns, however GRACE is better to detect interannual variation in water storage. Comparing TWS with rainfall data shows a phase lag of around one month between the maximum of the rainfall over a region and the maximum of TWS over the same region.

A Method for Estimating the Regional Initial and Constant Loss for Design Flood Estimation in West Peninsular Malaysia

Floods are known as one of the world’s most frequent and devastating events. Techniques to predict and estimate the size of floods is depend on the availability of hydrological data. Using the conceptual of lump model, rainfall-runoff method is widely used in design flood estimation, which represents the input of rainfall and catchment characteristics such as rainfall depth, rainfall intensity, baseflow and losses. 7o calculate the catchments runoff, amount of losses shall be determine accurately by considering various source of the rainfall losses such as evaporation, inßltration, interception, depression storage and loss in groundwater recharge. In Malaysia, the common technique to estimate the hydrological losses is using initial and constant loss method. Furthermore, the value has been used in Urban Stormwater Manual for Malaysia (USMA) are adopted from the other literatures which is not represented the value from local catchment.7he objective of this study is to derive the initial and constant loss values using the data from selected local catchments in west Peninsular Malaysia. 7he calculated initial and constant loss will be further used to derive design flood discharge based on the design rainfall. An initial loss and constant loss model was examined in this study to observe the loss rate parameters in heterogeneous catchments and evaluate their signißcance as well as their potential influence on design peak floods. 7he study has been utilised the rainfall and runoff data from 113 storms over 15 catchments. 7he loss parameters were obtained from model optimi3ation using the HEC-HMS Modeling program. From the analyses, the median initial loss is 21.54mm with the standard deviation 7.85mm. 7he value shows higher than the value adopted in USMA. Meanwhile, the value for constant loss is 8.07mm which between the range of USMA. Based on the ßndings of design initial loss analyses, the values of initial loss were 49.3, 57.6, 64.1, 69.4, 73.3 and 76.6 for ARI 2,5,10,20,50 and 100, respectively. 7he percentage error between design initial loss and constant loss method and flood frequency method shows good results which are most of the percentage error less than 35%. It shows that the design initial loss and constant loss method produce reasonable accurate results when compared to the rainfall-runoff method and flood frequency method. Based on the ßndings, it can be suggested that the regional design initial loss and the constant loss rates would be able to serve reasonably well in determining catchment loss for the design purposes.

Optimal Parameter Estimation for Muskingum Model Using a CSS-PSO Method

Limited availability of hydrologic data is a major hurdle for implementation of detailed hydrologic models. In cases where available data is limited, simple hydrologic models such as linear Muskingum model consisting of a minimum number (one or two) of model parameters are more desirable. As an alternative to the conventional mathematical approaches, this paper applies a new hybrid metaheuristic algorithm based on charged system search and particle swarm optimization for identifying the parameters of the linear Muskingum model. In order to evaluate the new algorithm, a numerical example is utilized and the results are compared to those of other algorithms. The results reveal the performance of the algorithm to optimize parameter estimation of the Muskingum model.

Modelling the hydrology of a catchment using a distributed and a semi‐distributed model

AbstractVarious hydrological models exist that describe the phases in the hydrologic cycle either in an empirical, semi‐mechanistic or fully mechanistic way. The way and level of detail for the different processes of the hydrologic cycle that needs to be described depends on the objective, the application and the availability of data. In this study the performance of two different models, the fully distributed MIKE SHE model and the semi‐distributed SWAT model, was assessed. The aim of the comparative study was to examine if both models are equally able to describe the different phases in the hydrologic cycle of a catchment, given the availability of hydrologic data in the catchment. For the comparison, historic data of the Jeker river basin, situated in the loamy belt region of Belgium, was used. The size of the catchment is 465 km2. The landscape is rolling, the dominant land use is farmland, and the soils vary from sandy‐loam to clay‐loam. The daily data of a continuous period of 6 years were used for the calibration and validation of both models. The results were obtained by comparing the performance of the two models using a qualitative (graphical) and quantitative (statistical) assessment, such as graphical representation of the observed and simulated river discharge, performance indices, the hydrograph maxima, the baseflow minima, the total accumulated volumes and the extreme value distribution of river flow data. The analysis revealed that both models are able to simulate the hydrology of the catchment in an acceptable way. The calibration results of the two tested models, although they differ in concept and spatial distribution, are quite similar. However, the MIKE SHE model predicts slightly better the overall variation of the river flow. Copyright © 2005 John Wiley & Sons, Ltd.