Influence of various in situ rainwater harvesting methods on soil moisture and growth of Tamarix ramosissima in the semiarid loess region of China

2006 ◽  
Vol 233 (1) ◽  
pp. 143-148 ◽  
Author(s):  
Xiao-Yan Li ◽  
Pei-Jun Shi ◽  
Yong-Liang Sun ◽  
Jia Tang ◽  
Zhi-Peng Yang
Keyword(s):  
Soil Moisture ◽  
Rainwater Harvesting ◽  
Tamarix Ramosissima ◽  
Loess Region

scholarly journals Evaluating the effect of in-situ rainwater harvesting techniques on maize production in moisture stress areas of humbo woreda, wolaita zone, southern Ethiopia

2020 ◽  
Vol 10 (1) ◽  
pp. 71-79
Author(s):  
W Naba ◽  
A Moges ◽  
A Gebremichael
Keyword(s):  
Soil Moisture ◽  
Grain Yield ◽  
Rainwater Harvesting ◽  
Block Design ◽  
Growing Season ◽  
Southern Ethiopia ◽  
Growth Stages ◽  
Dry Spell ◽  
Harvesting Techniques

The study was conducted to investigate the effect of different in-situ water harvesting structures as soil moisture conservation techniques under maize crop production in Abela Sippa kebele Wolaita zone, Ethiopia where rainfall variation is affecting agriculture with prolonged dry spells during critical crop growth stages. The experiment was laid out in a Randomized Complete Block Design, with three replications and four treatments. The four treatments used in the study were; Control, Targa, Tie-ridge and Zai pits. Findings from this study revealed that maize grain yield and yield components, such as, grain yield, dry matter biomass, and cob length were highly significant (p<0.05) on Targa. Soil-moisture content over the crop growing season at dry spell periods was significantly higher in Targa and Tie ridges than the control. Maize yield of (7150 kg ha-1), (6190 kg ha-1), (4500 kg ha-1) and (4900 kg ha-1) was obtained from Targa, Tie ridge, Zai pits and Control, respectively. Targa and Tie ridge treatments recorded higher net returns (29712 and 25164 kg ha-1) than Control (20370 kg ha-1) and Zai (14350 kg ha-1) treatments. The results revealed that the in-situ rainwater harvesting techniques could play great role in improving crop yield in dry periods. However, the utilization of the technology is surrounded by various constraints. The major constraints include labour, cost, lack of knowledge and crops planted on bunds. The findings suggest that Targa structure improved water availability during the growing season, thereby protecting crops from dry periods and it needs minimum cost, less labor power ,and easily constructed by local farmers (not require complicated knowledge). Int. J. Agril. Res. Innov. Tech. 10(1): 71-79, June 2020

scholarly journals Combining Rainwater Harvesting and Grass Reseeding to Revegetate Denuded African Semi-arid Landscapes

2021 ◽  
Author(s):  
Kevin Z. Mganga ◽  
Luwieke Bosma ◽  
Kevin O. Amollo ◽  
Theophilus Kioko ◽  
Nancy Kadenyi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Rainwater Harvesting ◽  
Cenchrus Ciliaris ◽  
Moisture Availability ◽  
Biomass Yields ◽  
Soil Moisture Availability ◽  
Semi Arid

AbstractIn African drylands, perennial grasses preferred by grazing livestock are disappearing at an alarming rate. This has led to recurrent livestock feed shortages threatening pastoralist’s livelihoods. Combining native grass reseeding and rainwater harvesting offers a viable and innovative solution to reverse this trend. However, studies to determine how biomass yields are affected by soil moisture availability attributed to in situ rainwater harvesting in African drylands are limited. We investigated how biomass yields of three grasses native to Africa, i.e., Enteropogon macrostachyus (Bush rye grass), Cenchrus ciliaris (African foxtail grass), and Eragrostis superba (Maasai love grass), are affected by soil moisture content in a typical semi-arid landscape. Rainwater harvesting structures included trenches, micro-catchments and furrows. Additionally, rain runoff was diverted from an adjacent road used as a catchment area. Soil moisture was measured between November 2018 and August 2019 using PlantCare Mini-Logger sensors installed at 40 and 50 cm depths and 0, 1, 5 and 15 m away from the trench. Quadrat method was used to determine biomass yields in August 2019. Peaks in soil moisture were observed after rainfall events. Soil moisture content gradually decreased after the rainy season, but was higher closer to the trench. This is attributed to the prolonged rainwater retention in the trenches. Biomass yields were in the order Eragrostis superba > Cenchrus ciliaris > Enteropogon macrostachyus. Biomass production was higher near the trenches for all the studied species. Sensitivity to soil moisture demonstrated by the magnitude to yield reduction during the growing season was in the order Eragrostis superba > Cenchrus ciliaris > Enteropogon macrostachyus. These results suggest that Eragrostis superba is more sensitive to drought stress than Enteropogon macrostachyus that is adapted to a wide range of soil moisture conditions. We demonstrated that in situ rainwater harvesting structures enhanced soil moisture availability and displayed great potential for revegetating denuded natural rangelands in semi-arid African landscapes. Thus, combining rainwater harvesting and reseeding techniques can produce measurable improvements in pastoral livelihoods and should be incorporated in dryland development policies in the region. Ultimately, incorporating such innovative strategies can strengthen the effectiveness of ecological restoration in African drylands to meet the objectives of the UN Decade on Ecosystem Restoration and achieving the UN Sustainable Development Goals. Graphical abstract

scholarly journals Evaluating the Effects of In-situ Rainwater Harvesting Techniques on Soil Moisture Conservation and Grain Yield of Maize (Zea mays L.) in Fedis District, Eastern Hararghe, Ethiopia

2018 ◽  
Vol 6 (9) ◽  
pp. 1129 ◽  
Author(s):  
Amisalu Milkias ◽  
Teshale Tadesse ◽  
Habtamu Zeleke
Keyword(s):  
Soil Moisture ◽  
Grain Yield ◽  
Crop Production ◽  
Rainwater Harvesting ◽  
Growth Stages ◽  
Soil Moisture Storage ◽  
Soil Moisture Conservation ◽  
Moisture Storage ◽  
Harvesting Techniques

In the drier farming regions of the world, where crop production is constrained by short growing period, unpredictable and short rainfall with sporadic run-off, in-situ rainwater harvesting is vital for successful crop production. In connection to this, a study was conducted in Fedis district of Oromia region during the main rainy seasons of 2015 and 2016 to evaluate the effects of in-situ rainwater harvesting techniques (Ridge Furrow (RF), Contour Ridge (CR), and Tied Ridge (TR)) on soil moisture conservation and grain yield of maize. A spilt-plot design was used and soil moisture content was measured at three growth stages of the crop to a depth of 60 cm with 20 cm interval. The results showed that water harvesting techniques significantly increased moisture conservation compared to the control, which was flat bed preparation. Averaged over the three stages, the TR, CR and RF treatments increased soil moisture storage by 134.59, 128.57, and 121.87%, respectively, compared to the control. The study also revealed that the in-situ rainwater harvesting techniques, due to the improved soil moisture storage, significantly affected grain yield of the maize. Averaged over the two years, the TR, CR, and FR increased the grain yield 143.14, 131.47 and 121.16%, respectively, over the control treatment. Therefore, in drier environments, such as Fedis, in-situ rainwater harvesting techniques can be recommended for better moisture conservation and subsequent improvement in crop production.

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.

scholarly journals Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-filling of Monthly ESA CCI Soil Moisture in China

2021 ◽  
Vol 13 (14) ◽  
pp. 2848
Author(s):  
Hao Sun ◽  
Qian Xu
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Water Resource Management ◽  
Large Scale ◽  
Gap Filling ◽  
Spatial Continuity ◽  
Study Results ◽  
Data Gaps

Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially for the area of China, due to the limitations in remote sensing of SM such as complex topography, human-induced radio frequency interference (RFI), and vegetation disturbances, etc. The data gaps make the CCI SM data cannot achieve spatial continuity, which entails the study of gap-filling methods. In order to develop suitable methods to fill the gaps of CCI SM in the whole area of China, we compared typical Machine Learning (ML) methods, including Random Forest method (RF), Feedforward Neural Network method (FNN), and Generalized Linear Model (GLM) with a geostatistical method, i.e., Ordinary Kriging (OK) in this study. More than 30 years of passive–active combined CCI SM from 1982 to 2018 and other biophysical variables such as Normalized Difference Vegetation Index (NDVI), precipitation, air temperature, Digital Elevation Model (DEM), soil type, and in situ SM from International Soil Moisture Network (ISMN) were utilized in this study. Results indicated that: 1) the data gap of CCI SM is frequent in China, which is found not only in cold seasons and areas but also in warm seasons and areas. The ratio of gap pixel numbers to the whole pixel numbers can be greater than 80%, and its average is around 40%. 2) ML methods can fill the gaps of CCI SM all up. Among the ML methods, RF had the best performance in fitting the relationship between CCI SM and biophysical variables. 3) Over simulated gap areas, RF had a comparable performance with OK, and they outperformed the FNN and GLM methods greatly. 4) Over in situ SM networks, RF achieved better performance than the OK method. 5) We also explored various strategies for gap-filling CCI SM. Results demonstrated that the strategy of constructing a monthly model with one RF for simulating monthly average SM and another RF for simulating monthly SM disturbance achieved the best performance. Such strategy combining with the ML method such as the RF is suggested in this study for filling the gaps of CCI SM in China.

scholarly journals Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale

2015 ◽  
Vol 19 (9) ◽  
pp. 3845-3856 ◽  
Author(s):  
F. Todisco ◽  
L. Brocca ◽  
L. F. Termite ◽  
W. Wagner
Keyword(s):  
Soil Moisture ◽  
Soil Loss ◽  
Large Scale ◽  
Central Italy ◽  
Remote Sensing Data ◽  
Loss Estimation ◽  
Balance Model ◽  
Estimation Accuracy ◽  
Soil Losses

Abstract. The potential of coupling soil moisture and a Universal Soil Loss Equation-based (USLE-based) model for event soil loss estimation at plot scale is carefully investigated at the Masse area, in central Italy. The derived model, named Soil Moisture for Erosion (SM4E), is applied by considering the unavailability of in situ soil moisture measurements, by using the data predicted by a soil water balance model (SWBM) and derived from satellite sensors, i.e., the Advanced SCATterometer (ASCAT). The soil loss estimation accuracy is validated using in situ measurements in which event observations at plot scale are available for the period 2008–2013. The results showed that including soil moisture observations in the event rainfall–runoff erosivity factor of the USLE enhances the capability of the model to account for variations in event soil losses, the soil moisture being an effective alternative to the estimated runoff, in the prediction of the event soil loss at Masse. The agreement between observed and estimated soil losses (through SM4E) is fairly satisfactory with a determination coefficient (log-scale) equal to ~ 0.35 and a root mean square error (RMSE) of ~ 2.8 Mg ha−1. These results are particularly significant for the operational estimation of soil losses. Indeed, currently, soil moisture is a relatively simple measurement at the field scale and remote sensing data are also widely available on a global scale. Through satellite data, there is the potential of applying the SM4E model for large-scale monitoring and quantification of the soil erosion process.

scholarly journals NOAA Satellite Soil Moisture Operational Product System (SMOPS) Version 3.0 Generates Higher Accuracy Blended Satellite Soil Moisture

2020 ◽  
Vol 12 (17) ◽  
pp. 2861
Author(s):  
Jifu Yin ◽  
Xiwu Zhan ◽  
Jicheng Liu
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Land Surface ◽  
Vital Role ◽  
Product System ◽  
Global Soil ◽  
One Stop ◽  
Satellite Sensors ◽  
Blended Data

Soil moisture plays a vital role for the understanding of hydrological, meteorological, and climatological land surface processes. To meet the need of real time global soil moisture datasets, a Soil Moisture Operational Product System (SMOPS) has been developed at National Oceanic and Atmospheric Administration to produce a one-stop shop for soil moisture observations from all available satellite sensors. What makes the SMOPS unique is its near real time global blended soil moisture product. Since the first version SMOPS publicly released in 2010, the SMOPS has been updated twice based on the users’ feedbacks through improving retrieval algorithms and including observations from new satellite sensors. The version 3.0 SMOPS has been operationally released since 2017. Significant differences in climatological averages lead to remarkable distinctions in data quality between the newest and the older versions of SMOPS blended soil moisture products. This study reveals that the SMOPS version 3.0 has overwhelming advantages of reduced data uncertainties and increased correlations with respect to the quality controlled in situ measurements. The new version SMOPS also presents more robust agreements with the European Space Agency’s Climate Change Initiative (ESA_CCI) soil moisture datasets. With the higher accuracy, the blended data product from the new version SMOPS is expected to benefit the hydrological, meteorological, and climatological researches, as well as numerical weather, climate, and water prediction operations.

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