Validation Of Hydrological Models Research Articles

Diagnosing modeling errors in global terrestrial water storage interannual variability

Abstract. Terrestrial water storage (TWS) is an integrative hydrological state that is key for our understanding of the global water cycle. The TWS observation from the GRACE missions has, therefore, been instrumental in the calibration and validation of hydrological models and understanding the variations in the hydrological storage. The models, however, still show significant uncertainties in reproducing observed TWS variations, especially for the interannual variability (IAV) at the global scale. Here, we diagnose the regions dominating the variance in globally integrated TWS IAV and the sources of the errors in two data-driven hydrological models that were calibrated against global TWS, snow water equivalent, evapotranspiration, and runoff data. We used (1) a parsimonious process-based hydrological model, the Strategies to INtegrate Data and BiogeochemicAl moDels (SINDBAD) framework and (2) a machine learning, physically based hybrid hydrological model (H2M) that combines a dynamic neural network with a water balance concept. While both models agree with the Gravity Recovery and Climate Experiment (GRACE) that global TWS IAV is largely driven by the semi-arid regions of southern Africa, the Indian subcontinent and northern Australia, and the humid regions of northern South America and the Mekong River basin, the models still show errors such as the overestimation of the observed magnitude of TWS IAV at the global scale. Our analysis identifies modeling error hotspots of the global TWS IAV, mostly in the tropical regions including the Amazon, sub-Saharan regions, and Southeast Asia, indicating that the regions that dominate global TWS IAV are not necessarily the same as those that dominate the error in global TWS IAV. Excluding those error hotspot regions in the global integration yields large improvements in the simulated global TWS IAV, which implies that model improvements can focus on improving processes in these hotspot regions. Further analysis indicates that error hotspot regions are associated with lateral flow dynamics, including both sub-pixel moisture convergence and across-pixel lateral river flow, or with interactions between surface processes and groundwater. The association of model deficiencies with land processes that delay the TWS variation could, in part, explain why the models cannot represent the observed lagged response of TWS IAV to precipitation IAV in hotspot regions that manifest as errors in global TWS IAV. Our approach presents a general avenue to better diagnose model simulation errors for global data streams to guide efficient and focused model development for regions and processes that matter the most.

Comparison of manual snow water equivalent (SWE) measurements: seeking the reference for a true SWE value in a boreal biome

Abstract. Manual measurement of snow water equivalent (SWE) is still important today for several applications such as hydrological model validation. This measurement can be performed with different types of snow tube sampler or by a snow pit. Although these methods have been performed for several decades, there is an apparent lack of information required to have a consensus regarding the best reference for “true” SWE. We define and estimate the uncertainty and measurement error of different methods of snow pits and snow samplers used in a boreal biome. Analysis was based upon measurements taken over five consecutive winters (2016–2020) from the same flat and open area. This study compares two snow pit methods and three snow samplers. In addition to including the Standard Federal sampler (SFS), this study documents the first use of two new large diameter samplers, the Hydro-Québec sampler (HQS) and Université Laval sampler (ULS). Large diameter samplers had the lowest uncertainty (2.6 % to 4.0 %). Snow pit methods had higher uncertainty due to instruments (7.1 % to 11.4 %), close to that of the SFS (mean = 10.4 %). Given its larger collected snow volume for estimating SWE and its lower uncertainty, we posit that ULS represents the most appropriate method of reference for “true” SWE. By considering ULS as the reference in calculating mean bias error (MBE), different snow pit methods overestimated SWE by 16.6 % to 26.2 %, which was much higher than SFS (8.4 %). This study suggests that large diameter samplers are the best method for estimating “true” SWE in a boreal biome.

Extensive Evaluation of Four Satellite Precipitation Products and Their Hydrologic Applications over the Yarlung Zangbo River

Satellite remote sensing precipitation products with high temporal–spatial resolution and large area coverage have great potential in hydrometeorological research. This paper analyzes the performance of four satellite products from 2000 to 2008 in the Yarlung Zangbo River Basin, namely the Tropical Rainfall Measuring Mission (TRMM), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), and Climate Prediction Center morphing method (CMORPH). The four products are evaluated from three aspects: spatial distribution, temporal characteristics, and hydrological simulation. The results show that: (1) the four products exhibit similar annual and daily precipitation patterns, with the highest daily precipitation accuracy concentrated in the center, followed by the east and west; (2) TRMM, CHIRPS, and CMORPH exhibit the largest positive bias for monthly precipitation estimation in December, while PERSIANN shows the largest positive bias in July. All products overestimate the precipitation of 0.1–5 mm/d, and underestimate the precipitation above 5 mm/d, especially for PERSIANN; (3) certain Products tend to perform better than others at elevations of 3000–4000 m and in relatively humid zones. TRMM shows relatively stable performance for various elevation and climate zones; (4) for hydrological model validation, TRMM has the best performance during the calibration period, although it is inferior to CHIRPS during the validation period. Overall, TRMM has the highest applicability in the Yarlung Zangbo River Basin; however, its impact on the uncertainty of hydrological modeling needs to be further studied.

Multivariate assimilation of satellite-based leaf area index and ground-based river streamflow for hydrological modelling of irrigated watersheds using SWAT+

• Performance of the SWAT + model with and without data assimilation (DA) is assessed. • Some modules of SWAT + source code are modified to be connected to the DA routine. • Multivariate and univariate assimilations of discharge and LAI data are compared. • High-resolution crop mapping is essential for LAI assimilation in irrigated watersheds. • Joint assimilation of streamflow and Sentinel-2′s LAI results in better simulation of streamflow, vegetation, and irrigation. Vegetation dynamics have different effects on the terrestrial water cycle and therefore play an important role in catchment-scale biophysical and hydrological modeling. Meanwhile, validation of hydrological models of irrigated watersheds concerning vegetation dynamics has rarely been studied. In this study, we propose a combinatorial approach for modelling irrigated watersheds; at first, based on Sentinel-2 (S2), we provide crop/land-use mapping; then we retrieve S2-based leaf area index (LAI) through the neural network algorithm and the PROSAIL model; finally, we employ sequential particle filter (PF) technique to explore the benefits of jointly assimilating high-resolution S2-LAI as well as in-situ river discharge observations for improving the predictive accuracy of SWAT + model. Moreover, we explain how SWAT + source code must be modified to implement the assimilation procedure. The developed methodology is applied to an irrigated watershed in Iran; we provide 66 raster LAI maps with a spatial resolution of 20 m for the study area related to January to December of 2019 as well as crop/land-use mapping of 2019 with a spatial resolution of 10 m. Results show that improvements in the hydrological simulation of LAI, evapotranspiration, and river discharge are achieved when we apply multivariate assimilation of S2-LAI and streamflow observations, compared to univariate assimilation scenarios. Results also reveal that LAI assimilation has a significant influence on the estimation of irrigation volume and timing.

Ardières‐Morcille in the Beaujolais, France: A research catchment dedicated to study of the transport and impacts of diffuse agricultural pollution in rivers

AbstractThe 8‐km2 Morcille catchment, which is a sub‐catchment of the 150‐km2 Ardières catchment in the Beaujolais region of France, is one of the first sites in Europe where research has been conducted on surface water contamination by pesticides. A consolidated hydrological and chemical dataset has been set up with data collected since 2002 on the Morcille River and since 2011 on the Ardières River. Additional data on the ecotoxicological and ecological impacts of pesticides on aquatic microbial communities and macroinvertebrates has also been recorded in both rivers since 2005. The ‘Site Atelier Ardières‐Morcille Dataset’ described here combines rainfall and stream water height measurements at gauged stations with concentrations of two trace elements and nine pesticides (mainly herbicides, fungicides and some of their metabolites) in both the Ardières and Morcille rivers. All contaminant concentration data showed spatial and temporal variability in water quality associated with pesticide use and rainfall patterns. This long‐term monitoring framework made it possible to estimate the persistence of two herbicides (diuron and norflurazon) after legislation banning them. It took 4 years for diuron and more than 10 years for norflurazon concentrations to fall below 0.1 μg/L. Concurrent biological data showed a gradient of impacts consistent with chemical anthropogenic pressure, and rapid recovery of phototrophic microbial communities after the diuron ban. Finally, monitoring data on pesticide transport in a grassed strip set on a slope of the Morcille catchment confirmed that vegetative filter strips can effectively reduce diuron fluxes in surface runoff (>80% abatement) and infiltration water (>70% abatement). The full dataset offers a valuable resource for the validation of hydrological models and the development of global approaches to better understand the pressure–transport–exposure–impact chain and aquatic community resilience at the small catchment scale.

A Compilation of Snow Cover Datasets for Svalbard: A Multi-Sensor, Multi-Model Study

Reliable and accurate mapping of snow cover are essential in applications such as water resource management, hazard forecasting, calibration and validation of hydrological models and climate impact assessments. Optical remote sensing has been utilized as a tool for snow cover monitoring over the last several decades. However, consistent long-term monitoring of snow cover can be challenging due to differences in spatial resolution and retrieval algorithms of the different generations of satellite-based sensors. Snow models represent a complementary tool to remote sensing for snow cover monitoring, being able to fill in temporal and spatial data gaps where a lack of observations exist. This study utilized three optical remote sensing datasets and two snow models with overlapping periods of data coverage to investigate the similarities and discrepancies in snow cover estimates over Nordenskiöld Land in central Svalbard. High-resolution Sentinel-2 observations were utilized to calibrate a 20-year MODIS snow cover dataset that was subsequently used to correct snow cover fraction estimates made by the lower resolution AVHRR instrument and snow model datasets. A consistent overestimation of snow cover fraction by the lower resolution datasets was found, as well as estimates of the first snow-free day (FSFD) that were, on average, 10–15 days later when compared with the baseline MODIS estimates. Correction of the AVHRR time series produced a significantly slower decadal change in the land-averaged FSFD, indicating that caution should be exercised when interpreting climate-related trends from earlier lower resolution observations. Substantial differences in the dynamic characteristics of snow cover in early autumn were also present between the remote sensing and snow model datasets, which need to be investigated separately. This work demonstrates that the consistency of earlier low spatial resolution snow cover datasets can be improved by using current-day higher resolution datasets.

Spatio-temporal analysis of remotely sensed and hydrological model soil moisture in the small Jičinka River catchment in Czech Republic

Abstract The knowledge of spatio-temporal dynamics of soil moisture within the catchment is very important for rainfall–runoff modelling in flood forecasting. In this study the comparison between remotely sensed soil moisture and soil moisture estimated from the SHETRAN hydrological model was performed for small and flashy Jičinka River catchment (75.9 km2) in the Czech Republic. Due to a relatively coarse spatial resolution of satellite data, the satellite soil moisture data were downscaled, by applying the method developed by Qu et al. (2015). The sub-grid variability of soil moisture was estimated on the basis of the mean soil moisture for the grid cell and the known hydraulic soil properties. The SHETRAN model was calibrated and verified to the observed streamflow hydrographs at the catchment outlet. The good correlation between the two different soil moisture information was obtained according to the majority of applied criteria. The results of the evaluation criteria indicate that the downscaled remotely sensed soil moisture data can be used as additional criteria for the calibration and validation of hydrological models for small catchments and can contribute to a better estimation of parameters, to reduce uncertainties of hydrological models and improve runoff simulations.

Statistical interpolation method for water quality data to improve water quality calibration and validation in watershed models

The importance of water resources forecasting is growing due to changes in precipitation caused by climate change and with hydrologic models are being used mainly for water resource management. Calibration and validation through adjustment of parameters are important processes for increasing the reliability of the application of hydrologic models. This is different from flow rates that can be estimated in real time through level-flow rating curve. The reliability of data depends on observation; therefore, using data with a short can increase the reliability of the results. In this study, daily loads of suspended solids (SS), total nitrogen (TN), and total phosphorous (TP) were generated using three statistical methods: general method, perfect prognosis method, and model output statistics (MOS). Fitness of calibration and validation of hydrologic models were then evaluated using correlation, R2, and anomaly analysis. When these three methods were applied to the Geumho and Hwang rivers, perfect prognosis method and the MOS method showed better results than the general method. For the Geumho River, correlation and R2 improved by up to 0.08% for SS, 0.04% for TN, and 0.12% for TP. For Hwang River, results improved by up to 27% for SS, 12% for TN, and 15% for TP. When the anomaly analysis results were represented by correlation and R2, they showed significant improvements of up to 0.86 in SS, 0.62 in TN, and 0.57 in TP for the Geumho River and up to 0.58 in SS, 0.49 in TN, and 0.49 in TP for the Hwang River. The statistical method suggested in this study can be applied when observation for the water quality data is long or data are insufficient.

A Decade of Water Storage Changes Across the Contiguous United States From GPS and Satellite Gravity

AbstractIncreased climate variability is driving changes in water storage across the contiguous United States (CONUS). Observational estimates of these storage changes are important for validation of hydrological models and predicting future water availability. We estimate CONUS terrestrial water storage anomalies (TWSA) from 2007–2017 using Global Positioning System (GPS) displacements, constrained by lower‐resolution TWSA observations from Gravity Recovery and Climate Experiment (GRACE) satellite gravity—a combination that provides higher spatiotemporal resolution than previous estimates. The relative contribution of seasonal, interannual, and subseasonal TWSA varies widely across CONUS watersheds, with implications for regional water security. Separately, we find positive correlation between TWSA and the El Niño/Southern Oscillation in the southeastern Texas‐Gulf and South Atlantic‐Gulf watersheds and an unexpected negative correlation in the southwest. In the western United States, atmospheric rivers (ARs) drive a large fraction of subseasonal TWSA, with the top 5% of ARs contributing 73% of total AR‐related TWSA increases.

Forcing the Penman-Montheith Formulation with Humidity, Radiation, and Wind Speed Taken from Reanalyses, for Hydrologic Modeling

The Penman-Monteith reference evapotranspiration (ET0) formulation was forced with humidity, radiation, and wind speed (HRW) fields simulated by four reanalyses in order to simulate hydrologic processes over six mid-sized nivo-pluvial watersheds in southern Quebec, Canada. The resulting simulated hydrologic response is comparable to an empirical ET0 formulation based exclusively on air temperature. However, Penman-Montheith provides a sounder representation of the existing relations between evapotranspiration fluctuations and climate drivers. Correcting HRW fields significantly improves the hydrologic bias over the pluvial period (June to November). The latter did not translate into an increase of the hydrologic performance according to the Kling-Gupta Efficiency (KGE) metric. The suggested approach allows for the implementation of physically-based ET0 formulations where HRW observations are insufficient for the calibration and validation of hydrologic models and a potential reinforcement of the confidence affecting the projection of low flow regimes and water availability.

An 11-year (2007–2017) soil moisture and precipitation dataset from the Kenaston Network in the Brightwater Creek basin, Saskatchewan, Canada

Abstract. Soil moisture and precipitation have been monitored in a hydrometeorological network situated within the Brightwater Creek basin, east of Kenaston, Saskatchewan, Canada, since 2007. The majority of the prairie landscape is annually cropped with some sections in pasture. This agricultural region is ideal for remote-sensing validation and calibration and, in conjunction with the flux tower situated within the network, hydrological model validation. Remote-sensing validation collaborations have included the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) and NASA's Soil Moisture Active Passive (SMAP). The network was developed at two spatial scales, one high-resolution set of sites installed over a 10 km × 10 km region and a second installed over 40 km × 40 km. The sites are all similar in design with three instrument depths for soil moisture and temperature, as well as precipitation measurement. The 2007–2017 dataset published in this paper has gone through a quality control review process, which involved both automated and manual processes. The dataset is limited to the summer months (1 May–30 September) due to the uncertainties and complexities of measurement in frozen soils and the freeze–thaw period each year. Data discussed in this publication are available at https://doi.org/10.20383/101.0116, and data beyond 2017 can be requested from the corresponding author.

Mesoscale Soil Moisture Patterns Revealed Using a Sparse In Situ Network and Regression Kriging

AbstractSoil moisture spatial patterns with length scales of 1‐100 km influence hydrological, ecological, and agricultural processes, but the footprint or support volume of existing monitoring systems, for example, satellite‐based radiometers and sparse in situ monitoring networks, is often either too large or too small to effectively observe these mesoscale patterns. This measurement scale gap hinders our understanding of soil water processes and complicates calibration and validation of hydrologic models and soil moisture satellites. One possible solution is to utilize geostatistical techniques that have proven effective for mapping static patterns in soil properties. The objective of this study was to determine how effectively dynamic, mesoscale soil moisture patterns can be mapped by applying regression kriging to the data from a sparse, large‐scale in situ network. The fully automated system developed here uses several data sets: daily soil moisture measurements from the Oklahoma Mesonet, sand content estimates from the Natural Resource Conservation Service Soil Survey Geographic Database, and an antecedent precipitation index computed from National Weather Service multisensor precipitation estimates. A multiple linear regression model is fitted daily to the observed data, and the residuals of that model are used in a semivariogram estimation and kriging routine to produce daily statewide maps of soil moisture at 5‐, 25‐, and 60‐cm depths at 800‐m resolution. During over 3 years of operation, this mapping system has revealed complex, dynamic, and depth‐specific mesoscale patterns, reflecting the shifting influences of both soil texture and precipitation, with a mean absolute error of ≤0.0576 cm3/cm3 across all three depths.

Statistics for sample splitting for the calibration and validation of hydrological models

Hydrological models have been widely applied in flood forecasting, water resource management and other environmental sciences. Most hydrological models calibrate and validate parameters with available records. However, the first step of hydrological simulation is always to quantitatively and objectively split samples for use in calibration and validation. In this paper, we have proposed a framework to address this issue through a combination of a hierarchical scheme through trial and error method, for systematic testing of hydrological models, and hypothesis testing to check the statistical significance of goodness-of-fit indices. That is, the framework evaluates the performance of a hydrological model using sample splitting for calibration and validation, and assesses the statistical significance of the Nash–Sutcliffe efficiency index (Ef), which is commonly used to assess the performance of hydrological models. The sample splitting scheme used is judged as acceptable if the Ef values exceed the threshold of hypothesis testing. According to the requirements of the hierarchical scheme for systematic testing of hydrological models, cross calibration and validation will help to increase the reliability of the splitting scheme, and reduce the effective range of sample sizes for both calibration and validation. It is illustrated that the threshold of Ef is dependent on the significance level, evaluation criteria (both regarded as the population), distribution type, and sample size. The performance rating of Ef is largely dependent on the evaluation criteria. Three types of distributions, which are based on an approximately standard normal distribution, a Chi square distribution, and a bootstrap method, are used to investigate their effects on the thresholds, with two commonly used significance levels. The highest threshold is from the bootstrap method, the middle one is from the approximately standard normal distribution, and the lowest is from the Chi square distribution. It was found that the smaller the sample size, the higher the threshold values are. Sample splitting was improved by providing more records. In addition, outliers with a large bias between the simulation and the observation can affect the sample values of Ef, and hence the output of the sample splitting scheme. Physical hydrology processes and the purpose of the model should be carefully considered when assessing outliers. The proposed framework in this paper cannot guarantee the best splitting scheme, but the results show the necessary conditions for splitting schemes to calibrate and validate hydrological models from a statistical point of view.

Evaluation of Surface Runoff Generation Processes Using a Rainfall Simulator: A Small Scale Laboratory Experiment

Nowadays, rainfall simulators are being used by many researchers in field or laboratory experiments. The main objective of most of these experiments is to better understand the underlying runoff generation processes, and to use the results in the process of calibration and validation of hydrological models. Many research groups have assembled their own rainfall simulators, which comply with their understanding of rainfall processes, and the requirements of their experiments. Most often, the existing rainfall simulators differ mainly in the size of the irrigated area, and the way they generate rain drops. They can be characterized by the accuracy, with which they produce a rainfall of a given intensity, the size of the irrigated area, and the rain drop generating mechanism. Rainfall simulation experiments can provide valuable information about the genesis of surface runoff, infiltration of water into soil and rainfall erodibility. Apart from the impact of physical properties of soil, its moisture and compaction on the generation of surface runoff and the amount of eroded particles, some studies also investigate the impact of vegetation cover of the whole area of interest. In this study, the rainfall simulator was used to simulate the impact of the slope gradient of the irrigated area on the amount of generated runoff and sediment yield. In order to eliminate the impact of external factors and to improve the reproducibility of the initial conditions, the experiments were conducted in laboratory conditions. The laboratory experiments were carried out using a commercial rainfall simulator, which was connected to an external peristaltic pump. The pump maintained a constant and adjustable inflow of water, which enabled to overcome the maximum volume of simulated precipitation of 2.3 l, given by the construction of the rainfall simulator, while maintaining constant characteristics of the simulated precipitation. In this study a 12-minute rainfall with a constant intensity of 5 mm/min was used to irrigate a corrupted soil sample. The experiment was undertaken for several different slopes, under the condition of no vegetation cover. The results of the rainfall simulation experiment complied with the expectations of a strong relationship between the slope gradient, and the amount of surface runoff generated. The experiments with higher slope gradients were characterised by larger volumes of surface runoff generated, and by shorter times after which it occurred. The experiments with rainfall simulators in both laboratory and field conditions play an important role in better understanding of runoff generation processes. The results of such small scale experiments could be used to estimate some of the parameters of complex hydrological models, which are used to model rainfall-runoff and erosion processes at catchment scale.

Are Model Transferability And Complexity Antithetical? Insights From Validation of a Variable‐Complexity Empirical Snow Model in Space and Time

AbstractThe related challenges of predictions in ungauged basins and predictions in ungauged climates point to the need to develop environmental models that are transferable across both space and time. Hydrologic modeling has historically focused on modelling one or only a few basins using highly parameterized conceptual or physically based models. However, model parameters and structures have been shown to change significantly when calibrated to new basins or time periods, suggesting that model complexity and model transferability may be antithetical. Empirical space‐for‐time models provide a framework within which to assess model transferability and any tradeoff with model complexity. Using 497 SNOTEL sites in the western U.S., we develop space‐for‐time models of April 1 SWE and Snow Residence Time based on mean winter temperature and cumulative winter precipitation. The transferability of the models to new conditions (in both space and time) is assessed using non‐random cross‐validation tests with consideration of the influence of model complexity on transferability. As others have noted, the algorithmic empirical models transfer best when minimal extrapolation in input variables is required. Temporal split‐sample validations use pseudoreplicated samples, resulting in the selection of overly complex models, which has implications for the design of hydrologic model validation tests. Finally, we show that low to moderate complexity models transfer most successfully to new conditions in space and time, providing empirical confirmation of the parsimony principal.

Sampling scheme optimization to map soil depth to petrocalcic horizon at field scale

Soil depth has played a key role in the development of soil survey, implementation of soil-specific management and validation of hydrological models. Generally, soil depth at field scale is difficult to map due to complex interactions of factors of soil formation at field scale. As a result, the conventional sampling schemes to map soil depth are generally laborious, time consuming and expensive. In this study, we presented, tested and evaluated a method to optimize the sampling scheme to map soil depth to petrocalcic horizon at field scale. The method was tested with real data at four agricultural fields localized in the southeast Pampas plain of Argentina. The purpose of the method was to minimize the sample dataset size to map soil depth to petrocalcic horizon based on ordinary cokriging, five calibration sample sizes (returned by Conditioned Latin hypercube –cLHS-), and apparent electrical conductivity (ECa) or elevation as variables of auxiliary information.The results suggest that (i) only 30% of samples collected on a 30-m grid are required to provide high prediction accuracy (R2>0.95) to map soil depth to petrocalcic horizon; (ii) an independent validation dataset based on 50% of the samples on a 30-m grid is adequate to validate the most realistic accuracy estimate; and (iii) ECa and elevation, as variables of auxiliary information, are sufficient to map soil depth to petrocalcic horizon. The method proposed provides a significant improvement over conventional to map soil depth and allows reducing cost, time and field labour. Extrapolation of the results to other areas needs to be tested.

Low spatial and inter-annual variability of evaporation from a year-round intensively grazed temperate pasture system

Ecosystem scale measurements of evaporation (E) from intensively managed pasture systems are important for informing water resource decision making and validation of hydrologic models and remote sensing methods. We measured E from a year round intensively grazed temperate pasture system in New Zealand using the eddy covariance method for three years (2012⿿2014). Evaporation varied by less than 3% both spatially (770⿿783mm) and temporally (759⿿776mm) at an annual scale. The low spatial and temporal variation largely occurred because E was strongly controlled by net radiation (r2=0.81, p<0.01, daytime, half-hourly), which did not vary much between sites and years. However, E was strongly limited when volumetric moisture content (VMC) declined below permanent wilting point causing a strong reduction in the decoupling coefficient and an increase in the Bowen ratio. Grazing events appeared to have no effect on E during autumn and winter but reduced E by up to 5% during summer and spring while complete removal of vegetation during autumn herbicide application reduced E by ⿼30%. This implied that over the pasture regrowth period soil water evaporation (ES) could provide up to 70% of E relative to a vegetated site (during autumn) and, given that grazing events removed about 60% of leaf area, these findings suggest ES was likely able to compensate for decreased transpiration post-grazing. Agreement between measured E (EEC) and FAO-56 reference crop E (Eo) was good when soil moisture limitation was not occurring. However, during periods of soil moisture limitation Eo exceeded EEC and a correction factor was needed. We trialled the water stress coefficient (Ks) and a simple three bin VMC correction factor (KVMC) and found the KVMC approach worked better at a daily and monthly scale while both approaches worked well at an annual scale.

MODSNOW-Tool: an operational tool for daily snow cover monitoring using MODIS data

Spatially distributed snow cover information is important for the assessment of climate-related variability of water resources and for calibration and validation of hydrological models in snow-dominated regions. Near-real-time snow cover data can be valuable for short term to seasonal streamflow prediction. Such information can be extracted using remote sensing techniques with good accuracy. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data, meanwhile available for more than 15 years, have been shown to be useful for monitoring snow cover extent in remote areas. This data can be processed and used with only 2-day delay, which is sufficient for many water resources management purposes. However, processing remote sensing data require knowledge and computational skills to handle large amounts of data. Moreover, cloud obscuration in optical remote sensing such as MODIS may lead to data gaps. These limitations impede the use of the freely available MODIS data for water resources management in developing countries, particularly in snow-dominated mountainous regions. To overcome this, we present the all-in-one software package MODSNOW-Tool. It processes raw MODIS data and eliminates cloud cover using advanced cloud removal algorithms. The ready-to-use output of the MODSNOW-Tool is a cloud-free snow cover map and a daily report, which includes spatiotemporal snow statistics for pre-defined river basins. The accuracy of cloud-eliminated MODSNOW snow cover maps was validated for 84 almost cloud-free days in the Karadarya river basin in Central Asia, and an average accuracy of 94 % was achieved. The MODSNOW-Tool can be used in operational and non-operational mode. In the operational mode, the tool is set up as a scheduled task on a local computer allowing automatic execution without user interaction and delivers snow cover maps on a daily basis. In the non-operational mode, the tool can be used to process historical time series of snow cover maps from MODIS.

Monthly and sub-monthly hydrological variability: in-orbit validation by GRACE level 1B observations

In this study, we present an approach to validate hydrological model output directly on the level of GRACE level 1B observations by analyzing K-band range-rate residuals. Modeled water mass variations are converted to simulated satellite observations and subtracted from the original measurements. This procedure bypasses the downward continuation and filtering steps generally required for water cycle analysis on the basis of gravity field maps. The goal of the study is twofold: (1) we demonstrate the feasibility of using residuals analysis for hydrological model validation in general and (2) we focus on the potential of the approach to investigate the signal content of temporally high-frequent (daily) modeled hydrological mass variations. In addition to the output of three different hydrological process models, we study mass changes computed from two different daily GRACE products. GRACE here serves as a reference, but its spatial resolution is limited and the daily models are not computed independently. Regarding aspect (1), our results show that the agreement of each of the models with GRACE varies strongly depending on geographical location. Aspect (2) is not only interesting for model validation, but it is also important in the context of improving the GRACE de-aliasing concept. We demonstrate that not only the daily GRACE models, but also the daily hydrological model output contains information on time scales smaller than 1 month. Realistically modeled or observed short-term hydrological mass changes may serve as additional de-aliasing product for GRACE and thus contribute to increasing the accuracy and resolution of future GRACE products.

Modeling future changes in the North-Estonian hydropower production by using SWAT

Climate change is altering temperature, precipitation, and other climatic parameters, affecting sectors dependent on water resources, e.g. energy production. The purpose of this study is to analyze the possible influences of climate change on hydropower potential in North Estonia. In Estonian run-of-river hydropower plants, energy comes mainly from water volume. Thus, changes in hydropower production are related to changes in river runoff. The Soil and Water Assessment Tool (SWAT) model is used to study runoff responses to climate change in Kunda, Keila and Valgejõe river basins. A sequential uncertainty fitting algorithm is used for calibration and validation of hydrological models. Two modeling studies from EURO-CORDEX high-resolution simulations are used: RACMO regional climate model (RCM) from the Netherlands (KNMI) and HIRHAM5 RCM from Denmark (DMI). Hydrological model efficiency is evaluated with coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE) and percent bias (PBIAS). The NSE values range from 0.71 to 0.77 during calibration and validation. The PBIAS reveals no significant bias. Daily discharge data of the baseline period (1971–2000) and the future period (2071–2100) for KNMI and DMI scenarios reveal an overall increase in hydropower potential. Larger changes are predicted by the DMI model, while KNMI prediction is lower, 25% and 45% respectively.