Hydrological Uncertainty Research Articles

Establishing a quantitative assessment methodology framework of water conservation based on the water balance method under spatiotemporal and different discontinuous ecosystem scales

Water conservation (WC) is an essential terrestrial ecosystem service that mitigates surface runoff and replenishes groundwater, which has received considerable attention under the dual pressures of climate change and human activity. However, there is insufficient understanding of the trends in WC changes on temporal (annual, monthly, daily), spatial, and ecosystem scales. This study proposed a quantitative assessment methodology framework (QAMF) for analyzing the spatiotemporal variation of WC under different discontinuous ecosystems. The QAMF mainly used models and methods such as the hydrological model (SWAT), calibration and uncertainty program (SWAT-CUP), WC calculation formula (water balance method), and spatial analysis method (empirical orthogonal function and wavelet analysis). It was applied to the source region of the Yellow River (SRYR), where the ecological landscape pattern underwent varying degrees of degradation, and WC capacity decreased. The results show that: Firstly, the constructed SWAT in the SRYR had high accuracy, and the proposed formula for calculating WC was suitable for multi-temporal scale analysis of WC in spatially distributed discontinuous basins. Secondly, the annual and monthly WC were respectively 81.00–184.13 mm and −28.58–107.64 mm, and daily WC was positive during extreme precipitation periods and negative during dry periods. The regulating effect of WC was fully reflected on the daily scale, partially reflected on the monthly scale, and absent on the annual scale. Third, the crucial WC area was mainly distributed in the southeast, and there was a significant primary yearly cycle of WC in the SRYR. Finally, different ecosystems exhibited different WC capabilities, and protecting the diversity of ecosystems played an essential role in maintaining and improving the WC function in the SRYR. This project has great scientific significance and technological support for scientifically evaluating the WC capacity in the SRYR.

Enhancing the predictability of flood forecasts by combining Numerical Weather Prediction ensembles with multiple hydrological models

Ensemble Prediction Systems (EPS) from a single Numerical Weather Prediction (NWP) model often miss out on a few extreme events. Similarly, a single hydrological model cannot capture all types of flows, such as flood peaks, occurring within a watershed due to the inaccurate representation of the real world processes in their mathematical structure. To focus on these aspects, this study proposes five ‘NWP+HM’ combinations (HM refers to Hydrologic Model) to investigate which combination performs better in providing quality flood forecasts. The EPS obtained from THORPEX Interactive Grand Global Ensemble (TIGGE) archive are forced into calibrated hydrologic models to generate ensemble flood forecasts (EFFs). The methodology also includes a machine-learning post-processing technique, Quantile Regression Forests (QRF), for temporal correction of bias and under-dispersion in the raw EPS data. The study finds that both the EPS used show similar performance in generating flood forecasts. However, the study demonstrates that the hydrologic model uncertainty has a more significant impact on capturing flood peaks compared to input model uncertainty. Finally, the simulated flood forecasts are verified for their quality using both deterministic and probabilistic metrics. For the above five combinations, an increase in leadtime up to 10 days is observed when the raw data are post-processed using QRF technique. The study identifies a computationally efficient ‘NWP+HM’ combination for use in the hydrological forecasting system, which improves the predictability of flood forecasts several days ahead.

Bayesian extreme learning machines for hydrological prediction uncertainty

In recent years, extreme learning machines (ELM) have been used to accurately predict a variety of hydrological variables (e.g., streamflow, precipitation, river water quality). Using the same model structure, ELM often obtains similar performance to multi-layer perceptron (MLP) networks without the need for an iterative learning process (backpropagation), resulting in faster training. However, despite the increasing popularity of ELM, the hydrology literature has not focused on training algorithms that can be used to generate probabilistic predictions for this method. This is an interesting research gap, as it is generally accepted that quantifying hydrological prediction uncertainty and producing probabilistic predictions (instead of point predictions or mean value predictions) is a prerequisite for reliable water resource management. Thus, for the first time, Bayesian ELM (BELM) and sparse BELM (SBELM) methods are adopted and applied for probabilistic streamflow simulation and multi-step ahead forecasting (1–3 days), using as a case study four watersheds from Mexico, Germany, Canada, and Belgium. Using deterministic and probabilistic metrics, BELM and SBELM are compared against Bayesian linear regression (BLR), MLP combined with Monte-Carlo dropout weights, and a deep learning method: long short-term memory network (LSTM) coupled with Monte-Carlo dropout weights. Adding time-lagged observations of streamflow and meteorological variables (up to 14 days), such as precipitation and potential evapotranspiration, improves the simulation accuracy up to a factor of 10. In general, both BELM and SBELM show more accurate point predictions than MLP, BLR, and LSTM. BELM and SBELM show similar performance in terms of accuracy and reliability. Although, BELM marginally outperforms SBELM by generating, on average, a narrower prediction interval width. The sparsity feature of SBELM reduces (on average) the network size by 14–83 %. BELM and SBELM produce more accurate and reliable predictions than LSTM and are up to 122 and 125 times more computationally efficient to train, respectively. The case study suggests that BELM and SBELM are promising probabilistic machine learning models for hydrological prediction that are attractive alternatives to physical (e.g., lumped conceptual) models and common deep learning models (e.g., LSTM).

Characterizing Errors Using Satellite Metadata for Eco‐Hydrological Model Calibration

AbstractUnderstanding the origins of errors between model predictions and catchment observations is a critical element in hydrologic model calibration and uncertainty estimation. Difficulties arise because there are a variety of error sources but only one measure of the total residual error between model predictions and catchment observations. One promising approach is to collect extra information a priori to characterize the data error before calibration. We implement here a new model calibration strategy for an ecohydrological model, using the satellite metadata information as a means to inform the model priors, to decompose data error from total residual error. This approach, referred to as Bayesian ecohydrological error model (BEEM), is first examined in a synthetic setting to establish its validity, and then applied to three real catchments across Australia. Results show that (a) BEEM is valid in a synthetic setting, as it can perfectly ascertain the true underlying error; (b) in real catchments the model error is reduced when utilizing the observation error variance as added error contributing to total error variance, while the magnitude of total residual error is more robust when utilizing metadata about the data quality proportionality as the basis for assigning total error variance; (c) BEEM improves model calibration by estimating the model error appropriately and estimating the uncertainty interval more precisely. Overall, our work demonstrates a new approach to collect prior error information in satellite metadata and reveals the potential for fully utilizing metadata about error sources in uncertainty estimation.

Improving the Performance of Hydrological Model Parameter Uncertainty Analysis Using a Constrained Multi-Objective Intelligent Optimization Algorithm

In the field of hydrological model parameter uncertainty analysis, sampling methods such as Differential Evolution based on Monte Carlo Markov Chain (DE-MC) and Shuffled Complex Evolution Metropolis (SCEM-UA) algorithms have been widely applied. However, there are two drawbacks which may introduce bad effects into the uncertainty analysis. The first disadvantage is that few optimization algorithms consider the physical meaning and reasonable range of the model parameters. The traditional sampling algorithms may generate non-physical parameter values and poorly simulated hydrographs when carrying out the uncertainty analysis. The second disadvantage is that the widely used sampling algorithms commonly involve only a single objective. Such sampling procedures implicitly introduce too strong an “exploitation” property into the sampling process, consequently destroying the diversity property of the sampled population, i.e., the “exploration” property is bad. Here, “exploitation” refers to using good already-existing solutions and making refinements to them, so that their fitness will improve further; meanwhile, “exploration” denotes that the algorithm searches for new solutions in new regions. With the aim of improving the performance of uncertainty analysis algorithms, in this research, a constrained multi-objective intelligent optimization algorithm is proposed that preserves the physical meaning of the model parameter using the penalty function method and maintains the population diversity using a Non-dominated Sorted Genetic Algorithm-II (NSGA-II) multi-objective optimization procedure. The representativeness of the parameter population is estimated on the basis of the mean and standard deviation of the Nash–Sutcliffe coefficient, and the diversity is evaluated on the basis of the mean Euclidean distance. The Chengcun watershed is selected as the study area, and uncertainty analysis is carried out. The numerical simulations indicate that the performance of the proposed algorithm is significantly improved, preserving the physical meaning and reasonable range of the model parameters while significantly improving the diversity and reliability of the sampled parameter population.

The enhanced future Flows and Groundwater dataset: development and evaluation of nationally consistent hydrological projections based on UKCP18

Abstract. This paper details the development and evaluation of the enhanced future FLows and Groundwater (eFLaG) dataset of nationally consistent hydrological projections for the UK, based on the latest UK Climate Projections (UKCP18). The projections are derived from a range of hydrological models. For river flows, multiple models (Grid-to-Grid, PDM (Probability Distributed Model) and GR (Génie Rural; both four- and six-parameter versions, GR4J and GR6J)) are used to provide an indication of hydrological model uncertainty. For groundwater, two models are used, a groundwater level model (AquiMod) and a groundwater recharge model (ZOODRM: zooming object-oriented distributed-recharge model). A 12-member ensemble of transient projections of present and future (up to 2080) daily river flows, groundwater levels and groundwater recharge was produced using bias-corrected data from the UKCP18 regional (12 km) climate ensemble. Projections are provided for 200 river catchments, 54 groundwater level boreholes and 558 groundwater bodies, all sampling across the diverse hydrological and geological conditions of the UK. An evaluation was carried out to appraise the quality of hydrological model simulations against observations and also to appraise the reliability of hydrological models driven by the regional climate model (RCM) ensemble in terms of their capacity to reproduce hydrological regimes in the current period. The dataset was originally conceived as a prototype climate service for drought planning for the UK water sector and so has been developed with drought, low river flow and low groundwater level applications as the primary objectives. The evaluation metrics show that river flows and groundwater levels are, for the majority of catchments and boreholes, well simulated across the flow and level regime, meaning that the eFLaG dataset could be applied to a wider range of water resources research and management contexts, pending a full evaluation for the designated purpose. Only a single climate model and one emissions scenario are used, so any applications should ideally contextualise the outcomes with other climate model–scenario combinations. The dataset can be accessed in Hannaford et al. (2022): https://doi.org/10.5285/1bb90673-ad37-4679-90b9-0126109639a9.

Evaluating the Feedback of the Reservoir Methane Cycle to Climate Warming under Hydrological Uncertainty

Freshwater reservoirs are widely recognized as methane (CH4) emission hotspots. Existing research has shown that temperature and hydrological conditions significantly affect wetland CH4 cycling processes. However, the feedback of the CH4 cycle to climate warming remains unclear for deep reservoirs where seasonal water thermal stratification exists. This study combined a reservoir CH4 cycling model and a Statistical DownScaling Model (SDSM) to evaluate reservoir CH4 cycling feedbacks under multiple climate change scenarios while accounting for hydrological uncertainty. Daily air temperatures in 2100 were predicted by the combination of the CanESM5 model and a SDSM. To address hydrological uncertainty, we selected three representative hydrological years (i.e., wet, normal, and dry) to create hydrological scenarios. Results showed that annual sediment CH4 production increased with warming, ranging 323.1–413.7 × 103 t C year−1 among multiple scenarios. Meanwhile, the CH4 oxidation percentage decreased with warming, which meant warming promoted sediment CH4 release non-linearly; 67.8–84.6% of sediment ebullient flux was ultimately emitted to the atmosphere (51.3–137.7 × 103 t C year−1), which showed ebullition was the dominant emission pathway. Higher air temperatures and drier conditions generally promote reservoir emissions. This study is helpful for predicting reservoir emissions while directing decision-making for reservoir sustainability.

Sea surface warming patterns drive hydrological sensitivity uncertainties

The increase in global-mean precipitation with global-mean temperature (hydrological sensitivity; eta) is constrained by the atmospheric energy budget, but its magnitude remains uncertain. Here we apply warming patch experiments to a climate model to demonstrate that the spatial pattern of sea surface warming can explain a wide range of eta. Warming in tropical strongly ascending regions produces eta values even larger than suggested by the Clausius–Clapeyron relationship (7% K−1), as the warming and moisture increases can propagate vertically and be transported globally through atmospheric dynamics. Differences in warming patterns are as important as different treatments of atmospheric physics in determining the spread of eta in climate models. By accounting for the pattern effect, the global-mean precipitation over the past decades can be well reconstructed in terms of both magnitude and variability, indicating the vital role of the pattern effect in estimating future intensification of the hydrological cycle.

Water supply, waste assimilation, and low‐flow issues facing the Southeast Piedmont Interstate‐85 urban archipelago

AbstractRapidly growing cities along the Interstate‐85 corridor from Atlanta, GA, to Raleigh, NC, rely on small rivers for water supply and waste assimilation. These rivers share commonalities including water supply stress during droughts, seasonally low flows for wastewater dilution, increasing drought and precipitation extremes, downstream eutrophication issues, and high regional aquatic diversity. Further challenges include rapid growth; sprawl that exacerbates water quality and infrastructure issues; water infrastructure that spans numerous counties and municipalities; and large numbers of septic systems. Holistic multi‐jurisdiction cooperative water resource planning along with policy and infrastructure modifications is necessary to adapt to population growth and climate. We propose six actions to improve water infrastructure resilience: increase water‐use efficiency by municipal, industrial, agricultural, and thermoelectric power sectors; adopt indirect potable reuse or closed loop systems; allow for water sharing during droughts but regulate inter‐basin transfers to protect aquatic ecosystems; increase nutrient recovery and reduce discharges of carbon and nutrients in effluents; employ green infrastructure and better stormwater management to reduce nonpoint pollutant loadings and mitigate urban heat island effects; and apply the CRIDA framework to incorporate climate and hydrologic uncertainty into water planning.

Exploration of dual-attention mechanism-based deep learning for multi-step-ahead flood probabilistic forecasting

In order to improve the flood forecasting accuracy and reflect the forecast uncertainty information in the Three Gorges Reservoir (TGR) interval-basin in China, this study integrates the feature and temporal dual-attention (DA) mechanism and recursive encoder-decoder (RED) structure into the long short-term memory (LSTM) neural network to develop a DA-LSTM-RED model. The feature attention acts on the input variables of the encoder, and the temporal attention mechanism acts on the hidden layer states extracted by the LSTM neural network during encoding process, prompting the proposed model to extract critical input information among different types and moments of input variables to improve the multi-step-ahead flood forecasting accuracy. Second, the copula-based Hydrological Uncertainty Processor (copula-HUP) is used to quantify the forecast uncertainty of the proposed model meanwhile creating multi-step-ahead flood probabilistic forecasts. Combining the long-term 6 h hydrologic data series of the Xiangjiaba-TGR interval-basin and the forecasted precipitation from the European Centre for Medium-Range Weather Forecasts (ECMWF), the effectiveness of the proposed model, the effect of forecast precipitation on multi-step-ahead flood forecasting, and the effect of different copula functions on the probabilistic forecast of copula-HUP are investigated, respectively. The results show that the DA-LSTM-RED model can effectively improve the forecasting accuracy for long forecast horizons (3-7d) compared to the LSTM-RED model, and the average absolute error metrics are reduced by 10%-17%. Meanwhile, the proposed model can identify input variables with a high correlation with the target output variables, which improves the interpretability of deep learning to a certain extent. The Student copula-HUP has the lowest RB and CRPS metrics than the Frank and Gaussian copula-HUP, which can better quantify the DA-LSTM-RED model's forecast uncertainty. Therefore, combining the proposed model with the Student copula-HUP can effectively reduce the forecasting uncertainty, enhance the forecasting reliability and accuracy for future horizons, and provide reliable risk information for the TGR flood control scheduling decision.

Evaluation of the Impact of Multi-Source Uncertainties on Meteorological and Hydrological Ensemble Forecasting

Evaluating the impact of multi-source uncertainties in complex forecasting systems is essential to understanding and improving the systems Previous studies have paid little attention to the influence of multi-source uncertainties in complex meteorological and hydrological forecasting systems. In this study, we developed a general ensemble framework based on Bayesian model averaging (BMA) for evaluating the impact of multi-source uncertainties in complex forecast systems. Based on this framework, we used eight numerical weather prediction products from the International Grand Global Ensemble (TIGGE) dataset, four hydrological models with different structures, and 1000 sets of parameters to comprehensively account for the input, structure, and parameter uncertainties. The framework’s application to the Chitan Basin in China revealed that the numerical weather prediction input uncertainty in the forecasting system was more significant than the hydrological model uncertainty. The hydrological model structure uncertainty was more prominent than the parameter uncertainty. The accuracy of the numerical weather prediction dominates the accuracy of the forecast of high flows. In addition, the structures and parameters of the hydrological model and their interactions contributed to the main uncertainty of the low flow forecasts. The streamflow was more realistically represented when the three uncertainty sources were considered jointly. By accounting for the significant uncertainty sources in complex forecast systems, the BMA ensemble forecasting produces more realistic and reliable predictions and reduces the influences of other incomplete considerations. The developed multi-source uncertainty assessment framework improves our understanding of the complex meteorological and hydrological forecasting system. Therefore, the framework is promising for improving the accuracy and reliability of complex forecasting systems.

Large Ensemble Diagnostic Evaluation of Hydrologic Parameter Uncertainty in the Community Land Model Version 5 (CLM5)

AbstractLand surface models such as the Community Land Model version 5 (CLM5) seek to enhance understanding of terrestrial hydrology and aid in the evaluation of anthropogenic and climate change impacts. However, the effects of parametric uncertainty on CLM5 hydrologic predictions across regions, timescales, and flow regimes have yet to be explored in detail. The common use of the default hydrologic model parameters in CLM5 risks generating streamflow predictions that may lead to incorrect inferences for important dynamics and/or extremes. In this study, we benchmark CLM5 streamflow predictions relative to the commonly employed default hydrologic parameters for 464 headwater basins over the conterminous United States (CONUS). We evaluate baseline CLM5 default parameter performance relative to a large (1,307) Latin Hypercube Sampling‐based diagnostic comparison of streamflow prediction skill using over 20 error measures. We provide a global sensitivity analysis that clarifies the significant spatial variations in parametric controls for CLM5 streamflow predictions across regions, temporal scales, and error metrics of interest. The baseline CLM5 shows relatively moderate to poor streamflow prediction skill in several CONUS regions, especially the arid Southwest and Central U.S. Hydrologic parameter uncertainty strongly affects CLM5 streamflow predictions, but its impacts vary in complex ways across U.S. regions, timescales, and flow regimes. Overall, CLM5's surface runoff and soil water parameters have the largest effects on simulated high flows, while canopy water and evaporation parameters have the most significant effects on the water balance.

Characterizing uncertainty in Community Land Model version 5 hydrological applications in the United States

Land surface models such as the Community Land Model Version 5 (CLM5) are essential tools for simulating the behavior of the terrestrial system. Despite the extensive application of CLM5, limited attention has been paid to the underlying uncertainties associated with its hydrological parameters and how these uncertainties affect water resource applications. To address this long-standing issue, we use five meteorological datasets to conduct a comprehensive hydrological parameter uncertainty characterization of CLM5 over the hydroclimatic gradients of the conterminous United States. Key datasets produced from the uncertainty characterization experiment include: a benchmark dataset of CLM5 default hydrological performance, parameter sensitivities for 28 hydrological metrics, and large-ensemble outputs for CLM5 hydrological predictions. The presented datasets will assist CLM5 calibration and support broad applications, such as evaluating drought and flood vulnerabilities. The datasets can be used to identify the hydroclimatological conditions under which parametric uncertainties demonstrate substantial effects on hydrological predictions and clarify where further investigations are needed to understand how hydrological prediction uncertainties interact with other Earth system processes.

Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe

Abstract. High-resolution large-scale predictions of hydrologic states and fluxes are important for many multi-scale applications, including water resource management. However, many of the existing global- to continental-scale hydrological models are applied at coarse resolution and neglect more complex processes such as lateral surface and groundwater flow, thereby not capturing smaller-scale hydrologic processes. Applications of high-resolution and physically based integrated hydrological models are often limited to watershed scales, neglecting the mesoscale climate effects on the water cycle. We implemented an integrated, physically based coupled land surface groundwater model, ParFlow-CLM version 3.6.0, over a pan-European model domain at 0.0275∘ (∼3 km) resolution. The model simulates a three-dimensional variably saturated groundwater-flow-solving Richards equation and overland flow with a two-dimensional kinematic wave approximation, which is fully integrated with land surface exchange processes. A comprehensive evaluation of multiple hydrologic variables including discharge, surface soil moisture (SM), evapotranspiration (ET), snow water equivalent (SWE), total water storage (TWS), and water table depth (WTD) resulting from a 10-year (1997–2006) model simulation was performed using in situ and remote sensing (RS) observations. Overall, the uncalibrated ParFlow-CLM model showed good agreement in simulating river discharge for 176 gauging stations across Europe (average Spearman's rank correlation (R) of 0.77). At the local scale, ParFlow-CLM model performed well for ET (R>0.94) against eddy covariance observations but showed relatively large differences for SM and WTD (median R values of 0.7 and 0.50, respectively) when compared with soil moisture networks and groundwater-monitoring-well data. However, model performance varied between hydroclimate regions, with the best agreement to RS datasets being shown in semi-arid and arid regions for most variables. Conversely, the largest differences between modeled and RS datasets (e.g., for SM, SWE, and TWS) are shown in humid and cold regions. Our findings highlight the importance of including multiple variables using both local-scale and large-scale RS datasets in model evaluations for a better understanding of physically based fully distributed hydrologic model performance and uncertainties in water and energy fluxes over continental scales and across different hydroclimate regions. The large-scale, high-resolution setup also forms a basis for future studies and provides an evaluation reference for climate change impact projections and a climatology for hydrological forecasting considering the effects of lateral surface and groundwater flows.

GEE can prominently reduce uncertainties from input data and parameters of the remote sensing-driven distributed hydrological model

The coupling of multisource remote sensing data and the lack of measured runoff introduce input data and model parameters uncertainties to the remote sensing-driven distributed hydrological model (RS-DHM). The PB satellite remote sensing datasets of the Google Earth Engine (GEE) are widely used in RS-DHM and remote sensing runoff inversion research, but whether GEE can reduce the two abovementioned uncertainties is still unknown. To answer this question, twelve remote sensing data sources provided by GEE were used in this study to drive a typical RS-DHM called the remote sensing-driven distributed time-variant gain model (RS-DTVGM) and the remote sensing runoff inversion technology called remote sensing hydrological station (RSHS), and the contribution of GEE to the improving hydrological model uncertainties was quantitatively analyzed from 2001 to 2020. The results showed that (1) the GEE-based improved data preparation not only effectively reduced the uncertainty in the input data with better spatial-temporal continuity and a 6.20 % reduction in the total area occupied by invalid grids, but also enhanced the operational efficiency by reducing the image number, memory size and data processing time of the satellite remote sensing data by 83.63 %, 99.53 %, and 98.73 %, respectively; (2) the GEE-based RSHS technology provided sufficient data support for parameter adjustment and accuracy validation of the RS-DTVGM, which effectively reduced the uncertainty in the model parameters and increased the Nash efficiency coefficient (NSE) in the calibration and validation period from 0.67 to 0.87 and 0.75, respectively; and (3) the calibrated RS-DTVGM was more reliable and robust, and its runoff and evapotranspiration were consistent with the actual statistical data. In the future, GEE and RSHS technology should be widely adopted to drive the RS-DHM to more quickly and easily provide reliable hydrological processes simulation results for integrated water resource management, therefore achieving win–win results in terms of efficiency and accuracy.

Subbasin Spatial Scale Effects on Hydrological Model Prediction Uncertainty of Extreme Stream Flows in the Omo Gibe River Basin, Ethiopia

Quantification of hydrologic model prediction uncertainty for various flow quantiles is of great importance for water resource planning and management. Thus, this study is designed to assess the effect of subbasin spatial scale on the hydrological model prediction uncertainty for different flow quantiles. The Soil Water Assessment Tool (SWAT), a geographic information system (GIS) interfaced hydrological model, was used in this study. Here, the spatial variations within the sub-basins of the Omo Gibe River basin in Ethiopia’s Abelti, Wabi, and Gecha watersheds from 1989 to 2020 were examined. The results revealed that (1) for the Abelti, Wabi, and Gecha watersheds, SWAT was able to reproduce the observed hydrograph with more than 85%, 82%, and 73% accuracy in terms of the Nash-Sutcliffe efficiency coefficient (NSE), respectively; (2) the variation in the spatial size of the subbasin had no effect on the overall flow simulations. However, the reproduction of the flow quantiles was considerably influenced by the subbasin spatial scales; (3) the coarser subbasin spatial scale resulted in the coverage of most of the observations. However, the finer subbasin spatial scale provided the best simulation closer to the observed stream flow pattern; (4) the SWAT model performed much better in recreating moist, high, and very-high flows than it did in replicating dry, low, and very-low flows in the studied watersheds; (5) a smaller subbasin spatial scale (towards to distributed model) may better replicate low flows, while a larger subbasin spatial scale (towards to lumped model) enhances high flow replication precision. Thus, it is crucial to investigate the subbasin spatial scale to reproduce the peak and low flows; (6) in this study, the best subbasin spatial scales for peak and low flows were found to be 79–98% and 29–42%, respectively. Hence, it is worthwhile to investigate the proper subbasin spatial scales in reproducing various flow quantiles toward sustainable management of floods and drought.

A Novel HydroEconomic - Econometric Approach for Integrated Transboundary Water Management Under Uncertainty

The optimal management of scarce transboundary water resources among competitive users is expected to be challenged by the effects of climate change on water availability. The multiple economic and social implications, including conflicts between neighbouring countries, as well as competitive sectors within each country are difficult to estimate and predict, to inform policy-making. In this paper, this problem is approached as a stochastic multistage dynamic game: we develop and apply a novel framework for assessing and evaluating different international strategies regarding transboundary water resources use, under conditions of hydrological uncertainty. The Omo-Turkana transboundary basin in Africa is used as a case study application, since it increasingly faces the above challenges, including the international tension between Kenya and Ethiopia and each individual country’s multi-sectoral competition for water use. The mathematical framework combines a hydro-economic model (water balance, water costs and benefits), and an econometric model (production functions and water demand curves) which are tested under cooperative and non-cooperative conditions (Stackelberg “leader–follower” game). The results show the cross-country and cross-sectoral water use—economic trade-offs, the future water availability for every game case, the sector-specific production function estimations (including residential, agriculture, energy, mining, tourism sectors), with nonparametric treatment, allowing for technical inefficiency in production and autocorrelated Total Factor Productivity, providing thus a more realistic simulation. Cooperation between the two countries is the most beneficial case for future water availability and economic growth. The study presents a replicable, sophisticated modelling framework, for holistic transboundary water management.

A large-sample investigation into uncertain climate change impacts on high flows across Great Britain

Abstract. Climate change may significantly increase flood risk globally, but there are large uncertainties in both future climatic changes and how these propagate into changing river flows. Here, the impact of climate change on the magnitude and frequency of high flows is analysed for Great Britain (GB) to provide the first spatially consistent GB projections to include both climate ensembles and hydrological model parameter uncertainties. We use the latest high-resolution (12 km) regional climate model ensemble from the UK Climate Projections (UKCP18). These projections are based on a perturbed-physics ensemble of 12 regional climate model simulations and allow exploration of climate model uncertainty beyond the variability caused by the use of different models. We model 346 larger (>144 km2) catchments across GB using the DECIPHeR hydrological modelling framework. Generally, results indicated an increase in the magnitude and frequency of high flows (Q10, Q1, and annual maximum) along the western coast of GB in the future (2050–2075), with increases in annual maximum flows of up to 65 % for western Scotland. In contrast, median flows (Q50) were projected to decrease across GB. Even when using an ensemble based on a single regional climate model (RCM) structure, all flow projections contained large uncertainties. While the RCM parameters were the largest source of uncertainty overall, hydrological modelling uncertainties were considerable in eastern and south-eastern England. Regional variations in flow projections were found to relate to (i) differences in climatic change and (ii) catchment conditions during the baseline period as characterised by the runoff coefficient (mean discharge divided by mean precipitation). Importantly, increased heavy-precipitation events (defined by an increase in 99th percentile precipitation) did not always result in increased flood flows for catchments with low runoff coefficients, highlighting the varying factors leading to changes in high flows. These results provide a national overview of climate change impacts on high flows across GB, which will inform climate change adaptation, and highlight the impact of hydrological model parameter uncertainties when modelling climate change impact on high flows.

Impact of land use land cover changes on flow uncertainty in Siliana watershed of northwestern Tunisia

It is widely admitted that changes in land use land cover (LULC) influence the hydrology of the catchment. However, how these changes affect hydrological model prediction uncertainty is still a raising question. In this paper we addressed this question by investigating the impacts of the rapid change in LULC in the Siliana catchment in Tunisia on monthly flow and magnitude of flow extremes using the SWAT hydrological model while quantifying the contribution of LULC to the model parameter and prediction uncertainty. At a first step, the SWAT model parameter and prediction uncertainty were estimated using the GLUE method and acceptable parameter sets were identified. Subsequently, the SWAT model was fed with historical LULC as derived from Landsat 5 and 8 satellite images for the years 1990, 2000, 2013 and 2019, and run with the acceptable parameter sets. The results show that the increase in olive plantations (+380 %), urban area (+200 %), and irrigated lands (+309 %) from 1990 to 2019, has LULC decreased monthly flow, high flows magnitude but did not impact low flows in particular over the previous two decades. The findings also suggest that model prediction uncertainty can mask LULC effects, suggesting that model results can be misleading without explicit consideration of uncertainty when assessing the hydrological impacts of changes in LULC.

Framework for a reliability-based analysis of local scour and its effect on pile response in clay

The analyses of axial and lateral capacity of a pile are significantly dependent on the appropriate estimation of scour depth, while the scour depth estimation procedure is uncertain due to the hydraulic, hydrologic, and geotechnical parameters uncertainty. Work herein is focused on developing a framework for reliability-based pier scour assessment methodology and demonstrate its integration with the concept of Load and Resistance Factor Design (LRFD) approach. Scour factors are proposed based on reliability level (β) and the associated probability of deceedance (POD). Three example applications of axially and laterally loaded pile design approach while including scour factor in the LRFD framework are demonstrated. Based on axial pile capacity analysis, the increase of pile length when the β-based scour assessment is used with the soil resistance factors, was estimated to be 26.5–29.6 % higher compared to using the deterministic scour with soil resistance factor. In the case of lateral pile response analysis, as β is increased from 2.0 to 3.0, the lateral pile head deflection increased by 46–132 % compared to the deterministically-estimated scour depth case. To obtain β= 3.0 for the considered example while maintaining the pile length unchanged, the pile diameter needed to be increased by 35.7 % compared to the base case pile’s diameter.