Capacidade de Armazenamento de Água no Solo (CAD) e Características Físicas dos Solos na Avaliação da Distribuição da Água das Chuvas na Alta Bacia do Ribeirão Santo Antônio

O estudo avalia a capacidade de armazenamento de água dos solos (CAD), utiliza como objeto de avaliação os latossolos da alta bacia do ribeirão Santo Antônio no município de Iporá-Goiás. O objetivo do estudo foi avaliar a dinâmica da água da chuva na manutenção do fluxo de água na bacia. Utilizou de dados de umidade dos solos em período de déficit hídrico e de excedente para comparar o comportamento dos solos na retenção de água e na permeabilidade. Utilizou a metodologia padrão na definição do CAD, porém com ajustes específicos na obtenção da umidade e na capacidade de campo (CC) e no ponto de murcha permanente PMP, que aqui foi denominado de ponto de menor umidade residual (PMUR). Informações como dados de chuva do período, balanço hídrico climatológico no período analisado (maio de 2018 a abril de 2019) e valores de vazão obtidos mensalmente no exutório durante o período de referência fizeram parte da análise. Um total de 43,5% da água disponibilizada pela chuva escoam superficialmente, 9,5% escoam em subsuperfície, 47% da água infiltra e/ou permanece retida nos solos. Os solos apresentaram boa drenagem e, cerca de 42% da água das chuvas garantem a perenidade da bacia. Soil Water Storage Capacity (AWSC) and Physical Characteristics of Soils in the Evaluation of Rainwater Distribution in the High Basin of Santo Antônio Stream. A B S T R A C T The study evaluates the water storage capacity of soils (AWSC). It use like object of evaluation the oxissoils located at the high Santo Antônio basin. The objective of the study was to evaluate the dynamics of rainwater in maintaining the flow of water in the basin through laboratory evaluations, in addition to testing a new methodology to define the destinations of the water that reached the surface. Was used soil moisture data in a period of water deficit and surplus to compare the behavior of soils in water retention and permeability. It used the standard methodology in the definition of the AWSC but with specific adjustments in obtaining the humidity and in the field capacity (FC) and in the permanent wilting point PWP, which here was called the point of lowest residual moisture (PLRM). Information such as rainfall data for the period, climatological water balance in the period analyzed (May 2018 to April 2019) and flow values obtained monthly in the exutory during the reference period were part of the analysis. A total of 43.5% of the water provided by the rain run off superficially, 9.5% seeps in subsurface, 47% of the water seeps and / or remains trapped in the soil. The soils had good drainage and about 42% of the rainwater guarantees the basin's perpetuity. Keywords: AWSC. Permeability. Moisture. Storage.

Diagnosis toward predicting mean annual runoff in ungauged basins

Prediction of mean annual runoff is of great interest but still poses a challenge in ungauged basins. 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 mean annual runoff on average across the study watersheds, indicating that the proposed new water balance model is promising for estimating mean annual runoff in ungauged watersheds. The underestimation of mean annual runoff is mainly caused by the underestimation of the area percentage of low soil water storage capacity due to neglecting the effect of land surface and bedrock topography. Higher spatial variability of soil water storage capacity estimated through the height above the nearest drainage (HAND) and topographic wetness index (TWI) 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. It leads to better diagnosis of the data requirement for predicting mean annual runoff in ungauged basins based on a newly developed process-based model finally.

Multiple abiotic and biotic drivers of soil water storage capacity in temperate forests recovering from disturbances

Background and aims Soil water storage capacity acts as a vital forest function to intercept rainfall and retain water for plant growth processes. However, whether or how plant functional trait diversity and composition regulate soil water storage capacity remains poorly understood. Methods Structural equation modeling (SEM) was used to detect the direct and indirect effects of multiple biotic (i.e., functional trait composition and functional diversity) and abiotic (topography and soil organic carbon) factors on soil water storage capacity, i.e., in terms of soil capillary water storage content (CW), soil non-capillary water storage content (NCW), and soil saturated water storage content (TSW), in temperate forests recovering from different logging disturbance intensity levels. Results The community-weighted mean of specific leaf area (CWMSLA) increased CW but decreased NCW directly, whereas improved NCW and TSW indirectly via soil organic carbon. Disturbance influenced soil water storage capacity mainly in indirect ways via promoting CWMSLA and soil organic carbon. Elevation increased NCW and TSW but decreased CW directly, and it also had indirect effects on soil water storage capacity via decreasing CWMSLA and soil organic carbon. Moreover, soil organic carbon influenced NCW and TSW directly or mediated the effects of elevation, disturbance, and CWMSLA on soil water storage capacity. Conclusions The quick return on investments trait of CWMSLA shows a positive effect on soil water storage capacity (CW and TSW), supporting the mass ratio mechanism in temperate forests recovering from disturbances. Soil organic carbon also presents additional importance to soil water storage capacity.

Diagnosis toward predicting mean annual runoff in ungauged basins

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.

The hydrological response of soil water storage capacity to human activities: A case study in the upper Yangtze River Basin, China

<p>The intensification of climate change and human activities can lead to non-stationarity of hydrological model parameters, which in turn affects the correctness of model simulation results. Previous studies mainly focus on impacts of climate change, while catchment hydrological responses to human activities require detailed investigation for sustainable water management. This study evaluates anthropogenic impacts on soil water storage capacity of the upper Yangtze River Basin by representing hydrological parameters as functions of human activity indicators. The Xinanjiang (XAJ) model is used since its parameter WM accounts for soil water storage capacity. In this study, time-variations of WM are identified by the split-sample calibration based on dynamic programming (SSC-DP). The variations are further related to ten indicators of human activities from five aspects: population, gross domestic product, farming, irrigation and reservoir construction. Then, the proposed WM functional form is selected by comparing the performance of a set of parameter functions of the identified human activity indicators during the validation period. The study shows that WM increases in 1976-2000, while a relatively high relationship is detected between WM and some indicators such as agricultural acreage, population and reservoir construction. It is further demonstrated that agricultural population has the greatest impact on soil water storage capacity and its linear functional form for WM is validated to be effective in 2001-2010 with best streamflow simulation, especially for low streamflow. These results can help understand the hydrological response to the increasing human development and contribute to adaptive development strategies for future water resource management.</p>

Inverting a comprehensive crop model in parsimonious data context using Sentinel 2 images and yield map to infer soil water storage capacity.

<p>Soil Water storage Capacity (SWSC) is an important quantity in the field of hydrology and agronomy to represent the hydrological functioning of a territory and/or the dynamics of a crop. SWSC spatial variability is often strong resulting from heterogeneity in texture and structure as well as soil depth. In situ measurement of SWSC is expensive, destructive and cannot be considered over a large area. Therefore, the characterization of SWSC by non-destructive methods is a mean of addressing the mapping issue. In this study we took profit of the new capacities offered by the Sentinel 2 mission, which allows characterizing relevant features in vegetation dynamic linked to stresses. In addition, yield map offers an additional source of information. Both yield and vegetation development are sensitive to several factors as the water and nitrogen supply, crop installation or pest. To isolate the influence of water supply, and therefore access parameters involved in the SWSC, an option is to delineate the effect of such factors by inverting a crop model able to simulate the observation together with the representation of most of influencing factors. The STICS crop model implemented in this study is suitable to consider interactions between carbon, nitrogen and water cycles, plant development and farming practices. The issue is then to demonstrate that parsimony in field characterization can be overcome by using satellite and yield observations to implement and invert comprehensive model such as STICS. A sensitivity analysis (Lammoglia et al. 2019) indicates that once plant variety parameters are calibrated, the parameters linked to crop installation, as the sowing depth and the sowing density, the initial soil mineral nitrogen and the SWSC are the main quantities to consider in an inversion procedure. The GLUE Bayesian method was used to retrieve the different parameters. The procedure was tested on non-irrigated winter durum wheat in a Mediterranean context in south-eastern France. The approach was evaluated in farm context 20 on heterogeneous fields over three years (2016-2018). Evaluation was made either by comparing inverted SWSC to observations and/or assessing the crop model performances on subsequent years. Soil heterogeneities are well captured by the method, but some heterogeneities interpreted as soil heterogeneities might be artefacts. A multi-year analysis is then necessary to get the permanent features that are most likely linked to soil properties. Discussion on the adding value of combining both soil vegetation dynamic (FAPAR, LAI) and yield, on the inversion strategy (calibration steps, data being considering, initialisation) and on the cost function (to reduce the impact of uncertainties on crop parameters) was made. The study has shown that LAI/FAPAR and yield observations make the use of complex model in data parsimonious context possible. In particular, the study highlights the importance of having frequent image acquisition, as it allows to capture short feature as the senescence rate which appears as an important proxy of the availability of water in the soil.</p><p>Lammoglia, A. Chanzy & M. Guerif, “Characterizing soil hydraulic properties from Sentinel 2 and STICS crop model” doi:10.1109/MetroAgriFor.2019.8909266, pp 312-316</p>

Rainfall–Runoff Processes and Modelling in Regions Characterized by Deficiency in Soil Water Storage

The partial runoff is complicated in semi-arid and some semi-humid zones in terms of what the runoff generates in partial vertical positions. The partial runoff is highlighted by horizontal soil heterogeneity as well. How to identify the partial runoff and develop a variable threshold for runoff generation is a great difficulty and challenge. In this work, the partial runoff is identified by using a variable active runoff layer structure, and a variable soil water storage capacity is proposed to act as a threshold for runoff generation. A variable layer-based runoff model (VLRM) for simulating the complex partial runoff was therefore developed, using dual distribution curves for variable soil water storage capacity over basin. The VLRM is distinct in that the threshold for runoff generation is denoted by variable soil water storage capacity instead of infiltration capacity or constant soil water storage capacity. A series of flood events in two typical basins of North China are simulated by the model, and also by the Xinanjiang model. Results demonstrate that the new threshold performs well and the new model outperforms the Xinanjiang model. The approach improves current hydrological modelling for complex runoff in regions with large deficiencies in soil water storage.

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

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.