scholarly journals Identifying uncertainties in simulated streamflow from hydrologic model components for climate change impact assessments

2019 ◽  
Author(s):  
Dongmei Feng ◽  
Edward Beighley
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Bayesian Model Averaging ◽  
Hydrologic Model ◽  
Process Models ◽  
Runoff Generation ◽  
Modeling Framework ◽  
Total Uncertainty ◽  
Physically Based ◽  
Model Components

Abstract. Assessing the impacts of climate change on hydrologic systems is critical for developing adaptation and mitigation strategies for water resource management, risk control and ecosystem conservation practices. Such assessments are commonly accomplished using outputs from a hydrologic model forced with future precipitation and temperature projections. The algorithms used in the hydrologic model components (e.g., runoff generation) can introduce significant uncertainties in the simulated hydrologic variables, yet the identification and quantification of such uncertainties is rarely studied. Here, a modeling framework is developed that integrates multiple runoff generation algorithms with a routing model and associated parameter optimizations. This framework is able to identify uncertainties from both hydrologic model components and climate forcings as well as associated parameterization. Three fundamentally different runoff generation approaches: runoff coefficient method (RCM, conceptual), variable infiltration capacity (VIC, physically-based, infiltration excess) and simple-TOPMODEL (STP, physically-based, saturation excess), are coupled with Hillslope River Routing model to simulate streamflow. A case study conducted in Santa Barbara County, California, reveals that the median changes are 1–10 % increases in mean annual discharge (Qm) and 10–40 % increases in annual maximum daily discharge (Qp) and 100-yr flood discharge (Q100). The Bayesian Model Averaging analysis indicates that the probability of increase in streamflow can be up to 85 %. However, the simulated discharge uncertainties are large (i.e., 230 % for Qm and 330 % for Qp and Q100) with general circulation models (GCMs) and emission scenarios accounting for more than half of the total uncertainty. Hydrologic process models contribute 10–30 % of the total uncertainty, while uncertainty due to hydrologic model parameterization is almost negligible (

scholarly journals Identifying uncertainties in hydrologic fluxes and seasonality from hydrologic model components for climate change impact assessments

2020 ◽  
Vol 24 (5) ◽  
pp. 2253-2267 ◽  
Author(s):  
Dongmei Feng ◽  
Edward Beighley
Keyword(s):  
Climate Change ◽  
Climate Change Impact ◽  
Impact Analysis ◽  
Risk Control ◽  
Hydrologic Model ◽  
Runoff Generation ◽  
Total Uncertainty ◽  
Physically Based ◽  
Change Impact ◽  
Model Components

Abstract. Assessing impacts of climate change on hydrologic systems is critical for developing adaptation and mitigation strategies for water resource management, risk control, and ecosystem conservation practices. Such assessments are commonly accomplished using outputs from a hydrologic model forced with future precipitation and temperature projections. The algorithms used for the hydrologic model components (e.g., runoff generation) can introduce significant uncertainties into the simulated hydrologic variables. Here, a modeling framework was developed that integrates multiple runoff generation algorithms with a routing model and associated parameter optimizations. This framework is able to identify uncertainties from both hydrologic model components and climate forcings as well as associated parameterization. Three fundamentally different runoff generation approaches, runoff coefficient method (RCM, conceptual), variable infiltration capacity (VIC, physically based, infiltration excess), and simple-TOPMODEL (STP, physically based, saturation excess), were coupled with the Hillslope River Routing model to simulate surface/subsurface runoff and streamflow. A case study conducted in Santa Barbara County, California, reveals increased surface runoff in February and March but decreased runoff in other months, a delayed (3 d, median) and shortened (6 d, median) wet season, and increased daily discharge especially for the extremes (e.g., 100-year flood discharge, Q100). The Bayesian model averaging analysis indicates that the probability of such an increase can be up to 85 %. For projected changes in runoff and discharge, general circulation models (GCMs) and emission scenarios are two major uncertainty sources, accounting for about half of the total uncertainty. For the changes in seasonality, GCMs and hydrologic models are two major uncertainty contributors (∼35 %). In contrast, the contribution of hydrologic model parameters to the total uncertainty of changes in these hydrologic variables is relatively small (<6 %), limiting the impacts of hydrologic model parameter equifinality in climate change impact analysis. This study provides useful information for practices associated with water resources, risk control, and ecosystem conservation and for studies related to hydrologic model evaluation and climate change impact analysis for the study region as well as other Mediterranean regions.

Improving the representation of the prairie pothole dynamics in land surface models

2020 ◽  
Author(s):  
Mohamed I. Ahmed ◽  
Amin Elshorbagy ◽  
Alain Pietroniro
Keyword(s):  
Surface Runoff ◽  
Land Surface ◽  
Flow Simulation ◽  
Hydrologic Model ◽  
Surface Model ◽  
Prairie Pothole ◽  
Runoff Generation ◽  
Dem Resolution ◽  
Physically Based ◽  
Streamflow Simulation

&lt;p&gt;The hydrography of the prairie basins is complicated by the existence of numerous land depressions, known as prairie potholes, which can retain a substantial amount of surface runoff. Consequently, the runoff production in the prairies follows a fill, spill, and merging mechanism, which results in a dynamic contributing area that makes the streamflow simulation challenging. Existing approaches to represent the potholes&amp;#8217; dynamics, in different hydrological models, use either a lumped or a series of reservoirs that contribute flow after exceeding a certain storage threshold. These approaches are simplified and do not represent the actual dynamics of the potholes nor their spatial water extents. Consequently, these approaches may not be useful in capturing the potholes&amp;#8217; complexities and may not be able to accurately simulate the complex prairie streamflow. This study advances towards more accurate and physically-based streamflow simulation in the prairies by implanting a physically-based runoff generation algorithm (Prairie Region Inundation MApping, PRIMA model) within the MESH land surface model, and is referred to as MESH-PRIMA. PRIMA is a recently developed hydrological routing model that can simulate the lateral movement of water over prairie landscape using topographic data provided via DEMs. In MESH-PRIMA, MESH handles the vertical water balance calculations, whereas PRIMA routes the water and determines the amount of water storage and surface runoff. The streamflow simulations of MESH-PRIMA (using different DEM resolution as a topographic input) and MESH with its existing conceptual pothole dynamics algorithm are tested on a number of pothole-dominated watersheds within Saskatchewan, Canada, and compared against observed flows. MESH-PRIMA provides improved streamflow and peak flow simulation, compared to that of MESH with its conceptual pothole algorithm, based on the metrics evaluated for the simulations. MESH-PRIMA shows potential for simulating the actual pothole water extents when compared against water areas obtained from remote sensing data. The use of different DEM resolution changes the resulting pothole water extent, especially for the small potholes as they are not detected in the coarse DEM. MESH-PRIMA can be considered as a hydraulic-hydrologic model that can be used for better understanding and accurate representation of the complex prairie hydrology.&lt;/p&gt;

scholarly journals Physically based modeling of rainfall-triggered landslides: a case study in the Luquillo forest, Puerto Rico

2013 ◽  
Vol 17 (9) ◽  
pp. 3371-3387 ◽  
Author(s):  
C. Lepore ◽  
E. Arnone ◽  
L. V. Noto ◽  
G. Sivandran ◽  
R. L. Bras
Keyword(s):  
Soil Moisture ◽  
Soil Column ◽  
Hydrologic Model ◽  
Richards Equation ◽  
Landslide Risk ◽  
Modeling Framework ◽  
Soil Moisture Dynamics ◽  
Physically Based ◽  
Spatially Distributed ◽  
Moisture Dynamics

Abstract. This paper presents the development of a rainfall-triggered landslide module within an existing physically based spatially distributed ecohydrologic model. The model, tRIBS-VEGGIE (Triangulated Irregular Networks-based Real-time Integrated Basin Simulator and Vegetation Generator for Interactive Evolution), is capable of a sophisticated description of many hydrological processes; in particular, the soil moisture dynamics are resolved at a temporal and spatial resolution required to examine the triggering mechanisms of rainfall-induced landslides. The validity of the tRIBS-VEGGIE model to a tropical environment is shown with an evaluation of its performance against direct observations made within the study area of Luquillo Forest. The newly developed landslide module builds upon the previous version of the tRIBS landslide component. This new module utilizes a numerical solution to the Richards' equation (present in tRIBS-VEGGIE but not in tRIBS), which better represents the time evolution of soil moisture transport through the soil column. Moreover, the new landslide module utilizes an extended formulation of the factor of safety (FS) to correctly quantify the role of matric suction in slope stability and to account for unsaturated conditions in the evaluation of FS. The new modeling framework couples the capabilities of the detailed hydrologic model to describe soil moisture dynamics with the infinite slope model, creating a powerful tool for the assessment of rainfall-triggered landslide risk.

scholarly journals Simulation and analysis of the impact of projected climate change on the spatially distributed waterbalance in Thuringia, Germany

2009 ◽  
Vol 21 ◽  
pp. 33-48 ◽  
Author(s):  
P. Krause ◽  
S. Hanisch
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Hydrological Model ◽  
Actual Evapotranspiration ◽  
Climate Data ◽  
Runoff Generation ◽  
Entire Area ◽  
Spatially Distributed ◽  
The Impact ◽  
Conceptual Hydrological Model

Abstract. The impact of projected climate change on the long-term hydrological balance and seasonal variability in the federal German state of Thuringia was assessed and analysed. For this study projected climate data for the scenarios A2 and B1 were used in conjunction with a conceptual hydrological model. The downscaled climate data are based on outputs of the general circulation model ECHAM5 and provide synthetic climate time series for a large number of precipitation and climate stations in Germany for the time period of 1971 to 2100. These data were used to compute the spatially distributed hydrological quantities, i.e. precipitation, actual evapotranspiration and runoff generation with a conceptual hydrological model. This paper discusses briefly the statistical downscaling method and its validation in Thuringia and includes an overview of the hydrological model. The achieved results show that the projected climate conditions in Thuringia follow the general European climate trends – increased temperature, wetter winters, drier summers. But, in terms of the spatial distribution and interannual variability regional differences occur. The analysis showed that the general increase of the winter precipitation is more distinct in the mid-mountain region and less pronounced in the lowland whereas the decrease of summer precipitation is higher in the lowland and less distinct in the mid-mountains. The actual evapotranspiration showed a statewide increase due to higher temperatures which is largest in the summer period. The resulting runoff generation in winter was found to increase in the mid-mountains and to slightly decrease in the lowland region. In summer and fall a decrease in runoff generation was estimated for the entire area due to lower precipitation and higher evapotranspiration rates. These spatially differentiated results emphasize the need of high resolution climate input data and distributed modelling for regional impact analyses.

scholarly journals Contrasting dynamics of hydrological processes in the Volta River basin under global warming

2021 ◽  
Author(s):  
Moctar Dembélé ◽  
Mathieu Vrac ◽  
Natalie Ceperley ◽  
Sander J. Zwart ◽  
Josh Larsen ◽  
...  
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
River Basin ◽  
General Circulation ◽  
Climate Models ◽  
Greenhouse Gas Emission ◽  
Local Population ◽  
Hydrologic Model ◽  
Mitigation Strategies ◽  
Low Flow

Abstract. A comprehensive evaluation of the impacts of climate change on water resources of the West Africa Volta River basin is conducted in this study, as the region is expected to be hardest hit by global warming. A large ensemble of twelve general circulation models (GCM) from CMIP5 that are dynamically downscaled by five regional climate models (RCM) from CORDEX-Africa is used. In total, 43 RCM-GCM combinations are considered under three representative concentration pathways (RCP2.6, RCP4.5 and RCP8.5). The reliability of each of the climate datasets is first evaluated with satellite and reanalysis reference datasets. Subsequently, the Rank Resampling for Distributions and Dependences (R2D2) multivariate bias correction method is applied to the climate datasets. The corrected simulations are then used as input to the fully distributed mesoscale Hydrologic Model (mHM) for hydrological projections over the twenty-first century (1991–2100). Results reveal contrasting changes in the seasonality of rainfall depending on the selected greenhouse gas emission scenarios and the future projection periods. Although air temperature and potential evaporation increase under all RCPs, an increase in the magnitude of all hydrological variables (actual evaporation, total runoff, groundwater recharge, soil moisture and terrestrial water storage) is only projected under RCP8.5. High and low flow analysis suggests an increased flood risk under RCP8.5, particularly in the Black Volta, while hydrological droughts would be recurrent under RCP2.6 and RCP4.5, particularly in the White Volta. Disparities are observed in the spatial patterns of hydroclimatic variables across climatic zones, with higher warming in the Sahelian zone. Therefore, climate change would have severe implications for future water availability with concerns for rain-fed agriculture, thereby weakening the water-energy-food security nexus and amplifying the vulnerability of the local population. The variability between climate models highlights uncertainties in the projections and indicates a need to better represent complex climate features in regional models. These findings could serve as a guideline for both the scientific community to improve climate change projections and for decision makers to elaborate adaptation and mitigation strategies to cope with the consequences of climate change and strengthen regional socio-economic development.

scholarly journals Hydrologic Model Evaluation and Assessment of Projected Climate Change Impacts Using Bias-Corrected Stream Flows

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2312
Author(s):  
Joseph A. Daraio
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Stream Flow ◽  
General Circulation ◽  
Model Performance ◽  
General Circulation Models ◽  
Hydrologic Model ◽  
Historical Period ◽  
Hydrologic Models ◽  
Stream Flows

Hydrologic models driven by downscaled meteorologic data from general circulation models (GCM) should be evaluated using long-term simulations over a historical period. However, simulations driven by GCM data cannot be directly evaluated using observed flows, and the confidence in the results can be relatively low. The objectives of this paper were to bias correct simulated stream flows from calibrated hydrologic models for two basins in New Jersey, USA, and evaluate model performance in comparison to uncorrected simulations. Then, we used stream flow bias correction and flow duration curves (FDCs) to evaluate and assess simulations driven by statistically downscaled GCMs for the historical period and the future time slices 2041–2070 and 2071–2099. Bias correction of stream flow from simulations increased confidence in the performance of two previously calibrated hydrologic models. Results indicated there was no difference in projected FDCs for uncorrected and bias-corrected flows in one basin, while this was not the case in the second basin. This result provided greater confidence in projected stream flow changes in the former basin and implied more uncertainty in projected stream flows in the latter. Applications in water resources can use the methods described to evaluate the performance of GCM-driven simulations and assess the potential impacts of climate change with an appropriate level of confidence in the model results.

scholarly journals Physically based modeling of rainfall-triggered landslides: a case study in the Luquillo Forest, Puerto Rico

2013 ◽  
Vol 10 (1) ◽  
pp. 1333-1373 ◽  
Author(s):  
C. Lepore ◽  
E. Arnone ◽  
L. V. Noto ◽  
G. Sivandran ◽  
R. L. Bras
Keyword(s):  
Soil Moisture ◽  
Soil Column ◽  
Hydrologic Model ◽  
Richards Equation ◽  
Landslide Risk ◽  
Modeling Framework ◽  
Soil Moisture Dynamics ◽  
Physically Based ◽  
Spatially Distributed ◽  
Moisture Dynamics

Abstract. This paper presents the development of a rainfall-triggered landslide module within a physically based spatially distributed ecohydrologic model. The model, Triangulated Irregular Networks Real-time Integrated Basin Simulator and VEGetation Generator for Interactive Evolution (tRIBS-VEGGIE), is capable of a sophisticated description of many hydrological processes; in particular, the soil moisture dynamics is resolved at a temporal and spatial resolution required to examine the triggering mechanisms of rainfall-induced landslides. The validity of the tRIBS-VEGGIE model to a tropical environment is shown with an evaluation of its performance against direct observations made within the Luquillo Forest (the study area). The newly developed landslide module builds upon the previous version of the tRIBS landslide component. This new module utilizes a numerical solution to the Richards equation to better represent the time evolution of soil moisture transport through the soil column. Moreover, the new landslide module utilizes an extended formulation of the Factor of Safety (FS) to correctly quantify the role of matric suction in slope stability and to account for unsaturated conditions in the evaluation of FS. The new modeling framework couples the capabilities of the detailed hydrologic model to describe soil moisture dynamics with the Infinite Slope model creating a powerful tool for the assessment of landslide risk.

scholarly journals Modelling streamflow response to climate change in data-scarce White Volta River basin of West Africa using a semi-distributed hydrologic model

2018 ◽  
Vol 10 (4) ◽  
pp. 907-930 ◽  
Author(s):  
Sulemana Abubakari ◽  
Xiaohua Dong ◽  
Bob Su ◽  
Xiaonong Hu ◽  
Ji Liu ◽  
...  
Keyword(s):  
Climate Change ◽  
West Africa ◽  
General Circulation ◽  
Management Practices ◽  
Evaluation Criteria ◽  
General Circulation Models ◽  
Hydrologic Model ◽  
Volta Basin ◽  
Monthly Streamflow ◽  
The Future

Abstract This study uses high resolution Climate Forecast System Reanalysis (CFSR), SWAT and two IPCC climate change (CC) scenarios (A1B and B1) combined with two general circulation models (GCMs) (HADCM3 and MPEH5) to evaluate impact of CC on streamflow in the White Volta basin of West Africa. The evaluation criteria (R2 and NSE &gt; 0.70 and PBIAS within ±25%) during calibration and validation showed good simulation of the basin hydrology. Using average streamflow from 1979 to 2008 as a baseline, there were uncertainties over the sign of variation of annual streamflow in the 2020s. Annually, streamflow change is projected to be within −4.00% to +13.00% in the 2020s and +3.00% to +16.00% in the 2050s. Monthly streamflow changes for most months vary between −13.00% and +32.00%. A shift in monthly maximum streamflow from September to August is projected, while the driest months (December, January and February) show no change in the future. Based on the model results, the White Volta basin will likely experience an increase in streamflow by the mid-21st century. This would call for appropriate investment into cost-effective adaptive water management practices to cater for the likely impact of CC on the future hydrology of the basin.

scholarly journals Quantification of hydrologic impacts of climate change in a Mediterranean basin in Sardinia, Italy, through high-resolution simulations

2014 ◽  
Vol 18 (12) ◽  
pp. 5201-5217 ◽  
Author(s):  
M. Piras ◽  
G. Mascaro ◽  
R. Deidda ◽  
E. R. Vivoni
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Annual Runoff ◽  
Hydrologic Model ◽  
Future Climate ◽  
Future Climate Change ◽  
Runoff Generation ◽  
Hydrologic Impacts ◽  
Generation Mechanisms ◽  
Impacts Of Climate Change

Abstract. Future climate projections robustly indicate that the Mediterranean region will experience a significant decrease of mean annual precipitation and an increase in temperature. These changes are expected to seriously affect the hydrologic regime, with a limitation of water availability and an intensification of hydrologic extremes, and to negatively impact local economies. In this study, we quantify the hydrologic impacts of climate change in the Rio Mannu basin (RMB), an agricultural watershed of 472.5 km2 in Sardinia, Italy. To simulate the wide range of runoff generation mechanisms typical of Mediterranean basins, we adopted a physically based, distributed hydrologic model. The high-resolution forcings in reference and future conditions (30-year records for each period) were provided by four combinations of global and regional climate models, bias-corrected and downscaled in space and time (from ~25 km, 24 h to 5 km, 1 h) through statistical tools. The analysis of the hydrologic model outputs indicates that the RMB is expected to be severely impacted by future climate change. The range of simulations consistently predict (i) a significant diminution of mean annual runoff at the basin outlet, mainly due to a decreasing contribution of the runoff generation mechanisms depending on water available in the soil; (ii) modest variations in mean annual runoff and intensification of mean annual discharge maxima in flatter sub-basins with clay and loamy soils, likely due to a higher occurrence of infiltration excess runoff; (iii) reduction of soil water content and actual evapotranspiration in most areas of the basin; and (iv) a drop in the groundwater table. Results of this study are useful to support the adoption of adaptive strategies for management and planning of agricultural activities and water resources in the region.

scholarly journals Evaluation of impact of climate change and anthropogenic change on regional hydrology

2018 ◽  
Author(s):  
Seungwoo Chang ◽  
Wendy Graham ◽  
Jeffrey Geurink ◽  
Nisai Wanakule ◽  
Tirusew Asefa
Keyword(s):  
Climate Change ◽  
Water Use ◽  
Groundwater Level ◽  
General Circulation ◽  
General Circulation Models ◽  
Hydrologic Model ◽  
Tampa Bay ◽  
Future Climate ◽  
Future Climate Change ◽  
Groundwater Levels

Abstract. General circulation models (GCMs) have been widely used to simulate current and future climate at the global scale. However, the development of frameworks to apply GCMs to assess potential climate change impacts on regional hydrologic systems and compliance with water resource regulations is more recent. It is important to predict potential impacts of future climate change on streamflows and groundwater levels to reduce risks and increase resilience in water resources management and planning. This study evaluated future streamflows and groundwater levels in the Tampa Bay region in west-central Florida using an ensemble of different GCMs, reference evapotranspiration (ET0) methods, and water use scenarios to drive an integrated hydrologic model (IHM). Eight GCMs were bias-corrected and downscaled using the Bias Correction and Stochastic Analog (BCSA) downscaling method and then used, together with three ET0 methods, to drive the IHM for eight different human water use scenarios. Results showed that changes in projected streamflow were most sensitive to GCM selection, however, projections of groundwater level change were sensitive to both GCM and water use scenario. Projected changes in streamflow and groundwater level were relatively insensitive to the ET0 methods evaluated in this study. Six of eight GCMs projected a decrease in streamflow and groundwater level in the future regardless of water use scenario or ET method. These results indicate a high probability of a reduction in future water supply in the Tampa Bay region if environmental regulations intended to protect current aquatic ecosystems do not adapt to the changing climate.

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