Field Drainage Strategies To Adapt Climate Change And Crop Production In The Lower Reaches Of The Yangtze River Basin,China

Climate change has been projected to produce adverse impacts on farmlands' water balance and agricultural productivity around the world.Considering such a threat to China's agricultural sector and water resources,this thesis firstly projected the future climatic changes and their adverse impacts on the water balance(using the DRAINMOD model)and irrigation requirements(IRs)(using the CROPWAT model)in the paddy lands that locate at the lower reaches of the Yangtze River Basin,and then some approaches were proposed to mitigate these adverse impacts of climate change.Following that and regarding the importance of the modeling approach as the most appropriate way that enables hydrologists to assess how the future climatic changes would impact farmlands' water balance and agricultural productivity,this thesis alerted to some of the DRAINMOD and CROPWAT models' limitations and then proposed novel approaches to overcome these limitations.These limitations are:?Considering a fixed value of the surface storage capacity(SSC)in the DRAINMOD model for the whole simulation period,which results in improper predictions of the water fate in agricultural lands if SSC changes due to any human or agricultural practices.?The adopted empirical approaches(in the CROPWAT model)that predict the rainwater fate.These approaches neglect many field conditions and management practices that have a direct and/or indirect impact on the rainwater fate.Considering the master role of rainwater,if best utilized,in decreasing IRS,these approaches cause the model to predict improper IRs.The proposed approaches to overcome such models' limitations include 1)a novel artificial neural network model(ANNM)that mimics the DRAINMOD model's performance and considers multiple values of SSC during the simulation period,and 2)a novel approach that adopts the detailed hydrological predictions of the rainwater fate(from the DRAINMOD model),rather than the empirical ones(which are adopted in CROPWAT model),to estimate IRs.This ensures accurate predictions of the rainwater fate,thus proper IRs estimates.Major findings are as below:1)The study area was projected to suffer from future warming alongside more frequent peak precipitation events,which increases future runoff by 33%(under the representative concentration pathway 45(RCP45))and 36.9%(RCP85)while decreasing future infiltration.As a result,a future drop[6%(RCP45)and 14.5%(RCP85)]in groundwater tables(GWTs)was projected to occur,which threatens the future rice productivity(as a major cultivated crop in these lands).2)Regarding the projected impacts of climate change on future IRs in the study area;CROPWAT simulations revealed that climatic changes will increase IRs in most future periods under both scenarios RCP45 and RCP85(up to more than 60%increase in future IRs during some periods,compared to IRs of the 2018 season).3)DRAINMOD simulations show that enhancing the future surface storage conditions in the study area will help to cope with the projected adverse impacts of climate change on the field water balance(by mitigating the future increase in runoff alongside rising GWTs).Also,CROPWAT simulations revealed that such enhancements in surface storage capacity will decrease future rainwater losses in the runoff,which allows better utilization of this rainwater and thus mitigating the projected increase in future IRs.4)To overcome the DRAINMOD model's limitation,represented in the inability to consider the changes in surface drainage conditions during the simulation period,an ANNM was developed to mimic the DRAINMOD model's performance and consider the variation in SSC.The developed model was validated by comparing its predictions of groundwater tables in the study area with the DRAINMOD's ones and there were good agreements between them[R2(0.97-0.99),MAE(2.4cm-4.6cm),NSE(0.99),and RMSE(5.1cm-7.5cm)].This reveals the applicability of the developed ANNM to predict the water balance in artificial-drained farmlands,with the advantage of considering the changes in SSC(thus surface drainage conditions)during the simulation period.5)Compared to the real IRs of the 2018 growing season in the study area,CROPWAT simulations revealed that adopting the empirical approaches(as in the model)to predict the rainwater fate in rainfed farmlands(like the study area)leads to improper IRs estimates due to the neglect of several field conditions and management practices that have a direct and/or indirect impact on the rainwater fate in farmlands.On the other hand,when the new proposed approach(which employs the DRAINMOD's detailed hydrological predictions of the rainwater fate,rather than empirical approaches)was applied to estimate IRs,there were great enhancements in IRs estimates.This is due to the adopted hydrological-detailed approaches(in the DRAINMOD model)that consider most of the field conditions and management practices when predicting the rainwater fate.This assures the necessity of having proper predictions of the rainwater fate for accurate IRs estimates.Findings from this thesis warn stakeholders generally of the great possibility of having severe adverse impacts on future agricultural productivity and water resources conservation due to the projected climate change,and specifically how such climatic changes would adversely impact the paddy lands at the lower reaches of the Yangtze River Basin.This thesis also states the necessity to ensure that hydrological and crop models consider(whether in their input module or calculation sequence)most of the field conditions and management practices that exist in the studied areas.Results revealed that such models' limitations(represented in the inability to consider these parameters)will mostly result in inaccurate simulations,thereby weakening the management of agricultural and water resources.Therefore the enhancements in simulating farmlands' water balance and IRs(that were proposed in this thesis)are supposed to result in a better understanding of hydrologic regimes in farmlands alongside better utilization of the available water resources,thus high agricultural productivity.