scholarly journals Large Scale Basins With Small to Negligible Slopes

2000 ◽  
Vol 31 (1) ◽  
pp. 15-26 ◽  
Author(s):  
Luis Silveira
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Subsurface Flow ◽  
Transport Processes ◽  
Runoff Generation ◽  
Soil Moisture Storage ◽  
Accumulated Rainfall ◽  
Physical Features ◽  
Rio Negro Basin ◽  
Surface And Subsurface Flow

In the first of this two-paper series, the main mechanisms for generation of runoff from rainfall in large basins with small to negligible slopes are analyzed using available data from the Río Negro basin in Uruguay. Topography and soils were examined in order to identify physical features that may influence the flow patterns. Soil moisture storage in space and soil moisture variability in time were also evaluated to relate rainfall and runoff generation. The study revealed the existence of strongly developed horizontal layers. Soil moisture depends essentially on vertical water transport processes due to the low morphological energy of the terrain. Surface and subsurface flow occurs during the season of low evapotranspiration where soils become progressively wet. Extreme storms in terms of accumulated rainfall are required to produce surface and subsurface flow during the season of high evapotranspiration. In the following paper, these observations and hypotheses are used to model a large basin with small to negligible slopes.

Large Scale Basins With Small to Negligible Slopes

2000 ◽  
Vol 31 (1) ◽  
pp. 27-40 ◽  
Author(s):  
Luis Silveira
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Hydrological Model ◽  
Transport Processes ◽  
Moisture Condition ◽  
Soil Moisture Condition ◽  
Soil Moisture Storage ◽  
High Soil Moisture ◽  
Rio Negro ◽  
Moisture Storage

Large basins with small to negligible slopes are seldom considered in the hydrological literature. An example of such basins is the Río Negro catchment in Uruguay. The first of this two-paper series showed the following special features: a) the existence of strongly developed horizontal layers and an essentially impervious B-horizon, b) significantly high soil moisture storage in terms of normally expected rainfall during a storm and c) the importance of vertical water transport processes to establish the soil moisture condition prior to a storm and its role concerning basin runoff response. These observations and hypotheses were taken into account by the lumped conceptual hydrological model called Hidro-Urfing through the percolation function and the basin runoff response function. This second paper shows its application to the Laguna I basin, a sub-basin of the Río Negro catchment with a surface area of 13,945 km2, and its ability to model the major storm hydrographs without any subdivision into smaller sub-basins. Modelling of low flows requires disaggregation of spatial-scale issues. A hydrological model of the entire Río Negro catchment did not previously exist.

scholarly journals Runoff reaction from extreme rainfall events on natural hillslopes: A data set from 132 large scale sprinkling experiments in south-west Germany

2019 ◽  
Author(s):  
Fabian Ries ◽  
Lara Kirn ◽  
Markus Weiler
Keyword(s):  
Large Scale ◽  
Subsurface Flow ◽  
West Germany ◽  
Runoff Generation ◽  
Large Variability ◽  
Maximum Rainfall ◽  
Rainfall Events ◽  
Extreme Precipitation Events ◽  
South West ◽  
Precipitation Events

Abstract. Pluvial or flash floods generated by heavy precipitation events cause high economic damages and loss of life worldwide. As discharge observations from such extreme occurrences are rare especially on the scale of small catchments or even hillslopes, data from artificial sprinkling experiments offer valuable information on runoff generation processes, overland and subsurface flow rates and response times. We conducted 132 large-scale sprinkling experiments on natural hillslopes at 23 sites with different soil types and geology on pastures and arable lands within the federal state of Baden-Württemberg in south-west Germany. The experiments were realized between 2016 and 2017. Simulated rainfall events of varying durations were based on a) the site-specific 100-year return periods of rainfall with different durations and b) the maximum rainfall intensity observed locally. The 100 m2 experimental area was divided into three individual plots and overland and subsurface flow, soil moisture and water level dynamics in the temporarily saturated soil zone were measured at 1-minute resolution. Furthermore, soil characteristics were described in detail for each site. The data was carefully processed and corrected for measurement errors and combined to a consistent and easy to use database. The experiments revealed a large variability of possible runoff responses to similar rainfall characteristics. In general, agricultural fields produced more overland flow than grassland. The latter generated hardly any runoff during the first simulated 100-year event on initially dry soils. The dataset provides valuable information on runoff generation variability from natural hillslopes and may be used for the development and evaluation of hydrological models, especially those considering physical processes governing runoff generation during extreme precipitation events. The dataset presented in this paper is freely available from the FreiDok plus data repository (https://doi.org/10.6094/UNIFR/149650, Ries et al., 2019).

scholarly journals Runoff reaction from extreme rainfall events on natural hillslopes: a data set from 132 large-scale sprinkling experiments in south-western Germany

2020 ◽  
Vol 12 (1) ◽  
pp. 245-255
Author(s):  
Fabian Ries ◽  
Lara Kirn ◽  
Markus Weiler
Keyword(s):  
Large Scale ◽  
Subsurface Flow ◽  
Economic Damage ◽  
Runoff Generation ◽  
Data Set ◽  
Large Variability ◽  
Rainfall Events ◽  
Extreme Precipitation Events ◽  
Western Germany ◽  
Precipitation Events

Abstract. Pluvial or flash floods generated by heavy precipitation events cause large economic damage and loss of life worldwide. As discharge observations from such extreme occurrences are rare, especially on the scale of small catchments or even hillslopes, data from artificial sprinkling experiments offer valuable information on runoff generation processes, overland and subsurface flow rates, and response times. We conducted 132 large-scale sprinkling experiments on natural hillslopes at 23 sites with different soil types and geology on pastures and arable land within the federal state of Baden-Württemberg in south-western Germany. The experiments were realized between 2016 and 2017. Simulated rainfall events of varying durations were based on (a) the site-specific 100-year return periods of rainfall with different durations and (b) the maximum rainfall intensity observed locally. The 100 m2 experimental area was divided into three individual plots, and overland and subsurface flow, soil moisture, and water level dynamics in the temporarily saturated soil zone were measured at 1 min resolution. Furthermore, soil characteristics were described in detail for each site. The data were carefully processed and corrected for measurement errors and combined into a consistent and easy-to-use database. The experiments revealed large variability in possible runoff responses to similar rainfall characteristics. In general, agricultural fields produced more overland flow than grassland. The latter generated hardly any runoff during the first simulated 100-year event on initially dry soils. The data set provides valuable information on runoff generation variability from natural hillslopes and may be used for the development and evaluation of hydrological models, especially those considering physical processes governing runoff generation during extreme precipitation events. The data set presented in this paper is freely available from the FreiDok plus data repository at https://doi.org/10.6094/UNIFR/151460 (Ries et al., 2019).

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 A high-resolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3

2015 ◽  
Vol 8 (3) ◽  
pp. 923-937 ◽  
Author(s):  
R. M. Maxwell ◽  
L. E. Condon ◽  
S. J. Kollet
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Numerical Models ◽  
Subsurface Flow ◽  
Hydrologic Model ◽  
Grand Challenge ◽  
Flow Equations ◽  
Continental Scale ◽  
Integrated Hydrologic Model ◽  
Surface And Subsurface Flow

Abstract. Interactions between surface and groundwater systems are well-established theoretically and observationally. While numerical models that solve both surface and subsurface flow equations in a single framework (matrix) are increasingly being applied, computational limitations have restricted their use to local and regional studies. Regional or watershed-scale simulations have been effective tools for understanding hydrologic processes; however, there are still many questions, such as the adaptation of water resources to anthropogenic stressors and climate variability, that can only be answered across large spatial extents at high resolution. In response to this grand challenge in hydrology, we present the results of a parallel, integrated hydrologic model simulating surface and subsurface flow at high spatial resolution (1 km) over much of continental North America (~ 6.3 M km2). These simulations provide integrated predictions of hydrologic states and fluxes, namely, water table depth and streamflow, at very large scale and high resolution. The physics-based modeling approach used here requires limited parameterizations and relies only on more fundamental inputs such as topography, hydrogeologic properties and climate forcing. Results are compared to observations and provide mechanistic insight into hydrologic process interaction. This study demonstrates both the feasibility of continental-scale integrated models and their utility for improving our understanding of large-scale hydrologic systems; the combination of high resolution and large spatial extent facilitates analysis of scaling relationships using model outputs.

scholarly journals Identifying dominant component of runoff yield processes: a case study in a sub-basin of the middle Yellow River

2021 ◽  
Author(s):  
Li Zhang ◽  
Caihong Hu ◽  
Shengqi Jian ◽  
Qiang Wu ◽  
Guang Ran ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Conservation ◽  
Human Activities ◽  
Large Scale ◽  
Overland Flow ◽  
Yellow River ◽  
Potential Evapotranspiration ◽  
Subsurface Flow ◽  
Runoff Generation ◽  
Runoff Reduction

Abstract The effects of long-term natural climate change and human activities on runoff generation mechanism in the middle Yellow River Basin are long-standing concerns. This study analyzed the characteristics of hydro-climatic variables in the meso-scale Tuweihe catchment based on the observed data for the period 1956–2016 and a climate elastic method. The spatial distribution of dominant runoff processes (DRP) following land use changes in case of rainfall was identified. The results show significant decreasing trends in annual runoff, whereas slightly downward trends are identified for annual precipitation and potential evapotranspiration, 1984 is detected as the mutation year of the study period. The average contributions of climate change and human activities to the runoff reduction in the Tuweihe catchment were 33.2% and 66.8%, respectively. In general, the influences of human activities on runoff are applied mostly through the alteration of the catchment characteristics. The dominant runoff processes changes between 1980 and 2015 show significant effects of large-scale soil and water conservation measures in the Tuweihe catchment. We found that Hortonian overland flow (HOF) and fast subsurface flow (SSF1) were the two main processes in 1980 (30.3% and 34.4% respectively), but the proportion of HOF decreased by 9.6% in 2015. The proportions of saturation overland flow (SOF) and SSF have increased to varying degrees, which means that the catchment is more prone to generate subsurface flow processes. Consequently, under similar rainfall conditions, the runoff yield of flood events decreases in the second period.

scholarly journals Simulation of groundwater and surface water over the continental US using a hyperresolution, integrated hydrologic model

2014 ◽  
Vol 7 (6) ◽  
pp. 7317-7349 ◽  
Author(s):  
R. M. Maxwell ◽  
L. E. Condon ◽  
S. J. Kollet
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Numerical Models ◽  
Subsurface Flow ◽  
Hydrologic Model ◽  
Grand Challenge ◽  
Flow Equations ◽  
Continental Scale ◽  
Integrated Hydrologic Model ◽  
Surface And Subsurface Flow

Abstract. Interactions between surface and groundwater systems are well-established theoretically and observationally. While numerical models that solve both surface and subsurface flow equations in a single framework (matrix) are increasingly being applied, computational limitations have restricted their use to local and regional studies. Regional or watershed, scale simulations have been effective tools in understanding hydrologic processes, however there are still many questions, such as the adaptation of water resources to anthropogenic stressors and climate variability, that need to be answered across large spatial extents at high resolution. In response to this "grand challenge" in hydrology, we present the results of a parallel, integrated hydrologic model simulating surface and subsurface flow at high spatial resolution (1 km) over much of continental North America (~ 6 300 000 or 6.3 million km2). These simulations provide predictions of hydrologic states and fluxes, namely water table depth and streamflow, at unprecedented scale and resolution. The physically-based modeling approach used here requires limited parameterizations and relies only on more fundamental inputs, such as topography, hydrogeologic properties and climate forcing. Results are compared to observations and provide mechanistic insight into hydrologic process interaction. This study demonstrates both the feasibility of continental scale integrated models and their utility for improving our understanding of large-scale hydrologic systems; the combination of high resolution and large spatial extent facilitates novel analysis of scaling relationships using model outputs.

PROTOTIPE APLIKASI PENYIRAMAN TANAMAN MENGGUNAKAN SENSOR KELEMBABAN TANAH BERBASIS MICRO CONTOLLER ATMEGA 328

2019 ◽  
Vol 5 (1) ◽  
pp. 97-106
Author(s):  
Rudi Budi Agung ◽  
Muhammad Nur ◽  
Didi Sukayadi
Keyword(s):  
Soil Moisture ◽  
Growth And Development ◽  
Large Scale ◽  
Simple Method ◽  
Moisture Sensor ◽  
Sensor Development ◽  
Soil Moisture Sensor ◽  
Application Systems ◽  
Harvesting Systems ◽  
Working Principle

The Indonesian country which is famous for its tropical climate has now experienced a shift in two seasons (dry season and rainy season). This has an impact on cropping and harvesting systems among farmers. In large scale this is very influential considering that farmers in Indonesia are stilldependent on rainfall which results in soil moisture. Some types of plants that are very dependent on soil moisture will greatly require rainfall or water for growth and development. Through this research, researchers tried to make a prototype application for watering plants using ATMEGA328 microcontroller based soil moisture sensor. Development of application systems using the prototype method as a simple method which is the first step and can be developed again for large scale. The working principle of this prototype is simply that when soil moisture reaches a certainthreshold (above 56%) then the system will work by activating the watering system, if it is below 56% the system does not work or in other words soil moisture is considered sufficient for certain plant needs.

scholarly journals Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

2021 ◽  
Vol 13 (2) ◽  
pp. 228
Author(s):  
Jian Kang ◽  
Rui Jin ◽  
Xin Li ◽  
Yang Zhang
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Spatial Scales ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Spatial Density ◽  
Linear Regression Method ◽  
Spatiotemporal Resolution ◽  
Pixel Scale

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

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