Small soil storage capacity limits benefit of winter snowpack to upland vegetation

2011 ◽  
Vol 25 (25) ◽  
pp. 3858-3865 ◽  
Author(s):  
T. J. Smith ◽  
J. P. McNamara ◽  
A. N. Flores ◽  
M. M. Gribb ◽  
P. S. Aishlin ◽  
...  
Keyword(s):  
Storage Capacity ◽  
Capacity Limits ◽  
Soil Storage ◽  
Soil Storage Capacity

scholarly journals A distributed Grid-Xinanjiang model with integration of subgrid variability of soil storage capacity

2016 ◽  
Vol 9 (2) ◽  
pp. 97-105 ◽  
Author(s):  
Wei-jian Guo ◽  
Chuan-hai Wang ◽  
Teng-fei Ma ◽  
Xian-min Zeng ◽  
Hai Yang
Keyword(s):  
Storage Capacity ◽  
Xinanjiang Model ◽  
Soil Storage ◽  
Soil Storage Capacity

scholarly journals Exploring the role of soil storage capacity for explaining deviations from the Budyko curve using a simple water balance model

2021 ◽  
Author(s):  
Jan Bondy ◽  
Jan Wienhöfer ◽  
Laurent Pfister ◽  
Erwin Zehe
Keyword(s):  
Water Balance ◽  
Storage Capacity ◽  
Field Capacity ◽  
Water Balance Model ◽  
Balance Model ◽  
Joint Control ◽  
Soil Storage ◽  
Wide Range ◽  
Evaporation Ratio ◽  
Soil Storage Capacity

Abstract. The Budyko curve is a widely used framework for predicting the steady-state water balance –solely based on the hydro-climatic setting of river basins. While this framework has been tested and verified across a wide range of climates and settings around the globe, numerous catchments have been reported to considerably deviate from the predicted behavior. Here, we hypothesize that storage capacity and field capacity of the root zone are important controls of the water limitation of evapotranspiration and thus deviations of the mean annual water balance from the Budyko curve. For testing our hypothesis, we selected 16 catchments of different climatic settings and varied the corresponding parameters of a simple water balance model that was previously calibrated against long-term data and investigated the corresponding variations of the simulated water balance in the Budyko space. We found that total soil storage capacity –by controlling water availability and limitation of evapotranspiration– explains deviations of the evaporation ratio (EVR) from the Budyko curve. Similarly, however to a lesser extent, the evaporation ratio showed sensitivity to alterations of the field capacity. In most cases, the parameter variations generated evaporation ratios enveloping the Budyko curve. The distinct soil storage volumes that matched the Budyko curve clustered at a normalized storage capacity equivalent to 5–15 % of mean annual precipitation. The second, capillarity-related soil parameter clustered at around 0.6–0.8, which is in line with its hydropedological interpretation. A simultaneous variation of both parameters provided additional insights into the interrelation of both parameters and their joint control on offsets from the Budyko curve. Here we found three different sensitivity patterns and we conclude the study with a reflection relating these offsets to the concept of catchment coevolution. The results of this study could also be useful to facilitate evaluation of the water balance in data-scarce regions, as they help constrain parameterizations for hydrological models a priori using the Budyko curve as a predictor.

Lithiation mechanisms and lithium storage capacity of reduced graphene oxide nanoribbons: a first-principles study

2017 ◽  
Vol 5 (10) ◽  
pp. 4912-4922 ◽  
Author(s):  
Kun-Han Lin ◽  
Chin-Lung Kuo
Keyword(s):  
First Principles ◽  
Density Functional ◽  
Storage Capacity ◽  
Density Functional Theory Calculations ◽  
Lithium Storage ◽  
Functional Theory ◽  
Capacity Limits ◽  
Graphene Oxide Nanoribbons ◽  
New Findings ◽  
Reduced Graphene

New findings on the lithiation mechanisms and the achievable Li capacity limits of various types of functional groups on the basal plane and those terminating the edge sites of graphene nanomaterials based on first-principles density functional theory calculations.

scholarly journals A new probability density function for spatial distribution of soil water storage capacity leads to SCS curve number method

2018 ◽  
Author(s):  
Dingbao Wang
Keyword(s):  
Distribution Function ◽  
Storage Capacity ◽  
Water Storage ◽  
Soil Water Storage ◽  
Water Storage Capacity ◽  
Soil Storage ◽  
Soil Water Storage Capacity ◽  
Soil Wetting ◽  
Scs Cn ◽  
New Distribution

Abstract. Following the Budyko framework, soil wetting ratio (the ratio between soil wetting and precipitation) as a function of soil storage index (the ratio between soil wetting capacity and precipitation) is derived from the SCS-CN method and the VIC type of model. For the SCS-CN method, soil wetting ratio approaches one when soil storage index approaches infinity, due to the limitation of the SCS-CN method in which the initial soil moisture condition is not explicitly represented. However, for the VIC type of model, soil wetting ratio equals soil storage index when soil storage index is lower than a certain value, due to the finite upper bound of the power distribution function of storage capacity. In this paper, a new distribution function, supported on a semi-infinite interval x ∈ [0, ∞), is proposed for describing the spatial distribution of storage capacity. From this new distribution function, an equation is derived for the relationship between soil wetting ratio and storage index. In the derived equation, soil wetting ratio approaches zero as storage index approaches zero; when storage index tends to infinity, soil wetting ratio approaches a certain value (≤ 1) depending on the initial storage. Moreover, the derived equation leads to the exact SCS-CN method when initial water storage is zero. Therefore, the new distribution function for soil water storage capacity explains the SCS-CN method as a saturation excess runoff model and unifies the surface runoff modeling of SCS-CN method and VIC type of model.

Metatheory of storage capacity limits

2001 ◽  
Vol 24 (1) ◽  
pp. 154-176 ◽  
Author(s):  
Nelson Cowan
Keyword(s):  
Conceptual Framework ◽  
Storage Capacity ◽  
Future Research ◽  
Memory Representation ◽  
Capacity Limits ◽  
Capacity Limit ◽  
Memory Store

Commentators expressed a wide variety of views on whether there is a basic capacity limit of 3 to 5 chunks and, among those who believe in it, about why it occurs. In this response, I conclude that the capacity limit is real and that the concept is strengthened by additional evidence offered by a number of commentators. I consider various arguments why the limit occurs and try to organize these arguments into a conceptual framework or “metatheory” of storage capacity limits meant to be useful in future research to settle the issue. I suggest that principles of memory representation determine what parts of the representation will be most prominent but that limits of attention (or of a memory store that includes only items that have been most recently attended) determine the 3- to 5-chunk capacity limit.

scholarly journals Diagnosis toward predicting mean annual runoff in ungauged basins

2020 ◽  
Author(s):  
Yuan Gao ◽  
Lili Yao ◽  
Ni-Bin Chang ◽  
Dingbao Wang
Keyword(s):  
Soil Water ◽  
Storage Capacity ◽  
Water Storage ◽  
Annual Runoff ◽  
Balance Model ◽  
Soil Water Storage ◽  
Ungauged Basins ◽  
Water Storage Capacity ◽  
Soil Water Storage Capacity ◽  
Soil Storage Capacity

Abstract. The present work diagnoses the prediction in mean annual runoff affected by the uncertainty in estimated distribution of soil water storage capacity. Based on a distribution function, a water balance model for estimating mean annual runoff is developed, in which the effects of climate variability and the distribution of soil water storage capacity are explicitly represented. As such, the two parameters in the model have explicit physical meanings, and relationships between the parameters and controlling factors on mean annual runoff are established. The estimated parameters from the existing data of watershed characteristics are applied to 35 watersheds. The results showed that the model could capture 88.2 % of the actual runoff on average, indicating that the proposed new water balance model is promising for estimating mean annual runoff in ungauged watersheds. The underestimation of runoff is mainly caused by the underestimation of the spatial heterogeneity of soil storage capacity due to neglecting the effect of land surface and bedrock topography. A higher spatial variability of soil storage capacity estimated through the Height Above the Nearest Drainage (HAND) indicated that topography plays a crucial role in determining the actual soil water storage capacity. The performance of mean annual runoff prediction in ungauged basins can be improved by employing better estimation of soil water storage capacity including the effects of soil, topography and bedrock. The purpose of this study is to diagnose the data requirement for predicting mean annual runoff in ungauged basins based on a newly developed process-based model.

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