Assessment of 13 Gridded Precipitation Datasets for Hydrological Modeling in a Mountainous Basin

Precipitation measurement with high spatial and temporal resolution over highly elevated and complex terrain in the eastern part of Turkey is an essential task to manage the water structures in an optimum manner. The objective of this study is to evaluate the consistency and hydrologic utility of 13 Gridded Precipitation Datasets (GPDs) (CPCv1, MSWEPv2.8, ERA5, CHIRPSv2.0, CHIRPv2.0, IMERGHHFv06, IMERGHHEv06, IMERGHHLv06, TMPA-3B42v7, TMPA-3B42RTv7, PERSIANN-CDR, PERSIANN-CCS, and PERSIANN) over a mountainous test basin (Karasu) at a daily time step. The Kling-Gupta Efficiency (KGE), including its three components (correlation, bias, and variability ratio), and the Nash-Sutcliffe Efficiency (NSE) are used for GPD evaluation. Moreover, the Hanssen-Kuiper (HK) score is considered to evaluate the detectability strength of selected GPDs for different precipitation events. Precipitation frequencies are evaluated considering the Probability Density Function (PDF). Daily precipitation data from 23 meteorological stations are provided as a reference for the period of 2015–2019. The TUW model is used for hydrological simulations regarding observed discharge located at the outlet of the basin. The model is calibrated in two ways, with observed precipitation only and by each GPD individually. Overall, CPCv1 shows the highest performance (median KGE; 0.46) over time and space. MSWEPv2.8 and CHIRPSv2.0 deliver the best performance among multi-source merging datasets, followed by CHIRPv2.0, whereas IMERGHHFv06, PERSIANN-CDR, and TMPA-3B42v7 show poor performance. IMERGHHLv06 is able to present the best performance (median KGE; 0.17) compared to other satellite-based GPDs (PERSIANN-CCS, PERSIANN, IMERGHHEv06, and TMPA-3B42RTv7). ERA5 performs well both in spatial and temporal validation compared to satellite-based GPDs, though it shows low performance in producing a streamflow simulation. Overall, all gridded precipitation datasets show better performance in generating streamflow when the model is calibrated by each GPD separately.

SWAT Application for Gajahwong River Streamflow Simulation

Gadjahwong River flows along Daerah Istimewa Yogyakarta (DIY) that is located in southern Java Island. Gadjahwong River has an important role for water supply purpose especially for agriculture activities. On the other hand, DIY is seeing 1.18% population growth each year. The population surge influences land cover change that can seize continuous discharge of Gadjahwong River. Therefore, continuous discharge simulation needs to be conducted for assessing Gadjahwong River water availability. Soil and Water Assessment Tool (SWAT) was employed for modelling Gadjahwong River streamflow discharge. The simulation result discharge was compared with observed data acquired at AWLR Wonokromo by using NSE and R2 statistical parameter. Finally, the statistical parameter was applied to justify quality of simulation. Findings showed that daily time step yielded NSE value of 0.61, R2 value of 0.79, and PBIAS value of -2.41%. Overall, the simulation showed good result based the statistical parameters.

The Impact of Gridded Weather Database on Soil Water Availability in Rice Crop Modeling

In recent years, there has been an increase in studies suggesting that gridded weather database (GWD) is a suitable source for simulating crop yield. Brazil has low geospatial coverage by measured weather database (MWD). Based on that, this study aimed to compare two different GWD sources, Daily Gridded (DG) and NASA/POWER (NP), on the simulated yield of upland rice (UR) against the MWD input. The GWD and MWD were obtained for seven locations across UR Brazilian region, considering a period ranging from 1984 to 2016. GWD and MWD were used to estimate rice potential (Yp) and attainable yield (Ya), in clay soil and sandy soil, using ORYZA (v3) model. DG had the best performance for all variables. GWD-based yields had a reasonable performance. However, DG had a slightly better performance than NP in all conditions, DG-based yields showed RMSE values of 0.57, 0.71 and 0.52 for Yp and Ya in clay and sandy soil, whereas NP showed RMSE values of 0.86, 0.91 and 0.64. DG also showed higher R² and d values for yields assessed. Both GWD overestimated Ya, these overestimations in DG-based yield were 3.54, 9.61, and 21.35% for Yp and Ya in clay and sandy soil respectively, in NP-based yield were 13.67, 18.45, 29.11%, showing that for both GWD-based yield increased as the soil type texture as well as water storage decreased. As a consequence, we do not recommend the use of precipitation data in daily time-step crop modeling.

Improvement of seasonal forecast correction by using weather regimes

<p>In order to develop seasonal forecast applications, raw forecast data generally need to be corrected to remove their systematic errors and drifts in time. In the climate community, methods based on quantile mapping techniques are quite common for their easy implementation. In the framework of the SECLI-FIRM project, we have tested a refinement of quantile mapping by conditioning the correction to weather regimes, in order to take large-scale circulation into account. For that purpose, we have used ADAMONT, a tool originally developed by Météo-France to correct climate projection scenarios. It was applied on four C3S seasonal forecast models over Europe, using ERA5 as a reference. Three parameters were treated at daily time-step: 2-metre temperature, precipitation and 10-metre wind-speed.</p><p>One of the main objectives of this study was to better understand the role weather regimes can play, if/when/where/for which parameter we gain in quality and predictability. For instance, a series of experiments were conducted on an idealized case of “perfect forecasts” of weather regimes, to point out the maximum benefits we could expect from the method.</p><p>Another focus of research was to test some strategies to optimize the positive impact of the introduction of weather regimes, by selecting members in one model ensemble or by using a multi-model approach. The selection was based on a sub-sampling of the best members in terms of weather regime frequency forecast, in order to determine the needed precision of weather regime forecast, for it to be useful in the correction.</p><p><span>We</span><span> will present the </span><span>main </span><span>results </span><span>of this work </span><span>and </span><span>some operational perspectives.</span></p>

Using a high-resolution regional climate model large ensemble to simulate the impact of extreme precipitation on flooding over small to medium-size catchments

<p>Recent studies show that the frequency and intensity of extreme precipitation will increase under a warmer climate. It is expected that extreme convective precipitation will scale at a larger than Clausius–Clapeyron rate and especially so for short-duration rainfall. This has implication on flooding risk, and especially so on small catchments (<500 km<sup>2</sup>) which have a quick response time and are therefore particularly vulnerable to short duration rainfall. The impact of the amplification of extreme precipitation as a function of catchment scale has not been widely studied because most of the climate change impact studies have been conducted at the daily time step or higher. This is because until recently the vast majority of climate model outputs have only been available at the daily time step.</p><p>This study has looked at the amplification of sub-daily, daily, and multiday extreme precipitation and flooding and its dependency on catchment scale. This work uses outputs from the Climex large-ensemble to study the amplification of extreme streamflow with return period from 2 to 300 years and durations from 1 to 24 hours over 133 North-American catchments. Using a large ensemble allows for the accurate empirical computation of extreme events with very large return periods.  Results indicate that future extreme streamflow relative increases are largest for smaller catchments, longer return period, and shorter rainfall durations. Small catchments are therefore more vulnerable to future extreme rainfall than their larger counterparts.</p>

Advantages of calibrating a daily rainfall-runoff model to monthly streamflow data

<p>Development of robust approaches for calibrating daily rainfall-runoff models to monthly streamflow data enable modelling platforms that operate at daily time step to be applied in practical situations. Here precipitation is available at the daily scale, but observed streamflow is available only at the monthly scale (e.g. predicting inflows into large dams). This study compares the performance of the daily GR4J hydrological model when calibrated against (1) daily and (2) monthly streamflow data. The performance comparison relies on a wide range of metrics and is undertaken for 508 Australian catchments. Two evaluation periods (1975–1992 and 1992–2015) and four objective functions (including sum-of-squared-errors of Box-Cox transformed streamflow and the Kling-Gupta efficiency) were tested.</p><p>Monthly calibration performs similar to or better than daily calibration in most sites and both periods in terms of bias and fit of the flow duration curve. This result remains the same when the flow duration curve is computed at the daily time step, which constitutes a significant finding of this study.</p><p>However, the performance of monthly calibration is worse than daily calibration for daily pattern metrics such as Nash-Sutcliffe efficiency in most sites and both periods. Significant improvement can be achieved if the flow-timing parameter of GR4J is regionalised, effectively reducing the number of calibrated parameters. Similar results are obtained for other pattern metrics and all objective functions.</p><p>These findings suggest that monthly calibration of rainfall-runoff models using daily-rainfall and monthly-streamflow data is a viable alternative to daily calibration when no daily streamflow data are available.</p>

Groundwater Recharge Indicators at the European scale

<p>One of the goals of the ERA4CS INDECIS project (http://www.indecis.eu/) is to use available climate datasets at the European scale to derive user-oriented indicators. In this framework, we adapted a methodology to compute the present and future groundwater recharge by precipitation at the European scale. This indicator of groundwater availability aims at supporting water resource management.</p><p>The scientific approach partly relies on two indexes related to precipitation infiltration at the watershed scale. The first one is the BaseFlow Index (BFI) which is considered as a fair approximation of the average infiltration coefficient for hydrogeological basins. The second one is the Network Development and Persistence Index (IDPR), a cartographic index calculated from the differences between the real river and the theoretical thalwegs networks. The IDPR provides a qualitative indication of infiltration versus runoff, and is now available at the European scale with a 50 m resolution. We computed the mean interannual BFI over the 1981 – 2010 period for more than 350 gauged and not influenced watersheds distributed over France, with various geological contexts and climates. These BFI values proved to be linearly correlated to the spatial average of the IDPR over these watersheds. The relationship between the two datasets established on these gauged basins was then applied to convert the European IDPR map into an effective precipitation infiltration ratio (EPIR) map.</p><p>The modelling process finally consisted in computing the effective precipitation at a daily time step on each cell of a mesh covering the European area. Three different water budget models were applied. The only parameter of these models is the soil water capacity provided by European Soil Data Centre. For the present period, the models were fed with the E-OBS datasets available on a 0.25 degree grid. Resulting time series were time-averaged and multiplied by the spatialized EPIR to provide a European map of annual potential recharge by precipitation infiltration. For the future periods, the same methodology can be applied. Ensemble simulations are in progress using EURO-CORDEX climate projections as input of the hydrological models.</p>

Daily mapping of Australian Plague Locust abundance

Locust population outbreaks have been a longstanding problem for Australian agriculture. Since its inception in the mid-1970s, The Australian Plague Locust Commission (APLC) is responsible for monitoring, forecasting and controlling populations of several locust pest species across inland eastern Australia (ca. two million km2). Ground surveys are typically targeted according to prevailing environmental conditions. However, due to the sheer size of the region and limited resources, such surveys remain sparse. Here we develop daily time-step statistical models of populations of Chortoicetes terminifera (Australian plague locust) that can used to predict abundances when observations are lacking, plus uncertainties. We firstly identified key environmental covariates of locust abundance, then examined their relationship with C. terminifera populations by interpreting the responses of Generalized Additive Models (GAM). We also illustrate how estimates of C. terminifera abundance plus uncertainties can be visualized across the region. Our results support earlier studies, specifically, populations peak in grasslands with high productivity, and decline rapidly under very hot and dry conditions. We also identified new relationships, specifically, a strong positive effect of vapour pressure and sunlight, and a negative effect of soil sand content on C. terminifera abundance. Our modelling tool may assist future APLC management and surveillance effort.

Challenges in flood modeling over data-scarce regions: how to exploit globally available soil moisture products to estimate antecedent soil wetness conditions in Morocco

The Mediterranean region is characterized by intense rainfall events giving rise to devastating floods. In Maghreb countries such as Morocco, there is a strong need for forecasting systems to reduce the impacts of floods. The development of such a system in the case of ungauged catchments is complicated, but remote-sensing products could overcome the lack of in situ measurements. The soil moisture content can strongly modulate the magnitude of flood events and consequently is a crucial parameter to take into account for flood modeling. In this study, different soil moisture products (European Space Agency Climate Change Initiative, ESA-CCI; Soil Moisture and Ocean Salinity, SMOS; Soil Moisture and Ocean Salinity by the Institut National de la Recherche Agronomique and Centre d'Etudes Spatiales de la Biosphère, SMOS-IC; Advanced Scatterometer, ASCAT; and ERA5 reanalysis) are compared to in situ measurements and one continuous soil-moisture-accounting (SMA) model for basins located in the High Atlas Mountains, upstream of the city of Marrakech. The results show that the SMOS-IC satellite product and the ERA5 reanalysis are best correlated with observed soil moisture and with the SMA model outputs. The different soil moisture datasets were also compared to estimate the initial soil moisture condition for an event-based hydrological model based on the Soil Conservation Service curve number (SCS-CN). The ASCAT, SMOS-IC, and ERA5 products performed equally well in validation to simulate floods, outperforming daily in situ soil moisture measurements that may not be representative of the whole catchment soil moisture conditions. The results also indicated that the daily time step may not fully represent the saturation state before a flood event due to the rapid decay of soil moisture after rainfall in these semiarid environments. Indeed, at the hourly time step, ERA5 and in situ measurements were found to better represent the initial soil moisture conditions of the SCS-CN model by comparison with the daily time step. The results of this work could be used to implement efficient flood modeling and forecasting systems in semiarid regions where soil moisture measurements are lacking.