scholarly journals The late-Holocene relationship between peatland water table depth and summer temperature in northern Poland

Author(s):  
Krzysztof Pleskot ◽  
Karina Apolinarska ◽  
Les C. Cwynar ◽  
Bartosz Kotrys ◽  
Mariusz Lamentowicz
Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2148
Author(s):  
Jonathan A. Lafond ◽  
Silvio J. Gumiere ◽  
Virginie Vanlandeghem ◽  
Jacques Gallichand ◽  
Alain N. Rousseau ◽  
...  

Integrated water management has become a priority for cropping systems where subirrigation is possible. Compared to conventional sprinkler irrigation, the controlling water table can lead to a substantial increase in yield and water use efficiency with less pumping energy requirements. Knowing the spatiotemporal distribution of water table depth (WTD) and soil properties should help perform intelligent, integrated water management. Observation wells were installed in cranberry fields with different water management systems: Bottom, with good drainage and controlled WTD management; Surface, with good drainage and sprinkler irrigation management; Natural, without drainage, or with imperfectly drained and conventional sprinkler irrigation. During the 2017–2020 growing seasons, WTD was monitored on an hourly basis, while precipitation was measured at each site. Multi-frequential periodogram analysis revealed a dominant periodic component of 40 days each year in WTD fluctuations for the Bottom and Surface systems; for the Natural system, periodicity was heterogeneous and ranged from 2 to 6 weeks. Temporal cross correlations with precipitation show that for almost all the sites, there is a 3 to 9 h lag before WTD rises; one exception is a subirrigation site. These results indicate that automatic water table management based on continuously updated knowledge could contribute to integrated water management systems, by using precipitation-based models to predict WTD.


Author(s):  
Sandeep Samantaray ◽  
Abinash Sahoo

Accurate prediction of water table depth over long-term in arid agricultural areas are very much important for maintaining environmental sustainability. Because of intricate and diverse hydrogeological features, boundary conditions, and human activities researchers face enormous difficulties for predicting water table depth. A virtual study on forecast of water table depth using various neural networks is employed in this paper. Hybrid neural network approach like Adaptive Neuro Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), Radial Basis Function Neural Network (RBFN) is employed here to appraisal water levels as a function of average temperature, precipitation, humidity, evapotranspiration and infiltration loss data. Coefficient of determination (R2), Root mean square error (RMSE), and Mean square error (MSE) are used to evaluate performance of model development. While ANFIS algorithm is used, Gbell function gives best value of performance for model development. Whole outcomes establish that, ANFIS accomplishes finest as related to RNN and RBFN for predicting water table depth in watershed.


Oecologia ◽  
2021 ◽  
Author(s):  
Jonathan W. F. Ribeiro ◽  
Natashi A. L. Pilon ◽  
Davi R. Rossatto ◽  
Giselda Durigan ◽  
Rosana M. Kolb

2010 ◽  
Vol 40 (8) ◽  
pp. 1485-1496 ◽  
Author(s):  
Sakari Sarkkola ◽  
Hannu Hökkä ◽  
Harri Koivusalo ◽  
Mika Nieminen ◽  
Erkki Ahti ◽  
...  

Ditch networks in drained peatland forests are maintained regularly to prevent water table rise and subsequent decrease in tree growth. The growing tree stand itself affects the level of water table through evapotranspiration, the magnitude of which is closely related to the living stand volume. In this study, regression analysis was applied to quantify the relationship between the late summer water table depth (DWT) and tree stand volume, mean monthly summertime precipitation (Ps), drainage network condition, and latitude. The analysis was based on several large data sets from southern to northern Finland, including concurrent measurements of stand volume and summer water table depth. The identified model demonstrated a nonlinear effect of stand volume on DWT, a linear effect of Ps on DWT, and an interactive effect of both stand volume and Ps. Latitude and ditch depth showed only marginal influence on DWT. A separate analysis indicated that an increase of 10 m3·ha–1 in stand volume corresponded with a drop of 1 cm in water table level during the growing season. In a subsample of the data, high bulk density peat showed deeper DWT than peat with low bulk density at the same stand volume.


2021 ◽  
Vol 131 ◽  
pp. 108122
Author(s):  
Thomas G. Sim ◽  
Graeme T. Swindles ◽  
Paul J. Morris ◽  
Andy J. Baird ◽  
Dan J. Charman ◽  
...  

2021 ◽  
Author(s):  
Michal Horsák ◽  
Veronika Horsáková ◽  
Marek Polášek ◽  
Radovan Coufal ◽  
Petra Hájková ◽  
...  

2021 ◽  
Author(s):  
Ain Kull ◽  
Iuliia Burdun ◽  
Gert Veber ◽  
Oleksandr Karasov ◽  
Martin Maddison ◽  
...  

<p>Besides water table depth, soil temperature is one of the main drivers of greenhouse gas (GHG) emissions in intact and managed peatlands. In this work, we evaluate the performance of remotely sensed land surface temperature (LST) as a proxy of greenhouse gas emissions in intact, drained and extracted peatlands. For this, we used chamber-measured carbon dioxide (CO<sub>2</sub>) and methane (CH<sub>4</sub>) data from seven peatlands in Estonia collected during vegetation season in 2017–2020. Additionally, we used temperature and water table depth data measured in situ. We studied relationships between CO<sub>2</sub>, CH<sub>4</sub>, in-situ parameters and remotely sensed LST from Landsat 7 and 8, and MODIS Terra. Results of our study suggest that LST has stronger relationships with surface and soil temperature as well as with ecosystem respiration (R<sub>eco</sub>) over drained and extracted sites than over intact ones. Over the extracted cites the correlation between R<sub>eco</sub> CO<sub>2</sub> and LST is 0.7, and over the drained sites correlation is 0.5. In natural sites, we revealed a moderate positive relationship between LST and CO<sub>2</sub> emitted in hollows (correlation is 0.6) while it is weak in hummocks (correlation is 0.3). Our study contributes to the better understanding of relationships between greenhouse gas emissions and their remotely sensed proxies over peatlands with different management status and enables better spatial assessment of GHG emissions in drainage affected northern temperate peatlands.</p>


2018 ◽  
Vol 23 (20) ◽  
pp. 10261-10285 ◽  
Author(s):  
Sucharita Pradhan ◽  
Shiv Kumar ◽  
Yogendra Kumar ◽  
Harish Chandra Sharma

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