Evaporation Estimates Research Articles

Seasonal variation of evapotranspiration, Priestley-Taylor coefficient and crop coefficient in diverse landscapes

Priestley-Taylor equation (PT) and the Penman-Monteith equation (PM) are commonly used methods for regional evapotranspiration monitoring, using the PT coefficient (αa) and PM crop/ vegetation coefficient (Kc). This paper investigates the seasonal changes in αa and Kc at five sites in Australia and China, to understand the relationship between environmental conditions and evapotranspiration when applying different evaporation estimation methods. The research shows that higher actual evapotranspiration does not lead to higher αa and Kc values. αa and Kc perform similarly in cropland and forest environments in both China and Australia. Both αa and Kc continuously increase to a peak during the growing season and then decrease to their lowest values during the winter season. Considering Kc’s similar performance to αa and its greater data processing requirements, Kc has few advantages for estimating regional evapotranspiration. Applying the Priestley-Taylor equation with a regional α indicator will enhance the accuracy and reduce the workload when estimating regional evapotranspiration for similar landcover types based on remote sensing.

Sigmoid Generalized Complementary Equation for Evaporation Over Wet Surfaces: A Nonlinear Modification of the Priestley‐Taylor Equation

AbstractThe deviations of the Priestley‐Taylor (PT) coefficient from a fixed value around 1.26 indicate a nonlinear dependence of wet surface evaporation () on the equilibrium evaporation (, which is the radiation term in Penman potential evaporation []). The linear PT equation with a fixed coefficient underestimates for small but overestimates for large , whereas the Penman equation with calibrated linear wind function has the opposite bias. In this study, the sigmoid generalized complementary (SGC) equation by Han and Tian (2018a), https://doi.org/10.1029/2017wr021755 was applied to estimate the wet surface evaporation by setting its asymmetric parameter to infinity. The SGC equation with one fixed parameter captures the nonlinear dependence of on over wet surfaces by including the aerodynamic component of with a reference wind function, and amends the shortages of the linear PT and Penman equation. By using datasets over open water surfaces of lakes and ocean, wetlands, and paddy fields, the validation results indicate that the wet surface SGC equation performed better than the linear PT and Penman equations on evaporation estimation. The success of the wet surface SGC equation has implications for the extension of the complementary principle to consider varying aerodynamic conditions over wet surfaces.

Does straw mulch alter soil evaporation, yield, and quality of sugarcane?

Cumulative soil evaporation estimation without CE(s − i) and with CE(s + i) inundation days viz. rainfall or irrigation days is an important aspect, affecting yield and quality of sugarcane, lacking in the literature. To this end, present replicated study conducted during spring 2019–2020 in irrigated sugarcane under semi-arid conditions, with differential rice straw mulch rates viz. 0, 4, 6, and 8 t ha−1 constituting T1, T2, T3, and T4 treatments, respectively. Attempts being made to delineate CE(s − i), CE(s + i) for which mini-lysimeters and pan evaporation (Ep) data used, respectively, and their effect on yield and quality parameters of sugarcane. From tillering to grand growth stage, CE(s − i) from T1 reduced to 2.4, 9.7, and 26.5% in T2, T3, and T4 plots, respectively whereas 7.14 and 15.3% reductions recorded while shifting from T2 to T3 and T3 to T4 plots. However, CE(s + i) reduced to 1.4, 14.4, and 5.6% while shifting from T1 to T2, T2 to T3, and T3 to T4 plots. Periodic growth parameters viz. cane height, width, Brix, and relative leaf water contents reported to improve but non-significantly in T2, T3, and T4 plots as compared to T1 plots. Averaged SPAD readings were reported to 7.6, 14.9, and 17% higher at 311 DAS in T2, T3 and T4 plots, compared to T1 plots. T3 plots reported significantly higher cane yields and commercial cane sugar (t ha−1) than T1 and T2 plots but at par with T4 plots. Therefore, T3 plots loaded with 6 t ha−1 of mulch reported to reduce CE(s − i), CE(s + i), and improve cane yield, and quality.

A Study on Characteristics and Comparison of Evaporation Estimation Methods in Bandung

This study aims to understand the characteristic of evaporation and to evaluate the evaporation estimation methods to be employed in Bandung by using observation data at three different land cover characteristics sites, namely, densely vegetated area (Baleendah), densely built-up area (Ujung Berung), and mix of buildings and vegetation area (ITB). Observation data used are hourly evaporation, vapour pressure deficit, temperature, relative humidity, wind speed, and radiation. The analysis was done mostly by using statistical methods such as regression analysis and error comparison. The result shows the dominant weather factor affecting the evaporation in ITB and Ujung Berung is vapour pressure deficit, and in Baleendah is solar radiation. The methods of evaporation estimations used in this study are Trabert, Schendel, Turc, and CIMIS-Penman methods. The result shows that the original constant values of those methods are significantly correlated. However, the Schendel is found the most overestimated, and the second is Turc. The best estimated evaporation in Baleendah, ITB, and Ujung Berung is calculated using CIMIS-Penman with one hour lag of radiation, Trabert, and Calibrated Schendel, respectively. The improvement of constant value was applied to Schendel and the result is better than the original constants.

A Study on Characteristics and Comparison of Evaporation Estimation Methods in Bandung

This study aims to understand the characteristic of evaporation and to evaluate the evaporation estimation methods to be employed in Bandung by using observation data at three different land cover characteristics sites, namely, densely vegetated area (Baleendah), densely built-up area (Ujung Berung), and mix of buildings and vegetation area (ITB). Observation data used are hourly evaporation, vapour pressure deficit, temperature, relative humidity, wind speed, and radiation. The analysis was done mostly by using statistical methods such as regression analysis and error comparison. The result shows the dominant weather factor affecting the evaporation in ITB and Ujung Berung is vapour pressure deficit, and in Baleendah is solar radiation. The methods of evaporation estimations used in this study are Trabert, Schendel, Turc, and CIMIS-Penman methods. The result shows that the original constant values of those methods are significantly correlated. However, the Schendel is found the most overestimated, and the second is Turc. The best estimated evaporation in Baleendah, ITB, and Ujung Berung is calculated using CIMIS-Penman with one hour lag of radiation, Trabert, and Calibrated Schendel, respectively. The improvement of constant value was applied to Schendel and the result is better than the original constants.

A Multi-Reservoir Study of the Impact of Uncertainty in Pool Evaporation Estimates on Water-Availability Models

Quantifying evaporative loss from reservoirs plays a critical role in sound water-availability management plans and in reservoir management. Various methods are used to quantify reservoir evaporation; however, each method carries a degree of uncertainty that propagates to model predictions of available water within a reservoir or a reservoir network. Herein, we explore the impact of uncertainty in reservoir evaporation on model outputs of historical and future water availability throughout the five major reservoirs in the Savannah River Basin in South Carolina, USA, using four different evaporation methods. Variability in the total available water is evaluated using the United States Army Corps of Engineers (USACE) 2006 Drought Contingency Plan hydrologic model of the Savannah River Basin, which incorporates recent water-management plans and reservoir controls. Results indicate that, during droughts, reservoir evaporation plays a large role in water-availability predictions, and uncertainty in evaporative losses produces significant uncertainty in modeled water availability for extreme events. For example, the return period for an event in which the availability of water in Lake Hartwell was reduced to 50% of full pool capacity varied from 38.2 years to 53.4 years, depending on the choice of evaporation parameterization. This is a variation of 40% in the return period, depending on the choice of evaporation method.

Estimation of Evapotranspiration and Its Components across China Based on a Modified Priestley–Taylor Algorithm Using Monthly Multi-Layer Soil Moisture Data

Although soil moisture (SM) is an important constraint factor of evapotranspiration (ET), the majority of the satellite-driven ET models do not include SM observations, especially the SM at different depths, since its spatial and temporal distribution is difficult to obtain. Based on monthly three-layer SM data at a 0.25° spatial resolution determined from multi-sources, we updated the original Priestley Taylor–Jet Propulsion Laboratory (PT-JPL) algorithm to the Priestley Taylor–Soil Moisture Evapotranspiration (PT-SM ET) algorithm by incorporating SM control into soil evaporation (Es) and canopy transpiration (T). Both algorithms were evaluated using 17 eddy covariance towers across different biomes of China. The PT-SM ET model shows increased R2, NSE and reduced RMSE, Bias, with more improvements occurring in water-limited regions. SM incorporation into T enhanced ET estimates by increasing R2 and NSE by 4% and 18%, respectively, and RMSE and Bias were respectively reduced by 34% and 7 mm. Moreover, we applied the two ET algorithms to the whole of China and found larger increases in T and Es in the central, northeastern, and southern regions of China when using the PT-SM algorithm compared with the original algorithm. Additionally, the estimated mean annual ET increased from the northwest to the southeast. The SM constraint resulted in higher transpiration estimate and lower evaporation estimate. Es was greatest in the northwest arid region, interception was a large fraction in some rainforests, and T was dominant in most other regions. Further improvements in the estimation of ET components at high spatial and temporal resolution are likely to lead to a better understanding of the water movement through the soil–plant–atmosphere continuum.

Evaporation from boreal reservoirs: A comparison between eddy covariance observations and estimates relying on limited data

AbstractHydrological models used for reservoir management typically lack an accurate representation of open‐water evaporation and must be run in a scarce data context. This study aims to identify an accurate means to estimate reservoir evaporation with simple meteorological inputs during the open‐water season, using long‐term eddy covariance observations from two boreal hydropower reservoirs with contrasting morphometry as reference. Unlike the temperate water bodies on which the majority of other studies have focused, northern reservoirs are governed by three distinct periods: ice cover in the cold season, warming in the summer and energy release in the fall. The reservoirs of interest are Eastmain‐1 (52°N, mean depth of 11 m) and Romaine‐2 (51°N, mean depth of 42 m), both located in eastern Canada. Four approaches are analysed herein: a combination approach, a radiation‐based approach, a mass‐transfer approach, and empirical methods. Of all the approaches, the bulk transfer equation with a constant Dalton number of 1.2 x 10−3 gave the most accurate estimation of evaporation at hourly time steps, compared with the eddy covariance observations (RMSE of 0.06 mm h−1 at Eastmain‐1 and RMSE of 0.04 mm h−1 at Romaine‐2). The daily series also showed good accuracy (RMSE of 1.38 mm day−1 at Eastmain‐1 and RMSE of 0.62 mm day−1 at Romaine‐2) both in the warming and energy release phases of the open‐water season. The bulk transfer equation, on the other hand, was incapable of reproducing condensation episodes that occurred soon after ice breakup. Basic and variance‐based sensitivity analyses were conducted, in particular to measure the variation in performance when the bulk transfer equation was applied with meteorological observations collected at a certain distance (~10–30 km) from the reservoir. This exercise illustrated that accurate estimates of open water evaporation require representative measurements of wind speed and water surface temperature.

The influence of solar radiation on plastic shrinkage cracking in concrete

Plastic shrinkage cracking (PSC) in concrete is related to the amount and rate of free pore water loss due to evaporation. This study investigates the influence of solar radiation on evaporation, the concrete temperature, plastic shrinkage and cracking. Literature suggests that exposure to solar radiation can either: increase the concrete temperature and, consequently, increase the rate of tensile strength gain resulting in less severe PSC or can increase the concrete temperature, resulting in a higher amount and rate of pore water loss to produce more severe PSC. The results of this study indicate that exposure to solar radiation significantly increases the amount and rate of pore water loss as well as plastic shrinkage and severity of PSC. To study PSC, many authors have estimated the evaporation rate from wind speed, air and concrete temperature and humidity which would result in significantly underestimating the actual evaporation rate if the concrete is exposed to moderate or high solar radiation. The accuracy of several radiation-related evaporation estimation models was evaluated by comparing the models to the actual rate of evaporation from specimens placed in the sun. A more accurate model was identified to estimate the evaporation in concrete specimens when exposed to solar radiation.

DIFFERENT METHODS FOR MEASURING EVAPORATION IN A TROPICAL RESERVOIR: THE CASE OF THE GAVIÃO RESERVOIR IN THE STATE OF CEARÁ

ABSTRACT Studies that allow the estimation of evaporation in reservoirs represent an important action for the adequate management of water resources. Thus, this study aimed at estimating evaporation in the tropical reservoir Gavião, located in the municipality of Pacatuba, Ceará, Brazil, and to verify the effect of these estimates on the water availability of the reservoir using the VYELAS model. The results of Penman's methods and the Water Balance were compared with the values obtained from the hydrostatic pressure sensor, the most accurate. It was possible to verify that, in relation to the pressure difference method, all the conventional methods overestimate the evaporation in the reservoir. The method that presented estimates closer to those obtained by the pressure difference sensor was that of Penman, based on data from an onboard station in the lake, with a deviation of only 12%. The method of water balance also presented reliable results for monthly average. The two methods usually accepted in the evaporation calculation (water balance ignoring the infiltration; and Penman's method for meteorological station data on land distant from the lake) presented the most disturbing evaporation values significantly altering the water availability. The results of the VYELAS model showed that evaporated flows, estimated by several methods, exceeded the reference flow by up to 83%. The results demonstrate the great sensitivity of the estimate of water availability in relation to the evaporation rate in the lake.

Optimal Calibration of Evaporation Models against Penman–Monteith Equation

We present an approach for the calibration of simplified evaporation model parameters based on the optimization of parameters against the most complex model for evaporation estimation, i.e., the Penman–Monteith equation. This model computes the evaporation from several input quantities, such as air temperature, wind speed, heat storage, net radiation etc. However, sometimes all these values are not available, therefore we must use simplified models. Our interest in free water surface evaporation is given by the need for ongoing hydric reclamation of the former Ležáky–Most quarry, i.e., the ongoing restoration of the land that has been mined to a natural and economically usable state. For emerging pit lakes, the prediction of evaporation and the level of water plays a crucial role. We examine the methodology on several popular models and standard statistical measures. The presented approach can be applied in a general model calibration process subject to any theoretical or measured evaporation.

Coherent Satellite Monitoring of the Water Cycle Over the Amazon. Part 2: Total Water Storage Change and River Discharge Estimation

AbstractIn companion paper 1, the SAtellite Water Cycle (SAWC) satellite data set integration approach was presented. SAWC accounts for (1) the closure of the water budget at the sub‐basin scale by (2) using upstream/downstream dependencies. Here, the SAWC database is used to reconstruct a missing water component. The total water storage change (dS) can be reconstructed prior to the Gravity Recovery and Climate Experiment (GRACE) period. In terms of seasonal and interannual variations, SAWC provides long‐term dS estimates that are highly consistent with the GRACE estimate (2002–2015). When compared to the extent of inundation, correlation of anomalies reaches 0.6 over sub‐basins presenting large floodplain. A strong relationship (correlation of 0.7) exists between the monthly dS and the El Niño index. The river discharge R can also be estimated using the SAWC approach during the GRACE period. A scheme was developed to estimate R continuously along the river through the indirect interpolation of discharge station measurements by using satellite estimates of dS, precipitation P, and evaporation E. These R estimates along the river were evaluated with 80 independent measurements and had a correlation of 0.86 and a Kling‐Gupta efficiency index of 0.71. A comprehensive monthly river discharge estimate was proposed for the first time across the Amazon basin over 13 years (2002–2015) at a 0.25° resolution. The SAWC framework is a pure observation tool for integrating a large range of satellite estimates, potentially facilitating the assimilation of such data into land surface models.

Mapping Evapotranspiration of Agricultural Areas in Ghana.

Climate change is having an adverse effect on the environment especially in sub-Sahara Africa, where capacity for natural resource management such as water is very low. The scope of the effect on land use types have to be estimated to inform proper remedy. A combined estimation of transpiration and evaporation from plants and soil is critical to determine annual water requirement for different land use. Evapotranspiration (ET) is a major component in the world hydrological cycle, and understanding its spatial dimensions is critical in evaluating the effects it has on regional land use. A measure of this component is challenging due to variation in rainfall and environmental changes. The mapping evapotranspiration with high resolution and internalized calibration (METRIC) method is employed to create evapotranspiration map for land use, using remotely sensed data by satellite, processed, and analyzed in ArcGIS. Normalized difference vegetation index (NDVI) was related to the availability of water for vegetation on different land use, and the results indicate a high evapotranspiration for vegetated land use with high NDVI than land use with low NDVI.

Evapotranspiration partitioning assessment using a machine-learning-based leaf area index and the two-source energy balance model with sUAV information.

Accurate quantification of the partitioning of evapotranspiration (ET) into transpiration and evaporation fluxes is necessary to understanding ecosystem interactions among carbon, water, and energy flux components. ET partitioning can also support the description of atmosphere and land interactions and provide unique insights into vegetation water status. Previous studies have identified leaf area index (LAI) estimation as a key descriptor of biomass conditions needed for the estimation of transpiration and evaporation. LAI estimation in clumped vegetation systems, such as vineyards and orchards, has proven challenging and is strongly related to crop phenological status and canopy management. In this study, a feature extraction model based on previous research was built to generate a total of 202 preliminary variables at a 3.6-by-3.6-meter-grid scale based on submeter-resolution information from a small Unmanned Aerial Vehicle (sUAV) in four commercial vineyards across California. Using these variables, a machine learning model called eXtreme Gradient Boosting (XGBoost) was successfully built for LAI estimation. The XGBoost built-in function requires only six variables relating to vegetation indices and temperature to produce high-accuracy LAI estimation for the vineyard. Using the six-variable XGBoost-based LAI map, two versions of the Two-Source Energy Balance (TSEB) model, TSEB-PT and TSEB-2T were used for energy balance and ET partitioning. Comparing these results with the Eddy-Covariance (EC) tower data, showed that TSEB-PT outperforms TSEB-2T on the estimation of sensible heat flux (within 13% relative error) and surface heat flux (within 34% relative error), while TSEB-2T outperforms TSEB-PT on the estimation of net radiation (within 14% relative error) and latent heat flux (within 2% relative error). For the mature vineyard (north block), TSEB-2T performs better than TSEB-PT in partitioning the canopy latent heat flux with 6.8% relative error and soil latent heat flux with 21.7% relative error; however, for the younger vineyard (south block), TSEB-PT performs better than TSEB-2T in partitioning the canopy latent heat flux with 11.7% relative error and soil latent heat flux with 39.3% relative error.

An Enhanced MOD16 Evapotranspiration Model for the Tibetan Plateau During the Unfrozen Season

AbstractEvapotranspiration (ET) is composed of soil evaporation (ETs), canopy transpiration (ETc), and canopy intercepted water evaporation (ETw), and it plays an indispensable role in the water and energy cycles of land surface processes. More accurate estimations of variations in ET are essential for natural hazard monitoring and water resource management. An improved algorithm for ETs with soil moisture and texture (SMT) and an optimized ETc based on the MOD16 model (MOD16‐STM) are proposed for the cold, arid, and semiarid regions of the Tibetan Plateau (TP). The nonlinear relationships between the soil surface resistance (rss) and soil surface hydration state in different soil textures were redefined by using five flux tower measurements over the TP. The value of the mean potential stomatal conductance per unit leaf area (CL) used for ETc calculation in grasslands was optimized at 0.0038 (m s−1). The MOD16‐STM was compared with the MOD16 and a soil water index‐based Priestley‐Taylor algorithm (SWI‐PT) at five independent sites. The results showed that the half‐hourly, daily, and monthly estimations from the MOD16‐STM were closer to observations than those from the other two models. The results indicated that the MOD16‐STM increased R2 by 0.2/0.05 and the index of agreement by 0.37/0.23 and deceased RMSE by 43.89/42.77 W m−2 and the absolute mean bias (|MB|) by 46.23/45.89 W m−2, when compared to the results of the MOD16 at daily/monthly scales. The results suggested that the MOD16‐STM will produce more accurate ET estimates over the flux sites and has great potential for ET estimation and land surface model improvement for the TP region.

Improving the Representation of Long‐Term Storage Variations With Conceptual Hydrological Models in Data‐Scarce Regions

AbstractIn the Luangwa basin in Zambia, long‐term total water storage variations were observed with Gravity Recovery and Climate Experiment, but not reproduced by a standard conceptual hydrological model that encapsulates our current understanding of the dominant regional hydrological processes. The objective of this study was to identify potential processes underlying these low‐frequency variations through combined data analysis and model hypothesis testing. First, we analyzed the effect of data uncertainty by contrasting observed storage variations with multi‐annual estimates of precipitation and evaporation from multiple data sources. Second, we analyzed four different combinations of model forcing and evaluated their skill to reproduce the observed long‐term storage variations. Third, we formulated alternative model hypotheses for groundwater export to potentially explain low‐frequency storage variations. Overall, the results suggest that the initial model's inability to reproduce the observed low‐frequency storage variations was partly due to the forcing data used and partly due to the missing representation of regional groundwater export. More specifically, the choice of data source affected the model's ability to reproduce annual maximum storage fluctuations, whereas the annual minima improved by adapting the model structure to allow for groundwater export from a deeper groundwater layer. This suggests that, in contrast to previous research, conceptual models can reproduce long‐term storage fluctuations if a suitable model structure is used. Overall, the results highlight the value of alternative data sources and iterative testing of model structural hypotheses to improve runoff predictions in a poorly gauged basin leading to enhanced understanding of its hydrological processes.

Forecasting lake evaporation under a changing climate with an integrated artificial neural network model: A case study Lake Nasser, Egypt

Understanding lake evaporation and the climate change role in evaporation is paramount for any water resources management system. The prediction of the climate's future changes is a very important step in planning lake future management decisions. This study analyzed Lake Nasser's evaporation in southern Egypt. Meteorological parameters were compiled from Aswan meteorological station near Lake Nasser. Also, CORDEX predicted climatological parameters from 2021 to 2050 were collected. Lake Nasser's evaporation prediction model using artificial neural networks technique was built. Statistics were calculated in the calibration and validation stages to find the most adequate model of the Lake evaporation calculation. The predictions of future evaporation were extracted from the model using predicted climatological data from CORDEX regional climate models. Trend analysis was done to assure the impacts of climate change on the lake. ANN model was developed and implemented successfully on Lake Nasser, which could be used to handle evaporation calculation over Lake Nasser. ANN model with training algorithm with 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22 neurons with 5 input variables were tested to find the best model evaporation estimation of the lake with the least number of neurons. ANN model with training algorithm with 20 neurons with 5 input variables performed the best for evaporation estimation of the lake. According to predicted climate data, about a 2% increase in lake Nasser evaporation could be predicted in the year 2050. It was noticed that the maximum predicted values of evaporation are in July to August months with a range from 7.04 mm/day to 9.64 mm/day. The peak value, the outlier of maximum evaporation, is about 11.16 mm/day. The minimum predicted values are in December to January months with a range from 3.50 mm/day to 6.81 mm/day. Trend analysis showed that the predicted climatological parameters are slightly higher than historical records.

Data Assimilation of Terrestrial Water Storage Observations to Estimate Precipitation Fluxes: A Synthetic Experiment

The Gravity Recovery and Climate Experiment (GRACE) mission and its Follow-On (GRACE-FO) mission provide unprecedented observations of terrestrial water storage (TWS) dynamics at basin to continental scales. Established GRACE data assimilation techniques directly adjust the simulated water storage components to improve the estimation of groundwater, streamflow, and snow water equivalent. Such techniques artificially add/subtract water to/from prognostic variables, thus upsetting the simulated water balance. To overcome this limitation, we propose and test an alternative assimilation scheme in which precipitation fluxes are adjusted to achieve the desired changes in simulated TWS. Using a synthetic data assimilation experiment, we show that the scheme improves performance skill in precipitation estimates in general, but that it is more robust for snowfall than for rainfall, and it fails in certain regions with strong horizontal gradients in precipitation. The results demonstrate that assimilation of TWS observations can help correct (adjust) the model’s precipitation forcing and, in turn, enhance model estimates of TWS, snow mass, soil moisture, runoff, and evaporation. A key limitation of the approach is the assumption that all errors in TWS originate from errors in precipitation. Nevertheless, the proposed approach produces more consistent improvements in simulated runoff than the established GRACE data assimilation techniques.

Estimation methodology and the significance of the atmospheric water exchange flux in the river-lake systems of selected lobelia lakes in the vicinity of the Tri-City in Poland

Abstract The article aims to present the methodology of estimating the atmospheric water exchange components in the lake. In the absence of direct precipitation and evaporation measurements, these water balance elements need to be estimated. However, the inadequate selection of precipitation and evaporation estimation methods causes the incorrect determination of the hydrological function of the lake and the effect it has on the formation of river drainage. Determination of the evaporation from the lake’s surface was based on the Davidov formula, which considered the monthly average surface temperature of a given lake. The saturated water vapour pressure under the lake’s monthly mean surface water temperature (TWP) was calculated according to ISO 13788 standard. The interpolation method, which is the inverse-distance deterministic method (IDW), was used to calculate precipitation reaching the lake surface. The calculations were made for three hydrological years diverse in terms of humidity and thermal conditions. The methodology for estimating the components of atmospheric water exchange was presented on a small river-lake system of the upper Gościcina River catchment, an example of a postglacial lake district area. The lake elements of this system are lobelia lakes, poorly known in terms of water circulation. At the beginning of the twenty-first century, unjustified activities regarding assessing the water circulation conditions in this river-lake system led to changes in water relations, causing environmental, financial and social losses.

Connotation analysis of parameters in the generalized nonlinear advection aridity model

The generalized nonlinear advection aridity model (GNAA) for evaporation (E) estimation can be expressed in a basic form with a single parameter αe-0, or an extended form using two parameters, αe-c and c. The implications of these model parameters in the model and the accurate estimation of E are receiving increasing attention. Our study shows that αe-0 and αe-c are affected by precipitation (P), the aridity index (Epa/P) and the climate seasonality and asynchrony index (SAI), etc, with which αe-0 has stronger correlations. This demonstrated that annual αe-0 and αe-c could cover the α parameter in the Priestley–Taylor formula, as well as other factors, particularly the aridity index. For the basic GNAA form, annual αe-0 is smaller than 1.00 in most catchments, but the GNAA model with the developed empirical formula between αe-0 and Epa/P can accurately estimate E at an annual scale. For the extended GNAA form, most annual αe-c were larger than 1.00, with a mean value of 1.08 for the Loess Plateau; αe-c tended to restore the original α values in the Priestley-Taylor equation. Functional differences exist between the basic and extended GNAA forms in estimating E and explaining the complementary relationship. Our results bridge some gaps in understanding the GNAA model from previous studies, and provide useful information to extend the application of the GNAA model.