Simulating Hydrologic Effects of Wildfire on a Small Sub-Alpine Watershed in New Mexico, U.S.

2021 ◽  
Vol 64 (1) ◽  
pp. 137-150
Author(s):  
C. David Moeser ◽  
Kyle R. Douglas-Mankin
Keyword(s):  
Soil Moisture ◽  
New Mexico ◽  
Soil Water ◽  
Surface Runoff ◽  
Hydrologic Model ◽  
Soil Water Storage ◽  
List Type ◽  
Watershed Model ◽  
Antecedent Soil Moisture ◽  
Runoff Change

HighlightsThis study calibrated a hydrologic model for pre- and postfire conditions and simulated postfire hydrologic response.Postfire rainfall-runoff was more influenced by canopy and soil water factors and less by antecedent soil moisture (ASM).For moderate to low ASM, postfire streamflow responded linearly to precipitation; prefire showed little response.Postfire streamflow increased and shifted from baseflow- to runoff-dominated, and runoff occurred across all ASM.Abstract. Streamflow records available before and after wildfire in a small, mixed conifer, sub-alpine monsoonal dominated watershed in New Mexico provided a unique opportunity to calibrate a watershed model (PRMS) for pre- and postfire conditions. The calibrated model was then used to simulate the hydrologic effects of fire. Simulated postfire surface runoff averaged 14.7 times greater than prefire for the 29-year simulation period. The relationship between precipitation and streamflow changed dramatically after wildfire, largely from a decreased influence of antecedent soil moisture (ASM) and increased influence of canopy factors (less interception) and soil factors (greater hydrophobicity, less infiltration) in controlling surface runoff. For higher ASM, simulated pre- and postfire streamflow was similarly variable. However, for moderate and lower ASM, soil water storage was too low to contribute baseflow for either prefire or postfire conditions, and thus postfire streamflow maintained a linear, surface runoff-dominated response to precipitation, whereas prefire streamflow showed little response. Postfire streamflow efficiency increased with ASM from a mean of 0.02 at the lowest ASM to 0.30 at the highest ASM, whereas prefire conditions showed no sensitivity to ASM at low to moderate ASM. Postfire streamflow increased (2.1 times greater median flow than prefire), particularly from increased surface runoff (14.7 times greater), which occurred across all ASM conditions. As a result, streamflow shifted from baseflow-dominated to surface runoff-dominated after wildfire. This result indicates that substantial increases in runoff efficiency (20% or more of precipitation volume) can occur across a range of ASM postfire, which may have severe consequences for flooding. This result also indicates that monitoring of soil moisture would enhance raingauge networks for early flood warning. Keywords: Landscape change, PRMS, Runoff change, Wildfire.

scholarly journals A new data assimilation approach for improving runoff prediction using remotely-sensed soil moisture retrievals

2009 ◽  
Vol 13 (1) ◽  
pp. 1-16 ◽  
Author(s):  
W. T. Crow ◽  
D. Ryu
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Surface Runoff ◽  
Surface Soil ◽  
Remotely Sensed ◽  
Hydrologic Model ◽  
Surface Soil Moisture ◽  
Antecedent Soil Moisture ◽  
Runoff Prediction ◽  
Moisture Conditions

Abstract. A number of recent studies have focused on enhancing runoff prediction via the assimilation of remotely-sensed surface soil moisture retrievals into a hydrologic model. The majority of these approaches have viewed the problem from purely a state or parameter estimation perspective in which remotely-sensed soil moisture estimates are assimilated to improve the characterization of pre-storm soil moisture conditions in a hydrologic model, and consequently, its simulation of runoff response to subsequent rainfall. However, recent work has demonstrated that soil moisture retrievals can also be used to filter errors present in satellite-based rainfall accumulation products. This result implies that soil moisture retrievals have potential benefit for characterizing both antecedent moisture conditions (required to estimate sub-surface flow intensities and subsequent surface runoff efficiencies) and storm-scale rainfall totals (required to estimate the total surface runoff volume). In response, this work presents a new sequential data assimilation system that exploits remotely-sensed surface soil moisture retrievals to simultaneously improve estimates of both pre-storm soil moisture conditions and storm-scale rainfall accumulations. Preliminary testing of the system, via a synthetic twin data assimilation experiment based on the Sacramento hydrologic model and data collected from the Model Parameterization Experiment, suggests that the new approach is more efficient at improving stream flow predictions than data assimilation techniques focusing solely on the constraint of antecedent soil moisture conditions.

scholarly journals A new data assimilation approach for improving hydrologic prediction using remotely-sensed soil moisture retrievals

2008 ◽  
Vol 5 (4) ◽  
pp. 2005-2044 ◽  
Author(s):  
W. T. Crow ◽  
D. Ryu
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Surface Runoff ◽  
Surface Soil ◽  
Remotely Sensed ◽  
Hydrologic Model ◽  
Surface Soil Moisture ◽  
Antecedent Soil Moisture ◽  
Hydrologic Prediction ◽  
Moisture Conditions

Abstract. A number of recent studies have focused on enhancing hydrologic prediction via the assimilation of remotely-sensed surface soil moisture retrievals into a hydrologic model. The majority of these approaches have viewed the problem from purely a state or parameter estimation perspective in which remotely-sensed soil moisture estimates are assimilated to improve the characterization of pre-storm soil moisture conditions in a hydrologic model, and consequently, its simulation of runoff response to subsequent rainfall. However, recent work has demonstrated that soil moisture retrievals can also be used to filter errors present in satellite-based rainfall accumulation products. This result implies that soil moisture retrievals have potential benefit for characterizing both antecedent moisture conditions (required to estimate sub-surface flow intensities and subsequent surface runoff efficiencies) and storm-scale rainfall totals (required to estimate the total surface runoff volume). In response, this work presents a new sequential data assimilation system that exploits remotely-sensed surface soil moisture retrievals to simultaneously improve estimates of both pre-storm soil moisture conditions and storm-scale rainfall accumulations. Preliminary testing of the system, via a synthetic twin data assimilation experiment based on the Sacramento hydrologic model and data collected from the Model Parameterization Experiment, suggests that the new approach is more efficient at improving stream flow predictions than data assimilation techniques focusing exclusively on the constraint of antecedent soil moisture conditions.

scholarly journals Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 37
Author(s):  
Tomás de Figueiredo ◽  
Ana Caroline Royer ◽  
Felícia Fonseca ◽  
Fabiana Costa de Araújo Schütz ◽  
Zulimar Hernández
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Regression Models ◽  
Meteorological Data ◽  
Water Storage ◽  
Model Performance ◽  
Soil Water Balance ◽  
Soil Water Storage ◽  
The Relationship

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

scholarly journals On the scaling characteristics of observed and simulated spatial soil moisture fields

2009 ◽  
Vol 16 (1) ◽  
pp. 141-150 ◽  
Author(s):  
M. Gebremichael ◽  
R. Rigon ◽  
G. Bertoldi ◽  
T. M. Over
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Spatial Correlation ◽  
Hydrologic Model ◽  
Invariance Property ◽  
Spatial Average ◽  
Hydrologic Models ◽  
Model Simulations

Abstract. By providing continuous high-resolution simulations of soil moisture fields, distributed hydrologic models could be powerful tools to advance the scientific community's understanding of the space-time variability and scaling characteristics of soil moisture fields. However, in order to use the soil moisture simulations from hydrologic models with confidence, it is important to understand whether the models are able to represent in a reliable way the processes regulating soil moisture variability. In this study, a comparison of the scaling characteristics of spatial soil moisture fields derived from a set of microwave radiometer observations from the Southern Great Plains 1997 experiment and corresponding simulations using the distributed hydrologic model GEOtop is performed through the use of generalized variograms. Microwave observations and model simulations are in agreement with respect to suggesting the existence of a scale-invariance property in the variograms of spatial soil moisture fields, and indicating that the scaling characteristics vary with changes in the spatial average soil water content. However, observations and simulations give contradictory results regarding the relationship between the scaling parameters (i.e. spatial organization) and average soil water content. The drying process increased the spatial correlation of the microwave observations at both short and long separation distances while increasing the rate of decay of correlation with distance. The effect of drying on the spatial correlation of the model simulations was more complex, depending on the storm and the simulation examined, but for the largest storm in the simulation most similar to the observations, drying increased the long-range correlation but decreased the short-range. This is an indication that model simulations, while reproducing correctly the total streamflow at the outlet of the watershed, may not accurately reproduce the runoff production mechanisms. Consideration of the scaling characteristics of spatial soil moisture fields can therefore serve as a more intensive means for validating distributed hydrologic models, compared to the traditional approach of only comparing the streamflow hydrographs.

scholarly journals Subsoiling and Sowing Time Influence Soil Water Content, Nitrogen Translocation and Yield of Dryland Winter Wheat

Agronomy ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 37 ◽  
Author(s):  
Yan Liang ◽  
Shahbaz Khan ◽  
Ai-xia Ren ◽  
Wen Lin ◽  
Sumera Anwar ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Winter Wheat ◽  
Grain Yield ◽  
Soil Water ◽  
Loess Plateau ◽  
Water Storage ◽  
Soil Water Storage ◽  
Yield Reduction ◽  
Sowing Time ◽  
N Accumulation

Dryland winter wheat in the Loess Plateau is facing a yield reduction due to a shortage of soil moisture and delayed sowing time. The field experiment was conducted at Loess Plateau in Shanxi, China from 2012 to 2015, to study the effect of subsoiling and conventional tillage and different sowing dates on the soil water storage, Nitrogen (N) accumulation, and remobilization and yield of winter wheat. The results showed that subsoiling significantly improved the soil water storage (0–300 cm soil depth) and increased the contribution of N translocation to grain N and grain yield (17–36%). Delaying sowing time had reduced the soil water storage at sowing and winter accumulated growing degree days by about 180 °C. The contribution of N translocation to grain yield was maximum in glume + spike followed by in leaves and minimum by stem + sheath. Moreover, there was a positive relationship between the N accumulation and translocation and the soil moisture in the 20–300 cm range. Subsoiling during the fallow period and the medium sowing date was beneficial for improving the soil water storage and increased the N translocation to grain, thereby increasing the yield of wheat, especially in a dry year.

scholarly journals Study on Variation of Surface Runoff and Soil Moisture Content in the Subgrade of Permeable Pavement Structure

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Lijun Hou ◽  
Yuan Wang ◽  
Fengchun Shen ◽  
Ming Lei ◽  
Xiang Wang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Water ◽  
Surface Runoff ◽  
Rainfall Intensity ◽  
Soil Moisture Content ◽  
Asphalt Pavement ◽  
Infiltration Rate ◽  
Runoff Coefficient ◽  
Flood Peak

The self-designed indoor simulated rainfall device was used to rain on five types of pavement structures with 4 types of rainfall intensity (2.5 mm/min, 3.4 mm/min, 4.6 mm/min, and 5.5 mm/min). The effect of rainfall intensity on the surface runoff, the relation between the subgrade soil moisture content changes, and the influence of initial soil water content on rain infiltration rate are studied. The test results show that the surface runoff coefficient of densely asphalted pavement is greater than 90% in drainage pavements and it has little influence on the reducing and hysteresis of the flood peak. The surface runoff coefficient of large-void asphalt pavement (permeable) is less than 40%. Although the large-void asphalt pavement (permeable) can reduce a small amount of surface runoff, it has no obvious effect on the reduction and hysteresis of the flood peak. In semipermeable pavement, with the increasing of the thickness of base (graded gravel), the surface runoff coefficient decreases at different rainfall intensities, parts of the surface runoff are reduced, and the arrival of flood peaks is delayed. In permeable roads, almost no surface runoff occurred. As time continued, the soil moisture content quickly reached a saturated state and presented a stable infiltration situation under the action of gravity and the gradient of soil water suction. As the initial moisture content increases, the initial infiltration rate decreases and the time to reach a stable infiltration rate becomes shorter. The drier the soil, the greater the initial infiltration rate and the higher the soil moisture content after infiltration stabilization. Permeable roads can greatly alleviate the pressure of urban drainage and reduce the risk of storms and floods.

The effect of soil moisture extremes on the pathways and forms of phosphorus lost in runoff from two contrasting soil types

Soil Research ◽  
2017 ◽  
Vol 55 (1) ◽  
pp. 19 ◽  
Author(s):  
B. Simmonds ◽  
R. W. McDowell ◽  
L. M. Condron
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Surface Runoff ◽  
Water Drop ◽  
Organic Soil ◽  
Surface Flow ◽  
Olsen P ◽  
Excess Surface ◽  
Infiltration Excess ◽  
Penetration Time

Soil moisture and Olsen P concentrations play an important role in phosphorus (P) losses in runoff. Under moisture-rich anaerobic conditions, the reduction of Fe-oxides dissolves P from the soil into solution that may be available for loss by transport processes. Under very dry conditions, soil hydrophobicity induced by soil organic C can exacerbate infiltration-excess surface flow and soil erosion. Our hypotheses were as follows: (1) rainfall applied to a dry soil would cause greater particulate P losses in surface runoff due to hydrophobicity; (2) P losses from a wet soil would be dominated by drainage and filtered P; and (3) both runoff processes would result in environmentally unacceptable P losses at agronomically productive Olsen P concentrations depending on the sorption capacity (anion storage capacity; ASC) of the soil. Superphosphate was added to a Brown and Organic soil (Olsen P initially 7 and 13mgL–1 respectively) to create a range of Olsen P concentrations. Soils were placed in boxes, soil moisture adjusted (<10% or 90% available water holding capacity) and artificial rainfall applied at a rate equivalent to a storm event (5-year return interval; 30–35mmh–1) and surface runoff and drainage collected. Surface runoff was measured as infiltration-excess surface flow from dry Organic soil (water drop penetration time >3600s), and as saturation-excess surface flow from the wet Brown soil (water drop penetration time <5s). Total P (TP) concentrations in surface flow from both soils increased linearly with Olsen P concentration. Compared with dry Organic soil, the wet Brown soil lost a greater proportion of TP as particulate via surface runoff. However, due to the high hydraulic conductivity and low ASC, the most important pathway for the Organic soil, wet or dry, was filtered P loss in drainage. These data can be used to more effectively target strategies to mitigate P losses.

Soil Moisture and Soil Water Storage Using Hydrological Modeling and Remote Sensing

2014 ◽  
pp. 307-345 ◽  
Author(s):  
Otto Corrêa Rotunno Filho ◽  
Afonso Augusto Magalhães de Araujo ◽  
Luciano Nóbrega Rodrigues Xavier ◽  
Daniel Medeiros Moreira ◽  
Rafael Carneiro Di Bello ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Soil Water ◽  
Hydrological Modeling ◽  
Water Storage ◽  
Soil Water Storage

scholarly journals Estimated soil water storage within a historical bunker during the growth period of vegetation

2018 ◽  
Vol 39 (1) ◽  
pp. 125-130 ◽  
Author(s):  
Łukasz Pardela ◽  
Tomasz Kowalczyk
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Water Retention ◽  
Growth Period ◽  
Vegetation Period ◽  
Soil Water Storage ◽  
Soil Profiles ◽  
Soil Water Retention ◽  
Average Value ◽  
Volumetric Soil Moisture

AbstractThe objective of the study was to estimate the variation of soil water retention on the site of a historical bunker, an element of the former Wrocław Fortress in Poland. Measurements of soil moisture in the study area were taken in the period from March to September, 2017. Measurements of volumetric soil moisture were taken by means of a hand-held gauge, type FOM/mts with an FP/mts probe, operating on the basis of the reflectometric technique TDR. Soil moisture measurements realized in the vegetation period demonstrated that soil moisture resources in profiles situated in the section of the bunker varied within the range of 37–135 mm in the layer of 50 cm, and 66–203 mm in the layer of 100 cm. The maximum differences of the average value of soil moisture of the soil profiles studied in the period covered by the measurements were 31 mm and 56 mm, respectively. This indicates a significant differentiation of the retention properties of soils used for the construction of individual shelter areas.

Elucidating soil moisture dynamics in agricultural landscapes under varying weather patterns

2021 ◽  
Author(s):  
Veronica Fritz ◽  
Thakshajini Thaasan ◽  
Andrew Williams ◽  
Ranjith Udawatta ◽  
Sidath Mendis ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Moisture ◽  
Soil Water ◽  
Water Cycle ◽  
Water Storage ◽  
Soil Water Storage ◽  
Storm Events ◽  
Weather Patterns ◽  
Land Use Types ◽  
Moisture Dynamics

&lt;p&gt;Changing weather patterns and anthropogenic land use change significantly alter the terrestrial water cycle. A key variable that modulates the water cycle on the land surface is soil moisture and its variability in time and space. Hydrological models are used to simulate key components of the water cycle including infiltration, soil storage and uptake by plants. However, uncertainties remain in accurately representing soil moisture dynamics in models. Here, with the aid of several sensors installed at a 30-ha experimental research facility, we attempt to quantify differences in soil water storage across multiple land use types &amp;#8211; cropped area, mosaic of turf grass and native plants, and an unkept weeded area as control land use. We will also discuss the accuracy of sensors to correctly measure soil water storage. Our study was conducted at an agricultural experimental station in Columbia, Missouri, USA. We use a variety of instruments to measure weather, evapotranspiration, and soil water. We used boundary layer scintillometers to measure near-surface turbulence, sensors to continuously track soil moisture and temperature, as well as weather stations for precipitation, air temperature, solar radiation and wind speed. &amp;#160;Changes in volumetric water content and soil temperature are measured at 5-minute intervals at 10-, 20-, and 40-cm soil depths to compare soil water storage among the three land use types. We also took soil samples before and after several storm events to calibrate the sensor readings at three sites. We, then, analyzed several storm events over a period of five months and compared the actual soil moisture and soil temperature dynamics at finer time intervals. With additional measurements of weather and boundary layer turbulence, we hope to reveal the landscape and weather control on soil moisture distribution across multiple land uses, and their subsequent impact on plant water uptake. Our preliminary results indicate that continuously disturbed agricultural lands depletes soil moisture at faster rates, which may present challenges in maintaining land productivity in the long term.&lt;/p&gt;

Sign in / Sign up

Export Citation Format

Share Document