ALBERT

Beschreibung: Evapotranspiration (ET) from tropical forests serves as a critical moisture source for regional and global climate cycles. However, the magnitude, seasonality, and interannual variability of ET in the Congo Basin remain poorly constrained due to a scarcity of direct observations, despite the Congo being the second-largest river basin in the world and containing a vast region of tropical forest. In this study, we applied a water balance model to an array of remotely sensed and in situ datasets to produce monthly, basin-wide ET estimates spanning April 2002 to November 2016. Data sources include water storage changes estimated from the Gravity Recovery and Climate Experiment (GRACE) satellites, in situ measurements of river discharge, and precipitation from several remotely sensed and gauge-based sources. An optimal precipitation dataset was determined as a weighted average of interpolated data by Nicholson et al. (2018), Climate Hazards InfraRed Precipitation with Station data version 2 (CHIRPS2) , and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record product (PERSIANN-CDR), with the relative weights based on the error magnitudes of each dataset as determined by triple collocation. The resulting water-balance-derived ET (ETwb) features a long-term average that is consistent with previous studies (117.2±3.5 cm yr−1) but displays greater seasonal and interannual variability than seven global ET products. The seasonal cycle of ETwb generally tracks that of precipitation over the basin, with the exception that ETwb is greater in March–April–May (MAM) than in the relatively wetter September–October–November (SON) periods. This pattern appears to be driven by seasonal variations in the diffuse photosynthetically active radiation (PAR) fraction, net radiation (Rn), and soil water availability. From 2002 to 2016, Rn, PAR, and vapor-pressure deficit (VPD) all increased significantly within the Congo Basin; however, no corresponding trend occurred in ETwb. We hypothesize that the stability of ETwb over the study period despite sunnier and less humid conditions may be due to increasing atmospheric CO2 concentrations that offset the impacts of rising VPD and irradiance on stomatal water use efficiency (WUE).

Beschreibung: Optimum management of irrigated crops in regions with shallow saline groundwater requires a careful balance between application of irrigation water and upward movement of salinity from the groundwater. Few field-validated surrogate models are available to aid in the management of irrigation water under shallow groundwater conditions. The objective of this research is to develop a model that can aid in the management using a minimum of input data that are field validated. In this paper a 2-year field experiment was carried out in the Hetao irrigation district in Inner Mongolia, China, and a physically based integrated surrogate model for arid irrigated areas with shallow groundwater was developed and validated with the collected field data. The integrated model that links crop growth with available water and salinity in the vadose zone is called Evaluation of the Performance of Irrigated Crops and Soils (EPICS). EPICS recognizes that field capacity is reached when the matric potential is equal to the height above the groundwater table and thus not by a limiting hydraulic conductivity. In the field experiment, soil moisture contents and soil salt conductivity at five depths in the top 100 cm, groundwater depth, crop height, and leaf area index were measured in 2017 and 2018. The field results were used for calibration and validation of EPICS. Simulated and observed data fitted generally well during both calibration and validation. The EPICS model that can predict crop growth, soil water, groundwater depth, and soil salinity can aid in optimizing water management in irrigation districts with shallow aquifers.