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Abstract
The Turkana Jet in northern Kenya is shown to modulate the climate of southwest Ethiopia’s Omo River Valley using in situ hydrometeorological data, satellite measurements, and atmospheric reanalyses from decadal to diurnal time scales. Temporal statistics from lowland (2.5°–5°N, 35°–38°E) and highland (6°–9°N, 35°–38°E) areas show that 850-hPa westward airflow over Lake Turkana is stronger in March and October but is weakened when western Indian Ocean sea temperatures become warmer than usual at intervals of 2–7 years. A case study on 24 March 2019 reveals how a stronger Turkana Jet induces warming and drying of the Omo Valley. A second case study on 27 September 2018 reveals Hadley cell subsidence over the southern flank of the Turkana Jet. We demonstrate how nocturnal airflow draining off the mountains joins the channelized jet. Satellite and atmospheric reanalyses exhibit realistic diurnal cycles in the east Omo mountains, but some products have incorrect phase and warm bias. Omo Valley soil moisture and runoff exhibit little trend in historical records and model projections; however, unpredictable multiyear wet and dry spells and a growing demand for water are ongoing concerns.
Abstract
The Turkana Jet in northern Kenya is shown to modulate the climate of southwest Ethiopia’s Omo River Valley using in situ hydrometeorological data, satellite measurements, and atmospheric reanalyses from decadal to diurnal time scales. Temporal statistics from lowland (2.5°–5°N, 35°–38°E) and highland (6°–9°N, 35°–38°E) areas show that 850-hPa westward airflow over Lake Turkana is stronger in March and October but is weakened when western Indian Ocean sea temperatures become warmer than usual at intervals of 2–7 years. A case study on 24 March 2019 reveals how a stronger Turkana Jet induces warming and drying of the Omo Valley. A second case study on 27 September 2018 reveals Hadley cell subsidence over the southern flank of the Turkana Jet. We demonstrate how nocturnal airflow draining off the mountains joins the channelized jet. Satellite and atmospheric reanalyses exhibit realistic diurnal cycles in the east Omo mountains, but some products have incorrect phase and warm bias. Omo Valley soil moisture and runoff exhibit little trend in historical records and model projections; however, unpredictable multiyear wet and dry spells and a growing demand for water are ongoing concerns.
Abstract
The Tigris–Euphrates dryland river basin has experienced a declining trend in terrestrial water storage (TWS) from April 2002 to June 2017. Using satellite observations and a process-based land surface model, we find that climate variations and direct human interventions explain ∼61% (−0.57 mm month−1) and ∼39% (−0.36 mm month−1) of the negative trend, respectively. We further disaggregate the effects of climate variations and find that interannual climate variability contributes substantially (−0.27 mm month−1) to the negative TWS trend, slightly greater than the decadal climate change (−0.25 mm month−1). Interannual climate variability affects TWS mainly through the nonlinear relationship between monthly TWS dynamics and aridity. Slow recovery of TWS during short wetting periods does not compensate for rapid depletion of TWS through transpiration during prolonged drying periods. Despite enhanced water stress, the dryland ecosystems show slightly enhanced resilience to water stress through greater partitioning of evapotranspiration into transpiration and weak surface “greening” effects. However, the dryland ecosystems are vulnerable to drought impacts. The basin shows straining ecosystem functioning after experiencing a severe drought event. In addition, after the onset of the drought, the dryland ecosystem becomes more sensitive to variations in climate conditions.
Significance Statement
The purpose of the research is to better understand climate impacts on terrestrial water storage over dryland regions with declining water storage. In our study, we disaggregate three components of climate impacts, namely, decadal climate change, interannual variability, and intra-annual variability. We then use observational datasets and a process-based model to quantify their individual effects on water storage. We find that interannual variability is the most significant climatic contributor to the declining water storage, mainly caused by prolonged drought periods and corresponding quick drying rates due to plant transpiration. We also find that the dryland ecosystem is sensitive and vulnerable to severe drought events. This study is important because 1) it provides a framework to investigate climate impacts on water fluxes and storages, 2) it highlights the importance of vegetation dynamics on dryland hydrology, and 3) it emphasizes the negative impacts of extreme hydroclimatological events on ecosystem functioning.
Abstract
The Tigris–Euphrates dryland river basin has experienced a declining trend in terrestrial water storage (TWS) from April 2002 to June 2017. Using satellite observations and a process-based land surface model, we find that climate variations and direct human interventions explain ∼61% (−0.57 mm month−1) and ∼39% (−0.36 mm month−1) of the negative trend, respectively. We further disaggregate the effects of climate variations and find that interannual climate variability contributes substantially (−0.27 mm month−1) to the negative TWS trend, slightly greater than the decadal climate change (−0.25 mm month−1). Interannual climate variability affects TWS mainly through the nonlinear relationship between monthly TWS dynamics and aridity. Slow recovery of TWS during short wetting periods does not compensate for rapid depletion of TWS through transpiration during prolonged drying periods. Despite enhanced water stress, the dryland ecosystems show slightly enhanced resilience to water stress through greater partitioning of evapotranspiration into transpiration and weak surface “greening” effects. However, the dryland ecosystems are vulnerable to drought impacts. The basin shows straining ecosystem functioning after experiencing a severe drought event. In addition, after the onset of the drought, the dryland ecosystem becomes more sensitive to variations in climate conditions.
Significance Statement
The purpose of the research is to better understand climate impacts on terrestrial water storage over dryland regions with declining water storage. In our study, we disaggregate three components of climate impacts, namely, decadal climate change, interannual variability, and intra-annual variability. We then use observational datasets and a process-based model to quantify their individual effects on water storage. We find that interannual variability is the most significant climatic contributor to the declining water storage, mainly caused by prolonged drought periods and corresponding quick drying rates due to plant transpiration. We also find that the dryland ecosystem is sensitive and vulnerable to severe drought events. This study is important because 1) it provides a framework to investigate climate impacts on water fluxes and storages, 2) it highlights the importance of vegetation dynamics on dryland hydrology, and 3) it emphasizes the negative impacts of extreme hydroclimatological events on ecosystem functioning.
Abstract
Supertyphoon rainstorms are apposite examples to evaluate the utility of multisource precipitation products in monitoring and forecasting short-duration heavy rainfall and the resulting intense floods. In this study, the record-breaking floods induced by Typhoon Lekima in Jiao River, China, were retrospectively forecasted. The Xinanjiang (XAJ) model was calibrated based on parameter regionalization derived from SOM+k-means clustering. Via XAJ, the performance of the currently prevailing atmosphere reanalysis (CLDASv2 and CMA-CMORP), quantitative precipitation estimation (QPE) (IMERG-ER and PERSIANN-CCS), and quantitative precipitation forecasts (QPFs) (GRAPES_MESO, ECMWF, and GFS) in monitoring and forecasting Lekima rainfall and flood was comprehensively evaluated. A three-component blended ensemble was proposed, by blending QPE nowcasts with the weighted ensemble of QPFs through a transition of the regional GRAPES_MESO, and compared with two conventional two-component blending methods. The results indicated that the parameter regionalization enabled an explicit consideration of the spatial heterogeneity of basin attributes as well as meteorology, resulting in a minimum NSE of 0.81. CLDASv2 and CMA-CMORPH provided superior spatiotemporal accuracy with a structural similarity index up to 0.75 and NSE > 0.9 for the flood simulation. PERSIANN-CCS rainfall and the driven flood were seriously underestimated by 70% and 80%, respectively. The real-time application of QPFs during the Lekima flood provided encouraging results with a lead time of 40 h. The three-component blended ensemble method resulted in more stable and accurate flood forecasts, especially for the flood peak on 9 August, which was improved by 80%. Our results are expected to present support for real-time flood preparation and mitigation with practical significance.
Abstract
Supertyphoon rainstorms are apposite examples to evaluate the utility of multisource precipitation products in monitoring and forecasting short-duration heavy rainfall and the resulting intense floods. In this study, the record-breaking floods induced by Typhoon Lekima in Jiao River, China, were retrospectively forecasted. The Xinanjiang (XAJ) model was calibrated based on parameter regionalization derived from SOM+k-means clustering. Via XAJ, the performance of the currently prevailing atmosphere reanalysis (CLDASv2 and CMA-CMORP), quantitative precipitation estimation (QPE) (IMERG-ER and PERSIANN-CCS), and quantitative precipitation forecasts (QPFs) (GRAPES_MESO, ECMWF, and GFS) in monitoring and forecasting Lekima rainfall and flood was comprehensively evaluated. A three-component blended ensemble was proposed, by blending QPE nowcasts with the weighted ensemble of QPFs through a transition of the regional GRAPES_MESO, and compared with two conventional two-component blending methods. The results indicated that the parameter regionalization enabled an explicit consideration of the spatial heterogeneity of basin attributes as well as meteorology, resulting in a minimum NSE of 0.81. CLDASv2 and CMA-CMORPH provided superior spatiotemporal accuracy with a structural similarity index up to 0.75 and NSE > 0.9 for the flood simulation. PERSIANN-CCS rainfall and the driven flood were seriously underestimated by 70% and 80%, respectively. The real-time application of QPFs during the Lekima flood provided encouraging results with a lead time of 40 h. The three-component blended ensemble method resulted in more stable and accurate flood forecasts, especially for the flood peak on 9 August, which was improved by 80%. Our results are expected to present support for real-time flood preparation and mitigation with practical significance.
Abstract
Drought events evolve simultaneously in space and time; hence, a proper characterization of an event requires the tracking of its full spatiotemporal evolution. Here we present a generalized algorithm for the tracking of drought events based on a three-dimensional application of the DBSCAN (density-based spatial clustering of applications with noise) clustering approach. The need for a generalized and flexible algorithm is dictated by the absence of a unanimous consensus on the definition of a drought event, which often depends on the target of the study. The proposed methodology introduces a set of six parameters that control both the spatial and the temporal connectivity between cells under drought conditions, also accounting for the local intensity of the drought itself. The capability of the algorithm to adapt to different drought definitions is tested successfully over a study case in Australia in the period 2017–20 using a set of standardized precipitation index (SPI) data derived from the ERA5 precipitation reanalysis. Insights on the possible range of variability of the model parameters, as well as on their effects on the delineation of drought events, are provided for the case of meteorological droughts in order to incentivize further applications of the methodology.
Abstract
Drought events evolve simultaneously in space and time; hence, a proper characterization of an event requires the tracking of its full spatiotemporal evolution. Here we present a generalized algorithm for the tracking of drought events based on a three-dimensional application of the DBSCAN (density-based spatial clustering of applications with noise) clustering approach. The need for a generalized and flexible algorithm is dictated by the absence of a unanimous consensus on the definition of a drought event, which often depends on the target of the study. The proposed methodology introduces a set of six parameters that control both the spatial and the temporal connectivity between cells under drought conditions, also accounting for the local intensity of the drought itself. The capability of the algorithm to adapt to different drought definitions is tested successfully over a study case in Australia in the period 2017–20 using a set of standardized precipitation index (SPI) data derived from the ERA5 precipitation reanalysis. Insights on the possible range of variability of the model parameters, as well as on their effects on the delineation of drought events, are provided for the case of meteorological droughts in order to incentivize further applications of the methodology.
Abstract
The demand of accurate, near-real-time radar-based quantitative precipitation estimation (QPE), which is key to feed hydrological models and enable reliable flash flood predictions, was highlighted again by the disastrous floods following after an intense stratiform precipitation field passing western Germany on 14 July 2021. Three state-of-the-art rainfall algorithms based on reflectivity Z, specific differential phase K DP, and specific attenuation A were applied to observations of four polarimetric C-band radars operated by the German Meteorological Service [DWD (Deutscher Wetterdienst)]. Due to the large vertical gradients of precipitation below the melting layer suggesting warm-rain processes, all QPE products significantly underestimate surface precipitation. We propose two mitigation approaches: (i) vertical profile (VP) corrections for Z and K DP and (ii) gap filling using observations of a local X-band radar, JuXPol. We also derive rainfall retrievals from vertically pointing Micro Rain Radar (MRR) profiles, which indirectly take precipitation gradients in the lower few hundreds of meters into account. When evaluated with DWD rain gauge measurements, those retrievals result in pronounced improvements, especially for the A-based retrieval partly due to its lower sensitivity to the variability of raindrop size distributions. The VP correction further improves QPE by reducing the normalized root-mean-square error by 23% and the normalized mean bias by 20%. With the use of gap-filling JuXPol data, the A-based retrieval gives the lowest errors followed by the Z-based retrievals in combination with VP corrections. The presented algorithms demonstrate the increased value of radar-based QPE application for warm-rain events and related potential flash flooding warnings.
Abstract
The demand of accurate, near-real-time radar-based quantitative precipitation estimation (QPE), which is key to feed hydrological models and enable reliable flash flood predictions, was highlighted again by the disastrous floods following after an intense stratiform precipitation field passing western Germany on 14 July 2021. Three state-of-the-art rainfall algorithms based on reflectivity Z, specific differential phase K DP, and specific attenuation A were applied to observations of four polarimetric C-band radars operated by the German Meteorological Service [DWD (Deutscher Wetterdienst)]. Due to the large vertical gradients of precipitation below the melting layer suggesting warm-rain processes, all QPE products significantly underestimate surface precipitation. We propose two mitigation approaches: (i) vertical profile (VP) corrections for Z and K DP and (ii) gap filling using observations of a local X-band radar, JuXPol. We also derive rainfall retrievals from vertically pointing Micro Rain Radar (MRR) profiles, which indirectly take precipitation gradients in the lower few hundreds of meters into account. When evaluated with DWD rain gauge measurements, those retrievals result in pronounced improvements, especially for the A-based retrieval partly due to its lower sensitivity to the variability of raindrop size distributions. The VP correction further improves QPE by reducing the normalized root-mean-square error by 23% and the normalized mean bias by 20%. With the use of gap-filling JuXPol data, the A-based retrieval gives the lowest errors followed by the Z-based retrievals in combination with VP corrections. The presented algorithms demonstrate the increased value of radar-based QPE application for warm-rain events and related potential flash flooding warnings.
Abstract
Uncertainty in quantitative precipitation forecasts (QPFs) from numerical weather prediction (NWP) models manifests in errors in the amounts of rainfall, storm structure, storm location, and timing, among other precipitation characteristics. In flash flood forecasting applications, errors in the QPFs can translate into significant uncertainty in forecasts of surface water flows and their impacts. In particular, the QPF errors in location and structure result in errors on flow paths, which can be highly detrimental in identifying locations susceptible to flash flood impacts. To account for this type of uncertainty, the neighboring pixel ensemble technique (NPET) was devised and implemented as a postprocessing algorithm of deterministic or ensemble outputs from a distributed hydrologic model. The aim of the technique is to address displaced hydrologic responses resulting from location biases in QPFs using a probabilistic approach. NPET identifies a sampling region surrounding each forecast pixel and builds an ensemble of surface water flow values considering the pixel’s physiographic similarities. The probabilistic information produced with NPET can be calibrated through a set of tunable parameters that are adjusted to account for NWP-specific QPF error characteristics. The utility of NPET is demonstrated for the Ellicott City flash flood event on 27 May 2018, using products and tools routinely used in the U.S. National Weather Service for warning operations. Results from this case demonstrate that NPET effectively conveys uncertainty information about QPF precipitation location in a hydrologic context.
Significance Statement
This study introduces a new method suitable for operational use called the neighboring pixel ensemble technique (NPET). NPET is an algorithm that generates ensemble-based streamflow forecasts accounting for the location uncertainties in quantitative precipitation forecasts (QPFs) without the requirement of multiple hydrologic model runs. NPET is capable of this feat through probabilistic assimilation of a priori QPF displacement information and its uncertainty. The application of NPET with the Flooded Locations and Simulated Hydrographs (FLASH) project shows the technique could be beneficial for flash flood warning operations in the U.S. National Weather Service (NWS). It is envisioned that the application of NPET with Warn-on-Forecast System (WoFS)-forced FLASH outputs will further enhance the quality of flash flood forecasts that support NWS warning operations.
Abstract
Uncertainty in quantitative precipitation forecasts (QPFs) from numerical weather prediction (NWP) models manifests in errors in the amounts of rainfall, storm structure, storm location, and timing, among other precipitation characteristics. In flash flood forecasting applications, errors in the QPFs can translate into significant uncertainty in forecasts of surface water flows and their impacts. In particular, the QPF errors in location and structure result in errors on flow paths, which can be highly detrimental in identifying locations susceptible to flash flood impacts. To account for this type of uncertainty, the neighboring pixel ensemble technique (NPET) was devised and implemented as a postprocessing algorithm of deterministic or ensemble outputs from a distributed hydrologic model. The aim of the technique is to address displaced hydrologic responses resulting from location biases in QPFs using a probabilistic approach. NPET identifies a sampling region surrounding each forecast pixel and builds an ensemble of surface water flow values considering the pixel’s physiographic similarities. The probabilistic information produced with NPET can be calibrated through a set of tunable parameters that are adjusted to account for NWP-specific QPF error characteristics. The utility of NPET is demonstrated for the Ellicott City flash flood event on 27 May 2018, using products and tools routinely used in the U.S. National Weather Service for warning operations. Results from this case demonstrate that NPET effectively conveys uncertainty information about QPF precipitation location in a hydrologic context.
Significance Statement
This study introduces a new method suitable for operational use called the neighboring pixel ensemble technique (NPET). NPET is an algorithm that generates ensemble-based streamflow forecasts accounting for the location uncertainties in quantitative precipitation forecasts (QPFs) without the requirement of multiple hydrologic model runs. NPET is capable of this feat through probabilistic assimilation of a priori QPF displacement information and its uncertainty. The application of NPET with the Flooded Locations and Simulated Hydrographs (FLASH) project shows the technique could be beneficial for flash flood warning operations in the U.S. National Weather Service (NWS). It is envisioned that the application of NPET with Warn-on-Forecast System (WoFS)-forced FLASH outputs will further enhance the quality of flash flood forecasts that support NWS warning operations.
Abstract
Extreme rainfall from extratropical cyclones and the distinctive hydrology of the winter season both contribute to flood extremes in the Mid-Atlantic region. In this study, we examine extreme rainfall and flooding from a winter season extratropical cyclone that passed through the eastern United States on 24/25 February 2016. Extreme rainfall rates during the 24/25 February 2016 time period were produced by supercell thunderstorms; we identify supercells through local maxima in azimuthal shear fields computed from Doppler velocity measurements from WSR-88D radars. Rainfall rates approaching 250 mm h−1 from a long-lived supercell in New Jersey were measured by a Parsivel disdrometer. A distinctive element of the storm environment for the 24/25 February 2016 storm was elevated values of convective available potential energy (CAPE). We also examine the climatology of atmospheric rivers (ARs)—like the February 2016 storm—based on an identification and tracking algorithm that uses Twentieth Century Reanalysis fields for the 66-yr period from 1950 to 2015. Climatological analyses suggest that AR frequency is increasing over the Mid-Atlantic region. An increase in AR frequency, combined with increasing frequency of elevated CAPE during the winter season over the Mid-Atlantic region, could result in striking changes to the climatology of extreme floods.
Abstract
Extreme rainfall from extratropical cyclones and the distinctive hydrology of the winter season both contribute to flood extremes in the Mid-Atlantic region. In this study, we examine extreme rainfall and flooding from a winter season extratropical cyclone that passed through the eastern United States on 24/25 February 2016. Extreme rainfall rates during the 24/25 February 2016 time period were produced by supercell thunderstorms; we identify supercells through local maxima in azimuthal shear fields computed from Doppler velocity measurements from WSR-88D radars. Rainfall rates approaching 250 mm h−1 from a long-lived supercell in New Jersey were measured by a Parsivel disdrometer. A distinctive element of the storm environment for the 24/25 February 2016 storm was elevated values of convective available potential energy (CAPE). We also examine the climatology of atmospheric rivers (ARs)—like the February 2016 storm—based on an identification and tracking algorithm that uses Twentieth Century Reanalysis fields for the 66-yr period from 1950 to 2015. Climatological analyses suggest that AR frequency is increasing over the Mid-Atlantic region. An increase in AR frequency, combined with increasing frequency of elevated CAPE during the winter season over the Mid-Atlantic region, could result in striking changes to the climatology of extreme floods.
Abstract
The variability of water year precipitation and selected blue oak tree-ring chronologies in California are both dominated by heavy precipitation delivered during just a few days each year. These heavy precipitation events can spell the difference between surplus or deficit water supply and elevated flood risk. Some blue oak chronologies are highly correlated with water year precipitation (r = 0.84) but are equally well correlated (r = 0.82) with heavy precipitation totals ≥25.4 mm (1 in., ≈95th percentile of daily totals, 1949–2004). The blue oak correlation with nonheavy daily totals is much weaker (<25.4 mm; r = 0.55). Consequently, some blue oak chronologies represent selective proxies for the temporal and spatial variability of heavy precipitation totals and are used to reconstruct the amount and number of days with heavy precipitation in northern California from 1582 to 2021. Instrumental and reconstructed heavy precipitation totals are strongly correlated with gridded atmospheric river–related precipitation over the western United States, especially in central California. Spectral analysis indicates that instrumental heavy precipitation totals may be dominated by high-frequency variability and the nonheavy totals by low-frequency variance. The reconstruction of heavy precipitation is coherent with instrumental heavy totals across the frequency domain and include concentrations of variance at ENSO and biennial frequencies. Return period analyses calculated using instrumental heavy precipitation totals are representative of the return periods in the blue oak reconstruction despite the large differences in series length. Decadal surges in the amount, frequency, and interannual volatility of heavy precipitation totals are reconstructed, likely reflecting episodes of elevated atmospheric river activity in the past.
Significance Statement
Tree-ring chronologies of blue oak are highly correlated with precipitation delivered to northern California during just the heaviest days of precipitation each year. The reconstruction of heavy precipitation indicates decadal episodes with a high frequency of extreme precipitation. These episodes of frequent heavy precipitation likely arose because of elevated atmospheric river activity and are relevant to the analysis of water supply and flood hazard in California.
Abstract
The variability of water year precipitation and selected blue oak tree-ring chronologies in California are both dominated by heavy precipitation delivered during just a few days each year. These heavy precipitation events can spell the difference between surplus or deficit water supply and elevated flood risk. Some blue oak chronologies are highly correlated with water year precipitation (r = 0.84) but are equally well correlated (r = 0.82) with heavy precipitation totals ≥25.4 mm (1 in., ≈95th percentile of daily totals, 1949–2004). The blue oak correlation with nonheavy daily totals is much weaker (<25.4 mm; r = 0.55). Consequently, some blue oak chronologies represent selective proxies for the temporal and spatial variability of heavy precipitation totals and are used to reconstruct the amount and number of days with heavy precipitation in northern California from 1582 to 2021. Instrumental and reconstructed heavy precipitation totals are strongly correlated with gridded atmospheric river–related precipitation over the western United States, especially in central California. Spectral analysis indicates that instrumental heavy precipitation totals may be dominated by high-frequency variability and the nonheavy totals by low-frequency variance. The reconstruction of heavy precipitation is coherent with instrumental heavy totals across the frequency domain and include concentrations of variance at ENSO and biennial frequencies. Return period analyses calculated using instrumental heavy precipitation totals are representative of the return periods in the blue oak reconstruction despite the large differences in series length. Decadal surges in the amount, frequency, and interannual volatility of heavy precipitation totals are reconstructed, likely reflecting episodes of elevated atmospheric river activity in the past.
Significance Statement
Tree-ring chronologies of blue oak are highly correlated with precipitation delivered to northern California during just the heaviest days of precipitation each year. The reconstruction of heavy precipitation indicates decadal episodes with a high frequency of extreme precipitation. These episodes of frequent heavy precipitation likely arose because of elevated atmospheric river activity and are relevant to the analysis of water supply and flood hazard in California.
Abstract
The Surface Water and Ocean Topography (SWOT) mission will allow the estimation of discharge in rivers wider than 100 m, filling important gaps in the network of in situ measurements. This novel source of discharge observations has the potential to enable significant progress toward closing Earth’s water budget and creating new understanding of the water cycle. Quantifying the uncertainty in the SWOT estimates of discharge, mapping the error sources, and understanding their relative importance is essential to the fulfillment of this potential. Here, we break the SWOT discharge production process into its essential parts: 1) retrieval of river width and water surface heights and slopes, 2) estimation of unobservable parameters, and 3) computation of discharge with the selected flow law, and through a Monte Carlo simulation study, we assess the sensitivity of the overall discharge error to each of these parts. We analyze the discharge error characteristics in terms of bias, error standard deviation, and the correlation between true and retrieved discharges and map the contribution of the essential discharge production elements to each of the error metrics. Our study revealed that biases in parameters are the most important source of discharge biases, yet we found that a larger than expected fraction of the discharge biases can be attributed to observation errors. Surprisingly, we found that parameter biases are also the most important contributor to discharge error standard deviation and to the deterioration of the correlation between truth and retrieved discharges, which were previously thought of as being controlled by observation errors.
Abstract
The Surface Water and Ocean Topography (SWOT) mission will allow the estimation of discharge in rivers wider than 100 m, filling important gaps in the network of in situ measurements. This novel source of discharge observations has the potential to enable significant progress toward closing Earth’s water budget and creating new understanding of the water cycle. Quantifying the uncertainty in the SWOT estimates of discharge, mapping the error sources, and understanding their relative importance is essential to the fulfillment of this potential. Here, we break the SWOT discharge production process into its essential parts: 1) retrieval of river width and water surface heights and slopes, 2) estimation of unobservable parameters, and 3) computation of discharge with the selected flow law, and through a Monte Carlo simulation study, we assess the sensitivity of the overall discharge error to each of these parts. We analyze the discharge error characteristics in terms of bias, error standard deviation, and the correlation between true and retrieved discharges and map the contribution of the essential discharge production elements to each of the error metrics. Our study revealed that biases in parameters are the most important source of discharge biases, yet we found that a larger than expected fraction of the discharge biases can be attributed to observation errors. Surprisingly, we found that parameter biases are also the most important contributor to discharge error standard deviation and to the deterioration of the correlation between truth and retrieved discharges, which were previously thought of as being controlled by observation errors.
Abstract
Developing effective methods for estimating regional-scale surface water storage change (ΔSW) has become increasingly important for water resources studies and environmental impact assessment. Three methods for estimating monthly ΔSW are proposed in this study, of which one is based on land surface runoff and two that use water body water budgets. Water areas observed by Landsat satellites for Canada’s entire landmass are used for evaluation of the results. The surface runoff method achieved the least satisfactory results, with large errors in the cold season or dry regions. The two water-budget methods demonstrated significant improvements, particularly when water area dynamics is considered in the estimation of the water body water budget. The three methods performed consistently across different climate regions in the country and showed better correlations with observations over wet climate regions than over dry regions with poorly connected hydrological system. The results also showed the impact of glacier and permanent snow melts over the Rocky Mountains on basin-scale surface water dynamics. The methods and outputs from this study can be used for calibrating and validating hydrological and climate models, assessing climate change and human disturbance impacts on regional water resources, and filling the ΔSW data gaps in GRACE-based total water storage decompositions studies.
Significance Statement
The purpose of this study is to develop and evaluate methods for estimating regional-scale surface water storage change. This is important because information on surface water dynamics is limited for water resources studies and environmental impact assessment. Our study makes available two new methods which significantly improve on surface water storage estimation from the traditional runoff model. A guide on controls of surface water dynamics is provided for regions under various hydroclimate and physiographic–hydraulic conditions and reveals the influence of glacier melt on surface water variations.
Abstract
Developing effective methods for estimating regional-scale surface water storage change (ΔSW) has become increasingly important for water resources studies and environmental impact assessment. Three methods for estimating monthly ΔSW are proposed in this study, of which one is based on land surface runoff and two that use water body water budgets. Water areas observed by Landsat satellites for Canada’s entire landmass are used for evaluation of the results. The surface runoff method achieved the least satisfactory results, with large errors in the cold season or dry regions. The two water-budget methods demonstrated significant improvements, particularly when water area dynamics is considered in the estimation of the water body water budget. The three methods performed consistently across different climate regions in the country and showed better correlations with observations over wet climate regions than over dry regions with poorly connected hydrological system. The results also showed the impact of glacier and permanent snow melts over the Rocky Mountains on basin-scale surface water dynamics. The methods and outputs from this study can be used for calibrating and validating hydrological and climate models, assessing climate change and human disturbance impacts on regional water resources, and filling the ΔSW data gaps in GRACE-based total water storage decompositions studies.
Significance Statement
The purpose of this study is to develop and evaluate methods for estimating regional-scale surface water storage change. This is important because information on surface water dynamics is limited for water resources studies and environmental impact assessment. Our study makes available two new methods which significantly improve on surface water storage estimation from the traditional runoff model. A guide on controls of surface water dynamics is provided for regions under various hydroclimate and physiographic–hydraulic conditions and reveals the influence of glacier melt on surface water variations.
