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This study aims to evaluate a remote sensing-based approach to allow estimation of the temporal and spatial distribution of crop evapotranspiration (ET) and irrigation water requirements over irrigated areas in semi-arid regions. The method is based on the daily step FAO-56 Soil Water Balance model combined with a time series of basal crop coefficients and the fractional vegetation cover derived from high-resolution satellite Normalized Difference Vegetation Index (NDVI) imagery. The model was first calibrated and validated at plot scale using ET measured by eddy-covariance systems over wheat fields and olive orchards representing the main crops grown in the study area of the Haouz plain (central Morocco). The results showed that the model provided good estimates of ET for wheat and olive trees with a root mean square error (RMSE) of about 0.56 and 0.54 mm/day respectively. The model was then used to compare remotely sensed estimates of irrigation requirements (RS-IWR) and irrigation water supplied (WS) at plot scale over an irrigation district in the Haouz plain through three growing seasons. The comparison indicated a large spatio-temporal variability in irrigation water demands and supplies; the median values of WS and RS-IWR were 130 (175), 117 (175) and 118 (112) mm respectively in the 2002–2003, 2005–2006 and 2008–2009 seasons. This could be attributed to inadequate irrigation supply and/or to farmers’ socio-economic considerations and management practices. The findings demonstrate the potential for irrigation managers to use remote sensing-based models to monitor irrigation water usage for efficient and sustainable use of water resources.

Accurate hydrological modelling to evaluate the impacts of climate and land use change on water resources is pivotal to sustainable management. Soil information is an important input in hydrological models but is often not available at adequate scale with appropriate attributes for direct parameterisation of the models. In this study, conducted in three quaternary catchments in the midlands of KwaZulu-Natal, three different soil information sets were used to configure SWAT+, a revised version of the Soil and Water Assessment Tool (SWAT). The datasets were: (i) the Land Type database (currently the only soil dataset covering the whole of South Africa), (ii) disaggregation of the Land Type database using digital soil mapping techniques (called DSMART), and (iii) a dataset where DSMART were complemented by field observations and interpretations of the hydropedological behaviour of the soils (DSMART+). Simulated streamflow was compared with measured streamflow at three weirs with long-term measurements, and the impact of the soil datasets on water balance simulations was evaluated. In general, the simulations were acceptable when compared to other studies, but could be improved through calibration and including small reservoirs in the model. The DSMART+ dataset yielded more accurate simulations of streamflow in all three catchments with Nash-Sutcliffe efficiencies increasing by between 9% and 67% when compared to the Land Type dataset. The value of the improved soil maps is, however, highlighted through the enhanced spatial detail of streamflow generation mechanisms and water balance components. The internal catchment processes are represented more accurately, and we argue that South Africa needs a detailed hydrological soil map for effective water resource management.

Proper irrigation management, especially for tomatoes that are sensitive to water, is the key to ensuring sustainable tomato production. Using a low-cost sensor coupled with IoT technology could help to achieve precise control of the moisture content in the plant root-zone soil and apply water on demand with minimum human intervention. An IoT-based precision irrigation system was developed for growing Momotaro tomato seedlings inside a dark chamber. Four irrigation thresholds, 5%, 8%, 12%, and 15%, and two irrigation systems, surface and subsurface drip irrigation, were compared to assess which threshold and irrigation system referred the ideal tomato seedling growth. As a result, the 12% soil moisture threshold applied through the subsurface drip irrigation system significantly (p < 0.05) increased tomato seedling growth in soil composed of a main blend of peat moss, vermiculite, and perlite. Furthermore, in two repeated experiments, a subsurface drip irrigation system with 0.86 distribution uniformity used 10% less water than the surface drip irrigation system. The produced tomato seedlings were transplanted to open fields for further assessment. A low-power wide area networking Long Range Wide Area Network (LoRaWAN) protocol was developed with remote monitoring and controlling capability for irrigation management. Two irrigation systems, including surface and subsurface drip irrigations, were used to compare which system resulted in higher tomato yields. The results showed that the subsurface drip irrigation system with 0.74 distribution uniformity produced 1243 g/plant, while each plant produced 1061 g in the surface drip irrigation system treatment. The results also indicated that the LoRaWAN-based subsurface drip irrigation system was suitable under outdoor conditions with easy operation and robust controlling capability for tomato production.

The ability to obtain an accurate measure of irrigation water use is urgently needed in order to provide further scientific guidance for irrigation practices. This investigation took soil moisture and precipitation as the study objects and quantitatively analyzed their relationship by establishing four models: a linear model, a logarithmic model, a soil water balance model, and a similarity model. The results from building models on every site clearly revealed the relationship between soil moisture and precipitation and confirmed the feasibility of estimating irrigation water use when soil moisture data are known. Four models combined with soil moisture data were used to estimate irrigation water use. First, the 16 sites which monitor soil moisture conditions in Hebi City were identified as study objects, from which everyday meteorological data (temperature, precipitation, atmospheric pressure, wind speed, sunshine duration) and soil moisture data from 2015 to 2020 (totaling six years) were collected. Second, the eligible data from the first four years in the date range were used to create four kinds of models (linear model, logarithmic model, soil water balance model, and similarity model) to estimate the amount of water input to the soil surface based on soil moisture. Third, the eligible data from the last two years in the established date range were used to verify the established models on every site and then judge the accuracy of the models. For example, for site 53990, the RMSE of the linear model, logarithmic model, soil water balance model, and similarity model was 10,547, 10,302, 8619, and 7524, respectively. The results demonstrate that the similarity model proposed in this study can express the quantitative relationship between soil moisture and precipitation more accurately than the other three models. Based on this conclusion, the eligible soil moisture data known in the specific site were ultimately used to estimate the irrigation water use in the field by the relationship expressed in the similarity model. Compared with the amount of irrigation water data recorded, the estimated irrigation water use yielded by the similarity model in this study was 18.11% smaller. In a future study, microwave satellite remote sensing of soil moisture data, such as SMAP and SMOS soil moisture data, will be used to evaluate the performance of estimated regional irrigation water use.

Excess irrigation may result in deep percolation and nitrate transport to groundwater. Furthermore, under Mediterranean climate conditions, heavy winter rains often result in high deep percolation, requiring the separate identification of the two sources of deep percolated water. An integrated methodology was developed to estimate the spatio-temporal dynamics of deep percolation, with the actual crop evapotranspiration (ETc act) being derived from satellite images data and processed on the Google Earth Engine (GEE) platform. GEE allowed to extract time series of vegetation indices derived from Sentinel-2 enabling to define the actual crop coefficient (Kc act) curves based on the observed lengths of crop growth stages. The crop growth stage lengths were then used to feed the soil water balance model ISAREG, and the standard Kc values were derived from the literature; thus, allowing the estimation of irrigation water requirements and deep drainage for independent Homogeneous Units of Analysis (HUA) at the Irrigation Scheme. The HUA are defined according to crop, soil type, and irrigation system. The ISAREG model was previously validated for diverse crops at plot level showing a good accuracy using soil water measurements and farmers’ irrigation calendars. Results show that during the crop season, irrigation caused 11 ± 3% of the total deep percolation. When the hotspots associated with the irrigation events corresponded to soils with low suitability for irrigation, the cultivated crop had no influence. However, maize and spring vegetables stood out when the hotspots corresponded to soils with high suitability for irrigation. On average, during the off-season period, deep percolation averaged 54 ± 6% of the annual precipitation. The spatial aggregation into the Irrigation Scheme scale provided a method for earth-observation-based accounting of the irrigation water requirements, with interest for the water user’s association manager, and at the same time for the detection of water losses by deep percolation and of hotspots within the irrigation scheme.

Potential effects of projected climate variability on base flow and groundwater storage in the North Fork Red River aquifer, Oklahoma (USA), were estimated using downscaled climate model data coupled with a numerical groundwater-flow model. The North Fork Red River aquifer discharges groundwater to the North Fork Red River, which provides inflow to Lake Altus. To approximate future conditions, Coupled Model Intercomparison Project Phase 5 climate data were downscaled to the watershed and a time-series of scaling factors were developed and interpolated for three climate scenarios (central tendency, warmer and drier, and less warm and wetter) representing future climate conditions for the period 2045–2074. These scaling factors were then applied to a soil-water-balance model to produce groundwater recharge and evapotranspiration estimates. A MODFLOW groundwater-flow model of the North Fork Red River aquifer used the scaled recharge and evapotranspiration data to estimate changes in base flow and water-surface elevation of Lake Altus. Compared to a baseline scenario, the mean percent change in annual base flow during 2045–2074 was −10.8 and −15.9% for the central tendency and warmer/drier scenarios, respectively; the mean percent change in annual base flow for the less-warm/wetter scenario was +15.7%. The mean annual percent change in groundwater storage for the central tendency, warmer/drier, and less-warm/wetter climate scenarios and the baseline are −2.7, −3.2, and +3.0%, respectively. The range of outcomes from the climate scenarios may be influenced by variability in the downscaled climate data for precipitation more than for temperature.

This paper presents a review of groundwater recharge assessment in some 50 carbonate aquifers of the extreme SW Mediterranean domain (Betic Cordillera, southern Spain), an area highly vulnerable to climate change. The selected aquifers represent a broad range of meteorological and geological conditions, and consequently results can be expanded eastward to other Mediterranean aquifers. The methods most commonly applied for recharge assessment over the past 20 years are aquifer water budget and soil water balance. While these methods support the first steps in groundwater management in Spain, precision in quantification requires other methods to be incorporated. They include chloride mass balance, diverse lumped model codes and empirical methods such as APLIS. In eastern areas of the Betic Cordillera, where semiarid conditions prevail, lumped and distributed models for recharge assessment and calibration are based on the time series of water table data. The mean annual rainfall, recharge and coefficient infiltration (as percentage of rainfall) are, respectively, 648, 262 mm/year and 38 %. A high correlation was observed between annual rainfall and annual recharge in all the studied aquifers. However, the infiltration coefficient can vary substantially even with the same annual rainfall recharge. This reflects significant differences in the degree of surface karstification and the development of the vegetal cover–soil–epikarst system in the carbonate aquifers of the Betic Cordillera. The results of this work may serve to improve the assessment and management of renewable water resources in western Mediterranean carbonate aquifers. The findings are moreover useful for future comparisons involving recharge assessments under different scenarios of climate change and changes in land use.

Two commonly applied groundwater recharge estimation techniques, namely soil–water balance (SWB) and the chloride mass balance (CMB) methods, were applied and compared to quantify and analyse groundwater recharge in the Akaki catchment, located in central Ethiopia. The semi-distributed SWB method estimated natural groundwater recharge at 105 mm/a (10 % of the mean annual areal precipitation, MAAP). The chloride mass balance method applied to the same catchment estimated mean annual groundwater recharge at 273 mm/a (25 % of the MAAP). The SWB recharge value is much less than the recharge estimated by CMB method, highlighting the importance of preferential flow path recharge mechanism which is not captured by the SWB method. In situ permeability measurements undertaken as part of this research and an earlier investigation based on environmental isotopes have demonstrated that the catchment gets recharged both from direct and preferential flow recharge mechanisms which explains the discrepancy between recharge estimated through the two methods. Therefore, SWB model result which only takes into consideration the piston-type flow recharge mechanism should be taken as the minimum possible groundwater recharge for the catchment characterized by fractured aquifers. The mean recharge estimate from the two methods represents the maximum possible recharge estimate for the catchment investigated. Although calibration of the recharge estimates was not possible, the SWB recharge estimate is found to be highly sensitive to changes in precipitation input. The findings of this research underline the fact that multiple recharge estimation methods are important to understand and capture possible recharge mechanisms and to reach to an acceptable recharge estimate.

Because arid and semi-arid regions have relatively low soil moisture, water vapor movement often occurs predominantly in the unsaturated zone, affecting the partitioning of energy among various land surface fluxes. To understand the hydrological processes of the unsaturated zone in desert areas, it is important to characterize the diurnal and spatial variations in soil water and vapor movement, which control recharge and discharge via the unsaturated zone. However, few studies have examined the pattern of soil water and vapor movement affected by rainfall in desert areas. To understand this process, field observations of desert soil physical parameters and micrometeorological variables were taken. These data were used to verify and calibrate the performance of an unsaturated–saturated zone soil water balance model, Hydrus-1D. Next, the diurnal pattern of the soil water and vapor was simulated under different climatic conditions, i.e., before, during and after rainfall. Two stages of thermal liquid and vapor movement were identified before rainfall. The thermal liquid flux fluctuates quickly and drastically, while the thermal vapor changes more moderately during and after rainfall. The changes in isothermal liquid and vapor flux differ from those of thermal liquid and vapor flux because of the change in the pressure head gradient under various wetness conditions. These findings offer insight into how water vapor affects soil water movement in the semiarid desert. They also improve our understanding of the liquid water and water vapor movement processes following rainfall.

With population growth and water scarcity, efficient crop production has drawn attention worldwide. In the Hexi Corridor, the largest production base of maize seed in China, it is desired to develop efficient irrigation strategies for seed maize. Considering the double criteria of yield and seed quality, an integrated water-saving and quality-guarantee uncertain programming approach (IWQUP) was developed in this study to help with agricultural sustainable development. The IWQUP combined deficit irrigation theory, soil-water balance, and multiple uncertainties. The water-flowering model (WFM) and kernel weight prediction model with water production functions were used to reflect the relationship among water consumption, crop yield, and seed quality. Meanwhile, to deal with the widespread existence of uncertainties in nature and the decision-making process, interval programming and fuzzy programming were integrated within the framework of IWQUP, along with the use of the genetic algorithm and Monte Carlo simulation. The results showed that when the climatic condition is moist, decision-makers may use a low tolerance level in order to reduce the water waste, enhance the water use efficiency, and guarantee a relatively high seed quality. When the climate is harsh, a high tolerance level to water use constraints is recommended in order to guarantee yield. In addition, optimistic decision-makers could choose a relatively high tolerance level, but in moist years they should be careful in order to avoid water waste. The established model was compared with three other models to represent its practicability for offering decision-makers various references under different scenarios.