Hydrological Uncertainty Research Articles

Climate change and water security in the northern slope of the Tianshan Mountains

Water security is under threat worldwide from climate change. A warming climate would accelerate evaporation and cryosphere melting, leading to reduced water availability and unpredictable water supply. However, the water crisis in the Northern Slope of Tianshan Mountains (NSTM) faces dual challenges because water demands for fast-growing urban areas have put heavy pressure on water resources. The mountain-oasis-desert system features glacier-fed rivers that sustain intensive water use in the oasis and end in the desert as fragile terminal lakes. The complex balance between water conservation and economic development is subtle. This paper investigates changes in hydroclimatic variables and water security-related issues on the NSTM. The spatiotemporal variations in glaciers, climatic variables, rivers, lakes and reservoirs, groundwater, surface water, human water use, and streamflow were analyzed for the past four decades. The results show that temperature in the NSTM exhibited an apparent upward trend with a more significant warming rate in the higher altitude regions. Glacier mass loss and shrinkage was strong. The average annual streamflow increased from 1980-1989 to 2006–2011 at most hydrological stations. The monthly dynamics of surface water area showed notable variability at both inter-annual and seasonal scales, revealing the impacts of both natural and anthropogenic drivers on surface water availability in the region. The terrestrial water storage anomaly showed a decreasing trend, which might be related to groundwater pumping for irrigation. Human water use for agriculture and industry grew with the increase in cultivated land area and gross domestic product (GDP). The increased agricultural water use was strongly associated with the expansion of oases. It is unclear whether water availability would remain high under future climatic and hydrological uncertainties, posing challenges to water management. In the context of rapid urban growth and climate change, balancing water for humans and nature is vital in achieving the Sustainable Development Goals (SDGs) in NSTM. This study provides a baseline understanding of the interplay among water, climate change, and socio-economic development in NSTM. It would also shed light on wise water management under environmental changes for other rapidly developing mountain-oasis-desert systems worldwide.

The hydrological effect and uncertainty assessment by runoff indicators based on SWMM for various LID facilities

Low Impact Development (LID) facilities are the key to realizing the source control of stormwater. However, there are few studies on the control effects of different LID facilities on runoff volume and runoff quality. In this paper, based on the Storm Water Management Model (SWMM), a Monte Carlo method was used to simulate the runoff process for five LID facilities. The correlation analysis and scoring of the three indicators of runoff coefficient, peak runoff, and Suspended Solid (SS) wash-off in the simulation results were conducted to evaluate the control effects of different LID facilities. The simulation results showed an evident law of decreasing marginal effect in the deployed area of LID facilities. In addition, the uncertainty of the hydrological response of permeable pavement is the smallest among the five types of LID facilities, and the uncertainty of vegetative swales is the largest. Under the suitable combination of parameters, the area percentage of permeable pavement reaching the upper limit of the control effect of three indicators is the smallest; the control effect of three indicators of infiltration trench is the best. Correlation analysis showed that the three indicators of the LID facilities could be estimated from each other through the regression relationship equation.

Improving probabilistic monthly water quantity and quality predictions using a simplified residual-based modeling approach

Uncertainty quantification between simulated and observed water quality simulations needs to be improved. This study generated and evaluated probabilistic hydrologic and water quality predictions in 18 locations across the U.S. using residual-based modeling. A Box-Cox transformation scheme group provided the best predictive uncertainties for all case studies. The tradeoffs in the performance metrics for a single variable predictive uncertainty in a single study watershed were more obvious than those for all hydrologic or water quality cases. Compared to a single realization of simulations, the ensemble average of hydrologic and water quality simulations better represented the predictive uncertainty, especially for large watersheds. This study recommends various opportunities via residual error scheme selection, data monitoring improvement, and hydrologic model enhancement to robust hydrologic and water quality predictive uncertainties. The results could improve the quantification of the predictive uncertainty of hydrologic and water quality simulations and guide probabilistic prediction enhancement.

A framework for drought adaption under deep uncertainties: Application of Portfolio theory (Markowitz 2.0)

Hydrological uncertainty, climate change, and water infrastructure development upstream transboundary basins may affect the safe access to water resources downstream, making socioeconomic and biophysical subsystems vulnerable. Therefore, it is vital to apply decision-making approaches to reduce vulnerability, especially in arid and semi-arid basins. This study evaluated an adaptation framework to reduce vulnerability through optimal water allocation in drought-prone areas. This framework was implemented in an area dependent on upstream transboundary water (Helmand River Basin). An integrated system dynamics model was developed using socioeconomic and biophysical factors identified by the Füssel Vulnerability Framework. The climatic variables of the basin were simulated and predicted by the output of AOGCM models. The trend of upstream annual precipitation and temperature changes during the 2021–2050 period based on the RCP4.5 emission scenario was −2% and +1.9 C compared to those in the historical period, respectively. The results indicated the drought intensification due to a 13% decrease in future runoff (2050) compared to the base period, leading to a reduction in value-added agriculture (8%) and the population (31%). Therefore, the latest version of portfolio optimization called Markowitz 2.0 was used. In order to decide on potential long-term growth versus short-term loss, an efficient frontier was formed between the expected return and the conditional value at risk (CVaR) based on the Markowitz 2.0 model. According to the model results, the development of the industrial sector as an approach with moderate (reasonable) risk could increase financial return by 43% and allocation to the environmental sector by 2.3 times. Finally, prioritizing the environmental sector was a high-risk approach with high returns that increased efficiency by 45%. The results showed that the proposed framework could help decision-makers, based on their risk-taking level, activate and develop decisions on water resource allocation and water-dependent productive sectors on the basis of efficient frontiers.

Risk analysis for the multi-reservoir flood control operation considering model structure and hydrological uncertainties

In the real-time flood control operation of a multi-reservoir system, the multi-reservoir real-time flood control hybrid operation (MRFCHO) model with reduced structure is an effective way to improve the efficiency, but the reduction of the model structure may cause greater flood risk. This paper focuses on this scientific problem and evaluates the reliability of the MRFCHO model in real-time flood control operation through risk analysis. Firstly, the flood risk is defined. Then, the Bayesian network risk analysis model is established through four parts: Bayesian network structure construction, training samples generation, parameter learning, and inference. This paper takes the flood control system in upstream of the Lutaizi point in the Huaihe River basin as a case study. The flood risk of the MRFCHO model caused by the model structure reduction is quantitatively described, the spatial distribution and propagation characteristics of flood risk are evaluated, and the flood risk caused by the double uncertainties of the model structure and flood forecasting is analyzed. Besides, the factors affecting the risk of the public flood control point are diagnosed. The results show that (1) the model structure reduction does not significantly increase the flood risk at the public flood control point, and there is no obvious propagation law of flood risk in space; (2) compared with only considering the model structure reduction, considering the double uncertainties of flood forecasting and model structure reduction has a 65% probability of increasing the risk; (3) Runoff from the Runheji point in the upper reaches of main stream and the lateral inflow of Lutaizi point are the main reasons for the risk of Lutaizi point.

Towards a More Robust Evaluation of Climate Model and Hydrological Impact Uncertainties

The uncertainty of climate model projections is recognized as being large. This represents a challenge for decision makers as the simulation spread of a climate model ensemble can be large, and there might even be disagreement on the direction of the climate change signal among the members of the ensemble. This study quantifies changes in the hydrological projection uncertainty due to different approaches used to select a climate model ensemble. The study assesses 16 Euro-CORDEX Regional Climate Models (RCMs) that drive three different conceptualizations of the MIKE-SHE hydrological model for the Ahlergaarde catchment in western Denmark. The skills of the raw and bias-corrected RCMs to simulate historical precipitation are evaluated using sets of nine, six, and three metrics assessing means and extremes in a series of steps, and results in reduction of projection uncertainties. After each step, the overall lowest-performing model is removed from the ensemble and the standard deviation is estimated, only considering the members of the new ensemble. This is performed for nine steps. The uncertainty of raw RCM outputs is reduced the most for river discharge (5 th , 50 th and 95 th percentiles) when using the set of three metrics, which only assess precipitation means and one ‘moderate’ extreme metrics. In contrast, the uncertainty of bias-corrected RCMs is reduced the most when using all nine metrics, which evaluate means, ‘moderate’ extremes and high extremes. Similar results are obtained for groundwater head (GWH). For the last step of the method, the initial standard deviation of the raw outputs decreases up to 38% for GWH and 37% for river discharge. The corresponding decreases when evaluating the bias-corrected outputs are 63% and 42%. For the bias corrected outputs, the approach proposed here reduces the projected hydrological uncertainty and provides a stronger change signal for most of the months. Thisanalysis provides an insight on how different approaches used to select a climatemodel ensemble affect the uncertainty of the hydrological projections and, in this case,reduce the uncertainty of the future projections.

Ensemble Climate and Streamflow Projections for the Assiniboine River Basin, Canada

The Assiniboine River Basin (ARB) is subject to an exceptionally variable precipitation regime of the Canadian Prairies, ranging between record droughts and unprecedented flooding in just the past decade. To assess the impact of a changing climate on hydroclimate variability in the ARB, we used the bias–corrected simulations from the Canadian Regional Climate Model (CanRCM4) to drive MESH model for two 60–year periods, a historical baseline (1951–2010) and future projection (2041–2100), under the Representative Concentration Pathway (RCP) 8.5 to simulate ARB flows at eight hydrometric stations. The precipitation is projected to increase in every season (~10–38%) except for summer (~−1–−5%). Minimum winter and maximum summer temperatures have the largest seasonal trends, increasing by 2–3 °C in the near future (2021–2050) and 5–6 °C in the far future (2051–2080). These climate changes produce higher winter river flows while peak runoff shifts by several weeks to earlier in the year. There is a shift in the magnitude and timing of extreme water levels. The ensemble of climate projections from a single model and one RCP to the variability and uncertainty in the future hydrology supports adaptation planning in the industrial sectors of Saskatchewan’s economy.

Is Precipitation Responsible for the Most Hydrological Model Uncertainty?

Rainfall-runoff modeling is highly uncertain for a number of different reasons. Hydrological processes are quite complex, and their simplifications in the models lead to inaccuracies. Model parameters themselves are uncertain—physical parameters because of their observations and conceptual parameters due to their limited identifiability. Furthermore, the main model input—precipitation is uncertain due to the limited number of available observations and the high spatio-temporal variability. The quantification of model output uncertainty is essential for their use. Most approaches used for the quantification of uncertainty in rainfall-runoff modeling assign the uncertainty to the model parameters. In this contribution, the role of precipitation uncertainty is investigated. Instead of a standard sensitivity analysis of the model output with respect to the input variations, it is investigated to what extent realistic precipitation fields could improve model performance. Realistic precipitation fields are defined as gridded realizations of precipitation which reproduce the observed values at the observation locations, with values which reproduce the distribution of the observed values and with spatial variability the same as the spatial variability of the observations. The above conditions apply to each observation time step. Through an inverse modeling approach based on Random Mixing precipitation fields fulfilling the above conditions and reproducing the discharge output better than using traditional interpolated observations can be obtained. These realizations show how much rainfall runoff models may profit from better precipitation input and how much remains for the parameter and model concept uncertainty. The methodology is applied using two hydrological models with a contrasting basis, SHETRAN and HBV, for three different mesoscale sub-catchments of the Neckar basin in Germany. Results show that up to 50% of the model error can be attributed to precipitation uncertainty. Further, inverting precipitation using hydrological models can improve model performance even in neighboring catchments which are not considered explicitly.

Hydrological evaluation of satellite and reanalysis-based rainfall estimates over the Upper Tekeze Basin, Ethiopia

Abstract Spatial rainfall data is an essential input to parametrically distributed hydrological models and is a main contributor to hydrological model uncertainty. Two important issues should be addressed before the use of satellite and reanalysis rainfall product at the basin level: (1) How useful are these rainfall estimates as forcing data for regional hydrological modelling? and (2) Which product should be selected at high-flow and low-flow conditions? This paper presents the hydrological performance of satellite and reanalysis rainfall products (CHIRPSv8 and EWEMBI) at three hydrological stations in the Upper Tekeze River Basin (UTB), northern Ethiopia. Results showed that the daily rainfall data from both CHIRPSv8 and EWEMBI are close to the rain gauge data, with relative errors 2.12 and 3.85%, respectively. The monthly streamflow simulated by the Soil and Water Assessment Tool (SWAT) model driven by CHIRPSv8 and EWEMBI had a Kling-Gupta Efficiency of 0.6–0.79 and 0.58–0.64, respectively. The hydrological performance during high-flow seasons is superior to low-flow seasons for both CHIRPSv8 and EWEMBI. In particular, CHIRPSv8 showed a relatively better hydrological performance than EWEMBI. This study provides insights on the usefulness of gridded rainfall products for hydrological modelling and conditions under which they can be used over the UTB and other similar basins.

Using a global sensitivity analysis to estimate the appropriate length of calibration period in the presence of high hydrological model uncertainty

Generally, reliable simulation results are obtained by calibrating and validating the parameters of a hydrological model through a split-sample test. To obtain adequate and stable calibration and validation results of a hydrological model, calibration data with sufficient length should be used; therefore, a study on the estimation of appropriate calibration data length is required. In this study, the appropriate calibration data length was estimated for three hydrological models (GR4J, IHACRES, and Sacramento models) with varying complexities using the Sobol global sensitivity analysis method. As a result of analyzing the appropriate calibration data length in three hydrological models for three dam catchments in Korea, a relatively stable simulation result could be derived using more than eight years of calibration data length. In addition, it was confirmed that the appropriate length of the calibration data increased as the size of the catchment decreased. In the case of the Sacramento model with the largest number of parameters, the variability of the optimum parameter values was high, even if the calibration period increased. Therefore, the variability size of the optimum parameter values is an improper scale for estimating the appropriate calibration data length when many parameter uncertainties exist. However, as a result of analyzing the parameter sensitivity of the Sacramento model, the variability of the parameter sensitivity decreased as the calibration period increased. The variability of model performance was also related to the variability of parameter sensitivity rather than to the variability of optimal parameter values. Therefore, parameter sensitivity analysis can be used to estimate the calibration data length for various hydrological models, including hydrological models with high uncertainty.

Multiscale assessments of hydroclimatic modelling uncertainties under a changing climate

Abstract Since the 1970s, climate change has led to decreasing water resources in the Sahel. To cope with climate change, reliable modelling of future hydroclimatic evolutions is required. This study uses multiclimate and hydrological modelling approaches to access past and future (1951–2100) hydroclimatic trends on nine headwater catchments of the Niger River Basin. Eight global climate models (GCMs) dynamically downscaled under the CORDEX CMIP5 project were used. The GCM data were bias-corrected with quantile–quantile mapping. Three rainfall–runoff models (IHACRES-CMD, IHACRES-CWI and Sacramento) were calibrated and validated with observed data and used to simulate runoff. The projected future runoff trend from 2061 to 2090 was compared across the three hydrological models to assess uncertainties from hydrological models. Results show that the bias correction positively enhanced the quality of eight GCMs across the nine catchments. An average Nash–Sutcliffe Efficiency (NSE) across the nine catchments was improved from 0.53 to 0.68 and the Kling–Gupta Efficiency (KGE) was enhanced from 0.65 to 0.83. The three hydrological models were calibrated and validated appropriately on the nine catchments. Despite this, high hydrological modelling uncertainties were witnessed with contrasting projected future runoff patterns by the three models. We recommended the use of ensembles of both climate and hydrological models to provide reliable hydroclimatic modelling.

Choosing between post-processing precipitation forecasts or chaining several uncertainty quantification tools in hydrological forecasting systems

Abstract. This study aims to decipher the interactions of a precipitation post-processor and several other tools for uncertainty quantification implemented in a hydrometeorological forecasting chain. We make use of four hydrometeorological forecasting systems that differ by how uncertainties are estimated and propagated. They consider the following sources of uncertainty: system A, forcing, system B, forcing and initial conditions, system C, forcing and model structure, and system D, forcing, initial conditions, and model structure. For each system's configuration, we investigate the reliability and accuracy of post-processed precipitation forecasts in order to evaluate their ability to improve streamflow forecasts for up to 7 d of forecast horizon. The evaluation is carried out across 30 catchments in the province of Quebec (Canada) and over the 2011–2016 period. Results are compared using a multicriteria approach, and the analysis is performed as a function of lead time and catchment size. The results indicate that the precipitation post-processor resulted in large improvements in the quality of forecasts with regard to the raw precipitation forecasts. This was especially the case when evaluating relative bias and reliability. However, its effectiveness in terms of improving the quality of hydrological forecasts varied according to the configuration of the forecasting system, the forecast attribute, the forecast lead time, and the catchment size. The combination of the precipitation post-processor and the quantification of uncertainty from initial conditions showed the best results. When all sources of uncertainty were quantified, the contribution of the precipitation post-processor to provide better streamflow forecasts was not remarkable, and in some cases, it even deteriorated the overall performance of the hydrometeorological forecasting system. Our study provides an in-depth investigation of how improvements brought by a precipitation post-processor to the quality of the inputs to a hydrological forecasting model can be cancelled along the forecasting chain, depending on how the hydrometeorological forecasting system is configured and on how the other sources of hydrological forecasting uncertainty (initial conditions and model structure) are considered and accounted for. This has implications for the choices users might make when designing new or enhancing existing hydrometeorological ensemble forecasting systems.

From Stream Flows to Cash Flows: Leveraging Evolutionary Multi‐Objective Direct Policy Search to Manage Hydrologic Financial Risks

AbstractHydrologic variability can present severe financial challenges for organizations that rely on water for the provision of services, such as water utilities and hydropower producers. While recent decades have seen rapid growth in decision‐support innovations aimed at helping utilities manage hydrologic uncertainty for multiple objectives, support for managing the related financial risks remains limited. However, the mathematical similarities between multi‐objective reservoir control and financial risk management suggest that the two problems can be approached in a similar manner. This paper demonstrates the utility of Evolutionary Multi‐Objective Direct Policy Search for developing adaptive policies for managing the drought‐related financial risk faced by a hydropower producer. These policies dynamically balance a portfolio, consisting of snowpack‐based financial hedging contracts, cash reserves, and debt, based on evolving system conditions. Performance is quantified based on four conflicting objectives, representing the classic tradeoff between “risk” and “return” in addition to decision‐makers’ unique preferences toward different risk management instruments. The dynamic policies identified here significantly outperform static management formulations that are more typically employed for financial risk applications in the water resources literature. Additionally, this paper combines visual analytics and information theoretic sensitivity analysis to improve understanding about how different candidate policies achieve their comparative advantages through differences in how they adapt to real‐time information. The methodology presented in this paper should be applicable to any organization subject to financial risk stemming from hydrology or other environmental variables (e.g., wind speed, insolation), including electric utilities, water utilities, agricultural producers, and renewable energy developers.

Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasts in the Upper Zambezi Basin and Its Sub-Basins Using Variational Ensemble Forecasting

The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed to improve operational real-time streamflow forecasts for the Upper Zambezi River Basin (UZRB), in Africa, utilizing the novel Variational Ensemble Forecasting (VEF) approach. In this regard, we describe and discuss the main steps required to implement, calibrate, and validate an operational hydrologic forecasting system (HFS) using VEF and Hydrologic Processing Strategies (HPS). The operational HFS was constructed to monitor daily streamflow and forecast them up to eight days in the future. The forecasting process called short- to medium-range (SR2MR) streamflow forecasting was implemented using real-time rainfall data from three Satellite Precipitation Products or SPPs (The real-time TRMM Multisatellite Precipitation Analysis TMPA-RT, the NOAA CPC Morphing Technique CMORPH, and the Precipitation Estimation from Remotely Sensed data using Artificial Neural Networks, PERSIANN) and rainfall forecasts from the Global Forecasting System (GFS). The hydrologic preprocessing (HPR) strategy considered using all raw and bias corrected rainfall estimates to calibrate three distributed hydrological models (HYMOD_DS, HBV_DS, and VIC 4.2.b). The hydrologic processing (HP) strategy considered using all optimal parameter sets estimated during the calibration process to increase the number of ensembles available for operational forecasting. Finally, inference-based approaches were evaluated during the application of a hydrological postprocessing (HPP) strategy. The final evaluation and reduction in uncertainty from multiple sources, i.e., multiple precipitation products, hydrologic models, and optimal parameter sets, was significantly achieved through a fully operational implementation of VEF combined with several HPS. Finally, the main challenges and opportunities associated with operational SR2MR streamflow forecasting using VEF are evaluated and discussed.

Quantile-Based Hydrological Modelling

Predictive uncertainty in hydrological modelling is quantified by using post-processing or Bayesian-based methods. The former methods are not straightforward and the latter ones are not distribution-free (i.e., assumptions on the probability distribution of the hydrological model’s output are necessary). To alleviate possible limitations related to these specific attributes, in this work we propose the calibration of the hydrological model by using the quantile loss function. By following this methodological approach, one can directly simulate pre-specified quantiles of the predictive distribution of streamflow. As a proof of concept, we apply our method in the frameworks of three hydrological models to 511 river basins in the contiguous US. We illustrate the predictive quantiles and show how an honest assessment of the predictive performance of the hydrological models can be made by using proper scoring rules. We believe that our method can help towards advancing the field of hydrological uncertainty.

Coupled Continuous-Time Markov Chain–Bayesian Network Model for Dam Failure Risk Prediction

Hydropower dams are critical infrastructure systems characterized by their complex, dynamic, and stochastic behaviors. The frequent variation in the hydrological and meteorological variables poses a higher probability of dam failure, highlighting the need to improve pertinent risk assessment approaches to predict failure risks, considering the uncertain states of such variables. Bayesian networks (BN) analysis has been a key risk assessment tool for decades; however, BN’s static acyclic nature is a recognized drawback. In this paper, a continuous-time Markov chain (CTMC) is coupled with a BN model to enable the dynamic assessment of dam failure risk. In this respect, BN is used to represent the interrelation among the system variables and simulate the propagation of uncertainties throughout the system, whereas the CTMC is adopted to describe the continuous transition of the system variables over their respective states. To demonstrate its applicability, the developed coupled BN-CTMC model was employed to predict the probability of failure of the Daisy Lake Dam in the province of British Columbia, Canada, under the uncertainty of reservoir water level, inflow, and wind speed states. The developed BN-CTMC modeling approach can aid in the development of reliable dam operation schemes and risk mitigation strategies through (1) adequately representing the propagation of the hydrological (e.g., inflow and reservoir water level) and meteorological (e.g., wind speed) variable uncertainties through dam system dynamical processes; (2) effectively quantifying dam failure risk under different operational conditions and failure scenarios; (3) accurately specifying the critical periods of dam system operational safety; and (4) providing in-depth understanding of the relationships between the dam system’s failure and associated variables over time.

Linear Versus Nonlinear (Convex and Concave) Hedging Rules for Reservoir Optimization Operation

AbstractHedging rules of reservoir operations decide the timing and magnitude of current water delivery reduction and consequent carryover water storage conservation based on the tradeoff between the utility of current and future water use. Different forms of hedging can be used for reservoir release decision. This study presents general optimality conditions for hedging with convex, concave, and linear relationships between reservoir water availability and release, using criteria based on the relative value of marginal value of risk tolerance (MVRT) with respect to current reservoir release versus carryover storage. MVRT is quantified to measure reservoir operators' risk tolerance change (sensitivity) to the change of reservoir release or carryover storage, reflecting their attitudes to the risk with current release and that with the carryover storage (i.e., future release), as well as their perception of future water availability uncertainty. Higher, equal, or lower MVRT with current release than that with carryover storage for future release corresponds to convex, linear or concave hedging. Various levels of risk tolerance toward hydrologic uncertainty underlie different hedging types for reservoir release decisions. Specifically, negative, positive, and null risk premiums (or risk seeking, risk aversion, and risk neutrality) result from convex, concave, and linear hedging rules, respectively. In general, high uncertainty will move the convex or concave curve closer to the linear curve. The MVRT‐based criteria for optimal hedging policies are illustrated through a real‐world case study.

Pressure-Flow Scour Due to Vertical Contraction: A Review

Climate and land-use/land-cover changes are amplifying hydrological uncertainties, which are increasing the probability of flood magnitudes exceeding their design values. Consequently, bridges are more likely to get submerged. The vertical contraction of flow during a submergence event causes the flow beneath the bridge to accelerate and transit from atmospheric flow to pressure flow. The pressure flow increases the velocity gradient and bed-shear stresses, thereby increasing the scouring potential of the flow and the risk of bridge failure. Laboratory and numerical experiments have been performed to relate the magnitude of pressure-flow scour to the flow geometry and flow, bed, and fluid properties. These developed prediction equations have found a place in design manuals. Nevertheless, many aspects of the pressure-flow scouring are confounding and are still unknown after 3 decades of research. In this article, an overview of the pressure-flow scour phenomenon is presented. The focus is on the important but special case of pressure flow with no pier that has received maximum attention in the literature. The experimental studies on pressure-flow scour are summarized, and inconsistencies and gaps in the reported results are highlighted. A compendium of data compiled from all existing data sets is used for evaluating the design equations and investigating their relative strengths and shortcomings. Based on the anomalies identified in the literature, the article questions the applicability of empirical and semitheoretical models for the pressure-flow scour phenomenon and suggests directions for future research.

Uncertainty propagation within a water level ensemble prediction system

Reliability of a water level ensemble prediction system (WL-EPS) is directly related to the quality of the available data and the processes integrated into each predictive layer. In this project, we evaluate a hydrometeorological ensemble prediction system (H-EPS) coupled to a hydraulic module (HM) developed on the Chaudière River watershed in Quebec, Canada. Ensemble streamflow forecasts account for the three main sources of uncertainty: meteorology (ensemble forecasts), initial conditions (through data assimilation), and model structure (multimodel). The HM consists of a 1D river model that is informed at the boundary conditions by the H-EPS outputs. In order to evaluate the forecasting system in an operational context, a sampling procedure is implemented on the hydrometeorological ensemble forecasts in order to limit the simulation time of the hydraulic component without altering the description of the uncertainty provided beforehand. Forecasts reliability and accuracy are evaluated, for events of various magnitude covering a wide range of flows, based on performance metrics such as the reliability diagram and the continuous rank probability score (CRPS). Overall, results show that the performances of the ensemble flow and water level forecasts provided by the proposed system are quite reliable over the range of forecast horizons evaluated. Water level forecasts are dependent on the quality of the streamflow forecast, which is coherent with the assumption that streamflow is the main source of uncertainty of the HM. However, at control point where flow and water level forecasts are available, a slight deterioration in the forecast reliability is noted from flow to water level; nonetheless, the reliability of the forecasts remains comparable, thus confirming the strong link between the H-EPS and HM uncertainties. Consideration of the uncertainty related to the roughness coefficient uncertainty turned out to have a negligible effect on the spread of the water level forecasts, confirming once more the domination of the hydrologic uncertainty.

Study on the different type of hydrological input towards the accuracy of catchment discharge hydrograph

For hydrological applications, several rainfall measurement techniques are available, each with its own spatial and temporal resolution and errors. When these rainfall datasets are used as input for hydrological models, errors and uncertainties are propagated throughout the hydrological system. This research paper is using rainfall data from two different measurement tools namely rain gauge and radar. Using these two types of measurement tools, it is expected dissimilar output due to the different working principle. Arau catchment in Perlis were selected as the study area. The rainfall data used is in the unit of intensity with time interval 10 minutes for both instruments. Storm Water Management Model 5 (SWMM5) were used as model to simulate the discharge of catchment area. Sensitivity analysis is carried out in this study to determine the major parameters that influence to shape of hydrograph and peak flow. Then, calibration process using five (5) storm events have been performed using available information such as conduits lengths, shape of conduits, impervious and pervious surface using trial and error process. Good correlation between observed and simulated hydrograph on 18 September 2006 has been found to be the best Correlation coefficient (r) and Root mean square error (RMSE) equal to 0.92 and 0.14 respectively. The same parameter used for that storm event was chosen to be applied in validation event. Validation results also in the acceptable range with r is found more than 50% correlation. Next, using the rainfall data captured by radar that converted using equation Z=40R1.6 developed by researcher [1] for the similar date of storm event and similar catchment conditions, all the hydrograph shape shows significant drop. As a conclusion, different type of measurement tools for rainfall gives significant different to the catchment discharge and can be seen that rain gauge are better to use to simulate rainfall compared to the radar.