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Abstract:Changes in hydrological processes due to rising temperatures and related effects on the socio-economy and people's livelihood are major concerns in Bangladesh. A study has been performed to assess the effects of increasing temperature on the groundwater levels and consequent changes in irrigation costs for groundwater-dependent irrigated agriculture in Northwest Bangladesh. A support vector machine (SVM) was used to model the temporal variations in groundwater level from rainfall, evapotranspiration, groundwater abstraction, and agricultural return flow. A multiple linear regression (MLR) model was developed to define the functional relationship between irrigation costs and groundwater levels. The model showed that average groundwater level during the major irrigation period (January-April) decreased by 0.15-2.01 m due to an increase in temperature of 1-5°C, which increased irrigation costs by 0.05-0.54 thousand Bangladesh Taka (BDT) per hector.

Assessment of possible changes in crops water stress due to climate alteration is essential for agricultural planning, particularly in arid regions where water supply is the major challenge for agricultural development. This study aims to project climatic water availability (CWA) and crop water demand (CWD) to outline the possible future agricultural water stress of Iraq for different radiative concentration pathways (RCPs). The ensemble means of downscaled precipitation and temperature projections of the selected global climate models (GCMs) were used in a simple water balance model for this purpose. The modified Mann–Kendall (mMK) trend test was employed to estimate the tendency in CWA and the Wilcoxon rank test to evaluate CWD alteration in three future time horizons compared to the base period (1971–2000). The results revealed a decrease in CWA at a rate of up to −34/year during 2010–2099 for RCP8.5. The largest declination would be in summer (−29/year) and an insignificant decrease in winter (−1.3/year). The study also showed an increase in CWD of all major crops for all scenarios. The highest increase in CWD would be for summer crops, approximately 320 mm, and the lowest for winter crops, nearly 32 mm for RCP8.5 in the far future (2070–2099). The decrease in CWA and increase in CWD would cause a sharp rise in crop water stress in Iraq. This study indicates that the increase in temperature is the main reason for a large increase in CWD and increased agricultural water stress in Iraq.

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Accurately estimating the amount of evaporation loss is necessary for scheduling and calculating irrigation water requirements. In this study, four machine learning (ML) modeling approaches, extreme learning machine (ELM), gradient boosting machine (GBM), quantile random forest (QRF), and Gaussian process regression (GPR), have been developed to estimate the monthly evaporation loss over two stations located in Iraq. Monthly climatical parameters have been used as an input variable for simulating the evaporation rate. Several statistical measures (e.g., mean absolute error (MAE), correlation coefficient (R), mean absolute percentage error (MAPE), and modified index of agreement (Md)), as well as graphical inspection, were used to compare the performances of the applied models. The results showed that the GBM model has much better performance in predicting monthly evaporation over two stations compared to other applied models. For the first case study which was in Diyala, the results showed a prediction enhancement in terms of MAE and RMSE by 7.17%, 21.01%; 16.51%, 15.74%; and 23.14%, 26.64%; using GBM compared to ELM, GPR, and QRF, respectively. However, for the second case study (in Erbil), the prediction enhancement was improved in terms of reduction of MAE and RMSE by 10.88%, 9.24%; 15.24%, 5%; and 16.06%, 15.76%; respectively, compared to ELM, GPR, and QRF models. The results of the proposed GMBM model can therefore assist local stakeholders in the management of water resources.