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Agriculture plays a vital role in the socio-economic development. For agricultural production, whether rainfed or using irrigation, water is a key requirement. Therefore, a thorough understanding of the hydrological processes in agricultural lands is essential to address a wide range of issues, including soil moisture condition, crop water requirement, agricultural productivity, water efficiency, soil erosion, and solute transport.
This session is intended to address and advance our understanding of the role of hydrological processes in agricultural lands. Some of the topics and questions of interest are: (1) modelling the impacts of climate change on water balance and agricultural productivity at watershed scale; (2) identification of dominant hydrological factors and how they can be measured locally for improving water supply to crops; (3) effects of irrigation schemes on regional evapotranspiration and soil moisture content; (4) effects of artificial drainage on water regime and solute transport at different spatial scales; (5) aquifer vulnerability to high rates of fertilizer and pesticide applications; (6) multi-process and multi-scale water and energy transitions in agricultural lands; (7) water and energy responses to water-saving practice; and (8) linking hydrological issues with other environmental issues, including removal of natural vegetation, droughts and floods, and soil erosion. We welcome abstracts addressing the above topics or other topics related to hydrological processes in agricultural lands.

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Convener: Jun Niu | Co-conveners: Noel Aloysius, Bellie Sivakumar
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| Attendance Fri, 08 May, 08:30–10:15 (CEST)

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Chat time: Friday, 8 May 2020, 08:30–10:15

Chairperson: Jun Niu, Noel Aloysius,Bellie Sivakumar
D1 |
EGU2020-1067
Burak Bulut, M. Tugrul Yilmaz, and Mehdi H. Afshar

Monitoring agricultural crop conditions during the growing season and estimating potential crop yields are important for evaluating seasonal production. The accurate and timely assessment of the losses in crop yields caused by a natural disaster, such as drought, may be critical for countries where their economies are reliant on their agricultural productivity. Early assessment of the reduction in crop yields can prevent a catastrophic situation and help meet the demands of strategic planning.

In this study, the Multiple Linear Regression model was used to estimate the wheat yields in Turkey. Remotely sensed-, model-, and in-situ-based measurements of affecting variables of crop productivity (i.e., precipitation, land surface temperature, soil moisture, wind, and Normalized Vegetation Difference Index) were extracted over selected areas in which yield data were available on them. The datasets are collected using different time scales (e.g., before/during sowing period, growing season, one/two months before harvest, etc.).

The cross-validation of more than 700 different model combinations over more than total 700 different administrative divisions (i.e., districts, provinces, and regions) showed that by using the best model selected for each district, on average, a correlation value of 0.65 and a mean absolute error of 35 kg/da can be obtained between estimated and observed yield values. While, this consistency is more pronounced over the districts located in the Central Anatolia region where the average production of the wheat in them is more than the rest of districts in the country. Overall, regional differences of the selected predictors of observed yield data, suggest that the land surface temperature can provide a useful exploratory and predictive tool for wheat yield estimation across the country.

How to cite: Bulut, B., Yilmaz, M. T., and H. Afshar, M.: Estimation of Wheat Yield using Remotely Sensed and Modeled Data over Turkey, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1067, https://doi.org/10.5194/egusphere-egu2020-1067, 2019

D2 |
EGU2020-2263
Ying Ma

The increase in atmosphere carbon dioxide (CO2) concentrations has been the most important environmental change experienced by agricultural systems. It is still uncertain whether grain yield of the global food crop of maize will remain unchanged under a future elevated CO2 (eCO2) environment. A water transformation dynamic processes experimental device (WTDPED) was developed using a chamber coupled with two weighing lysimeters and a groundwater supply system to explore the water-related yield responses of maize to eCO2. Two experiments were conducted via the WTDPED under eCO2 (700 ppm) and current CO2 (400 ppm) concentrations. Seasonal changes in multiple ecophysiological indicators and related hydrological processes were compared between these two experiments. The results showed that the leaf nitrogen (N) content, chlorophyll content, net photosynthesis rate, and transpiration rate (Tr) consistently decreased during the seedling to filling stages but notably increased at the maturity stage due to eCO2 (P<0.05). Nevertheless, the effects were not significant over the entire growing season or for other indicators, i.e., the leaf carbon (C) content, C/N ratio, and leaf area index (P>0.05). Significant decreases in crop height (mean of 15.9%, P<0.05) associated with notable increases in stem diameter (mean of 14.9%, P<0.05) were found throughout the growing season. Dry matter per corncob at the final harvest decreased slightly under eCO2 (mean of 7.7 g, P >0.05). Soil moisture was not conserved by the decline of Tr ahead of the filling stage when soil evaporation was likely promoted by eCO2 instead. The total evapotranspiration changed little (0.2%) over the entire growing season. Although the leaf water use efficiency increased significantly at every growth stage (mean of 27.3%, P<0.05), the grain yield, water productivity and irrigation water use efficiency were not improved noticeably by eCO2. This study is critical to accurately predict future crop yield and hydrological changes under climate change.

How to cite: Ma, Y.: Seasonal responses of maize growth and water use to elevated CO2 based on WTDPED experiments: evidences from multiple ecophysiological indicators, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2263, https://doi.org/10.5194/egusphere-egu2020-2263, 2020

D3 |
EGU2020-6268
| Highlight
Haiyun Shi and Suning Liu

This study develops a recursive approach to long-term prediction of monthly precipitation using genetic programming (GP), and the study area is the Three-River Headwaters Region (TRHR) in China. The daily precipitation data recorded at 29 meteorological stations during 1961-2014 are collected, among which the data during 1961-2000 are used for calibration and the remaining data are for validation. To develop this approach, first, the preliminary estimations of annual precipitation are computed based on a statistical method. Second, the percentage of the monthly precipitation for each month of a year is calculated as the mean monthly precipitation divided by the mean annual precipitation during the study period, and then the preliminary estimation of monthly precipitation for each month of a year is obtained. Third, GP is adopted to improve the preliminary estimations through establishing the relationship of the observations with the preliminary estimations at the past and current times. The calibration and validation results reveal that the recursive approach involving GP can provide the more accurate predictions of monthly precipitation. Finally, this approach is used to predict the monthly precipitation over the TRHR till 2050.

How to cite: Shi, H. and Liu, S.: A recursive approach to long-term prediction of monthly precipitation using genetic programming: case of the Three-River Headwaters Region, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6268, https://doi.org/10.5194/egusphere-egu2020-6268, 2020

D4 |
EGU2020-8144
Rike Becker, Stephan Schulz, Ralf Merz, Tim aus der Beek, and Christoph Schüth

The study focuses on the assessment of climate change impacts on the water balance and agricultural productivity in a semi-arid, meso-scale irrigation system, in Punjab, Pakistan.

To simulate potential future water balance changes in our intensively irrigated agricultural study area, we chose the widely used Soil & Water Assessment Tool (SWAT) model software. Using the SWAT model, we were able to incorporate detailed irrigation management strategies into the analysis, and to account for spatially distributed plant physiognomic dynamics and their effects on the local water balance.

Climate change data is taken from the Coordinated Regional Climate Downscaling Experiment (CORDEX; www.cordex.org), which provides a suite of regional climate projections based on Global Climate Models of the Coupled Model Intercomparison Project, Phase 5 (CMIP5). We take into account medium (RCP 4.5) and high (RCP 8.5) greenhouse gas emission scenarios from the IPCC - Fifth Assessment Record (AR5) and study their short (until 2030) and medium term (until 2050) impacts.

The assessment shows the following interesting results regarding climate change impacts on future agricultural productivity in our study area:

  • Temperature stress on plant growth will increase significantly
  • A substantial reduction in future summer crop yields can be expected
  • Temperature stress induces the reduction of biomass production, which causes a decrease in transpiration and hence a decrease in actual evapotranspiration
  • Reduced transpiration counteracts the temperature-induced increase in potential evapotranspiration, which leads to surprisingly low increases in future irrigation water demand despite the significant warming
  • Temperature stress related adaption strategies (e.g. more heat tolerant crops) are under these circumstances more important than increasing irrigation efficiency
  • Even though overall changes in water demand are surprisingly low, higher pressures on surface water and groundwater resources can be expected due to changes in plant growing cycles: Future temperature patterns are expected to speed up the plant growing cycle and increase irrigation demands during the early growing stages. In our study area, this alters the share of irrigation water supply sources (i.e. rain, surface water and groundwater) and leads to higher demands of surface water and particularly groundwater resources, while rainfall contributions decrease.

The study discusses the above mentioned climate change impacts and their interaction. It focuses on the importance of temperature vs. water stress, and elaborates on their implications for potential climate adaption strategies.

How to cite: Becker, R., Schulz, S., Merz, R., aus der Beek, T., and Schüth, C.: Effects of temperature and water stress on agricultural productivity in a semi-arid irrigation system under changing climate, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8144, https://doi.org/10.5194/egusphere-egu2020-8144, 2020

D5 |
EGU2020-11637
| Highlight
Ainis Lagzdins, Linda Grinberga, Arturs Veinbergs, Ritvars Sudars, and Kaspars Abramenko

This study was conducted based on systematic and regular water quality and quantity monitoring activities carried out as a part of the Agricultural Runoff monitoring programme in Latvia. This programme was initiated in 1995 and since then aims to document and evaluate the current status and long-term trends in nutrient concentrations and losses at different spatial and temporal scales as affected by meteorological, hydrological, and farming conditions.

Water sampling and flow measurements were carried out at several spatial scales where subsurface and open drainage systems have been installed including 16 experimental plots, 3 subsurface drainage fields, 3 small agricultural catchments, 24 small and medium size rivers. In addition, 21 groundwater monitoring well was established at 6 locations to investigate the effects of agricultural activities on groundwater quality. Water samples were collected on a monthly basis and analyzed for nitrate-nitrogen, ammonium-nitrogen, total nitrogen, orthophosphate-phosphorus, total phosphorus. Continuous flow measurements were made at experimental plots, subsurface drainage fields and small agricultural catchments using hydraulic measurement structures, pressure sensors and data loggers.

The long-term monitoring data (1995 – 2019) showed that water quantity and quality in subsurface and open drainage systems were strongly affect by meteorological conditions at the research site mainly in terms of annual and seasonal patterns of precipitation. Moreover, the flooding conditions in 2017 and drought conditions in 2018 and 2019 indicated that the agronomic activities at the research sites such as crops, tillage operations, types and application rates of fertilizers have a minor role on water quality leaving the agricultural fields. Intensive precipitation outside the growing season in 2017 resulted in the highest nutrient losses, while drought conditions in 2018 resulted in the lowest nutrient losses since this monitoring programme was established. One year of flooding and two consecutive years of drought have emphasized that more specific water and nutrient retention measures are needed in agricultural areas to secure timely removal of excess water from fields and water storage for later use. The analysis of nitrate-nitrogen concentrations obtained at different scales of measurements showed that nutrient concentrations, especially nitrate-nitrogen, decrease if the scale of measurements increases with the highest concentrations at the experimental plot scale followed by subsurface drainage fields, small catchments, and rivers.

How to cite: Lagzdins, A., Grinberga, L., Veinbergs, A., Sudars, R., and Abramenko, K.: The effects of flooding and drought on water quantity and quality in agricultural drainage systems and streams in Latvia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11637, https://doi.org/10.5194/egusphere-egu2020-11637, 2020

D6 |
EGU2020-13406
Yang Lu and Justin Sheffield

Global population is projected to keep increasing rapidly in the next 3 decades, particularly in dryland regions of the developing world, making it a global imperative to enhance crop production. However, improving current crop production in these regions is hampered by yield gaps due to poor soils, lack of irrigation and other management practices. Here we develop a crop modelling capability to help understand gaps, and apply to dryland regions where data for parametrizing and testing models is generally lacking. We present a data assimilation framework to improve simulation capability by assimilating in-situ soil moisture and vegetation data into the FAO AquaCrop model. AquaCrop is a water-driven model that simulates canopy growth, biomass and crop yield as a function of water productivity. The key strength of AquaCrop lies in the low requirement for input data thanks to its simple structure. A global sensitivity analysis is first performed using the Morris screening method and the variance-based Extended Fourier Amplitude Sensitivity Test (EFAST) method to identify the key influential parameters on the model outputs. We begin with state-only updates by assimilating different combinations of soil moisture and vegetation data (vegetation indices, biomass, etc.), and different filtering/smoothing assimilation strategies are tested. Based on the state-only assimilation results, we further evaluate the utility of joint state-parameter (augmented-states) assimilation in improving the model performance. The framework will eventually be extended to assimilate remote sensing estimates of soil moisture and vegetation data to overcome the lack of in-situ data more generally in dryland regions.

How to cite: Lu, Y. and Sheffield, J.: Improving Dryland Crop Simulation by Assimilating Soil Moisture and Vegetation Data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13406, https://doi.org/10.5194/egusphere-egu2020-13406, 2020

D7 |
EGU2020-16229
| Highlight
Francesca Casale and Daniele Bocchiola

We present here preliminary results in fulfilment of the project IPCC MOUPA (Interdisciplinary Project for assessing current and expected Climate Change impacts on MOUntain PAstures) project, funded by Fondazione Cariplo of Italy, aimed to i) evaluate potentially modified productivity of pasture lands under climate change scenarios, and subsequent on socio-economic, wildlife and biodiversity impacts, within the Italian Alps, and ii) propose management strategies for pasture and multi-functional use of mountain areas.

In high mountain areas pastures are a source of living for local communities, and further agriculture and livestock supply ecosystems services (ES). In the last century, increase of temperature nearby +1.5°C was observed in the Alpine region, to increase hereon, and future climate scenarios display potential reduction of water availability, with an increase in precipitation extremes, potentially impacting soil moisture, vegetation, and pasture dynamics (phenology/timing), deeply dependent upon precipitation, temperature, and snow cover.

We here defined some fragility indices (FIs), to sketch the effects of climate change on pastures in the Alps, with special focus on Valtellina valley, in the central Alps of Italy. FIs can be used to highlight pressures experienced by pastures, and thresholds for failure, and to develop policies to i) determine zones needing particular management, and adaptation, ii) monitor trends of global environmental stability, iii) evaluate the overall impact of climate change and anthropic influence, and iv) investigate the dynamics of pasture fragility. We chose indices of climate, productivity, and water usage. Some of these FIs can be evaluated starting from observations, but others have to be calculated using models of pasture growth, and water availability. For this reason, a pasture model Poli-Pasture has been set up to simulate the pasture growth, and to evaluate FIs in the target area.

To explore the broad range of variability under uncertain future climate, FIs are calculated for present conditions of pastures, and for future projected conditions using i) three climatic scenarios of AR5 of IPCC (RCP 2.6, RCP 4.5 and RCP 8.5) as depicted by three Global Circulation Models GCMs (EC-Earth, Echam6.0, CCSM4), and ii) four climatic scenarios of the AR6 (RCP 2.6, RCP 4.5, RCP 7.0, RCP 8.5) depicted by three GCMs (EC-Earth3, Echam6.3, CESM2), and some preliminary conclusion are reported for future pasture dynamics, and management therein.

How to cite: Casale, F. and Bocchiola, D.: Effects of prospective climate change on pasture productivity in the Italian Alps., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16229, https://doi.org/10.5194/egusphere-egu2020-16229, 2020

D8 |
EGU2020-172
| Highlight
Kaushika Gujjanadu Suryaprakash and Hari Prasad Kotnur Suryanarayana Rao

India is primarily an agronomic country and most of the cropping in the Rabi season depends on the rainwater availability. With the ill effects of climate change cropping up, the agriculture sector is expected to take a major hit. This study takes a technical approach on the impact of climate change on the irrigation requirement of wheat cropping by studying the future irrigation requirement based on the temperature and rainfall that can be expected to occur in the future timelines. A root water uptake model involving the solution of the non-linear Richards equation to assess the root-zone moisture movement is formulated and validated. The inputs of the model include the crop data, which, in this case is obtained by field experimentation at the irrigation field laboratory at IIT Roorkee and weather data, which is obtained from the CANESM2 General circulation model for the historical and projected timescales. The historical GCM data for thirty years is bias corrected using the observed data from the India Meteorological department (IMD). The validated root water uptake model is applied to the historical and projected data for a 60 year span for two emission scenarios for RCP 4.5 and 8.5. The output was obtained as soil moisture profiles and frequencies of irrigation required. It was seen that for both the mild and high emission scenarios, the number of irrigation events per cropping period increased. This increase is assessed using variability analysis and for its impacts on the water resources management systems. The variability assessment showed the variation of the irrigation water requirement on annual and decadal scales. This is useful in understanding the historical and expected crop water requirement in view of the climate change effects.

How to cite: Gujjanadu Suryaprakash, K. and Kotnur Suryanarayana Rao, H. P.: Variability assessment of Irrigation Requirement for Winter Wheat Cropping Under Changing Climate., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-172, https://doi.org/10.5194/egusphere-egu2020-172, 2019

D9 |
EGU2020-1060
Navjot Kaur and Susanta Paikaray

Groundwater and Sutlej river water are major sources of irrigation in Rupnagar district of Punjab. Water quality was examined for their agricultural suitability using a total of 54 surface water (16 from Sutlej and 6 from Sirsa River) and groundwater (total 32 of ~160 m depth) samples from Pre- (June 2019) and post-monsoon (Dec 2018) seasons. On-site parameters (electrical conductivity, pH, total dissolved solids) indicate permissible pH (pH 6.6-8.2) and conductivity (147-1953 μS/cm), while 18.5% of samples are brackish salt to salt category type on salinity index. The results of these parameters were further interpreted and measured with different irrigation indexes like sodium percent (SP), sodium adsorption ratio (SAR), residual sodium carbonate (RSC), chloride concentrations and Wilcox diagram. Similarly, most of the samples (except Sutlej river water samples) were found to be above permissible limits with respect to SP (5.36-81.01) and RSC (0-6.23), but SAR is indicative of suitability for irrigation purposes (0.11-8.3). The suitability for irrigation as per SAR is because of low sodium content in all the samples relative to calcium and magnesium. The Wilcox diagram of pre-monsoon samples indicate high, medium and low saline to low sodium hazard except 1 sample with high saline to medium sodium hazard and salinity-sodium hazard in post-monsoon is comparatively lower than that of pre-monsoon. However careful observation of the complete data analysis suggests that all the parameters in Sutlej river water samples were found to be suitable for irrigation while most of the groundwater samples and 3 samples from Sirsa river were unfit for irrigation purposes as inferred from SP, RSC and Wilcox diagram.

How to cite: Kaur, N. and Paikaray, S.: Assessment of groundwater and surface water quality for irrigation suitability in Rupnagar District, Punjab, India, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1060, https://doi.org/10.5194/egusphere-egu2020-1060, 2019

D10 |
EGU2020-3786
Jun Niu and Shaozhong Kang

Crop production in North China largely depends on irrigation, which is mainly from groundwater in Northwest China. Groundwater abstractions are decreasing the groundwater levels, and threatening the fragile ecological systems of arid regions. Here, we examine the dynamic relations between groundwater level and irrigation water for the last three decades in Heihe River basin in China. The average groundwater decline level, attributed to the irrigation water consumption for the farmland area over the past three decades, was calculated. Moreover, the future possible changes are estimated with different RCP senarios. Effective water-saving measures and strategies are expected to adopt to maintain both groundwater levels and agricultural productivity for the coming decades.

How to cite: Niu, J. and Kang, S.: Environmental burdens of groundwater extraction for irrigation over an agricultural land in Northwest China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3786, https://doi.org/10.5194/egusphere-egu2020-3786, 2020

D11 |
EGU2020-4877
Jonathan Jupke and Ralf Schäfer

A large number of chemicals such as pharmaceuticals, pesticides and industrial chemicals are in daily use. In Europe alone, an estimated 100,000 chemicals are in current use, of which 30,000 are produced in quantities larger than one ton per year. Chemicals can enter freshwater ecosystems as an intended (e.g. deliberate emission as in the case of pesticides) or unintended (e.g. wastewater discharge as in the case of pharmaceuticals) byproduct of their use. In the environment, many chemicals (hereafter called toxicants) can exert adverse toxic effects on freshwater organisms and in turn on ecosystem functions. The potential toxic effects of chemicals are often examined within the context of Ecological Risk Assessment (ERA). ERA consists of standardized procedures and methods to evaluate the environmental risks of ecological systems. An open question is to what extent ERA needs to account for differences between recipient ecosystems that are subject to chemical exposure. For example, in the European context, is a single ecological threshold concentration per substance sufficient or is the sensitivity of the organism’s dependent on water body size, geology or climate.

As previous studies have shown that the latter factors influence the community composition of algae and invertebrates, we aim to compare the sensitivity of communities across macroecological gradients. 

We established a typology of small streams for eight European countries that captures the major macroecological gradients and identified typical ecological assemblages for each type. The typology is based on the Catchment & Characteristics Modelling 2 database and incorporates catchment properties such as climate, geology, and altitude as well as river attributes such as sinuosity and flow regime statistics. The latter are derived from modeled daily discharge values. Through CLARA-clustering of the resulting data, we obtained a classification into 14 stream classes. We focused on smaller rivers as they constitute the majority of river length, host a higher share of biodiversity than large rivers, and are more susceptible to pollution. The presented typology is built from the ground up with openly accessible data. All code will be made publicly available; thus, it will be easy to update, modify, and extend the typology. Beyond our application the typology can be used to regionalize ecological and hydrological models, to inventory the number and state of different river types or to develop individualized conservation programs.

Based on the identified typical assemblages we can also present preliminary relative sensitivities of stream types towards different toxicants.

How to cite: Jupke, J. and Schäfer, R.: A unified typology for small European rivers , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4877, https://doi.org/10.5194/egusphere-egu2020-4877, 2020

D12 |
EGU2020-9283
Simon Stisen, Raphael Schneider, and Anker Lajer Højberg

About half of the Danish agricultural land is artificially drained to make land arable and increase crop yield. Those artificial drains, mostly in the form on tile drains, have a significant effect on the groundwater flow patterns and the whole water cycle. Consequently, the drainage system must also be represented in hydrological models that are used to understand and simulate, for example, recharge patterns, groundwater flow paths, or the transport and retention of nutrients. However, representation of drain in regional- and large-scale hydrological models is challenging due to i) issues with scale, ii) a lack of data on the distribution of the drain network, and iii) a lack of direct observations of drain flow. This calls for more indirect methods to inform such models.

We assume that drain flow leaves a signal in certain hydrograph signatures, as it impacts the generation of streamflow. Based on a dataset of observed discharge covering all of Denmark, and simulation results from regional-scale hydrological models, we use machine learning regressors to shed light on possible correlations between hydrograph signatures and artificial drainage. Building up on this step, we run a series of calibration exercises on a hydrological model of the agriculturally dominated Norsminde catchment, Denmark (~100 km2). The model is set up in the DHI MIKE SHE software, as distributed coupled groundwater-surface water models with a grid size of 100 m. The different calibration exercises differed in the objective functions used: either we only use conventional stream flow metrics (KGE), or also include hydrograph signatures that showed sensitive towards drain flow in our regression analysis. We then evaluate the results from the different calibration exercises, in terms of how well the model reproduces directly observed drain flow, and spatial drainage patterns.

Despite including hydrologic signatures in the calibration process, the representation of drain flow in large-scale models remains challenging. Eventually, the insight gained from this and similar studies will be incorporated in the National Water Resources Model for Denmark, to help improving national targeted regulation of nitrate application through fertilizers.

How to cite: Stisen, S., Schneider, R., and Lajer Højberg, A.: Including hydrologic signatures in the calibration of a groundwater-surface water model to improve representation of artificial drain, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9283, https://doi.org/10.5194/egusphere-egu2020-9283, 2020

D13 |
EGU2020-9933
Silvio J. Gumiere, Paul Celicourt, Jonathan Lafond, and Alain Rousseau

The Bécancour River watershed, located half way between Montréal and Québec City in the Province of Québec (Canada) and covering a surface area of about 2600 km2, is dominated by forest in the upstream portion and agriculture in the downstream portion. The production of cranberries (Vaccinium macrocarpon) is an important feature of this watershed. This crop not only relies on abundant water resources for frost protection, soil moisture management, and harvest and winter flooding, but also on tiled drainage system which together impact the watershed hydrology and flow patterns.

This study aims at modelling the impacts of cranberry farms on the hydrologic regimes of the Bécancour River watershed in Québec, Canada. We dispose of groundwater level and soil tension data at the root zone from two distinct cranberry farms, meteorological data, Ground-penetrating radar (GPR) and stratigraphy data, and LIDAR data collected over a period of 5 years starting in 2014. We setup the hydrological model using the well-known finite-element-based model named FEFLOW to simulate the hydrological behavior of two cranberry farms in the watershed. The preliminary results are promising and demonstrate the potential of the model in a) depicting and understanding hydrological changes in the watershed and b) supporting decision-making regarding water resources management for agricultural production in the region.

How to cite: Gumiere, S. J., Celicourt, P., Lafond, J., and Rousseau, A.: Modelling the impacts of cranberry farms on the hydrologic regimes of the Bécancour River watershed in Québec, Canada, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9933, https://doi.org/10.5194/egusphere-egu2020-9933, 2020

D14 |
EGU2020-11926
Hoejeong Jeong, Jae-Hyun Ryu, Sang-il Na, and Jaeil Cho

  In 1980s, Crop Water Stress Index (CWSI) is suggested to indicate the water stress of crops. CWSI is based on the leaf energy balance, which is closely related to leaf temperature. To calculate CWSI, meteorological factors such as air temperature and vapor pressure deficit should be measured besides leaf temperature. As recent technology has been developed, leaf temperature can be easily observed by thermal camera or infrared thermometer. Stomatal conductance (gs, mmol m-2 s-1) is one of the critical factors to understand crop photosynthesis and water demand. In addition, the behaviors of gs can represent the biotic and abiotic plant stresses. In abnormal condition, such as drought, insects or disease, gs getting lower. The observation of gs will make better to evaluate and predict crop growth and conditions. Therefore, the time series data of gs is useful for the monitoring of crop growth and the quick detection of abnormal crop condition in smart-farming system but there are some limitations to measure gs continuously and easily.

  We assume that there is some relationship between CWSI and gs because both has strong relation to leaf temperature. Thus, the aim of this study is to investigate possibility of estimation of gs using CWSI which is derived from thermal image. Through the data collected from literatures, negative correlations between CWSI and gs were revealed. The slope of correlation was changed according to crop types. In addition, as a result of simulation, there is almost linear negative relationship between CWSI and gs, and the slope was determined by maximum stomatal conductance (gs_max). Field measurement in this study was also demonstrated to identify such correlation. Further, various methods to measure CWSI were tested. This relationship will contribute to not only monitoring of crop stress for irrigation scheduling in smart farm system but also estimating evapotranspiration, photosynthesis, and crop yield.

How to cite: Jeong, H., Ryu, J.-H., Na, S., and Cho, J.: Estimation of Stomatal Conductance using Crop Water Stress Index based on the Thermal Image at a Leaf Scale, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11926, https://doi.org/10.5194/egusphere-egu2020-11926, 2020

D15 |
EGU2020-12520
| Highlight
Congying Han

Spatiotemporal Variability of Potential Evaporation in Heihe River Basin Influenced by Irrigation

Congying Han1,2, Baozhong Zhang1,2, Songjun Han1,2

1 State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China.

2 National Center of Efficient Irrigation Engineering and Technology Research-Beijing, Beijing 100048, China.

Corresponding author: Baozhong Zhang (zhangbaozhong333@163.com)

Abstract: Potential evaporation is a key factor in crop water requirement estimation and agricultural water resource planning. The spatial pattern and temporal changes of potential evaporation calculated by Penman equation (EPen) (1970-2017) in Heihe River Basin (HRB), Northwest China were evaluated by using data from 10 meteorological stations, with a serious consideration of the influences of irrigation development. Results indicated that the spatial pattern of annual EPen in HRB was significantly different, among which the EPen of agricultural sites (average between 1154 mm and 1333 mm) was significantly higher than that of natural sites (average between 794 mm and 899 mm). Besides, the coefficient of spatial variation of the aerodynamic term (Eaero) was 0.4, while that of the radiation term (Erad) was 0.09. The agricultural irrigation water withdrawal increased annually before 2000, but decreased significantly after 2000 which was influenced by the agricultural development and the water policy. Coincidentally, the annual variation of Epen in agricultural sites decreased at -40 mm/decade in 1970-2000 but increased at 60 mm/decade in 2001-2017, while that in natural sites with little influence of irrigation, only decreased at -0.5mm/decade in 1970-2000 but increased at 11 mm/decade in 2001-2017. So it was obvious that irrigation influenced Epen significantly and the change of Epen was mainly caused by the aerodynamic term. The analysis of the main meteorological factors that affect Epen showed that wind speed had the greatest impact on Epen of agricultural sites, followed by relative humidity and average temperature, while the meteorological factors that had the greatest impact on Epen of natural sites were maximum temperature, followed by wind speed and relative humidity.

How to cite: Han, C.: Spatiotemporal Variability of Potential Evaporation in Heihe River Basin Influenced by Irrigation , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12520, https://doi.org/10.5194/egusphere-egu2020-12520, 2020

D16 |
EGU2020-14008
Elisa Bjerre and Anker Lajer Højberg

Diffuse nitrogen pollution is a major cause of degraded water quality in rivers and groundwater across Europe. In artificially drained agricultural catchments, nitrate leaching from the root zone is either transmitted directly to streams by tile drains or transported to the groundwater system. Thus, the partitioning of the water flux to drains, the drainage fraction, is an indicator of surface-water/groundwater vulnerability to nitrogen application. This information can be used to target mitigation measures like drain filter technologies and cover crops. Hydrological models are usually employed to assist water management. Yet, for many decision-making applications numerical models are computationally too time-consuming. Additionally, as models are simplifications of the complex natural system, model results are inherently imprecise for grid-scale, and thus field-scale, predictions. To overcome these barriers, we develop metamodels to make predictions of drainage fraction. We train random forest and gradient boosted regression trees statistical metamodels to MIKE SHE-derived 16-year averages of drainage fraction in a regional groundwater model (100x100m) in Denmark. We explore the effects of mappable and non-mappable predictor variables on model performance. The metamodels are used to identify the most important predictor variables for drainage fraction prediction. Based on this, we investigate how grid cells of similar characteristics can be clustered in homogeneous subsets, in which the drainage fraction variability can be used as an uncertainty estimate. The findings could potentially support decision making on spatially differentiated regulation of nitrate emissions.

How to cite: Bjerre, E. and Lajer Højberg, A.: Metamodeling for predicting drainage fraction in groundwater: Development of a decision-support tool, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14008, https://doi.org/10.5194/egusphere-egu2020-14008, 2020

D17 |
EGU2020-14894
| Highlight
Bellie Sivakumar

Modeling the dynamics of streamflow continues to be highly challenging. The present study proposes a new approach to study the temporal dynamics of streamflow. The approach couples the concepts of complex networks and chaos theory. Applications of the concepts of complex networks for studying streamflow dynamics have been gaining momentum in recent years. A key step in such applications is the construction of the network – a network is a set of points (nodes) connected by lines (links). The present study uses the concept of phase-space reconstruction, an essential first step in chaos theory-based methods, for network construction to study the temporal dynamics of streamflow. The phase-space reconstruction involves representation of a single-variable time series in a multi-dimensional phase space using delay embedding. The reconstructed phase space is treated as a network, with the reconstructed vectors (rather than the original time series) serving as the nodes and the connections between them serving as the links. With this network construction, the clustering coefficient of the individual nodes and the entire network is calculated to assess the node and network strengths. The approach is employed to a large number of streamflow time series observed in the United States. The results indicate the usefulness and effectiveness of the phase-space reconstruction-based approach for network construction. The implications of the outcomes for identification of the appropriate type and complexity of model as well as for classification of catchments are discussed.

How to cite: Sivakumar, B.: Temporal Dynamics of Streamflow Using Complex Networks, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14894, https://doi.org/10.5194/egusphere-egu2020-14894, 2020

D18 |
EGU2020-19626
Arturs Veinbergs and Ainis Lagzdins

The threshold groundwater levels limiting the drainage depth and tile drain runoff as well as runoff recession and runoff partitioning are case-specific.  These are the characteristics that are usually necessary for setting up and calibration processes for such models as HYPE (Lindström et al. 2010) and SWAT (Neitsch et al. 2002).  

The objective of the present study is to identify the thresholds of groundwater levels and runoff rates that limit the formations of such runoff components as base flow and tile drain runoff. This study utilizes the data that represents the daily runoff measurements in open ditch with such characteristics as total length 2.4 km, basin area 368 ha, loamy soils, agricultural lands with subsurface drainage systems installed in 98% of the area, average tile depth 1.2m below ground surface.

The runoff components were partly separated from the daily runoff hydrographs through the analysis of storm runoff recession gradients (eq.1) and groundwater level fluctuations during the period from 2006. to 2015. Baseflow and tile drain runoff ware calculated as beeing linearly dependent on daily groundwater level fluctuations (eq.2).

  Rci=Qi+1/Qi,     (1)

Qx=fx(GWT)=ax*GWT+bx ,      (2)

Where: Rci – recession gradient; Qi and Qi+1– runoff of day i and i+1 respectively;  Qx – runoff component; GWT– groundwater level; ax and bx– slope and intercept of a linear function.

Nash-Sutcliffe efficiency (NSE) and percent bias (PBIAS) were used for comparison of calculated and separated runoff components.

The results indicate a decrease in drainage intensity and reduction in specific yield during the study period. The groundwater level of 1.18m below ground surface limit the existence of the tile drain runoff, that, furthermore,  is similar for rising and falling groundwater level. The results reveal that runoff could be contributed by 35%, 57% and 8% of baseflow, tile drain runoff and surface runoff respectively.

How to cite: Veinbergs, A. and Lagzdins, A.: The Identification of Hydrological Threshold Variables, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19626, https://doi.org/10.5194/egusphere-egu2020-19626, 2020

D19 |
EGU2020-21451
Mengge Lu, Huaiwei Sun, and Shanzhen Yi

              The agricultural demands and supply were expected to grow all of the more severe with increasing population. Growing degree days is the dominant factors associated with the quality and quantity of many agricultural crops. In order to find out the effects of historical climate changes on agricultural production, we investigated the trends and changes of growth degree days (GDD) and heat degree days (HDD) in the main maize producing area of China by using the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) data. In this study, we find that the GDD and HDD increased slightly during 1861-2005 with more abruptly increases in GDD, which lead to better production environment in Central and Eastern China for maize. However, the climatic trend rates showed large variation in spatial, as GDD shows an upward trend in Hebei and Shandong provinces and HDD was on the rise in Shandong and Shanxi provinces. The GDD and HDD in the northern part of Hebei province and the northern part of Shanxi province are lower, but have a higher rising trend. Therefore, the future heat resources are better for maize production, but the risk of extreme high temperature is increased. This result indicates a necessary to the crop layout in these areas.

Appendix. List of figures

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

How to cite: Lu, M., Sun, H., and Yi, S.: Changes of growing degree days in the main maize producing area of China during past years, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21451, https://doi.org/10.5194/egusphere-egu2020-21451, 2020