Inferring Soil Moisture Memory from Streamflow Observations Using a Simple Water Balance Model

Soil moisture is known for its integrative behavior and resulting memory characteristics. Soil moisture anomalies can persist for weeks or even months into the future, making initial soil moisture a potentially important contributor to skill in weather forecasting. A major difficulty when investigating soil moisture and its memory using observations is the sparse availability of long-term measurements and their limited spatial representativeness. In contrast, there is an abundance of long-term streamflow measurements for catchments of various sizes across the world. We investigate in this study whether such streamflow measurements can be used to infer and characterize soil moisture memory in respective catchments. Our approach uses a simple water balance model in which evapotranspiration and runoff ratios are expressed as simple functions of soil moisture; optimized functions for the model are determined using streamflow observations, and the optimized model in turn provides information on soil moisture memory on the catchment scale. The validity of the approach is demonstrated with data from three heavily monitored catchments. The approach is then applied to streamflow data in several small catchments across Switzerland to obtain a spatially distributed description of soil moisture memory and to show how memory varies, for example, with altitude and topography

Satellite and in situ observations for advancing global Earth surface modelling: a review

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort

Invited perspectives: A research agenda towards disaster risk management pathways in multi-(hazard-)risk assessment

Whilst the last decades have seen a clear shift in emphasis from managing natural hazards to managing risk, the majority of natural-hazard risk research still focuses on single hazards. Internationally, there are calls for more attention for multi-hazards and multi-risks. Within the European Union (EU), the concepts of multi-hazard and multi-risk assessment and management have taken centre stage in recent years. In this perspective paper, we outline several key developments in multi-(hazard-)risk research in the last decade, with a particular focus on the EU. We present challenges for multi-(hazard-)risk management as outlined in several research projects and papers. We then present a research agenda for addressing these challenges. We argue for an approach that addresses multi-(hazard-)risk management through the lens of sustainability challenges that cut across sectors, regions, and hazards. In this approach, the starting point is a specific sustainability challenge, rather than an individual hazard or sector, and trade-offs and synergies are examined across sectors, regions, and hazards. We argue for in-depth case studies in which various approaches for multi-(hazard-)risk management are co-developed and tested in practice. Finally, we present a new pan-European research project in which our proposed research agenda will be implemented, with the goal of enabling stakeholders to develop forward-looking disaster risk management pathways that assess trade-offs and synergies of various strategies across sectors, hazards, and spatial scales

Introduction of a simple-model-based land surface dataset for Europe

Land surface hydrology can play a crucial role during extreme events such as droughts, floods and even heat waves. We introduce in this study a new hydrological dataset for Europe that consists of soil moisture, runoff and evapotranspiration (ET). It is derived with a simple water balance model (SWBM) forced with precipitation, temperature and net radiation. The SWBM dataset extends over the period 1984–2013 with a daily time step and 0.5° × 0.5° resolution. We employ a novel calibration approach, in which we consider 300 random parameter sets chosen from an observation-based range. Using several independent validation datasets representing soil moisture (or terrestrial water content), ET and streamflow, we identify the best performing parameter set and hence the new dataset. To illustrate its usefulness, the SWBM dataset is compared against several state-of-the-art datasets (ERA-Interim/Land, MERRA-Land, GLDAS-2-Noah, simulations of the Community Land Model Version 4), using all validation datasets as reference. For soil moisture dynamics it outperforms the benchmarks. Therefore the SWBM soil moisture dataset constitutes a reasonable alternative to sparse measurements, little validated model results, or proxy data such as precipitation indices. Also in terms of runoff the SWBM dataset performs well, whereas the evaluation of the SWBM ET dataset is overall satisfactory, but the dynamics are less well captured for this variable. This highlights the limitations of the dataset, as it is based on a simple model that uses uniform parameter values. Hence some processes impacting ET dynamics may not be captured, and quality issues may occur in regions with complex terrain. Even though the SWBM is well calibrated, it cannot replace more sophisticated models; but as their calibration is a complex task the present dataset may serve as a benchmark in future. In addition we investigate the sources of skill of the SWBM dataset and find that the parameter set has a similar impact on the simple model results as the choice of the forcing dataset. The newly derived SWBM dataset is of relevance for a range of applications given the deficit of available land datasets.ISSN:1748-9326ISSN:1748-931

High-resolution European daily soil moisture derived with machine learning (2003-2020)

Machine learning (ML) has emerged as a novel tool for generating large-scale land surface data in recent years. ML can learn the relationship between input and target, e.g. meteorological variables and in-situ soil moisture, and then estimate soil moisture across space and time, independently of prior physics-based knowledge. Here we develop a high-resolution (0.1{\deg}) daily soil moisture dataset in Europe (SoMo.ml-EU) using Long Short-Term Memory trained with in-situ measurements. The resulting dataset covers three vertical layers and the period 2003-2020. Compared to its previous version with a lower spatial resolution (0.25{\deg}), it shows a closer agreement with independent in-situ data in terms of temporal variation, demonstrating the enhanced usefulness of in-situ observations when processed jointly with high-resolution meteorological data. Regional comparison with other gridded datasets also demonstrates the ability of SoMo.ml-EU in describing the variability of soil moisture, including drought conditions. As a result, our new dataset will benefit regional studies requiring high-resolution observation-based soil moisture, such as hydrological and agricultural analyses. The SoMo.ml-EU is available at figshare.Comment: 16 pages, 5 figure

Advancing land surface model development with satellite-based Earth observations

The land surface forms an essential part of the climate system. It interacts with the atmosphere through the exchange of water and energy and hence influences weather and climate, as well as their predictability. Correspondingly, the land surface model (LSM) is an essential part of any weather forecasting system. LSMs rely on partly poorly constrained parameters, due to sparse land surface observations. With the use of newly available land surface temperature observations, we show in this study that novel satellite-derived datasets help improve LSM configuration, and hence can contribute to improved weather predictability. We use the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) and validate it comprehensively against an array of Earth observation reference datasets, including the new land surface temperature product. This reveals satisfactory model performance in terms of hydrology but poor performance in terms of land surface temperature. This is due to inconsistencies of process representations in the model as identified from an analysis of perturbed parameter simulations. We show that HTESSEL can be more robustly calibrated with multiple instead of single reference datasets as this mitigates the impact of the structural inconsistencies. Finally, performing coupled global weather forecasts, we find that a more robust calibration of HTESSEL also contributes to improved weather forecast skills. In summary, new satellite-based Earth observations are shown to enhance the multi-dataset calibration of LSMs, thereby improving the representation of insufficiently captured processes, advancing weather predictability, and understanding of climate system feedbacks.ISSN:1027-5606ISSN:1607-793

Evaluation of precipitation datasets against local observations in southwestern Iran

This study provides a comprehensive evaluation of a great variety of state-of-the-art precipitation datasets against gauge observations over the Karun basin in southwestern Iran. In particular, we consider (a) gauge-interpolated datasets (GPCCv8, CRU TS4.01, PREC/L, and CPC-Unified), (b) multi-source products (PERSIANN-CDR, CHIRPS2.0, MSWEP V2, HydroGFD2.0, and SM2RAIN-CCI), and (c) reanalyses (ERA-Interim, ERA5, CFSR, and JRA-55). The spatiotemporal performance of each product is evaluated against monthly precipitation observations from 155 gauges distributed across the basin during the period 2000–2015. This way, we find that overall the GPCCv8 dataset agrees best with the measurements. Most datasets show significant underestimations, which are largest for the interpolated datasets. These underestimations are usually smallest at low altitudes and increase towards more mountainous areas, although there is large spread across the products. Interestingly, no overall performance difference can be found between precipitation datasets for which gauge observations from Karun basin were used, versus products that were derived without these measurements, except in the case of GPCCv8. In general, our findings highlight remarkable differences between state-of-the-art precipitation products over regions with comparatively sparse gauge density, such as Iran. Revealing the best-performing datasets and their remaining weaknesses, we provide guidance for monitoring and modelling applications which rely on high-quality precipitation input