DRYP 1.0: A parsimonious hydrological model of DRYland Partitioning of the water balance

Abstract. Dryland regions are characterized by water scarcity and are facing major challenges under climate change. One difficulty is anticipating how rainfall will be partitioned into evaporative losses, groundwater, soil moisture and runoff (the water balance) in the future, which has important implications for water resources and dryland ecosystems. However, in order to effectively estimate the water balance, hydrological models in drylands need to capture the key processes at the appropriate spatiotemporal scales including spatially restricted and temporally brief rainfall, high evaporation rates, transmission losses and focused groundwater recharge. Lack of available data and the high computational costs of explicit representation of ephemeral surface-groundwater interactions restrict the usefulness of most hydrological models in these environments. Therefore, here we have developed a parsimonious hydrological model (DRYP) that incorporates the key processes of water partitioning in dryland regions, and we tested it in the data-rich Walnut Gulch Experimental Watershed against measurements of streamflow, soil moisture and evapotranspiration. Overall, DRYP showed skill in quantifying the main components of the dryland water balance including monthly observations of streamflow (Nash efficiency (NSE) ~0.7), evapotranspiration (NSE > 0.6) and soil moisture (NSE ~0.7). The model showed that evapotranspiration consumes > 90 % of the total precipitation input to the catchment, and that < 1 % leaves the catchment as streamflow. Greater than 90 % of the overland flow generated in the catchment is lost through ephemeral channels as transmission losses. However, only ~35 % of the total transmission losses percolate to the groundwater aquifer as focused groundwater recharge, whereas the rest is lost to the atmosphere as riparian evapotranspiration. Overall, DRYP is a modular, versatile and parsimonious Python-based model which can be used to anticipate and plan for climatic and anthropogenic changes to water fluxes and storage in dryland regions

Download Full-text

DRYP 1.0: a parsimonious hydrological model of DRYland Partitioning of the water balance

Geoscientific Model Development ◽

10.5194/gmd-14-6893-2021 ◽

2021 ◽

Vol 14 (11) ◽

pp. 6893-6917

Author(s):

E. Andrés Quichimbo ◽

Michael Bliss Singer ◽

Katerina Michaelides ◽

Daniel E. J. Hobley ◽

Rafael Rosolem ◽

...

Keyword(s):

Soil Moisture ◽

Water Balance ◽

Groundwater Recharge ◽

Overland Flow ◽

Hydrological Model ◽

Hydrological Models ◽

Distributed Hydrological Model ◽

Transmission Losses ◽

Total Transmission ◽

Groundwater Aquifer

Abstract. Dryland regions are characterised by water scarcity and are facing major challenges under climate change. One difficulty is anticipating how rainfall will be partitioned into evaporative losses, groundwater, soil moisture, and runoff (the water balance) in the future, which has important implications for water resources and dryland ecosystems. However, in order to effectively estimate the water balance, hydrological models in drylands need to capture the key processes at the appropriate spatio-temporal scales. These include spatially restricted and temporally brief rainfall, high evaporation rates, transmission losses, and focused groundwater recharge. Lack of available input and evaluation data and the high computational costs of explicit representation of ephemeral surface–groundwater interactions restrict the usefulness of most hydrological models in these environments. Therefore, here we have developed a parsimonious distributed hydrological model for DRYland Partitioning (DRYP). The DRYP model incorporates the key processes of water partitioning in dryland regions with limited data requirements, and we tested it in the data-rich Walnut Gulch Experimental Watershed against measurements of streamflow, soil moisture, and evapotranspiration. Overall, DRYP showed skill in quantifying the main components of the dryland water balance including monthly observations of streamflow (Nash–Sutcliffe efficiency, NSE, ∼ 0.7), evapotranspiration (NSE > 0.6), and soil moisture (NSE ∼ 0.7). The model showed that evapotranspiration consumes > 90 % of the total precipitation input to the catchment and that < 1 % leaves the catchment as streamflow. Greater than 90 % of the overland flow generated in the catchment is lost through ephemeral channels as transmission losses. However, only ∼ 35 % of the total transmission losses percolate to the groundwater aquifer as focused groundwater recharge, whereas the rest is lost to the atmosphere as riparian evapotranspiration. Overall, DRYP is a modular, versatile, and parsimonious Python-based model which can be used to anticipate and plan for climatic and anthropogenic changes to water fluxes and storage in dryland regions.

Download Full-text

Estimating surface runoff and groundwater recharge in an urban catchment using a water balance approach

Hydrogeology Journal ◽

10.1007/s10040-021-02385-1 ◽

2021 ◽

Vol 29 (7) ◽

pp. 2411-2428

Author(s):

Robin K. Weatherl ◽

Maria J. Henao Salgado ◽

Maximilian Ramgraber ◽

Christian Moeck ◽

Mario Schirmer

Keyword(s):

Soil Moisture ◽

Water Balance ◽

Groundwater Recharge ◽

Surface Runoff ◽

Urban Areas ◽

Water Cycle ◽

Land Use Changes ◽

Curve Number ◽

Hydrograph Separation ◽

Balance Approach

AbstractLand-use changes often have significant impact on the water cycle, including changing groundwater/surface-water interactions, modifying groundwater recharge zones, and increasing risk of contamination. Surface runoff in particular is significantly impacted by land cover. As surface runoff can act as a carrier for contaminants found at the surface, it is important to characterize runoff dynamics in anthropogenic environments. In this study, the relationship between surface runoff and groundwater recharge in urban areas is explored using a top-down water balance approach. Two empirical models were used to estimate runoff: (1) an updated, advanced method based on curve number, followed by (2) bivariate hydrograph separation. Modifications were added to each method in an attempt to better capture continuous soil-moisture processes and explicitly account for runoff from impervious surfaces. Differences between the resulting runoff estimates shed light on the complexity of the rainfall–runoff relationship, and highlight the importance of understanding soil-moisture dynamics and their control on hydro(geo)logical responses. These results were then used as input in a water balance to calculate groundwater recharge. Two approaches were used to assess the accuracy of these groundwater balance estimates: (1) comparison to calculations of groundwater recharge using the calibrated conceptual HBV Light model, and (2) comparison to groundwater recharge estimates from physically similar catchments in Switzerland that are found in the literature. In all cases, recharge is estimated at approximately 40–45% of annual precipitation. These conditions were found to closely echo those results from Swiss catchments of similar characteristics.

Download Full-text

Predicting Rainfall and Runoff Through Satellite Soil Moisture Data and SWAT Modelling for a Poorly Gauged Basin in Iran

Water ◽

10.3390/w11030594 ◽

2019 ◽

Vol 11 (3) ◽

pp. 594 ◽

Cited By ~ 9

Author(s):

Majid Fereidoon ◽

Manfred Koch ◽

Luca Brocca

Keyword(s):

Soil Moisture ◽

Hydrological Model ◽

Assessment Tool ◽

Swat Model ◽

Rainfall Data ◽

Coefficient Of Determination ◽

Hydrological Models ◽

Streamflow Prediction ◽

Soil Moisture Data ◽

Monthly Runoff

Hydrological models are widely used for many purposes in water sector projects, including streamflow prediction and flood risk assessment. Among the input data used in such hydrological models, the spatial-temporal variability of rainfall datasets has a significant role on the final discharge estimation. Therefore, accurate measurements of rainfall are vital. On the other hand, ground-based measurement networks, mainly in developing countries, are either nonexistent or too sparse to capture rainfall accurately. In addition to in-situ rainfall datasets, satellite-derived rainfall products are currently available globally with high spatial and temporal resolution. An innovative approach called SM2RAIN that estimates rainfall from soil moisture data has been applied successfully to various regions. In this study, first, soil moisture content derived from the Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E) is used as input into the SM2RAIN algorithm to estimate daily rainfall (SM2R-AMSRE) at different sites in the Karkheh river basin (KRB), southwest Iran. Second, the SWAT (Soil and Water Assessment Tool) hydrological model was applied to simulate runoff using both ground-based observed rainfall and SM2R-AMSRE rainfall as input. The results reveal that the SM2R-AMSRE rainfall data are, in most cases, in good agreement with ground-based rainfall, with correlations R ranging between 0.58 and 0.88, though there is some underestimation of the observed rainfall due to soil moisture saturation not accounted for in the SM2RAIN equation. The subsequent SWAT-simulated monthly runoff from SM2R-AMSRE rainfall data (SWAT-SM2R-AMSRE) reproduces the observations at the six gauging stations (with coefficient of determination, R² > 0.71 and NSE > 0.56), though with slightly worse performances in terms of bias (Bias) and root-mean-square error (RMSE) and, again, some systematic flow underestimation compared to the SWAT model with ground-based rainfall input. Additionally, rainfall estimates of two satellite products of the Tropical Rainfall Measuring Mission (TRMM), 3B42 and 3B42RT, are used in the calibrated SWAT- model after bias correction. The monthly runoff predictions obtained with 3B42- rainfall have 0.42 < R2 < 0.72 and−0.06 < NSE < 0.74 which are slightly better than those obtained with 3B42RT- rainfall, but not as good as the SWAT-SM2R-AMSRE. Therefore, despite the aforementioned limitations, using SM2R-AMSRE rainfall data in a hydrological model like SWAT appears to be a viable approach in basins with limited ground-based rainfall data.

Download Full-text

Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa

Hydrology and Earth System Sciences ◽

10.5194/hess-12-751-2008 ◽

2008 ◽

Vol 12 (3) ◽

pp. 751-767 ◽

Cited By ~ 28

Author(s):

T. Vischel ◽

G. G. S. Pegram ◽

S. Sinclair ◽

W. Wagner ◽

A. Bartsch

Keyword(s):

South Africa ◽

Remote Sensing ◽

Soil Moisture ◽

Hydrological Model ◽

Regional Scale ◽

Remotely Sensed ◽

Hydrological Models ◽

Good Correspondence ◽

Physically Based

Abstract. The paper compares two independent approaches to estimate soil moisture at the regional scale over a 4625 km2 catchment (Liebenbergsvlei, South Africa). The first estimate is derived from a physically-based hydrological model (TOPKAPI). The second estimate is derived from the scatterometer on board the European Remote Sensing satellite (ERS). Results show a good correspondence between the modelled and remotely sensed soil moisture, particularly with respect to the soil moisture dynamic, illustrated over two selected seasons of 8 months, yielding regression R2 coefficients lying between 0.68 and 0.92. Such a close similarity between these two different, independent approaches is very promising for (i) remote sensing in general (ii) the use of hydrological models to back-calculate and disaggregate the satellite soil moisture estimate and (iii) for hydrological models to assimilate the remotely sensed soil moisture.

Download Full-text

Modeling the effect of flood and drip irrigation on groundwater recharge

10.5194/egusphere-egu2020-9514 ◽

2020 ◽

Author(s):

Sandra Pool ◽

Félix Francés ◽

Alberto Garcia-Prats ◽

Cristina Puertes ◽

Manuel Pulido-Velázquez ◽

...

Keyword(s):

Water Balance ◽

Groundwater Recharge ◽

Drip Irrigation ◽

Hydrological Model ◽

Agricultural Area ◽

Annual Rainfall ◽

Irrigated Agriculture ◽

Irrigation Technology ◽

Simulation Results ◽

Irrigation Modernization

Irrigation modernization, here defined as the replacement of traditional flood irrigation systems by pressurized drip-irrigation technology, has been widely promoted with the aim to move towards a more sustainable use of freshwater resources in irrigated agriculture. However, the scale sensitivity of irrigation efficiency challenged the predominantly positive value attributed to irrigation modernization and asked for an integrated evaluation of the technological change at various scales. The aim of this study is therefore to contribute to an improved understanding of the hydrological functioning in a landscape under irrigation modernization. We used local field observations to propose a regional scale modeling approach that allowed to specifically simulate the difference in water balance as a function of irrigation method and crop type. The approach focused on the modification of the spatial input data and had therefore the benefit of being relatively independent of the final choice of the hydrological model. We applied the proposed approach to the semi-arid agricultural area of Valencia (Spain), where regional information about the use of irrigation technologies and irrigation volumes at farm level were available. The distributed hydrological model Tetis was chosen to simulate the daily water balance from 1994 to 2015 for an area of 913 km2 at a spatial resolution of 200 m. Model simulations were based on a random selection of parameter values that were subsequently evaluated in a multi-objective calibration framework. Multiple process scales were addressed within the framework by considering the annual evaporative index, monthly groundwater level dynamics, and daily soil moisture dynamics for evaluation. Simulation results were finally analyzed with a focus on groundwater recharge, which is of particular interest for environmental challenges faced within the study area. Simulation results of groundwater recharge for the entire agricultural area indicated a considerable variability in annual recharge (values from 112 mm up to 337 mm), whereby recharge was strongly controlled by annual rainfall volumes. Annual recharge in flood-irrigated areas tended to exceed annual recharge in drip irrigated-areas except for years with above average rainfall volumes. The observed rainfall dependency could be explained by the fact that recharge in drip-irrigated areas almost exclusively occurred during rainy days, whereby a few heavy rainfall events could produce the majority of annual recharge. Our results indicated interesting differences but also commonalities in groundwater recharge for flood and drip irrigation, and therefore emphasized the importance of explicitly considering irrigation technology when modelling irrigated agricultural areas.

Download Full-text

A new numerical model for simulating top surface soil moisture and runoff

Engineering Computations ◽

10.1108/ec-01-2017-0031 ◽

2018 ◽

Vol 35 (3) ◽

pp. 1344-1363 ◽

Cited By ~ 1

Author(s):

Jiongfeng Chen ◽

Wan-chang Zhang

Keyword(s):

Soil Moisture ◽

Stream Flow ◽

Hydrological Model ◽

Surface Soil ◽

Flow Simulation ◽

Hydrological Models ◽

Distributed Hydrological Model ◽

Surface Soil Moisture ◽

Content Type ◽

Simulation Results

PurposeThis paper aims to construct a simplified distributed hydrological model based on the surveyed watershed soil properties database.Design/methodology/approachThe new established model requires fewer parameters to be adjusted than needed by former hydrological models. However, the achieved stream-flow simulation results are similar and comparable to the classic hydrological models, such as the Xinanjiang model and the TOPMODEL.FindingsGood results show that the discharge and the top surface soil moisture can be simultaneously simulated, and that is the exclusive character of this new model. The stream-flow simulation results from two moderate hydrological watershed models show that the daily stream-flow simulation achieved the classic hydrological results shown in the TOPMODEL and Xinanjiang model. The soil moisture validation results show that the modeled watershed scale surface soil moisture has general agreement with the obtained measurements, with a root-mean-square error (RMSE) value of 0.04 (m3/m3) for one of the one-measurement sites and an averaged RMSE of 0.08 (m3/m3) over all measurements.Originality/valueIn this paper, a new simplified distributed hydrological model was constructed.

Download Full-text

Testing the Robustness of a Physically-Based Hydrological Model in Two Data Limited Inland Valley Catchments in Dano, Burkina Faso

Hydrology ◽

10.3390/hydrology7030043 ◽

2020 ◽

Vol 7 (3) ◽

pp. 43

Author(s):

Mouhamed Idrissou ◽

Bernd Diekkrüger ◽

Bernhard Tischbein ◽

Boubacar Ibrahim ◽

Yacouba Yira ◽

...

Keyword(s):

Soil Moisture ◽

Water Balance ◽

Burkina Faso ◽

Hydrological Model ◽

Model Performance ◽

Surface Flow ◽

Small Scale ◽

Model Parameters ◽

Inland Valley ◽

Physically Based

This study investigates the robustness of the physically-based hydrological model WaSiM (water balance and flow simulation model) for simulating hydrological processes in two data sparse small-scale inland valley catchments (Bankandi-Loffing and Mebar) in Burkina Faso. An intensive instrumentation with two weather stations, three rain recorders, 43 piezometers, and one soil moisture station was part of the general effort to reduce the scarcity of hydrological data in West Africa. The data allowed us to successfully parameterize, calibrate (2014–2015), and validate (2016) WaSiM for the Bankandi-Loffing catchment. Good model performance concerning discharge in the calibration period (R2 = 0.91, NSE = 0.88, and KGE = 0.82) and validation period (R2 = 0.82, NSE = 0.77, and KGE = 0.57) was obtained. The soil moisture (R2 = 0.7, NSE = 0.7, and KGE = 0.8) and the groundwater table (R2 = 0.3, NSE = 0.2, and KGE = 0.5) were well simulated, although not explicitly calibrated. The spatial transposability of the model parameters from the Bankandi-Loffing model was investigated by applying the best parameter-set to the Mebar catchment without any recalibration. This resulted in good model performance in 2014–2015 (R2 = 0.93, NSE = 0.92, and KGE = 0.84) and in 2016 (R2 = 0.65, NSE = 0.64, and KGE = 0.59). This suggests that the parameter-set achieved in this study can be useful for modeling ungauged inland valley catchments in the region. The water balance shows that evaporation is more important than transpiration (76% and 24%, respectively, of evapotranspiration losses) and the surface flow is very sensitive to the observed high interannual variability of rainfall. Interflow dominates the uplands, but base flow is the major component of stream flow in inland valleys. This study provides useful information for the better management of soil and scarce water resources for smallholder farming in the area.

Download Full-text

Groundwater recharge estimates combining soil isotope profiles and classical soil water monitoring techniques

10.5194/egusphere-egu21-9061 ◽

2021 ◽

Author(s):

Nina Krüger ◽

Christoph Külls ◽

Marcel Kock

Keyword(s):

Soil Moisture ◽

Water Balance ◽

Groundwater Recharge ◽

Soil Water ◽

Soil Profile ◽

Tunable Diode Laser ◽

Soil Water Balance ◽

Advection Dispersion ◽

Flow Processes ◽

Percolation Rate

To improve knowledge of hydrological and hydrogeological flow processes and their dependency on climate conditions it is becoming increasingly important to integrate sensors technology, independent observation methods, and new modeling techniques. Established isotope methods are usually regarded as a supplement and extension to classical hydrological investigation methods but are rarely included in soil water balance models. However, the combination could close knowledge gaps and thus lead to more precise and realistic predictions and therefore to better water management. Within the Wasserpfad project, a project of the Department of Civil Engineering at the TH L&#252;beck, soil moisture has been measured since May 2018. SMT100 soil moisture sensors from TRUEBNER GmbH are used at depths of 20, 40, 60, and 80 cm. Next to the station a 2m deep soil profile was taken in 2020, to estimate groundwater recharge using stable isotope equilibration methods and cryogenic extraction combined with soil water balance modeling. Vertical profiles of stable isotopes have been determined with a 10-cm resolution and measured with Tunable Diode Laser spectrometry. Percolation through the soil profile has been estimated based on the convolution of a seasonal input function using advection-dispersion transport models. Percolation rate estimate based on environmental isotope profiles results in 230 mm per year. Fitting of the advection-dispersion equation using a sinusoidal isotope input fitted to available time series provides an estimate of 255 mm per year. This difference is due to the dispersion effect on the isotope minima and maxima. The result of modeling the soil moisture data with a soil water balance model integrating the Richards equation for water transport and Penmen-Monteith based calculation of actual evaporation is used to verify the percolation rates. The analysis of soil moisture and isotope data by modeling provides a direct and efficient way to estimate the percolation rate. The combination of isotope methods with classical hydrological measuring techniques offers the possibility to verify results, to calibrate models, or to investigate the limits of isotope methods. Thus, flow processes can be predicted more reliably in the future.

Download Full-text

Improvement of uncertainty estimation in global hydrological models by using high resolution satellite data as an interpolator

10.5194/egusphere-egu2020-4323 ◽

2020 ◽

Author(s):

Rogier Westerhoff ◽

Frederika Mourot ◽

Conny Tschritter

Keyword(s):

High Resolution ◽

Water Balance ◽

Groundwater Recharge ◽

Satellite Data ◽

Spatial Scales ◽

Remotely Sensed ◽

Google Earth ◽

Data Driven ◽

Hydrological Models ◽

Water Balance Components

Global hydrological models often ingest remotely-sensed observations supported by ground-truthed data in attempts to better quantify water balance components, e.g. soil water content, evapotranspiration, runoff/discharge, groundwater recharge. However, the scaling up process from local observations to that global, coarse, scale contains large uncertainty, often undermining the relevance of water balance calculations.With recent more advanced high-resolution satellite data, freely available at 10m spatial resolution and (sub-) weekly temporal resolution, there is a possibility to reduce uncertainty in that upscaling. However, there are two challenges in doing so when working with global models: exponential increase of computational effort, and the need for quantifying the yet unknown uncertainty of assumptions that coarse global model cells and their underlying equations bring.This study hypothesises that a bottom-up approach with high-resolution satellite data and in situ observations will better constrain and quantify uncertainty. By using these more spatially-explicit data, we make the case that the estimation of water balance components should become more data-driven. We propose a more data-driven model that improves uncertainty of estimation and scalability by using more sophisticated, remotely-sensed interpolation layers.Our case study shows New Zealand-wide estimates of evapotranspiration and groundwater recharge at two resolutions: 1km x 1km, using an earlier developed model and MODIS satellite data; and a novel approach at 10m x 10m using Sentinel-1 and Sentinel-2 data to better incorporate impervious areas (e.g., anthropogenic urbanised land covers) and small land patches (e.g., small forestry areas). We then study the implications of using different spatial scales and quantify the differences between 10m x 10m versus 1km x 1km model pixel estimates. Our method is applied in the Google Earth Engine, a free-for-research high performance cloud computing facility, hence providing powerful computational resources and making our approach easily scalable to global, regional and catchment scales.&#160;Finally, we discuss what underlying model assumptions in traditional models could be changed to facilitate a method that works consistently at these different scales.

Download Full-text

The Role of Hydrological Signatures in Calibration of Conceptual Hydrological Model

Water ◽

10.3390/w12123401 ◽

2020 ◽

Vol 12 (12) ◽

pp. 3401

Author(s):

Eva Melišová ◽

Adam Vizina ◽

Linda R. Staponites ◽

Martin Hanel

Keyword(s):

Time Series ◽

Soil Moisture ◽

Goodness Of Fit ◽

Hydrological Model ◽

Hydrological Models ◽

Balance Assessment ◽

The Czech Republic ◽

Hydrological Signatures

Determining an optimal calibration strategy for hydrological models is essential for a robust and accurate water balance assessment, in particular, for catchments with limited observed data. In the present study, the hydrological model Bilan was used to simulate hydrological balance for 20 catchments throughout the Czech Republic during the period 1981–2016. Calibration strategies utilizing observed runoff and estimated soil moisture time series were compared with those using only long-term statistics (signatures) of runoff and soil moisture as well as a combination of signatures and time series. Calibration strategies were evaluated considering the goodness-of-fit, the bias in flow duration curve and runoff signatures and uncertainty of the Bilan model. Results indicate that the expert calibration and calibration with observed runoff time series are, in general, preferred. On the other hand, we show that, in many cases, the extension of the calibration criteria to also include runoff or soil moisture signatures is beneficial, particularly for decreasing the uncertainty in parameters of the hydrological model. Moreover, in many cases, fitting the model with hydrological signatures only provides a comparable fit to that of the calibration strategies employing runoff time series.

Download Full-text