Solar radiation (Rs) is an essential input for estimating reference crop evapotranspiration, ETo. An accurate estimate of ETo is the first step involved in determining water demand of field crops. The objective of this study was to assess the ac-curacy of fifteen empirical solar radiations (Rs) models and determine its effects on ETo estimates for three sites in humid tropical environment (Abakaliki, Nsukka, and Awka). Meteorological data from the archives of NASA (from 1983 to 2005) was used to derive empirical constants (calibration) for the different models at each location while data from 2006 to 2015 was used for validation. The results showed an overall improvement when comparing measured Rs with Rs determined us-ing original constants and Rs using the new constants. After calibration, the Swartman–Ogunlade (R2 = 0.97) and Chen 2 models (RMSE = 0.665 MJ∙m–2∙day–1) performed best while Chen 1 (R2 = 0.66) and Bristow–Campbell models (RMSE = 1.58 MJ∙m–2∙day–1) performed least in estimating Rs in Abakaliki. At the Nsukka station, Swartman–Ogunlade (R2 = 0.96) and Adeala models (RMSE = 0.785 MJ∙m–2∙day–1) performed best while Hargreaves–Samani (R2 = 0.64) and Chen 1 mod-els (RMSE = 1.96 MJ∙m–2∙day–1) performed least in estimating Rs. Chen 2 (R2 = 0.98) and Swartman–Ogunlade models (RMSE = 0.43 MJ∙m–2∙day–1) performed best while Hargreaves–Samani (R2 = 0.68) and Chen 1 models (RMSE = 1.64 MJ∙m–2∙day–1) performed least in estimating Rs in Awka. For estimating ETo, Adeala (R2 =0.98) and Swartman–Ogunlade models (RMSE = 0.064 MJ∙m–2∙day–1) performed best at the Awka station and Swartman–Ogunlade (R2 = 0.98) and Chen 2 models (RMSE = 0.43 MJ∙m–2∙day–1) performed best at Abakaliki while Angstrom–Prescott–Page (R2 = 0.96) and El-Sebaii models (RMSE = 0.0908 mm∙day–1) performed best at the Nsukka station.

Evaporation and evapotranspiration is crucial part of hydrological and water resource management studies e.g. water footprinting. Proper methods for estimating evaporation/potential evapotranspiration using limited climatic data are critical if the availability of climatic data is extremely limited. In a large scale studies are very often used generalized (modelled or gridded) input data. For a large scale water footprint studies is also important to find methods as simple as possible with quantifiable error. In our study, nine simple temperature-based empirical equations were compared with a long term time series of real evaporation data from a 20 m2 tank at Hlasivo station. In the first step, we used real temperature measured at Hlasivo station for validation of equations. In the second step, the gridded temperature data (interpolated datasets) derived from the meteorological stations were used. For both datasets, the differences between observed and predicted values were categorized into three groups of accuracy and the statistical indices of each equation were calculated. Very good results were achieved with the Hamon equation from 1961 and the Oudin equation for both datasets with index of agreement (d) higher than 0.9, cross-correlation coefficient (R2) around 0.7 and root mean square error (RMSE) around 0.5 mm∙(24 h)–1The Kharrufa equation, which was developed for semi-arid or arid areas, also provides results with sufficient accuracy. Comparison of the results with similar studies showed a lower accuracy of very simple equations against more complex equations, which have RMSE lower than 0.25 mm∙(24 h)–1. But for some kind of studies, quantifiable errors with sufficient accuracy can be more important than the absolute accuracy.

Lysimeters represent the ideal tool for direct measurement of soil water balance components in soil profiles. Changes in the water content in a soil monolith can be measured with sufficient accuracy by the precise lysimeter weighing system.Water content changes in soil monolith as derived from lysimeter mass represent one of the basic water balance compo-nent. This paper deals with the development and comparison of individual soil water balance components in two different soil profiles from the Easter-Slovakian-Lowland. Two lysimeter vessels were filled monolithically with two different soil profiles covered with grass: one sandy soil profile from locality Poľany and one silty-loam soil profile from locality Vysoká nad Uhom. A constant groundwater level of 1 m below ground level was maintained in both soil profiles. Under the same meteorological conditions, all differences in the development of water balance components were caused only by the differences in soil profiles. The actual evapotranspiration and water flows at the bottom of the soil profiles were compared. Sandy soils are generally considered to be more prone to drought than silty-loam soils. Under the specific conditions of this experiment (maintaining a constant groundwater level) the opposite was shown, when the silty-loam soil profile was more prone to drought than sandy soil profile. Sandy soilprofile from Poľany reacted more quickly to precipitation (or evaporation). Due to the higher hydraulic conductivity of the sandy soil compared to the silty-loamy soil, the groundwater level response to external stimuli was much faster.

The relative relationships “yield – evapotranspiration” were used long time ago. The well known linear relationship yi = 1 – ky (1 – ei), where yi is relative yield, ky – yield response factor and ei – relative evapotranspiration was proposed. It’s usually assumed that ky is constant for a given crop and climatic conditions. It was found, however, that ky for late variety of maize H 708 varied through the study years (1984–1990) in the Plovdiv region (South Bulgaria, altitude 150 m). During the dry years it was significantly higher than in the medium and humid years. The range of ky for maize in this location was 1.12–1.90, the average value being 1.50. The climate in the Sofia region (the ex-perimental field of Chelopechene, altitude 550 m) is comparatively more humid. The two regions approximately outlined the boundaries of the appropriate economical conditions for grain maize pro-duction. The experiments in the Sofia region were carried out in the years 1994–2000. The seven years results for mean variety maize showed that the relationships “yield – evapotranspiration” and, respectively, ky varied at these climatic conditions too. The highest ky value was 1.41 for the driest year (2000) and the lowest value – 1.05 for the most wet years (1995, 1999). The value of ky for av-erage years was 1.21. The yield response factor ky is of more significance when the relative evapotranspiration is less than 0.7–0.8. Thus, the extreme or the average values of ky could be used for the corresponding climatic regions. The relationships between ky and relative yield were estab-lished without considering irrigation.

Drought is regarded as one of the environmental constraints threatening agriculture worldwide. Melatonin is a pleiotropic molecule prevalent in plants capable of promoting plant endogenous resilience to many environmental challenges including drought. Banana is an important staple food consumed in developing countries especially in Africa. In this research, we studied the role of melatonin in the growth of bananas subjected to drought under the Egyptian semi-arid conditions. To achieve this objective, a field experiment on banana (Musa spp., cv. Williams) mother plants and first ratoon was conducted on a private farm for two seasons - 2019 and 2020. Three irrigation treatments, 100, 90 and 80% irrigation water requirements (IWR) were used in conjunction with four concentrations of melatonin as a foliar spray (0 μmol, 40 μmol, 60 μmol, and 80 μmol) to determine the effect of both treatments on banana plant performance under drought. The results showed that there was a substantial difference between treatments, with the foliar application of melatonin at 80 μmol concentration improving most of the yield attributes, relative water content, total chlorophyll and proline with water deficit. However, the foliar application of the molecule lowered the biochemical characteristics mostly at 80% IWR under the Egyptian semi-arid conditions. Overall, there was a concentration-dependent response with regards to IWR for the two seasons 2019 and 2020.

Satellite-based irrigation performance is a valuable tool for improving yields in irrigated areas across the world and requires adequate land for long-term development. This study aimed to increase irrigation performance and yield gap variation of rainfed crops using the database of FAO’s Water Productivity Open Access Portal (WaPOR) and the Global Yield Gap Atlas. The evapotranspiration ( ET) performance of irrigation is expressed in equity ( CV of ET), reliability, adequacy ( CV of ET), and water productivity (kg∙m^-3</p>). The rainfed crops are interpreted in terms of metric tonnes/ha. Specifically, 20,325 km²</p> of suitable pastoral land across eight sub-classes was converted to rainfed rice, sugarcane, maize, and vegetable crops. Results showed that the R²</p> value was 0.97 at Baro Itang and –0.99 at Sor Metu, with the Baro Gambella sub-catchment having the largest yield gap of 4.435.2, 8.870.4, and 10.080∙106 kg when the yield increased by 1/3, 2/3, and 3/4. On the other hand, Gumero Gore had the smallest yield gap of 10,690, 29,700, and 33,750 kg, respectively. The management regime was 2.87, 0.87, and 0.35 kg∙m^-3</p> for growers in the estate, farmer association, and individual, respectively. The study concludes that no single irrigation technique can be considered the best, and a thorough analysis of spatiotemporal variation of the irrigation performance indicators and the yield gap in the water-scarce lower Baro watershed is required.

Atmospheric precipitation is the major input to the soil water balance. Its amount, intensity, and temporal distribution have an indubitable influence on soil moisture. The aim of the study (conducted in the years 2010–2013) was to evaluate soil water balance in an apple orchard as determined by daily rainfall. The amount and intensity of rainfall and daily evapotranspiration were measured using an automatic weather station. Changes in soil water content was carried out using capacitance probes placed at a depth of 20, 40 and 60 cm. The most common were single events of rainfall of up to 0.2 mm, while 1.3–3.6 mm rains delivered the greatest amount of water. A significant correlation was found between the amount of daily rainfall and changes in water content of individual soil layers. The 15–45 cm and 15–65 cm layers accumulated the greatest amount of high rainfall. The study showed a significant influence of the initial soil moisture on changes in the water content of the analysed layers of the soil profile. The lower its initial moisture content was, the more rainwater it was able to accumulate.

Optimal estimation of water balance components at the local and regional scales is essential for many applications such as integrated water resources management, hydrogeological modelling and irrigation scheduling. Evapotranspiration is a very important component of the hydrological cycle at the soil surface, particularly in arid and semi-arid lands. Mapping evapotranspiration at high resolution with internalised calibration (METRIC), trapezoid interpolation model (TIM), two-source energy balance (TSEB), and soil-plant-atmosphere and remote sensing evapotranspiration (SPARSE) models were applied using Landsat 8 images for four dates during 2014–2015 and meteorological data. Surface energy maps were then generated. Latent heat flux estimated by four models was then compared and evaluated with those measured by applying the method of Bowen ratio for the various days. In warm periods with high water stress differences and with important surface temperature differences, METRIC proves to be the most robust with the root-mean-square error ( RMSE) less than 40 W∙m ^–2. However, during the periods with no significant surface temperature and soil humidity differences, SPARSE model is superior with the RMSE of 35 W∙m ^–2. The results of TIM are close to METRIC, since both models are sensitive to the difference in surface temperature. However, SPARSE remains reliable with the RMSE of 55 W∙m ^–2 unlike TSEB, which has a large deviation from the other models. On the other hand, during the days when the temperature difference is small, SPARSE and TSEB are superior, with a clear advantage of SPARSE serial version, where temperature differences are less important.

A machine learning model was developed to support irrigation decisions. The field research was conducted on ‘Gala’ apple trees. For each week during the growing seasons (2009–2013), the following parameters were determined: precipitation, evapotranspiration (Penman–Monteith formula), crop (apple) evapotranspiration, climatic water balance, crop (apple) water balance (AWB), cumulative climatic water balance (determined weekly, ΣCWB), cumulative apple water balance (ΣAWB), week number from full bloom, and nominal classification variable: irrigation, no irrigation. Statistical analyses were performed with the use of the WEKA 3.9 application software. The attribute evaluator was performed using Correlation Attribute Eval with the Ranker Search Method. Due to its highest accuracy, the final analyses were performed using the WEKA classifier package with the J48graft algorithm. For each of the analysed growing seasons, different correlations were found between the water balance determined for apple trees and the actual water balance of the soil layer (10–30 cm). The model made correct decisions in 76.7% of the instances when watering was needed and in 87.7% of the instances when watering was not needed. The root of the classification tree was the AWB determined for individual weeks of the growing season. The high places in the tree hierarchy were occupied by the nodes defining the elapsed time of the growing season, the values of ΣCWB and ΣAWB.

Flooding in the northern part of The Netherlands has caused serious economic threats to densely populated areas. Therefore a project has been carried out in a pilot area to assess the retention of water in two river basins as a way to reduce flooding. The physically-based groundwater and sur-face water model SIMGRO was used to model the hydrology of the basins. The model was calibrated using discharges and groundwater levels. Scenarios of measures to assess the possibility of retaining water in the basin were then defined and tested. The first measure was the retention of higher dis-charges using culverts or gates in the upstream part of the basin. The second measure was to make the streams shallower and thereby, increase flood plain storage. The last measure was flood water storage in a designated area in the downstream part of one basin. The analysis indicates that holding water in the upstream parts of the basins proved to be feasible and can result in significant reductions of peak flows.

River intermittence was studied based on data from hydrological monitoring in Poland. We screened the entire state database and two another data sources applying the criterion for zero-flow event: discharge less than 0.0005 m ³∙s ^–1, and found five intermittent rivers with catchment area from 9.2 to 303.7 km ². We aimed at finding associations between intermittence and climatic driving forces (temperature and precipitation), and between intermittence and anthropogenic activity. We used the Spearman correlation coefficient, circular statistics, and statistical tests for trend.
The concentration of zero-flow days, mostly in summer, and the decreasing trend in the standardised precipitation evapotranspiration index ( SPEI) in all catchments at various aggregation levels, and an increasing trend in the total number of zero-flow days and in the maximum length of zero flow events in two rivers, were detected. The strong negative correlation (–0.62 ≤ ρ < 0) between intermittence and the SPEI backward lagged in time showed that intermittence resulted from prolonged deficits in climatic water balance due to increasing evapotranspiration. The reaction of the Noteć catchment, amplified by the anthropogenic pressure (brown coal mines), was reflected in the atypical shape of the rose diagram and in inhomogeneities in river discharges.
The results show that the rose diagram can serve as an indicator of the degree of anthropogenic impact on runoff conditions.

Improving water productivity (WP) through deficit irrigation is crucial in water-scarce areas. To practice deficit irriga-tion, the optimum level of water deficit that maximizes WP must be investigated. In this study, a field experiment was con-ducted to examine WP of the three treatments at available soil water depletion percentage (����) of 25% (reference), 45% and 65% using a drip irrigation system. Treatments were arranged in a randomized complete block design. The water deficit was allowed throughout the growth stages after transplanting except for the first 15 days of equal amounts of irrigations during the initial growth stage and 20 days enough spring season rainfall during bulb enlargement periods. Physical WP in terms of water use efficiency (WUEf) for treatments T1, T2, and T3 was 9.44 kg∙m–3, 11 kg∙m–3and 10.6 kg∙m–3 for mar-ketable yields. The WUEf and economic water productivity were significantly improved by T2 and T3. The WUEf differ-ence between T2 and T3 was insignificant. However, T2 can be selected as an optimal irrigation level. Hence, deficit irriga-tion scheduling is an important approach for maximizing WP in areas where water is the main constraint for crop produc-tion. The planting dates should be scheduled such that the peak water requirement periods coincide with the rainy system.

In the present research, a scripting cartographic technique for the environmental mapping of Ethiopia using climate and topographic datasets is developed. The strength of the Generic Mapping Tools (GMT) is employed for the effective visualisation of the seven maps using high-resolution data: GEBCO, TerraClimate, WorldClim, CRUTS 4.0 in 2018 by considering the solutions of map design. The role of topographic characteristics for climate variables (evapotranspiration, downward surface shortwave radiation, vapour pressure, vapour pressure deficit and climatic water deficit) is explained. Topographic variability of Ethiopia is illustrated for geographically dispersed and contrasting environmental setting in its various regions: Afar, Danakil Depression, Ethiopian Highlands, Great Rift Valley, lowlands and Ogaden Desert. The relationships between the environmental and topographic variables are investigated with aid of literature review and the outcomes are discussed. The maps are demonstrated graphically to highlight variables enabling to find correlations between the geographic phenomena, their distribution and intensity. The presented maps honor the environmental and topographic data sets within the resolution of the data. Integration of these results in the interpretation maps presented here brings new insights into both the variations of selected climate variables, and the topography of Ethiopia.

According to the SRES A1B climate change scenario, by the end of the 21st century temperature in Poland will increase by 2–4°C, no increase in precipitation totals is predicted. This will rise crop irrigation needs and necessity to develop irrigation systems. Due to increase in temperature and needs of sustainable agriculture development some changes in crop growing structure will occur. An increase interest in high protein crops cultivation has been noted last years and further extension of these acreage is foreseen. Identifying the future water needs of these plants is crucial for planning and implementing sustainable agricultural production. In the study, the impact of projected air temperature changes on soybean water needs, one of the most valuable high-protein crops, in 2021–2050 in the Kuyavia region in Poland was analysed. The calculations based on meteorological data collected in 1981–2010 were considered as the reference period. Potential evapotranspiration was adopted as a measure of crop water requirements. The potential evapotranspiration was estimated using the Penman–Monteith method and crop coefficient. Based on these estimations, it was found that in the forecast years the soybean water needs will increase by 5% in the growing period (from 21 April to 10 September), and by 8% in June–August. The highest monthly soybean water needs increase (by 15%) may occur in August. The predicted climate changes and the increase in the arable crops water requirements, may contribute to an increase in the irrigated area in the Kuyavia region and necessity of rational management of water resources.

The purpose of this study is to develop mathematical models based on artificial intelligence: Models based on the support vectors regression (SVR) for drought forecast in the Ansegmir watershed (Upper Moulouya, Morocco). This study focuses on the prediction of the temporal aspect of the two drought indices (standardized precipitation index – SPI and standardized precipitation-evapotranspiration index – SPEI) using six hydro-climatic variables relating to the period 1979–2013. The model SVR3-SPI: RBF, ε = 0.004, C = 20 and γ = 1.7 for the index SPI, and the model SVR3-SPEI: RBF ε = 0.004, C = 40 and γ = 0.167 for the SPEI index are significantly better in comparison to other models SVR1, SVR2 and SVR4. The SVR model for the SPI index gave a correlation coefficient of R = 0.92, MSE = 0.17 and MAE = 0.329 for the learning phase and R = 0.90, MSE = 0.18 and MAE = 0.313 for the testing phase. As for the SPEI index, the overlay is slightly poorer only in the case of the SPI index between the observed values and the predicted ones by the SVR model. It shows a very small gap between the observed and predicted values. The correlation coefficients R = 0.88 for the learning, R = 0.86 for testing remain higher and corresponding to a quadratic error average MSE = 0.21 and MAE = 0.351 for the learning and MSE = 0.21 and MAE = 0.350 for the testing phase. The prediction of drought by SVR model remain useful and would be extremely important for drought risk management.

The Lamongan Regency is an area in East Java, Indonesia, which often experiences drought, especially in the south. The Corong River basin is located in the southern part of Lamongan, which supplies the irrigation area of the Gondang Reservoir. Drought monitoring in the Corong River basin is very important to ensure the sustainability of the agricultural regions. This study aims to analyse the causal relationship between meteorological and agricultural drought indices represented by standardised precipitation evapotranspiration index ( SPEI) and standard normalisation difference vegetation index ( NDVI), using time series regression. The correlation between NDVI and SPEI lag 4 has the largest correlation test results between NDVI and SPEI lag, which is 0.41. This suggests that the previous four months of meteorological drought impacted the current agricultural drought. A time series regression model strengthens the results, which show a causal relationship between NDVI and SPEI lag. According to the NDVI–SPEI-1 lag 4 time series model, NDVI was influenced by NDVI in the previous 12 periods, and SPEI-1 in the last four periods had a determinant coefficient value of 0.4. This shows that the causal model between SPEI-1 and NDVI shows a fairly strong relationship for drought management in agricultural areas (irrigated areas) and is considered a reliable and effective tool in determining the severity and duration of drought in the study area.