Rainfall Input Research Articles

Smart Disaster Mitigation: The Role of IoT and Machine Learning

This prototype output research has the aim of creating a dashboard that is used to monitor river water levels. The dashboard created will display data in real-time and prediction results. The use of the dashboard is to minimize the risk in the event of flooding caused by river overflow. The way this prototype works is to take data from sensors that have been installed at several points. The recorded data will be stored in a database using the working principles of the Internet of Things. For predictions, machine learning is used to produce future river water level figures. The machine learning used is using time series regression with rainfall input and river water level output. Long-term output data is needed, therefore to forecast future rainfall the Hybrid method is used. Data generated from sensors as well as from prediction results are stored in one database. From the database, data visualization is displayed along with important figures used for river overflow intelligence. Therefore, the dashboard is very useful for people living around the river flow.

Magnesium and groundwater flow relationship in karst aquifers: a tool for exploitation management of springs

Karst aquifers are characterized by different types of groundwater flow, related to different types of permeability due to the simultaneous presence of matrix, fractures and conduits. The presence of a well-developed karst conduit system leads to a rapid circulation of groundwater within the aquifer and a pulse-type response of the spring flow to the rainfall inputs, with a potential fast transport of contaminants from the hydrogeological basin surface to the discharge zones. Supported by hydro chemical analyses of spring water samples and single discharge measurements, it was possible to develop specific mass balance models, correlating ion content to spring flowrates. Specifically, Mg2+ content revealed a reliable application for spring baseflow separation in karst settings. Once the local model has been set, its conservative behaviour, in mostly limestone-dominant aquifers, allows using Mg2+ as a natural tracer of groundwater flow, distinguishing conduit flow (overflow) and diffuse flow (baseflow) occurrence in the spring outlet, without additional discharge measurements. In karst settings, the difficulty in continuously monitoring the spring discharge values makes this application interesting for exploitation management. This study shows the results obtained for two springs located in Central Italy, confirming that monitoring groundwater quality in karst environments is often the key for successfully characterizing springs and assessing the total yield when direct measurements are not available.

Applicability Assessment of GPM IMERG Satellite Heavy-Rainfall-Informed Reservoir Short-Term Inflow Forecast and Optimal Operation: A Case Study of Wan’an Reservoir in China

Satellite precipitation estimate (SPE) dedicated to reservoir inflow forecasting is very attractive as it can provide near-real-time information for reservoir monitoring. However, the potential of SPE retrievals with fine temporal resolution in supporting the high-quality pluvial flood inflow forecast and robust short-term operation of a reservoir remains unclear. In this study, the hydrological applicability of half-hourly Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM IMERG) heavy rainfall data was explored using a synthetic experiment of flood inflow forecast at sub-daily to daily lead times and resultant reservoir short-term operation. The event-based flood forecast was implemented via the rainfall–runoff model GR4H driven by the forecasted IMERG. Then, inflow forecast-informed reservoir multi-objective optimal operation was conducted via a numerical reservoir system and assessed by the risk-based robustness indices encompassing reliability, resilience, vulnerability for water supply, and flood risk ratio for flood prevention. Selecting the Wan’an reservoir located in eastern China as the test case, the results show that the flood forecast forced with IMERG exhibits slightly lower accuracy than that driven by the gauge rainfall across varying lead times. For a specific robustness index, its trends between IMERG and gauge rainfall inputs are comparable, while its magnitude depends on varying lead times and scale ratios (i.e., the reservoir scale). The pattern that the forecast errors in IMERG increase with the lead time is changed in the resultant inflow forecast series and dynamics in the robustness indices for the optimal operation decision. This indicates that the flood forecast model coupled with reservoir operation system could partly compensate the original SPE errors. Our study highlights the acceptable hydrological applicability of IMERG rainfall towards reservoir inflow forecast for robust operation, despite the intrinsic error in SPE.

Investigating groundwater recharge using hydrogen and oxygen stable isotopes in Kabul city, a semi-arid region

There are significant concerns about the sustainability of groundwater, and the inhabitants that depend on it, due to rapid groundwater depletion from the alluvium aquifers in Kabul city. Sustainable groundwater management in Kabul requires an understanding of the sources and rates of groundwater recharge, however, both these parameters are poorly quantified. In this study, we examined the stable isotopic composition (δ18O and δ2H) of groundwater and surface water from the Upper Kabul River and Logar River. Utilizing the hydrograph separation approach, we assessed the percentage contribution of river water to groundwater, including the uncertainty analysis of its estimation. Our results, based on isotopic analysis, demonstrated that precipitation was the primary source of groundwater recharge in the Central Kabul sub-basin. Mixed recharge from the river, precipitation and irrigation return flow governed groundwater recharge in the Logar sub-basin. In Paghman and Lower Kabul, and Upper Kabul sub-basins, more rainfall input was observed besides the river contribution to groundwater recharge. We have noted substantial spatial and depth-related variation in the contribution of the river water to groundwater recharge. In the study area, the river water contribution (fraction contribution) to groundwater recharge has changed from over 60 ± 5 % (on average) in 2007 to less than 50 ± 5 % (on average) in 2020. We documented significant groundwater level depletion in the Central Kabul sub-basin and western parts of Kabul city (Paghman and Upper Kabul sub-basins). The present study provides important insights into the local water cycle in Kabul City, which is critical for developing sustainable management strategies for groundwater sources in this semi-arid region.

VALIDATION OF TROPICAL RAINFALL MEASURING MISSION (TRMM) RAINFALL DATA FOR THE KAPUAS HULU DISTRICT AREA

Rainfall is a critical element in the hydrological cycle. Therefore, accurate rainfall inputs are essential for reliable hydrological predictions in various water resource analyses. Many areas in the tropics depend on rainfall for multiple needs because rainfall has an essential influence on numerous aspects of human life, especially in water resources planning and management. However, not all areas have rainfall recording stations using manual rain and gauges or automatic rainfall recorders, so rainfall records are rarely complete; this is also the case in Kapuas Hulu District, West Kalimantan Province.This research article presents the results of TRMM satellite rainfall data research in Kapuas Hulu District, with validated rainfall data, 1-day maximum rainfall data, and rainfall amount data. Validation stages are carried out by calibrating, verifying, and correcting rainfall data. As a comparison station (dependent variable) is the BMKG Pangsuma observation station, KPH-06 Nanga Kantuk, KPH-08 Ng. Silat, KPH-09 Jongkong, KPH-13 Tepuai in 1998-2019.The results of the analysis show that the best correlation value on the amount of rainfall data and 1-day maximum rainfall data obtained from Grid 30 TRMM of 0.83 and 0.82 with the observation station is BMKG Pangsuma Station, and then the equation obtained from the analysis results on the grid is used as an equation for correction. The data correction equation for the amount of rainfall is Y = 1.1578 X - 98.5547, and the 1-day maximum rainfall data correction equation is Y = 1.2036 X - 28.4197.

Analyzing the Pollution Sources and Mechanisms of Urban Rivers Based on Identifying the Molecular Signature of Dissolved Organic Matter

Dissolved organic matter (DOM) is an essential component of river pollutants. Under the new situation of black water treatment in urban areas of China, in view of the widespread problem of unclear sources of multiple pollutants, further analysis of DOM components in urban rivers from the molecular level is a key link to deeply explore the sources, causes, and mechanism of river pollution so as to achieve efficient management. In this study, the urban rivers in the central city were selected as the research object, and a total of five rivers were selected that were seriously affected by the discharge sewage of four combined and separated sewer systems, respectively. Using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS), this study identified the molecular formulae and analyzed the elemental composition and compound groups of DOM in water and sediment samples at each site in dry and wet weather. The results showed that:①although CHO molecules and lignins were the main compounds in the urban river DOM, the high proportion of lipids, proteins, and heteroatomic compounds (especially CHOS molecules) revealed the anthropogenic pollution in rivers, which also led to the increase in DOC, TN, and NH+4-N. ②Surfactants such as C17H28O3S and C18H30O3S were ubiquitous in all urban rivers, which could be used as markers of domestic wastewater pollution. ③In wet weather, the rainfall inputs, storm runoffs, and hydraulic disturbance jointly led to the increase in the proportion of CHO molecules and lignin compounds; the decrease in proteins and lipids; the rise of DOC, TN, and NH+4-N concentrations in river water; and the decrease in DOC, TN, and NH+4-N concentrations and proteins and lipids in river sediments. ④The abundance of multi-heteroatomic compounds and condensed aromatics in the combined sewer system was higher than that in the separated sewer system, which may have been more severely polluted by domestic wastewater and storm runoff, especially kitchen wastewater. This study provides new insight for clarifying the critical causes of pollution in the new stage and provides an essential basis for further precision prevention and control of water pollution.

Changes in flood-associated rainfall losses under climate change

Climate change is expected to impact the severity and frequency of floods, which has implications for flood risk management. Design floods and derived flood frequency curves obtained using event-based rainfall-runoff models are widely used in industry to assess flood risks for planning and design purposes. For these approaches it is necessary to have (a) rainfall inputs, and (b) rainfall losses specified, the latter representing the amount of rainfall that is either intercepted, stored on the surface, or infiltrated into the soil and does not contribute to the flood hydrograph. There is extensive research on changes in flooding under climate change that focus on projections of rainfall. However, there is little research into projections of rainfall losses under climate change, despite the knowledge that their changes will modulate the flood response. Here, we present one of the first studies seeking to quantify how rainfall losses, as represented by estimates of initial and continuing losses used in event-based models, are projected to change under climate change.We identify dependencies between rainfall losses and antecedent soil moisture in around half (over 200) of the largely unregulated catchments (i.e. watersheds) in Australia analysed in this study and use these relationships to project rainfall losses under climate change. Near universal increases in both the mean and variance of both initial losses and continuing losses are projected in these catchments, suggesting that increased rainfall losses could offset the impact of increased rainfalls for frequently occurring floods and result in an increased variance in flood responses.

Application of the Single Exponential Smoothing Method For Flood Disaster Prediction

The country of Indonesia is seen as one that is particularly vulnerable to natural catastrophes as well as calamities brought on by human activity. A disaster that may be brought on by both natural and human sources is a flood. Disasters brought on by flooding are unpredictable occurrences that frequently cause losses in the form of property damage, the theft of assets, and lost productivity at work and in school. Through this prediction information system, the people can find out the level of risk of flooding through excessive rainfall. in order to better anticipate and prepare for all possibilities that occur before the flood, the method used is Single Exponential Smoothing. This method was chosen because of the simple way the system works to find predictive values ??through past data. With this system, researchers can input rainfall data taken from the Meteorology, Climatology and Geophysics Agency, then the data is processed through the system and if rainfall gets high results. The risk of flooding will also be very high and a warning will be given to the public so that better prepared for the risk of flooding. The results obtained from this study are the results of an analysis of the exponential method single to obtain accurate rainfall prediction information with data MAD, MSE and MAPE.

A Neural Network-Based Hydrological Model for Very High-Resolution Forecasting Using Weather Radar Data

Many hydro-meteorological disasters in small and steep watersheds develop quickly and significantly impact human lives and infrastructures. High-resolution rainfall data and machine learning methods have been used as modeling frameworks to predict those events, such as flash floods. However, a critical question remains: How long must the rainfall input data be for an empirical-based hydrological forecast? The present article employed an artificial neural network (ANN)hydrological model to address this issue to predict river levels and investigate its dependency on antecedent rainfall conditions. The tests were performed using observed water level data and high-resolution weather radar rainfall estimation over a small watershed in the mountainous region of Rio de Janeiro, Brazil. As a result, the forecast water level time series only archived a successful performance (i.e., Nash–Sutcliffe model efficiency coefficient (NSE) > 0.6) when data inputs considered at least 2 h of accumulated rainfall, suggesting a strong physical association to the watershed time of concentration. Under extended periods of accumulated rainfall (>12 h), the framework reached considerably higher performance levels (i.e., NSE > 0.85), which may be related to the ability of the ANN to capture the subsurface response as well as past soil moisture states in the watershed. Additionally, we investigated the model’s robustness, considering different seeds for random number generating, and spacial applicability, looking at maps of weights.

Source apportionment of nitrogen pressures at a Chalk-fed groundwater-dependent wetland

Abstract In Groundwater-Dependent Terrestrial Ecosystems (GWDTEs), atmospheric nitrogen (N) inputs have often been studied in isolation from terrestrial groundwater and surface water inputs. We describe for the first time the development and application of a combined atmospheric and terrestrial N source apportionment methodology, able to identify contributing catchment and N loadings to GWDTEs. We combined all N inputs using a site-specific conceptual model supported by 12 months’ monitoring for a Chalk-fed GWDTE at Newbald Becksies, East Yorkshire. We discuss implications for effective catchment management, wetland protection and development of a source apportionment methodology. Potential sources of nitrate include: atmospheric deposition, mineralization, leaching from agricultural soils, manure heaps, septic tanks, sewer and mains water leakage. Atmospheric deposition was calculated from measurements of ammonia and nitrogen dioxide concentrations together with rainfall inputs of ammonium and nitrate. Quantification of agricultural sources used the FarmScoper modelling tool to estimate nitrate leaching in the groundwater catchment. Comparison between modelled nitrate concentrations in leachate (15–17 mg N l −1 ) and observed groundwater nitrate concentrations (12.3–19.8 mg N l −1 ) are good. The majority of nitrate is leached from arable land. FarmScoper allows mitigation scenarios to be tested, supporting measures to reduce nitrate within a groundwater catchment.

Assessment of TRMM rainfall data for flood modelling in three contrasting catchments in Java, Indonesia

Abstract This study investigates the use of the Tropical Rainfall Measurement Mission's (TRMM) rainfall data for predicting water flows and flood events in three catchments on the island of Java, Indonesia, namely, Ciliwung, Citarum and Bengawan Solo. The Shetran model was used for rainfall-runoff simulations, with rainfall input obtained from measured rain gauges (hourly and daily) and TRMM (3-hourly and daily). The daily Nash Sutcliffe Efficiency (NSE) values for the model calibration period were 0.75, 0.70 and 0.85 using rain gauge data and 0.44, 0.44 and 0.75 using the TRMM rainfall data. For the validation period, the NSE values were 0.71, 0.62 and 0.89 using rain gauge data and 0.26, 0.61 and 0.58 for the TRMM data. The Critical Success Index for predicting flooding events was improved using rain gauge data compared to using TRMM data. The results demonstrate that rain gauge data are systematically superior to TRMM rainfall data when used for simulating discharges and predicting flooding events. These findings suggest that rain gauge data are preferred for flood early warning systems in tropical rainfall regimes and that if TRMM or similar satellite rainfall data are used, the evaluated flood risks should be treated with extreme caution.

Ecosystem responses of shallow thermokarst lakes to climate-driven hydrological change: Insights from long-term monitoring of periphytic diatom community composition at Old Crow Flats (Yukon, Canada).

Shallow waterbodies are abundant in Arctic and subarctic landscapes where they provide productive wildlife habitat and hold cultural and socioeconomic importance for Indigenous communities. Their vulnerability to climate-driven hydrological and limnological changes enhances a need for long-term monitoring data capable of tracking aquatic ecosystem responses. Here, we evaluate biological and inferred physicochemical responses associated with a rise in rainfall-generated runoff and increasingly positive lake water balances in Old Crow Flats (OCF), a 5600 km2 thermokarst landscape in northern Yukon. This is achieved by analyzing periphytic diatom community composition in biofilms accrued on artificial-substrate samplers at 14 lakes collected mostly annually during 2008-2019 CE. Results reveal that diatom communities at 10 of the 14 lakes converged toward a composition typical of lakes with rainfall-dominated input waters. These include six of nine lakes that were not initially dominated by rainfall input. The shifts in diatom community composition infer rise of lake-water pH and ionic content, and they reveal that northern shallow lake ecosystems are responsive to climate-driven increases in rainfall. Based on data generated during the 12 -year-long monitoring period, we conclude that lakes located centrally within OCF are most vulnerable to rapid climate-driven hydroecological change due to flat terrain, larger lake surface area, and sparse terrestrial vegetation, which provide less resistance to lake expansion, shoreline erosion, and sudden drainage. This information assists the local Indigenous community and natural resource stewardship agencies to anticipate changes to traditional food sources and inform adaptation options.

Monsoon-induced response of algal chlorophyll to trophic state, light availability, and morphometry in 293 temperate reservoirs

Eutrophication management is one of the greatest environmental challenges for lacustrine systems worldwide. The empirically predicted models between algal chlorophyll (CHL-a) and total phosphorus (TP) provide a basis for managing eutrophication in lakes and reservoirs, but other environmental factors influencing the empirical relations must be considered. Here, we tested the impacts of morphological and chemical variables, as well as the effect of the Asian monsoon, on the functional response of CHL-a to TP using two-year data of 293 agricultural reservoirs. This study was based on the approaches of empirical models (linear and sigmoidal), CHL-a:TP ratio, and trophic state index deviation (TSID). Algal CHL-a exhibited a strong log-linear relation with TP on the basis of 2-year average data (R2 = 0.69, p < 0.001), whereas it had a more sigmoidal relation in terms of monsoon-seasonal averages (R2 = 0.52, p < 0.001). The linear segment of the CHL-a–TP relation aligned with the gradient of TP (10 mg/L < TP < 100 mg/L) from mesotrophic to eutrophic conditions. The transfer efficiency of TP to CHL-a based on the 2-year mean CHL-a:TP was high (0.6 <) across all assessed agricultural systems. CHL-a:TP showed insignificant correlations with reservoir morphological variations, but it decreased (<0.5) in eutrophic and hypereutrophic systems during the monsoon season (July–August). Because TP and total suspended solids (TSS) have become increasingly abundant, light conditions become insufficient for algal growth during and after the monsoon season. Light-limited conditions become more prevalent in hypereutrophic systems with shallow depth and high dynamic sediment ratio (DSR) because of the intense rainfall inputs and wind-induced sediment resuspension of the post-monsoon season. TSID reflected the degree of phosphorus limitation and the reduction in underwater light corresponding to changes in reservoir water chemistry (ionic content, TSS, and TN:TP ratio), trophic state gradient, and morphological metrics (mainly mean depth and DSR). Our findings suggest that monsoon-induced changes in water chemistry and light attenuation, which are also associated with anthropogenic pollutant runoffs and reservoir morphology, are critical factors that influence the functional response of algal CHL-a to TP in temperate reservoirs. Modeling and assessing eutrophication should therefore take into account monsoon seasonality along with individual morphological features further.

Assessment of Object-Level Flood Impact in an Urbanized Area Considering Operation of Hydraulic Structures

Urban flooding has become one of the most common natural hazards threatening people’s lives and assets globally due to climate change and rapid urbanization. Hydraulic structures, e.g., sluicegates and pumping stations, can directly influence flooding processes and should be represented in flood modeling and risk assessment. This study aims to present a robust numerical model by incorporating a hydraulic structure simulation module to accurately predict the highly transient flood hydrodynamics interrupted by the operation of hydraulic structures to support object-level risk assessment. Source-term and flux-term coupling approaches are applied and implemented to represent different types of hydraulic structures in the model. For hydraulic structures such as a sluicegate, the flux-term coupling approach may lead to more accurate results, as indicated by the calculated values of NSE and RMSE for different test cases. The model is further applied to predict different design flood scenarios with rainfall inputs created using Intensity-Duration-Frequency relationships, Chicago Design Storm, and surveyed data. The simulation results are combined with established vehicle instability formulas and depth-damage curves to assess the flood impact on individual objects in an urbanized case study area in Zhejiang Province, China.

Hydrological Model Adaptability to Rainfall Inputs of Varied Quality

AbstractNumerous studies have evaluated the reliability and hydrologic utility of various rainfall data sets through hydrological modeling. However, the calibration of hydrological models compensates for errors in rainfall inputs. The drivers, conditions, and factors affecting the calibration of hydrological models given the accuracy of rainfall inputs are not well understood. Here, we explore hydrological model adaptability to rainfall inputs of varied quality and its potential mechanisms. Twenty‐eight rainfall products from multiple sources are collected for a headwater catchment in the Southern United States. These rainfall data sets include measurements from rain gauges, weather radars, satellites, reanalysis products, and Weather Research and Forecasting model simulations. Such rainfall data sets with varied errors are used to independently calibrate a widely used conceptual Xin'anjiang (XAJ) hydrological model. Results suggest that the hydrological model can often adapt well to two scenarios of inaccurate rainfall inputs producing high‐performance streamflow simulations. This adaptive ability is controlled by an adaptable threshold of the overall bias of the rainfall inputs. Moreover, hydrological model adaptability to rainfall inputs is further influenced by how event‐based rainfall bias shapes the overall rainfall bias, especially from those of heavy rainstorms. The hydrological model can adapt to those rainfall inputs that contain important information content for model calibration. Notably, the adaptability to rainfall inputs of the XAJ model is mainly controlled by a bias reduction through adjustment of evapotranspiration and soil moisture storage, yielding satisfactory effective rainfall. The study quantitatively sheds new light on hydrological model adaptability to rainfall input quality.

Rainfall–streamflow response times for diverse upland UK micro-basins: quantifying hydrographs to identify the nonlinearity of storm response

AbstractWhile it is known that antecedent conditions and rainfall profiles contribute to the nonlinearity of streamflow response and that hydrograph shape can be dependent on the nature of rainfall inputs, how antecedent conditions (with similar rainfall inputs) impact hydrograph shape is less known. Here, a data-based mechanistic (DBM) approach is applied to quantify hydrograph shape, in terms of timing and volume, for the purposes of comparing hydrographs across 17 micro-basins at selected localities in upland UK over a 4-year period. The analysis demonstrates the nonlinearity of storm response for small catchments and revealed that with low antecedent conditions and/or small rainfall inputs there was a high variance in hydrograph shape quantifiers and that these variances decrease (at rates micro-basin dependent) as the micro-basins became wetter or as the storms increased in size, potentially converging to a more stable response.

Two novel error-updating model frameworks for short-to-medium range streamflow forecasting using bias-corrected rainfall inputs: Development and comparative assessment

Accurate inflow forecasts with sufficient lead-time are highly crucial for efficient reservoir operation, for which, this study advocates the popular MIKE11-NAM-HD (MIKE) standalone conceptual hydrological model and its two error-updating frameworks for 1–5 days ahead inflow forecasting. These two modelling frameworks integrate the recently developed Wavelet-based Nonlinear AutoRegressive neural network with eXogenous inputs (WNARX) and a novel nested smoothing-based Long Short-Term Memory (LSTM) network as the error-updating models, with the standalone MIKE11-NAM-HD model, respectively. The available rainfall forecasts are bias-corrected using the Gaussian and Archimedean copulas, enhanced Kohonen Self-Organizing Maps (eKSOM) and hybrid Copula–eKSOM (Cop-SOM) approaches. The MIKE11-NAM-HD forced with the raw India Meteorological Department–Multi-model Ensemble (IMD-MME) and the selected bias-corrected rainfall forecasts are, hereafter named as rMIKE and bcMIKE, respectively. These proposed frameworks are tested for daily streamflow forecasting into the Hirakud multi-purpose reservoir in eastern India. The results reveal that the hybrid Cop-SOM could provide improved rainfall forecast skills with up to 5 days lead-time as compared to the standalone Copula and eKSOM. The standalone rMIKE and bcMIKE forecasting frameworks are useful for up to 2 days lead-time; whereas the bcMIKE-WNARX and bcMIKE-LSTM error-updating frameworks are useful up to 5 days lead-time with the Nash-Sutcliffe Efficiency (NSE) of 0.67–0.88 and 0.81–0.92, respectively. Overall, the LSTM proves to be a robust error-forecasting model at 1–5 days lead-time with reliable reproduction of peak flows; and the MIKE-LSTM framework forced with the Cop-SOM based bias-corrected rainfall forecasts has the lowest model prediction uncertainty with the narrowest confidence bands.

Comparing machine-learning-based black box techniques and white box models to predict rainfall-runoff in a northern area of Iraq, the Little Khabur River.

The rainfall-runoff process is one of the most complex hydrological phenomena. Estimating runoff in the basin is one of the main conditions for planning and optimal use of rainfall. Using machine learning models in various sciences to investigate phenomena for which statistical information is available is a helpful tool. This study investigates and compares the abilities of HEC-HMS and TOPMODEL as white box models and adaptive neural fuzzy inference system (ANFIS) and gene expression programming (GEP) as black box models in rainfall-runoff simulation using 5-year statistical data. Using the inputs of rainfall and temperature of the previous day and discharge in the steps of the previous 2 days reduced the prediction error of both models. Examining the role of different parameters in improving the accuracy of simulations showed that the temperature as an effective parameter in cold months reduces the amount of prediction error. A comparison of R2, RMSE, and MBE showed that black box models are more effective forecasting tools. Among the black box models, the ANFIS model with R2 = 0.82 has performed better than the GEP model with R2 = 0.76. For white box models, the HEC-HMS and TOPMODEL had R2 equal to 0.3 and 0.25, respectively.

Soil erosion control from trash residues at varying land slopes under simulated rainfall conditions.

Trash mulches are remarkably effective in preventing soil erosion, reducing runoff-sediment transport-erosion, and increasing infiltration. The study was carried out to observe the sediment outflow from sugar cane leaf (trash) mulch treatments at selected land slopes under simulated rainfall conditions using a rainfall simulator of size 10 m × 1.2 m × 0.5 m with the locally available soil material collected from Pantnagar. In the present study, trash mulches with different quantities were selected to observe the effect of mulching on soil loss reduction. The number of mulches was taken as 6, 8 and 10 t/ha, three rainfall intensities viz. 11, 13 and 14.65 cm/h at 0, 2 and 4% land slopes were selected. The rainfall duration was fixed (10 minutes) for every mulch treatment. The total runoff volume varied with mulch rates for constant rainfall input and land slope. The average sediment concentration (SC) and sediment outflow rate (SOR) increased with the increasing land slope. However, SC and outflow decreased with the increasing mulch rate for a fixed land slope and rainfall intensity. The SOR for no mulch-treated land was higher than trash mulch-treated lands. Mathematical relationships were developed for relating SOR, SC, land slope, and rainfall intensity for a particular mulch treatment. It was observed that SOR and average SC values correlated with rainfall intensity and land slope for each mulch treatment. The developed models' correlation coefficients were more than 90%.

Development and Application of Digital Twin Analysis Module for Low Impact Development Techniques

In this study, a digital twin analysis module capable of LID (Low Impact Development) interpretation for various rainfall scenarios was developed. The low-impact development digital twin model consists of a rainfall input module, a SWMM (Storm Water Management Model) -based LID analysis module, a reservoir tracking-based LID analysis module, and a post-processing module. Rainfall scenarios that can be applied in the developed model can simulate stochastic rainfall by frequency and daily heavy rainfall over the last 10 years. Additionally, the results of rainfall-loss-outflow for subwatersheds and inflow-outflow for total LID facilities (five types) are provided. If the LID DT (Digital Twin) simulator developed in this way is used, various water environment information such as the water circulation improvement and water quality reduction effects in the target area can be provided in a series of processes from the low-impact development facility planning stage to the operation management stage.