How uncertain are precipitation and peak flow estimates for the July 2021 flooding event?

The disastrous July 2021 flooding event made us question the ability of current hydrometeorological tools in providing timely and reliable flood forecasts for unprecedented events. This is an urgent concern since extreme events are increasing due to global warming, and existing methods are usually limited to more frequently observed events with the usual flood generation processes. For the July 2021 event, we simulated the hourly streamflows of seven catchments located in western Germany by combining seven partly polarimetric, radar-based quantitative precipitation estimates (QPEs) with two hydrological models: a conceptual lumped model (GR4H) and a physically based, 3D distributed model (ParFlowCLM). GR4H parameters were calibrated with an emphasis on high flows using historical discharge observations, whereas ParFlowCLM parameters were estimated based on landscape and soil properties. The key results are as follows. (1) With no correction of the vertical profiles of radar variables, radar-based QPE products underestimated the total precipitation depth relative to rain gauges due to intense collision–coalescence processes near the surface, i.e., below the height levels monitored by the radars. (2) Correcting the vertical profiles of radar variables led to substantial improvements. (3) The probability of exceeding the highest measured peak flow before July 2021 was highly impacted by the QPE product, and this impact depended on the catchment for both models. (4) The estimation of model parameters had a larger impact than the choice of QPE product, but simulated peak flows of ParFlowCLM agreed with those of GR4H for five of the seven catchments. This study highlights the need for the correction of vertical profiles of reflectivity and other polarimetric variables near the surface to improve radar-based QPEs for extreme flooding events. It also underlines the large uncertainty in peak flow estimates due to model parameter estimation.</p

Advanced Remote Sensing Precipitation Input for Improved Runoff Simulation : Local to regional scale modelling

Accurate precipitation data are crucial for hydrological modelling and rainwater runoff management. Precipitation variability exists through a wide range of spatial and temporal scales and cannot be captured well using sparse rain gauge networks. This limitation is further emphasised for urban and mountainous catchments, especially under global warming, causing an increased frequency of extreme events. Recent advances in remote sensing (RS) techniques make monitoring precipitation possible over larger areas at more regular resolutions than conventional rain gauge networks. The RS data can be biased mainly due to the indirect estimations prone to multiple error sources and temporally discrete observations. The wealth of spatiotemporal precipitation data by RS, however, calls for developing data-driven solutions for both the bias correction and hydrological modelling that, in turn, requires new procedures to assure generalization of the existing methods. The present dissertation comprises a comprehensive summary followed by five appended papers, attempting to evaluate quantitative precipitation estimations (QPE) by state-of-the-art instruments/products for local and regional hydrological applications. Accordingly, two recently installed dual polarimetric doppler X-band weather radars (X-WRs) in southern Sweden and multiple Global Precipitation Mission (GPM) products in Iran were studied at the relevant scales for urban hydrology (1–5-min and sub-km) and large water supply river–reservoir system operation (daily-monthly and 0.1°), respectively. The validation against rain gauge observations (Paper I and II) showed a significant dependency of the X-WR and GPM precipitation errors on the radial distance and regional precipitation pattern, respectively. Taking observations from local tipping bucket rain gauges at the 1–30-km ranges as a reference, the apparent problems with a single X-WR is related to the attenuation during heavy rains and overshooting (at higher elevation angle scans). An internationally bias-corrected GPM product called GPM-IMERG-Final shows a generally good correlation to synoptic observations of over 300 rain gauges in Iran except for extreme observations that are much better predicted by the GPM-IMERG Late product during spring, summer, and autumn seasons. To leverage the wealth of spatiotemporally complete and validated precipitation data for hydrological modelling, two novel data-driven procedures using artificial neural networks (ANNs) were developed. As in Paper III, the formulation of the new ANN input variables, namely, ECOVs and CCOVs, representing the event- and catchment-specific areal precipitation coverage ratios, improve monthly runoff estimations in all the studied sub-catchments of the Karkheh River basin (KRB) in the mountainous semi-arid climate of western Iran. Merging the doppler and dual-polarization data in the overlapping coverage of the two XWRs (Paper IV) via an ANN-based QPE improves rainfall detection and accuracy. ANN-assisted estimation of rainfall quantiles, compared to the merging with an empirically based regression model, also shows better results especially related to the extreme 5-min data. Finally, Paper V describes the impact of human activities such as agricultural developments that can equally affect the runoff variation. This fact is considered in Paper III by including MODIS Terra products as additional inputs

How uncertain are precipitation and peak flow estimates for the July 2021 flooding event?

The disastrous July 2021 flooding event made us question the ability of current hydrometeorological tools in providing timely and reliable flood forecasts for unprecedented events. This is an urgent concern since extreme events are increasing due to global warming, and existing methods are usually limited to more frequently observed events with the usual flood generation processes. For the July 2021 event, we simulated the hourly streamflows of seven catchments located in western Germany by combining seven partly polarimetric, radar-based quantitative precipitation estimates (QPEs) with two hydrological models: a conceptual lumped model (GR4H) and a physically based, 3D distributed model (ParFlowCLM). GR4H parameters were calibrated with an emphasis on high flows using historical discharge observations, whereas ParFlowCLM parameters were estimated based on landscape and soil properties. The key results are as follows. (1) With no correction of the vertical profiles of radar variables, radar-based QPE products underestimated the total precipitation depth relative to rain gauges due to intense collision–coalescence processes near the surface, i.e., below the height levels monitored by the radars. (2) Correcting the vertical profiles of radar variables led to substantial improvements. (3) The probability of exceeding the highest measured peak flow before July 2021 was highly impacted by the QPE product, and this impact depended on the catchment for both models. (4) The estimation of model parameters had a larger impact than the choice of QPE product, but simulated peak flows of ParFlowCLM agreed with those of GR4H for five of the seven catchments. This study highlights the need for the correction of vertical profiles of reflectivity and other polarimetric variables near the surface to improve radar-based QPEs for extreme flooding events. It also underlines the large uncertainty in peak flow estimates due to model parameter estimation.</p

Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SM measurement and monitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security

Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World

Remotely sensed geophysical datasets are being produced at increasingly fast rates to monitor various aspects of the Earth system in a rapidly changing world. The efficient and innovative use of these datasets to understand hydrological processes in various climatic and vegetation regimes under anthropogenic impacts has become an important challenge, but with a wide range of research opportunities. The ten contributions in this Special Issue have addressed the following four research topics: (1) Evapotranspiration estimation; (2) rainfall monitoring and prediction; (3) flood simulations and predictions; and (4) monitoring of ecohydrological processes using remote sensing techniques. Moreover, the authors have provided broader discussions on how to capitalize on state-of-the-art remote sensing techniques to improve hydrological model simulations and predictions, to enhance their skills in reproducing processes for the fast-changing world