Safeguarding water resources for society and ecosystems requires a comprehensive understanding of hydrological fluxes within the Critical Zone, Earth's living skin where the atmosphere, hydrosphere, biosphere, and lithosphere meet. For decades, tracer-aided mixing models have been used to track water flow paths through the Critical Zone, mapping the journey of water particles from atmospheric moisture to groundwater. Recent advances in novel tracer measurements and modeling methodologies offer new insights into hydrological partitioning within the Critical Zone, enabling improved quantification of water fluxes across scales ranging from microscopic to macroscopic. Advanced tracer-aided modeling approaches enable more rigorous testing of assumptions and improved quantification of uncertainties. In this review, we (a) summarize state-of-the-art tracer and modeling techniques, with an emphasis on stable water isotope tracers, (b) synthesize insights emerging from new approaches, and (c) highlight opportunities to apply these methods in interdisciplinary Critical Zone research.

Quantification of precipitation partitioning into evaporation and runoff is crucial for predicting future water availability. Within the widely used Budyko framework, which relates the long-term aridity index to the long-term evaporative index, curvilinear relationships between these indices (i.e. parametric Budyko curves) allow for the quantification of precipitation partitioning under prevailing climatic conditions. A common assumption is that movement along a specific Budyko curve with changes in the aridity index over time can be used as a predictor for catchment responses to changing climatic conditions. However, various studies have reported deviations around these curves, which raises questions about the usefulness of the method for future predictions. To investigate whether parametric Budyko curves still have predictive power, we quantified the global, regional, and local evolution of deviations of catchments from their parametric Budyko curves over multiple subsequent 20-year periods throughout the last century based on historical long-term water balance data from over 2000 river catchments worldwide. This process resulted in up to four 20-year distributions of annual deviations from the long-term mean parametric curve for each catchment. To use these distributions of deviations to predict future deviations, the temporal stability of these four distributions of deviations was evaluated between subsequent periods of time. On average, it was found that the majority (62 %) of study catchments did not significantly deviate from their expected parametric Budyko curves. Out of the remaining 38 % of catchments that deviated from their expected curves, the long-term magnitude of median deviations remains minor, with 70 % of catchments falling within the range of ±0.025 of the expected evaporative index. When these median deviations were expressed as relative changes in discharge, catchments in arid regions showed higher susceptibility to larger discharge shifts compared to those in humid regions. Furthermore, a significant majority of catchments, constituting around the same percentage, was found to have stable distributions of deviations across multiple time periods, making them well suited to statistically predict future deviations with high predictive power. These findings suggest that while trajectories of change in catchments do not strictly follow the expected long-term mean parametric Budyko curves, the deviations are minor and quantifiable. Consequently, taking into account these deviations, the parametric formulations of the Budyko framework remain a valuable tool for predicting future evaporation and runoff under changing climatic conditions within quantifiable margins of error.

Urban areas, characterized by dense populations and many socio-economic activities, increasingly suffer from floods, droughts, and heat stress due to land use and climate change. Traditionally, the urban thermal environment and water resources management have been studied separately, using urban land surface models (ULSMs) and urban hydrological models (UHMs). However, as our understanding deepens and the urgency to address future climate disasters grows, it becomes clear that hydrological disasters—such as floods, droughts, severe urban thermal environments, and more frequent heat waves—are actually not isolated events but compound events. This underscores the close interaction between the water cycle and the energy balance. Consequently, the existing separation between ULSMs and UHMs creates significant obstacles to better understanding urban hydrological and meteorological processes, which is crucial for addressing the high risks posed by climate change. Defining the future direction of process-based models for hydro-meteorological predictions and assessments is essential for better managing climate disasters and evaluating response measures in densely populated urban areas. Our review focuses on three critical aspects of urban hydro-meteorological simulation: similarities, differences, and gaps among different models; existing gaps in physical process implementations; and efforts, challenges, and potential for model coupling and integration. We find that ULSMs inadequately represent water surfaces and hydraulic systems, while UHMs lack explicit surface energy balance solutions and detailed building representations. Coupled models show potential for simulating urban hydro-meteorological environments, but face challenges at regional and neighborhood scales. Our review highlights the need for interdisciplinary communication between the urban climatology and urban water management communities to enhance urban hydro-meteorological simulation models.

Droughts have an increasing impact on the entire European continent. As the frequency and intensity of droughts rise in many parts of Europe, the implementation of effective drought adaptation and mitigation strategies becomes increasingly important. However, it is not known how diverse tools are used in drought management with increasing drought severity. This study explores the role of Decision Support Tools (DSTs) in strategic and operational drought management in the Netherlands. Through a survey among national and regional water authorities, this study shows the increasing reliance of water managers on field measurements, Data Information Systems (DISs), stakeholder consultation, and legislation with increasing drought severity. Weather forecasts and expert knowledge remain important throughout all drought management phases. Despite the increased use of DISs with drought severity, the use of hydrological models does not follow the same trend. DISs, which often incorporate hydrological models, reveal a ‘hidden’ use of these models. Rather than serving as ‘key artifacts’ for modelers, they become active ‘participants’ in broader data systems during advanced phases of drought management. All these aspects influence key responsibilities in model use including appropriateness and transferability, reproducibility, and transparency. These factors are critical to consider when aiming to bridge the gap between science and policy in the application and development of DSTs.

The miombo woodland is the largest dry woodland formation in sub-Saharan Africa, covering an estimated area of 2.7–3.6 million km2. Compared to other global ecosystems, the miombo woodland demonstrates unique interactions between plant phenology and climate. For instance, it experiences an increase in the leaf area index (LAI) during the dry season. However, due to limited surface exchange observations in the miombo region, there is a lack of information regarding the effect of these properties on miombo woodland evaporation. It is crucial to have a better understanding of miombo evaporation for accurate hydrological and climate modelling in this region. Currently, the only available regional evaporation estimates are based on satellite data. However, the accuracy of these estimates is questionable due to the scarcity of field estimates with which to compare. Therefore, this study aims to compare the temporal dynamics and magnitudes of six satellite-based evaporation estimates – the Topography-driven Flux Exchange (FLEX-Topo) model, Global Land Evaporation Amsterdam Model (GLEAM), Moderate-Resolution Imaging Spectrometer (MODIS) MOD16 product, operational Simplified Surface Energy Balance (SSEBop) model, Thornthwaite–Mather climatic Water Balance (TerraClimate) dataset, and Water Productivity through Open access of Remotely sensed derived data (WaPOR) – during different phenophases in the miombo woodland of the Luangwa Basin, a representative river basin in southern Africa. The goal of this comparison is to determine if the temporal dynamics and magnitudes of the satellite-based evaporation estimates align with the documented feedback between miombo woodland and climate. In the absence of basin-scale field observations, actual evaporation estimates based on the multi-annual water balance (Ewb) are used for comparison. The results show significant discrepancies among the satellite-based evaporation estimates during the dormant and green-up and mid-green-up phenophases. These phenophases involve substantial changes in miombo species' canopy phenology, including the co-occurrence of leaf fall and leaf flush, as well as access to deeper moisture stocks to support leaf flush in preparation for the rainy season. The satellite-based evaporation estimates show the highest agreement during the senescence phenophase, which corresponds to the period of high temperature, high soil moisture, high leaf chlorophyll content, and highest LAI (i.e. late rainy season into the cool-dry season). In comparison to basin-scale actual evaporation, all six satellite-based evaporation estimates appear to underestimate evaporation. Satellite-based evaporation estimates do not accurately represent evaporation in this data-sparse region, which has a phenology and seasonality that significantly differ from the typical case in data-rich ground-truth locations. This may also be true for other locations with limited data coverage. Based on this study, it is crucial to conduct field-based observations of evaporation during different miombo species phenophases to improve satellite-based evaporation estimates in miombo woodlands.

Amplified eruptive outbreaks of bark beetles as a consequence of climate change can cause tree mortality that significantly affects terrestrial water and carbon fluxes. However, the lack of field-scale observations of underlying physiological mechanisms currently hampers the expression of such ecosystem disturbances in predictive modelling. Based on a unique flux tower dataset from a subalpine forest located in the Rocky Mountains, mechanisms of stomatal response to an extensive bark beetle outbreak were investigated using various models and parametrizations. The datasets cover a decade, including the periods of pre-infestation, infestation, and post-infestation. Field measurements showed considerable decreases in evapotranspiration (ET), transpiration (T), and leaf area index (LAI) during the two-year infestation period compared to the pre-infestation period. Model interpretations of observed water and carbon fluxes indicated that the overall reductions in T were not solely due to decreased LAI, but also to changes in physiological behaviours. The summer season's canopy-scale stomatal conductance was significantly reduced during the infestation period, from 0.0018 to 0.0011 m s⁻¹. One primary reason for the observed variations is likely that the bark beetle infestation hampers the water transport in the xylem. The damage of xylem has important implications for water use efficiency (WUE), which also significantly influences the parameterization of stomatal conductance. When using stomatal conductance models to forecast ecosystem dynamics, it is crucial to recalibrate the model's parameters to ensure the accurate depiction of stomatal dynamics during various infestation periods. The neglect of the temporal variability of canopy-scale stomatal conductance under ecosystem disturbances (e.g., bark beetle infestations) in current earth system models, therefore, requires specific attention in assessments of large-scale water and carbon balances.

Urban areas, characterized by dense populations and many socioeconomic activities, increasingly suffer from floods, droughts and heat stress due to land use and climate change. Traditionally, the urban thermal environment and water resource management have been studied separately, using urban land-surface models (ULSMs) and urban hydrological models (UHMs). However, as our understanding deepens and the urgency to address future climate disasters grows, it becomes clear that hydroclimatological extremes – such as floods, droughts, severe urban thermal environments and more frequent heat waves – are actually not always isolated events but can be compound events. This underscores the close interaction between the water cycle and the energy balance. Consequently, the existing separation between ULSMs and UHMs creates significant obstacles in better understanding urban hydrological and meteorological processes, which is crucial for addressing the high risks posed by climate change. Defining the future direction of process-based models for hydrometeorological predictions and assessments is essential for better managing extreme events and evaluating response measures in densely populated urban areas. Our review focuses on three critical aspects of urban hydrometeorological simulation: similarities, differences and gaps among different models; existing gaps in physical process implementations; and efforts, challenges and potential for model coupling and integration. We find that ULSMs inadequately represent water surfaces and hydraulic systems, while UHMs lack explicit surface energy balance solutions and detailed building representations. Coupled models show the potential for simulating urban hydrometeorological environments but face challenges at regional and neighbourhood scales. Our review highlights the need for interdisciplinary communication between the urban climatology and the urban water management communities to enhance urban hydrometeorological simulation models.

Storage change in heat in the soil is one of the main components of the energy balance and is essential in studying the land-Atmosphere heat exchange. However, its measurement proves to be difficult due to (vertical) soil heterogeneity and sensors easily disturbing the soil. Improvements in the precision and resolution of distributed temperature sensing (DTS) equipment has resulted in its widespread use in geoscientific studies. Multiple studies have shown the added value of spatially distributed measurements of soil temperature and soil heat flux. However, due to the spatial resolution of DTS measurements (g1/430gcm), soil temperature measurements with DTS have generally been restricted to (horizontal) spatially distributed measurements. This paper presents a device which allows high-resolution measurements of (vertical) soil temperature profiles by making use of a 3D-printed screw-like structure. A 50gcm tall probe is created from segments manufactured with fused-filament 3D printing and has a helical groove to guide and protect a fiber-optic (FO) cable. This configuration increases the effective DTS measurement resolution and will inhibit preferential flow along the probe. The probe was tested in the field, where the results were in agreement with the reference sensors. The high vertical resolution of the DTS-measured soil temperature allowed determination of the thermal diffusivity of the soil at a resolution of 2.5gcm, many times better than what is feasible using discrete probes. A future improvement in the design could be the use of integrated reference temperature probes, which would remove the need for DTS calibration baths. This could, in turn, support making the probes "plug and play"into the shelf instruments without the need to splice cables or experience in DTS setup design. The design can also support the integration of an electrical conductor into the probe and allow heat tracer experiments to derive both the heat capacity and the thermal conductivity over depth at high resolution.

Terrestrial microwave links are increasingly being used to estimate path-averaged precipitation by determining the attenuation caused by rainfall along the link path, mostly with commercial microwave links from cellular telecommunication networks. However, the temporal resolution of these rainfall estimates and the method to derive them are often determined by the temporal sampling strategy that is employed by the mobile network operators. Currently, the links are most often sampled at a temporal resolution of 15 min with a recording of the minimum and maximum values, while more recently, a form of instantaneous sampling with possible intervals up to 1 s has also been set up. For rainfall research purposes, often high temporal resolutions in combination with averaged values are preferred. However, it is uncertain how these various temporal sampling strategies affect the estimated rainfall intensity. Here we aim to understand how temporal sampling strategies affect the measured rainfall intensities using microwave links. To do so, we use data from three collocated microwave links, two 38 GHz and one 26 GHz, sampled at 20 Hz and covering a 2.2 km path over the city of Wageningen, the Netherlands. We aggregate the microwave link power levels to multiple time intervals (1 s to 60 min) and use a mean, instantaneous, and minimum and maximum value to characterize the signal. Based on the aggregated data, we compute rainfall intensities and compare these with 20 Hz rainfall estimates, such that we isolate errors and uncertainties caused by the sampling strategies from instrumental effects, such as different biases between instruments and representativeness errors. In general, our results show that for all sampling strategies, an increase in sampling time interval reduces the performance of the rainfall estimates, which especially holds for the instantaneous sampling strategy. Even the mean sampling strategy, which generally performs best of all strategies, is sensitive to this reduction in temporal resolution and could lead to significant underestimations. This sensitivity of the mean sampling to the temporal resolution seems to be largely affected by the non-linear relation between attenuation and rainfall. The min–max sampling strategy is mostly prone to minor underestimations or large overestimations of the path-averaged rainfall intensities. Moreover, our results, including a comparison with theoretical events, show that the attenuation due to wet antennas not only affects the comparison between the rainfall estimates obtained with a microwave link and another reference instrument but also has a significant influence on the performance of the rainfall retrieval algorithm, especially for devices with relatively long duration of the wet-antenna attenuation combined with the longer sampling time intervals. Overall, this study demonstrates the effect a selected sampling strategy can have on rainfall intensity estimates using (commercial) microwave links.

The trend and magnitude of actual evaporation across the phenophases of miombo woodlands are unknown. This is because estimating evaporation in African woodland ecosystems continues to be a challenge, as flux observation towers are scant if not completely lacking in most ecosystems. Furthermore, significant phenophase-based discrepancies in both trend and magnitude exist among the satellitebased evaporation estimates (i.e. Global Land Evaporation Amsterdam Model (GLEAM), moderate resolution imaging spectroradiometer (MODIS), operational simplified surface energy balance (SSEBop), and water productivity through open-access remotely sensed derived data (WaPOR)), making it difficult to ascertain which of the estimates are close to field conditions. Despite the many limitations with estimation of evaporation in woodlands, the development and application of the distributed temperature system (DTS) is providing deepened insights and improved accuracy in woodland energy partitioning for evaporation assessment. In this study, the Bowen ratio distributed temperature sensing (BRDTS) approach is used to partition available energy and estimate actual evaporation across three canopy phenophases of the miombo woodland, covering the entire 2021 dry season (May–October) and early rain season (November– December) at a representative site in Mpika in Zambia, southern Africa. To complement the field experiment, four satellite-based evaporation estimates are compared to the field observations. Our results show that actual evaporation of the miombo woodland appears to follow the trend of the net radiation, with the lowest values observed during the phenophase with the lowest net radiation in the cool dry season and the highest values during the phenophase with peak net radiation in the early rainy season. It appears the continued transpiration during the driest period in the dormant phenophase (with lowest canopy cover and photosynthetic activities) may be influenced by the species-dependent adapted physiological attributes such as access to moisture in deep soils (i.e. due to deep rooting), plant water storage, and the simultaneous leaf fall and leaf flush among miombo plants. Of the four satellite-based evaporation estimates, only the WaPOR has a similar trend to the field observations across the three phenophases. However, all four satellitebased estimates underestimate the actual evaporation during the dormant and green-up phenophases. Large coefficients of variation in actual evaporation estimates among the satellite-based estimates exist in the dormant and green-up phenophases and are indicative of the difficulty in estimating actual evaporation in these phenophases. The differences between field observations and satellite-based evaporation estimates can be attributed to the model structure, processes, and inputs.

Stormwater is a vital resource and dynamic driver of terrestrial ecosystem processes. However, processes controlling interactions during and shortly after storms are often poorly seen and poorly sensed when direct observations are substituted with technological ones. We discuss how human observations complement technological ones and the benefits of scientists spending more time in the storm. Human observation can reveal ephemeral storm-related phenomena such as biogeochemical hot moments, organismal responses, and sedimentary processes that can then be explored in greater resolution using sensors and virtual experiments. Storm-related phenomena trigger lasting, oversized impacts on hydrologic and biogeochemical processes, organismal traits or functions, and ecosystem services at all scales. We provide examples of phenomena in forests, across disciplines and scales, that have been overlooked in past research to inspire mindful, holistic observation of ecosystems during storms. We conclude that technological observations alone are insufficient to trace the process complexity and unpredictability of fleeting biogeochemical or ecological events without the shower thoughts produced by scientists' human sensory and cognitive systems during storms.

Plant transpiration accounts for about half of all terrestrial evaporation. Plants need water for many vital functions including nutrient uptake, growth and leaf cooling. The regulation of plant water transport by stomata in the leaves leads to the loss of 97% of the water that is taken up via their roots, to the atmosphere. Measuring plant-water dynamics is essential to gain better insight into its roles in the terrestrial water cycle and plant productivity. It can be measured at different levels of integration, from the single cell micro-scale to the ecosystem macro-scale, on time scales from minutes to months. In this contribution, we give an overview of state-of-the-art techniques for plant-water dynamics measurement and highlight several promising innovations for future monitoring. Some of the techniques we will cover include: gas exchange for stomatal conductance and transpiration monitoring, lysimetry, thermometry, heat-based sap flow monitoring, reflectance monitoring including satellite remote sensing, ultrasound spectroscopy, dendrometry, accelometry, scintillometry, stable water isotope analysis and eddy covariance. To fully assess water transport within the soil-plant-atmosphere continuum, a variety of techniques are required to monitor environmental variables in combination with biological responses at different scales. Yet this is not sufficient: to truly account for spatial heterogeneity, a dense network sampling is needed.

Thornthwaite's formula is globally an optimum candidate for large-scale applications of potential evapotranspiration and aridity assessment at different climates and landscapes since it has lower data requirements compared to other methods and especially from the ASCE-standardized reference evapotranspiration (formerly FAO-56), which is the most data-demanding method and is commonly used as the benchmark method. The aim of the study is to develop a global database of local coefficients for correcting the formula of monthly Thornthwaite potential evapotranspiration (Ep) using as benchmark the ASCE-standardized reference evapotranspiration method (Er). The validity of the database will be verified by testing the hypothesis that a local correction coefficient, which integrates the local mean effect of wind speed, humidity, and solar radiation, can improve the performance of the original Thornthwaite formula. The database of local correction coefficients was developed using global gridded temperature, rainfall, and Er data of the period 1950-2000 at 30arcsec resolution (1km at Equator) from freely available climate geodatabases. The correction coefficients were produced as partial weighted averages of monthly Er/Ep ratios by setting the ratios' weight according to the monthly Er magnitude and by excluding colder months with monthly values of Er or Ep <45mm per month because their ratio becomes highly unstable for low temperatures. The validation of the correction coefficients was made using raw data from 525 stations of Europe; California, USA; and Australia including data up to 2020. The validation procedure showed that the corrected Thornthwaite formula Eps using local coefficients led to a reduction of RMSE from 37.2 to 30.0mmm-1 for monthly step estimations and from 388.8 to 174.8mmyr-1 for annual step estimations compared to Ep using as a benchmark the values of the Er method. The corrected Eps and the original Ep Thornthwaite formulas were also evaluated by their use in Thornthwaite and UNEP (United Nations Environment Program) aridity indices using as a benchmark the respective indices estimated by Er. The analysis was made using the validation data of the stations, and the results showed that the correction of the Thornthwaite formula using local coefficients increased the accuracy of detecting identical aridity classes with Er from 63% to 76% for the case of Thornthwaite classification and from 76% to 93% for the case of UNEP classification. The performance of both aridity indices using the corrected formula was extremely improved in the case of non-humid classes. The global database of local correction factors can support applications of reference evapotranspiration and aridity index assessment with the minimum data requirements (i.e., temperature) for locations where climatic data are limited.

Despite the importance of forests in the water and carbon cycles, accurately measuring their contribution remains challenging, especially at night. During clear-sky nights current models and theories fail, as non-turbulent flows and spatial heterogeneity become more important. One of the standing issues is the ‘decoupling’ of the air masses in and above the canopy, where little turbulent exchange takes place, thus preventing proper measurement of atmospheric fluxes. Temperature inversions, where lower air is colder and thus more dense, can be both the cause and result of this decoupling. With Distributed Temperature Sensing (DTS) it is now possible to detect these temperature inversions, and increase our understanding of the decoupling mechanism. With DTS we detected strong inversions within the canopy of a tall Douglas Fir stand. The inversions formed in on clear-sky nights with low turbulence, and preferentially formed in the open understory. A second inversion regularly occurred above the canopy. Oscillations in this upper inversion transferred vertically through the canopy and induced oscillations in the lower inversion. We hypothesize that the inversions could form due to a local suppression of turbulent motions along the height of the canopy. This was supported by a 1-D conceptual model, which showed that a local inversion layer would always form within the canopy if the bulk inversion (over the full canopy) was strong enough. Due to the near-continuous vertical motion and specific height the inversions occur at, a very high measurement density (better than ∼2 m) and measurement frequency (>0.1 Hz) are required to detect them. Consequently, it could be possible that the observed inversions are a regular feature in similarly structured forests, but are generally not directly observed. With DTS it is possible to detect and describe these types of features, which will aid in improving our understanding of atmospheric flows over complex terrain such as forests.

Forest evaporation exports a vast amount of water vapor from land ecosystems into the atmosphere. Meanwhile, evaporation during rain events is neglected or considered of minor importance in dense ecosystems. Air convection moves the water vapor upwards leading to the formation of large invisible vapor plumes, while the identification of visible vapor plumes has not yet been studied. This work describes the formation process of vapor plumes in a tropical wet forest as evidence of evaporation processes happening during rain events. In the dry season of 2018 at La Selva Biological Station (LSBS) in Costa Rica it was possible to spot visible vapor plumes within the forest canopy. The combination of time-lapse videos at the canopy top with conventional meteorological measurements along the canopy profile allowed us to identify the driver conditions required for this process to happen. This phenomenon happened only during rain events. Visible vapor plumes during the daytime occurred when the following three conditions are accomplished: presence of precipitation (P), air convection, and a lifting condensation level value smaller than 100 m at 43 m height (z lcl.43).

Deforestation can considerably affect transpiration dynamics and magnitudes at the catchment scale and thereby alter the partitioning between drainage and evaporative water fluxes released from terrestrial hydrological systems. However, it has so far remained problematic to directly link reductions in transpiration to changes in the physical properties of the system and to quantify these changes in system properties at the catchment scale. As a consequence, it is difficult to quantify the effect of deforestation on parameters of catchment-scale hydrological models. This in turn leads to substantial uncertainties in predictions of the hydrological response after deforestation but also to a poor understanding of how deforestation affects principal descriptors of catchment-scale transport, such as travel time distributions and young water fractions. The objectives of this study in the Wüstebach experimental catchment are therefore to provide a mechanistic explanation of why changes in the partitioning of water fluxes can be observed after deforestation and how this further affects the storage and release dynamics of water. More specifically, we test the hypotheses that (1) post-deforestation changes in water storage dynamics and partitioning of water fluxes are largely a direct consequence of a reduction of the catchment-scale effective vegetation-accessible water storage capacity in the unsaturated root zone (SU, max) after deforestation and that (2) the deforestation-induced reduction of SU, max affects the shape of travel time distributions and results in shifts towards higher fractions of young water in the stream. Simultaneously modelling streamflow and stable water isotope dynamics using meaningfully adjusted model parameters both for the pre- and post-deforestation periods, respectively, a hydrological model with an integrated tracer routine based on the concept of storage-age selection functions is used to track fluxes through the system and to estimate the effects of deforestation on catchment travel time distributions and young water fractions Fyw.

It was found that deforestation led to a significant increase in streamflow accompanied by corresponding reductions of evaporative fluxes. This is reflected by an increase in the runoff ratio from CR=0.55 to 0.68 in the post-deforestation period despite similar climatic conditions. This reduction of evaporative fluxes could be linked to a reduction of the catchment-scale water storage volume in the unsaturated soil (SU, max) that is within the reach of active roots and thus accessible for vegetation transpiration from ∼258 mm in the pre-deforestation period to ∼101 mm in the post-deforestation period. The hydrological model, reflecting the changes in the parameter SU, max, indicated that in the post-deforestation period stream water was characterized by slightly yet statistically not significantly higher mean fractions of young water (Fyw∼0.13) than in the pre-deforestation period (Fyw∼0.12). In spite of these limited effects on the overall Fyw, changes were found for wet periods, during which post-deforestation fractions of young water increased to values Fyw∼0.37 for individual storms. Deforestation also caused a significantly increased sensitivity of young water fractions to discharge under wet conditions from dFyw/dQ=0.25 to 0.36.

Overall, this study provides quantitative evidence that deforestation resulted in changes in vegetation-accessible storage volumes SU, max and that these changes are not only responsible for changes in the partitioning between drainage and evaporation and thus the fundamental hydrological response characteristics of the Wüstebach catchment, but also for changes in catchment-scale tracer circulation dynamics. In particular for wet conditions, deforestation caused higher proportions of younger water to reach the stream, implying faster routing of stable isotopes and plausibly also solutes through the sub-surface.

While evaporation is the largest water consumer of terrestrial water, its importance is often (limitedly) linked to increasing crop productivities. As a consequence, our knowledge of the evaporation process is highly biased by agricultural settings, and results in erroneous estimates of evaporation for other land surfaces and especially for forest systems. The reason why crop and forest systems differ has to do with the vegetation height and what is happening in the space between the plant top and surface. Forests are multi-layered systems, where under the tallest tree species, lower vegetation layers are present. These lower vegetation layers transpire, but at a different rate then the main vegetation, since the atmospheric conditions are different under the canopy. Additionally, the sub-vegetation layers, and also the forest floor, intercept water. Next to different atmospheric conditions per layer, the interception process is highly complex due to differences in interception capacity and a time delay caused by the cascade of water when water flows from the top canopy down to the forest floor. Lastly, forests also have the capacity to store heat and vapor in the air column, biomass, and soil. While this energy storage can be up to 110 W/m2 it is often neglected in evaporation models. To get a better understanding of what is happening inside a forest, for the purpose of evaporation modeling, we should make use of new sensing techniques that allow identifying the rainfall, energy, and evaporation partitioning. This will help to improve evaporation estimates for tall vegetation, like forest, and allow spatial up scaling.

The interception of precipitation by vegetation has important consequences for climate and water resources. Although canopy interception has been studied for centuries, many fundamental unknowns remain. We present persistent questions that reflect challenges in measuring, representing, and understanding how terrestrial ecosystems intercept, partition, and transport precipitation—down to soils or back to the atmosphere. In summary of this book, we outline future needs and simultaneously provide a primer for those interested in precipitation interception processes.

Accurate estimation of transpiration (Tr) is important in the development of precise irrigation scheduling and to enhance water-use efficiency in agricultural production. In this study, the air temperature (Ta) and relative humidity (RH) were measured at three different heights (0.5, 1.0, and 1.8 m above the ground near the plant canopy) parameterize aerodynamic resistance (ra) based on the heat transfer coefficient method and to estimate Tr using the Stanghellini model (SM) during two growing seasons of cucumber in a greenhouse. The canopy resistance (rc) was parameterized by an exponential relationship of stomata resistance and solar radiation, and the estimated Tr was compared to the values measured with lysimeters. After parameterization of ra and rc, the efficiency (EF) and the Root Mean Square Error (RMSE) of the estimated Tr by the SM based on micrometeorological data at a height of 0.5 m were 95% and 18 W m−2, respectively, while the corresponding values were 86% and 29 W m−2 at a height of 1.8 m for the autumn planting season. For the spring planting season, the EF and RMSE were 92% and 34 W m−2 at a height of 0.5 m, while the corresponding values were 81% and 56 W m−2 at a height of 1.8 m, respectively. This work demonstrated that when micrometeorological data within the canopy was applied alongside the data measured above the canopy, the SM led to better agreement with the lysimeter measurements.

Understanding the canopy cover relationship with canopy water content and canopy temperature in the Miombo ecosystem is important for studying the consequences of climate change. To better understand these relationships, we studied the satellite data-based land surface temperature (LST) as proxy for canopy temperature, leaf area index (LAI), and the normalized difference vegetation index (NDVI) as proxies for canopy cover. Meanwhile, the normalized difference infrared index (NDII) was used as a proxy for canopy water content. We used several statistical approaches including the correlated component regression linear model (CCR.LM) to understand the relationships. Our results showed that the most determinant factor of variations in the canopy cover was the interaction between canopy water content (i.e., NDII) and canopy temperature (i.e., LST) with coefficients of determination (R²) ranging between 0.67 and 0.96. However, the coefficients of estimates showed the canopy water content (i.e., NDII) to have had the largest percentage of the interactive effect on the variations in canopy cover regardless of the proxy used i.e., LAI or NDVI. From 2009-2018, the NDII (proxy for canopy water content) showed no significant (at alpha level 0.05) trend. However, there was a-n significant upward trend in LST (proxy for canopy temperature) with a magnitude of 0.17 °C/year. Yet, the upward trend in LST did not result in significant (at alpha level 0.05) downward changes in canopy cover (i.e., proxied by LAI and NDVI). This result augments the observed least determinant factor characterization of temperature (i.e., LST) on the variations in canopy cover as compared to the vegetation water content (i.e., NDII).