scholarly journals Diel streamflow cycles suggest more sensitive snowmelt-driven streamflow to climate change than land surface modeling

2021 ◽  
Author(s):  
Sebastian A. Krogh ◽  
Lucia Scaff ◽  
Gary Sterle ◽  
James Kirchner ◽  
Beatrice Gordon ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Detection Methods ◽  
Land Surface Modeling ◽  
Western Us ◽  
Observational Technique ◽  
Physically Based ◽  
Late 21St Century ◽  
Physically Based Models ◽  
Time Substitution

Abstract. Climate warming may cause mountain snowpacks to melt earlier, reducing summer streamflow and threatening water supplies and ecosystems. Few observations allow separating rain and snowmelt contributions to streamflow, so physically based models are needed for hydrological predictions and analyses. We develop an observational technique for detecting streamflow responses to snowmelt using incoming solar radiation and diel (daily) cycles of streamflow. We measure the 20th percentile of snowmelt days (DOS20), across 31 watersheds in the western US, as a proxy for the beginning of snowmelt-initiated streamflow. Historic DOS20 varies from mid-January to late May, with warmer sites having earlier and more intermittent snowmelt-mediated streamflow. Mean annual DOS20 strongly correlates with the dates of 25 % and 50 % annual streamflow volume (DOQ25 and DOQ50, both R2 = 0.85), suggesting that a one-day earlier DOS20 corresponds with a one-day earlier DOQ25 and 0.7-day earlier DOQ50. Empirical projections of future DOS20 (RCP8.5, late 21st century), using space-for-time substitution, show that DOS20 will occur 11 ± 4 days earlier per 1 °C of warming, and that colder places (mean November–February air temperature, TNDJF <−8 °C) are 70 % more sensitive to climate change on average than warmer places (TNDJF > 0 °C). Moreover, empirical space-for-time based projections of DOQ25 and DOQ50 are about four and two times more sensitive to earlier streamflow than those from NoahMP-WRF. Given the importance of changing streamflow timing for headwater resources, snowmelt detection methods such as DOS20 based on diel streamflow cycles may constrain hydrological models and improve hydrological predictions.

Upscaling runoff and evapotranspiration fluxes in the Little Washita watershed using physically-based hillslope models

2020 ◽  
Author(s):  
Fanny Picourlat ◽  
Emmanuel Mouche ◽  
Claude Mugler
Keyword(s):  
Land Surface ◽  
Reference Model ◽  
Drainage Density ◽  
Equivalent Model ◽  
Distributed Model ◽  
Climatic Data ◽  
Good Prediction ◽  
Surface Models ◽  
Physically Based ◽  
Physically Based Models

&lt;p&gt;Several authors in the literature, such as Khan (2014) and Loritz (2017), have previously suggested that 3D catchment hydrology can be predicted from 2D hillslope simulations. Following this idea, we propose an upscaling methodology for runoff and evapotranspiration fluxes. The first step consists of a geomorphic analysis of the studied watershed. The average mean slope and hillslope length are then used to build a 2D equivalent-hillslope model. The validity of the methodology is tested by comparing the resulting water balance with a 3D physically-based distributed model. 2D fluxes of the equivalent hillslope are converted into 3D by using the drainage density. This upscaling methodology is applied to the Little Washita (LW) watershed (Oklahoma, USA). Both the 3D reference model and the 2D equivalent model are built with the physically-based distributed code HydroGeoSphere, which is forced by LW reanalysis climatic data. Two decades are simulated. Regarding the evapotranspiration, the upscaling methodology with only one equivalent hillslope gives a good prediction of 3D fluxes. However, a combination of several hillslopes is needed for simulating the 3D flow rate at the basin&amp;#8217;s outlet. This work on the decrease of model dimensionality is a first step in the upscaling process from 3D physically-based models to 1D column models used in global Land Surface Models.&lt;/p&gt;

scholarly journals Scale issues in soil moisture modelling: problems and prospects

1996 ◽  
Vol 20 (3) ◽  
pp. 273-291 ◽  
Author(s):  
Rezaul Mahmood
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Characterization ◽  
Large Scale ◽  
Small Scale ◽  
Soil Moisture Storage ◽  
Surface Atmosphere ◽  
Physically Based ◽  
Physically Based Models ◽  
Moisture Storage

Soil moisture storage is an important component of the hydrological cycle and plays a key role in land-surface-atmosphere interaction. The soil-moisture storage equation in this study considers precipitation as an input and soil moisture as a residual term for runoff and evapotranspiration. A number of models have been developed to estimate soil moisture storage and the components of the soil-moisture storage equation. A detailed discussion of the impli cation of the scale of application of these models reports that it is not possible to extrapolate processes and their estimates from the small to the large scale. It is also noted that physically based models for small-scale applications are sufficiently detailed to reproduce land-surface- atmosphere interactions. On the other hand, models for large-scale applications oversimplify the processes. Recently developed physically based models for large-scale applications can only be applied to limited uses because of data restrictions and the problems associated with land surface characterization. It is reported that remote sensing can play an important role in over coming the problems related to the unavailability of data and the land surface characterization of large-scale applications of these physically based models when estimating soil moisture storage.

scholarly journals Flood4castRTF: A Real-Time Urban Flood Forecasting Model

2021 ◽  
Vol 13 (10) ◽  
pp. 5651
Author(s):  
Michel Craninx ◽  
Koen Hilgersom ◽  
Jef Dams ◽  
Guido Vaes ◽  
Thomas Danckaert ◽  
...  
Keyword(s):  
Climate Change ◽  
Real Time ◽  
Model Building ◽  
Change Scenario ◽  
Climate Change Scenarios ◽  
Urban Flood ◽  
Flood Modelling ◽  
Physically Based ◽  
Physically Based Models ◽  
Modelling Approach

Worldwide, climate change increases the frequency and intensity of heavy rainstorms. The increasing severity of consequent floods has major socio-economic impacts, especially in urban environments. Urban flood modelling supports the assessment of these impacts, both in current climate conditions and for forecasted climate change scenarios. Over the past decade, model frameworks that allow flood modelling in real-time have been gaining widespread popularity. Flood4castRTF is a novel urban flood model that applies a grid-based approach at a modelling scale coarser than most recent detailed physically based models. Automatic model set-up based on commonly available GIS data facilitates quick model building in contrast with detailed physically based models. The coarser grid scale applied in Flood4castRTF pursues a better agreement with the resolution of the forcing rainfall data and allows speeding up of the calculations. The modelling approach conceptualises cell-to-cell interactions while at the same time maintaining relevant and interpretable physical descriptions of flow drivers and resistances. A case study comparison of Flood4castRTF results with flood results from two detailed models shows that detailed models do not necessarily outperform the accuracy of Flood4castRTF with flooded areas in-between the two detailed models. A successful model application for a high climate change scenario is demonstrated. The reduced data need, consisting mainly of widely available data, makes the presented modelling approach applicable in data scarce regions with no terrain inventories. Moreover, the method is cost effective for applications which do not require detailed physically based modelling.

scholarly journals A Method for Extracting Some Key Terrain Features from Shaded Relief of Digital Terrain Models

2020 ◽  
Vol 12 (17) ◽  
pp. 2809
Author(s):  
Meirman Syzdykbayev ◽  
Bobak Karimi ◽  
Hassan A. Karimi
Keyword(s):  
Land Surface ◽  
Basic Research ◽  
Probabilistic Methods ◽  
Detection Methods ◽  
Computational Techniques ◽  
Surface Evolution ◽  
Dem Analysis ◽  
High Degree ◽  
Terrain Features ◽  
Shaded Relief

Detection of terrain features (ridges, spurs, cliffs, and peaks) is a basic research topic in digital elevation model (DEM) analysis and is essential for learning about factors that influence terrain surfaces, such as geologic structures and geomorphologic processes. Detection of terrain features based on general geomorphometry is challenging and has a high degree of uncertainty, mostly due to a variety of controlling factors on surface evolution in different regions. Currently, there are different computational techniques for obtaining detailed information about terrain features using DEM analysis. One of the most common techniques is numerically identifying or classifying terrain elements where regional topologies of the land surface are constructed by using DEMs or by combining derivatives of DEM. The main drawbacks of these techniques are that they cannot differentiate between ridges, spurs, and cliffs, or result in a high degree of false positives when detecting spur lines. In this paper, we propose a new method for automatically detecting terrain features such as ridges, spurs, cliffs, and peaks, using shaded relief by controlling altitude and azimuth of illumination sources on both smooth and rough surfaces. In our proposed method, we use edge detection filters based on azimuth angle on shaded relief to identify specific terrain features. Results show that the proposed method performs similar to or in some cases better (when detecting spurs than current terrain features detection methods, such as geomorphon, curvature, and probabilistic methods.

scholarly journals 2 °C vs. High Warming: Transitions to Flood-Generating Mechanisms across Canada

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1494
Author(s):  
Bernardo Teufel ◽  
Laxmi Sushama
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Climate Model ◽  
Regional Climate ◽  
Adaptation Measures ◽  
Relative Contribution ◽  
Late 21St Century ◽  
Transient Climate Change ◽  
Projected Changes ◽  
Change Mitigation

Fluvial flooding in Canada is often snowmelt-driven, thus occurs mostly in spring, and has caused billions of dollars in damage in the past decade alone. In a warmer climate, increasing rainfall and changing snowmelt rates could lead to significant shifts in flood-generating mechanisms. Here, projected changes to flood-generating mechanisms in terms of the relative contribution of snowmelt and rainfall are assessed across Canada, based on an ensemble of transient climate change simulations performed using a state-of-the-art regional climate model. Changes to flood-generating mechanisms are assessed for both a late 21st century, high warming (i.e., Representative Concentration Pathway 8.5) scenario, and in a 2 °C global warming context. Under 2 °C of global warming, the relative contribution of snowmelt and rainfall to streamflow peaks is projected to remain close to that of the current climate, despite slightly increased rainfall contribution. In contrast, a high warming scenario leads to widespread increases in rainfall contribution and the emergence of hotspots of change in currently snowmelt-dominated regions across Canada. In addition, several regions in southern Canada would be projected to become rainfall dominated. These contrasting projections highlight the importance of climate change mitigation, as remaining below the 2 °C global warming threshold can avoid large changes over most regions, implying a low likelihood that expensive flood adaptation measures would be necessary.

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