Observed Discharge Data Research Articles

A probabilistic machine learning framework for daily extreme events forecasting

Forecasting daily extreme events is crucial, particularly amidst the escalating severity of tropical storms and hurricanes in the East and Gulf coasts of the United States. The intensity of these hydrometeorological events, exacerbated by climate variability and change, has resulted in catastrophic floodings, posing significant risks to public safety and infrastructure. In response to the shortcomings associated with the deterministic rainfall-runoff models, specifically to account for uncertainties, we develop a probabilistic framework to enhance the accuracy and reliability of streamflow forecasting during extreme events. The core of our framework is a Monte Carlo-based deep learning model based on Long Short-Term Memory (LSTM) to account for model uncertainty. We employed the wavelet transform in our probabilistic framework to decompose observed discharge data, breaking down the data into its constituent components to identify trends. Furthermore, to account for the aleatory uncertainty in model input, we perturb the forcing datasets. Therefore, the proposed probabilistic framework accounts for both types of uncertainty, aleatory and epistemic. The model efficacy is tested across twenty-four basins in Southeast Texas, with a particular focus on the extreme conditions during Hurricane Harvey event. The results show that the proposed model outperforms the basic LSTM model by 45%, 40%, 9%, and 40% in NSE, KGE, PCC, and RMSE, respectively. This validation demonstrates the model’s robustness and applicability in real-world scenarios and underscores its potential as an effective tool for decision-makers in planning and risk management during extreme hydrological events.

Improving the estimation approach of percentage of impervious area for the storm water management model — A case study of the Zengwen reservoir watershed, Taiwan

The Zengwen Reservoir, located within the Zengwen River watershed, is a crucial water supply source in southern Taiwan. Water resources can be estimated using the rainfall-runoff model of the Storm Water Management Model (SWMM). However, the percentage of impervious area (PIA) is one of the significant factors influencing the SWMM. The purpose of this study is to utilize remote sensing imagery to rapidly and accurately estimate land use as PIA for the SWMM rainfall-runoff model. The rainfall-runoff model of SWMM was calibrated and validated based on observed discharge data in 2005 and 2017. The results of goodness-of-fit indicators of NSE value and R2 value showed in the acceptable range of 0.745, 0.764 in 2005, 0.715, and 0.883 in 2007, respectively. The modified composite of the built-up index and PIA (MCBI-PIA) was used for rainfall-runoff simulation in 2005, 2009, 2014, 2017, and 2021. The simulation results revealed the NSE value varied from 0.484 to 0.851, and the R2 value between 0.519 and 0.894 which represented a statistically acceptable performance of the simulation model. It indicates that the proposed method can be applied to estimate the PIA for land use patterns during different periods and utilized as the actual PIA for rainfall-runoff simulation with the SWMM.

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

Hydrological information is essential for planning water resource projects and for simulating hydrological models to calibrate and generate streamflow data. This study evaluates the performance of distributed and semi-distributed hydrological models in the Tamakoshi River Basin (TRB), Nepal. The models assessed include the Spatial Process in Hydrology (SPHY), Hydrologiska Byråns Vattenbalansavdelning (HBV), and Hydrologic Engineering Centre Hydrologic Modeling System (HEC-HMS). The models were evaluated using metrics such as Nash-Sutcliffe Efficiency (NSE) ranging from 0.62 to 0.77, Coefficient of Determination (R2) from 0.76 to 0.77, and Percentage Bias (P Bias) from 26.16 % to −3.54 % during daily calibration. All three models were successfully calibrated and validated on a daily and monthly basis using observed discharge data at the Busti station in the TRB. The SPHY model performed better during the calibration period compared to the validation period, while the HBV and HEC-HMS models showed better performance during validation than calibration. Among the statistical indicators, the coefficient of determination closely matched, but NSE and P Bias showed some deviations. This comparative analysis of calibrated and validated models is valuable for filling gaps in hydrological data records and for selecting appropriate hydrological models for planning, managing, and developing water resource projects in the TRB.

Application of SWAT Model for Hydrological Simulation of Rapti River Basin

This study aimed at application of SWAT model for hydrological simulations of Rapti River Basin (RRB) water systems. The Rapti River originates from Nepal and then it comes in India. SWAT (Soil and Water Assessment Tool) model was used for hydrological simulation of the RRB surface and sub surface water systems. SWAT is a comprehensive, semi-distributed river basin model that requires a large number of input parameters, which complicates model parameterization and calibration. The RRB was discretised into 4 sub-basins and 630 hydrological response units (HRUs) and calibration and validation was carried out at Bagasoti using monthly flow data of 11 years, respectively. We first calibrated the model in SWAT-CUP which is a decision-making framework that incorporates a semi-automated approach (SUFI2) using manual calibration and incorporating sensitivity and uncertainty analysis. Parameter sensitivity analysis helps focus the calibration and uncertainty analysis and is used to provide statistics for goodness-of-fit. In this study Calibration has been done between simulated and observed discharge data (1974-1985) for 50 simulations with 6 parameters that is Curve number (CN2 = 0.945), Groundwater delay (GW_DELAY = 50), Baseflow alpha factor (ALPHA_BF = 0.58), Manning's "n" value for the main channel (CH_N2 = 0.15), Effective hydraulic conductivity in main channel alluvium (CH_K2 = 10.20) and Available water capacity of the soil layer (SOL_AWC = 0.28). The results were analysed and compared with the observational data. The model performance evaluation showed acceptable ranges of values (i.e., Nash Sutcliff was 0.75 and R2 was 0.71). After model calibration, in order to predict water balance, the model was validated by using the best parameter.

A hydro-climatic approach for extreme flood estimation in mountainous catchments

Prediction of rainfall-runoff process, peak discharges, and finally flood hydrograph is essential for flood risk management and river engineering projects. In most previous studies in this field, the precipitation rates have been entered into the models without considering seasonal and monthly distribution. In this study, the daily precipitation data of 144 climatology stations in Iran were used to evaluate the seasonal and monthly pattern of flood-causing precipitation. Then, by determining the rainy seasons and seasonal fit of precipitation with a probabilistic model and using regional precipitation, a semi-distributed conceptual model of rainfall-runoff (MORDOR-SD) was trained and validated using the observed discharge data. Flood prediction was performed using climatic data, modeling of hydrological conditions, and extreme flow data with high performance. According to the results, the Nash–Sutcliffe and Kling–Gupta coefficients were 0.69 and 0.82 for the mean daily streamflow, 0.98 and 0.98 for the seasonal streamflow, 0.98 and 0.94 for the maximum discharges, and 0.57 and 0.78 for low flows, respectively. Moreover, the maximum daily discharges in different return periods were estimated using the results of the MORDOR-SD model, considering the probability distribution function of the probabilistic model of central precipitation (MEWP), the probabilistic model of adjacent precipitation, and probability distribution function of the previous precipitation. Finally, the extreme flows were predicted and compared using different methods including the SCHADEX, regional flood analysis, GRADEX, and AGREGEE. The results showed that the methods GRADEX, AGREGEE, and SCHADEX have the highest performance in predicting extreme floods, respectively.

Hydraulic Simulation for Flow One Dimension of Shatt Al-Hilla River

Floods are considered the most serious disaster among all-natural catastrophes because they occur more frequently than any other natural hazard and strongly impact more individuals than all other natural disasters. Therefore, it is necessary to study flood risk. Hillah River is critical in securing industrial, agricultural, and civil water in three Iraqi governorates: Babil, Diwaniyah, and Muthanna. Its primary source is the Euphrates River, which extends approximately 100 kilometers within Babil Governorate – the study area. This research aims to evaluate and study the river's capacity and predict future floods by developing scenarios for anticipated events. Also, a hydraulic model was developed to assess the manning coefficient for Shatt Al-Hillah. The one-dimensional HEC-RAS 6.0.1 program has been used to simulate water flow in the river, incorporating over 350 cross-sections spaced at 250-meter intervals surveyed in 2018 by the Department of Water Resources in Babil Governorate. The model was calibrated using observed discharge data from 2004 to 2022 in Shatt al-Hillah. Subsequently, it was compared with a range of water levels by varying manning factors. The calibration results indicated that a roughness coefficient of 0.023 was suitable for unstable flow conditions, and the least mean square root error between the measured and simulated water levels was 0.053. The simulation results showed that the current capacity of Al-Hilla River was 205 m3/s, such that it cannot pass the design discharge of 303 m3/s. After conducting scenarios greater than 205 m3/s, the results showed that increasing the discharge increased the areas exposed to flooding so that when the discharge was 450 m3/s, flooding of the submerged areas increased. With a percentage of 92.2%, the northern side of Babil Governorate will be more vulnerable to flooding than the southern side because the southern part levels are lower than the northern part.

Application of Arc-SWAT Model for Water Budgeting and Water Resource Planning at the Yeralwadi Catchment of Khatav, India

Every facet of life, including human habitation, economic development, food security, etc., depends on water as a valuable resource. Due to the burgeoning population and rapid urbanization, water availability needs to be simulated and measured using hydrologic models and trustworthy data. To fulfill this aim, the SWAT model was processed in this work. The SWAT model was formulated to estimate the hydrological parameters of Yeralwadi using meteorological data from IMD (India Meteorological Department) for the period 1995-2020. The observed discharge data was collected from the HDUG Nasik group and used in the calibration and validation of the Model. The SWAT model was corrected & validated through the SUFI-II algorithm in SWAT-CUP to get a better result. The model’s sensitivity is checked by using statistical parameters like Nash-Sutcliffe Efficiency (NSE) and a coefficient of determination (R2). NSE values were 0.72 and 0.80 in calibration and validation, and R2 were 0.80 & 0.76 in calibration and validation, respectively, indicating the acceptance of the model. Results show that 40.6% of the total yearly precipitation was lost by evapotranspiration. The estimated total discharge from the Yeralwadi catchment was 55.6%, out of which 41.2% was surface runoff and 14.4% was baseflow. The other 17.8% was made up of percolation into confined and unconfined aquifers, which served as soil and groundwater storages. The surface runoff is influenced by Curve number (CnII), SOL_AWC, ESCO, and base flow was influenced by ALPHA-BF and GW_REVAP. This study will be useful to water managers and researchers to develop sustainable water resource management and to alleviate the water scarcity issues in the study basin.

Penentuan Debit Aliran Berbasis Data Satelit GPM pada Daerah Tangkapan Air (Studi Kasus: Waduk Sepaku Semoi)

Sepaku Semoi Reservoir is located in Tengin Baru Village, Sepaku District, North Penajam Paser Regency, East Kalimantan Province, and has a water catchment area of 70.19 km2. This research aims to determine the amount of flow discharge entering the Sepaku Semoi Reservoir Catchment Area using the Mock Method. The rain data used is GPM satellite data as an alternative to the unavailability of rain recording posts in the Sepaku Semoi Reservoir Water Catchment Area. GPM satellite data needs to be calibrated and validated against rain recording posts in the field to determine the accuracy of the data. The highest GPM satellite data calibration is the Intercept linear regression equation with a value of R2 = 0.852. The best validation of GPM satellite data was shown in the 2004 period with a value of RMSE=65.04; KR=13%; R=0.91. The annual average maximum discharge is 6.26 m3/sec and the minimum is 3.17 m3/sec. Validation of the Mock Method flow rate is shown with a value of NSE=0.78; R=0.79. Based on the results of the analysis that has been carried out, concluded that an alternative in predicting observed discharge data in the field can be done using the Mock Method based on GPM satellite data.

Quantitative analysis of hydropower potential in the upper Beas basin using geographical information system and MIKE 11 Nedbor Afrstromnings Model (NAM)

AbstractThe present study aims to identify potential locations for small‐scale hydroelectric power (HEP) stations in hilly regions for the purpose of generating renewable energy. A rainfall‐runoff (R‐R) model of the Beas River catchment was established using the MIKE 11 NAM to estimate the available discharge. The model was calibrated and validated over the period of June‐2015–May‐2018 and June‐2018–May‐2020, respectively, using daily observed discharge data at the Pandoh Dam site. The model exhibited good performance with a coefficient of determination (R2) of 0.82 during calibration and 0.70 during validation and a water balance of −0.01% and −18%, respectively. However, Lmax, CK1, CK2 and CQOF are found most sensitive parameters during the calibration. Further, thirteen major streams of order five or higher were selected for the assessment of hydropower potential, resulting in the identification of 131 potential run‐of‐river (ROR) hydropower sites. The hydropower potential at two proposed sites, Bhang SHEP (9 MW) and Raison SHEP (18 MW), was estimated to be 11 and 15 MW, respectively, using 90% dependable flow. The results demonstrate the effectiveness of using Digital Elevation Model (DEM) and Geographic Information System (GIS) techniques for determining hydropower potential in ungauged basins in the Himalayas.

Enhancing Understanding of Hydrologic Processes in the Shafe Watershed, Ethiopia

Research on the combined impact of watershed attributes on streamflow is crucial in water resources planning and management, particularly due to the strong link between landscape modification and watershed attributes. However, identifying critical watershed attributes remains a challenge. This study focuses on investigating the impact of watershed attributes on streamflow in the Hare catchment of Ethiopia’s rift valley lakes basin. The Soil and Water Assessment Tool (SWAT) rainfall–runoff model was used, calibrated, and validated against observed discharge data to identify remedial measures for streamflow generation. The model’s performance was evaluated using criteria such as R2, NSE, PBIAS, and RSR, which yielded satisfactory values. The study found significant changes in land cover, with forest and shrub land declining and agricultural land expanding. Comparing mean annual streamflow between 1998, 2009, and 2021, streamflow at 2021 land use and land cover increased by 13.03% compared to 2009, which had already increased by 16.05% compared to 1998. The study also examined the impact of climate variations by manipulating meteorological data length and average slope. Overall, this study provides valuable insights into the relationship between watershed attributes and streamflow, emphasizing the importance of considering land cover changes and climate variations for effective water resource management in the Hare catchment.

Evaluation of Satellite-based Rainfall Data in Flood Prediction

Rainfall-runoff transformation is a solution to the difficulty of obtaining observed discharge data in flood prediction analysis. Rainfall-runoff transformation requires observed rainfall data with a high rate of accuracy spatially. However, observed rainfall data is also often not available. Satellite rainfall data is commonly used to replace observed rainfall data. However, the accuracy of satellite rainfall data still needs to be tested. This study applied rainfall-runoff transformation to the observed rainfall data and the PERSIANN, GPM, and GSMaP satellite rainfall data in the Opak Watershed using GAMA I SUH method, which were then compared with the observed hydrograph at the AWLR Kretek during the flood event that occurred in Yogyakarta Province due to Cyclone Cempaka to evaluate their accuracy. The results showed that the GPM data generated a hydrograph that is the closest to the observed hydrograph, both the shape and the peak of the hydrograph.

Separation of Floodplain Flow and Bankfull Discharge: Application of 1D Momentum Equation Solver and MIKE 21C

A floodplain is an area of low-lying land adjacent to a river, stream, or other water body that is regularly inundated by water during periods of high flow. Floodplains typically have relatively flat terrain and are composed of sediments deposited by the river over time. Floodplain flow refers to the movement of water across the surface of the floodplain during periods of high flow. This flow can occur as a result of water spilling over the river banks or seeping into the ground and then re-emerging on the surface of the floodplain. Bankfull discharge is the flow of water that just fills the channel of a river or stream to the top of its banks. It is the point at which the river or stream is at its maximum capacity without overflowing onto the floodplain. Bankfull discharge is often used as a reference point for assessing flood risk and planning floodplain management strategies. To examine the bank-to-bank hydro-morphodynamics of a river, it is necessary to comprehend the flow distribution throughout the main stream and floodplain. Along with river hydraulics, bankfull discharge is a crucial parameter for estimating river bank erosion. For evaluating the distribution and generation of river flow over the floodplain and main stream, a variety of modeling tools and approaches are available. This study investigates methods for separating floodplain flow and bankfull discharge from observed discharge data using the one-dimensional momentum equation. A two-dimensional modeling tool (MIKE 21C) was also employed to investigate the usefulness of the proposed method in a region with an enormous floodplain.

Water Resources Assessment for Raw Water Purposes in Serang Watershed, Kulonprogro

One source of raw water for drinking water is river water. The raw water source for drinking water must meet the requirements for quantity, continuity, and quality. Raw water for drinking water must meet class I water quality requirements. This study aims to assess of the potential for water availability and water quality in the Serang River. Analysis of water availability used statistical methods and simulated rainfall-runoff with the HEC-HMS model. Calibration of the hydrological model was carried out in sub-watersheds where there were observational discharge data. Water availability is reviewed in the second-order river. Water quality status was analyzed using the Pollutant Index and Storet methods. The results of the analysis of water availability for reliable discharge with a probability of 80%, 90%, and 95% in several sections of the Serang River to order 2 rivers can be presented in the form of a flow duration curve. The results of the analysis of the status of water quality in several sections of the upstream Serang river based on the Storet method and the Polluter Index method show that in general, it is in a slightly polluted condition.

Optimizing the Use of Meninting Multipurpose Reservoir Water in West Lombok District

The construction of the Meninting Reservoir was planned with the objective of meeting the irrigation and domestic water demands on Lombok Island. It served as a multipurpose reservoir, with a maximum storage volume of 12.18 million m³, mainly for supplying irrigation and domestic water. The reservoir had considerable potential for water availability, which could be used to supply water to the South Lombok region with limited water availability but had agricultural land potential. Therefore, this study aimed to evaluate the potential of Meninting Reservoir water availability and its optimum utilization for irrigation and domestic purposes. The irrigation water demand for 1,559.29 ha and domestic water demand of 150 ls-1 was fulfilled by Meninting Reservoir. Water availability in this Reservoir was estimated with the F.J. Mock method of rainfall-runoff model using 25 years of daily rainfall data from Gunung Sari and Sesaot rain gauge stations. The calibration process of the rainfall-runoff parameters models employed observed discharge data from the Aiknyet water level gauge station. The formula for optimizing reservoir water release was prepared using the linear programming method based on operational water level limits, inflow discharge, irrigation, and non-irrigation water requirements, including domestic water. The optimal average annual cropping intensity was 203.96%, 215.87%, and 241.41% for dry, normal, and wet years, respectively. The service reliability of irrigation and domestic water demands reached 100% for all inflow discharge conditions. The k-factor value met the minimum limit of 0.70 and 0.85 for irrigation and domestic water demands, respectively.

Modeling Hydrological Responses to Land Use Change in Sejnane Watershed, Northern Tunisia

Land use change is a crucial driving factor in hydrological processes. Understanding its long-term dynamics is essential for sustainable water resources management. This study sought to quantify and analyze land use change between 1985 and 2021 and its impacts on the hydrology of the Sejnane watershed, northern Tunisia. Remote sensing and a SWAT model using the SUFI-2 algorithm to identify the most sensitive parameters were used to achieve this objective. Land use maps were developed for 1985, 2001 and 2021. For the last 37 years, the watershed experienced a slight decrease in forest, scrubland and forage crops, a significant reduction in grassland, and a conspicuous expansion of olive trees and vegetable crops. Given the scarcity of observed discharge data, a SWAT model was calibrated for the period 1997–2010 and validated for 2011–2019. Model performance was good for both calibration (NSE = 0.78, PBIAS = −6.6 and R2 = 0.85) and validation (NSE = 0.70, PBIAS = −29.2 and R2 = 0.81). Changes in land use strongly affected the water balance components. Surface runoff and percolation were the most influenced, showing an increase in runoff and a decrease in percolation by 15.5% and 13.8%, respectively. The results revealed that the construction of the Sejnane dam, the extension of irrigated perimeters and olive tree plantations were the major contributors to changes in hydrology.

Hydrological modelling of largest braided river of India using MIKE Hydro River software with rainfall runoff, hydrodynamic and snowmelt modules

Abstract The Brahmaputra River is one of the world's largest river systems, India's largest braided river, and its springtime runoff and downstream streamflow are mostly due to snowmelt processes. This study analysed and used Tropical Rainfall Measuring Mission (TRMM)/Global Forecast System (GFS) rainfall, PET, and snowmelt data as inputs to a Rainfall–Runoff (RR) model, which is based on the MIKE Hydro River NAM software package. The mathematical model was calibrated against the available observed discharge data for the sub-catchments. The model performed reasonably well and simulated discharge in good agreement with observed discharge in terms of timing, rate, volume, and shape of the hydrograph. During the calibration procedure, the optimum values of the nine RR-NAM parameters are obtained. The performance of each model has been checked against measured discharge using a coefficient of determination (R2). It is observed that the value of R2 varies from 0.6 to 0.86. This is deemed acceptable for the purposes of this study. In addition to R2, the overall Water Balance error is also checked. The WBL error is less than 6%. Despite the inherent uncertainties in hydrological modelling, it is determined that the calibrated RR-NAM model can be utilized for the Brahmaputra basin's Flood Forecasting and Early Warning System design, as well as water resource management and planning.

Calibrating a hydrological model in ungauged small river basins of the northeastern Tibetan Plateau based on near-infrared images

River discharge monitoring sites are scarce in high mountain basins, especially on the Tibetan Plateau where rivers are widely distributed. The potential for estimating river discharge using remote sensing has been widely utilized for large continental rivers, while its application to smaller regional rivers has been limited. To address this issue, we investigate the reliability of calibrating a hydrological model for two small subbasins lacking discharge data in the upstream region of the Heihe River located on the Tibetan Plateau, based solely on multiple pixel ratio (MPR) data derived from near-infrared satellite images. Using six independent calibration schemes based on discharge or MPR data, the value of MPR data for hydrological modeling is explored. We first calibrate a model using observed discharge data and Nash-Sutcliffe efficiencies (NSEs) based on non-dominated sorting genetic algorithm II (Q-NSE scheme). We then use estimated discharge values derived from MPR data and identify different parameter sets that result in the highest NSEs in a Monte Carlo-based uncertainty framework (Q-NSE-1, Q-NSE-2 and Q-NSE-3 schemes). Spearman rank coefficients between MPR data and observed discharge data are then used as an alternative way to calibrate the model (MPR-SR scheme). The MPR-SR scheme is also applied with a simple water-balance filter, which removes parameter sets, which result in either underestimated or overestimated total discharge volumes (MPR-SRF scheme). The Babao River Basin and Yeniugou River Basin are used to compare the different calibration schemes and to perform parameter sensitivity analyses. The MPR-SRF calibration scheme achieved better performance in both river basins, with NSEs of 0.63 and 0.62, respectively, during the validation period. This study demonstrates the potential of the MPR-SRF calibration scheme to enhance models of small ungauged river basins lacking ground discharge observation data, and provides insights into how remote sensing data can be more effectively combined with hydrological modelling.

On the transferability of snowmelt runoff model parameters: Discharge modeling in the Chandra-Bhaga Basin, western Himalaya

Snowmelt runoff plays a major role in the glacierized and snow-covered basins in the western Himalaya. Modeling is the most helpful tool to quantify snowmelt contribution in mountainous rivers. However, the model calibration is very difficult because of the scarcity of ground observations in the Himalaya. We applied snowmelt runoff model (SRM) in a reference catchment of Chhota Shigri Glacier in the Chandra-Bhaga Basin, western Himalaya. Three model parameters [temperature lapse rate and recession coefficients (x and y)] among the nine model parameters were constrained using extensive field observations while initial values of other parameters were adopted from previous studies and calibrated, and the model was calibrated and validated against the observed discharge data. The daily discharge was simulated over 2003–2018 for both Chhota Shigri Catchment and Chandra-Bhaga Basin using snow cover area (SCA), precipitation, and temperature as inputs. The simulated mean annual discharges were 1.2 ± 0.2 m3/s and 55.9 ± 12.1 m3/s over 2003–2018 for Chhota Shigri Catchment and Chandra-Bhaga Basin, respectively. The reconstructed discharge was mainly controlled by summer temperature and summer SCA in the Chhota Shigri Catchment and summer SCA and summer precipitation in the Chandra-Bhaga Basin. The decadal comparison showed an increase (11% and 9%) and early onset (10 days and 20 days) of maximum monthly discharge over 2011–2018 compared to 2003–2010 in both catchment and basin scales. The model output is almost equally sensitive to the “degree day factor” and “runoff coefficient for snow” in the Chhota Shigri Catchment and most sensitive to the “runoff coefficient for snow” in the Chandra-Bhaga Basin. Though the SRM parameters were constrained/calibrated in a data-plenty reference catchment of Chhota Shigri Glacier, their application resulted in large discharge overestimation at the basin scale and were not transferable in the same basin i.e., Chandra-Bhaga Basin. Extreme care must be taken while using SRM parameters from other basins.

Evaluation of precipitation forecasts for five-day streamflow forecasting in Narmada River basin

ABSTRACT The accuracy of quantitative rainfall forecasts (QPFs) obtained from numerical weather prediction (NWP) models plays a crucial role in setting up a catchment streamflow forecasting system. Additionally, a suitable hydrological model is required. This study addresses input and model uncertainty in developing a five-day streamflow forecasting system in Narmada River Basin. We use deterministic and ensemble QPFs obtained from the Japan Meteorological Agency (JMA), National Centre for Medium Range Weather Forecasting (NCMRWF), and European Centre for Medium-Range Weather Forecasts (ECMWF). We use two hydrological models, the Soil and Water Assessment Tool (SWAT) and variable infiltration capacity (VIC), to generate streamflow forecasts. By comparing simulated streamflow forecasts with the observed discharge data, our results indicate that the forecast accuracy of NCMRWF is better than other forecasting products for lead times of two to five days. The streamflow generated using VIC produces better results than that obtained from the SWAT hydrological model.

Climate Change Impacts on Streamflow in the Krishna River Basin, India: Uncertainty and Multi-Site Analysis

In Peninsular India, the Krishna River basin is the second largest river basin that is overutilized and more vulnerable to climate change. The main aim of this study is to determine the future projection of monthly streamflows in the Krishna River basin for Historic (1980–2004) and Future (2020–2044, 2045–2069, 2070–2094) climate scenarios (RCP 4.5 and 8.5, respectively), with the help of the Soil Water and Assessment Tool (SWAT). SWAT model parameters are optimized using SWAT-CUP during calibration (1975 to 1990) and validation (1991–2003) periods using observed discharge data at 5 gauging stations. The Cordinated Regional Downscaling EXperiment (CORDEX) provides the future projections for meteorological variables with different high-resolution Global Climate Models (GCM). Reliability Ensemble Averaging (REA) is used to analyze the uncertainty of meteorological variables associated within the multiple GCMs for simulating streamflow. REA-projected climate parameters are validated with IMD-simulated data. The results indicate that REA performs well throughout the basin, with the exception of the area near the Krishna River’s headwaters. For the RCP 4.5 scenario, the simulated monsoon streamflow values at Mantralayam gauge station are 716.3 m3/s per month for the historic period (1980–2004), 615.6 m3/s per month for the future1 period (2020–2044), 658.4 m3/s per month for the future2 period (2045–2069), and 748.9 m3/s per month for the future3 period (2070–2094). Under the RCP 4.5 scenario, lower values of about 50% are simulated during the winter. Future streamflow projections at Mantralayam and Pondhugala gauge stations are lower by 30 to 50% when compared to historic streamflow under RCP 4.5. When compared to the other two future periods, trends in streamflow throughout the basin show a decreasing trend in the first future period. Water managers in developing water management can use the recommendations made in this study as preliminary information and adaptation practices for the Krishna River basin.