Showing papers in "Hydrology and Earth System Sciences Discussions in 2017"

Large-scale hydrological model river storage and discharge correction using a satellite altimetry-based discharge product

Abstract: . Land surface models (LSMs) are widely used to study the continental part of the water cycle. However, even though their accuracy is increasing, inherent model uncertainties can not be avoided. In the meantime, remotely sensed observations of the continental water cycle variables such as soil moisture, lakes and river elevations are more frequent and accurate. Therefore, those two different types of information can be combined, using data assimilation techniques to reduce a model's uncertainties in its state variables or/and in its input parameters. The objective of this study is to present a data assimilation platform that assimilates into the large-scale ISBA-CTRIP LSM a punctual river discharge product, derived from ENVISAT nadir altimeter water elevation measurements and rating curves, over the whole Amazon basin. To deal with the scale difference between the model and the observation, the study also presents an initial development for a localization treatment that allows one to limit the impact of observations to areas close to the observation and in the same hydrological network. This assimilation platform is based on the ensemble Kalman filter and can correct either the CTRIP river water storage or the discharge. Root mean square error (RMSE) compared to gauge discharges is globally reduced until 21 % and at Obidos, near the outlet, RMSE is reduced by up to 52 % compared to ENVISAT-based discharge. Finally, it is shown that localization improves results along the main tributaries.

Climate change impacts on hydroclimatic regimes and extremes over Andean basins in central Chile

Abstract: . This study examines the projections of hydroclimatic regimes and extremes over Andean basins in central Chile (∼ 30–40 S). We have used daily precipitation and temperature data based on observations to drive and validate the VIC macro-scale hydrological model in the region of interest at a 0.25 × 0.25 degree resolution. Historical (1960–2005) and projected, following the RCP8.5 scenario (2006–2099), daily precipitation and temperatures from 26 CMIP5 climate models are bias corrected and used to drive the VIC model to obtain regional hydroclimate projections. Following the robust drying and warming shown by CMIP5 models in this region, the VIC model simulations indicate decreases in annual runoff of about 40 % by the end of the century, larger that the projected precipitation decreases (up to 30 %). Center timing of runoff shifts to earlier dates, 3–5 weeks by the end of the century. The Andes snowpack is projected to be less than half of the reference period by mid-century. The projected hydroclimatic regime is also expected to increase the severity and frequency of extreme events. The probability of having extended droughts, such as the recently experienced mega-drought (2010–2015), increases to up to 5 events/100 years. On the other hand, probability density function of annual maximum daily runoff indicates an increase in the frequency of flood events. The estimated return periods of annual maximum runoff events depict more drastic changes and increase in the flood risk as longer return periods are considered (e.g. 25-yr and 50-yr).

Global-scale evaluation of 23 precipitation datasets using gaugeobservations and hydrological modeling

Abstract: We undertook a comprehensive evaluation of 23 gridded (quasi-)global (sub-)daily precipitation ( P ) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76 086 gauges worldwide. Another ten gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the conceptual model HBV against streamflow records for each of 9053 small to medium-sized ( 2 ) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite- and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the the satellite- and reanalysis-based CHIRP V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gaugebased CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed those indirectly incorporating gauge data through other multi-source datasets (PERSIANN-CDR V1R1 and PGF). Our results highlight large differences in estimation accuracy, and hence, the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite- and reanalysis-based P estimates.

Bias correction of daily satellite-based rainfall estimates for hydrologic forecasting in the Upper Zambezi, Africa

Abstract: . The Zambezi Basin is located in the semi-arid region of southern Africa and is one of the largest basins in Africa. The Upper Zambezi River Basin (UZRB) is sparsely gauged (only 11 rain gauges are currently accessible), and real-time rainfall estimates are not readily available. However, Satellite Precipitation Products (SPPs) may complement that information, thereby allowing for improved real-time forecasting of streamflows. In this study, three SPPs for the UZRB are bias-corrected and evaluated for use in real-time forecasting of daily streamflows: (1) CMORPH (Climate Prediction Center’s morphing technique), (2) PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and (3) TRMM-3B42RT (Tropical Rainfall Measuring Mission). Two approaches for bias correction (Quantile Mapping and a Principal Component-based technique) are used to perform Bias Correction (BC) for the daily SPPs; for reference data, the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) was used. The two BC approaches were evaluated for the period 2001–2016. The bias-corrected SPPs were then used for real-time forecasting of streamflows at Katima Mulilo in the UZRB. Both BC approaches significantly improve the accuracy of the streamflow forecasts in the UZRB.

Climate change alters low flows in Europe under a 1.5, 2, and 3 degree global warming

Abstract: There is growing evidence that climate change will alter water availability in Europe. Here, we investigate how hydrological low flows are affected under different levels of future global warming (i.e., 1.5, 2 and 3 K). The analysis is based on a multi-model ensemble of 45 hydrological simulations based on three RCPs (rcp2p6, rcp6p0, rcp8p5), five CMIP5 GCMs (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, NorESM1-M) and three state-of-the-art hydrological models (HMs: mHM, Noah-MP, and PCR-GLOBWB). High resolution model results are available at the unprecedented spatial resolution of 5 km across the pan-European domain at daily temporal resolution. Low river flow is described as the percentile of daily streamflow that is exceeded 90 % of the time. It is determined separately for each GCM/HM combinations and the warming scenarios. The results show that the change signal amplifies with increasing warming levels. Low flows decrease in the Mediterranean while they increase in the Alpine and Northern regions. In the Mediterranean, the level of warming amplifies the signal from −12 % under 1.5 K to −35 % under 3 K global warming largely due to the projected decreases in annual precipitation. In contrast, the signal is amplified from p22 % (1.5 K) to p45 % (3 K) in the Alpine region because of the reduced snow melt contribution. The changes in low flows are significant for regions with relatively large change signals and under higher levels of warming. Nevertheless, it is not possible to distinguish climate induced differences in low flows between 1.5 and 2 K warming because of the large variability inherent in the multi-model ensemble. The contribution by the GCMs to the uncertainty in the model results is generally higher than the one by the HMs. However, the uncertainty due to HMs cannot be neglected. In the Alpine and Northern region as well as the Mediterranean, the uncertainty contribution by the HMs is partly higher than those by the GCMs due to different representations of processes such as snow, soil moisture and evapotranspiration.

SWAT-CUP for Calibration of Spatially Distributed Hydrological Processes and Ecosystem Services in a Vietnamese River Basin Using Remote Sensing

Abstract: . Distributed hydrological models are usually calibrated against the measured outflow of a certain drainage area, provided flow data is available. A close match with flow does however not mean that the spatially distributed hydrological processes are properly understood and simulated. In this paper, remotely sensed precipitation, evapotranspiration (ET) and leaf area index (LAI) from open access data sources were used to calibrate the SWAT model for the Day Basin, a tributary of the Red River in Vietnam. The efficacy of the SWAT-CUP parameter sensitivity and optimization model developed by Abbaspour (2015) was tested with spatial remote sensing input parameters. The innovation is that the parameters of the soil-vegetation processes were optimized for every Hydrological Response Unit for which remotely sensed monthly ET and LAI values were available. Such level of detail cannot be achieved from flow measurements, which are the integrated result of many processes over large areas. A total of 15 soil-vegetation process parameters were calibrated. The SUFI2 algorithm in SWAT-CUP appeared to be an adequate practical tool for the calibration process. Simulated monthly ET correlations with remote sensing estimates showed an R2 = 0.71 and NSE = 0.65 while monthly LAI showed correlations of R2 = 0.59 and NSE = 0.57 over a five year validation period. Accumulated modelled ET over the 5-year calibration period amounted to 5713 mm compared to 6015 mm of remotely sensed ET: a non-significant difference of 302 mm (5.3 %). Because river flow was not optimized during the calibration process, it could be used as an independent validation of the calibrated model simulations. The monthly flow at two flow measurement stations were adequately estimated (R2 = 0.78 and 0.55, NSE = 0.71 and 0.63 for Phu Ly and Ninh Binh, respectively). The estimated total water withdrawal from the Red River was 1.934 billion m3/yr with a peak flow of approximately 200 m3/s during the months of February and July. The availability of a reliable set of parameters will make SWAT a useful tool for optimizing water conservation, agricultural outputs, and ecosystem services such as reduced soil erosion, better water quality standards, carbon sequestration, micro-climate cooling amongst others. Such calibrated distributed eco-hydrological models can be used for appraising scenarios of green growth.

Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an Unmanned Aerial Vehicle

Abstract: . High-quality bathymetric maps of inland water bodies are a common requirement for hydraulic engineering and hydrological science applications. Remote sensing methods, e.g. space-borne and airborne multispectral or LIDAR, have been developed to estimate water depth, but are ineffective for most inland water bodies, because of water turbidity and attenuation of electromagnetic radiation in water. Surveys conducted with boats equipped with sonars can retrieve accurate water depths, but are expensive, time-consuming, and are unsuitable for non-navigable water bodies. We develop and assess a novel approach to retrieve accurate and high resolution bathymetry maps. We measured accurate water depths using a tethered floating sonar controlled by an Unmanned Aerial Vehicle (UAV) in a Danish lake and in a few river cross sections. The developed technique combines the advantages of remote sensing techniques with the potential of bathymetric sonars. UAV surveys can be conducted also in non-navigable, inaccessible, or remote water bodies. The tethered sonar can measure bathymetry with an accuracy of ca. 2.1 % of the actual depth for observations up to 35 m, without being significantly affected by water turbidity, bedform or bed material.

Comparison and validation of eight satellite rainfall products over the rugged topography of Tekeze-Atbara Basin at different spatial andtemporal scales

Abstract: . Satellite rainfall products are considered important options for acquiring rainfall estimates in the absence of ground measurements. However, estimates from these products need to be validated as their accuracy can be affected by geographical position, topography, and climate, as well as by the algorithms used to derive rainfall from satellite measurements. Eight satellite-based rainfall products (TRMM, CHIRPS, RFEv2, ARC2, PERSIANN, GPCP, CMAP and CMORPH) were evaluated against ground observations over the complex topography of the upper Tekeze-Atbara basin in Ethiopia. The performance was evaluated at various temporal (daily, monthly, seasonal) and spatial (point, sub-basin, basin) scales over the period 2002–2015. Results show that CHIRPS, TRMM, and RFEv2 performed well and were able to capture the rainfall measured by rain gauges. The BIAS and correlation of these products were within ±25 % and > 0.5 over different time steps. The remaining products poorly performed at daily time step with higher BIAS (up to ±200 %) and lower correlation ( 2500 m a.s.l. Compared to the lowlands, the BIAS at highlands increased by 35 % whilst the correlation dropped by 28 %. Underestimation and overestimation of rainfall dominated in the mountainous and lowland areas, respectively. CMORPH and TRMM overestimated while the remaining products underestimated the rainfall at all spatiotemporal scales. CMAP, ARC2, and GPCP estimates were the most affected by large underestimation. Unlike in temporal scale, the performance of the products did not show a uniform pattern with respect to spatial scale. Their performance improved from point to aerial comparisons in the lowlands whereas it slightly reduced at highland areas. Poor performance in the highlands contributed to a slightly lower performance at basin scale compared to the pixel-to-point comparison. Our results show that rainfall estimates from CHIRPS and TRMM have a consistently good agreement with ground rainfall at different spatiotemporal scales in the upper Tekeze-Atbara basin. Interpolating the sparse and unevenly distributed rain gauges over the complex terrains however introduces unknown uncertainties with respect to the actual rainfall.

Spatial and temporal Trend Analysis of Long Term rainfall records in data-poor catchments with missing data, a case study of Lower Shire floodplain in Malawi for the Period 1953–2010

Abstract: . This paper investigated the long-term trends in precipitation from 16 stations located in the lower Shire catchment in Malawi over the period 1953–2010. Annual trend analysis was first considered, and in order to take into account seasonality and serial correlation, the different months of the year are considered. Trend significance was determined using the nonparametric Mann-Kendall (MK) test statistic while the determination of the trends magnitudes was achieved using Sen’s slope method. The homogeneity of trends was examined using the Van Belle and Hughes method. The results indicate that annual precipitation has increased, whereas, monthly precipitation revealed an upward trend in wet seasons (November to April) and a downward trend in dry seasons (May to October). The monthly peak trend analysis has shown upward trend in rainy months at all stations.

Hydrologic modeling of a Himalayan mountain basin by using the SWAT mode

Abstract: . A large population depends on runoff from Himalayan rivers which have high hydropower potential; floods in these rivers are also frequent. Current understanding of hydrologic response mechanism of these rivers and impact of climate change is inadequate due to limited studies. This paper presents results of modeling to understand the hydrologic response and compute the water balance components of a Himalayan river basin in India viz. Ganga up to Devprayag. Soil and Water Assessment Tool (SWAT) model was applied for simulation of the snow/rainfed catchment. SWAT was calibrated with daily streamflow data for 1992–98 and validated with data for 1999–2005. Manual calibration was carried out to determine model parameters and quantify uncertainty. Results indicate good simulation of streamflow; main contribution to water yield is from lateral and ground water flow. Water yield and ET for the catchments varies between 43–46 % and 57–58 % of precipitation, respectively. The contribution of snowmelt to lateral runoff for Ganga River ranged between 13–20 %. More attention is needed to strengthen spatial and temporal hydrometeorological database for the study basins for improved modeling.

Frequently used drought indices reflect different drought conditionson global scale

Abstract: . Drought is an abnormal and prolonged deficit in available water. Possible drought impacts are crop losses, famine, fatalities, power blackouts and degraded ecosystems. These severe socio-economic and environmental impacts show the need to carefully monitor drought conditions using a suitable index. Our objective is to provide an intercomparison of frequently used physical drought indices to show to which degree they are interchangeable for monitoring drought in precipitation, soil moisture, groundwater and streamflow. Physical indices are commonly introduced to predict drought impacts, because appropriate drought impact indices are still missing. Correlations (R) between frequently used indices for different drought types were calculated at the global scale. We have made the index timeseries available to the community for future studies. Precipitation drought indices show low to intermediate correlations (ranging from R = 0.1 to 0.75), soil moisture drought indices show an even lower similarity (R = 0.25). Indices for streamflow drought show the highest correlation (R = 0.5 to 0.95). Additionally, meteorological drought indices do not capture the soil moisture drought correctly (R = 0.0 to 0.6) nor streamflow drought (R = 0.0 to 0.7). These findings have implications for drought monitoring systems: (i) for each drought type, a different index should carefully be identified; (ii) drought indices that are designed to monitor the same drought type show large discrepancies in their anomalies and hence drought detection; (iii) there is no single superior physical drought index that is capable of accurately capturing the diverse set of drought impacts in all parts of the hydrological cycle.

The temporally varying roles of rainfall, snowmelt and soil moisture for debris flow initiation in a snow dominated system: the compound trigger concept

Abstract: Debris flows represent a severe hazard in mountain regions. Though significant effort has been made to predict such events, the trigger conditions as well as the hydrologic disposition of a watershed at the time of debris flow occurrence are not well understood. Traditional intensity-duration threshold techniques to establish trigger conditions generally do not account for distinct influences of rainfall, snowmelt, and antecedent moisture. To improve our knowledge on the connection between debris flow initiation and the hydrologic system and to overcome the above limitations, this study explores the use of a semi-distributed conceptual rainfall-runoff model, linking different system variables such as soil moisture, snowmelt, or runoff with documented debris flow events in the inner Pitztal watershed, western Austria. The model was run on a daily basis between 1953 and 2012. Analyzing a range of modelled system state and flux variables at days on which debris flows occurred, three distinct dominant trigger mechanisms could be clearly identified. While the results suggest that for 68 % (17 out of 25) of the observed debris flow events during the study period high-intensity rainfall was the dominant trigger, snowmelt was identified as dominant trigger for 24 % (6 out of 25) of the observed debris flow events. In addition, 8 % (2 out of 25) of the debris flow events could be attributed to the combined effects of low-intensity, long-lasting rainfall and transient storage of this water, causing elevated antecedent soil moisture conditions. The results also suggest a relatively clear temporal separation between the distinct trigger mechanisms, with high-intensity rainfall as trigger being limited to mid- and late summer. The dominant trigger in late spring/early summer is snowmelt. Based on the discrimination between different modelled system states and fluxes and more specifically, their temporally varying importance relative to each other, rather than their absolute values, this exploratory study demonstrates that already the use of a relatively simple hydrological model can prove useful to gain some more insight into the importance of distinct debris flow trigger mechanisms in a compound trigger concept, highlighting in particular the relevance of snowmelt contributions and the switch between mechanisms in early- to mid-summer in snow dominated systems.

Waning habitats due to climate change: effects of streamflow and temperature changes at the rear edge of the distribution of a cold-water fish

Abstract: Climate change affects aquatic ecosystems altering temperature and precipitation patterns, and the rear edge of the distribution of cold-water species is especially sensitive to them. The main goal was to predict in detail how change in air temperature and precipitation will affect streamflow, the thermal habitat of a cold-water fish (brown trout, Salmo trutta Linnaeus 1758), and their synergistic relationships at the rear edge of its natural distribution. 31 sites in 14 mountain rivers and streams were studied in Central Spain. Models at several sites were built using regression trees for streamflow, and a non-linear regression method for stream temperature. Nine global climate models simulations for the RCP4.5 and RCP8.5 (Representative Concentration Pathways) scenarios were downscaled to a local level. Significant streamflow reductions were predicted in all basins (max. −49 %) by the year 2099, showing seasonal differences between them. The stream temperature models showed relationships between models parameters, geology and hydrologic responses. Temperature was sensitive to the streamflow in one set of streams, and summer reductions contributed to additional stream temperature increases (max. 3.6 °C), although the most deep-aquifer dependent sites better resisted warming. The predicted increase in water temperature reached up to 4.0 °C. Temperature and streamflow changes will cause a shift of the rear edge of the species distribution. However, geology conditioned the extent of this shift. Approaches like these should be useful in planning the prevention and mitigation of negative effects of climate change by differentiating areas based on the risk level and viability of fish populations.

Errors and adjustments for single-Alter shielded and unshielded weighing gauge precipitation measurements from WMO-SPICE

Abstract: Although precipitation has been measured for many centuries, precipitation measurements are still beset with significant inaccuracies. Solid precipitation is particularly difficult to measure accurately, and differences between winter-time precipitation measurements from different measurement networks or different regions can exceed 100 %. Using precipitation gauge results from the World Meteorological Organization Solid Precipitation Intercomparison Experiment (WMO-SPICE), errors in precipitation measurement caused by gauge uncertainty, spatial variability in precipitation, hydrometeor type, crystal habit, and wind were quantified. The methods used to calculate gauge catch efficiency and correct known biases are described. Adjustments, in the form of transfer functions that describe catch efficiency as a function of air temperature and wind speed, were derived using measurements from eight separate WMO-SPICE sites for both unshielded and single-Alter shielded weighing precipitation gauges. The use of multiple sites to derive such adjustments makes these results unique and more broadly applicable to other sites with various climatic conditions. In addition, errors associated with the use of a single transfer function to correct gauge undercatch at multiple sites were estimated.

Towards assimilation of crowdsourced observations for different levels of citizen engagement: the flood event of 2013 in the Bacchiglione catchment

Abstract: Accurate flood predictions are essential to reduce the risk and damages over large urbanized areas. To improve prediction capabilities, hydrological measurements derived by traditional physical sensors are integrated in real-time within mathematic models. Recently, traditional sensors are complemented with low-cost social sensors. However, measurements derived by social sensors (i.e. crowdsourced observations) can be more spatially distributed but less accurate. In this study, we assess the usefulness for model performance of assimilating crowdsourced observations from a heterogeneous network of static physical, static social and dynamic social sensors. We assess potential effects on the model predictions to the extreme flood event occurred in the Bacchiglione catchment on May 2013. Flood predictions are estimated at the target point of Ponte degli Angeli (Vicenza), outlet of the Bacchiglione catchment, by means of a semi-distributed hydrological model. The contribution of the upstream sub-catchment is calculated using a conceptual hydrological model. The flow is propagated along the river reach using a hydraulic model. In both models, a Kalman filter is implemented to assimilate the real-time crowdsourced observations. We synthetically derived crowdsourced observations for either static social or dynamic social sensors because crowdsourced measures were not available. We consider three sets of experiments: (1) only physical sensors are available; (2) probability of receiving crowdsourced observations and (3) realistic scenario of citizen engagement based on population distribution. The results demonstrated the importance of integrating crowdsourced observations. Observations from upstream sub-catchments assimilated into the hydrological model ensures high model performance for high lead time values. Observations next to the outlet of the catchments provide good results for short lead times. Furthermore, citizen engagement level scenarios moved by a feeling of belonging to a community of friends indicated flood prediction improvements when such small communities are located upstream a particular target point. Effective communication and feedback is required between water authorities and citizens to ensure minimum engagement levels and to minimize the intrinsic low-variable accuracy of crowdsourced observations.

How runoff components affect the export of DOC and nitrate: along-term and high-frequency analysis

Abstract: . We monitored dissolved organic carbon (DOC) and nitrate concentrations and fluxes in situ with a UV-Vis spectrometer for two years at a high temporal resolution of 15 minutes in the forested Weierbach headwater catchment. The catchment exhibits a characteristic double peak runoff response to incident rainfall during periods with wet initial conditions. When initial conditions are dry, only the first discharge peak occurs. During our observations, both DOC and nitrate concentrations increased during the first discharge peak, while only nitrate concentrations were elevated during the second discharge peak. Relying on additional biweekly end-member data of precipitation, throughfall, soil water and groundwater, we linked the first peak to near surface flowpaths and the second peak to shallow groundwater reactions and subsurface flowpaths. The mass export of DOC and nitrate is largely controlled by the discharge yield. Nevertheless, this relationship is altered by changing flowpaths during different wetness conditions in the catchment. Due to the absence of second discharge peaks during dry conditions, the DOC export is more relevant and the nitrate export is less relevant during dry catchment states. The study highlights the benefits of in-situ, long-term, and high-frequency monitoring for comparing DOC and nitrate export with runoff components that are changing rapidly during events as well as gradually between seasons.

Improving Calibration and Validation of Cosmic-Ray NeutronSensors in the Light of Spatial Sensitivity – Theory and Evidence

Abstract: In the last years the method of cosmic-ray neutron sensing (CRNS) has gained popularity among soil hydrologists, physicists, and land-surface modelers. The sensor provides continuous soil moisture data, averaged over several hectares and tens of decimeters depth. However, the signal still may contain unidentified features of hydrological processes, and many calibration datasets are often required in order to find reliable relations between neutrons and water dynamics. Recent insights into environmental neutrons accurately described the spatial sensitivity of the sensor and thus allowed to quantify the contribution of individual sample locations to the CRNS signal. Consequently, data points of calibration and validation datasets are suggested to be averaged using a more physically-based weighting approach. In this work, a revised sensitivity function is used to calculate weighted averages of point data. The approach is extensively tested with two calibration and four time series datasets from a variety of sites and conditions. In all cases, the revised averaging method robustly improved the performance of the CRNS product and even helped to reveal otherwise hidden hydrological processes. The presented approach increases the overall accuracy of CRNS products and will have impact on all their applications in agriculture, hydrology, and modeling.

HESS Opinions: The complementary merits of top-down and bottom-up modelling philosophies in hydrology

Abstract: In hydrology, the two somewhat competing modelling philosophies of bottom-up and top-down approaches are the basis of most process-based models. Differing mostly (1) in their respective degree of detail in resolving the modelling domain and (2) in their respective degree of explicitly treating conservation laws, these two philosophies suffer from similar limitations. Nevertheless, a better understanding of their respective basis (i.e. micro-scale vs. macro-scale) as well as their respective short comings bears the potential of identifying the complementary value of the two philosophies for improving our models. In this manuscript we analyse several frequently communicated beliefs and assumptions to identify, discuss and emphasize the functional similarity of the two modelling philosophies. We argue that deficiencies in model applications largely do not depend on the modelling philosophy but rather on the way a model is implemented. Based on the premises that top-down models can be implemented at any desired degree of detail and that any type of model remains to some degree conceptual we argue that a convergence of the two modelling strategies may hold some value for progressing the development of hydrological models.

Economic impacts of drought risks for water utilities through Severity-Duration-Frequency framework under climate change scenarios

Abstract: . Climate variability and increasing water demands prioritize the need to implement planning strategies for urban water security in the long and medium term. However, actions to manage the drought risk impacts entail great complexity, such as the calculation of economic losses derived from the combination of severity, duration and frequency under uncertainties in the climate projections. Thus, new approaches of risk aversion are needed, as an integrated framework for resilience gap assessment, for water utilities to cope with droughts, thereby linking drivers of climate, hydrology and human demands. This paper aims to present the economic impacts of risk aversion for water utilities through a framework linking severity, duration and frequency (SDF) of droughts under climate change scenarios. This new model framework addresses the opportunity cost that represent the preparedness for risk aversion to cope with potential future impacts of droughts, involving a set of options for planning of water resources, under different demands and climate projections. The methodology integrates the hydrological simulation procedures, under radiative climate forcing scenarios RCP 4.5 and 8.5, from a regional climate model Eta-INPE, with time horizons of 2007–2040, 2041–2070, and 2071–2099, linked to Water Evaluation and Planning system (WEAP) hydrologic model and under stationary and non-stationary water supply demand assumptions. The model framework is applied to the Cantareira Water Supply System for Sao Paulo Metropolitan Region, Brazil, with severe vulnerability to droughts. By using hydrological simulations with WEAP, driven by Eta-INPE Regional Climatic Model base line scenarios (1962–2005), were characterized the SDF curves. On the one hand, water tariff price associated to calibrated and modelled scenarios constitute supply/demand proxies of the water warranty time delimited by drought duration. Then, profit loss analysis scenarios are assessed for the regional water utility. On the other hand, for drought resilience gap, results show water utility profit losses per period between 1.3 % and 10.3 % of the regional GDP in 2016. Although future economic impacts vary in a same order, non-stationary demand trends impose larger differences in the drought resilience gap, when the future securitization are linked to regional climate outputs.

Saturated Hydraulic Conductivity and Textural Heterogeneity of Soils

Abstract: . Saturated hydraulic conductivity K sat is an important soil parameter that highly depends on soil's particle size distribution (PSD). The nature of this dependency is explored in this work in two ways, (1) by using the Information Entropy as a heterogeneity parameter of the PSD and (2) using descriptions of PSD in forms of textural triplets, different than the usual description in terms of the triplet of sand, silt and clay contents. The power of this parameter, as a descriptor of K sat and log( K sat ) , was tested on a database of > 19 K soils. We found coefficients of determination of up to 0.977 for log( K sat ) using a triplet that combines very coarse, coarse, medium and fine sand as coarse particles, very fine sand as intermediate particles, and silt and clay as fines. The power of the correlation is analysed for different textural classes and different triplets. Overall, the use of textural triplets different than traditional, combined with IE , may provide a useful tool for predicting K sat values.

Beaver dam influences on streamflow hydraulic properties and thermal regimes

Abstract: . Beaver dams alter channel hydraulics which in turn change the geomorphic templates of streams. Variability in geomorphic units, the building blocks of stream systems, and water temperature, critical to stream ecological function, define habitat heterogeneity and availability. While prior research has shown the impact of beaver dams on stream hydraulics, geomorphic template, or temperature, the connections or feedbacks between these habitat measures are not well understood. This has left questions regarding relationships between temperature variability at different spatial scales to hydraulic properties such as flow depth and velocity that are dependent on the geomorphology. We combine detailed predicted hydraulic properties, field based maps with an additional classification scheme of geomorphic units, and detailed water temperature observations throughout a study reach to demonstrate the relationship between these factors at different spatial scales (reach, beaver dam complexes, and geomorphic units). Over a three week, low flow period we found temperature to vary 2 °C between the upstream and downstream extents of the reach with a net warming of 1 °C during the day and a net cooling of 0.5 °C at night. At the beaver dam complex scale, net warming of 1.15 °C occurred during the day with variable cooling at night. Regardless of limited temperature changes at these larger scales, the temperaure variability in a beaver dam complex reached up to 10.5 °C due to the diversity of geomorphic units within the complex. At the geomorphic unit scale, the highly altered flow velocity and depth distributions within primary units provide an explanation of the temperature variability within the dam complex. Riffles, with the greatest velocity variability and least depth variability, have the smallest temperature variability and range. The lowest velocity variability occurred within margins, pools, and backwaters which exhibit the widest temperature ranges, but range from shallow to deep. Overall, the predicted flow hydraulic properties for different geomorphic units suggest that velocity is the primary factor in determining the variability of water temperature. However, water depth can also play a role as it impacts warming patterns and can dictate thermal stratification. These findings begin to link key attributes of different geomorphic units to thermal variability and illustrates the value of the geomorphic variability associated with the development of beaver dam complexes.

Hydro-Climatic Modelling of an Ungauged Basin in Kumasi, Ghana

Abstract: . The Owabi catchment which is about 69 km2 provides about 20 % of water needs of the Kumasi metropolis has been in recent times prone to high anthropogenic activities that threaten water resource management. The Soil-Water-Assessment-Tool (SWAT) was used to assess the extent of these activities on the hydrology on the catchment from 1986 to 2015. Specifically, the model simulated historic and projected stream-flow and water balance. Initial results revealed the forest and topography played major role in water loss at the catchment as evapotranspiration and surface runoff were the dominant modulating processes. Monthly calibration/validation of the model yielded satisfactory results with NSE (0.66/0.67), R2 (0.67/0.67), PBIAS (8.2 %/8.0 %) and RSR (0.59/0.58). Nine sensitive parameters of which the catchment slope (CN2) ranked principal were found to control runoff amounts into the river. The model uncertainty was also quite low as the 95PPU enveloped about 50 % of the observed streamflow within a width of 0.45–0.55. Furthermore, future streamflow predictions were modelled under RCP2.6, RCP4.5 and RCP8.5 climatic scenarios, and two landuse scenarios, landuse category 1 and 2 (LU1 and LU2). An increasing trend of the downscaled rainfall totals between 2021 to 2050 for all RCPs were observed. This will positively impact streamflow generation at the catchment under LU1. There is an expected deficit of streamflow amounts under LU2 relative to LU1, and a marginal reduction as compared to the baseline. In general, the model proved efficient in determining the hydrology parameters in the catchment and therefore has potential to be used for further modelling of water quality and pollution to aid effective water resource decisions at the catchment.

Proximate and underlying drivers of socio-hydrologic change in the upper Arkavathy watershed, India

Abstract: . Addressing water security in the developing world involves predicting water availability under unprecedented rates of population and economic growth. Yet the combination of rapid change, inadequate data and human modifications to watersheds poses a challenge, as researchers face a poorly constrained water resources prediction problem. This case study of the data-scarce, upper Arkavathy watershed, near the city of Bengaluru in southern India, attempts to systematically explain the observed disappearance of surface and groundwater in recent decades. The study asks three questions: 1) Can we quantify the change attributable to different drivers? 2) Can we anticipate change? 3) What policy lessons can be drawn? Field experiments, isotopic studies, borewell scans and sensors were deployed to understand hydrologic processes over five years. These were used in a historical reconstruction of the catchment over three decades that replicates the decline. The multi-scale model of the upper Arkavathy, quantifies the contributions of soil and water conservation measures, groundwater depletion and eucalyptus plantations to the decline in surface and groundwater resources. The model results indicate that the catchment hydrology cannot be reconstructed without explicitly including human feedbacks. The system is influenced by both endogenous drivers (social feedbacks to changes in water availability such as irrigation efficiency improvements, soil and water conservation measures and deeper borewells) and exogenous drivers (technological change, pro-development governance and economic forces due to urbanization, which provided access to capital and markets for high-value crops). The research suggests that in a system where productivity of the landscape is limited by water, economic drivers will always push for maximization of water abstraction and use. Unsustainability of resource use is inevitable, in the absence of credible controls on abstraction and use.

An intercomparison of approaches for improving predictability in operational seasonal streamflow forecasting

Abstract: For much of the last century, forecasting centers around the world have offered seasonal streamflow predictions to support water management. Recent work suggests that the two major avenues to advance seasonal predictability are improvements in the estimation of initial hydrologic conditions (IHCs) and the incorporation of climate information. This study investigates the marginal benefits of a variety of methods using IHC and/or climate information, focusing on seasonal water supply forecasts (WSFs) in five case study watersheds located in the U.S. Pacific Northwest region. We specify two benchmark methods that mimic standard operational approaches – statistical regression against IHCs, and model-based ensemble streamflow prediction (ESP) – and then systematically inter-compare WSFs across a range of lead times. Additional methods include: (i) statistical techniques using climate information either from standard indices or from climate reanalysis variables; and (ii) several hybrid/hierarchical approaches harnessing both land surface and climate predictability. In basins where atmospheric teleconnection signals are strong, and when watershed predictability is low, climate information alone provides considerable improvements. For those basins showing weak teleconnections, custom predictors from reanalysis fields were more effective in forecast skill than standard climate indices. ESP predictions tended to have high correlation skill but greater bias compared to other methods, and climate predictors failed to substantially improve these deficiencies within a trace weighting framework. Lower complexity techniques were competitive with more complex methods, and the hierarchical expert regression approach introduced here (HESP) provided a robust alternative for skillful and reliable water supply forecasts at all initialization times. Three key findings from this effort are: (1) objective approaches supporting methodologically consistent hindcasts open the door to a broad range of beneficial forecasting strategies; (2) the use of climate predictors can add to the seasonal forecast skill available from IHCs; and (3) sample size limitations must be handled rigorously to avoid over-trained forecast solutions. Overall, the results suggest that despite a rich, long heritage of operational use, there remain a number of compelling opportunities to improve the skill and value of seasonal streamflow predictions.

Uncertainty analysis of hydrological return period estimation, takingthe upper Yangtze River as an example

Abstract: . Return period estimation plays an important role in the engineering practices of water resources and disaster management, but uncertainties accompany the calculation process. Based on the daily discharge records at two gauging stations (Cuntan and Pingshan) on the upper Yangtze River, three sampling methods (SMs; (annual maximum, peak over threshold, and decadal peak over threshold), five distribution functions (DFs; gamma, Gumbel, lognormal, Pearson III, and general extreme value), and three parameterization methods (PMs; maximum likelihood, L-Moment, and method of moment) were applied to analyze the uncertainties in return period estimation. The estimated return levels based on the different approaches were found to differ considerably at each station. The range of discharge for a 20-year return period was 63,800.8–74,024.1 m3 s−1 for Cuntan and 23,097.8–25,595.3 m3 s−1 for Pingshan, when using the 45 combinations of SMs, DFs, and PMs. For a 1000-year event, the estimated discharge ranges increased to 74,492.5–125,658.0 and 27,339.2–41,718.1 m3 s−1 for Cuntan and Pingshan, respectively. Application of the analysis of variance method showed that the total sum of the squares of the estimated return levels increased with the widening of the return periods, suggestive of increased uncertainties. However, the contributions of the different sources to the uncertainties were different. For Cuntan, where the discharge changed significantly, the SM appeared to be the largest source of uncertainty. For Pingshan, where the discharge series remained almost stable, the DF contributed most to the uncertainty. Therefore, multiple uncertainty sources in estimating return periods should be considered to meet the demands of different planning purposes. The research results also suggest that uncertainties of return level estimation could be reduced if an optimized DF were used, or if the decadal peak over threshold SM were used, which is capable of representing temporal changes of hydrological series.

Optimizing micro watershed management for soil erosion control under various slope gradient and vegetation cover conditions using SWAT modeling

Abstract: . This study evaluated parameters of soil erosion and optimization of micro watersheds by applying a semidistributed basin-scale Soil and Water Assessment Tool (SWAT) model in various small watersheds of the Chakwal and Attock districts of Pothwar, Pakistan. The model was calibrated and validated on a daily basis for a small catchment (Catchment-25) of the Dhrabi watershed without any soil conservation structures. Statistical measures (R2 and EN-S) were used to evaluate model performance; the model performed satisfactorily well for both surface runoff and sediment yield estimations, with the R2 and EN-S values both being greater than 0.75, during calibration (2009–2010) and validation (2011). The model was applied to various small watershed sites in the Chakwal and Attock districts after successful calibration and validation. Soil erosion estimation was performed at these sites having loose stone soil and water conservation structures and being under various slope gradient and vegetation cover conditions. The structures had significant effects, and the average sediment yield reduction engendered by the loose stone structures at the various sites varied from 54 to 98 %. The sediment yield and erosion reductions were also compared under conditions involving vegetation cover change. Agricultural land with winter wheat crops had a higher sediment yield level than did fallow land with crop residue, which facilitated sediment yield reduction along with the soil conservation structures. Analyzing various slope gradients revealed that all selected sites had a maximum slope area of less than 5 %; stone structures were installed at these sites to reduce sediment yield. Based on slope classification analysis, the model was upscaled for the whole districts of Chakwal and Attock. The results indicated that 60 % of Chakwal (4095 km2) and Attock (3918 km2) by area lies in a slope range of 0–4 %; this thus implies that considerable potential exists for implementing soil conservation measures by installing stone structures. Estimates revealed that minimum sediment yield reductions of 122,850 t year−1 in Chakwal District and 117,540 t year−1 in Attock District could be achieved by installing loose stone structures in 60 % of the agricultural areas of both districts having a slope of 0–4 %; these findings can serve as a reference for policymakers and planners. The overarching findings of this study show that the SWAT model provides reliable results for sediment yield and soil erosion estimation, which can be used in rocky mountainous watersheds for erosion control and watershed management.

Long-term projections of global water use for electricity generation under the Shared Socioeconomic Pathways and climate mitigation scenarios

Abstract: . Electricity generation may become a key factor that accelerates water scarcity. In this study, we estimated the future global water use for electricity generation from 2005 to 2100 in 17 global sub-regions. Twenty-two future global change scenarios were examined, consisting of feasible combinations of five socioeconomic scenarios of the Shared Socioeconomic Pathways (SSPs) and six climate mitigation scenarios based on four forcing levels of representative concentration pathways (RCPs) and two additional forcing levels, to assess the impacts of socioeconomic and climate mitigation changes on water withdrawal and consumption for electricity generation. Climate policies such as targets of greenhouse gas (GHG) emissions are determined by climate mitigation scenarios. Both water withdrawal and consumption were calculated by multiplying the electricity generation of each energy source (e.g., coal, nuclear, biomass, and solar power) and the energy source-specific water use intensity. The future electricity generation dataset was derived from the Asia-Pacific Integrated/Computable General Equilibrium (AIM/CGE) model. Estimated water withdrawal and consumption varied significantly among the SSPs. In contrast, water withdrawal and consumption differed little among the climate mitigation scenarios even though GHG emissions depend on them. There are two explanations for these outcomes. First, electricity generation for energy sources requiring considerable amounts of water varied widely among the SSPs, while it did not differ substantially among the climate mitigation scenarios. Second, the introduction of more carbon capture and storage strategies increased water withdrawal and consumption under stronger mitigation scenarios, while the introduction of more renewable energy decreased water withdrawal and consumption. Therefore, the socioeconomic changes represented by the SSPs had a larger impact on water withdrawal and consumption for electricity generation, compared with the climate mitigation changes represented by the climate mitigation scenarios. The same trends were observed on a regional scale, even though the composition of energy sources differed completely from that on a global scale.

Toward seamless hydrologic predictions across scales

Abstract: Land surface and hydrologic models (LSM/HM) are used at diverse spatial resolutions ranging from 1–10 km in catchment-scale applications to over 50 km in global-scale applications. Application of the same model structure at different spatial scales requires that the model estimates similar fluxes independent of the model resolution and fulfills a flux-matching condition across scales. An analysis of state-of-the-art LSMs and HMs reveals that most do not have consistent and realistic parameter fields for land surface geophysical properties. Multiple experiments with the mHM, Noah-MP, PCR-GLOBWB and WaterGAP models are conducted to demonstrate the pitfalls of poor parameterization practices currently used in most operational models, which are insufficient to satisfy the flux-matching condition. These examples demonstrate that J. Dooge's 1982 statement on the unsolved problem of parameterization in these models remains true. We provide a short review of existing parameter regionalization techniques and discuss a method for obtaining seamless hydrological predictions of water fluxes and states across multiple spatial resolutions. The multiscale parameter regionalization (MPR) technique is a practical and robust method that provides consistent (seamless) parameter and flux fields across scales. A general model protocol is presented to describe how MPR can be applied to a specific model, with an example of this application using the PCR-GLOBWB model. Applying MPR to PCR-GLOBWB substantially improves the flux-matching condition. Estimation of evapotranspiration without MPR at 5 arcmin and 30 arcmin spatial resolutions for the Rhine river basin results in a difference of approximately 29 %. Applying MPR reduce this difference to 9 %. For total soil water, the differences without and with MPR are 25 % and 7 %, respectively.

Scaling down hyporheic exchange flows: from catchments to reaches

Abstract: . Rivers are not isolated systems but continuously interact with the subsurface from upstream to downstream. In the last few decades, research on the hyporheic zone (HZ) from many perspectives has increased appreciation of the hydrological importance and ecological significance of connected river and groundwater systems. Although recent reviews, modelling and field studies have explored hydrological, biogeochemical and ecohydrological processes in the HZ at relatively small scales (bedforms to reaches), a comprehensive understanding of the factors driving the hyporheic exchange flows (HEF) at larger scales is still missing. To date, there is fragmentary information on how hydroclimatic, hydrogeologic, topographic, anthropogenic and ecological factors interact to drive hyporheic exchange flows at large scales. Further evidence is needed to link hyporheic exchange flows across scales. This review aims to conceptualize interacting factors at catchment, valley and reach scales that control spatial and temporal variations in hyporheic exchange flows. The implications of these drivers are discussed for each scale, and co-occurrences across scale are highlighted in a case of study. By using a multi-scale perspective, this review connects field observations and modelling studies to identify broad and general patterns of HEF in different catchments. This multi-scale perspective is useful to devise approaches to interpret hyporheic exchange across multiscale heterogeneities, to infer scaling relationships, and to inform watershed management decisions.

Global synthesis of forest cover effects on long-term water balance partitioning in large basins

Abstract: . Global changes in forest cover have been related to major scientific and social challenges. There are important uncertainties about the potential effects of ongoing forest loss on continental water balances. Here we present an observation-based analysis of long-term water balance partitioning (precipitation divided into evaporation and runoff) in 22 large basins of the world, whereby we identify two partitioning patterns likely related to biophysical mechanisms that depend on the presence and abundance of forests. In less forested basins, evaporation dominates water balance and, as forest cover increases, this dominance of evaporation over runoff is reduced. When forest is the predominant cover, both components account for nearly half of precipitation in the long-term water balance. The distinction between these two patterns is not fully explained by differences between water- and energy-limited environments, but requires consideration of other biophysical properties that affect precipitation and its conversion into evaporation and runoff. Our results indicate that forest cover is an effective descriptor of basin attributes that are relevant for characterizing long-term water balance partitioning in large basins of the world. Further, our results provide insights to understanding and predicting the potential consequences of forest loss on continental water availability, a critical determinant for multiple ecological and societal processes.