Human-induced alterations in Landscape Patterns (LP) significantly influence hydrological processes, yet their comprehensive impact on rainfall-runoff-sediment relationships (RRSR) remains unexplored. This study introduces the Sediment Transport Modulus per unit of erosive Rainfall (STMR) as a novel metric, alongside the Sediment Transport Modulus (STM) and Suspended Sediment Concentration (SSC), to characterize RRSR. Using hydrometeorological data (1963-2022) and land use maps (1990-2022), the Hushan River Watershed (HSRW) in eastern Poyang Lake, China, was selected to examine the effects of LP on RRSR through LP metrics calculation, correlation analysis, redundancy analysis, and multiple linear regression. The results reveal significant increases in STM, STMR, and SSC, accompanied by notable LP changes during the study period. Overall, LP had a stronger influence on RRSR than sediment yield alone. While the LP of cultivated and forest land showed limited effects on RRSR compared to STM, constructed land exhibited a more pronounced impact. Notably, LP influence diminished when sediment yields exceeded a threshold. Key contributors to RRSR variability included Shannon's Diversity Index, the percentage of cultivated and forest land, and patch density of cultivated and constructed land. It can be concluded that LP effectively predicted RRSR variations. Strategic LP management, such as enhancing the boundaries between forest and cultivated land, reducing the fragmentation of construction land, and strengthening soil erosion control measures along riverbanks can reduce the transfer of soil during the runoff convergence process. These insights are crucial for optimizing landscapes to enhance ecological resilience.

Study regions: The Wangjiaba (WJB) watershed, located in the upper Huaihe River Basin in China. Study focus: An attributing framework has been proposed combining the Double Mass Curve (DMC) and the Soil and Water Assessment Tools (SWAT) model to identify the contributions of climate variability, Land use (LU) change, and Other Human Activities (OHA) to the variations in runoff-sediment processes within the WJB. New hydrological insights for the region: The studied period was able to be separated into three sub-periods (P-1: 1981-1991, P-2: 1992-2009, and P-3: 2010-2019) using the DMC, and the SWAT model could simulate runoff and Sediment Yields Load (SYL) properly during different sub-periods after calibration. Generally, the runoff, SYL, and Suspended Sediment Concentration (SSC) within the WJB exhibited a decrease trend with a change rate of -1.3 mm a(-1), -8.49x10(4) t a(-1), and -0.01 kg m(-3) a(-1), respectively. Substantially, climate variability decreases runoff, SYL, and SSC from P-1 to P-3; LU change decreases runoff, SYL, and SSC from P-2 to P-3; OHA decreases SYL and SSC from P-1 to P-2, but increases SYL and SSC from P-2 to P-3. It should be noticed that the OHA has increased the SYL significantly especially over the downstream of WJB from P-2 to P-3. It is essential to enhance soil erosion prevention measures in the future under the background of global climate change.

Variable heat fluxes over the Tibetan Plateau (TP) interact thermally with the atmosphere, affecting weather in surrounding areas, particularly in the Middle and Lower Yangtze River (MLYR). However, the circulation patterns and time-lag effects between TP heat fluxes and MLYR precipitation remain unclear. This study identified 577 large-scale daily heavy precipitation events (LSDHPEs) in the MLYR from 1980 to 2022. We analyzed the weather causation and spatiotemporal correlations between the TP surface heat fluxes and MLYR LSDHPEs using self-organizing map clustering, singular value decomposition, and harmonic analysis of time series. The results found two dominant synoptic patterns of LSDHPEs at 500 hPa: one, driven by anticyclonic and cyclonic circulations coinciding with shifts in the West Pacific subtropical high and South Asian high, increased from 2000 to 2022; the other, influenced by MLYR cyclonic circulation, showed a significant decrease. For the first time, we revealed lagged effects of the latent heat anomalies (with a lag time of 1–10 d and 130–200 d) and sensible heat anomalies (with a lag time of 2–4 months) over the TP during LSDHPEs in the MLYR. The results may enhance our understanding of TP heat flux anomalies as precursor signals for early warning of heavy rainfall and flooding in the MLYR.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a scientific mission that collects high spatio‐temporal resolution (∼70 m, 1–5 days average revisit time) thermal images since its launch on 29 June 2018. As a predecessor of future missions, one of the main objectives of ECOSTRESS is to retrieve and understand the spatio‐temporal variations in terrestrial evapotranspiration (ET) and its responses to soil water availability and atmospheric aridity. In the European ECOSTRESS Hub (EEH), by taking advantage of land surface temperature (LST) retrievals, we generated ECOSTRESS ET products over Europe and Africa using three models with different structures and parameterization schemes, namely Surface Energy Balance System (SEBS) and Two Source Energy Balance (TSEB) parametric models, as well as the non‐parametric Surface Temperature Initiated Closure (STIC) model. A comprehensive evaluation of the EEH ET products was conducted with respect to flux measurements from 19 eddy covariance sites in Europe over six different biomes with diverse aridity levels. Results revealed comparable performances of STIC and SEBS (RMSE of ∼70 W m −2 ). However, the relatively complex TSEB model produced a higher RMSE of ∼90 W m −2 . Comparison between STIC ET estimates and the operational ECOSTRESS ET product from NASA PT‐JPL model showed a larger RMSE (around 50 W m −2 higher) for the PT‐JPL ET estimates. Substantial overestimation (>80 W m −2 ) in PT‐JPL ET estimates was evident over shrublands and savannas, presumably due to weak constraint of LST in the model. Overall, the EEH supports ET retrieval for the future high‐resolution thermal missions.

<p>Accurate estimation of carbon assimilation and allocation plays a significant role in the plant growth and terrestrial ecosystems. The STEMMUS-SCOPE model integrates photosynthesis, fluorescence emission, and transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum system, and has good performances in estimating water, energy, and carbon fluxes. However, the plant growth states (i.e., leaf area index (LAI) and plant height (PH)) are needed as inputs for running the STEMMUS-SCOPE model, and are obtained either from interpolating observations or taking as constants over the time. As a result, the physical interactions are not adequately captured between radiative transfer, plant growth and soil water movements. The objective of this study is to consider the plant growth in STEMMUS-SCOPE model via coupling a crop growth module (i.e., WOFOST module). The coupled STEMMUS-SCOPE-WOFOST model was evaluated with plant functioning measurements. The results indicate that the simulation of LAI and PH is significantly improved and consistent with the dynamic of the water stress and gross primary production (GPP). Besides, the additional generated state variables (i.e., the biomass of root, leaf, stem as well as yield) can also agree well with the observations. Finally, the interactions between the land surface fluxes, soil moisture dynamic and plant growth are all well simulated. The STEMMUS-SCOPE-WOFOST model provides a mechanistic window to link the satellite observation of solar-induced fluorescence to above- and below-ground biomass, land surface fluxes, and root zone soil moisture, in a physically consistent manner.</p>

Although soil moisture is a key factor of hydrologic and climate applications, global continuous high resolution soil moisture datasets are still limited. Here we use physics-informed machine learning to generate a global, long-term, spatially continuous high resolution dataset of surface soil moisture, using International Soil Moisture Network (ISMN), remote sensing and meteorological data, guided with the knowledge of physical processes impacting soil moisture dynamics. Global Surface Soil Moisture (GSSM1 km) provides surface soil moisture (0–5 cm) at 1 km spatial and daily temporal resolution over the period 2000–2020. The performance of the GSSM1 km dataset is evaluated with testing and validation datasets, and via inter-comparisons with existing soil moisture products. The root mean square error of GSSM1 km in testing set is 0.05 cm3/cm3, and correlation coefficient is 0.9. In terms of the feature importance, Antecedent Precipitation Evaporation Index (APEI) is the most important significant predictor among 18 predictors, followed by evaporation and longitude. GSSM1 km product can support the investigation of large-scale climate extremes and long-term trend analysis.

Lake stratification and mixing processes can influence gas and energy transport in the water column and water–atmosphere interactions, thus impacting limnology and local climate. Featuring the largest high-elevation inland lake zone in the world, comprehensive and comparative studies on the evolution of lake stratification and mixing and their driving forces are still quite limited. Here, using valuable temperature chain measurements in four large lakes (Nam Co, Dagze Co, Bangong Co, and Paiku Co) and a “small lake” adjacent to Nam Co, our objectives are to investigate the seasonal and diurnal variations of epilimnion depth (Ep, the most important layer in stratification and mixing process) and to analyze the driving force differences between “small lake” and Nam Co. Results indicate that Ep estimated by the methods of the absolute density difference (<0.1 kg m−3) from the surface and the Lake-Analyzer were quite similar, with the former being more reliable and widely applicable. The stratification and mixing in the four large lakes showed a dimictic pattern, with obvious spring and autumn turnovers. Additionally, the stratification form during heat storage periods, with Ep quickly locating at depths of approximately 10–15 m, and, after that, increasing gradually to the lake bottom. Additionally, the diurnal variation in Ep can be evidenced both in the large and small lakes when temperature measurements above 3 m depth are included. For Nam Co, the dominant influencing factors for the seasonal variation of Ep were the heat budget components (turbulent heat fluxes and radiation components), while wind speed only had a relatively weak positive correlation (r = 0.23). In the “small lake”, radiation components and wind speed show high negative (r = −0.43 to −0.59) and positive (r = 0.46) correlation, with rare correlations for turbulent heat flux. These reported characteristics have significance for lake process modeling and evaluation in these high-elevation lakes.

With rapid economic development, extensive human activity has changed landscape patterns (LPs) dramatically, which has further influenced hydrological processes. However, the effects of LPs changes on hydrological processes, especially for the streamflow–sediment relationship in the subtropical monsoon climate zone, have not been reported. In our study, 10 watersheds with different sizes in the subtropical monsoon climate zone of southeastern China were chosen as the study area, and the effect of the 14 most commonly used landscape metrics (LMs) on 4 typical hydrological indices (water yields (WY), the runoff coefficient (RC), the soil erosion modulus (SEM), and the suspended sediment concentration (SSC)) were analyzed based on land use maps and hydrological data from 1990 to 2019. The results reveal that the LP characteristics within the study area have changed significantly. The number of patches and landscape shape indices were significantly positively correlated with watershed size (p < 0.01). For most watersheds, the largest patch index was negatively correlated with WY, RC, and SEM, and the perimeter area fractal dimension was positively correlated with WY, RC, SEM, and SSC. The effects of several LMs on the hydrological indices had scale effects. WY/RC and the interspersion and juxtaposition index were negatively correlated in most larger watersheds but were positively correlated in most smaller watersheds. Similar results were found for Shannon’s diversity/evenness index and SEM. In general, an increase in a small patch of landscape and in landscape diversity would increase WY, the fragmentation of LPs would result in more soil erosion, and LPs would affect the relationship between streamflow and sediment yield. As a result, a proper decrease in landscape fragmentation and physical connectivity in the subtropical monsoon climate zone of southeastern China would benefit soil erosion prevention. These results enhance the knowledge about the relationship between LPs and hydrological processes in the subtropical monsoon climate zone of southeastern China and benefit local water and soil conservation efforts.

Precipitation is an important component of water circulation and an essential input for various hydrological models. A high quality, high spatial resolution, and long-term precipitation dataset would benefit hydrological investigations, particularly for regions having insufficient precipitation records. The upper Huaihe River Basin (UHRB) was selected as the research location in this study, and the accuracies of three precipitation products (PPs: a high-resolution daily gridded precipitation dataset for China (HRLT), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center Global (CPC) precipitation dataset) were assessed at multiple spatio-temporal scales comparing with the gauge precipitation (GP) for 2000-2019. Subsequently, the applicability of the three PPs on streamflow (Q) and sediment yield (SY) simu-lations, as well as the impact on parameter sensitivity, were compared using the Soil and Water Assessment Tool (SWAT) model. The results showed that the accuracy of the three PPs were ranked as CPC > HRLT > PERSIANN-CDR on the watershed average scale, HRLT would underestimate the extreme precipitation; and PERSIANN-CDR would overestimate the annual precipitation. On the grid-to-point scale, PERSIANN-CDR was found to be the most stable with high accuracy, followed by CPC and HRLT on all temporal scales. The ability of these PPs to detect rainfall events was ranked as CPC > HRLT > PERSIANN-CDR. The sensitivity of the Q parameters changed with the variation in the precipitation input. The sensitive parameters for GP were distributed on average for almost all processes, while the sensitive parameters for PPs mainly controlled the groundwater and evapo-transpiration processes. Among all the PPs, the performance of CPC in the Q and SY simulations was found to be the best, followed by HRLT and PERSIANN-CDR, and all the PPs could simulate SY better than Q in spatial distribution. HRLT has the potential to be used in long-term hydrological simulations in ungauged or small watersheds based on its high spatial resolution compared to other products.

Accurate information on surface soil moisture (SSM) content at a global scale under different climatic conditions is important for hydrological and climatological applications. Machine-learning-based systematic integration of in situ hydrological measurements, complex environmental and climate data, and satellite observation facilitate the generation of reliable data products to monitor and analyse the exchange of water, energy, and carbon in the Earth system at a proper space–time resolution. This study investigates the estimation of daily SSM using 8 optimised machine learning (ML) algorithms and 10 ensemble models (constructed via model bootstrap aggregating techniques and five-fold cross-validation). The algorithmic implementations were trained and tested using International Soil Moisture Network (ISMN) data collected from 1722 stations distributed across the world. The result showed that the K-neighbours Regressor (KNR) had the lowest root-mean-square error (0.0379 cm3 cm−3) on the “test_random” set (for testing the performance of randomly split data during training), the Random Forest Regressor (RFR) had the lowest RMSE (0.0599 cm3 cm−3) on the “test_temporal” set (for testing the performance on the period that was not used in training), and AdaBoost (AB) had the lowest RMSE (0.0786 cm3 cm−3) on the “test_independent-stations” set (for testing the performance on the stations that were not used in training). Independent evaluation on novel stations across different climate zones was conducted. For the optimised ML algorithms, the median RMSE values were below 0.1 cm3 cm−3. GradientBoosting (GB), Multi-layer Perceptron Regressor (MLPR), Stochastic Gradient Descent Regressor (SGDR), and RFR achieved a median r score of 0.6 in 12, 11, 9, and 9 climate zones, respectively, out of 15 climate zones. The performance of ensemble models improved significantly, with the median RMSE value below 0.075 cm3 cm−3 for all climate zones. All voting regressors achieved r scores of above 0.6 in 13 climate zones; BSh (hot semi-arid climate) and BWh (hot desert climate) were the exceptions because of the sparse distribution of training stations. The metric evaluation showed that ensemble models can improve the performance of single ML algorithms and achieve more stable results. Based on the results computed for three different test sets, the ensemble model with KNR, RFR and Extreme Gradient Boosting (XB) performed the best. Overall, our investigation shows that ensemble machine learning algorithms have a greater capability with respect to predicting SSM compared with the optimised or base ML algorithms; this indicates their huge potential applicability in estimating water cycle budgets, managing irrigation, and predicting crop yields.

&lt;p&gt;Water stress factor is utilized to describe drought effects on plant growth in land surface models (LSMs). Accurately representing water stress is critical to understand the impact of climate change on plant and ecosystem. Models use various approaches to describe the responses of vegetation to water stress. Some models assumed water stress causes stomata closure to attenuate gas exchange process, while others assumed water stress reduces the maximum rate of carboxylation (Vcmax) to slow photosynthesis. Only a few models considered both constraints. However, which parameterization can better capture the dry condition is still controversial. A reliable detection and attribution of the impact of water stress on plant is necessary for understanding the consequence of climate change on the ecosystem from a mechanism aspect. In this study, an empirical stomatal conductance scheme (proposed by Ball et al. in1987, called &amp;#8220;BB_gs&amp;#8221;) and a unified stomatal conductance model (proposed by Medlyn et al. 2011, called &amp;#8220;ME_gs&amp;#8221;) were coupled into STEMMUS-SCOPE model to explore the discrepancy between empirical and optimal approaches. Three scenarios were designed to represent the effect of water stress on gas exchange (gs_w), photosynthesis (Vcmax_w) and both processes (gs &amp; Vcmax_w). The coupled model was implemented for three sites with different plant function types, including C3 grassland, C3 shrub, and C4 cropland. Results showed that the optimal stomatal conductance scheme has better performance than the empirical approach because the optimal method considers the realistic stomata regulation. The Vcmax_w scheme captured the drought effects better than other schemes. The results improved our understanding on regional ecosystem functioning under the context of climate change. &amp;#160;&amp;#160;&lt;/p&gt;

Commercial Microwave Links (CMLs), that provide the underlying framework for data transfer between cellular network base stations have been found effective for monitoring rainfall. Wireless infrastructure of this kind is deployed widely by communication providers across Africa and can be used as a complementary monitoring device to the sparse proprietary resources that exist currently, and at minimal cost, or as a substitute tool in the many cases where alternatives do not exist. Here we focus on the potential that lies in this novel approach to acquire valuable information required for agricultural needs over the poverty-stricken countries of Africa.

Study region: The Xixian Watershed is an agricultural watershed in the upper Huaihe River basin, China, which suffers severe soil erosion. Study focus: Converting the types of agricultural production without losing their agricultural purposes would be a choice for mitigating soil erosion compared to the Grain for Green Program (GGP). The Conversion of Land Use and its Effect at Small regional extent (CLUE-S) model and the Soil and Water Assessment Tool (SWAT) model were used, and four land-use scenarios (S1: preference for dry land; S2: historical trend; S3: preference for paddy land; and S4: grain for green) were designed based on different preference in agricultural land and the GGP. The responses of runoff, sediment yields, and economic benefits were compared under each land-use scenario. New hydrological insights for the region: We find that S1 increases runoff and sediment yields, S3 and S4 decrease runoff and sediment yields, and S2 slightly increases runoff and reduces sediment yields. Different types of agricultural land mainly affected runoff by redistributing surface and subsurface runoffs. Paddy land would increase subsurface runoff and decrease surface runoff and sediment yields, in contrast to dry land. The economic benefit could be increased under S3 and be opposite under other scenarios. Promoting paddy farming in a water-rich watershed can mitigate soil erosion, conserve groundwater, and gain more economic benefits simultaneously.

At the level of a watershed, the conservation of mass imposes that the net moisture transport through the atmospheric boundaries is balanced by the river discharge and an accumulation/depletion in terrestrial sources such as the soil, surface waters and groundwater. There are considerable uncertainties connected with modelled water balance components, especially since most models only simulate part of the system: either the atmosphere, the surface or the subsurface. Uncertainties in boundary conditions propagate as biases in the simulated results. For example, not accounting for anthropogenic groundwater extraction potentially introduces biases in arid regions, where groundwater is a non-negligible source of moisture for the atmosphere. The use of observations is therefore an important aid to evaluate model performances and to detect possible biases in water balance components. In this contribution, we compare total water storage changes derived from the Gravity Recovery Climate Experiment (GRACE) and its follow-on mission, with modelled components of the water balance. We use ERA5 reanalysis data to compute (net) atmospheric transports, and river discharge from GloFAS (Global Flood Awareness System). Furthermore, we use precipitation estimates (e.g. from GPCC) together with evapotranspiration from the Surface Energy Balance System (SEBS). We finally perform an accounting of the water balance components for the world’s largest watersheds and show to what extent we can find agreements, inconsistencies and biases in the data and models.

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Rainfall erosivity (RE) is an important factor in the soil erosion process, which cannot be altered by human intervention alone. The Huaihe River Basin (HRB) is a large agricultural watershed, suffering severe water erosion, investigating the spatio-temporal variation of RE is essential for local soil erosion prevention. The linear regression, Yue-Pilon method, and the Hurst exponent were used in analyzing the spatio-temporal variation within the HRB during 1960-2018. The slip correlation analysis and F-test were applied to obtain the correlation between climatic indices [the Arctic Oscillation (AO), the Southern Oscillation (SOI), the North Pacific Index (NPI), and the Nino 4SST index (SST)] and RE in the HRB. Results revealed that the annual average RE were 4280, 5061, 4068, 4886, and 4089 MJ mm hm(-2) h(-1) within the HRB, upper-HRB, middle-HRB, lower-HRB, and the Yishusi River Watershed, respectively. The annual average RE will increase within the upper-HRB and lower-HRB and decrease within the middle-HRB and the Yishusi River Watershed in the future. The seasonal average RE was ranked as summer > autumn = spring > winter in the HRB. The spatiotemporal difference was significant in the HRB, and different sub-regions exhibited a different trend in seasonal RE, except for the winter RE that increased significantly in all sub-regions (p < 0.05). The highest monthly RE occurred in July, with RE during May-September accounting for approximately 87% of the annual RE in the HRB. The AO and NPI had significant correlations with RE (p < 0.05) both on the annual and monthly scales with different lag times. The monthly AO, SOI, SST, and NPI were significantly correlated with RE in the long term with different lag times in different months (p < 0.05). These findings could provide potential predictive factors for RE prediction and help prevent soil erosion within the HRB.

NASA’s Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error <0.04 m3/m3). The validation approach also includes other (“sparse network”) in situ SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products’ performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the L-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the ESA Soil Moisture Ocean Salinity mission, although there are differences in some regions. The high-resolution (3-km) SM retrieval product, generated by combining Copernicus Sentinel-1 data with SMAP observations, performs within expectations. Currently, however, there is limited availability of 3-km CVS data to support extensive validation at this spatial scale. The most recent (version 5) SMAP Level 4 SM data assimilation product providing surface and root-zone SM with complete spatio-temporal coverage at 9-km resolution also meets performance requirements. The SMAP SM validation program will continue throughout the mission life; future plans include expanding it to forested and high-latitude regions.

Commercial microwave link (MWL) used by mobile telecom operators for data transmission can provide hydro-meteorologically valid rainfall estimates according to studies in the past decade. For the first time, this study investigated a new method, the MSG technique, that uses Meteosat Second Generation (MSG) satellite data to improve MWL rainfall estimates. The investigation, conducted during daytime, used MSG optical (VIS0.6) and near IR (NIR1.6) data to estimate rain areas along a 15 GHz, 9.88 km MWL for classifying the MWL signal into wet–dry periods and estimate the baseline level. Additionally, the MSG technique estimated a new parameter, wet path length, representing the length of the MWL that was wet during wet periods. Finally, MWL rainfall intensity estimates from this new MSG and conventional techniques were compared to rain gauge estimates. The results show that the MSG technique is robust and can estimate gauge comparable rainfall estimates. The evaluation scores every three hours of RMSD, relative bias, and r2 based on the entire evaluation period results of the MSG technique were 2.61 mm h−1, 0.47, and 0.81, compared to 2.09 mm h−1, 0.04, and 0.84 of the conventional technique, respectively. For convective rain events with high intensity spatially varying rainfall, the results show that the MSG technique may approximate the actual mean rainfall estimates better than the conventional technique.

Over the last two decades, prevalent technologies and Internet of Things (IoT) systems have been found to have potential for carrying out environmental monitoring. The data generated from these infrastructures are readily available and have the potential to provide massive spatial coverage. The costs involved in using these data are minimal since the records are already generated for the original uses of these systems. Commercial microwave links, which provide the underlying framework for data transfer between cellular network base stations, are one example of such a system and have been found useful for monitoring rainfall. Wireless infrastructure of this kind is deployed widely by communication providers across Africa and can thus be used as a rainfall monitoring device to complement the sparse proprietary resources that currently exist or to substitute for them where alternatives do not exist. Here we focus this approach’s potential to acquire valuable information required for agricultural needs across Africa using Kenya as an example.

In 2018 we celebrated 25 years of development of radar altimetry, and the progress achieved by this methodology in the fields of global and coastal oceanography, hydrology, geodesy and cryospheric sciences. Many symbolic major events have celebrated these developments, e.g., in Venice, Italy, the 15th (2006) and 20th (2012) years of progress and more recently, in 2018, in Ponta Delgada, Portugal, 25 Years of Progress in Radar Altimetry. On this latter occasion it was decided to collect contributions of scientists, engineers and managers involved in the worldwide altimetry community to depict the state of altimetry and propose recommendations for the altimetry of the future. This paper summarizes contributions and recommendations that were collected and provides guidance for future mission design, research activities, and sustainable operational radar altimetry data exploitation. Recommendations provided are fundamental for optimizing further scientific and operational advances of oceanographic observations by altimetry, including requirements for spatial and temporal resolution of altimetric measurements, their accuracy and continuity. There are also new challenges and new openings mentioned in the paper that are particularly crucial for observations at higher latitudes, for coastal oceanography, for cryospheric studies and for hydrology.Thepaperstarts with a general introduction followed by a section on Earth System Science including Ocean Dynamics, Sea Level, the Coastal Ocean, Hydrology, the Cryosphere and Polar Oceans and the ‘‘Green ” Ocean, extending the frontier from biogeochemistry to marine ecology. Applications are described in a subsequent section, which covers Operational Oceanography, Weather, Hurricane Wave and Wind Forecasting, Climate projection. Instruments’ development and satellite missions’ evolutions are described in a fourth section. A fifth section covers the key observations that altimeters provide and their potential complements, from other Earth observation measurements to in situ data. Section 6 identifies the data and methods and provides some accuracy and resolution requirements for the wet tropospheric correction, the orbit and other geodetic requirements, the Mean Sea Surface, Geoid and Mean Dynamic Topography, Calibration and Validation, data accuracy, data access and handling (including the DUACS system). Section 7 brings a transversal view on scales, integration, artificial intelligence, and capacity building (education and training). Section 8 reviews the programmatic issues followed by a conclusion. (cid:1) 2021 COSPAR. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).