Streamflow Discharge Research Articles

Assessment of Water Resources Potential of Ogun River for Irrigation Purposes at Middle Ogun Irrigation Project

The Middle Ogun Irrigation Project (MOIP) was conceived by the Federal Government of Nigeria to boost agricultural food production in the southwestern part of the country using modern irrigation facilities. However, water supply from the Ikere Gorge dam has been intermittent, particularly at the intake weir of this project, despite the enormous water resources on the dam. Therefore, this project assessed the water resources potential at the Odo Ogun intake weir guage station of the Middle Ogun Irrigation Project and evaluated the seasonal irrigation requirements on irrigation fields. The Flow Duration Curve (FDC) was derived at the gauge site for streamflow discharge from four quartiles and percentage reliability of Q95%, Q75%, Q50% and Q25%, which gave 32.40, 55.01, 60.00 and 90.00 m3/s, respectively. Hydrological modelling of the catchment was carried out using the MapWindow Soil and Water Assessment Tool (MWSWAT) for ten delineated sub-basins on the catchment and the average annual yield from the catchment was 10,094.00 mm/year. The net seasonal irrigation requirement of crops grown was evaluated using CROPWAT 8.0 and gave 428.46 mm per planting season.

Causality Analysis and Prediction of Riverine Algal Blooms by Combining Empirical Dynamic Modeling and Machine Learning Techniques

AbstractRiver algal blooms have become a global environmental problem due to their large impact range and environmental hazards. However, the complex mechanisms underlying these blooms make prediction and prevention challenging. Here, we employed empirical dynamic modeling (EDM) and machine learning to reveal the causes and predict diatom blooms from 2003 to 2017 in the Han River of China. The diatom cell density ranged from 0.1 to 5.1 × 107 cells L−1, whereas algal blooms often lasted for 10 days with density exceeding 107 cells L−1. The EDM results elucidated that, under consistent high nutrient concentrations, algal blooms were primarily regulated by eight environmental factors: water temperature in the Han River; water levels, flow velocities, and streamflow discharges in the Han River and the Yangtze River; and water level variation in the Han River. The poor performance (coefficient of determination R2 < 0) of the multiple linear regression, EDM, and random forest models indicated the challenge of predicting algae density. Therefore, we used machine learning classification models to predict algal blooms occurrences. With the resampling techniques to account for imbalanced data, machine learning models achieved perfect classification prediction (Kappa value = 1) of the 13 algae‐bloom events with a 10‐day lead time during a 15‐year period, providing an important reference for preemptive warning of riverine algal blooms.

Using of hydrological model and geospatial tool to assess climate change impact on the hydropower potential of the White Bandama watershed in Côte d'Ivoire (West Africa)

Abstract. This study was carried out in the White Bandama watershed (WBW) in Côte d'Ivoire (West Africa). The objective is to assess the impacts of future climate change (CC) on the hydropower potential (HPP) of the WBW. The methodology is based on coupling the SWAT (Soil and Water Assessment Tool) hydrological model with the Geographic Information System (GIS) QGIS to assess HPP on streams and evaluate the impacts of future CC on HPP of the watershed. Historical and climate projection data (precipitation, minimum and maximum temperature) for a set of three Regional Climate Models (RCM) from CORDEX-AFRICA (CCCma-CanRCM4, CCLM4-8-17 and REMO 2009) under RCP 4.5 were used. The biases of the ensemble mean were corrected by the Delta-change method. The relative change of streamflow discharge and HPP was assessed as the relative difference between the projection periods (2041–2070 and 2071–2100) and the reference period (1976–2005). The results showed a total of 22 future hydropower potential sites in the watershed. These sites were identified, geolocated and classified according to their potential capacity of generation in 82 % as small (1–25 MW potential capacity), 9 % as medium (25–100 MW potential capacity) and 9 % as large (more than 100 MW potential capacity) hydropower. The climate models' ensemble projected an upward trend for both the annual mean discharge of rivers and HPP of the WBW according to RCP 4.5 for the periods 2041–2070 and 2071–2100. On the annual cycle, the months of August and September will record the highest monthly mean flows between 150 and 200 m3 s−1 while the months from November to April will record low monthly mean flows in the WBW.

An Imputing Technique for Surface Water Extent Timeseries with Streamflow Discharges

A continuous and multi-decadal surface water extent (SWE) record is vital for water resources management, flood risk assessment, and comprehensive climate change impact studies. The advancements in remote sensing technologies offer a valuable tool for monitoring surface water with high temporal and spatial resolution. However, challenges persist due to image gaps resulting from sensor issues and adverse weather conditions during data collection. To address this issue, one way to fill the gaps is by leveraging in situ measurements such as streamflow discharges (SFDs). We investigate the relationship between SFDs and Landsat-derived SWE in the New England region watersheds (eight-digit hydrological unit code (HUC)) on a monthly scale. While previous studies indicate the relationship exists, it remains elusive for larger domains. Recent research suggests using monthly average SFD data from a single stream gage to fill the gaps in SWE. However, as SWE represents a monthly maximum value, relying on a single gage with average values may not capture the complex dynamics of surface water. Our study introduces a novel approach by replacing the monthly average SFD with the maximum day streamflow discharge anomaly (SFDA) within a month. This adjustment aims to better reflect extreme scenarios, and we explore the relationship using ridge regression, incorporating data from all stream gages in the study domain. The SWE and SFDA are both transformed to stabilize the variance. We found that there is no discernible correlation between the magnitude of the correlation and the size of the basins. The correlations vary based on HUC and display a wide range, indicating the variances of the importance of stream gages to each HUC. The maximum correlation is found when the stream gage is located outside of the target HUC, further verifying the complex relationship between SWE and SFDA. Covering over 30 years of data across 45 HUCs, the imputing technique using ridge regression shows satisfactory performance for most of the HUCs analyzed. The results show that 41 out of 45 HUCs achieve a root-mean-square error (RMSE) of less than 10, and 44 out of 45 HUCs exhibit a normalized root-mean-square error (NRMSE) of less than 0.1. Of 45 HUCs, 42 have an R-squared (R2) score higher than 0.7. The Nash–Sutcliffe efficiency index (Ef) shows consistent results with R2, with the relative bias ranging from –0.02 to 0.03. The established relationship serves as an effective imputing technique, filling gaps in the time series of SWE. Moreover, our approach facilitates the identification and visualization of the most significant gages for each HUC, contributing to a more refined understanding of surface water dynamics.

Statistical evaluation of a long‐memory process using the generalized entropic value‐at‐risk

AbstractThe modeling and identification of time series data with a long memory are important in various fields. The streamflow discharge is one such example that can be reasonably described as an aggregated stochastic process of randomized affine processes where the probability measure, we call it reversion measure, for the randomization is not directly observable. Accurate identification of the reversion measure is critical because of its omnipresence in the aggregated stochastic process. However, the modeling accuracy is commonly limited by the available real‐world data. We resolve this issue by proposing the novel upper and lower bounds of a statistic of interest subject to ambiguity of the reversion measure. Here, we use the Tsallis value‐at‐risk (TsVaR) as a convex risk functional to generalize the widely used entropic value‐at‐risk (EVaR) as a sharp statistical indicator. We demonstrate that the EVaR cannot be used for evaluating key statistics, such as mean and variance, of the streamflow discharge due to the blowup of some exponential integrand. We theoretically show that the TsVaR can avoid this issue because it requires only the existence of some polynomial moment, not exponential moment. As a demonstration, we apply the semi‐implicit gradient descent method to calculate the TsVaR and corresponding Radon–Nikodym derivative for time series data of actual streamflow discharges in mountainous river environments.

Geophysics‐Informed Hydrologic Modeling of a Mountain Headwater Catchment for Studying Hydrological Partitioning in the Critical Zone

AbstractHydrologic modeling has been a useful approach for analyzing water partitioning in catchment systems. It will play an essential role in studying the responses of watersheds under projected climate changes. Numerous studies have shown it is critical to include subsurface heterogeneity in the hydrologic modeling to correctly simulate various water fluxes and processes in the hydrologic system. In this study, we test the idea of incorporating geophysics‐obtained subsurface critical zone (CZ) structures in the hydrologic modeling of a mountainous headwater catchment. The CZ structure is extracted from a three‐dimensional seismic velocity model developed from a series of two‐dimensional velocity sections inverted from seismic travel time measurements. Comparing different subsurface models shows that geophysics‐informed hydrologic modeling better fits the field observations, including streamflow discharge and soil moisture measurements. The results also show that this new hydrologic modeling approach could quantify many key hydrologic fluxes in the catchment, including streamflow, deep infiltration, and subsurface water storage. Estimations of these fluxes from numerical simulations generally have low uncertainties and are consistent with estimations from other methods. In particular, it is straightforward to calculate many hydraulic fluxes or states that may not be measured directly in the field or separated from field observations. Examples include quickflow/subsurface lateral flow, soil/rock moisture, and deep infiltration. Thus, this study provides a useful approach for studying the hydraulic fluxes and processes in the deep subsurface (e.g., weathered bedrock), which needs to be better represented in many earth system models.

Orlicz risks for assessing stochastic streamflow environments: a static optimization approach

This study applies novel risk measures, called Orlicz risks, to the risk and uncertainty evaluation of the streamflow discharge as a primary driver of hydrological and hydraulic processes of interest in civil and environmental engineering. We consider the mixed moving average process governing the discharge whose statistics are explicitly represented as some product of a time-scale characterizing the flow attenuation and a jump moment governing the size and frequency of jumps. The classical Orlicz risks are extended so that not only the upper tail risk but also the lower one of the jump size and attenuation of the discharge can be evaluated within a single mathematical framework. Further, the risk and uncertainty can be individually quantified in a tractable manner by the proposed Orlicz risks. Computing the Orlicz risks reduces to solving a pair of novel static optimization problems that are solvable semi-analytically. The risk and uncertainty involved in the streamflow dynamics can be consistently evaluated by specifying few user-dependent parameters. The associated Radon–Nikodym derivatives as the worst-case model uncertainties are obtained as byproducts. Sufficient conditions for the well-posedness of the Orlicz risks are discussed and numerical algorithms for computing them are presented. We finally apply the proposed framework to a statistical analysis of the streamflow discharge time series data collected at mountainous river environments.

Long short-term memory models to quantify long-term evolution of streamflow discharge and groundwater depth in Alabama

Long short-term memory (LSTM) models have been shown to be efficient for rainfall-runoff modeling, and to a lesser extent, for groundwater depth forecasting. In this study, LSTMs were applied to quantify the spatiotemporal evolution of surface and subsurface hydrographs in Alabama in the Southeastern United States, where water sustainability has not been fully quantified across spatiotemporal scales. First, the surface water LSTM model with extensive dynamic (precipitation and other weather variables) and static (basin characteristics) inputs predicted the main characteristics of streamflow for six years at 19 gauged basins in Alabama. The model tended to underestimate extremely high streamflow but adding drainage density as an input feature slightly improved the predictions of extreme events. Second, to predict the groundwater depth evolution, a groundwater LSTM (GW-LSTM) model was proposed and applied using static inputs capturing the aquifers' hydrogeological properties and dynamic inputs of meteorological information. Three precipitation scenarios were also explored to evaluate the groundwater hydrograph evolution in the next two decades. The GW-LSTM model predicted the general trend of daily groundwater depth fluctuations (at 21 wells distributed across Alabama from 1990 to 2021) including most extremely high groundwater levels, and recovered groundwater depth for locations withheld from model training and validation. This study, therefore, extended the application of LSTMs in quantifying the spatiotemporal evolution of surface water and groundwater, two manifestations of a single integrated resource.

Dual stochastic descriptions of streamflow dynamics under model ambiguity through a Markovian embedding

Hamilton–Jacobi–Bellman equation (HJBE) and backward stochastic differential equation (BSDE) are the two faces of stochastic control. We explore their equivalence focusing on a system of self-exciting and affine stochastic differential equations (SDEs) arising in streamflow dynamics. Our SDE is a finite-dimensional Markovian embedding of an infinite-dimensional jump-driven process called the superposition of continuous-state branching processes (a supCBI process). We formulate new ergodic control problems to evaluate the worst-case streamflow discharge in the long run and derive their HJBEs and ergodic BSDEs. The constant ambiguity aversion classically used in assessing model ambiguity must be modified in our case so that the optimality equations become well-posed. With a suitable modification of the ambiguity-aversion coefficient depending on the distributed reversion speed, we demonstrate that the solutions to the optimality equations are equivalent to each other in the sense that they lead to the same result. Finally, we apply the proposed framework to the computation of realistic cases with an existing record of discharge through a numerical Markovian embedding.

Inland Flooding and Rainfall from Hurricane Irene and Tropical Storm Lee (2011): Coupled Atmosphere–Wave–Ocean Model Simulations and Remote Sensing and In Situ Observations with a Machine Learning Tool

Abstract Inland flooding from landfalling tropical cyclones (TCs) is a major cause of death and damage to property and infrastructure worldwide. The mid-Atlantic region of the United States was devastated by Hurricane Irene and Tropical Storm Lee during late August–early September 2011, when the two storms produced sequential heavy rainfall and record flooding. Many rivers and streams reached their all-time record discharge to date. This study aims at 1) better understanding and predicting TC rainfall using various observed rainfall products and a high-resolution coupled atmosphere–wave–ocean model, namely, the Unified Wave Interface-Coupled Model (UWIN-CM), 2) characterizing inland flooding using streamflow data, and 3) improving prediction of TC-induced inland flooding using UWIN-CM and a machine learning K-nearest-neighbor (KNN) model. The results show that there is a wide range of uncertainty in satellite and radar–gauge-observed rainfall products in terms of rain-rate distribution and cumulative rainfall over the mid-Atlantic region. UWIN-CM rainfall is closer to the radar–gauge data than satellite data over land. Streamflow in most large rivers (>500 cfs) peaked after Lee, which reflects the sequential rainfall contributions of the two storms. The rainfall–streamflow–discharge response times were dependent on the size of the stream and the peak rain rates. To better predict rainfall and flooding, UWIN-CM and observed rainfall are used with the machine learning KNN regression model for prediction of severity of TC-induced inland flooding hazard. These results demonstrate the value of a stepped approach for rainfall and flood prediction toward a fully coupled atmosphere–ocean–land/hydrology model in the future.

Quantifying the impact of climate variability and human activities on streamflow variation in Taoer River Basin, China.

Climate variability and human activity are the two driving forces that alter the hydrological cycle and spatiotemporal distribution of water resources. Using the Taoer River Basin (TRB) as an example, this study analyzed the impacts of climate variability and human activities on streamflow discharge in various periods and the resulting hydrological alterations. First, historical streamflow data were divided into four periods (baseline period and altered periods 1, 2, and 3). Based on the proposed basic identification framework, four assessment methods (the hydrological sensitivity method, distributed hydrological model, linear regression model, and runoff restoring computation) are used and provided relatively consistent estimates of streamflow attribution. Climate variability is the driving factor for streamflow changes, and the relative contributions in altered periods 1, 2, and 3 are 81% (+ 50.34mm), 68% (+ 13.37mm), and 53% (-19.23mm), respectively. In addition, climate variability and reservoir construction have different impacts on the hydrological regime at different periods, and reservoir regulation's effect on the hydrological regime depends on climatic conditions. Combined with this case study, we further discuss the necessity of breakpoint selection and period subdivision in the attribution of streamflow changes, and analyze the applicability of different methods with current ideas for improvement. This study not only has practical significance for water resource planning and adaptive policy formulation in the TRB but also provides a useful reference for similar studies.

Performance of a Rectangular-Shaped Surface Velocity Radar for River Velocity Measurements

Recent advances in streamflow discharge measurement instrumentation have improved upon or following replacements of conventional devices and methodologies. Among the advanced instruments, surface velocity radar (SVR) has been increasingly used for flood measurements and normal flow discharge, thus enabling noncontact measurements of surface velocities by using microwaves. It is effective even when direct access to rivers is difficult, such as during the flood season. Unfortunately, systematic and comprehensive evaluation of surface velocity measurement accuracy in conditions in situ is rarely conducted. The microwave footprint on the water surface, as emitted from the SVR, depends on the monitoring configurations (e.g., tilt and yaw angles) and can affect the accuracy of velocity measurements. In this study, we selected an SVR with a rectangular front shape and evaluated its measurement accuracy in a real-scale river experiment facility wherein surface velocity was validated against concurrently measured, large-scale particle image and acoustic Doppler velocimetry. Comparative results showed that the spatial autocorrelation of the velocity distribution inside the SVR footprint was crucial such that the measurement configuration for low-tilting and high-yawing angles should be avoided especially for locations near the channel edge with substantially reduced velocity.

Estimation of Water Balance Components of Patapur Micro Watershed in the Tungabhadra River Basin Using QSWAT Model in QGIS Environment

Aims: Estimation of water balance components of a micro watershed by employing efficient calibrated and validated SWAT model helps to understand each components of water balance and are important to plan agricultural water management, climate change impact assessment, flow forecasting, water quality assessment etc. This water balance study minimizes possibility of drought and mismanagement, and hence will lead to a proper utilization of accessible water resource.
 Place and Duration of Study: In the present study, QSWAT hydrological model was calibrated and validated using measured runoff data from the outlet of the micro watershed and then put to use for long term simulations in Patapur micro watershed, Raichur District, Karnataka using weather, land use and land cover, soil and digital elevation model for the period of 37 years (1980-2016).
 Methodology: The QSWAT model was set up using the input data of Patapur micro watershed and was accurately calibrated and validated using the measured runoff data. The calibrated was used for long term simulation from 1980-2016 and then water balance components of the micro watershed was estimated.
 Results: The results revealed that the QSWAT model performed better in simulating the runoff and other water balance components. The daily calibration statistics results for behavioral parameters in SWAT-CUP for stream flow discharge during the period 2012-2014 are R2, NS, PBIAS and RSR values between measured and simulated by model was found to be 0.88, 0.87, -21.30 and 0.36, respectively indicating the model performance for daily calibration was very good in terms of both R2 and NS value as their value being >0.75 as per the performance ratings of hydrological model.
 The water yield that is draining out of the watershed includes surface runoff, lateral flow and groundwater contribution to stream flow minus the transmission losses (water lost as deep percolation and evapo-transpiration) which amounts to 168.40 mm. The annual water balance components for the watershed indicated that out of 527.70 mm of annual precipitation, 322.50 mm and 114.91 mm was lost by evapo-transpiration and surface runoff, respectively. The water balance also revealed that 82.86 mm was contributed to groundwater by percolating into shallow aquifer which was followed by 43.40 mm of base flow but the ground water recharge and storage is very meager that accounts to only 4.14 mm that is the matter of concern over the micro watershed. The simulation model indicates that 58.77 to 64.54% by rainfall was lost by evapotranspiration and very less amount lost through the lateral flow. Groundwater flow and percolation were contributing 3.68 to 10.13% and 9.95 to 19.82 % respectively, from total rainfall. During the highest rainfall year, about 33.22, 10.13% and 1.10% of the rainfall was transformed into surface runoff, groundwater flow and lateral flow respectively. During lowest rainfall year, about 8.31%, 3.68% and 1.35% of the rainfall was transformed into surface runoff, groundwater flow and lateral flow respectively.

Evaluating Spatiotemporal Variations of Groundwater–Surface Water Interaction Using an Integrated Hydrological Model in Huashan Basin, China

Quantifying the spatiotemporal variations of basin-scale surface water (SW)–groundwater (GW) interactions is vital for the conjunctive management of water resources in the basin. In this study, an integrated hydrological model (SWAT-MODFLOW) is used to simulate the SW–GW system in the Huashan Basin. The numerical model was calibrated and validated using the streamflow observations of the watershed outlet and the groundwater levels of the long-term monitoring wells from 2016 to 2020 in the study area. The model results show that the SWAT–MODFLOW can achieve a better fit for the streamflow discharge, compared with the results in the single SWAT model, with R2 (coefficient of correlation) and NSE (Nash-Sutcliffe efficiency coefficient) of 0.85 and 0.83, respectively. The water table fitting results indicate that R2 and RMSE can reach 0.95 and 0.88, respectively. The water budgets analysis demonstrates that the average rate (0.5281 m3/s) of GW abstraction to SW is larger than the rate (0.1289 m3/s) of SW recharge to GW. Moreover, the exchange rate of SW and GW gradually reaches a peak value from June to August, and the lowest value is shown in April, for each hydrological year. Based on the IPPC6 CanESM5 dataset supplied by the Canadian Climate Centre, the regional precipitation scenario subject to climate change was predicted by the ASD (Auto Statistical Downscaling Model) a statistical downscaling method, under the climate scenarios of SSP2_4.5 and SSP5_8.5. The SW–GW interaction pattern was modeled under the future scenarios in the study area. The current (2016–2020) average annual rate of the SW–GW interaction is considered as the base value. Subject to the SSP2_4.5 scenario, the average exchange rate of the SW recharge to GW is 0.1583 m3/s, which is an increase of 22.8%. The average exchange rate of the GW discharge to SW is 0.5189 m3/s which is a reduction of 0.017%. Subject to the SSP5_8.5 scenario, the average exchange rate of SW recharge to GW is 0.1469 m3/s, which is an increase of 14.7%. The average exchange rate of the GW discharge to SW is 0.5953 m3/s, which is an increases of 12.7%. The results can assist in water resource management in the basin, by identifying potential locations of nutrient transport from the aquifer to the river, as well as changes in spatial variability under future climatic conditions.

Modeling and computation of an integral operator Riccati equation for an infinite-dimensional stochastic differential equation governing streamflow discharge

We propose a linear-quadratic (LQ) control problem of streamflow discharge by optimizing an infinite-dimensional jump-driven stochastic differential equation (SDE). Our SDE is a superposition of Ornstein-Uhlenbeck processes (supOU process), generating a sub-exponential autocorrelation function observed in actual data. The integral operator Riccati equation is heuristically derived to determine the optimal control of the infinite-dimensional system. In addition, its finite-dimensional version is derived with a discretized distribution of the reversion speed and computed by a finite difference scheme. The optimality of the Riccati equation is analyzed by a verification argument. The supOU process is parameterized based on the actual data of a perennial river. The convergence of the numerical scheme is analyzed through computational experiments. Finally, we demonstrate the application of the proposed model to realistic problems along with the Kolmogorov backward equation for the performance evaluation of controls.

Impact of climate change on water availability in Bhagirathi River Basin, India

ABSTRACT The paper aims at simulating streamflow using Hydrologic Engineering Center Hydrological Modelling System (HEC-HMS) under different Representative Concentration Pathways (RCP’s) to access the impact of climate change on hydrological regime of Bhagirathi River at Tehri Dam. The input for the study is precipitation and temperature which are obtained from remote-sensing data. The temperature and precipitation data daily products are obtained from Tropical Rain Monitoring Mission (TRMM) and Atmospheric Infrared Sounder (AIRS) missions and the respective future anomalies were obtained from World Bank Climate Change Knowledge Portal (CCKP). The streamflow simulation is done for RCP’s 4.5 and 8.5 under the models csiro_mk3_6_0, bcc_csm1_1, ccsm4 and mri_cgcm3. The study found that the combination of remote-sensing data and HEC-HMS model has been successful in simulating streamflow in the region. The region will experience an increase in streamflow discharge under changing climate over the next coming years. The simulation also indicates that there will be a change in temporal distribution pattern of the discharge. Amidst these uncertainties, the predictions also give an insight into the planning and management of constructed or to be constructed hydraulic structures in the basin.

Impacts of seasonally frozen ground on streamflow recession in the Red River of the North Basin

AbstractThe projected rising temperatures due to climate change can alter the frozen ground's duration and thickness in the cold regions of the Northern Hemisphere, leading to changes in surface and subsurface contributions to streamflow. The initial step in understanding the impacts of the changing climate on the hydrologic response of the cold climate watersheds is to investigate the effects of the cold climate processes (e.g., freeze–thaw processes) on watershed response. However, complexities associated with the spatiotemporal data collection of cold climate processes have limited the capabilities of hydrologic models in simulating cold climate watershed responses. This study aims to evaluate the impacts of seasonally frozen soil on the streamflow recession in the Red River of the North Basin (RRB). To the best of our knowledge, this study is one of the first attempts to quantify the impacts of frozen soil on the storage‐discharge relationship of the RRB. We define a set of hypotheses to evaluate how cold climate processes influence the surface and subsurface flow paths and affect the linearity/non‐linearity of the storage‐discharge relationship in spring and summer. The results confirmed that the cold climate processes control the recession behavior of the RRB. It was found that the streamflow discharge is linearly related to the storage during spring, whereas during summer, the relationship was less linear. Statistical analyses revealed that the antecedent winter soil temperature and snowpack control the seasonal dynamics of hydrograph recession. Particularly, results indicated a more linear storage‐discharge relationship for antecedent winters characterized by colder soil temperatures and larger snowpack. These findings highlight the importance of the cold climate hydrologic processes in cold climate basins such as the RRB.

Classification analysis of blue and green water quantities for a large-scale watershed of southwest China

Traditional blue water resources assessment and management may not meet the needs of sustainable water resource utilization; ignoring the number of green water resources will underestimate the availability of water resources. To rationally allocate and scientifically manage the limited water resources, it is necessary to divide the rich and poor flow situation of blue water and green water. The MIKE SHE-MIKE HYDRO integrated coupled model was selected and used in the Yalong River basin to ascertain the blue and green water in the hydrological cycle. The model was calibrated by matching simulated discharge against observed streamflow discharge at the Tongziling Station. At the same time, the research analyzed the component of green water and the total amount of blue water or green water on a temporal scale. The set pair analysis (SPA) was introduced to classify blue water and green water, which can not only understand the amount and distribution characteristics of water resources in the Yalong River Basin but also rationally allocate the total of water resources in the basin from the perspective of the regional water cycle. Furthermore, according to the situation of blue water and green water in the basin, the related policies are formulated to realize the efficient utilization of water resources in the Yalong River basin.

Investigating Historical Baseflow Characteristics and Variations in the Upper Yellow River Basin, China.

The baseflow of the Yellow River is vital and important for water resource management and for understanding the hydrological cycle and ecohydrology setting in this arid and semi-arid basin. This study uses a Lyne and Hollick digital filtering technique to investigate the behaviors of the baseflow and the baseflow index in the upper reaches of the Yellow River Basin (China). The observed streamflow discharges along the river were used to analyze the baseflow trend, persistence, and periodic characteristics during the period of 1950–2000. The results show that the average baseflow and BFI in the upper reaches of the Yellow River exhibit a decreasing trend and will continue to decline in the future. Generally, the annual average baseflow and BFI for the most upstream areas of the Yellow River show little difference, while the baseflow and BFI exhibit significant differences for the downstream areas. The filtered annual baseflow varied between 128 × 108 m3/year and 193 × 108 m3/year for the Yellow River. The BFI ranged from 0.54 to 0.65, with an average of 0.60. This indicates that on average, 60% of the long-term streamflow is likely controlled by groundwater discharge and shallow subsurface flow. Statistics show that two periodic variations were observed in the baseflow evolution process. The results indicate that on average, the first and second main cycles of baseflow behaviors occur at 28 years and 12–17 years, respectively. Correspondingly, the estimation indicates that the abrupt change points tend to appear in the 1960s, the 1980s, and the 1990s. An improved understanding of baseflow behaviors can help guide future strategies to manage the river regime, its water resources, and water quality.

Comparison of the hydrological time series modeling by the floods in river Indus of Pakistan

Today, in the field of science and technology, huge forecasting applications are used by scholars to forecast future values. Nowadays, using estimating the flood forecasting for peak flow discharges is very common for the risk assessment annually by quantitative data collections from different resources. The very famous and longest rivers of Pakistan i.e. Indus River and other rivers too like River Jhelum, River Kabul, and River Chenab are the prime sources of flooding. These rivers are the prime tributaries of the Indus River System. Pakistan's longest river, River Indus, is connected with the seven (7) gauge stations called Dams and barrages, and they are playing a vital role in the generation of electricity and also in irrigation for Pakistan. In this research paper, we calculated the flood risk for the Indus using the streamflow discharges on the daily basis. At present, Adaptive Neuro-Fuzzy Inference System (ANFIS) model is widely used to analyze these hydrological time series data. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) merges the potentiality of Fuzzy Inference Systems (FIS) and Artificial Neural Networks (ANN) to work out problems of different kinds. For this purpose, we used the data for the years from 2002 to 2012 daily (6-months each year) streamflow period. In our analysis, the root means square error (RMSE) shows that the ANFIS model generated more satisfactory results than other models with minimum prediction errors. The ANFIS model is more reliable and has the feasibility of integrating the essence of a fuzzy system into the real world.1–28