GIS-Based Surface Runoff Modeling Using Empirical Technique For A River Basin In South India

Precipitation is the primary source of fresh water in the world. Surface runoff will happen when the amount of rainfall is greater than the soil’s infiltration capacity. In most water resource applications, runoff is the most important hydrological variable. Aside from these rainfall characteristics, there are a number of catchment-specific elements that have a direct impact on runoff amount and volume. This research focuses on estimating surface runoff over the lower Vellar basin, a river basin in the southern part of India, by integrating Soil Conservation Service-Curve Number (SCS-CN) method with GIS. This technique is one of the most common methods used by hydrologists for estimating surface runoff. Curve Number (CN) is an index established by the Natural Resource Conservation Service (NRCS) to denote the potential for stormwater runoff. The nature of the watershed is explored first by creating land use and land cover pattern followed by the preparation of slope, drainage, and location maps. The area taken for this study is the lower Vellar basin situated in the Cuddalore District of Tamil Nadu, India. The curve number is analyzed using the rainfall data of 15 years (2001-2015) and the runoff is being calculated. The watershed pattern of the study area is also explored being analyzed and executed. Preservation of the runoff water is also discussed.

Landsat 8 Data as a Source of High Resolution Sea Surface Temperature Maps in the Baltic Sea

Sea surface temperature (SST) is a key hydrological variable which can be monitored via satellite. One source of thermal data with a spatial resolution high enough to study sub-mesoscale processes in coastal waters may be the Landsat mission. The Thermal Infrared Sensor on board Landsat 8 collects data in two bands, which allows for the use of the well-known nonlinear split-window formula to estimate SST (NLSST) using top-of-the-atmosphere (TOA) brightness temperature. To calibrate its coefficients a significant number of matchup points are required, representing a wide range of atmospheric conditions. In this study over 1200 granules of satellite data and 12 time series of in situ measurements from buoys and platforms operating in the Baltic Sea over a period of more than 6 years were used to select matchup points, derive NLSST coefficients and evaluate the results. To filter out pixels contaminated by clouds, ice or land influences, the IdePix algorithm was used with Quality Assessment Band and additional test of the adjacent pixels. Various combinations of flags were tested. The results show that the NLSST coefficients derived previously for coastal areas, characterised by a more humid atmosphere, might overestimate low SST values. Formulas derived for the Baltic Sea produced biases close to 0 °C and RMSEs in the range of 0.49–0.52 °C.

Streamflow Estimation at Ungauged Basin using Modified Group Method of Data Handling

Among the foremost frequent and vital tasks for hydrologist is to deliver a high accuracy estimation on the hydrological variable, which is reliable. It is essential for flood risk evaluation project, hydropower development and for developing efficient water resource management. Presently, the approach of the Group Method of Data Handling (GMDH) has been widely applied in the hydrological modelling sector. Yet, comparatively, the same tool is not vastly used for the hydrological estimation at ungauged basins. In this study, a modified GMDH (MGMDH) model was developed to ameliorate the GMDH model performance on estimating hydrological variable at ungauged sites. The MGMDH model consists of four transfer functions that include polynomial, hyperbolic tangent, sigmoid and radial basis for hydrological estimation at ungauged basins; as well as; it incorporates the Principal Component Analysis (PCA) in the GMDH model. The purpose of PCA is to lessen the complexity of the GMDH model; meanwhile, the implementation of four transfer functions is to enhance the estimation performance of the GMDH model. In evaluating the effectiveness of the proposed model, 70 selected basins were adopted from the locations throughout Peninsular Malaysia. A comparative study on the performance was done between the MGMDH and GMDH model as well as with other extensively used models in the area of flood quantile estimation at ungauged basins known as Linear Regression (LR), Nonlinear Regression (NLR) and Artificial Neural Network (ANN). The results acquired demonstrated that the MGMDH model possessed the best estimation with the highest accuracy comparatively among all models tested. Thus, it can be deduced that MGMDH model is a robust and efficient instrument for flood quantiles estimation at ungauged basins.

Cloud Transform Algorithm Based Model for Hydrological Variable Frequency Analysis

Hydrological variable frequency analysis is a fundamental task for water resource management and water conservancy project design. Given the deficiencies of higher distribution features for the upper tail section of hydrological variable frequency curves and the corresponding safer resulting design of water conservancy projects utilizing the empirical frequency formula and Pearson type III function-based curve fitting method, the normal cloud transform algorithm-based model for hydrological variable frequency analysis was proposed through estimation of the sample empirical frequency by the normal cloud transform algorithm, and determining the cumulative probability distribution curve by overlapping calculation of multiple conceptual cloud distribution patterns, which is also the primary innovation of the paper. Its application result in northern Anhui province, China indicated that the varying trend of the cumulative probability distribution curve of annual precipitation derived from the proposed approach was basically consistent with the result obtained through the traditional empirical frequency formula. Furthermore, the upper tail section of the annual precipitation frequency curve derived from the cloud transform algorithm varied below the calculation result utilizing the traditional empirical frequency formula, which indicated that the annual precipitation frequency calculation result utilizing the cloud transform algorithm was more optimal compared to the results obtained by the traditional empirical frequency formula. Therefore, the proposed cloud transform algorithm-based model was reliable and effective for hydrological variable frequency analysis, which can be further applied in the related research field of hydrological process analysis.

Data-Driven Approach for Rainfall-Runoff Modelling Using Equilibrium Optimizer Coupled Extreme Learning Machine and Deep Neural Network

Rainfall-runoff (R-R) modelling is used to study the runoff generation of a catchment. The quantity or rate of change measure of the hydrological variable, called runoff, is important for environmental scientists to accomplish water-related planning and design. This paper proposes (i) an integrated model namely EO-ELM (an integration of equilibrium optimizer (EO) and extreme learning machine (ELM)) and (ii) a deep neural network (DNN) for one day-ahead R-R modelling. The proposed R-R models are validated at two different benchmark stations of the catchments, namely river Teifi at Glanteifi and river Fal at Tregony in the UK. Firstly, a partial autocorrelation function (PACF) is used for optimal number of lag inputs to deploy the proposed models. Six other well-known machine learning models, called ELM, kernel ELM (KELM), and particle swarm optimization-based ELM (PSO-ELM), support vector regression (SVR), artificial neural network (ANN) and gradient boosting machine (GBM) are utilized to validate the two proposed models in terms of prediction efficiency. Furthermore, to increase the performance of the proposed models, paper utilizes a discrete wavelet-based data pre-processing technique is applied in rainfall and runoff data. The performance of wavelet-based EO-ELM and DNN are compared with wavelet-based ELM (WELM), KELM (WKELM), PSO-ELM (WPSO-ELM), SVR (WSVR), ANN (WANN) and GBM (WGBM). An uncertainty analysis and two-tailed t-test are carried out to ensure the trustworthiness and efficacy of the proposed models. The experimental results for two different time series datasets show that the EO-ELM performs better in an optimal number of lags than the others. In the case of wavelet-based daily R-R modelling, proposed models performed better and showed robustness compared to other models used. Therefore, this paper shows the efficient applicability of EO-ELM and DNN in R-R modelling that may be used in the hydrological modelling field.

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

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.

The importance of geology when estimating catchment-scale streamflow characteristics: Application of a new technique for hydrologic similarity and regionalization

<p>Expanding the scientific understanding of global hydrological processes is a key research area for hydrologists. Research in this area can allow hydrologists to make better predictions in ungauged basins and catchments under climate change scenarios. Though hydrological processes are largely understood at a laboratory-scale, catchment-scale processes are much more complex and unknown. Previous studies at the catchment-scale have shown catchment geology is largely irrelevant in determining components of streamflow. Laboratory-scale experiments, however, have revealed that this is unlikely. This contradiction indicates the current techniques for determining hydrological variable importance in the literature are insufficient. In this paper, we quantify the influence of the interaction amongst climatic, geological, and topographical features on a large set of hydrological signatures in snow-dominated regions across North America, using Stable Extrapolative Marginal Contribution Feature Importance. The preliminary results show that when we consider interaction effects among climatic and geophysical features, and remove the influence of correlation, geological features show considerable importance at the catchment scale. We contend that this study contributes to the scientific understanding of catchment-scale hydrological processes, especially in cold, ungauged basins.</p>

Capturing watershed water balance with a physically-based two-hydrological-variable model: Application to the Little Washita basin

<p>Hydrological processes import across scales is known to constitute a key challenge to improve their representation in large-scale land surface models. Since these models describe continental hydrology with vertical one dimensional infiltration and evapotranspiration, the challenge mainly resides in the dimensionality reduction of the processes. Departing from the catchment three-dimensional scale, previous work has shown that an equivalent two-dimensional hillslope model is able to simulate long term watershed water balance with good accuracy. This work has been done on the Little Washita basin (Ok, USA) using the integrated code HydroGeoSphere. Following this framework, we show that hillslope hydrology can be described by using realistic simplifying assumptions, such as linear water table profile. These assumptions allow the writing of an analytical model relying on two hydrological variables: the seepage face extension, which describe the intersection length between the water table and the land surface, and the water table slope. The last step of the work will be to use these key variables and this simplified description of the driving processes for importing small-scale hydrological processes into large-scale models.</p>

Climate change induced modifications of hydro-meteorological extreme events and their impacts on water resources for agriculture in the Mediterranean

<p>It is common today to consider that climate is expected to change or even climate change is present and evident.  A changing climate leads to changes in the frequency, intensity, spatial extent, duration, and timing of climate extremes, and may result in unprecedented events. Changes in extremes of a climate variable are not always related in a simple way to changes in the mean of the same variable or a hydrological variable, and in some cases may be of opposite sign to a change in the mean of the variable.  Also, the changes vary from one geographical region to another.   In this review paper, examples of climate change impact studies on hydro-meteorological extremes, i.e. extreme precipitation, floods and droughts, in the Mediterranean region, are presented and discussed.  In this geographical area, agriculture is the main consumer of water, demanding 60-90% of the total water use. The impacts of the climate change induced modifications of hydro-meteorological extremes and water management practices on the availability of surface water and groundwater resources are also discussed.</p>

Using stable isotopes to estimate the time since disconnection of pools in temporary rivers

<p>Temporary rivers, characterized by shifts between flowing water, disconnected pools and dry periods, represent over 50% of the world’s river network and future climatic projections suggest their increase. These rivers are understudied, especially when only disconnected pools remain, because gauging stations or hydrological models do not inform of what happens after the cessation of flow. In addition, most of biological indicators for water quality are designed for flowing waters and their adequacy for temporary rivers is uncertain.</p><p>The development of biological metrics adequate for the assessment of disconnected pools is difficult, because the high species replacement during and following flow cessation. For this reason, one hydrological variable of paramount importance for the assessment of ecological quality of disconected pools is the time since disconnection from the river flow.</p><p>The objective of our work is to present a methodology to estimate the time since disconnection of pools from the river flow. This methodology, following the Gonfiantini (1986) model, is based on the sampling of water stable isotopes in disconnected pools. For pools disconnected from the groundwater, knowing the isotopic modification of the water in time due to evaporation, allows to estimate the relative volume of water evaporated since the pool has been disconnected. However, this approach gets complicated when pools have relevant rainfall inputs or exchanges with groundwater.</p><p>Within the Vallcebre research area (42º12’N and 1º49’E), two artificial pools, one covered with a transparent lid to prevent the input of rainfall and another uncovered, were installed to validate this methodology in controlled conditions. From July to November 2020, water volume of these pools were weekly measured and sampled for isotopic analysis. In parallel, meteorological variables were monitored and rainfall was also sampled for water stable isotopes.</p><p>To develop and validate an operational methodology for estimating the time since disconnection, we first calculated the relative amount of evaporated water based on the variations of isotopic composition of the covered pool samples, and estimated the time since disconnection (for a given natural pool) using the potential evaporation calculated from the meteorological data. For the uncovered pool, the information of amount and isotopic composition of rainfall was added in a mass balance model. Additionally, the same estimations were calculated with standard information (i.e. the meteorological data obtained from the National Meteorological Service and precipitation isotopes data from the Global Network of Isotopes in Precipitation (GNIP) of the International Atomic Energy Agency). Finally, measured volumes changes in pools, were used to assess the limitations of the operational methodology and the sensitivity of the results to meteorological conditions.</p><p>Our approach suggests that changes in isotopic composition can be a reliable method to estimate time since disconnection of pools in temporary rivers to better assess their ecological quality.</p>