Increase In Evapotranspiration Research Articles

The biogeophysical impacts of land cover changes in Northern Hemisphere permafrost regions

The permafrost zone in the Northern Hemisphere has experienced substantial land cover changes (LCCs) in recent decades, resulting in significant impacts on climate and ecosystems by regulating land–atmosphere interactions, energy, water fluxes, and carbon exchange. However, the response of LCCs to climate change is highly variable, especially in terms of biogeophysical effects in high latitude regions. The goal of this study therefore is to quantify the biogeophysical effects of LCCs in Northern Hemisphere permafrost regions. We combine land cover and climate data and separate natural and biogeophysical impacts of LCCs to estimate the contribution on the surface and shallow soil temperatures. From 2001 to 2020, approximately 26 % of the permafrost zone experienced LCCs. LCCs caused a decrease in albedo (−0.15 ± 1.03 %) and an increase in snow depth (2.04 ± 31.96 cm), evapotranspiration (2.19 ± 11.37 mm), and surface heat flux (0.09 ± 1.36 W/m2), resulting in an increase in temperatures: 0.03 ± 0.42 K (surface temperature), 0.07 ± 0.46 K (0–10 cm soil temperature), 0.07 ± 0.45 K (10–100 cm soil temperature), and 0.06 ± 0.43 K (100–200 cm soil temperature), which could increase the risk of permafrost degradation. Seasonally, LCCs exacerbate the temperature difference between winter and summer. Albedo dominates surface temperature changes, with about 87.5 % of surface temperature changes coinciding with albedo changes. Soil temperatures are regulated by snowpack and surface heat fluxes, in addition to albedo.

Vegetation greening amplifies shallow soil temperature warming on the Tibetan Plateau

Vegetation changes are expected to alter soil thermal regimes, consequently modifying climate feedbacks related to frozen ground thawing and carbon cycling in cold regions. The Tibetan Plateau (TP) contains diverse alpine ecosystems and the largest area of frozen ground in low–mid latitude regions. Evidence suggests ongoing vegetation greening and permafrost degradation during the past several decades on the TP. However, the effect of vegetation changes on soil thermal regimes on the TP is not well understood. Here, we quantify the response of shallow soil temperature change to vegetation greening on the TP using remote–sensing data, in–situ observations, and physics–based modelling. Our results show that over the past 20 years, vegetation greening on the TP was accompanied a notable decrease in the area of bare land by approximately 0.7% (5000 km2). Annual mean soil temperature showed a significant warming trend of 0.57 °C decade–1 (p < 0.05) during the period 1983–2019, exceeding the warming rate of surface air temperature. Changes in vegetation resulted in a warming effect on annual shallow soil temperature of 0.15 ± 0.33 °C across the TP during the period 2000–2019. The warming effect varies with frozen soil types: 0.24 ± 0.48 °C in permafrost, 0.18 ± 0.36 °C in seasonally frozen ground, and 0.11 ± 0.32 °C in unfrozen ground. The net warming effect was caused by a decrease in albedo and increase in radiation penetrating the canopy, outweighing the cooling effect related to a limited increase in evapotranspiration.

Climate Change Scenarios for Impact Assessment: Lower Zab River Basin (Iraq and Iran)

Selecting appropriate climate change scenarios is crucial, as it influences the outcomes of climate change impact studies. Several storylines could be used to investigate the sensitivity of water resource schemes to weather variability and improve policymakers’ adaptation strategies. This study proposes a comprehensive and generic methodology for assessing the future climate change impact on semi-arid and arid zones at the basin scale by comparing delta perturbation scenarios to the outcomes of seven collections of GCMs (general circulation models). The findings indicate that the two scenarios predicted nearly identical declines in average reservoir discharges over a monthly timescale. Consequently, their maximum values are almost similar. The projected decrease in the streamflow for the period 2080–2099 is approximately 48%—the same as the ratio from the delta perturbation scenario of Future16 (a 30% precipitation decrease and a 30% potential evapotranspiration increase). Furthermore, delta perturbation scenarios allow the impacts of model sensitivity to climate change to be clearly identified in relation to GCM scenarios. Delta perturbation scenarios allow for an extensive collection of possible climate changes at the regional scale. In addition, delta perturbation scenarios are simpler to create and use; therefore, they might complement GCM scenarios.

Water Resources Management under Climate Change: A Review

Climate change affects water resources through the decrease in rainfall and the increase in temperatures and evapotranspiration. An indirect impact of climate change is also the increase in water uses by human activities. In this review, 320 papers were retrieved, of which 134, spanning five continents and dealing with impacts and solutions, were selected to be used to better understand the effects of climate change on water resources, ecosystems, human health, security, and socio-economic aspects. Here, suggestions and proposals towards solutions by scientists from around the world, tips and ideas to deal with climate change, and the best solutions for future water management are presented. The main solutions highlighted concern integrated water resource management, political direction, policies, an increase in knowledge, and new technologies. Furthermore, most of the analyzed papers underline that water resource management needs to incorporate the protection and restoration of ecosystems and their services. Nature-based solutions need to be the starting point of new scientific and innovative ways to deal with climate change and look towards future climate adaptation. In this complex evolution of the water resource, the political position of Italy is also shown, illustrating what actions could be implemented for water resource management.

Study on the Influence of Vegetation Restoration on Evapotranspiration in Mountainous Areas of the Luan River Basin

The study employed the PML (Penman–Monteith–Leuning) model to simulate the evolution law of long-sequence evapotranspiration in the mountainous region of the Luan River basin. Additionally, this study conducted a quantitative analysis to determine the effect of restoration on evapotranspiration water consumption. From 1981 to 2020, the results indicated that there were significantly less fluctuations in precipitation in the mountainous region of the Luan River basin than there were fluctuations in discharge. The restoration of vegetation in the mountainous region of the Luan River basin caused a mean annual growth rate of 3.47 mm in evapotranspiration. A linear positive correlation was observed between the evapotranspiration and vegetation NDVIs (normalized difference vegetation indexes) in mountainous regions. Specifically, for each 0.01 increase in the NDVI, there was an approximate 8.3 mm increase in evapotranspiration. When comparing the time periods of 1995–2001 and 2002–2020, it was observed that evapotranspiration increased by 70 mm. Furthermore, the evapotranspiration rate in the southeastern region exhibits significant variation, peaking at over 50 mm per year. In contrast, the northwest experiences variations of less than 10 mm per year. A quantitative analysis of the relationship between the evolution of mountain evapotranspiration and the response law of vegetation restoration is presented in this study; this information can be used as a guide when developing practical vegetation restoration strategies.

The Role of Parameterizations and Model Coupling on Simulations of Energy and Water Balances – Investigations With the Atmospheric Model WRF and the Hydrologic Model WRF‐Hydro

AbstractThe distributed hydrologic model WRF‐Hydro can operate in a fully‐coupled mode with the atmospheric Weather, Research and Forecasting (WRF) model. WRF‐Hydro enhances the modeling of terrestrial hydrologic processes in coupled WRF/WRF‐Hydro by simulating lateral surface and subsurface water flows. The objectives of this study are (a) to examine the effect of WRF‐Hydro on the surface energy and water balance in fully‐coupled WRF/WRF‐Hydro simulations and (b) to examine the impact of five WRF physics parameterizations on WRF‐Hydro streamflow. The study area is the Mediterranean island of Cyprus and 31 mountainous watersheds. The domain‐average soil moisture was 20% higher in the coupled WRF/WRF‐Hydro, relative to the standalone WRF model, during a 1‐year simulation. The higher soil moisture could explain the increase in latent heat (36%) and evapotranspiration (33%). The increase in these fluxes was less with a modification in the model transpiration parameterization to represent nocturnal transpiration and the use of remote sensing leaf area index data. The simulated precipitation of the coupled model increased up to 3%, relative to WRF. Two‐year long WRF‐Hydro simulations gave a median Nash‐Sutcliffe Efficiency for daily streamflow of the 31 watersheds of 0.5 for observed precipitation forcing and between −1.9 and 0.2 for the forcing of the five WRF parameterizations. This study showed that the enhancement of the standalone WRF model with lateral water flow processes in the coupled mode with WRF‐Hydro modifies the terrestrial energy and water balance. The improved terrestrial process representation should be considered for future hydrological cycle studies with WRF.

Development of anthropogenic water regulation for Community integrated Earth System model (CIESM)

This study examines the impact of anthropogenic water regulation (AWR) on hydroclimatic systems by incorporating an AWR module into the Community Integrated Earth System Model (CIESM), validated against GRACE satellite data. This approach assesses the influence of human activities, including irrigation and groundwater extraction, on global and regional hydrology and climate. Key findings include: Implementation of the AWR module significantly improves terrestrial water storage (TWS) simulation accuracy in CIESM. The correlation coefficient for global-scale TWS improved from 0.33 in the control (CTRL) to 0.89 in the experiment (EXP). Notably, the model's performance enhanced in Northern India and the North China Plain, while the Central United States showed limited improvement. AWR markedly alters the global water cycle, evidenced by substantial increases in soil moisture (about 0.04 to 0.02 m3/m3) and evapotranspiration. These changes have led to increased latent heat flux (around 5 W/m2) and a decrease in temperature by 0.1 °C to 1 °C in heavily irrigated regions. The study highlights the role of AWR in modifying the energy balance, particularly in agricultural areas where irrigation exerts a significant cooling effect. Despite its potential, the study identifies considerable uncertainties in coupling AWR within the CIESM model, related to inherent model limitations, incomplete water intake data, variable groundwater levels, and simplifications in water transfer parameters. Future research should aim to refine these aspects, focusing on enhancing the physical mechanisms and performance of AWR modules in hydroclimatic simulations. In conclusion, this research underscores the substantial modifications in hydrological and climatic conditions due to human activities. The improved AWR scheme within CIESM provides valuable insights for understanding and predicting climate change impacts on water resources, demonstrating its utility in simulating complex hydroclimatic changes at both global and regional scales.

The Projected Response of the Water Cycle to Global Warming Over Drylands in East Asia

AbstractClimate change exacerbates the threat of water scarcity over the drylands in East Asia (DEA), the world's most densely populated arid region. The water cycle continuously supplies water to support all life. Previous studies have focused on the change in individual hydrological components over DEA; however, how the projected water cycle changes under climate warming remains unclear. We demonstrate the projected response of the water cycle to global warming in different seasons utilizing the Coupled Model Intercomparison Project Phase 6. Winter in the DEA presents an intensification of the water cycle, reflected in coherent increases in evapotranspiration (E), precipitation (P), runoff, and surface soil moisture. In contrast, summer will experience a weakened water cycle in the northwestern DEA, while the southeastern part exhibits the opposite trend. From the surface and atmospheric water balance perspective, we further attribute the changes in E and P to gain a more comprehensive understanding. The increasing E is attributed to the combined effects of P and the vapor pressure deficit during summer, whereas it is dominated by P in winter. The increased P in summer is primarily attributed to the horizontal dynamic and vertical thermodynamic components associated with the strengthening and westward expansion of East Asia summer monsoon in the future. During winter, the increased P is mainly due to the vertical dynamic and horizontal thermodynamic components associated with the enhancement of vertical ascending motion and increased moisture.

Assessing Future Hydrological Variability in a Semi-Arid Mediterranean Basin: Soil and Water Assessment Tool Model Projections under Shared Socioeconomic Pathways Climate Scenarios

Climate is one of the main drivers of hydrological processes, and climate change has caused worldwide effects such as water scarcity, frequent floods and intense droughts. The purpose of this study was to analyze the effects of climate change on the water balance components, high flow and low flow stream conditions in a semi-arid basin in Iran. For this reason, the climate outputs of the CanESM5 model under Shared Socioeconomic Pathways (SSP) scenarios SSP126, SSP245, and SSP585 were spatially downscaled by the Statistical Downscaling Model (SDSM). The hydrological process was simulated by the Soil and Water Assessment Tool (SWAT) model. Key findings include a 74% increase in evapotranspiration, a reduction by up to 9.6% in surface runoff, and variations in discharge by up to 53.6%. The temporal analysis of snow melting changes revealed an increase in the volume of snow melting during winter months and a reduction in the volume during spring. The projected climate change is expected to cause notable variations in high and low flow events, particularly under the SSP585 scenario, which anticipates significant peaks in flow rates. This comprehensive analysis underscores the pressing need for adaptive strategies in water resource management to mitigate the anticipated impacts of climate variability.

Enhancing Low-Flow Forecasts: A Multi-Model Approach for Rainfall–Runoff Models

The expected change in rainfall patterns and the increase in evapotranspiration due to climate change leads to earlier droughts, which aggravate water shortages. To ensure the sustainable management of water resources in these conditions, it is necessary to forecast their evolution. The use of hydrological models is essential for monitoring the water crisis. The conceptual hydrological models used in this study are MEDOR, GR4J, and HBV. They are applied in the Nahr Ibrahim watershed, which is a typical Lebanese Mediterranean basin. While these models simplify complex natural systems, concerns persist about their reliability in addressing drought challenges. In order to reduce the uncertainties, this study develops new robust methods that can improve model simulations. First, a particular series concerning low flows is constructed with the use of hydrological low-flow indices. The multi-model approach is utilized to reach a more accurate unique series while combining the low-flow series generated from the models. This combination is accomplished by using the simple average method, weighted average, artificial neural networks, and genetic algorithms. Better results are generated with the use of these methods. Accordingly, this study led to an improvement in model performances while increasing the reliability of low-flow forecasts.

Analysis of change process of NPP dominated by human activities in Northwest Hubei, China, from 2000 to 2020.

Clarifying the spatial distribution of the impact of different human disturbance activities on the net primary productivity (NPP) in regions with single climatic conditions is of considerable importance to ecological protection. Time-series NPP from 2000 to 2020 was simulated in Northwest Hubei, China, and the effects of the climate and human activities on the NPP changes were separated. Research results showed that from 2000 to 2020, the NPP change with an area of 10,166.63 km2 in Northwest Hubei is influenced by climate and human activities. Among them, human activities account for as high as 84.53%. From 2000 to 2020, the NPP in Northwest Hubei showed a slight upward trend at a rate of 1.61g C m-2year-1. The significantly increased NPP accounted for 21.4% of the total, which was mainly distributed in north of Northwest Hubei. And the farming of cultivated land led to the increase of NPP in west as well as the reduced human distribution in cultivated land, which was scattered in forests. Only 6.67% of the total area demonstrated a significantly decreased NPP, which was distributed mainly in the central affected by the expansion of rural-urban land and change of broad-leaved forests to shrubs and in southeast regions of Northwest Hubei caused by the increase in potential evapotranspiration. This study refined the driving factors of spatial heterogeneity of NPP changes in Northwest Hubei, which is conducive to rational planning of terrestrial ecosystem protection measures.

Spatiotemporal characteristics of meteorological drought events in 34 major global river basins during 1901–2021

Meteorological drought is a crucial driver of various types of droughts; thus, identifying the spatiotemporal characteristics of meteorological drought at the basin scale has implications for ecological and water resource security. However, differences in drought characteristics between river basins have not been clearly elucidated. In this study, we identify and compare meteorological drought events in 34 major river basins worldwide using a three-dimensional drought-clustering algorithm based on the standardised precipitation evapotranspiration index on a 12-month scale from 1901 to 2021. Despite synchronous increases in precipitation and potential evapotranspiration (PET), with precipitation increasing by more than three times the PET, 47 % (16/34) of the basins showed a tendency towards drought in over half their basin areas. Drought events occurred frequently, with more than half identified as short-term droughts (lasting less than or equal to three months). Small basins had a larger drought impact area, with major drought events often originating from the basin boundaries and migrating towards the basin centre. Meteorological droughts were driven by changes in sea surface temperature (SST), especially the El Niño Southern Oscillation (ENSO) or other climate indices. Anomalies in SST and atmospheric circulation caused by ENSO events may have led to altered climate patterns in different basins, resulting in drought events. These results provide important insights into the characteristics and mechanisms of meteorological droughts in different river basins worldwide.

Spatial–Temporal Water Balance Evaluation in the Nile Valley Upstream of the New Assiut Barrage, Egypt, Using WetSpass-M

The components of water balance (WBC) that involve precipitation, evapotranspiration, runoff, irrigation, and groundwater recharge are critical for understanding the hydrological cycle and water management of resources in semi-arid and arid areas. This paper assesses temporal and spatial distributions of surface runoff, actual evapotranspiration, and groundwater recharge upstream of the New Assiut Barrage (NAB) in the Nile Valley, Upper Egypt, using the WetSpass-M model for the period 2012–2020. Moreover, this study evaluates the effect of land cover/land use (LULC) alterations in the study period on the WBC of the NAB. The data provided as input for the WetSpass-M model in the structure of raster maps using the Arc-GIS tool. Monthly meteorological factors (e.g., temperature, rainfall, and wind speed), a digital elevation model (DEM), slope, land cover, irrigation cover, a soil map, and depth to groundwater are included. The long-term temporal and spatial mean monthly irrigation and precipitation (127 mm) is distributed as 49% (62 mm) actual evapotranspiration, 15% (19 mm) groundwater recharge, and 36% (46 mm) surface runoff. The replacement of cropland by built-up areas was recognized as the primary factor responsible for the major decrease in groundwater, an increase in evapotranspiration and an increase in surface runoff between LCLU in 2012 and 2020. The integration of the WetSpass model with GIS has shown its effectiveness as a powerful approach for assessing WBC. Results were more accurate and reliable when hydrological modeling and spatial analysis were combined. The results of this research can help make well-informed decisions about land use planning and sustainable management of water resources in the upstream area of the NAB.

Potential impacts of climate change on the sudan-sahel region in West Africa – Insights from Burkina Faso

The Sudan-Sahel region has long been vulnerable to environmental change. However, the intensification of global warming has led to unprecedented challenges that require a detailed understanding of climate change for this region. This study analyzes the impacts of climate change for Burkina Faso using eleven climate indices that are highly relevant to Sudan-Sahelian societies. The full ensemble of statistically downscaled NEX-GDDP-CMIP6 models (25 km) is used to determine the projected changes for the near (2031–2060) and far future (2071–2100) compared to the reference period (1985–2014) for different SSPs. Validation of the climate models against state-of-the-art reference data (CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) and ERA5 (ECMWF Reanalysis v5)) shows reasonable performance for the main climate variables with some biases. Under the SSP5–8.5, Burkina Faso is projected to experience a substantial temperature increase of more than 4.3 °C by the end of the century. Rainfall amount is projected to increase by 30 % under the SSP5–8.5, with the rainy season starting earlier and lasting longer. This could increase water availability for rainfed agriculture but is offset by a 20 % increase in evapotranspiration. The country could be at increased risk of flooding and heavy rainfall in all SSPs and future periods. Due to the pronounced temperature increase, heat stress, and cooling degree days are expected to strongly increase under the SSP8.5 scenarios, especially in the western and northern parts. Under the SSP1–2.6 and SSP2–4.5, the projected changes are much lower for the country. Thus, timely implementation of climate change mitigation measures can significantly reduce climate change impacts for this vulnerable region and strengthen population resilience for a sustainable future.

On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration

Abstract. Deep learning (DL) rainfall–runoff models outperform conceptual, process-based models in a range of applications. However, it remains unclear whether DL models can produce physically plausible projections of streamflow under climate change. We investigate this question through a sensitivity analysis of modeled responses to increases in temperature and potential evapotranspiration (PET), with other meteorological variables left unchanged. Previous research has shown that temperature-based PET methods overestimate evaporative water loss under warming compared with energy budget-based PET methods. We therefore assume that reliable streamflow responses to warming should exhibit less evaporative water loss when forced with smaller, energy-budget-based PET compared with temperature-based PET. We conduct this assessment using three conceptual, process-based rainfall–runoff models and three DL models, trained and tested across 212 watersheds in the Great Lakes basin. The DL models include a Long Short-Term Memory network (LSTM), a mass-conserving LSTM (MC-LSTM), and a novel variant of the MC-LSTM that also respects the relationship between PET and evaporative water loss (MC-LSTM-PET). After validating models against historical streamflow and actual evapotranspiration, we force all models with scenarios of warming, historical precipitation, and both temperature-based (Hamon) and energy-budget-based (Priestley–Taylor) PET, and compare their responses in long-term mean daily flow, low flows, high flows, and seasonal streamflow timing. We also explore similar responses using a national LSTM fit to 531 watersheds across the United States to assess how the inclusion of a larger and more diverse set of basins influences signals of hydrological response under warming. The main results of this study are as follows: The three Great Lakes DL models substantially outperform all process-based models in streamflow estimation. The MC-LSTM-PET also matches the best process-based models and outperforms the MC-LSTM in estimating actual evapotranspiration. All process-based models show a downward shift in long-term mean daily flows under warming, but median shifts are considerably larger under temperature-based PET (−17 % to −25 %) than energy-budget-based PET (−6 % to −9 %). The MC-LSTM-PET model exhibits similar differences in water loss across the different PET forcings. Conversely, the LSTM exhibits unrealistically large water losses under warming using Priestley–Taylor PET (−20 %), while the MC-LSTM is relatively insensitive to the PET method. DL models exhibit smaller changes in high flows and seasonal timing of flows as compared with the process-based models, while DL estimates of low flows are within the range estimated by the process-based models. Like the Great Lakes LSTM, the national LSTM also shows unrealistically large water losses under warming (−25 %), but it is more stable when many inputs are changed under warming and better aligns with process-based model responses for seasonal timing of flows. Ultimately, the results of this sensitivity analysis suggest that physical considerations regarding model architecture and input variables may be necessary to promote the physical realism of deep-learning-based hydrological projections under climate change.

Irrigation Water Management in a Water-Scarce Environment in the Context of Climate Change

Climate change has a considerable impact on irrigated agriculture, which is vital for food and fiber production. In this study, in the context of climate change, simulation model CROPWAT 8 was employed to compute the reference evapotranspiration, and net irrigation water requirement for wheat, barley, maize, sugar beet, potato, tomato, and date palm. In addition, the WaterGEMS model was utilized to design a new sprinkler irrigation system to run long-term simulations of hydraulic behavior within pressurized pipe networks to irrigate 43 acres for two arid sites (Siwa Oasis and West Elminya fields) inside the 1.5-million-acre groundwater irrigation project in the Egyptian western desert. Five scenarios for climate change were employed in the current (1991–2023), representative concentration path (RCP) 8.5 greenhouse gas emission scenarios for the 2040s, 2060s, 2080s, and 2100s. Mean ETo values for the current scenario show 4.56 and 5.7 mm for the Siwa Oasis and West Elminya fields, respectively. The climate changes cause an increase of the reference evapotranspiration by 4.6, 5.9, 9.4, and 12.7% for RCP: 8.5 greenhouse gas emissions for the 2040s, 2060s, 2080s, and 2100s scenarios, respectively, for the Siwa Oasis field. On the other hand, an increased ratio for the reference evapotranspiration by 4.2, 5.4, 8.6, and 11.6% was observed for the scenarios in the West Elminya field, respectively. The designed sprinkler system indicated a capacity of 111.4 m3 h−1 and 167 m3 h−1 for Siwa and West Elminya fields, respectively. The study suggests using crop patterns for wheat, barley, potato, and sugar beet to save irrigation water.Graphical abstract

Understanding climate change impacts on drought in China over the 21st century: a multi-model assessment from CMIP6

The future state of drought in China under climate change remains uncertain. This study investigates drought events, focusing on the region of China, using simulations from five global climate models (GCMs) under three Shared Socioeconomic Pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5) participating in the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b). The daily Standardized Precipitation Evapotranspiration Index (SPEI) is employed to analyze drought severity, duration, and frequency over three future periods. Evaluation of the GCMs’ simulations against observational data indicates their effectiveness in capturing historical climatic change across China. The rapid increase in CO2 concentration under high-emission scenarios in the mid- and late-future century (2040–2070 and 2071–2100) substantially influences vegetation behavior via regulation on leaf stomata and canopy structure. This regulation decelerates the increase in potential evapotranspiration, thereby mitigating the sharp rise in future drought occurrences in China. These findings offer valuable insights for policymakers and stakeholders to develop strategies and measures for mitigating and adapting to future drought conditions in China.

Trade-off of ecosystem productivity and water use related to afforestation in southcentral USA under climate change

The increase of tree canopy cover due to woody plant encroachment and tree plantations modifies both carbon and water dynamics. The tradeoffs between ecosystem net primary productivity (NPP) and water use with increasing tree cover in different climate conditions, particularly under future climate scenarios, are not well understood. Within the climate transition zone of the southern Great Plains, USA, we used the Soil and Water Assessment Tool+ (SWAT+) to investigate the combined impacts of increasing tree cover and climate change on carbon and water dynamics in three watersheds representing semiarid, subhumid, and humid climates. Model simulations incorporated two land use modifications (Baseline: existing tree cover; Forest +: increasing evergreen tree cover), in conjunction with two climate change projections (the RCP45 and the RCP85), spanning two time periods (historic: 1991–2020; future: 2070–2099). With climate change, the subhumid and humid watersheds exhibited a greater increase in evapotranspiration (ET) and a corresponding reduction in runoff compared to the semi-arid watershed, while the semi-arid and subhumid watersheds encountered pronounced losses in water availability for streams (>200 mm/year) due to increasing tree cover and climate change. With every 1 % increase in tree cover, both NPP and water use efficiency were projected to increase in all three watersheds under both climate change scenarios, with the subhumid watershed demonstrating the largest increases (>0.16 Mg/ha/year and 170 %, respectively). Increasing tree cover within grasslands, either through woody plant expansion or afforestation, boosts ecosystem NPP, particularly in subhumid regions. Nevertheless, this comes with a notable decrease in water resources, a concern made worse by future climate change. While afforestation offers the potential for greater NPP, it also brings heightened water scarcity concerns, highlighting the importance of tailoring carbon sequestration strategies within specific regions to mitigate unintended repercussions on water availability.

Emerging investigator series: predicted losses of sulfur and selenium in european soils using machine learning: a call for prudent model interrogation and selection.

Reductions in sulfur (S) atmospheric deposition in recent decades have been attributed to S deficiencies in crops. Similarly, global soil selenium (Se) concentrations were predicted to drop, particularly in Europe, due to increases in leaching attributed to increases in aridity. Given its international importance in agriculture, reductions of essential elements, including S and Se, in European soils could have important impacts on nutrition and human health. Our objectives were to model current soil S and Se levels in Europe and predict concentration changes for the 21st century. We interrogated four machine-learning (ML) techniques, but after critical evaluation, only outputs for linear support vector regression (Lin-SVR) models for S and Se and the multilayer perceptron model (MLP) for Se were consistent with known mechanisms reported in literature. Other models exhibited overfitting even when differences in training and testing performance were low or non-existent. Furthermore, our results highlight that similarly performing models based on RMSE or R2 can lead to drastically different predictions and conclusions, thus highlighting the need to interrogate machine learning models and to ensure they are consistent with known mechanisms reported in the literature. Both elements exhibited similar spatial patterns with predicted gains in Scandinavia versus losses in the central and Mediterranean regions of Europe, respectively, by the end of the 21st century for an extreme climate scenario. The median change was -5.5% for S (Lin-SVR) and -3.5% (MLP) and -4.0% (Lin-SVR) for Se. For both elements, modeled losses were driven by decreases in soil organic carbon, S and Se atmospheric deposition, and gains were driven by increases in evapotranspiration.

The maritime continent’s rainforests modulate the local interannual evapotranspiration variability

The interannual variation of evapotranspiration in tropical rainforests is thought to be small, despite the variability of precipitation. Here we investigated the cause of this phenomenon in the Maritime Continent using observations, reanalysis data and model simulations. We find that evapotranspiration’s interannual variation is dampened by the self-compensating mechanism of canopy evaporation and transpiration. During El Niño, when precipitation is lower than climatology, canopy evaporation decreases due to less interception, while canopy transpiration increases in response to increased incoming solar radiation, resulting in a compensating effect and a small interannual variation of evapotranspiration. Deforestation, however, eliminates transpiration’s dampening effect and, thus, amplifies the interannual variation of evapotranspiration significantly. This increase in evapotranspiration’s interannual variation due to deforestation further affect the local hydrological cycle, resulting in decreased interannual variation of precipitation. The results highlight the impacts of deforestation and emphasize the critical role of tropical rainforests in the hydroclimatological cycle.