Evaporation Estimates Research Articles

Estimation of evaporation and soil moisture index based length of growing period for enhanced agricultural production in Bihar

Estimation of evaporation and soil moisture index based length of growing period for enhanced agricultural production in Bihar

Neural Network based Predictors for Evaporation Estimation at Jabalpur in Central India

Free water evaporation is an imperative parameter for estimation of crop water requirement, and irrigation scheduling. This study aims to evaluate different techniques to estimate evaporation with weather parameters inputs. Multilayer Perception (MLR), Radial Basis Function (RBF) based neural network, traditional statistical Linear Regression (LR) approach and conventional empirical methods of Linacre and Christianson were used to estimate the evaporation at Jabalpur station situated under Kymore Plateau and Satpura Hills Agro-climatic Zone of Madhya Pradesh in the Central India. The weather parameters considered for estimation of evaporation are temperature, humidity, sunshine hours and wind speed. Results indicate that MLP and RBF based models with input of all selected weather parameters is able to estimate evaporation much precisely than LR and empirical approaches. It was found that higher accuracy may be obtained with multiple weather data input and low accuracy with only temperature input. It was observed that with temperature used as input the performance accuracy reduces in estimating evaporation with the selected models. However, neural network approach seems to produce better results as compared to statistical and empirical approach. The neural network based model RBF found more efficient in estimation of evaporation as compared to MLP. This study suggests that evaporation can be estimated by RBF model of a station, where there is no standard instrument available for its observation.

Intra-Seasonal and Intra-Annual Variation of the Latent Heat Flux Transfer Coefficient for a Freshwater Lake

In the case of lakes, evaporation is one of the most significant losses of water and energy. Based on high-frequency eddy-covariance (EC) measurements between May and September of 2019, the offshore heat and water vapor exchanges are evaluated for the large (~600 km2) but shallow (~3.2 m deep) Lake Balaton (Transdanubian region, Hungary). The role of local driving forces of evaporation in different time scales (from 20 min to one month) is explored, such as water surface and air temperatures, humidity, atmospheric stability, net radiation, and energy budget components. EC-derived water vapor roughness lengths and transfer coefficients (Cq) show an apparent intra-seasonal variation. Different energy balance-based evaporation estimation methods (such as the Priestley-Taylor and the Penman-Monteith) confirm this observation. Furthermore, this has suggested the existence of an intra-annual variation in these parameters. This hypothesis is verified using ten years of water balance measurements, from which, as a first step, evaporation rates and, second, transfer coefficients are derived on a monthly scale. Cq is highly reduced in winter months (~1 × 10−3) compared to summer months (~2.5 × 10−3) and strongly correlated with net radiation. The application of time-varying Cq significantly increases the accuracy of evaporation estimation when the Monin-Obukhov similarity theory-based aerodynamic method is applied. The determination coefficient increases to 0.84 compared to 0.52 when a constant Cq is employed.

Comparison of evaporation estimation methods for water surface under floating coverage in arid areas

The main purpose of this study is to evaluate evaporation estimation methods for water surfaces under floating coverage in arid areas. The natural water surface evaporation methods (namely, the mass transfer method (MT), Bowen ratio energy balance (BREB) method, Penman method, the Penman-Monteith method (PM), and the Priestley-Taylor method (PT)) are corrected to establish the covered water surface evaporation methods, which are based on the micrometeorology near the covered water surface, energy balance components, and Bowen ratio. The radiation term of the natural water surface evaporation model is corrected by introducing the Bowen ratio of the entire surface, which includes the uncovered water surface and the floating cover surface. The mass transfer coefficient and aerodynamic impedance in the aerodynamic terms of the natural water surface evaporation model were corrected. Taking the evaporation of the water surface covered by spheres (coverage rate of spheres is 76.4%) as an example, based on the rose change characteristics of the above parameters, the effects of these modified evaporation models are comprehensively evaluated from three different time scales (even months, odd months and the complete test period). The evaluation methods include the root mean square error (RMSE), index of agreement (D), mean bias error (MBE), and coefficient of determination (R2). The results show that under three different time scales, the Penman-Monteith model is the most promising and superior in estimating water surface evaporation under floating coverage (for the complete test period, RMSE=0.11 mm·d−1, D=0.99, MBE= 0.07 mm·d−1, and R2 =0.99); the Priestley-Taylor model is slightly less effective in estimating evaporation than the Penman-Monteith model, and the mass transfer model and the Penman model are less effective in estimating evaporation. Furthermore, the analysis of the Penman-Monteith model shows that the evaporation of the radiation term (69.5%) is higher than that of the aerodynamic term (30.5%) during the evaporation of the water surface under floating coverage, while the evaporation of the radiation term is dominant. Moreover, during the complete test period, the weights of the radiation evaporation and aerodynamic evaporation changed significantly with time. The proportion of radiation evaporation varies from 54.8% to 81.7%, while that of aerodynamic evaporation varies from 18.3% to 45.2%.

Development and evaluation of hybrid deep learning long short-term memory network model for pan evaporation estimation trained with satellite and ground-based data

Evaporation, as a core process within the global hydrological cycle, requires reliable methods to monitor its variation, for decision-making in agriculture, irrigation systems and dam operations, also in other areas of hydrology and water resource management. Accurate monitoring of pan evaporation (Ep) is one the most popular approaches to understand the evaporative process. This work aims to construct a hybrid Long Short-Term Memory (LSTM) predictive model that is coupled with Neighbourhood Component Analysis for feature selection to predict Ep in drought-prone regions in Queensland, Australia (Amberley, Gatton, Oakey, & Townsville). Utilizing the daily-scale dataset [31 August 2002 to 22 September 2020], the performance of the proposed deep learning (DL) hybrid model, denoted as NCA-LSTM, is compared with competitive benchmark models, i.e., standalone LSTM, other types of DL, single hidden layer neuronal architecture and decision tree-based method. The testing results reveal the lowest Relative Root Mean Square Error ≤20%, Absolute Percentage Bias ≤14.5% and the highest Kling-Gupta Efficiency ≥87% attained by the NCA-LSTM hybrid model (relative to benchmark models) tested for Amberley, Gatton, and Oakey sites. In respect to the predictive efficiency, the proposed NCA-LSTM hybrid model, improved with feature selection, outperforms all benchmark models, indicating its future utility in the prediction of daily Ep. In practical sense, the predictive model developed for Ep estimation provides an accurate estimation of evaporative water loss in hydrological cycle and therefore, can be implemented in areas of irrigation management, planning of irrigation-based agriculture, and mitigation of financial losses to agricultural and related sectors where, regular monitoring and forecasting of water resources are a vital part of sustainable livelihood and business.

Changes in Terrestrial Evaporation across Poland over the Past Four Decades Dominated by Increases in Summer Months

Given the importance of terrestrial evaporation (ET) for the water cycle, a fundamental understanding of the water quantity involved in this process is required. As recent observations reveal a widespread ET intensification across the world, it is important to evaluate regional ET variability. The specific objectives of this study are the following: (1) to assess annual and monthly ET trends across Poland, and (2) to reveal seasons and regions with significant ET changes. This study uses the ET estimates acquired from the Global Land Evaporation Amsterdam Model (GLEAM) dataset allowing for multi-year analysis (1980–2020). The Mann–Kendall test and the Sen’s slope were applied to estimate the significance and magnitude of the trends. The results show that a rising temperature, along with small precipitation increase, led to the accelerated ET of 1.36 mm/y. This was revealed by increased transpiration and interception loss not compensated by a decrease in bare soil evaporation and sublimation. The wide-spread higher water consumption especially occurred during the summer months of June, July, and August. Comparing the two subperiods of 1980–2020, it was found that in 2007–2020, the annual ET increased by 7% compared to the reference period of 1980–2006. These results can serve as an important reference for formulating a water resources management strategy in Poland.

Modelling the gas–particle partitioning and water uptake of isoprene-derived secondary organic aerosol at high and low relative humidity

Abstract. This study presents a characterization of the hygroscopic growth behaviour and effects of different inorganic seed particles on the formation of secondary organic aerosols (SOAs) from the dark ozone-initiated oxidation of isoprene at low NOx conditions. We performed simulations of isoprene oxidation using a gas-phase chemical reaction mechanism based on the Master Chemical Mechanism (MCM) in combination with an equilibrium gas–particle partitioning model to predict the SOA concentration. The equilibrium model accounts for non-ideal mixing in liquid phases, including liquid–liquid phase separation (LLPS), and is based on the AIOMFAC (Aerosol Inorganic–Organic Mixtures Functional groups Activity Coefficients) model for mixture non-ideality and the EVAPORATION (Estimation of VApour Pressure of ORganics, Accounting for Temperature, Intramolecular, and Non-additivity effects) model for pure compound vapour pressures. Measurements from the Cosmics Leaving Outdoor Droplets (CLOUD) chamber experiments, conducted at the European Organization for Nuclear Research (CERN) for isoprene ozonolysis cases, were used to aid in parameterizing the SOA yields at different atmospherically relevant temperatures, relative humidity (RH), and reacted isoprene concentrations. To represent the isoprene-ozonolysis-derived SOA, a selection of organic surrogate species is introduced in the coupled modelling system. The model predicts a single, homogeneously mixed particle phase at all relative humidity levels for SOA formation in the absence of any inorganic seed particles. In the presence of aqueous sulfuric acid or ammonium bisulfate seed particles, the model predicts LLPS to occur below ∼ 80 % RH, where the particles consist of an inorganic-rich liquid phase and an organic-rich liquid phase; however, this includes significant amounts of bisulfate and water partitioned to the organic-rich phase. The measurements show an enhancement in the SOA amounts at 85 % RH, compared to 35 % RH, for both the seed-free and seeded cases. The model predictions of RH-dependent SOA yield enhancements at 85 % RH vs. 35 % RH are 1.80 for a seed-free case, 1.52 for the case with ammonium bisulfate seed, and 1.06 for the case with sulfuric acid seed. Predicted SOA yields are enhanced in the presence of an aqueous inorganic seed, regardless of the seed type (ammonium sulfate, ammonium bisulfate, or sulfuric acid) in comparison with seed-free conditions at the same RH level. We discuss the comparison of model-predicted SOA yields with a selection of other laboratory studies on isoprene SOA formation conducted at different temperatures and for a variety of reacted isoprene concentrations. Those studies were conducted at RH levels at or below 40 % with reported SOA mass yields ranging from 0.3 % up to 9.0 %, indicating considerable variations. A robust feature of our associated gas–particle partitioning calculations covering the whole RH range is the predicted enhancement of SOA yield at high RH (> 80 %) compared to low RH (dry) conditions, which is explained by the effect of particle water uptake and its impact on the equilibrium partitioning of all components.

Calculating net radiation of freshwater reservoir to estimate spatial distribution of evaporation using satellite images

Evaporation over freshwater bodies is one of the main sources of water loss. However, evaporation estimation models using net radiation (Rn) over water bodies have not yet provided proper efficiency, the fact which has led to several attempts to improve such models using satellite images. Therefore, the purpose of this study is to introduce a model for estimation of Rn through using satellite images, and to utilize such model in evaporation calculation models in order to produce an evaporation map for freshwater bodies. In this regard, two reservoirs of Taleghan and Amirkabir dams in Iran were selected as the areas under study. Thirty-three Landsat satellite images from the years 2010 to 2018 along with the corresponding meteorological data were used in the implementations. A new simplified equation was introduced for calculating Rn and the subsequent estimation of potential evaporation over freshwater bodies using remote sensing images. Evaporation was generally estimated using FAO56-PM, Priestley-Taylor, Kimberly-Penman, and Hargreaves-Samani models, and the results were then compared with Pan evaporation data. The obtained results were also compared with the results of two energy balance models, including Surface Energy Balance System (SEBAL) and Mapping Evapotranspiration at high Resolution and with Internalized Calibration (METRIC). The results of this study indicated that Kimberly-Penman model with coefficient of determination (R2) of 0.76, Root Mean Square Error (RMSE) of 1.29 (mm), and Nash-Sutcliffe (NSE) of 0.65, and FAO56-PM model with R2 = 0.75, RMSE = 1.76 (mm), and NSE = 0.36 outperformed when compared to other similar methods. Moreover, the proposed model proved higher performance in estimating evaporation over freshwater bodies compared with SEBAL and METRIC models.

A framework based on high-resolution imagery datasets and MCS for forecasting evaporation loss from small reservoirs in groundwater-based agriculture

In arid and semi-arid regions, evaporation from small irrigation reservoirs can be a significant source of water loss. Since groundwater is a major source of water supply for irrigation, evaporation losses from irrigation water reservoirs represent a challenging aspect in aquifers governance in such limited-water areas. Estimating these losses is crucial for water resource managers to regulate irrigation reservoirs development and to implement appropriate mitigation measures. Several practical challenges make the individual inventory and monitoring of small irrigation reservoirs unfeasible, especially in large irrigation perimeters with dynamic irrigated surfaces. Thus, significant uncertainty is generally associated with the determinist estimation of evaporation from small irrigation reservoirs. This study is an attempt to develop remote sensing and Monte Carlo Simulation (MCS)-based framework to estimate the evaporation loss from small irrigation water reservoirs used for storing groundwater pumped from the Berrechid aquifer, in Morocco. To that end, remote sensing datasets were validated using data of 49 known reservoirs to identify small reservoirs and their surface area over the growing season. An Exploratory Data Analysis (EDA) of the remotely sensed results was conducted to process the outlier values. Meanwhile, MCS was implemented using 20000 iterations for developing a probabilistic model to estimate the annual evaporation loss associated with exceedance probabilities. The results showed that for an exceedance probability of 90% the associated annual evaporation loss is about 1.50 Mm3·yr−1 with a median of about 1.84 Mm3·yr−1. A sensitivity analysis (SA) of the model was conducted which revealed that the model is more sensitive to the pan coefficient (Cp) followed by reservoir area, and pan evaporation (EVP) for dry months. As for wet months, the SA showed that the model is more sensitive to the daily EVP. Overall, the study provides a new insight for forecasting evaporation loss from small reservoirs and, therefore, will help decision-makers to consider the uncertainty in evaluating the economic viability of mitigation measures. Furthermore, the methodology developed could be valuable in estimating the evaporation loss from the reservoirs in poorly monitored zones.

Determination of the asymmetric parameter in complementary relations of evaporation in alpine grasslands of the Tibetan Plateau

Complementary equations have been widely applied to evaporation (E) estimation in poorly monitored areas and under changing conditions. It is essential to determine a priori the key parameter called the asymmetric parameter (or land surface parameter in the sigmoid equation) b for predictive application of complementary equations. A previous study (Wang et al., 2020a) suggested ecosystem mean b values at the global scale, which, however, exhibit significant variation for specific ecosystem types, especially for grasslands. This brings a challenge for accurate estimation of E. In this study, taking the alpine grassland ecosystem on the Tibetan Plateau (TP) as an example, eddy covariance data from 10 EC monitoring sites across TP were used to investigate b and its control factors. Our results show that 1) b ranges from 0.48 to 2.56 with a mean value of 1.52 ± 0.65 (mean ± standard deviation) in alpine grasslands of the TP, and b is significantly correlated (R2 = 0.97, p-value < 0.01) with the mean annual surface soil water content, which results in an empirical formula to determine b; 2) the sigmoid generalized complementary equation aided by the estimated b exhibits a high efficiency of evaporation calculation with a mean Nash–Sutcliffe coefficient of 0.88 on the TP; and 3) a small perturbation in b could cause a high bias in the E estimation, especially in areas of low soil moisture. The proposed empirical formula to estimate b lays the foundation for accurate evaporation prediction on the TP under fast-changing conditions.

Data Intelligence Model and Meta-Heuristic Algorithms-Based Pan Evaporation Modelling in Two Different Agro-Climatic Zones: A Case Study from Northern India

Precise quantification of evaporation has a vital role in effective crop modelling, irrigation scheduling, and agricultural water management. In recent years, the data-driven models using meta-heuristics algorithms have attracted the attention of researchers worldwide. In this investigation, we have examined the performance of models employing four meta-heuristic algorithms, namely, support vector machine (SVM), random tree (RT), reduced error pruning tree (REPTree), and random subspace (RSS) for simulating daily pan evaporation (EPd) at two different locations in north India representing semi-arid climate (New Delhi) and sub-humid climate (Ludhiana). The most suitable combinations of meteorological input variables as covariates to estimate EPd were ascertained through the subset regression technique followed by sensitivity analyses. The statistical indicators such as root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), Willmott index (WI), and correlation coefficient (r) followed by graphical interpretations, were utilized for model evaluation. The SVM algorithm successfully performed in reconstructing the EPd time series with acceptable statistical criteria (i.e., NSE = 0.937, 0.795; WI = 0.984, 0.943; r = 0.968, 0.902; MAE = 0.055, 0.993 mm/day; and RMSE = 0.092, 1.317 mm/day) compared with the other applied algorithms during the testing phase at the New Delhi and Ludhiana stations, respectively. This study also demonstrated and discussed the potential of meta-heuristic algorithms for producing reasonable estimates of daily evaporation using minimal meteorological input variables with applicability of the best candidate model vetted in two diverse agro-climatic settings.

Estimation of evapotranspiration in Eucalyptus plantation and mixed forests based on air temperature and humidity

Soil evaporation, Air temperature and humidity inside and outside the forest and river flows were continually monitored in eucalyptus plantations (EU) and coniferous broadleaved mixed forests (MFs) to investigate the reasons for the decreased runoff recorded in EU. The applicabilities of four estimation methods based on air temperature and humidity were evaluated and verified. Our results show that: (1) the average annual solar radiation penetration rate of the EU canopies was 1.75 times that of the MFs canopies, and temperatures were lower inside the two forests than outside the forests. The daily average temperatures inside the EU and MFs were 20.7 ℃ and 20.8 ℃, respectively, which were 1.5 ℃ and 1.4 ℃ lower than those outside the forests. The daily average relative humidity was 0.8% higher in the MFs than in the EU. (2) The evapotranspiration of EU and MFs accounted for 80.2% and 76.3% of the total rainfall respectively, of which the soil evaporation was 12.8% and 8.9% respectively, and the runoff production coefficients were 0.198 and 0.237 respectively. The main reason for the decrease of the EU production was that soil evaporation in EU was 43.8% higher than that in MFs. (3) The deviation of EU and MFs evapotranspiration (ET0) from the measured values was estimated by the ET0Valian(T, RH) and ET0Alt/rs(T, RH) methods as less than 10%. (4) Using the product of the extraterrestrial solar radiation and the surface solar emissivity of the forest instead of the extraterrestrial solar radiation in these four formulas, the variation trend of the estimated forest soil evaporation was highly similar to the measured value. ET0Valian(T, RH) and ET0Alt/rs(T, RH) had the highest accuracy in soil evaporation estimation (2.0–3.4% lower than the measured values). The results provide a theoretical basis for studying the ecohydrology of subtropical plantations in the Northern Hemisphere.

Estimation of Infiltration Volumes and Rates in Seasonally Water-Filled Topographic Depressions Based on Remote-Sensing Time Series.

In semi-arid ecoregions of temperate zones, focused snowmelt water infiltration in topographic depressions is a key, but imperfectly understood, groundwater recharge mechanism. Routine monitoring is precluded by the abundance of depressions. We have used remote-sensing data to construct mass balances and estimate volumes of temporary ponds in the Tambov area of Russia. First, small water bodies were automatically recognized in each of a time series of high-resolution Planet Labs images taken in April and May 2021 by object-oriented supervised classification. A training set of water pixels defined in one of the latest images using a small unmanned aerial vehicle enabled high-confidence predictions of water pixels in the earlier images (Cohen’s Κ = 0.99). A digital elevation model was used to estimate the ponds’ water volumes, which decreased with time following a negative exponential equation. The power of the exponent did not systematically depend on the pond size. With adjustment for estimates of daily Penman evaporation, function-based interpolation of the water bodies’ areas and volumes allowed calculation of daily infiltration into the depression beds. The infiltration was maximal (5–40 mm/day) at onset of spring and decreased with time during the study period. Use of the spatially variable infiltration rates improved steady-state shallow groundwater simulations.

Long-term mean evaporation estimates with two calibration-free models

Long-term mean evaporation estimates with two calibration-free models

Modelling daily net radiation of open water surfaces using land-based meteorological data

Accurate quantification of net irradiance of open water (Rn water) is of paramount importance for the estimation of open water evaporation, which is critical for the efficient management of water resources. Alternatively, model estimates of Rn water are often used when quality measurements of Rn water are not readily available for the water storage of interest. A Daily Penman, Monteith, Equilibrium Temperature Hargreaves-Samani (DPMETHS) model has been developed for the estimation of Rn water using land-based meteorological data. The DPMETHS model is a spreadsheet-based iterative procedure that computes Rn water using daily land-based meteorological measurements of solar irradiance (Rs land), minimum and maximum air temperatures (Tmin and Tmax), minimum and maximum relative humidity (RHmin and RHmax) and average wind speed (Uland). In this study, the DPMETHS model was evaluated using daily Rn water in-situ measurements acquired from 5 sites in both hemispheres, representing very different climatic conditions. Results showed reasonable model performance at all 5 sites, with the coefficient of determination (r2) values greater than 0.85 and root mean square error (RMSE) values ranging from 0.60 MJ∙m-2 for Stratus Ocean (East Pacific Ocean) to 1.89 MJ∙m-2 for Midmar Dam (South Africa). The results of this study suggested that the DPMETHS model can be reliably used to estimate Rn water for a wide range of climatic conditions. The performance of the DPMETHS model depends on the representativeness of the land-based meteorological data to the weather conditions above the open water surface. The DPMETHS model is user-friendly with minimal computational and data requirements that allows easy data handling and visual inspection.

Technical note: Evaporating water is different from bulk soil water in &amp;lt;i&amp;gt;δ&amp;lt;/i&amp;gt;&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;H and &amp;lt;i&amp;gt;δ&amp;lt;/i&amp;gt;&amp;lt;sup&amp;gt;18&amp;lt;/sup&amp;gt;O and has implications for evaporation calculation

Abstract. Soil evaporation is a key process in the water cycle and can be conveniently quantified using δ2H and δ18O in bulk surface soil water (BW). However, recent research shows that soil water in larger pores evaporates first and differs from water in smaller pores in δ2H and δ18O, which disqualifies the quantification of evaporation from BW δ2H and δ18O. We hypothesized that BW had different isotopic compositions from evaporating water (EW). Therefore, our objectives were to test this hypothesis first and then evaluate whether the isotopic difference alters the calculated evaporative water loss. We measured the isotopic composition of soil water during two continuous evaporation periods in a summer maize field. Period I had a duration of 32 d, following a natural precipitation event, and period II lasted 24 d, following an irrigation event with a 2H-enriched water. BW was obtained by cryogenically extracting water from samples of 0–5 cm soil taken every 3 d; EW was derived from condensation water collected every 2 d on a plastic film placed on the soil surface. The results showed that when event water was heavier than pre-event BW, δ2H of BW in period II decreased, with an increase in evaporation time, indicating heavy water evaporation. When event water was lighter than the pre-event BW, δ2H and δ18O of BW in period I and δ18O of BW in period II increased with increasing evaporation time, suggesting light water evaporation. Moreover, relative to BW, EW had significantly smaller δ2H and δ18O in period I and significantly smaller δ18O in period II (p&lt;0.05). These observations suggest that the evaporating water was close to the event water, both of which differed from the bulk soil water. Furthermore, the event water might be in larger pores from which evaporation takes precedence. The soil evaporative water losses derived from EW isotopes were compared with those from BW. With a small isotopic difference between EW and BW, the evaporative water losses in the soil did not differ significantly (p&gt;0.05). Our results have important implications for quantifying evaporation processes using water stable isotopes. Future studies are needed to investigate how soil water isotopes partition differently between pores in soils with different pore size distributions and how this might affect soil evaporation estimation.

TEMPERATURE-DRIVEN EVAPORATION ANALYSIS OVER BALLIKAYA CATCHMENT

Hydrometeorological analysis such as terrain characteristics, flood forecasts and water potentials have become possible through computer models today in parallel with rapidly ever-evolving technology. While the surface-runoff during a storm has linearly and highly correlation with effective precipitation, it is inversely proportional to hydrological losses, including interception, infiltration, transpiration and evaporation. However, flood estimation analysis, in especially rural areas in Turkey, is still not widely and accurately usable, due to difficulties in accessing the necessary ground-based data records such as evaporation, which affects much of the rainfall. Although direct measurement of evaporation is not available now, indirect methods such as evaporation pans have been developed to give acceptable results. On the one hand, observation networks are built by Turkish State Meteorological Service for the estimation of evaporation from open water bodies, these measurements, on the other hand, are only obtained at large climatological stations such as city centers and generally for only six months of the year. In this sense, evaporation values, in order to apply rainfall-runoff analysis, were estimated by Kharrufa, Blaney-Criddle, and modified Blaney-Criddle methods to be used in Monthly Average models. While the highest underestimations were obtained in modified Blaney-Criddle method, Kharrufa method outperformed the other Blaney-Criddle products. The results, confirmed by analysis of the temperature-based Kharrufa approach in two urban areas close to the study basin, show to be appliable for estimation of long-term hydrographs. The analyzed method can be applied over rural watersheds lacking in-situ evaporation measurements as well as enables more accurate rainfall-runoff simulations processes with calibrated and verified hydrological models.

Relative humidity gradients as a key constraint on terrestrial water and energy fluxes

Abstract. Earth's climate and water cycle are highly dependent on terrestrial evapotranspiration and the associated flux of latent heat. Although it has been hypothesized for over 50 years that land dryness becomes embedded in atmospheric conditions through evaporation, underlying physical mechanisms for this land–atmosphere coupling remain elusive. Here, we use a novel physically based evaporation model to demonstrate that near-surface atmospheric relative humidity (RH) fundamentally coevolves with RH at the land surface. The new model expresses the latent heat flux as a combination of thermodynamic processes in the atmospheric surface layer. Our approach is similar to the Penman–Monteith equation but uses only routinely measured abiotic variables, avoiding the need to parameterize surface resistance. We applied our new model to 212 in situ eddy covariance sites around the globe and to the FLUXCOM global-scale evaporation product to partition observed evaporation into diabatic vs. adiabatic thermodynamic processes. Vertical RH gradients were widely observed to be near zero on daily to yearly timescales for local as well as global scales, implying an emergent land–atmosphere equilibrium. This equilibrium allows for accurate evaporation estimates using only the atmospheric state and radiative energy, regardless of land surface conditions and vegetation controls. Our results also demonstrate that the latent heat portion of available energy (i.e., evaporative fraction) at local scales is mainly controlled by the vertical RH gradient. By demonstrating how land surface conditions become encoded in the atmospheric state, this study will improve our fundamental understanding of Earth's climate and the terrestrial water cycle.

Estimating water balance components and their uncertainty bounds in highly groundwater-dependent and data-scarce area: An example for the Upper Citarum basin

Study regionUpper region of the Citarum basin in Indonesia. Study focusAssessing water balance components in data-scarce regions using different approaches could result in different outcomes. In the upper reaches of the Citarum River in West Java, Indonesia, for example, many previous studies found the groundwater storage to be depleting, while GRACE identifies a contrasting trend of increasing groundwater storage change. Therefore, in this study, we aim to improve the accuracy of water balance components estimates in the Upper Citarum basin. Firstly, we estimate groundwater abstraction volumes based on population size and a review of literature. Estimates of the other water balance components, namely the rainfall, actual evaporation, discharge, and groundwater storage change are derived from various global datasets and available measurements. We also use a distributed hydrological model, wflow_sbm, to yield additional estimates of discharge and actual evaporation. We compare each basin water balance estimate and quantify the uncertainty of some components using the Extended Triple Collocation (ETC) method. New hydrological insights for the regionETC application on four different rainfall estimates suggests a preference of using CHIRPS product in the study area as it delivers r2 of 0.56 and RMSE of 6.52 mm/day, compared to estimates from rainfall station (r2=0.39, RMSE = 8.57 mm/day), SACA&D (r2=0.29, RMSE = 10.46 mm/day), and TRMM (r2=0.56, RMSE = 8.61 mm/day). With CHIRPS rainfall forcing, wflow_sbm model estimates of average daily actual evaporation and discharge are obtained. The results for actual evaporation (2.67 mm/day) are plausible with a narrow difference of less than 0.50 mm/day among other estimates. The simulated discharge results in a daily average of 5.38 mm/day, estimated between observation data (3.65 mm/day) and GloFAS-ERA5 product (6.12 mm/day). Combining precipitation, actual evaporation, and discharge with a groundwater abstraction estimate of 0.57 mm/day, the wflow_sbm-based groundwater storage change is estimated at a daily storage depletion of 0.82 mm/day. Using GLEAM actual evaporation estimates (3.13 mm/day) and observed daily discharge, on the other hand, results in surplus water of 0.45 mm/day for groundwater storage change. These results demonstrate the high uncertainty in capturing subsurface hydrological processes, although the groundwater storage change estimates are found close to the estimates based upon GRACE gravimetric satellite data of 0.25 mm/day, with a variance of 1.57 mm/day. To aid in estimating current and future basin-scale groundwater level changes to support operational water management and policy in the Citarum basin, considering the massive groundwater abstraction, a focus on subsurface hydrological components quantification is of great importance for future research.

Modeling of pan evaporation based on the development of machine learning methods

For effective planning and management of water resources and implementation of the related strategies, it is important to ensure proper estimation of evaporation losses, especially in regions that are prone to drought. Changes in climatic factors, such as changes in temperature, wind speed, sunshine hours, humidity, and solar radiation can have a significant impact on the evaporation process. As such, evaporation is a highly non-linear, non-stationary process, and can be difficult to be modeled based on climatic factors, especially in different agro-climatic conditions. The aim of this study, therefore, is to investigate the feasibility of several machines learning (ML) models (conditional random forest regression, Multivariate Adaptive Regression Splines, Bagged Multivariate Adaptive Regression Splines, Model Tree M5, K- nearest neighbor, and the weighted K- nearest neighbor) for modeling the monthly pan evaporation estimation. This study proposes the development of newly explored ML models for modeling evaporation losses in three different locations over the Iraq region based on the available climatic data in such areas. The evaluation of the performance of the proposed model based on various evaluation criteria showed the capability of the proposed weighted K- nearest neighbor model in modeling the monthly evaporation losses in the studies areas with better accuracy when compared with the other existing models used as a benchmark in this study.