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Information on water balance components such as evapotranspiration and groundwater recharge are crucial for water management. Due to differences in physical conditions, but also due to limited budgets, there is not one universal best practice, but a wide range of different methods with specific advantages and disadvantages. In this study, we propose an approach to quantify actual evapotranspiration, groundwater recharge and water inflow, i.e. precipitation and irrigation, that considers the specific conditions of irrigated agriculture in warm, arid environments. This approach does not require direct measurements of precipitation or irrigation quantities and is therefore suitable for sites with an uncertain data basis. For this purpose, we combine soil moisture and energy balance monitoring, remote sensing data analysis and numerical modelling using Hydrus. Energy balance data and routine weather data serve to estimate ET0. Surface reflectance data from satellite images (Sentinel‐2) are used to derive leaf area indices, which help to partition ET0 into energy limited evaporation and transpiration. Subsequently, first approximations of water inflow are derived based on observed soil moisture changes. These inflow estimates are used in a series of forward simulations that produce initial estimates of drainage and ETact, which in turn help improve the estimate of water inflow. Finally, the improved inflow estimates are incorporated into the model and then a parameter optimization is performed using the observed soil moisture as the reference figure. Forward simulations with calibrated soil parameters result in final estimates for ETact and groundwater recharge. The presented method is applied to an agricultural test site with a crop rotation of cotton and wheat in Punjab, Pakistan. The final model results, with an RMSE of 2.2% in volumetric water content, suggest a cumulative ETact and groundwater recharge of 769 and 297 mm over a period of 281 days, respectively. The total estimated water inflow accounts for 946 mm, of which 77% originates from irrigation. [ABSTRACT FROM AUTHOR]

Climate change and variability threaten the sustainability of future food production, especially in semi-arid regions where water resources are limited and irrigated agriculture is widespread. Increasing temperatures will exacerbate evaporative losses and increase plant water needs. In this regard, higher irrigation intensities have been posited as a solution to mitigate climate change impacts in these regions. Here, using the agro-hydrological model SWAT and the biophysical crop model APSIM, we show that this mitigation measure is oversimplified. We find that heat stress, driven by strong temperature increases, might be the dominating factor in controlling future crop yields and plant water needs. Our analysis encompasses agricultural areas of the Lower Chenab Canal System in Punjab, Pakistan (15,000 km2), which is part of the Indus River irrigation system, the largest irrigation system in the world, covering major cotton, rice and maize cropping zones. Climate models project a strong increase in temperature over the study region of up to 1.8 °C (±0.5 °C) until the mid-century. Both models predict a decline in future crop yields for maize and rice crops, while cotton yields are less effected by rising temperatures and strongly benefit from elevated atmospheric CO 2 concentrations. For a high carbon emission scenario, the models simulate yield declines for maize of up to −10% (APSIM) and −19% (SWAT); for rice yields of up to −4% (APSIM) to −26% (SWAT), and for cotton yields of −1% (APSIM) to +11% (SWAT), until 2050, relative to the baseline scenario 1996–2005. Our modeling results further suggest that irrigation demands do not align with increasing temperature trends. Average irrigation demands increase less under higher temperatures. Overall, our study emphasizes the role of elevated heat stress, its effects on agricultural productivity as well as water demand, and its implications for climate change adaption strategies to mitigate adverse impacts in an intensively irrigated region. Climate change is one of the most important challenge facing agriculture, and hence future food security. Farmers struggle more and more to ensure a reliable food production. This is especially true for semi-arid regions where water resources are limited but at the same time urgently needed for irrigation, such as in the Lower Chenab Canal Area, in Punjab, Pakistan. The search for adaptation measures to weaken the negative impacts of climate change on agricultural systems becomes increasingly important. One option could be the intensification of irrigation. We use two models to simulate crop growth processes under various climate conditions. Our results show that heat stress would be the dominating stress for plants in the selected study region and that this heat stress might even lead to a reduction in water demand. Intensive irrigation can not necessarily help to prevent climate change-related yield losses. The models further predict that rising CO 2 can have a positive effect on crop yield by enhancing plant growth. Nevertheless, our results indicate that these positive effects could not compensate the negative impacts of heat stress under constantly rising temperatures. • Increased heat stress strongly reduces future crop yields of rice, maize and cotton. • Lowered plant productivity due to heat stress markedly reduces plant water demand. • Intensified irrigation to increase future crop yields is not a viable climate change adaptation measure. [ABSTRACT FROM AUTHOR]

The study to be presented, follows a diagnostic approach to assess the estimation of actual ET over a complex and highly diverse agricultural area. We use a process-based and spatially distributed hydrological model to estimate ET, which integrates detailed soil-vegetation dynamics, and compare the results to remote sensing derived ET data. The Soil and Water Assessment Tool (SWAT) model is set up to simulate ET of an entirely managed meso-scale irrigation scheme, in the semi-arid Punjab region of Pakistan, which is characterized by diverse and small scale cropping patterns and a high temporal and spatial climate variability. Remote sensing derived ET data, based on the Surface Energy Balance Algorithm (SEBAL), is used for comparison and to assess spatial ET variability. The estimation of actual ET in this diverse and complex hydrological setting differs between both approaches. The study shows clearly that:• Temporal variability of ET is captured well by the remotely sensed data, but shows lower accuracies during wet seasons when cloud cover reduces the reliability of remote sensing data. • During seasons with homogeneous crop types, i.e. low spatial heterogeneity, remote sensing and model estimates show a satisfactory match. • During seasons when crop patterns are highly diverse, the remote sensing and modelled ET deviate significantly, due to scaling challenges. Especially for small-scale agricultural areas, the scaling of remote sensing data to a mean ET value per model unit (here: hydrological response units) cannot resolve the SWAT ET variability when crop patterns are highly diverse. • A correction of the remote sensing data, based on a crop coefficient based agricultural model, increases the spatial variability of the remote sensing ET.• Using this modified ET product a better match between modelled and remote sensing ET can be achieved, while maintaining the large scale heterogeneity from the remote sensing data and the small scale heterogeneity from the modelled data. The study discusses the reasons behind the mentioned differences in ET estimations and shows that a higher spatial variability of remotely sensed ET data can be achieved if detailed crop characteristics are taken into account. [ABSTRACT FROM AUTHOR]