Multi-Spatial Resolution Rainfall-Runoff Modelling—A Case Study of Sabari River Basin, India

Hydrological models are an effective tool for the estimation of peak floods and runoff in planning water development and flood mitigation/adaptation. Continuous hydrologic modeling can determine the relationships between hydrologic processes and environmental changes over long time periods. Therefore, the selection of a theoretically robust and functionally reliable hydrological model is crucial for the effective management of flood risk within a basin. The Soil and Water Assessment Tool (SWAT) is one of the most widely used hydrological models for assessing the impacts of climate change on discharge in large basins. In contrast, the Hydrologic Engineering Centers’ Hydrologic Modeling System (HEC-HMS) has been one of the most rapidly evolving and promising hydrological models, with its latest version (v4.9) supporting both fully distributed and semidistributed hydrological modeling approaches. This study was conducted to compare the performances of SWAT and HEC-HMS in long-term continuous simulations in the Pearl River Basin, which is the second largest river basin in terms of discharge in China. For purposes of comparison, both models employed identical input data and comparable configurations. The results demonstrate that both models are capable of predicting river discharge at designated station satisfactories, with the Nash-Sutciffe coefficient exceeding 0.7. Benefiting from its elaborate use of the modified Soil Conservation Service (SCS) loss model and more advanced automatic calibration program, SWAT obtained more accurate results than HEC-HMS in the validation period. However, HEC-HMS is distinguished by its customizable options for constructing hydrological models, and it exhibits considerable potential for application in large-scale river basins such as the Pearl River Basin, enabling long-term, continuous hydrological simulations.

Meenachil river basin, located in southern part of Kerala, is frequently liable to flood. It is an area predominant with agricultural land and falls under tropical humid zone. Hence, water resources planning and management and understanding of rainfall-runoff relationship along with its land characteristics, is necessary for irrigation scheduling, flood control and design of various engineering structures. HEC-HMS(Hydrologic Engineering Centers Hydrologic Modeling System) which is a widely used rainfall-runoff model was chosen for the simulation of watershed responses and generation of flood hydrographs. This research was done to establish the peak discharge rate of the river as well as calibrate and validate the HEC-HMS model for the Meenachil subbasin and obtain long-term flow data. The performance parameters NSE (Nash–Sutcliffe model efficiency) coefficient and R² (coefficient of determination) values were obtained above 0.7. The Error in Peak Flow and Volume were calculated below 20%. All these values indicated satisfactory performance of the model simulation both in the calibration (2013-2016) and validation (2017-2018) period. Curve number (61.46-79.93 mm), Initial abstraction(10.29-31.87 mm) and Lag time(208.27 min and 4456.2 min) were found to be the most sensitive parameters of the model. Simulated and observed stream flow values (viz. lowest peak flow of river was found during the year 2013 and it was predicted as 168.9 m³/s and observed as 158.5 m³/s) indicated that the model was able to predict and present credible results for the sub basin.

One main natural hazard resulting from interactions between rainfall-runoff is flooding. Extreme precipitation causes surface water flow to rise, hence breaking river systems and causing flooding. This work models and forecasts flood dynamics in the Kunhar River basin with the Hydrologic Engineering Centre's Hydrologic Modelling System (HEC-HMS). It shows how well the technology can combine several hydrological data points to precisely project flood paths. These studies have repeatedly shown the dependability of HEC-HMS over many geographical and climatic environments, therefore confirming its fit for thorough hydrological research. HEC-HMS included obtaining thorough datasets comprising historical hydrological data from NASA, spatial information from ARCGIS, and meteorological data from WAPDA in addition to flow rates and water levels. We started the basin model in HEC-HMS by including Digital Elevation Models (DEMs) to define watershed boundaries and record topography. Terrain preprocessing came next to solve discontinuities and guarantee correct water flow modelling. Combining several datasets, the model was designed to reflect the coordinate system and underwent hydrological study to replicate surface water flow, accumulation, and stream networks inside the basin. With subbasin-10 showing the largest peak flow of 132 cubic meters per second during severe rainfall events, especially on August 7, 2013, results revealed that HEC-HMS effectively forecasted peak discharges in the subbasins of the Kunhar River. With an estimate 90% accuracy rate, this proved the great dependability of HEC-HMS in flood prediction. The results show that the model can help with flood management planning and foresee flood circumstances. HEC-HMS's value in designing flood barriers and enhancing watershed management particular to each subbasin. It emphasizes the need of revised hydrological models to consider land use and increasing temperature.

Wetland mapping by fusing fine spatial and hyperspectral resolution images

Hydrological modeling and the hydrological response to land-use/land-cover changes induced by human activities have gained enormous research interest over the last few decades. The study presented here analyzes the spatial and qualitative changes in the rainfall–runoff that have resulted from the land-cover changes between 1985–2014 in the Godavari River Basin using the Hydrologic Engineering Centre-Hydrologic Modeling System(HEC-HMS) model and remote sensing—GIS (geographic information system) techniques. The purpose of this paper is to analyze the dynamics of land-use/land-cover (LULC) changes for the years 1985, 1995, 2005, and 2014 for the Godavari Basin. The findings reveal an increase of 0.64% of built-up land, a decrease of 0.92% in shrubland, and an increase of 0.56% in waterbodies between 1985–2014. The LULC change detection results between the years 1985–2014 indicated a drastic change in the cropland, forest, built-up land, and water bodies among all of the other classes. The urbanization and agricultural activities are the major reasons for the increase of cropland, built-up land, and water bodies, at the expense of decreases in shrubland and forest. The study had an overall classification accuracy of 92% and an overall Kappa coefficient of 0.9. The HEC-HMS model is used to simulate the hydrology of the Godavari Basin. The analyses carried out were mainly focussed on the impact of LULC changes on the streamflow pattern. The surface runoff was simulated for the year 2014 to quantify the changes that have taken place due to changes in LULC. The observed and the simulated peak streamflow was found to be the same i.e., 56,780 m3/s on 9 September 2014. In the validation part, the linear regression method was used to correlate the observed and simulated streamflow data at the prominent gauge station of the Badrachalam outlet for the Godavari River Basin and give a correlation coefficient value of 0.83. It was found that the HEC-HMS model is compatible and works better for the rainfall–runoff modeling, as it takes into account the various parameters that are influencing the process. The hydrological modeling that was carried out using the HEC-HMS model has brought out the significant impact of LULCC on rainfall–runoff at the Pranhita sub-basinscale, indicating the model’s ability to successfully accommodate all of the environmental and landscape variables. The study indicates that deforestation at the cost of urbanization and cropland expansions leads to decreases in the overall evapotranspiration (ET) and infiltration, with an increase in runoff. The results of the study show that the integration of remote sensing, GIS, and the hydrological model (HEC-HMS) can solve hydrological problems in a river basin.

Assessing habitat differentiation between coexisting species: The role of spatial scale

An artificial neural network is well known as a flexible mathematical tool that has the ability to generalize patterns in imprecise or noisy and ambiguous input and output data sets. The radial basis function (RBF) method is applied to model the relationship between rainfall and runoff for Sungai Bekok Catchment (Johor, Malaysia) and Sungai Ketil catchment (Kedah, Malaysia). The RBF is used to predict the streamflow hydrograph based on storm events. Evaluation on the performance of RBF is demonstrated based on errors (between predicted and actual) and comparison with the results of the Hydrologic Engineering Center hydrologic modeling system model. It is obvious that the RBF method offers an accurate modeling of streamflow hydrograph.

Abstract. Despite showing great success of applications in many commercial fields, machine learning and data science models generally show limited success in many scientific fields, including hydrology (Karpatne et al., 2017). The approach is often criticized for its lack of interpretability and physical consistency. This has led to the emergence of new modelling paradigms, such as theory-guided data science (TGDS) and physics-informed machine learning. The motivation behind such approaches is to improve the physical meaningfulness of machine learning models by blending existing scientific knowledge with learning algorithms. Following the same principles in our prior work (Chadalawada et al., 2020), a new model induction framework was founded on genetic programming (GP), namely the Machine Learning Rainfall–Runoff Model Induction (ML-RR-MI) toolkit. ML-RR-MI is capable of developing fully fledged lumped conceptual rainfall–runoff models for a watershed of interest using the building blocks of two flexible rainfall–runoff modelling frameworks. In this study, we extend ML-RR-MI towards inducing semi-distributed rainfall–runoff models. The meaningfulness and reliability of hydrological inferences gained from lumped models may tend to deteriorate within large catchments where the spatial heterogeneity of forcing variables and watershed properties is significant. This was the motivation behind developing our machine learning approach for distributed rainfall–runoff modelling titled Machine Induction Knowledge Augmented – System Hydrologique Asiatique (MIKA-SHA). MIKA-SHA captures spatial variabilities and automatically induces rainfall–runoff models for the catchment of interest without any explicit user selections. Currently, MIKA-SHA learns models utilizing the model building components of two flexible modelling frameworks. However, the proposed framework can be coupled with any internally coherent collection of building blocks. MIKA-SHA's model induction capabilities have been tested on the Rappahannock River basin near Fredericksburg, Virginia, USA. MIKA-SHA builds and tests many model configurations using the model building components of the two flexible modelling frameworks and quantitatively identifies the optimal model for the watershed of concern. In this study, MIKA-SHA is utilized to identify two optimal models (one from each flexible modelling framework) to capture the runoff dynamics of the Rappahannock River basin. Both optimal models achieve high-efficiency values in hydrograph predictions (both at catchment and subcatchment outlets) and good visual matches with the observed runoff response of the catchment. Furthermore, the resulting model architectures are compatible with previously reported research findings and fieldwork insights of the watershed and are readily interpretable by hydrologists. MIKA-SHA-induced semi-distributed model performances were compared against existing lumped model performances for the same basin. MIKA-SHA-induced optimal models outperform the lumped models used in this study in terms of efficiency values while benefitting hydrologists with more meaningful hydrological inferences about the runoff dynamics of the Rappahannock River basin.

Continuous hydrological records at the desired site with spatial and temporal coverage are essential to water resources management and flood prevention. Hydrological gauging stations and observations are limited at transboundary Himalayan River basins in developing countries. This study is carried out to estimate discharge at ungauged sites from donor catchments based on calibrated discharge. In the Tamakoshi River basin, Spatial Process in HYdrology (SPHY), Hydrologiska Byråns Vattenbalansavdelning (HBV)-light, and Hydrologic Engineering Centre Hydrologic Modelling System (HEC HMS) models were used for hydrological simulation. The ungauged discharge estimated at Benighat is based on daily, monthly, and annual bases from upstream gauged stations. Statistical indices were obtained as NSE > 0.62, R2 > 0.77, and PBias <26% in the simulation of these models. The low flow prediction is more reliable than the high flow prediction. The deviation in predicted flow mostly appeared in high flow periods. All the hydrological models simulated ungauged streamflow were similar in the flow pattern, and the estimated discharge at the ungauged receiver site was greater than at the donor site. Comparative simulations are better alternatives to estimate ungauged discharge than individual simulations. This research will support future reference to generate streamflow data at ungauged sites and can help fill the data recording gap at similar mountain river basins.

Evaluating a thermal image sharpening model over a mixed agricultural landscape in India

The Lafa River Basin (LRB) experiences increased frequency and magnitude of floods, causing increases in areal and depth of inundation. This translates into huge economic losses and loss of human lives. The effective management of flood disaster of the LRB is hinged on the identification of the factors responsible for the floods, modelling of the flood situations and a quantitative assessment of the hazard zones. Hydrologic Engineering Centre-Hydrologic Modeling System (HEC-HMS) and Hydrologic Engineering Centre-River Analysis System (HEC-RAS) models have been integrated with Geographical Information System (GIS) to identify the driving forces behind the floods, and assess the hazard zones of the Basin. Physiographical characteristics are responsible for the floods and analysis indicate that the LRB is a small urbanised elongated drainage basin of area 27.945 km2 with predominantly steep slopes, and impervious surface soils. The Peak flows produced by 2, 15, 25, 50, and 100 year return period storms have been found to inundate 233.88 ha, 292.92 ha, 298.36 ha, 305.00 ha, and 311.24 ha respectively. 1.68 %, 5.54 %, 5.91 %, 6.41 %, and 6.63 % respectively of total inundation areas were above inundated depth of 2 m and are located around culvert locations on the Lafa stream. Keywords-Basin, Flood plain, Hydrological analysis, Modelling, Peak flows

Evaluation of distributed and semi-distributed hydrological models in complex River Basin system, Nepal

Hydrological models are a conceptual representation of a simplified hydrological cycle. The hydrological cycle is the water cycle that circulates water from the land surface to the atmosphere and back again to the land. With the use of Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS), an event-based model and a continous hydrological model were established in Zijinguan watershed of Daqinghe River basin. To study the loss methods provided by HEC-HMS and its appropriateness for model fitting, is the main objective of this paper. The watershed was delineated with HEC-GeoHMS in ArcGIS, and its properties were extracted from a Digital Elevation Model (DEM) of 30 m × 30 m. The HEC-HMS includes a soil conservation service curve number (SCS-CN) method and a soil moisture calculation (SMA) loss method, simulating the event and continuous runoff, respectively. SCS Unit hydrograph and Muskingum were used for flow routing. Specifically, eight rainfall events were selected for calibrating (6 events) and verifying (2 events) the event-based model. Similarly, for the continuous model the wet seasons of eight different years were used for calibration and verification. The calibrated parameters of the events model were used in the continuous model. The soil moisture and evapotranspiration data were decoded from the satellite data to set in the continuous model. The performance of SCS-CN and SMA models was compared. During the calibration period, the values of NSE, PEV and PEPF range from 0.605 to 0.744, 3.1 to 13.58%, and 11.104 to 27.72%, and during validation they are 0.527 to 0.634, 4.35% to 5.01%, and 13.66% to 27.88%, for SCS-CN model. For the SMA model, the values of NSE, PEV and PEPF during calibration range from 0.434 to 0.604, 2.879 to 34.326%, −4.831 to 57.48%, and during validation they are 0.094 to 0.624, −19.52% to −12.55%, and 40.213% to 50.15%. Overall, the performance of SCS-CN model is found more satisfactory than that of SMA model.

In recent years, Weather Surveillance Radar-1988 Doppler base data have become available at substantially finer spatial resolution—i.e., 0.25 km  0.5o (0.25 km  1o for use in precipitation processing) versus the legacy 1 km  1o. Quantitative precipitation estimation (QPE) at this higher resolution may yield greater accuracy in the discretization of precipitation to delineated stream and river basins, in turn resulting in operational benefit in improved performance of hydrologic models and forecasting tools. To assess this potential, 1-h radar QPEs were determined from two experimental, S-band radar systems across several spatial resolutions—starting with base reflectivity data at or near the newly available, fine resolution, and recombining them up to, and including, the legacy resolution. Next, 1-h QPE was calculated at these various resolutions (based on the traditional Z–R relationship) and matched against 1-h rain gauge accumulations from dense gauge networks. After performing several steps of manual quality control, the dataset contained over 9500 warm-season gauge–radar pairs; these were assessed in various sub-groups and configurations (including stratification by precipitation intensity and range from the radar) and in simulated small stream basins. In various statistical analyses, however, the error differences between the fine- and legacy-resolutions were not statistically significant. Several possible causes for this supposed counterintuitive result were investigated, including (1) increased sampling error/noisiness in smaller versus larger sample bins, (2) sub-beam factors acting on falling hydrometeors, and (3) the matter that temporal sampling frequencies employed operationally were not increased commensurately with the increase in spatial sampling resolution.

The high resolution of remote sensors has evolved to capture the fine details of the Earth’s surface features in remote-sensing (RS) data. There is a trade-off between this fine spatial resolution and the temporal resolution of global space-borne sensors. Global space-borne sensors are not good enough to acquire an image at fine spatial and high temporal (FSHT) resolutions simultaneously. In this article, we propose a computationally efficient technique to create a FSHT resolution image using a ground-based data processing system. Resourcesat-2, part of the Indian Space Research Organization’s (ISRO’s) mission, carries linear imaging self-scanners (LISS III and LISS IV) and an advanced wide-field sensor (AWiFS) on board. The spatial and temporal resolutions of LISS III are 23.5 m and 24 days and those of AWiFS are 56 m and 5 days, respectively. The proposed method creates a synthetic FSHT resolution image with 23.5 m spatial and 5 day temporal resolution. This method is referred to as ‘LISS III spatial and AWiFS temporal’ (LSAT) data fusion. The LSAT data fusion method is based on a sub-pixel relationship between the images of a single AWiFS–LISS III image pair, which was acquired before or after the ‘prediction date’: a synthetic LISS III image for the time is ‘predicted’ (synthesized) using an AWiFS image at time and a single AWiFS–LISS III image pair at time , where . The LSAT model was tested on simulated and real data sets acquired by the LISS III and AWiFS sensors. The proposed method was compared with the recently developed spatiotemporal data fusion methods. The experimental results demonstrate that the method is computationally efficient and shows consistent prediction accuracy in retrieving surface reflectance changes.