Theories and calculation methods for regional objective ET

An accurate estimation of evapotranspiration (ET) is critical to understanding the water cycle in watersheds and promoting the sustainable utilization of water resources. Although there are various ET products in the Yellow River Basin, various ET products have many uncertainties due to input data, parameterization schemes, and scale conversion, resulting in significant uncertainties in regional ET data products. To reduce the uncertainty of a single product and obtain more accurate ET data, more accurate ET data can be obtained by fusing different ET data. Addressing this challenge, by calculating the uncertainty of three ET data products, namely global land surface satellite (GLASS) ET, Penman–Monteith–Leuning (PML)-V2 ET, and reliability-affordable averaging (REA) ET, the weight of each product is obtained to drive the Bayesian three-cornered Hat (BTCH) algorithm to obtain higher quality fused ET data, which are then validated at the site and basin scales, and the accuracy has significantly improved compared to a single input product. On a daily scale, the fused data’s root mean square error (RMSE) is 0.78 mm/day and 1.14 mm/day. The mean absolute error (MAE) is 0.53 mm/day and 0.84 mm/day, respectively, which has a lower RMSE and MAE than the model input data; the correlation coefficients (R) are 0.9 and 0.83, respectively. At the basin scale, the RMSE and MAE of the annual average ET of the fused data are 11.77 mm/year and 14.95 mm/year, respectively, and the correlation coefficient is 0.84. The results show that the BTCH ET fusion data are better than single-input product data. An analysis of the fused ET data on a spatiotemporal scale shows that from 2001 to 2017, the ET increased in 85.64% of the area of the Yellow River Basin. Fluctuations in ET were greater in the middle reaches of the Yellow River than in the upstream and downstream regions. The BTCH algorithm has indispensable reference value for regional ET estimation research, and the ET data after BTCH algorithm fusion have higher data quality than the original input data. The fused ET data can inform the development of management strategies for water resources in the YRB and provide a deeper understanding of the regional water supply and demand balance mechanism.

Satellite retrieval of actual evapotranspiration in the Tibetan Plateau: Components partitioning, multidecadal trends and dominated factors identifying

Our study area is the Xilin River basin. Remote sensing approach was used to retrieve ecological water use by using MODIS images from Apr. to Sept. of 2000、2007、2010–2014, and using auxiliary environmental data from the same time periods. The results showed the spatial distribution of ecological water use was consistent with the land surface condition. The ecological water use had the trend of strip reduction from the upstream to the middle and lower reaches. The annual variation was extremely related to the precipitation. Large amount of precipitation in the year, ecological water use was larger, and vice versa. The areas with higher ecological water use were forest land and farmland, followed by unutilized land. Grassland and construction land were lower. The order of ecological water use of different land use type was basically: forest land> farmland>unutilized land>construction land>grassland. In the year of abundant water, the time of the maximum value of ecological water use appeared in June-July with the time of precipitation. In dry year, the time of the maximum value had no rule. The single factor correlation analysis of the monthly ecological water use and monthly meteorological factors showed that the most relevant meteorological factor is precipitation. The correlation coefficient is 0.776.

Water resource optimization allocation is essential for maintaining ecological balance, promoting economic development, and ensuring social stability within river basins. However, many existing studies treated ecological water requirements as static baselines in allocation models, failing to address the dynamic utilization of water resources efficiently under varying hydrological conditions. In the Tao’er River Basin, a typical semiarid river basin, the combined effects of climate change and human activities have led to a severe imbalance between water supply and demand, particularly in regard to the competition between ecological water replenishment for the Xianghai Wetland, agricultural irrigation, and social water use. There is an urgent need for optimized water resource allocation to ensure the coordinated and sustainable development of these sectors. In this study, we developed the WEP-L model, a coupled natural–societal system, using various ecohydrological monitoring data to analyze the water supply‒demand balance under different hydrological conditions. We proposed an allocation plan that considers ecological marginal benefits, agricultural needs, and water use guarantees: 1.04 × 108 m3 for ecological supplementation and 5.16 × 108 m3 for agricultural water in the flat year, adjusted to 0.94 × 108 m3 and 3.60 × 108 m3 in the dry year, respectively. Furthermore, by incorporating reservoir water supply rules into the WEP-L model, we simulated the feasibility of intra-annual water allocation. The proposed allocation scheme concentrates ecological water in spring during flat years and distributes it at a ratio of 80% in spring and 20% in autumn during dry years. This study emphasizes the dynamic adjustment of ecological water resources on the basis of environmental conditions and societal needs, aiming to maximize ecological and social benefits. It provides scientific support for optimizing ecological water allocation and offers practical guidance for wetland conservation, agricultural water management, and water resource policy-making.

Multiple-risk assessment of water supply, hydropower and environment nexus in the water resources system

Grain for Green project could result in high evapotranspiration (ET), exacerbate water shortage, and intensify conflicts in water demand between ecosystems and humans in many water‐limited areas. However, it is still unclear how vegetation restoration would influence ET in humid karst regions where abundant precipitation but quick leakage. Particularly, it is challenging that existing ET measurement methods are not applicable simultaneously for various ecosystems in such highly heterogeneous and fragmented landscapes. To solve this issue, this study, for the first time, develops a new ET measurement system, by integrating multi‐techniques, to quantify ET for multiple vegetation restoration types and identify their controlling factors in a humid karst region for three consecutive years. The results show that (a) for different succession stages during natural restoration, the ET of secondary forest (680 ± 7 mm yr−1) > shrub (564 ± 16 mm yr−1) > grass (546 ± 12 mm yr−1) > cropland (513 ± 8 mm yr−1) (P < 0.05); (b) ET of all ecosystems are far less than that of precipitation (1,059 ± 63 mm yr−1) but closer to potential ET (849 ± 11 mm yr−1), and soil moisture has been maintained at >0.20 cm3cm−3 in the growing season; (c) solar radiation and vapor pressure deficit are the two predominant factors on the ET (P < 0.01), while there is no significant relationship between ET and soil moisture (P > 0.05). The results suggest that vegetation restoration returned from cropland in humid karst regions increased ET but may not induce water scarcity for vegetation sustainability, at least under normal climatic conditions.

The use of remote sensing technology to estimate regional evapotranspiration has been carried out for many years. Recently, with the advancements in quantification of remote sensing and the access of MODIS data, more scientists have been using MODIS data to monitoring regional evapotranspiration (ET) instead of the NOAA/AVHRR data. The surface energy balance algorithm for land (SEBAL) model combined with NOAA/AVHRR and MODIS data separately is applied to estimate the 24-hour regional evapotranspiration in a semi-arid agricultural area of northern China. And the SEBAL regional evapotranspiration model calculated results from MODIS and NOAA/AVHRR data are compared with the in-situ measured ground surface evaporation. The analysis shows that in estimating regional evapotranspiration of the satellite based application, MODIS data is more appropriate than NOAA/AVHRR data.

Coupling physical constraints with machine learning for satellite-derived evapotranspiration of the Tibetan Plateau

As an important energy absorption process in the Earth’s surface energy balance, evapotranspiration (ET) from vegetation and bare soil plays an important role in regulating the environmental temperatures. However, little research has been done to explore the cooling effect of ET on the urban heat island (UHI) due to the lack of appropriate remote-sensing-based estimation models for complex urban surface. Here, we apply the modified remote sensing Penman–Monteith (RS-PM) model (also known as the urban RS-PM model), which has provided a new regional ET estimation method with the better accuracy for the urban complex underlying surface. Focusing on the city of Xuzhou in China, ET and land surface temperature (LST) were inversed by using 10 Landsat 8 images during 2014–2018. The impact of ET on LST was then analyzed and quantified through statistical and spatial analyses. The results indicate that: (1) The alleviating effect of ET on the UHI was stronger during the warmest months of the year (May–October) but not during the colder months (November–March); (2) ET had the most significant alleviating effect on the UHI effect in those regions with the highest ET intensities; and (3) in regions with high ET intensities and their surrounding areas (within a radius of 150 m), variation in ET was a key factor for UHI regulation; a 10 W·m−2 increase in ET equated to 0.56 K decrease in LST. These findings provide a new perspective for the improvement of urban thermal comfort, which can be applied to urban management, planning, and natural design.

Actual evapotranspiration (ET) is a major water use flux in a basin water balance with crucial significance for water resources management and planning. Mapping ET with good accuracy has been the subject of ongoing research. Such mapping is even more challenging in heterogeneous and data-scarce regions. The main objective of our research is to estimate ET using daily Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and Global Land Data Assimilation System (GLDAS) weather datasets based on the operational simplified surface energy balance (SSEBop) algorithm at a 1-km spatial scale and 8-day temporal resolution for the Mara Basin (Kenya/Tanzania). Unlike previous studies where the SSEBop algorithm was used, we use a seasonally-varying calibration coefficient for determining the “cold” reference temperature. Our results show that ET is highly variable, with a high inter-quartile range for wetlands and evergreen forest (24% to 29% of the median) and even up to 52% of the median for herbaceous land cover and rainfed agriculture. The basin average ET accounts for about 66% of the rainfall with minimal inter-annual variability. The basin scale validation using nine-years of monthly, gridded global flux tower-based ET (GFET) data reveals that our ET is able to explain 64% of the variance in GFET while the MOD16-NB (Nile Basin) explains 72%. We also observe a percent of bias (PBIAS) of 1.1% and 2.8%, respectively for SSEBop ET and MOD16-NB, indicating a good reliability in the ET estimates. Additionally, the SSEBop ET explains about 52% of the observed variability in the Normalized Difference Vegetation Index (NDVI) for a 16-day temporal resolution and 81% for the annual resolution, pointing to an increased reliability for longer aggregation periods. The annual SSEBop ET estimates are also consistent with the underlying primary (i.e., water and energy) and secondary (i.e., soil, topography, geology, land cover, etc.) controlling factors across the basin. This paper demonstrated how to effectively estimate and evaluate spatially-distributed and temporally-varying ET in data-scarce regions that can be applied elsewhere in the world where observed hydro-meteorological variables are limited.

Simulating regional full-chain water balance using a novel surface water-groundwater coupled model

Based on the relevant data of nature, society, economics and water resources of eight sub-areas of Taihang Mountain from 2016, this paper selected six main evaluation factors including use efficiency of water resources, water deficient ratio, irrigation ration of arable land, precipitation amount of precipitation, per capita water resources and ecological water use ratio, and quantitatively researched water resources carrying capacity of eight sub-areas by using the fuzzy comprehensive evaluation method. The evaluated results show that the comprehensive grade of the study area was higher than 0.614, and the current water exploitation and utilization have reached a considerable scale, but still has certain potentiality. Overall, water resources supply and demand are able to partly satisfy the regional development in the the study area. For the eight sub-areas, the water resources carrying capacity status is very different. Handan, Xingtai and Shijiazhuang have a weak water resources carrying capacity, while water resources carrying capacity of other sub-areas is relatively strong. Finally, according to different regions of the water resources carrying condition, this paper puts forward the corresponding measures in Taihang Mountain Area.

Abstract: In this study, seven widely used potential evapotranspiration (ETo) methods were evaluated by comparing with the FAO-56 Penman-Monteith method (PM method) to provide useful information for selecting appropriate ETo equations under data-limited condition in Beijing, China. Statistical methods and parameters, namely linear regression, root mean squared error (RMSE) and mean bias error (MBE), were used to evaluate the seven ETo methods. Results showed that ETo estimated using Kimberly–Penman method have fairly close agreement with the PM method (referring to standard ETo), considering the coefficient of determination (R2) of 0.96, RMSE of 0.42 mm/day, and a coefficient of efficiency (E) of 0.96. Locally calibrated Penman and Doorenbos–Pruitt methods also have better agreement with the PM method, correspondingly with R2 of 0.99 and 0.95, RMSEs of 0.24 mm/day and 0.21 mm/day, and coefficients of efficiency of 1.02 and 0.99, respectively. The ETo is the most sensitive to vapor pressure deficit (VPD) and net radiation in the Beijing area. Hence, the VPD-based and VPD-radiation combined ETo methods were developed and calibrated. Results showed that the two developed methods performed well in ETo estimation. By fully considering the data-limit situation, the calibrated Turc method, VPD-based method and VPD-radiation-combined method may be attractive alternatives to the more complex Penman−Monteith method in Beijing. Keywords: potential evapotranspiration, Penman-Monteith method, determination coefficient, coefficient of efficiency, Beijing DOI: 10.3965/j.ijabe.20130604.002 Citation: Liu H J, Li Y, Zhang R H, Huang G H. Evaluation and modification of potential evapotranspiration methods in Beijing, China. Int J Agric & Biol Eng, 2013; 6(4): 9－18.

Accurate estimation of evapotranspiration (ET) is essential both at the regional and local scales for many management tasks. Numerous methods for estimating ET with various complexities and combinations exist which may be broadly classified as direct and indirect methods. Information on ET estimation uncertainties cannot be overemphasized and ignoring them can misguide decision-making in management of water resources. This study reviews the uncertainties in ET estimations and suggests ways to reduce them. Identified in this study are uncertainties associated with ET methods and input data, uncertainties due to spatial and temporal scales, and uncertainties based on region. Many studies have the ET method related uncertainties. The ground-based techniques generally used as a standard for comparing other methods have considerable uncertainty (10–30%) associated with the input components. The errors from the input reflect in the estimated ET output irrespective of the model used. Datasets from satellite products are based on in-situ network forcing as well as on model’s estimation and remote sensing (RS), and they are prone to errors as a result of differences in in-situ measurements, scale, sensor calibration and basics of model theory and parametrization. Generally, uncertainties associated with ET were found to vary temporally. Also, homogeneity and stability of potential evapotranspiration (PET) were worse in space than in time, indicating that the temporal distribution of PET was more uniform and stable compared to spatial distribution. Some ET RS products showed less uncertainty in coarse resolution and comparatively high uncertainty in fine resolution. This study identified five ways to minimize uncertainties in ET estimations. Minimizing uncertainty in ET estimation will definitely improve planning, management and use of water resources especially where accurate estimations are required.

Irrigation water is crucial for global food security, but it is increasingly vulnerable to climate change. Accurate projections of irrigation water demand under climate change scenarios are essential for water resource planning in an area like the Awash basin, where water is scarce. This study utilizes Coupled Model Intercomparison Project Phase 6 (CMIP6) General Circulation Models (GCMs) to project future crop and irrigation water demand under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5) for the 2030s, 2050s, and 2080s in the Awash River Basin. The ensemble model outputs were used after bias correction to calculate reference evapotranspiration (ETo) in the basin, followed by projecting crop and irrigation water demand for selected stations. CMIP6 climate projections were bias-corrected and used to estimate reference evapotranspiration (ETo) and future crop water demand. This study demonstrated an improved simulation of climate variables using the ensemble model compared to individual models. The linear scaling bias correction method outperformed for rainfall and minimum temperature, while variance scaling and distribution mapping were more effective for maximum temperature in the basin. Maximum temperature increased by 0.5, 1.2, and 1.6 °C under SSP2-4.5, and 0.6, 1.6, and 2.8 °C under SSP5-8.5, for the 2030s, 2050s, and 2080s, respectively, compared to the baseline period. The minimum temperature increased by 0.8, 1.5, and 2.0 °C under SSP2-4.5, and 1.0, 2.2, and 3.6 °C under SSP5-8.5, for the same periods. Precipitation showed a spatial heterogeneity, decreasing in a few stations and increasing in the majority of the stations, especially during the 2080s under SSP5-8.5. ETo generally increased, ranging from a 2.1% decrease to a 22.6% increase. Most crops showed increased crop and irrigation water demands, except wheat, which experienced reductions of up to 5.0% in crop water demand and 15.6% in irrigation demand, which is associated with seasonal shifts. Maize, tomato, onion, tropical fruits, and sugarcane exhibited varying increases in water demand, ranging from 1.7 to 13.3% due to varying levels of climate change impact for different seasons and annual periods. However, irrigation water demand fluctuates between a 4.6% decrease and a 9.0% increase under different climate change scenarios, revealing a growing pressure on water resources. This study provides a comprehensive assessment of crop and irrigation water demand in the basin using bias-corrected CMIP6 ensemble projections, offering better reliable estimates. The findings provide essential evidence to guide agricultural policies and practices toward climate-resilient irrigation planning, crop selection, and water allocation in the Awash River Basin. Moreover, the study underscores the need for adaptive irrigation strategies, such as precision agriculture, water-saving technologies, crop calendar adjustment, and water storage infrastructure, to ensure sustainable water management in the basin.