ABSTRACT:

Understanding the projected changes in annual maximum peak discharge is important to improve resiliency in water resource planning and design at the community level. Currently, much of the literature on climate change impacts focuses on analyses at the regional scale. In this study we use a hydrologic model to evaluate the projected changes in annual maximum peak discharge at the community-level across Iowa under two emission scenarios, Representative Concentration Pathway 4.5 and 8.5 (RCP4.5 and RCP8.5). We utilize climate forcings from global climate models part of the Coupled Model Intercomparison Projected Phase 5 (CMIP5) from 1950 to 2100. Our simulations show a detectable increase in annual maximum discharge for 70% of the Iowa communities under RCP8.5. However, under RCP4.5 only 2% of the communities are projected to face an increase in annual maximum discharge by the end of the 21st century; furthermore, precipitation intensity under this scenario is not projected to increase in the latter half of the 21st century. Our results point to a larger increase in precipitation intensity and annual maximum discharge under RCP8.5 compared to RCP4.5, especially in the second half of this century. This pattern is present in the changes in flood peak distributions, with changes detected earlier in the current century under RCP4.5. This study provides a basis for analyzing climate change impacts at a local decision-making scale.

This resource provides the codes and some data used for this analysis.

ABSTRACT:

This repository contains codes for a study titled "Evaluation of CMIP6 HighResMIP for hydrologic modeling of annual maximum discharge in Iowa" submitted to Water Resources Research (Article DOI: 10.1029/2022WR034166) by Alexander T. Michalek, Gabriele Villarini, Taereem Kim, Felipe Quintero, Witold F. Krajewski, and Enrico Scoccimarro.

The resources include R codes for data analysis, figures, and precipitation bias-correction and downscaling. Additionally, codes are provided related to the setup of the hydrologic model (HLM) utilized in the study and found at https://asynch.readthedocs.io/en/latest/index.html. Finally, a subset of data from the simulations is provided for which the analysis is conducted. Full simulation datasets and CMIP6 forcings are not provided as they are too large to store and can be provided upon reasonable request.

Abstract:
The High-Resolution Model Intercomparison Project (HighResMIP) experiments from the Coupled Model Intercomparison Phase 6 (CMIP6) represent a broad effort to improve the resolution, and performance of climate models. The HighResMIP suite provides high spatial resolution (i.e., 25- and 50-km) forcings that have been shown to improve the representation of climate processes. However, little is known about their suitability for hydrologic applications. We use outputs from the HighResMIP suite to simulate annual maximum discharge with the Hillslope-Link Model (HLM) at ~1000 river communities across Iowa. First, we assess whether the runoff from the climate models can be directly routed through the river network model in HLM to estimate annual maximum discharge. Runoff-based simulations can capture the empirical distribution of flood peaks in five of the ten models/members assessed. Next, we force the HLM with precipitation, temperature, and potential evapotranspiration from HighResMIP models to simulate flood peaks, finding all models/members produce empirical distributions similar to our reference. However, significant biases exist in the model/member forcings as correct flood response is being generated for the wrong reason. To improve their suitability for community-level assessment, we use nine statistical approaches to bias-correct and downscale HighResMIP precipitation to a 4-km resolution. The bias-correction and downscaling of climate model precipitation performs well for all models/members. Furthermore, we do not find significant changes in the magnitude flood peak projections for Iowa based on the HLM forced with HighResMIP outputs, or based on routed runoff, while there are indications that the variability in flood peaks is projected to increase across the state.

ABSTRACT:

Structural connectivity describes how landscapes facilitate the transfer of matter and plays a critical role in the flux of water, solutes, and sediment across the Earth’s surface. The strength of a landscape’s connectivity is a function of climatic and tectonic processes, but the importance of these drivers is poorly understood, particularly in the context of climate change. Here, we provide global estimates of structural connectivity at the hillslope level and develop a model to describe connectivity accounting for tectonic and climate processes. We find that connectivity is primarily controlled by tectonics with climate as a second order control. However, we show climate change is projected to alter global-scale connectivity at the end of the century (2070 to 2100) by up to 4% for increasing greenhouse gas emission scenarios. Notably, the Ganges River, the world’s most populated basin, is projected to experience a large increase in connectivity. Conversely, the Amazon River and the Pacific coast of Patagonia are projected to experience the largest decreases in connectivity. Modeling suggests that as the climate warms, it could lead to increased erosion in source areas, while decreased rainfall may hinder sediment flow downstream, affecting landscape connectivity that poses implications for human and environmental health.

ABSTRACT:

Understanding the projected changes in annual maximum peak discharge is important to improve resiliency in water resource planning and design at the community level. Currently, much of the literature on climate change impacts focuses on analyses at the regional scale. In this study we use a hydrologic model to evaluate the projected changes in annual maximum peak discharge at the community-level across Iowa under two emission scenarios, Representative Concentration Pathway 4.5 and 8.5 (RCP4.5 and RCP8.5). We utilize climate forcings from global climate models part of the Coupled Model Intercomparison Projected Phase 5 (CMIP5) from 1950 to 2100. Our simulations show a detectable increase in annual maximum discharge for 27% of Iowa’s communities under RCP8.5. However, under RCP4.5 none of the communities are projected to face an increase in annual maximum discharge by the end of the 21st century; furthermore, precipitation intensity under this scenario is not projected to increase in the latter half of the 21st century. Our results point to a larger increase in precipitation intensity and annual maximum discharge under RCP8.5 compared to RCP4.5, especially in the second half of this century. The projected flood peak distributions tend to become statistically different from the historical ones later in the 21st century under RCP8.5 than under RCP4.5. This study provides a basis for analyzing climate change impacts at a local decision-making scale.

This resource provides the codes and some data used for this analysis.

ABSTRACT:

The following is the code utilized to conduct the analysis for "Disentangling the sources of uncertainties in the projection of floods risks in the Central United States (Iowa)" located in Geophysical Research Letters.

All data used in this study is publicly available. Climate model data was downloaded from the WCRP Coupled Model Intercomparison Project (Phase 6) data portal found at https://esgf-node.llnl.gov/search/cmip6/. Information for the Hillslope Link Model can be obtained at https://asynch.readthedocs.io/en/latest/index.html. Hydrologic simulations results are available across the study site at https://iowafloodfrequency.iihr.uiowa.edu/.

Abstract:
Climate change projections are uncertain and what drives this uncertainty and how it propagates to flood impacts are not well understood. Here we explore the projected changes in flood impacts across Iowa (central United States) by forcing a hydrologic model with downscaled global climate model (GCM) outputs and four Shared Socioeconomic Pathways (SSP126, SSP245, SSP370, and SSP585). Our results point to projected increasing magnitude and variability in flooding across the state, especially for high-emission scenarios (i.e., SSP370 and SSP585). Moreover, we partition the flood impacts’ projections into (1) the response of the GCMs to anthropogenic forcing, (2) scenario uncertainty, and (3) internal climate variability. We find scenario uncertainty plays a small role, while model uncertainty and internal climate variability dominate the flood impacts’ projections, with the contribution of model uncertainty increasing towards the end of this century. Our results provide information about future flood impacts, as well as insights into the largest sources of uncertainty.

ABSTRACT:

This repository contains codes for a study titled "Evaluation of CMIP6 HighResMIP for hydrologic modeling of annual maximum discharge in Iowa" submitted to Water Resources Research (Article DOI: 10.1029/2022WR034166) by Alexander T. Michalek, Gabriele Villarini, Taereem Kim, Felipe Quintero, Witold F. Krajewski, and Enrico Scoccimarro.

The resources include R codes for data analysis, figures, and precipitation bias-correction and downscaling. Additionally, codes are provided related to the setup of the hydrologic model (HLM) utilized in the study and found at https://asynch.readthedocs.io/en/latest/index.html. Finally, a subset of data from the simulations is provided for which the analysis is conducted. Full simulation datasets and CMIP6 forcings are not provided as they are too large to store and can be provided upon reasonable request.

Abstract:
The High-Resolution Model Intercomparison Project (HighResMIP) experiments from the Coupled Model Intercomparison Phase 6 (CMIP6) represent a broad effort to improve the resolution, and performance of climate models. The HighResMIP suite provides high spatial resolution (i.e., 25- and 50-km) forcings that have been shown to improve the representation of climate processes. However, little is known about their suitability for hydrologic applications. We use outputs from the HighResMIP suite to simulate annual maximum discharge with the Hillslope-Link Model (HLM) at ~1000 river communities across Iowa. First, we assess whether the runoff from the climate models can be directly routed through the river network model in HLM to estimate annual maximum discharge. Runoff-based simulations can capture the empirical distribution of flood peaks in five of the ten models/members assessed. Next, we force the HLM with precipitation, temperature, and potential evapotranspiration from HighResMIP models to simulate flood peaks, finding all models/members produce empirical distributions similar to our reference. However, significant biases exist in the model/member forcings as correct flood response is being generated for the wrong reason. To improve their suitability for community-level assessment, we use nine statistical approaches to bias-correct and downscale HighResMIP precipitation to a 4-km resolution. The bias-correction and downscaling of climate model precipitation performs well for all models/members. Furthermore, we do not find significant changes in the magnitude flood peak projections for Iowa based on the HLM forced with HighResMIP outputs, or based on routed runoff, while there are indications that the variability in flood peaks is projected to increase across the state.

ABSTRACT:

This contains the data and codes for the study: "Understanding the impact of precipitation bias-correction and statistical downscaling methods on projected changes in flood extremes" by Michalek et. al. (2023). The code for the analysis is provided below. The file name provided the order of the steps taken for the analysis. Note any precipitation related files are not included as they are too large for Hydroshare. Abstract: This study evaluates five bias correction and statistical downscaling (BCSD) techniques for daily precipitation and examines their impacts on the projected changes in flood extremes (i.e., 1%, 0.5%, and 0.2% floods). We use climate model outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to conduct hydrologic simulations across watersheds in Iowa and determine historical and future flood extreme estimates based on generalized extreme value distribution fitting. Projected changes in these extremes are examined with respect to four Shared Socioeconomic Pathways (SSPs) alongside five BCSD techniques. We find the magnitude of future annual exceedance probability (AEPs) estimates are expected to increase for the future under all SSPs, especially for the emission scenarios with higher greenhouse gases concentrations (i.e., SSP370 and SSP585). Our results also suggest the choice of BCSD impacts the magnitude of the projected changes, with the SSPs that exert limited sensitivity compared to the choice of downscaling method. The variability in projected flood changes across Iowa is similar across the downscaling technique but increases as the AEP increases. Our findings provide insights into the impact of downscaling techniques on flood extremes’ projections and useful information for climate planning across the state.

ABSTRACT:

Understanding changes in the hazard component of climate risk is important to inform societal resilience planning under a changing climate. Here, we examine local changes in wind speed, rainfall, and flooding related to tropical cyclones (TCs) and compare them across statistical and dynamical modeling approaches. Our focus region is the Delaware River Basin, located in the northeastern United States. We pair event-based downscaling with large ensemble climate model information to capture the details of extreme TC wind, rain, and flooding, and their likelihood, in a changing climate. We identify local TCs in the Community Earth System Model 2 Large Ensemble (CESM2-LENS). We find fewer TCs in the future, but these future TCs have higher wind speeds and are wetter. We also find that TCs produce heavier 3-day precipitation distributions than all other summertime weather events, with TCs constituting a larger percentage of the upper tail of the full precipitation distribution. With this information, we identify a small collection of 200-year return events and compare the resulting TC rain and wind across dynamical and statistical downscaling methods. We find that dynamical downscaling produces peak rain rates far higher than CESM or statistical downscaling methods. It can also produce very different future changes in precipitation totals for the small set of events considered here. This leads to vastly different flood responses. Overall, our results highlight the need to interpret future changes of event-based simulations in the context of downscaling method limitations.

ABSTRACT:

This hydroshare provides the source code utilized for the model runs, calibration, input processing, data analysis and figure creation for the manuscript under review at JAWRA. The abstract of the manuscript is as follows: In this study, we evaluate the performance of the TETIS model structure of the Hillslope-Link Model (HLM), which is a distributed hydrologic model. We explore performance across the contiguous United States (CONUS) at 5046 United States Geological Survey (USGS) streamgages. Specifically, we compare observed daily discharge from 1981 through 2020 with long term continuous simulations from the HLM TETIS. To obtain model parameters across CONUS, we run calibration by partitioning the study area based on 234 HydroSHEDS level 5 basins and calibrating to a single representative location near the outlet of each basin. Next, we utilize the remaining USGS gages for validation. We assess the model performance with the Kling Gupta Efficiency (KGE) and bias. We find the median KGE across CONUS is 0.43, with 80% of the gages above 0 and 43% above 0.5. Furthermore, our results show there is a dependence of the model performance on climate regions, with arid basins performing worse than basins in cold and temperate regions. To improve the model performance, we recalibrate these arid basins and highlight an overall performance improvement. Next, we compare model performance between simulations with different precipitation inputs to examine the robustness of the selected model parameters. Overall, our study highlights the model’s flexibility in performing across regions with different runoff generation mechanisms and provides a basis for future.