Discharge Estimates Research Articles

Dynamic Assimilation of Deep Learning Predictions to a Process-Based Water Budget

A three-step data assimilation (DA) of deep learning (DL) predictions to a process-based water budget is developed and applied to produce an active, operational water balance for groundwater management. In the first step, an existing water budget model provides forward model predictions of aquifer storage from meteorological observations, estimates of pumping and diversion discharge, and estimates of recharge. A Kalman filter DA approach is the second step and generates updated storage volumes by combining a long short-term memory (LSTM) network, a DL method, and predicted “measurements” with forward model predictions. The third “correction” step uses modified recharge and pumping, adjusted to account for the difference between Kalman update storage and forward model predicted storage, in forward model re-simulation to approximate updated storage volume. Use of modified inputs in the correction provides a mass-conservative water budget framework that leverages DL predictions. LSTM predictor “measurements” primarily represent missing observations due to missing or malfunctioning equipment. Pumping and recharge inputs are uncertain and unobserved in the study region and can be adjusted without contradicting measurements. Because DL requires clean and certain data for learning, a common-sense baseline facilitates interpretation of LSTM generalization skill and accounts for feature and outcome uncertainty when sufficient target data are available. DA, in contrast to DL, provides for explicit uncertainty analysis through an observation error model, which allows the integrated approach to address uncertainty impacts from an LSTM predictor developed from limited outcome observations.

Refining fine sediment source identification through integration of spatial modelling, concentration monitoring and source tracing: A case study in the Great Barrier Reef catchments

Excess fine sediment delivery is a major contributor to the declining health of the Great Barrier Reef and identifying the dominant source areas of fine sediment has been critical to prioritising erosion remediation programs. The Bowen River catchment within the Burdekin Basin has been recognised as a major contributor and hence received considerable research investment over the last two decades. This study adopts a novel approach to integrate three independently derived sediment budgets produced from a catchment scale sediment budget model (Dynamic SedNet), targeted tributary water quality monitoring and geochemical sediment source tracing to refine and map the sediment source zones within the Bowen catchment. A four year study of water quality monitoring combined with modelled discharge estimates and geochemical source tracing both identified that the Little Bowen River and Rosella Creek were the largest sources of sediment in the Bowen River catchment. Both data sets contradicted initial synoptic sediment budget model predictions due to inadequate representation of hillslope and gully erosion. Recent improvements in model inputs have resulted in predictions that are consistent with the field data and are of finer resolution within the identified source areas. Priorities for further investigation of erosion processes are also revealed. Examining the benefits and limitations of each method indicates that these are complimentary methods which can effectively be used as multiple lines of evidence. An integrated dataset such as this provides a higher level of certainty in the prediction of fine sediment sources than a single line of evidence dataset or model. The use of high quality, integrated datasets to inform catchment management prioritisation will provide greater confidence for decision makers when investing in catchment management.

Spatiotemporal estimation of fresh submarine groundwater discharge across the coastal shorelines of Oahu Island, Hawaii

Abstract The integrated hydrological model is a powerful tool that is used to assess the temporal distribution of fresh groundwater discharge especially in coastal areas. The coastal regions of Hawaii are examples of crucial natural resources for the Hawaiian economy and general ecological health. To fully comprehend the intricate interactions between coastal hydrology processes and ecosystems, it is necessary to evaluate the fresh submarine groundwater discharge (FSGD) at the Heeia shoreline using an integrated hydrological modeling technique. Under steady-state settings, the results showed that the present daily average of FSGD is around 0.43 m3/days across 1 m of the shoreline. However, we showed that the FSGD values were considerably impacted by climate change, groundwater head of the coastal aquifer, recharge rate, and sea level rise, particularly by the end of the 21st century. The post-development FSGD fluxes were 1.5–3.5 times greater than the freshwater transported by the Heeia stream, demonstrating the considerable contribution of the FSGD to the coastal zones of Heeia. The results also showed an exponential association between the FSGD and the groundwater level for the coastal unconfined aquifer.

Flood Mitigation and Pollution Abatement in Kaduna Metropolis Through Engineering Assessment and Analytical Hierarchy Process Design

Pollution abatement and flood control activities require effective water resource planning, engineering assessment, sophisticated technology, and appropriate hydraulic structure designs. This paper x-rays flood mitigation and pollution abatement strategies that can be adopted in the Kaduna metropolis in Nigeria. Analytical hierarchy process of design, questionnaires, engineering assessment approach, and standard method for estimation of the runoff discharge was adopted for this research. Estimating water balance components and QSWAT Hydrological Model can be used with the QGIS interface for a greener environment. Suitable hydraulic systems were designed for long-term flood control in the River Kaduna catchment area through an analytic hierarchy Process. Statistical analysis, manning equations, and rational methods were utilized for adequate assessment and planning. The hydraulic discharge capacity of culverts, open channels, and other hydraulic structures was carefully checked. Flooding greatly impacts infrastructural development, and inadequate drainage systems contribute to it. Mitigation strategies, adequate water resource planning, and management will greatly benefit from addressing flood-related issues in the study area. This research provides information on the flood vulnerability of infrastructures and mitigation strategies that can be adopted in the study area. Viable policies and management strategies can be utilized to avert losses traceable to floods in developing and developed countries.

Extending global river gauge records using satellite observations

Long-term, continuous, and real-time streamflow records are essential for understanding and managing freshwater resources. However, we find that 37% of publicly available global gauge records (N = 45 837) are discontinuous and 77% of gauge records do not contain real-time data. Historical periods of social upheaval are associated with declines in gauge data availability. Using river width observations from Landsat and Sentinel-2 satellites, we fill in missing records at 2168 gauge locations worldwide with more than 275 000 daily discharge estimates. This task is accomplished with a river width-based rating curve technique that optimizes measurement location and rating function (median relative bias = 1.4%, median Kling-Gupta efficiency = 0.46). The rating curves presented here can be used to generate near real-time discharge measurements as new satellite images are acquired, improving our capabilities for monitoring and managing river resources.

Machine Learning Framework with Feature Importance Interpretation for Discharge Estimation: A Case Study in Huitanggou Sluice Hydrological Station, China

Accurate and reliable discharge estimation plays an important role in water resource management as well as downstream applications such as ecosystem conservation and flood control. Recently, data-driven machine learning (ML) techniques showed seemingly insurmountable performance in runoff forecasting and other geophysical domains, but they still need to be improved in terms of reliability and interpretability. In this study, focusing on discharge estimation and management, we developed an ML-based framework and applied it to the Huitanggou sluice hydrological station in Anhui Province, China. The framework contains two ML algorithms, the ensemble learning random forest (ELRF) and the ensemble learning gradient boosting decision tree (ELGBDT). The SHapley Additive exPlanation (SHAP) was introduced into our framework to interpret the impact of the model features. In our framework, the correlation analysis of the dataset can provide feature information for modeling, and the quartile method was utilized to solve the outlier problem of the dataset. The Bayesian optimization algorithm was adopted to optimize the hyperparameters of the ensemble ML models. The ensemble ML models are further compared with the traditional stage–discharge rating curve (SDRC) method and the single ML model. The results show that the estimation performance of the ensemble ML models is superior to that of the SDRC and the single ML model. In addition, an analysis of the discharge estimation without considering the flow state was performed. This analysis reveals that the ensemble ML models have strong adaptability. The ensemble ML models accurately estimate the discharge, with a coefficient of determination of 0.963, a root mean squared error of 31.268, and a coefficient of correlation of 0.984. Our framework can prove helpful to improve the efficiency of short-term hydrological estimation and simultaneously provide the interpretation of the impact of the hydrological features on estimation results.

Turning Lakes Into River Gauges Using the LakeFlow Algorithm

AbstractRivers and lakes are intrinsically connected waterbodies yet they are rarely used to hydrologically constrain one another with remote sensing. Here we begin to bridge the gap between river and lake hydrology with the introduction of the LakeFlow algorithm. LakeFlow uses river‐lake mass conservation and observations from the Surface Water and Ocean Topography (SWOT) satellite to provide river discharge estimates of lake and reservoir inflows and outflows. We test LakeFlow performance at three lakes using a synthetic SWOT data set assuming the maximum measurement errors defined by the mission science requirements, and we include modeled lateral inflow and lake evaporation data to further constrain the mass balance. We find that LakeFlow produces promising discharge estimates (median Nash‐Sutcliffe efficiency = 0.88, relative bias = 14%). LakeFlow can inform water resources management by providing global lake inflow and outflow estimates, highlighting a path for recognizing rivers and lakes as an interconnected system.

A copula model of extracting DEM-based cross-sections for estimating ecological flow regimes in data-limited deltaic-branched river systems

For operational flood control and estimating ecological flow regimes in deltaic branched-river systems with limited surveyed cross-sections, accurate river stage and discharge estimation using public domain Digital Elevation Model (DEM)-extracted cross-sections are challenging. To estimate the spatiotemporal variability of streamflow and river stage in a deltaic river system using a hydrodynamic model, this study demonstrates a novel copula-based framework to obtain reliable river cross-sections from SRTM (Shuttle Radar Topographic Mission) and ASTER (Advanced Spaceborne Thermal Emission and Reflection) DEMs. Firstly, the accuracy of the CSRTM and CASTER models was assessed against the surveyed river cross-sections. Thereafter, the sensitivity of the copula-based river cross-sections was evaluated by simulating river stage and discharge using MIKE11-HD in a complex deltaic branched-river system (7000 km2) of Eastern India having a network of 19 distributaries. For this, three MIKE11-HD models were developed based on surveyed cross-sections and synthetic cross-sections (CSRTM and CASTER models). The results indicated that the developed Copula-SRTM (CSRTM) and Copula-ASTER (CASTER) models significantly reduce biases (NSE>0.8; IOA>0.9) in the DEM-derived cross-sections and hence, are capable of satisfactorily reproducing observed streamflow regimes and water levels using MIKE11-HD. The performance evaluation metrics and uncertainty analysis indicated that the MIKE11-HD model based on the surveyed cross-sections simulates with higher accuracies (streamflow regimes: NSE>0.81; water levels: NSE>0.70). The MIKE11-HD model based on the CSRTM and CASTER cross-sections, reasonably simulates streamflow regimes (CSRTM: NSE>0.74; CASTER: NSE>0.61) and water levels (CSRTM: NSE>0.54; CASTER: NSE>0.51). Conclusively, the proposed framework is a useful tool for the hydrologic community to derive synthetic river cross-sections from public domain DEMs, and simulate streamflow regimes and water levels under data-scarce conditions. This modelling framework can be easily replicated in other river systems of the world under varying topographic and hydro-climatic conditions.

Nutrient inputs from subarctic rivers into Hudson Bay

Hudson Bay (HB), a large subarctic inland sea, is impacted by rapid climate change and anthropogenic disturbance. HB plays crucial roles in supporting resident and migratory species of birds and marine mammals, providing subsistence to coastal communities, and exporting nutrients into the western Labrador Sea. To better constrain the impact of river nutrients on the HB ecosystem and to obtain a contemporary reference point by which future change can be evaluated, we estimated fluxes of nitrate plus nitrite (N), phosphate (P), and silicate using contemporary and historical nutrient data in conjunction with discharge estimates produced by three global climate models. Concentrations and molar ratios of the different nutrients exhibited large contrasts between different sectors of HB, which is attributed to the diversity of geological settings across distinct watersheds. With respect to the needs of primary producers, river waters were characterized by a shortage of P during winter and spring (N:P molar ratios in dissolved nutrients >16), nearly balanced N:P ratios in summer, and a shortage of N during fall (N:P < 16). Southwestern rivers made the largest regional contribution to the total annual delivery of all nutrients, followed by modest contributions from southern and eastern rivers, and minor ones from northwestern rivers. While the regulation of river flow in the Nelson and La Grande rivers had no discernible impact on nutrient concentrations and ratios, it clearly shifted nutrient transports toward the winter when biological activity in the estuaries is reduced. Finally, the potential amount of new production supported by riverine N inputs was nearly two orders of magnitude (1.8 × 1011 g C yr−1) lower than the new production supported by marine nutrients (7.3 × 1012 g C yr−1). Although the potential contribution of river nutrients to new primary production is small (2.4%) at the bay-wide scale, it can be significant locally.

Estimates of persistent inward currents in lower limb motoneurons are larger in females than in males.

Noninvasive recordings of motor unit (MU) spike trains help us understand how the nervous system controls movement and how it adapts to various physiological conditions. The majority of participants in human and nonhuman animal physiology studies are male, and it is assumed that mechanisms uncovered in these studies are shared between males and females. However, sex differences in neurological impairment and physical performance warrant the study of sex as a biological variable in human physiology and performance. To begin addressing this gap in the study of biophysical properties of human motoneurons, we quantified MU discharge rates and estimates of persistent inward current (PIC) magnitude in both sexes. We decomposed MU spike trains from the tibialis anterior (TA), medial gastrocnemius (MG), and soleus (SOL) using high-density surface electromyography and blind source separation algorithms. Ten participants of each sex performed slow triangular (10 s up and down) isometric contractions to a peak of 30% of their maximum voluntary contraction. We then used linear mixed-effects models to determine if peak discharge rate and estimates of PICs were predicted by the fixed effects of sex, muscle, and their interaction. Despite a lack of sex-differences in peak discharge rates across all muscles, estimates of PICs were larger [χ2(1) = 6.26, P = 0.012] in females [4.73 ± 0.242 pulses per second (pps)] than in males (3.81 ± 0.240 pps). These findings suggest that neuromodulatory drive, inhibitory input, and/or biophysical properties of motoneurons differ between the sexes and may contribute to differences in MU discharge patterns.NEW & NOTEWORTHY Sex-related differences in motoneuron analyses have emerged with greater inclusion of female participants, however, mechanisms for these differences remain unclear. Estimates of persistent inward currents (i.e., ΔF) in motoneurons of the lower limb muscles were larger in females than in males. This suggests neuromodulatory drive, monoaminergic signaling, intrinsic motoneuron properties, and/or descending motor commands may differ between the sexes, which provides a potential mechanism underlying previously reported sex-related differences in motoneuron discharge patterns.

Estimating river bathymetry from multisource remote sensing data

River bathymetry is a key variable in estimating river discharge by remote sensing. However, traditional methods of mapping river bathymetry are expensive and time-consuming, which hinders the estimation of river discharge. In this study, we propose and test a method for estimating river bathymetry by combining remotely sensed observations of water level (z) and river width (w) with a nonlinear z-w relationship derived from a generalized channel cross-sectional shape. The results from five reaches of the upper Yangtze River show that the absolute relative error for the estimate is between 1.25% and 6.18%, indicating the ability of this method to provide accurate estimates. The channel exposure (e) is the main limitation of the proposed method, and the river bathymetry estimates improve significantly with increasing e. For the upper Yangtze River, averaging reaches over an appropriate length can reduce variability in river bathymetry estimate, especially when data are reach-averaged over a length of 10 km. Considering that a priori bathymetric information is not required and its computational process is relatively simple, the proposed method could open up the possibility of bathymetry modeling of global rivers.

Flood Frequency Analysis of Jiadhal River Basin, India using Log Pearson Type III Distribution Method

Flood is considered as the most widespread and disastrous natural calamity in the world which adversely affects the socio-economic condition and lives of the people living within the floodplain areas of the world. The Jiadhal River basin of Dhemaji district, Assam experiences a high magnitude of flood almost every year, leaving the people of that area in jeopardy. In this regard, flood frequency analysis is considered an important step for proper planning and managing the region in terms of floods. Flood frequency analysis (FFA) is one of the utmost important and necessary measures taken to develop any floodplain region. In this present study, a FFA of the Jiadhal River Basin is carried out using the Log Pearson Type III (LP3) probability distribution method which is used to predict future flood events based on historical records. The data used in the study was for a period of 46 years. The method was used to predict the flood discharge estimations at 2, 10, 25, 50 and 100 years recurrence period. The result says that after 10 years the return period will surpass the water-carrying capacity of the Jiadhal River.

Deducing the stage-discharge relationship of rectangular broad and sharp-crested contraction devices

Flumes with a local contraction of the channel width are a common way for accurately measuring the flow discharge. In this paper, the outflow process of rectangular broad and sharp-crested contraction devices is modeled using the dimensional analysis and the incomplete self-similarity condition. The proposed theoretical stage-discharge relationship is tested using measurements available in the literature. The proposed power equation is characterized by a value of the exponent close to 1.5 and a coefficient depending on the contraction ratio. The proposed flow-measuring flumes are characterized by a good accuracy of the measured discharge (ranging from −2.71 to 3.28% for the broad-crested contraction device and in the range from −0.24 to 0.31% for the sharp-crested one). Even if both flume types have good accuracy of the measured discharge (less than the limit of ±5% suggested in the literature), the developed analysis demonstrated that the sharp-crested device is characterized by the lowest values of the errors in the estimate of discharge, and this result increases the interest of this device in practical applications. The advantage of the proposed approach is combining an accurate estimate of the flow discharge and working regardless of the discharge coefficient estimate.

A Framework for Estimating Global River Discharge From the Surface Water and Ocean Topography Satellite Mission

AbstractThe Surface Water and Ocean Topography (SWOT) mission will vastly expand measurements of global rivers, providing critical new data sets for both gaged and ungaged basins. SWOT discharge products (available approximately 1 year after launch) will provide discharge for all river that reaches wider than 100 m. In this paper, we describe how SWOT discharge produced and archived by the US and French space agencies will be computed from measurements of river water surface elevation, width, and slope and ancillary data, along with expected discharge accuracy. We present for the first time a complete estimate of the SWOT discharge uncertainty budget, with separate terms for random (standard error) and systematic (bias) uncertainty components in river discharge time series. We expect that discharge uncertainty will be less than 30% for two‐thirds of global reaches and will be dominated by bias. Separate river discharge estimates will combine both SWOT and in situ data; these “gage‐constrained” discharge estimates can be expected to have lower systematic uncertainty. Temporal variations in river discharge time series will be dominated by random error and are expected to be estimated within 15% for nearly all reaches, allowing accurate inference of event flow dynamics globally, including in ungaged basins. We believe this level of accuracy lays the groundwork for SWOT to enable breakthroughs in global hydrologic science.

Drainage area estimates for synorogenic clastic wedges in the Central Appalachian Basin (sink) with implications for terrane accretion in the hinterland (source)

AbstractSource‐to‐sink studies typically utilize the sedimentary record preserved in basins to infer source area parameters such as estimates of drainage area and discharges of fluvial systems in the hinterland, the characterization of which remains elusive as few geomorphic elements of the hinterland are preserved. Clastic wedges within foreland basin settings often contain fluvial deposits, and the scale of fluvial architectural elements can be used to estimate drainage area and discharge within the accreted hinterland. This study uses thicknesses of fluvial architectural elements within the Late Ordovician Taconic, the Late Devonian‐Early Mississippian Acadian, and the Late Mississippian to Early and Middle Pennsylvanian Alleghanian clastic wedges, together with climatic conditions interpreted from palaeosol data, to estimate the drainage areas and discharges of the river systems which drained the hinterlands of the accreted Taconic and Acadian terrains and the Alleghanian suture of Laurentia and Gondwana. Published detrital zircon age spectra provide insight into ages of source areas of sediments as well as the timing of terrane accretion. The increase in hinterland drainage areas calculated for Late Devonian Acadian Orogeny river systems compared to Late Ordovician Taconic Orogeny river systems can be attributed to the increase in size of accreted terranes to the east of the foreland basins during that period of time. The decrease in hinterland drainage area from the Late Devonian Acadian Orogeny to the Middle to Late Pennsylvanian Alleghanian Orogeny is attributed to the westward migration of the drainage divide as the Alleghanian orogeny proceeded. It is inferred that the drainage divide migrated towards the drier leeward side of the mountain range and caused drainage basins to shrink and split into smaller drainage basins.

Satellite remote sensing of river discharge: a framework for assessing the accuracy of discharge estimates made from satellite remote sensing observations

This research presents an evaluation of the accuracy and uncertainty of estimates of river discharge made using satellite observed data sources as input to a modified form of Manning’s equation. Conventional U.S. Geological Survey (USGS) streamflow gaging station data and in-situ measurements of width, depth, height, slope, discharge, and velocity from 30 USGS gage sites were used as ground-truth to assess accuracy. This study explores accuracy in relation to the amount of ground truth information available, the number of calibration points available, and the accuracy of the input data. This research indicates that remotely sensed discharge estimates associated with the modified Manning equation may be expected to have an uncertainty in range of 10% overall given a sufficient number of calibration points. The uncertainty associated with the modified Manning algorithm increased markedly for depths <3 meters (m) and for discharges <1000 cubic meters per second (m3 / s) for many rivers after calibration. Rivers that exhibit (1) a wide range of flow conditions, (2) a significant number of dams in the watershed and along the channel, and (3) a high baseflow index are more likely to have relatively large errors overall and particularly at the low end of the streamflow range. Uncertainty in remotely sensed measurements of water-surface elevation (WSE) and width in the expected range (WSE, + / − 10 cm; Width, + / − 15 m) introduces uncertainty in the discharge estimates on the order of 10% and is greatest at the low end of discharge as rivers get shallower and narrower. As WSE and width measurement uncertainty increases, discharge uncertainty increases accordingly. In general, the observation errors are greater than the errors associated with the algorithm for a well-calibrated model (e.g., 20 calibration points).

Seasonality in groundwater recharge in Coastal Southwestern India and its hydrological implications based on stable isotopes (δ18O, δD)

This study observes and interprets the spatio-temporal variation in the oxygen and hydrogen isotopic composition (δ18O, δD) of shallow groundwater in the Southwestern Coastal state of India, Kerala, in terms of seasonally varying rainfall, physiography, and hydrogeological settings of this region. This study identifies effective groundwater recharge sources and their relative contributions and provides first-order estimates of submarine groundwater discharge by interpreting the spatio-temporal variations in the stable isotopes of oxygen and hydrogen in groundwater, collected from 225 locations for pre-monsoon and post-monsoon seasons in conjunction with the isotopic composition of rainfall. Spatio-temporal variations in the δ18O and d-excess, with other hydrogeological data, reveals that: (1) ∼4.9 Billion Cubic Meter (BCM) of fresh water from rainfall during May to October mixes with ∼5.9 BCM of residual pre-monsoon groundwater across Kerala. (2) ∼ 11% area of Kerala is additionally recharged by the NE monsoon rainfall. (3) The annual groundwater recharge varies physiographically from ∼2.2 BCM in the lowland to ∼1.5 BCM in the midland and ∼1.2 BCM in highland regions. (4) On an annual basis, 41% of replenishable groundwater is drained as SGD into the Arabian Sea.

Environment and shipping drive environmental DNA beta-diversity among commercial ports.

The spread of nonindigenous species by shipping is a large and growing global problem that harms coastal ecosystems and economies and may blur coastal biogeographical patterns. This study coupled eukaryotic environmental DNA (eDNA) metabarcoding with dissimilarity regression to test the hypothesis that ship-borne species spread homogenizes port communities. We first collected and metabarcoded water samples from ports in Europe, Asia, Australia and the Americas. We then calculated community dissimilarities between port pairs and tested for effects of environmental dissimilarity, biogeographical region and four alternative measures of ship-borne species transport risk. We predicted that higher shipping between ports would decrease community dissimilarity, that the effect of shipping would be small compared to that of environment dissimilarity and shared biogeography, and that more complex shipping risk metrics (which account for ballast water and stepping-stone spread) would perform better. Consistent with our hypotheses, community dissimilarities increased significantly with environmental dissimilarity and, to a lesser extent, decreased with ship-borne species transport risks, particularly if the ports had similar environments and stepping-stone risks were considered. Unexpectedly, we found no clear effect of shared biogeography, and that risk metrics incorporating estimates of ballast discharge did not offer more explanatory power than simpler traffic-based risks. Overall, we found that shipping homogenizes eukaryotic communities between ports in predictable ways, which could inform improvements in invasive species policy and management. We demonstrated the usefulness of eDNA metabarcoding and dissimilarity regression for disentangling the drivers of large-scale biodiversity patterns. We conclude by outlining logistical considerations and recommendations for future studies using this approach.

Investigations of Multi-Platform Data for Developing an Integrated Flood Information System in the Kalu River Basin, Sri Lanka

Flood early warning systems (FEWS) are crucial for flood risk management; however, several catchments in the developing world are still far behind in all aspects of FEWS and thus, they encounter devastating damage recurrently due to limitations in data, knowledge, and technologies. This paper presents a catchment-scale integrated flood information system by incorporating present-day multi-platform data and technologies (e.g., ground and satellite rainfall observation, ensemble rainfall forecasts, and flood simulation) and evaluates their performance in a poorly gauged prototype basin (i.e., the Kalu River basin). Satellite rainfall products obtained in real time (GSMaP-NOW) and near-real time (GSMaP-NRT) can detect heavy rainfall events well and bias-corrected products can further improve rainfall estimations and flood simulations. Particularly, GSMaP-NRT, which outperformed GSMaP-NOW in both rainfall and discharge estimations, is suitable for near-real-time flood-related applications. Ensemble rainfall forecasts showed good performance in predicting alarming signals of heavy rainfall and peak flow with uncertainties in the amounts and timings of the events. Information derived from both satellite and ensemble forecasts on heavy rainfall, simulated flood signals, and their possible range of probabilities is promising and can help minimize the data gaps and improve the knowledge and technology of experts and policy-makers in poorly gauged basins.

Accurate Discharge Estimation Based on River Widths of SWOT and Constrained At-Many-Stations Hydraulic Geometry

River discharge monitoring is an important component of the hydrology objectives of Surface Water and Ocean Topography mission (SWOT). River discharge can be estimated Solely using river widths and At Many-stations Hydraulic Geometry (AMHG), but the accuracy is low due to the parameters of At a-station Hydraulic Geometry (AHG) given by AMHG deviate from the truth. In view of this, a Constrained At-Many-Stations Hydraulic Geometry (CAMHG) is proposed to optimize AHG parameters. The performance of CAMHG is verified in three reaches of the Yangtze River using river widths derived from SWOT. After using CAMHG, the relative root mean square error (RRMSE) of estimated discharge reduce 100.1% to 24.4%, 1137.1% to 49.9% and 48.6% to 45.5% for Hankou, Shashi and Luoshan respectively. In addition, CAMHG can also weaken the accuracy difference of estimated discharge in dry and wet seasons benefited from its more reliable AHG parameters. Thus, the proposed CAMHG can dramatically improves the accuracy of discharge estimations and it is meaningful for the discharge calculation after SWOT data release.