Regional estimation of daily to annual regional evapotranspiration with MODIS data in the Yellow River Delta wetland

Evapotranspiration (ET) from the wetland of the Yellow River Delta (YRD) is one of the important components in the water cycle, which represents the water consumption by the plants and evaporation from the water and the non-vegetated surfaces. Reliable estimates of the total evapotranspiration from the wetland is useful information both for understanding the hydrological process and for water management to protect this natural environment. Due to the heterogeneity of the vegetation types and canopy density and of soil water content over the wetland (specifically over the natural reserve areas), it is difficult to estimate the regional evapotranspiration extrapolating measurements or calculations usually done locally for a specific land cover type. Remote sensing can provide observations of land surface conditions with high spatial and temporal resolution and coverage. In this study, a model based on the Energy Balance method was used to calculate daily evapotranspiration (ET) using instantaneous observations of land surface reflectance and temperature from MODIS when the data were available on clouds-free days. A time series analysis algorithm was then applied to generate a time series of daily ET over a year period by filling the gaps in the observation series due to clouds. A detailed vegetation classification map was used to help identifying areas of various wetland vegetation types in the YRD wetland. Such information was also used to improve the parameterizations in the energy balance model to improve the accuracy of ET estimates. This study showed that spatial variation of ET was significant over the same vegetation class at a given time and over different vegetation types in different seasons in the YRD wetlan

Influence of topography and moisture and nutrient availability on green alder function on the low arctic tundra, NT

The Arctic has warmed by at least 3°C over the past 50 years and this rapid warming is expected to continue. Climate warming is driving the proliferation of shrubs across the tundra biome with implications for energy balance, climate, hydrology, nutrient cycling, and biodiversity. Changes in tundra plant water use attributable to shrub expansion are predicted to increase evapotranspirative water loss which may amplify local warming and reduce run-off. However, little is known about the extent to which shrubs will enhance evapotranspirative water loss in these systems. Direct measures of shrub water use are needed to accurately predict evapotranspiration rates and the associated hydrological and energetic impacts. In addition, it is crucial that we understand the abiotic factors that drive shrub distribution and physiological function to forecast further changes in tundra ecosystem function. Shrubs are expanding in areas that have a higher potential of accumulating moisture, such as drainage channels and hill slopes. Shrub expansion may be limited by variation in water and nutrient availability across topographic gradients. Nevertheless, the associations between shrub function and abiotic limitations remain understudied. To address these knowledge gaps, we measured sap flow, stem water potential, and a range of functional traits of green alder (Alnus viridis) shrubs and quantified water and nutrient availability in shrub patches on the low arctic tundra of the Northwest Territories. Frost table depth was a significant negative driver of sap flow and underlies decreased surface water availability with thaw. This was further supported through significantly lower stem water potential values as the growing season progressed. Shrubs in upslope locations had significantly lower water potentials relative to shrubs in downslope locations, demonstrating topographic variation in shrub water status. Shrubs in channels and at the tops of patch slopes significantly differed in leaf functional traits representing leaf investment, productivity, and water use efficiency. Channel shrubs reflected traits associated with higher resource availability and productivity whereas shrubs at the tops of patches reflected the opposite. This work provides insight into the abiotic drivers of tall shrub water use and productivity, both of which will be essential for predicting ecosystem function

Hydrological changes on water resources

Water is one of the essential elements for the nature and human being. The development of good practise of managing water resources are necessary to maintain sufficient availability and to support socio-economic activities and preserve natural ecosystems. For these reasons, it is fundamental to improve the knowledge of cause-effect relations that drives hydrological cycle, which determines water availability. Climate and land use (LU) are two of the main drivers of the water cycle and indeed, the knowledge of their influence on hydrology is a fundamental research question. Of course, the future water availability is strictly related to future climatic and LU scenarios and then a critical role is assumed by the prediction and assessment of these two. A climate and LU change impact study will be developed to investigate the near-future water availability in the Mediterranean area. In detail, on the basis of the state of art and the actual knowledge, the main objective of this dissertation is to estimate the probability density function (pdf) of annual surface runoff Q in transient climate and LU conditions in the island of Sardinia (Italy). The study case has been selected due to the ongoing important process of climate change, overexploitation and degradation of natural resources affecting the entire island (see e.g. ISPRA, ENEA and CIRCE studies). These analyses might have a strategic importance for stakeholders and government agencies that are interested in the management of water resources due to the well-known issue of water availability in the Mediterranean area. The knowledge of the near-future impact of climate and LU change could be useful to establish regional guidelines and good practices to avoid the ongoing reduction of water resources in Sardinia. After a detailed review of the existing methodologies for describing and detecting climate and LU change and their influence in hydrological processes, a methodology based on the Budyko’s theory that aims at assessing near future Q pdf in a closed form has been adopted. Five parameters are requested, referring to mean and standard deviation of annual rainfall P and annual potential evapotranspiration PET and Fu’s parameter ω. Sets of these parameters will be assessed to define different climatic and LU scenarios for the near future. EUROCORDEX and Land Use CORINE projects will be used to represent climate and LU in the present and in the near future. Results showed that in the near future Q will decrease due to the reduction of P and the increase of PET. The variability of Q will decrease due the reduction of variability of P. Finally, it has been observed that in Sardinia the main driver in the change of Q pdf will be climate change, while the LU plays a secondary role

Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches

In this research, climate classification maps over the Korean Peninsula at 1 km resolution were generated using the satellite-based climatic variables of monthly temperature and precipitation based on machine learning approaches. Random forest (RF), artificial neural networks (ANN), k-nearest neighbor (KNN), logistic regression (LR), and support vector machines (SVM) were used to develop models. Training and validation of these models were conducted using in-situ observations from the Korea Meteorological Administration (KMA) from 2001 to 2016. The rule of the traditional Koppen-Geiger (K-G) climate classification was used to classify climate regions. The input variables were land surface temperature (LST) of the Moderate Resolution Imaging Spectroradiometer (MODIS), monthly precipitation data from the Tropical Rainfall Measuring Mission (TRMM) 3B43 product, and the Digital Elevation Map (DEM) from the Shuttle Radar Topography Mission (SRTM). The overall accuracy (OA) based on validation data from 2001 to 2016 for all models was high over 95%. DEM and minimum winter temperature were two distinct variables over the study area with particularly high relative importance. ANN produced more realistic spatial distribution of the classified climates despite having a slightly lower OA than the others. The accuracy of the models using high altitudinal in-situ data of the Mountain Meteorology Observation System (MMOS) was also assessed. Although the data length of the MMOS data was relatively short (2013 to 2017), it proved that the snowy, dry and cold winter and cool summer class (Dwc) is widely located in the eastern coastal region of South Korea. Temporal shifting of climate was examined through a comparison of climate maps produced by period: from 1950 to 2000, from 1983 to 2000, and from 2001 to 2013. A shrinking trend of snow classes (D) over the Korean Peninsula was clearly observed from the ANN-based climate classification results. Shifting trends of climate with the decrease/increase of snow (D)/temperate (C) classes were clearly shown in the maps produced using the proposed approaches, consistent with the results from the reanalysis data of the Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC)

Evapotranspiration Mapping for Forest Management in California's Sierra Nevada

We assessed the response of densely forested watersheds with little apparent annual water limitation to forest disturbance and climate variability, by studying how past wildfires changed forest evapotranspiration, and what past evapotranspiration patterns imply for the availability of subsurface water storage for drought resistance. We determined annual spatial patterns of evapotranspiration using a top-down statistical model, correlating measured annual evapotranspiration from eddycovariance towers across California with NDVI (Normalized Difference Vegetation Index) measured by satellite, and with annual precipitation. The study area was the Yuba and American River watersheds, two densely forested watersheds in the northern Sierra Nevada. Wildfires in the 1985-2015 period resulted in significant post-fire reductions in evapotranspiration for at least 5 years, and in some cases for more than 20 years. The levels of biomass removed in medium-intensity fires (25- 75% basal area loss), similar to magnitudes expected from forest treatments for fuels reduction and forest health, reduced evapotranspiration by as much 150-200 mm yr-1 for the first 5 years. Rates of recovery in post-wildfire evapotranspiration confirm the need for follow-up forest treatments at intervals of 5-20 years to sustain lower evapotranspiration, depending on local landscape attributes and interannual climate. Using the metric of cumulative precipitation minus evapotranspiration (P-ET) during multi-year dry periods, we found that forests in the study area showed little evidence of moisture stress during the 1985-2018 period of our analysis, owing to relatively small reliance on interannual subsurface water storage to meet dry-year evapotranspiration needs of vegetation. However, more-severe or sustained drought periods will push some lower-elevation forests in the area studied toward the cumulative P-ET thresholds previously associated with widespread forest mortality in the southern Sierra Nevada

Machine learning models to predict daily actual evapotranspiration of citrus orchards under regulated deficit irrigation

Precise estimations of actual evapotranspiration (ETa) are essential for various environmental issues, including those related to agricultural ecosystem sustainability and water management. Indeed, the increasing demands of agricultural production, coupled with increasingly frequent drought events in many parts of the world, necessitate a more careful evaluation of crop water requirements. Artificial Intelligence-based models represent a promising alternative to the most common measurement techniques, e.g. using expensive Eddy Covariance (EC) towers. In this context, the main challenges are choosing the best possible model and selecting the most representative features. The objective of this research is to evaluate two different machine learning algorithms, namely Multi-Layer Perceptron (MLP) and Random Forest (RF), to predict daily actual evapotranspiration (ETa) in a citrus orchard typical of the Mediterranean ecosystem using different feature combinations. With many features available coming from various infield sensors, a thorough analysis was performed to measure feature importance, scatter matrix observations, and Pearson's correlation coefficient calculation, which resulted in the selection of 12 promising feature combinations. The models were calibrated under regulated deficit irrigation (RDI) conditions to estimate ETa and save irrigation water. On average up to 38.5% water savings were obtained, compared to full irrigation. Moreover, among the different input variables adopted, the soil water content (SWC) feature appears to have a prominent role in the prediction of ETa. Indeed, the presented results show that by choosing the appropriate input features, the accuracy of the proposed machine learning models remains acceptable even when the number of features is reduced to only 4. The best performance was achieved by the Random Forest method, with seven input features, obtaining a root mean square error (RMSE) and a coefficient of determination (R2) of 0.39 mm/day and 0.84, respectively. Finally, the results show that the joint use of SWC, weather and satellite data significantly improves the performance of evapotranspiration forecasts compared to models using only meteorological variables

Mapping evapotranspiration variability over a complex oasis-desert ecosystem based on automated calibration of Landsat 7 ETM+ data in SEBAL

Fragmented ecosystems of the desiccated Aral Sea seek answers to the profound local hydrologically- and water-related problems. Particularly, in the Small Aral Sea Basin (SASB), these problems are associated with low precipitation, increased temperature, land use and evapotranspiration (ET) changes. Here, the utility of high-resolution satellite dataset is employed to model the growing season dynamic of near-surface fluxes controlled by the advective effects of desert and oasis ecosystems in the SASB. This study adapted and applied the sensible heat flux calibration mechanism of Surface Energy Balance Algorithm for Land (SEBAL) to 16 clear-sky Landsat 7 ETM+ dataset, following a guided automatic pixels search from surface temperature T-s and Normalized Difference Vegetation Index NDVI (). Results were comprehensively validated with flux components and actual ET (ETa) outputs of Eddy Covariance (EC) and Meteorological Station (KZL) observations located in the desert and oasis, respectively. Compared with the original SEBAL, a noteworthy enhancement of flux estimations was achieved as follows: - desert ecosystem ETa R-2 = 0.94; oasis ecosystem ETa R-2 = 0.98 (P < 0.05). The improvement uncovered the exact land use contributions to ETa variability, with average estimates ranging from 1.24 mm to 6.98 mm . Additionally, instantaneous ET to NDVI (ETins-NDVI) ratio indicated that desert and oasis consumptive water use vary significantly with time of the season. This study indicates the possibility of continuous daily ET monitoring with considerable implications for improving water resources decision support over complex data-scarce drylands