Modeling Of Hydrological Processes Research Articles

Understanding the spatiotemporal variability in nonpoint source nutrient loads and its effect on water quality in the upper Xin’an river basin, Eastern China

Understanding the spatiotemporal characteristics of major nonpoint sources (NPSs) are crucial for protecting river water quality in drinking water source areas. This study employed a geomorphology-based nonpoint source pollution (GBNP) model to simulate hydrological processes together with NPS pollutant transport and transformation processes from 1980 to 2019 in the upper Xin’an River basin, an important drinking water headwater. The simulations revealed the spatiotemporal characteristics of nitrogen and phosphorus loads and their influences on river water quality. The results showed that the long-term average total nitrogen (TN) and total phosphorus (TP) loads were 3219 kg∙km−2∙yr−1 and 293 kg∙km−2∙yr−1, respectively. The proportional coefficients for TN and TP entering the rivers from the watershed were 0.40 and 0.37. The TN and TP loads during the April to July flood season accounted for 65.0 % and 63.2 % of the annual total, respectively. Annual TN load was significantly correlated with annual runoff depth, soil erosion and TN input in the interannual variation, while the interannual variation of TP load was significantly correlated with annual runoff depth and soil erosion. The long-term average TN and TP loads showed spatial patterns similar to those of land use and runoff depth, respectively. The daily TN concentrations showed a clockwise hysteresis relationship with discharge during flood events, while the daily TP concentrations had a single positive correlation with discharge throughout a whole year. Without atmospheric N deposition, the average TN load would decrease from 3122 kg∙km−2∙yr−1 to 1266 kg∙km−2∙yr−1 in 2000–2009. The TN load from atmospheric deposition accounted for 65 %-71 % of the total load, which implies that river water quality protection must include local basin eco-environmental protection and air pollution control over broader areas. This study showed that the distributed modelling of hydrological and biogeochemical processes is crucial to understand the spatiotemporal variability in nonpoint source nutrient loads over the watershed and is helpful to identifying key factors affecting river water quality.

Reconstructing Digital Terrain Models from ArcticDEM and WorldView-2 Imagery in Livengood, Alaska

ArcticDEM provides the public with an unprecedented opportunity to access very high-spatial resolution digital elevation models (DEMs) covering the pan-Arctic surfaces. As it is generated from stereo-pairs of optical satellite imagery, ArcticDEM represents a mixture of a digital surface model (DSM) over a non-ground areas and digital terrain model (DTM) at bare grounds. Reconstructing DTM from ArcticDEM is thus needed in studies requiring bare ground elevation, such as modeling hydrological processes, tracking surface change dynamics, and estimating vegetation canopy height and associated forest attributes. Here we proposed an automated approach for estimating DTM from ArcticDEM in two steps: (1) identifying ground pixels from WorldView-2 imagery using a Gaussian mixture model (GMM) with local refinement by morphological operation, and (2) generating a continuous DTM surface using ArcticDEMs at ground locations and spatial interpolation methods (ordinary kriging (OK) and natural neighbor (NN)). We evaluated our method at three forested study sites characterized by different canopy cover and topographic conditions in Livengood, Alaska, where airborne lidar data is available for validation. Our results demonstrate that (1) the proposed ground identification method can effectively identify ground pixels with much lower root mean square errors (RMSEs) (<0.35 m) to the reference data than the comparative state-of-the-art approaches; (2) NN performs more robustly in DTM interpolation than OK; (3) the DTMs generated from NN interpolation with GMM-based ground masks decrease the RMSEs of ArcticDEM to 0.648 m, 1.677 m, and 0.521 m for Site-1, Site-2, and Site-3, respectively. This study provides a viable means of deriving high-resolution DTM from ArcticDEM that will be of great value to studies focusing on the Arctic ecosystems, forest change dynamics, and earth surface processes.

Seasonal and Morphological Controls on Nitrate Retention in Arctic Deltas

AbstractEstimates of nitrate loading to the Arctic Ocean are limited by the lack of field observations within deltas partly due to logistical constraints. To overcome this limitation, we use a remote sensing framework to estimate retention of nitrate in Arctic deltas. We achieve this by coupling hydrological and biogeochemical process models at the network scale for five major Arctic deltas. Binary masks of delta channels were used to simulate flow direction and magnitude through networks. Models were parameterized using historical and seasonal observations. Simulated nitrate retention ranged from 2.9% to 15% of the incoming load. Retention rates were largest during winter but smallest during spring conditions when increased discharges export large nitrate masses to the coast. Under future climate scenarios, retention rates fall by ∼1%–10%. Arctic deltas have an important effect on the magnitude of nitrate entering Arctic seas and the inclusion of processing in deltas can improve flux estimates.

Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity

Saturated hydraulic conductivity (Ksat) and other soil (hydraulic) properties are fundamental for applications that depend on modeling hydrological processes, such as the quantification of future groundwater recharge rates. Yet, for most areas in the world, local soil information is lacking. Additionally, access to local soil surveys is often restricted or costly. Available global and regional digital soil mapping (DSM) products differ in scale and degree of data aggregation, as well as in spatial coverage. Ksat – and soil properties in general – are also characterized by a high spatial variability at all scales. Most often, there is no single data product available that covers the whole study area and still displays the variability of local soil observations. Thus, it is often a challenge to combine and predict soil data from different sources and resolutions while preserving the characteristically high spatial variability of soil properties.This study develops and compares two approaches for producing spatially distributed Ksat maps. First, an indirect approach based on two machine learning (ML) models – eXtreme Gradient Boosting (XGBoost) and feed-forward neural network (FNN) – that are trained with available local soil data sources and environmental raster datasets to predict the soil parameters sand, silt, clay, and organic matter content. Ksat is then determined by applying existing pedotransfer-functions (PTFs) on these regionalized soil parameters. Second, a direct approach in which ML models are directly trained with available soil hydraulic datasets to predict Ksat.Both approaches are applied to predict Ksat for Austria. While the resulting soil property maps of the indirect approach are able to largely reproduce the original data variability, the prediction of Ksat includes high levels of uncertainties and the predicted vertical distribution of Ksat is not plausible. The spatial distribution of Ksat in the direct approach resembles available global Ksat maps. In the existing global Ksat maps as well as in the results of the direct approach the small-scale variability of Ksat is reduced. In both approaches XGBoost outperforms FNN. The derived soil property maps help to reduce current gaps in soil data availability for Austria, but also highlight the need for additional Ksat field data acquisition.

High-Performance Forecasting of Spring Flood in Mountain River Basins with Complex Landscape Structure

We propose the methodology of building the process-driven models for medium-term forecasting of spring floods (including catastrophic ones) in the mountainous areas, the hydrological analysis of which is usually much more complicated in contrast to plains. Our methodology is based on system analytical modeling of complex hydrological processes in 34 river basins of the Altai-Sayan mountain country. Consideration of 13 types of landscapes as autonomous hydrological subsystems influencing rivers’ runoff (1951–2020) allowed us to develop the universal predictive model for the most dangerous April monthly runoff (with ice motion), which is applicable to any river basin. The input factors of the model are the average monthly air temperature and monthly precipitation for the current autumn–winter period, as well as the data on the basin landscape structure and relief calculated by GIS tools. The established universal dependences of hydrological runoffs on meteorological factors are quite complex and formed under influence of solar radiation and physical–hydrological patterns of melting snow cover, moistening, freezing, and thawing of soils. The model shows the greatest sensitivity of April floods to the landscape composition of river basins (49% of common flood variance), then to autumn precipitation (9%), winter precipitation (3%), and finally, to winter air temperature (0.7%). When it is applied to individual river basins, the forecast quality is very good, with the Nesh–Sutcliffe coefficient NSE = 0.77. In terms of the accuracy of process-driven predictive hydrological models for the mountainous areas, the designed model demonstrates high-class performance.

The Effect of Design Rainfall Patterns on Urban Flooding Based on the Chicago Method

Design rainfall is the basis for deriving design floods in areas where rainfall data are lacking and has a significant impact on the construction of water engineering facilities and municipal engineering designs. The Chicago rainfall pattern method has great applicability for urban short-duration design rainfall. In order to analyze the influence of design storm rainfall patterns on urban flooding, numerical models of hydrological and hydrodynamic processes were applied to simulate design rainfall with different recurrence periods and different rain peaks and were also used to compare and analyze the total amount of water accumulation and inundation extent by taking the central city of Zhoukou as an example. The results show that when the design rainfall recurrence period is less than 20 years, the total volume and inundation extent of waterlogging in design rainfall with a smaller peak ratio is larger. When the return period is greater than 20 years, the pattern is reversed. However, as the return period grows, the difference in peak inundation volume due to different peak rainfall amounts decreases. This study has certain guiding significance for urban flood forecasting and early warning efforts.

Role of in-situ point instruments in the estimation of variability in soil saturated hydraulic conductivity

ABSTRACT Saturated hydraulic conductivity (Ks ) is among the most important soil parameters for modeling hydrological processes, and its field-scale variability plays a significant role for soils subjected to light and moderate rainfalls. It is often assessed by either making point-scale measurements using permeameters and infiltrometers or coupling probabilistic models with field-scale infiltration experiments under natural/artificial rainfall conditions. The former requires numerous measurements across the study area, and the latter is constrained by the infiltration experiment’s rainfall pattern, thereby restricting these approaches to only partially resolving the spatial variability of Ks , over a field. This study investigated the applicability of three in situ infiltration devices – the double-ring infiltrometer, Commonwealth Scientific and Industrial Research Organisation (CSIRO) version of the tension permeameter, and Guelph permeameter – using a Bayesian framework. Results indicate the disparate estimates of Ks distribution parameters obtained from each instrument’s infiltration data. Using field-scale rainfall-runoff data, a posterior coarsening method is proposed to reconcile the point estimates from different instruments.

Modeling Post‐Wildfire Hydrologic Response: Review and Future Directions for Applications of Physically Based Distributed Simulation

AbstractWildfire is a growing concern as climate shifts. The hydrologic effects of wildfire, which include elevated hazards and changes in water quantity and quality, are increasingly assessed using numerical models. Post‐wildfire application of physically based distributed models provides unique insight into the underlying processes that affect water resources after wildfire. This work reviews and synthesizes post‐wildfire applications of physically based distributed models by examining the scales and geographic/ecohydrologic distribution of model applications, hydrologic response process representation, model parameterization, and model performance metrics. Highlighted gaps and opportunities for advancing physically based distributed hydrologic response modeling after wildfire include the following: (a) applying models in under‐represented geographic (S. America, Africa, Asia) and ecohydrologic regions (arid or dry subhumid climates), (b) incorporating all four major streamflow generation mechanisms (infiltration excess, saturation excess, subsurface storm flow, and groundwater flow), (c) representing integrated vadose zone and saturated zone processes to better capture subsurface streamflow generation, (d) building new remotely sensed model parameterization methods for precipitation interception, infiltration, and overland flow that account for burn severity and recovery, (e) incorporating distributed state variables (e.g., soil moisture, groundwater levels) in model performance assessment, (f) designing model intercomparison studies, including field datasets specifically for post‐wildfire model development and validation, (g) linking mechanistic vegetation regrowth models with hydrologic models to improve simulation of process shifts as ecosystems recover, and (h) creating a new community modeling framework to integrate modeling advances across the wildfire science community.

ADVANCES IN THE TRAINING ON WATER RESOURCES AT THE UNIVERSITY OF CARABOBO

This article deals with advances in the training on water resources at the University of Carabobo (UC), Venezuela, carried out by a research unit belonging to the UC Engineering Faculty renamed as Center of Hydrological and Environmental Research (CIHAM UC, in Spanish) from 2007 to the present. CIHAM-UC operated as Center of Hydraulic Research (CIH-UC, in Spanish) since 1971. CIHAM-UC is focused on six research lines: 1) Integrated Management of Watersheds: Monitoring and Environmental Management. 2) Modeling of hydrological and environmental variables. 3) Environmental Risk Assessment: Vulnerability and Adaptability. 4) Environment, 5) Extreme Hydrological Process Modeling: Droughts and Floods. 6) Climate change. The main CIHAM-UC’s scientific products have consisted of academic projects, theses, scientific articles and books to support undergraduate and graduate programs at UC, to enhance the impact of the UC Engineering Faculty in the economic and social environment of the region and the country.

A monthly distributed water and salt balance model in irrigated and non-irrigated lands of arid irrigation district with shallow groundwater table

Water scarcity and soil salinization are two major problems threatening the sustainability of irrigation districts in arid regions with shallow groundwater table. To study water and salt balances in arid irrigation districts, we developed a monthly distributed water and salt balance model for arid irrigation district (DAHMID-S) with shallow groundwater table by integrating models of major hydrological processes and the associated salt transport processes, including irrigation, evapotranspiration, soil water and groundwater and salt balances, drainage of water and salt through ditches, and interior drainage of water and associated dry drainage of salt between irrigated and non-irrigated lands. The model was successfully applied to the Hetao Irrigation District (HID), the largest irrigation district in the arid region of China. Results indicate that evapotranspiration is the dominant water consumption component in the HID and accounts for about 95% of the total irrigation and precipitation, while drainage through ditches accounts for 10.5% of the total irrigation. Only 49.4% of the introduced salt is discharged from the HID through drainage ditches. Consequently, most parts of the HID are in the state of salt accumulation except the northern Wulate sub-irrigation district, and the problem of salt accumulation is more serious in the western and southern HID under more favorable conditions of water use. Dry drainage plays a crucial role in salt balance of both irrigated and non-irrigated lands, which results in desalinization in most irrigated lands while salt accumulation in non-irrigated lands. Appropriate measures should be taken to solve water and salt-related problems to improve the sustainability of the HID. In the western HID, irrigation water diversion should be decreased, and the drainage system should be further improved to decrease the salt accumulation to a lower level. Meanwhile, the irrigation water diversion can be increased in the southeastern HID by planting more corn and wheat. Besides, irrigation management should be improved to reduce the surface water return flow to increase the groundwater drainage capacity, especially in the southwestern HID.

Accuracy of methods for simulating daily water surface evaporation evaluated by the eddy covariance measurement at boreal flux sites

Evaporation from the surface of water is a vital component of the hydrological cycle. Accurately simulating evaporation is crucial for modeling hydrological processes, ensuring ecosystem water efficiency, and managing water resources. Previous methods for evaluating evaporation have generally been limited to a single site and on a regional scale. Based on eddy covariance measurements of 14 boreal flux sites, the accuracy of 26 methods for simulating daily water surface evaporation was comprehensively evaluated in this study. Most methods accurately simulated daily evaporation at most sites with a KGE >0.60. The combination methods with simultaneous calibration of the energy and aerodynamic terms and the double-parameter aerodynamic methods based on the linear and exponential functions showed the best performance, with the median KGE of the evaluated sites ranging from 0.72 to 0.76. The Bowen ratio energy balance (BREB) method embedded with Tw, Rs-based two-variable empirical methods, double-parameter aerodynamic methods, and the combination method of the energy term based on net radiation (Rn) and the aerodynamic term based on the exponential function method performed better than the other methods of the same type. The relative error (RE) simulated by most methods was generally within ± 30 %, with the median RE of all sites within ± 10 % for each method. The combination methods tended to overestimate the level of evaporation, whereas the BREB-type and aerodynamic methods tended to underestimate the extent of evaporation. The simulation accuracy of the daily evaporation showed significant variance among the sites. It showed the best model performance at two FLUXNET and two sites taken from the literature, in which 75 % of the methods were able to accurately simulate daily evaporation with a Kling–Gupta efficiency (KGE) >0.80. All methods performed poorly at the three sites from the literature and were mainly due to the lack of measured variables with a strong correlation with the latent heat flux (LE). The FLUXNET sites generally showed better performance. The observed LE of the sites with the best model performance generally showed a unimodal distribution throughout the year. The simulation accuracy of non-unimodal distribution sites mainly depended on the correlation between the LE and related variables. The combination of the energy term based on Rn and the aerodynamic term based on the power function method showed the best model stability. This finding indicated that the calibrated method was robust and showed high stability in simulating daily evaporation, and it was recommended for application in other sites or regions. The optimization of parameters calibrated in this study may improve the accuracy of simulating the daily evaporation for the application of the Penman and Priestley–Taylor equations when the water heat storage measurement is missing.

Assessing the spatial variability of saturated soil hydraulic conductivity at the watershed scale using the sequential Gaussian co-simulation method

Accurate spatial characterization of saturated soil hydraulic conductivity (KSat) is vital for modeling hydrological processes. An effective representation of KSat by field sampling is very difficult on a watershed scale, due the heterogeneity of landscape characteristics for wide land surfaces. To improve the estimation of spatial distributions of KSat at this scale, Sequential Gaussian Simulation (SGS) and Sequential Gaussian Co-Simulation (SGCS) algorithms were applied. The study was conducted in a 70 ha watershed, with 178 sampling points. In addition to KSat the database is composed of Bulk Density (BD), Total Porosity (TP), Macroporosity (Mac), Microporosity (Mic) and Organic Carbon (OC). The Land Use (LU) was also quantified as a regionalized variable. A principal component analysis generated additional two regionalized variables (PC1 and PC2). The simulations were evaluated by faithfully reproducing the stochastic and spatial characteristics of the KSat original data. To estimate the KSat, a univariate SGS was run first. The reproduction of the KSat histograms was satisfactory, while the semivariograms of the simulated fields underestimated the sill. The SGCS was run using five different secondary variables: BD; Mac; LU; PC1 and PC2. All SGCS satisfactorily represented the original KSat histogram, with a slight overestimation for some simulated fields. The semivariogram was very well reproduced for SGCS, being an indication that the process was quite efficient for all secondary variables. The smallest uncertainties were identified for the SGCS that used BD and Mac as secondary variables while the greatest uncertainties were associated with the SGS. The results of this study demonstrate that soil hydrological attributes as BD and Mac are a reliable auxiliary dataset to improve the spatial assessment of KSat by SGCS at watershed scale.

An algorithm for deriving the topology of belowground urban stormwater networks

Abstract. Belowground urban stormwater networks (BUSNs) are critical for removing excess rainfall from impervious urban areas and preventing or mitigating urban flooding. However, available BUSN data are sparse, preventing the modeling and analysis of urban hydrologic processes at regional and larger scales. We propose a novel algorithm for estimating BUSNs by drawing on concepts from graph theory and existing, extensively available land surface data, such as street network, topography, and land use/land cover. First, we derive the causal relationships between the topology of BUSNs and urban surface features based on graph theory concepts. We then apply the causal relationships and estimate BUSNs using web-service data retrieval, spatial analysis, and high-performance computing techniques. Finally, we validate the derived BUSNs in the metropolitan areas of Los Angeles, Seattle, Houston, and Baltimore in the US, where real BUSN data are partly available to the public. Results show that our algorithm can effectively capture 59 %–76 % of the topology of real BUSN data, depending on the supporting data quality. This algorithm has promising potential to support large-scale urban hydrologic modeling and future urban drainage system planning.

Temperature lapse rate estimation and snowmelt runoff simulation in a high-altitude basin

As a key parameter of hydrological process modeling, the near-surface air temperature lapse rate reflects the vertical changes in air temperature characteristics in alpine basins but often lacks the support of sufficient ground observation data. This study estimated the lapse rate of the Lhasa River Basin (LRB) from the monthly air temperature dataset (2001–2015), which was derived based on good relationships between the observed air temperature at eight gauged stations and the corresponding gridded land surface temperature of MODIS. The estimated annual average air temperature lapse rate was approximately 0.62 °C/100 m. The monthly lapse rate in different years varied seasonally in the range of 0.45–0.8 °C/100 m; the maximum was in May, and the relatively low value occurred from September to January. The snow cover in the zones with relatively low altitudes showed seasonal variation, which was consistent with the air temperature variation. Permanent snow cover appeared in the area above 5000 m and expanded with increasing elevation.

Quantitative evaluation of runoff variation and its driving forces based on multi-scale separation framework

Study regionThe Xiangjiang River (XJR) basin, China. Study focusOn multiple time scales (annual, quarterly, and monthly), there are few comprehensive and comprehensive quantitative evaluation frameworks for the factors that drive changes in watershed runoff. The focus of this study is to use the Range of Variability Approach (RVA) to quantitatively analyze the changes in the overall hydrological regime of the basin. The Budyko model and the deterministic hydrological process model (ABCD model) were then used to quantitatively separate the factors affecting runoff in the monthly window, and to clarify the differences in the runoff driving force at different time scales. New hydrological insights for the regionAfter the mutation, the runoff of the XJR basin increased by 11%, and the changes in the five groups of hydrological and ecological indicators indicated that the ecological response process of the basin was undergoing negative changes. The contributions of the driving forces on different time scales show significant differences: the climatic factor is the dominant factor of the runoff change on the interannual scale. However, on the seasonal scale, human activities have a greater weight in spring, accounting for 82 % of the total. On a monthly scale, it presents a more complicated situation. Human activities contribute significantly in February, May, and October (60 %, 52 %, and 54 %), while climate factors are the main driving factor in the remaining months (51–170 %).

Integrated modeling of hydrological processes and groundwater recharge based on land use land cover, and climate changes: A systematic review

Groundwater is the main available freshwater resource and therefore its use, management and sustainability are closely related to the Sustainable Development Goals (SDGs). However, Land Use Land Cover (LULC) and climate change are among the factors impacting groundwater recharge. The use of land-use and climate data in conjunction with hydrological models are valuable tools for assessing these impacts on river basins. This systematic review aimed at assessing the integrated modeling approach for evaluating hydrological processes and groundwater recharge based on LULC and climate change. The analysis is based on 200 peer-reviewed articles indexed in Scopus, and the Web of Science. Continuous research and the development of context-specific groundwater recharge models are essential to increase the long-term viability of water resources in any basin. The long-term impacts of natural and anthropogenic drivers on river basin interactions require integrating knowledge and modeling capabilities across biophysical responses, environmental problems, policies, economics, social, and data.

Ecological Sustainability Assessment of Water Distribution for the Maintenance of Ecosystems, their Services and Biodiversity

Water provision and distribution are subject to conflicts between users worldwide, with agriculture as a major driver of discords. Water sensitive ecosystems and their services are often impaired by man-made water shortage. Nevertheless, they are not sufficiently included in sustainability or risk assessments and neglected when it comes to distribution of available water resources. The herein presented contribution to the Sustainable Development Goals Clean Water and Sanitation (SDG 6) and Life on Land (SDG 15) is the Ecological Sustainability Assessment of Water distribution (ESAW-tool). The ESAW-tool introduces a watershed sustainability assessment that evaluates the sustainability of the water supply-demand ratio on basin level, where domestic water use and the water requirements of ecosystems are considered as most important water users. An ecological risk assessment estimates potential impacts of agricultural depletion of renewable water resources on (ground)water-dependent ecosystems. The ESAW-tool works in standard GIS applications and is applicable in basins worldwide with a set of broadly available input data. The ESAW-tool is tested in the Danube river basin through combination of high-resolution hydro-agroecological model data (hydrological land surface process model PROMET and groundwater model OpenGeoSys) and further freely available data (water use, biodiversity and wetlands maps). Based on the results, measures for more sustainable water management can be deduced, such as increase of rainfed agriculture near vulnerable ecosystems or change of certain crops. The tool can support decision making of authorities from local to national level as well as private enterprises who want to improve the sustainability of their supply chains.

Coupled modelling of hydrological processes and grassland production in two contrasting climates

Abstract. Projections of global climate models suggest that ongoing human-induced climate change will lead to an increase in the frequency of severe droughts in many important agricultural regions of the world. Eco-hydrological models that integrate current understanding of the interacting processes governing soil water balance and plant growth may be useful tools to predict the impacts of climate change on crop production. However, the validation status of these models for making predictions under climate change is still unclear, since few suitable datasets are available for model testing. One promising approach is to test models using data obtained in “space-for-time” substitution experiments, in which samples are transferred among locations with contrasting current climates in order to mimic future climatic conditions. An important advantage of this approach is that the soil type is the same, so that differences in soil properties are not confounded with the influence of climate on water balance and crop growth. In this study, we evaluate the capability of a relatively simple eco-hydrological model to reproduce 6 years (2013–2018) of measurements of soil water contents, water balance components and grass production made in weighing lysimeters located at two sites within the TERENO-SoilCan network in Germany. Three lysimeters are located at an upland site at Rollesbroich with a cool, wet climate, while three others had been moved from Rollesbroich to a warmer and drier climate on the lower Rhine valley floodplain at Selhausen. Four of the most sensitive parameters in the model were treated as uncertain within the framework of the GLUE (generalized likelihood uncertainty estimation) methodology, while the remaining parameters in the model were set according to site measurements or data in the literature. The model satisfactorily reproduced the measurements at both sites, and some significant differences in the posterior ranges of the four uncertain parameters were found. In particular, the results indicated greater stomatal conductance as well an increase in dry-matter allocation below ground and a significantly larger maximum root depth for the three lysimeters that had been moved to Selhausen. As a consequence, the apparent water use efficiency (above-ground harvest divided by evapotranspiration) was significantly smaller at Selhausen than Rollesbroich. Data on species abundance on the lysimeters provide one possible explanation for the differences in the plant traits at the two sites derived from model calibration. These observations showed that the plant community at Selhausen had changed significantly in response to the drier climate, with a significant decrease in the abundance of herbs and an increase in the proportion of grass species. The differences in root depth and leaf conductance may also be a consequence of plasticity or acclimation at the species level. Regardless of the reason, we may conclude that such adaptations introduce significant additional uncertainties into model predictions of water balance and plant growth in response to climate change.

Deriving Snow Depth From ICESat-2 Lidar Multiple Scattering Measurements: Uncertainty Analyses

The application of diffusion theory and Monte Carlo lidar radiative transfer simulations presented in Part I of this series of study suggests that snow depth can be derived from the first-, second- and third-order moments of the lidar backscattering pathlength distribution. These methods are now applied to the satellite ICESat-2 lidar measurements over the Arctic sea ice and land surfaces of Northern Hemisphere. Over the Arctic sea ice, the ICESat-2 retrieved snow depths agree well with co-located IceBridge snow radar measured values with a root-mean-square (RMS) difference of 7.8 cm or 29.2% of the mean snow depth. The terrestrial snow depths derived from ICESat-2 show drastic spatial variation of the snowpack along ICESat-2 ground tracks over the Northern Hemisphere, which are consistent with the University of Arizona (UA) and Canadian Meteorological Centre (CMC) gridded daily snow products. The RMS difference in snow depths between ICESat-2 and UA gridded daily snow products is 14 cm, or 28% of the mean UA snow depth. To better understand these results, we also discuss the possible sources of errors in ICESat-2 derived snow depths, including surface roughness within the laser footprint, atmospheric forward scattering, solar background noise, and detector dark current. Simulation results indicate that the snow depth errors would be less than 5 cm if the standard deviation of pulse spreading due to surface roughness is within 50 cm. Our results demonstrate that the ICESat-2 lidar measurements can be used to reliably derive snow depth, which is a critical geophysical parameter for cryosphere studies including sea ice thickness estimation and also provides important constraints in the modeling of terrestrial hydrological processes.

The tales that the distribution tails of non-Gaussian autocorrelated processes tell: efficient methods for the estimation of the k-length block-maxima distribution

ABSTRACT The focal point of this work is the estimation of the distribution of maxima without the use of classic extreme value theory and asymptotic properties, which may not be ideal for hydrological processes. The problem is revisited from the perspective of non-asymptotic conditions, and regards the so-called exact distribution of block-maxima of finite-sized k-length blocks. First, we review existing non-asymptotic approaches/models, and introduce an alternative and fast model. Next, through simulations and comparisons (using asymptotic and non-asymptotic models), involving intermittent processes (e.g. rainfall), we highlight the capability of non-asymptotic approaches to model the distribution of maxima with reduced uncertainty and variability. Finally, we discuss an alternative use of such models that concerns the theoretical estimation of the multi-scale probability of obtaining a zero value. This is a useful finding when the scope is the multi-scale modelling of intermittent hydrological processes (e.g. intensity–duration–frequency models). The work also provides step-by-step recipes and an R package.